Impact Magazine Autumn 2018

D R I V I N G I M P R O V E M E N T W I T H O P E R AT I O N A L R E S E A R C H A N D D E C I S I O N A N A LY T I C S

AUTUMN 2018

M.R. ENABLES DUTCH RAILWAYS TO ADJUST TIMETABLES QUICKLY IN EXTREME WEATHER

© Ivonne Wierink/Shutterstock.com

Optimisation keeps the Dutch moving

ASSESSING BUSINESS ­I NSURANCE RISK Zurich Insurance gains benefits from cutting-edge ­analytics that creates opportunities for stakeholders

O.R. HELPS IMPROVE CHILDREN’S HEART ­S ERVICES Soft and hard methods combine to improve care for infant heart patients

CORMSIS Centre for Operational Research, Management Science & Information Systems

Helping people and organisations make better decisions through advanced mathematical and analytical modelling and enhanced problem understanding →→ One of the largest OR/MS groups in the UK →→ World-class research with demonstrable real-world impact for 50 years →→ 70 researchers and circa 170 UK and international MSc students annually

→→ Strong links with industry, commerce, public sector and non-governmental organisations →→ Dedicated CORMSIS Industry Liaison Officers →→ Extensive UK & international alumni network

Areas of Expertise

Optimisation

Healthcare

Simulation

Predictive and Prescriptive Analytics

Transportation and Logistics

Risk and Uncertainty

Engage with us →→ Study

→→ Training programmes and courses

→→ Case studies and events

→→ Research

→→ Sponsorship (MSc Summer Projects → and PhD students)

→→ Scholarships and awards

→→ Consultancy

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Contact us: www.soton.ac.uk/cormsis CORMSIS@southampton.ac.uk @cormsis CORMSIS @ University of Southampton

E D I TO R I A L Our lead story this autumn tells how Dutch Railways cope with severe winter weather. Timely? Their timetables and crew/rolling stock schedules are changed whenever extreme weather is on the horizon. The planners can completely overhaul the entire timetable less than 16 hours in advance, and so offer passengers some certainty when planning their journeys. What a contrast with the experience of millions of UK rail passengers during the last few months. The railway theme is also present in another story from the continent.

In Finland, work in cooperation with O.R. consultants has optimised locomotive maintenance operations, following previous work to optimise locomotive driver rosters.

Another couple of articles demonstrate the continued relevance of analytical work in health. Matthias Ehrgott reports on his work, initially with the Royal Preston Hospital’s Rosemere Cancer Centre focussing on prostate cancer and continuing with looking at more complex cancer cases affecting the head and neck with Leeds St James Hospital, to reduce the level of trial and error involved in treatments. We can also read how a judicious blend of so-called hard and soft O.R. has helped to improve the services provided for infants with congenital heart disease.

Finally, I’m pleased to highlight an article dealing with financial services, the first since the first issue of Impact. Neil Robinson recounts the work of researchers from the Swiss Federal Institute of Technology and Zurich Insurance which has offered significant potential for innovation and digitalisation in the Business Interruption (BI) business. Their work shows how cutting-edge analytics can provide opportunities for stakeholders as BI insurance moves up global corporate agendas.

It’s good to see that O.R. and analytics continue to make an impact in many different environments. I hope you enjoy reading these stories and the others, and not just when you are stuck on a train in bad weather. Electronic copies of all issues continue to be available at https://issuu.com/orsimpact. For future issues of this free magazine, please subscribe at http://www.getimpactmagazine.co.uk/.

The OR Society is the trading name of the Operational Research Society, which is a registered charity and a company limited by guarantee.

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Graham Rand

OPERATIONAL RESEARCH AND DECISION ANALYTICS

Operational Research (O.R.) is the discipline of applying appropriate analytical methods to help those who run organisations make better decisions. It’s a ‘real world’ discipline with a focus on improving the complex systems and processes that underpin everyone’s daily life - O.R. is an improvement science. For over 70 years, O.R. has focussed on supporting decision making in a wide range of organisations. It is a major contributor to the development of decision analytics, which has come to prominence because of the availability of big data. Work under the O.R. label continues, though some prefer names such as business analysis, decision analysis, analytics or management science. Whatever the name, O.R. analysts seek to work in partnership with managers and decision makers to achieve desirable outcomes that are informed and evidence-based. As the world has become more complex, problems tougher to solve using gut-feel alone, and computers become increasingly powerful, O.R. continues to develop new techniques to guide decision making. The methods used are typically quantitative, tempered with problem structuring methods to resolve problems that have multiple stakeholders and conflicting objectives.

Impact aims to encourage further use of O.R. by demonstrating the value of these techniques in every kind of organisation – large and small, private and public, for-profit and not-for-profit. To find out more about how decision analytics could help your organisation make more informed decisions see www.scienceofbetter.co.uk. O.R. is the ‘science of better’.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Contents

JORS is published 12 times a year and is the flagship journal of the Operational Research Society. It is the aim of JORS to present papers which cover the theory, practice, history or methodology of OR. However, since OR is primarily an applied science, VOLUME 00 NUMBER 00 MONTH 00 it is a major objective of the journal to attract and ISSN: 0960-085X publish accounts of good, practical case studies.

Consequently, papers illustrating applications of OR 00 to real problems are especially welcome.

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Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

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Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

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Satis quinquennalis fiducias imputat gulosus agricolae.

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Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

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Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

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Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

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Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

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Real applications of OR - forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling A wide variety of environments - community OR, education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation Technical approaches - decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation

THE EUROP JOURNAL O INFORMATIO SYSTEMS

Editors-in-Chief: Thomas Archibald, University of Edinburgh Jonathan Crook, University of Edinburgh

VOLUME 00

T&F STEM @tandfSTEM

Dov Te’eni @tandfengineering NUMBER 00

Explore more today… bit.ly/2ClmiTY MONTH 2018

CO N T E N T S 7

HOW O.R. HELPS NETHERLANDS RAILWAYS COPE WITH EXTREME WEATHER CONDITIONS Pieter-Jan Fioole and Dennis Huisman explain how O.R. helps Netherlands Railways offer passengers adjusted timetables in severe weather conditions

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RISK AND REWARD Neil Robinson reports how researchers from the Swiss Federal Institute of Technology and Zurich Insurance have demonstrated the benefits of an analytical approach to Business Interruption insurance

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IMPROVING EMPLOYER ENGAGEMENT FOR ACTION WEST LONDON Shamim Rahman, David Millson, Darren Holland and Abdul Khaled discuss a Pro Bono O.R. project to assist Action West London with the processes involved in securing employment for disadvantaged individuals

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HARTLEY MCMASTER – SUM PEOPLE Mathew Davies describes the wide-ranging work of the analytics consultancy Hartley McMaster

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OPTIMISING HOPE Matthias Ehrgott gives insight into how Data Envelopment Analysis can identify patients where an improvement to a cancer treatment plan is feasible

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BLENDING HARD AND SOFT O.R. TO IMPROVE CHILDREN’S HEART SERVICES

4 Seen Elsewhere

Analytics making an impact 11 The Data Series – evolution of

programming languages Louise Maynard-Atem looks at how programming languages have evolved over time 22 Universities making an impact

Brief reports of two postgraduate student projects 39 Revenue Management in the

digital economy Arne K. Strauss and Nursen Aydin give insight into how revenue management has become more strategic and more centralised in the new connected world 43 Not only … . but also

Geoff Royston looks at a recent book that argues that the rigorous logical framework for argumentation that is used in mathematics should be applied more widely

Brian Clegg describes the work of O.R. researchers, working with Great Ormond Street Hospital, who combined soft and hard methods to improve the quality of services for infants with congenital heart disease

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OPTIMISING LOCOMOTIVE MAINTENANCE TIMES FOR VR GROUP LTD. IN FINLAND Joonas Ollila and Otto Sormunen explain how O.R. enabled the optimisation of the long-term plan of locomotive maintenance operations

DISCLAIMER The Operational Research Society and our publisher Informa UK Limited, trading as Taylor & Francis Group, make every effort to ensure the accuracy of all the information (the “Content”) contained in our publications. However, the Operational Research Society and our publisher Informa UK Limited, trading as Taylor & Francis Group, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by the Operational Research Society or our publisher Informa UK Limited, trading as Taylor & Francis Group. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. The Operational Research Society and our publisher Informa UK Limited, trading as Taylor & Francis Group, shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions​

Reusing Articles in this Magazine

All content is published under a Creative Commons Attribution-NonCommercial-NoDerivatives License which permits noncommercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

SEEN ELSEWHERE

Image courtesy Elin Garø

ACTIONABLE ANALYTICS

Actionable can now be added to descriptive, diagnostic, predictive, prescriptive and decision as adjectives modifying analytics. In a post on Greenbook blog, see http://bit.ly/ 2ITB3Wt, Tara Grabowsky, presents three case studies using data analytics to predict, identify and intervene in healthcare research to improve the lives of patients. She states that ‘analytics is the means to transform “big data” into something meaningful and actionable.

We must collect/aggregate the best quality of data and we must apply the best transformation techniques including state-of-the-art machine learning techniques. But that is just the beginning. From there we must extract actionable intelligence, and finally, we must ensure that we can measure the impact of our outputs’.

She concludes ‘that we need physicians and data scientists working together to ensure that we analyse healthcare data in a meaningful way, and extract measurable value. It is critical, in this age of burgeoning healthcare data, that we create our teams from people who know healthcare and medicine, and people who know data analytics’.

PERSONAL PREDISPOSITIONS MAY DRIVE CHOICE

In INFORMS’ journal Marketing Science (Volume 37, pp. 445–468), researchers Onesun Steve Yoo of the School of Management at University College London and Rakesh Sarin of the Anderson School of Management at the University of California, Los Angeles found that as consumers we

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IMPACT © THE OR SOCIETY

make choices with bounded rationality – we ‘accelerate’ our decision-making processes, and rely on some predispositions, whilst at the same time keep an open mind to evidence for making alternative choices.

Suppose you are choosing between two holiday resorts for your next holiday.

There will be many factors available to inform your choice: location, climate, quality, cost-effectiveness and availability of amenities, for instance. If one of the factors driving your decision is quality, you put yourself in the position of defining just what ‘quality’ means to you, but quality is a difficult concept to define.

The study reveals that although quality could be the key ‘non-price’ consideration, consumers often lack knowledge and encounter missing or conflicting information about product quality. When product quality is ambiguous, apparently consumers do not compute and compare subjective expected utilities, but rather rely on their initial preference or liking for a product to simplify the decision process. The researchers argue that marketers can reinforce the power

of brand loyalty, by emphasising the competitive advantage provided by their brand name or publishing case studies or testimonials for it. These serve to demonstrate their products can be perceived as being of ‘superior quality’ to those of their competitors, or those which exist as generics.

TWITTER PREDICTS THE FUTURE

The research of Costas Milas, Professor of Finance at the Liverpool University’s Management School, shows that Twitter is better at predicting the financial future than even the most sophisticated financial tools. This is especially true in periods of negative economic news when traditional models that use only financial variables might prove inadequate. He is now extending his research on prediction to things like Google search trends and he argues that search can predict how Brexit negotiations are likely to unfold. To find out more you can listen to podcast 29 at https://news.liverpool.ac.uk/podcasts/

ORGANIZATIONS STRUGGLE TO EXTRACT VALUE FROM OPERATIONAL DATA

A recent report, sponsored by Devo Technology, surveyed 400 business, IT and security decision-makers, found 88% of business, security and IT professionals can’t access the data needed to do their jobs effectively.

Organizations depend on operational data to drive improvements in all areas of an enterprise, but the survey reveals that data volumes are affecting productivity.

1. Business, IT and security users face obstacles with enterprise-wide data access. While data is everywhere, creating a unified view of this information is challenging.

2. Organizations struggle with the negative impacts of data silos. More data is being generated by IT, sensors, devices, business applications, customer clicks, etc., resulting in more data silos.

To download the full report, visit https://go.devo.com/organization-datasurvey

stay ahead of the human responses.’

His company’s simulation platform is particularly useful, he said, for exploring what he called, ‘tail events’ such as trade wars or other unpredictable policy outcomes.

Simudyne will also assist Barclay plc by hosting hands-on workshops and seminars delivered by industry experts, academics and Simudyne staff. This work will form the basis of a computational simulation ‘Centre of Excellence’ inside Barclays.

Read more at http://newsroom. barclays.com.

AI ON THE MARCH

A study by Moorfields Eye Hospital in London and Google’s DeepMind has found that AI enabled machines can learn to read optical coherence tomography eye scans and detect more than 50 eye conditions. Doctors hope AI could soon play a major role in helping to identify patients who need urgent treatment. In a trial based on 1000 cases, AI performed as well as two of the world’s leading retina specialists, with an error rate of only 5.5%. More at http://bit.ly/2wM7nm2

The latest forecasts by PwC are that AI is likely to increase the need for as many new jobs as it is likely to displace. The problem is that the skills needed for these new jobs will be very different from those who are likely to be made redundant.

Jobs in health, education and professional scientific and technical services are the ones most likely to increase while manufacturing, transport and storage and public administration could be amongst the biggest losers. The indications are that up to 20% of the workforce could be affected over the next 20 years. More at https://pwc.to/2MMSp9a

SIMULATING BANKS

FIGHTING FLU WITH DATA

Simudyne is partnering with Barclays plc to simulate millions of possible future scenarios using agent-based modelling techniques to account for the ability of agents - the people, firms, traders and other influences - to deviate from rationality, optimise or exploit their environment.

According to Justin Lyon, Simudyne’s CEO, ‘Given a policy change, these models can be used to see how a system will reconfigure itself, allowing machine intelligence to

Professor Eva Lee, Georgia Institute of Technology, explains in an article in Scientific American (June 25) how OR and analytics can be used to help develop a universal flu vaccine.

Essentially, this involves identifying the best immune correlates for protection.

It can identify those people who are most likely to be protected by a vaccine and those least likely to be protected.

The DAMIP model that Lee and colleagues have developed identifies genes for predicting whether someone will

AI READS EYE SCANS

produce high levels of antibodies against a flu shot a few days after vaccination.

After scanning the extent to which carefully selected genes are turned on in white blood cells, the researchers can predict on day three, with up to 90% accuracy, who will make high levels of antibodies against a standard flu shot four weeks after vaccination. It often takes several weeks after vaccination for an individual to develop sufficient levels of protective antibodies against the influenza virus. The ability to predict who will meet these criteria within a few days after vaccination and identify non-responders is of great value from a public health perspective.

Kateryna Kon/Shutterstock.com

Key findings include:

AUGMENTED ANALYTICS

No sooner has “actionable” been added as a modifying adjective to “analytics” (see first item) than along comes “augmented” to be added to the lexicon.

In a Gartner Report, Augmented Analytics is the Future of Data and Analytics, Rita L. Sallam, Cindi Howson and Carlie J. Idoine, seek to shed light on the transformational effect of automation on data analysis.

The aim of the report is to provide a clear path for data and analytics leaders to leverage augmented analytics tools and technologies that empower employees throughout an organization to effectively and efficiently solve more complex problems. The authors discuss technologies available (including DataRobot) that enable users of all skill levels to uncover actionable insights from massive amounts of data.

IMPACT | AUTUMN 2018

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Certified Analytics Professional (CAP®) The OR Society now offers Certified Analytics Professional, an exambased analytics qualification established by INFORMS in the USA. This is complementary to our own accreditation programme, and does not create an exclusive ‘one-or-the-other’ choice.

What is CAP?

CAP is the premier global professional certification for analytics practitioners. Those who meet CAP’s high standards and pass the rigorous exam distinguish themselves and create greater opportunities for career enhancement. Earning the CAP credential requires meeting the eligibility requirements for experience and education, effective mastering of “soft skills”, committing to the CAP Code of Ethics, and passing the CAP exam. For organisations seeking to enhance their ability to transform complex data into valuable insights and actions, CAP provides a trusted means to identify, recruit, and retain the very best analytics talent.

CAPs in the workforce

■ 20% of Fortune 100 companies have CAP on staff including ■ ■

Bank of America, General Motors, Boeing, Chevron, DuPont, IBM, JPMorgan Chase, Chase, Lockheed Martin, UPS, and more.

Visit www.certifiedanalytics.org to search the database for CAP professionals.

Add CAP Preferred to your job postings to receive résumés from the most eligible analytics professionals.

CAP Benefits

■ Building Capability: CAP-accredited employees and ■

professionals provide the unique capability to leverage the power and promise of analytics.

Driving Credibility: Having CAP professionals on your team demonstrates you have the top talent in place and are committed to the highest ethical standards of analytics practice.

Creating Opportunity: Encouraging employees and professionals to pursue their CAP certification creates new opportunities for success and provides new avenues for organisational analytics-based growth.

■ Focused on seven domains of analytics process:

■ ■

I Business Problem Framing II Analytics Problem Framing III Data IV Methodology Selection V Model Building VI Deployment VII Lifecycle Management Computer-based exam administered through the CAP test vendor network Managed by the Institute for Operations Research and the Management Sciences (INFORMS)

Are you eligible?

Earning the CAP credential includes meeting eligibility requirements for experience and education.

Degree Level

Degree Area

Experience

MSc/MA or Higher

Related Area

3 Years

BSc/BA

Related Area

5 Years

BSc/BA

Non-Related Area

7 Years

Fees and pricing

For OR Society members, fees will be billed in sterling equivalent at the prevailing PayPal exchange rate.

Exam Fee: OR Society Member

$495

£380*

Exam Fee: Non-members

$695

£533*

Annual Maintenance Fee Payable beginning 4th year of certification

$100

£77*

Why should I hire CAPs?

Member Re-examination Fee

$300

£230*

■ ■ ■ ■ ■

Nonmember Re-examination Fee

$400

£307*

Processing Fee on Approved Refunds

$100

£77*

Appeals Processing Fee

$150

£115*

■

Proven pre-qualified analytics talent Improves your organisation’s analytics capability Maintains continuous professional development Provides long-term professional growth Increases your competitive advantage

Programme at a glance

■ Globally recognised credential based on practice of ■ ■

analytics professional Vendor and software neutral Created by teams of subject matter experts from practice, academia and government

*GBP conversion estimated as at 6 September 2017.

For more information about the CAP programme www.theorsociety.com/cap 0121 233 9300 www.certifiedanalytics.org info@certifiedanalytics.org

H OW O. R . H E L P S N E T H E R L A N D S R A I LWAYS CO P E W I T H E X T R E M E W E AT H E R CO N D I T I O N S

PIETER-JAN FIOOLE AND DENNIS HUISMAN

EXTREME WINTER CONDITIONS in the Netherlands during the winters of 2009–2012 resulted on the Dutch rail network in a full standstill of operations on several occasions and subsequently complete chaos. Netherlands Railways (NS) is the largest passenger railway

operator in the Netherlands. NS operates around 5000 trains daily and carries approximately 1.3 million passengers on a working day. This operation requires extensive planning and scheduling for smooth and optimal operation of its 3000 train crew members and almost

IMPACT Š 2018 THE AUTHORS

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1000 units of rolling stock. To optimise this process NS has implemented O.R. tooling for rolling stock and crew scheduling. This achievement received the prestigious Franz Edelman award in 2008.

Netherlands Railways operates around 5000 trains daily and carries approximately 1.3 million passengers on a working day

However, since 2012 NS has used the existing knowledge to change the timetable and crew/rolling stock schedules in case of extreme weather predictions in order to make the timetable more robust. Nowadays we are able to completely overhaul the entire timetable less than 16 hours in advance. What follows explains how this process has been designed and how O.R. tools are of the utmost importance to reschedule rolling stock and crew quickly. Jantina Woudstra, Head of Operation Control comments: ‘O.R. tools enable train operations to adjust the entire system’s timetable within 16 hours in order to deal with extreme (weather) circumstances and to offer our travellers predictive and controlled train operations.’

THE ADJUSTED TIMETABLE FOR EXTREME WEATHER CONDITIONS

Technical failures on infrastructure (such as switches and signals) and defect rolling stock can result in disruptions. An extensive analysis in 2012 demonstrated that these disruptions can accumulate in a so-called out-of-control situation.

In such a situation, dispatchers

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lose their overview over the system, which can eventually lead to the termination of all railway traffic in a certain area. In the winters of 2009–2012, this happened several times in the Amsterdam and Utrecht area.

Extreme weather, for example, snow, will raise the chance of a technical failure a lot; therefore, a major disrupted train operation is quite likely. To reduce the probability of an out-of-control situation, the timetable is made more robust.

We do this by reducing the total amount of trains by approximately 20%. Basically, this means that on all parts of the network where the normal frequency is four or six trains per hour, we reduce the frequency to two trains per hour. If the frequency is normally only two trains per hour, the frequency is not changed.

THE DECISION PROCESS TO REDUCE THE TIMETABLE

The decision-making process starts when weather prediction exceeds

certain thresholds (for example a certain snow level) for the next day.

Decision makers come together in a meeting at 10:30, where they will decide whether or not to start the scheduling process to adjust the timetable. If a positive decision is taken, the next step is to evaluate the predefined timetable scenario for the specific day. If the specific situation gives reason to slightly adjust the scenario, these changes are made and the scenario is sent to a team that will adjust the rolling stock schedule and to a team that will adjust the crew schedules (both for drivers and guards). At 16:00, decision makers join again to make a final decision whether or not the timetable for the next day will be adjusted. If not, the rescheduling process is stopped. If the decision is taken to really change the timetable, this information is sent to the Dutch press and announced on all stations.

In addition, the travel plan tool on the website is updated. Rolling stock and crew schedules should be finished at around 22:00 in the evening, in order to import the

new schedules into the dispatching systems and send the updates to all crew members.

O.R. tools enable train operations to deal with extreme (weather) circumstances and to offer our travellers predictive and controlled train operations

ADJUSTING THE ROLLING STOCK SCHEDULES

While on some parts of the network fewer trains are operated, the remaining trains will be (a lot) busier than on a normal day. Rolling stock that would operate a cancelled train could now be used to extend the remaining trains, which still would result in a reasonable seating probability for the passengers despite the cancellation of trains. This requires a major rescheduling of rolling stock.

Normally, a lot of shunting is done in order to use the rolling stock as efficiently as possible.

However, to minimise the risk of technical failures of rolling stock, shunting is not allowed in case of an adjusted timetable. The rescheduling is done using an existing Integer Linear Programming model "TAM" and solved to optimality using CPlex. In regular scheduling, the objective is a multi-criteria objective of operational costs and seating probability. While there is no reliable estimate of passenger numbers in case of a reduced timetable, (some passengers may decide to stay at home as a result of the media exposure), we do not optimise on seating probability.

Instead, we maximise the number of trains that have at least equal capacity on parts of the network that do not have a reduction, and maximise the number of trains that have higher capacity on parts of the network that do have a reduction in number of trains. Moreover, the number of changes is kept as low as possible in the objective function.

The model can be solved in around 10 minutes, while importing the data and parameter setting usually takes a couple of hours. Some iterations are

necessary to come up with a satisfactory rolling stock schedule. Normally it is possible to extend almost half of all trains that are still running in the adjusted timetable.

If the decision is taken to change the timetable, this information is sent to the Dutch press and announced on all stations

ADJUSTING THE CREW SCHEDULES

On a normal weekday NS has about 1000 duties for drivers and around 1100 duties for guards. In addition to the normal rules for crew scheduling, there are some additional rules that have to be taken into account. The most important one is that the start time of the existing duties is fixed and cannot be changed. The reason for this is that we cannot assume that any change of the starting time can be communicated to the crew member on time. So we expect the crew member to arrive at work at the previously planned time and at that point in time give the information of his rescheduled duty. The scheduling problem is formulated as a set covering model and solved with a column generation approach. For the drivers this means the number of tasks is 20% less than normal, while the number of crew members stays the same. This means that feasibility of the problem is not that difficult and around 80 duties remain empty in the rescheduled solution. These empty duties will be used as additional reserve duties and thus contribute to a more robust plan. In the regular planning process, the most important objective is minimising the number of duties.

IMPACT | AUTUMN 2018

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CHANGED DUTIES Rolling stock

430 (68 %)

Train drivers

669 (65 %)

Conductors

674 (62 %)

TABLE 1 REDUCED TIMETABLE ON WEDNESDAY FEBRUARY 14, 2013

However, as this number is fixed in this case, we optimise on minimal number of duty changes. All changes have to be communicated, so the fewer duties are changed, the less communication needs to be done which also reduces the risk that something goes wrong due to miscommunication. The computation time of the algorithm is around 2 hours while data import and correct parameter setting takes around the same amount of time. Usually at least two iterations are needed in order to come up with a satisfactory schedule.

NS uses the CREWS software suite, supplied by Portuguese company Siscog. Software and automatic support is available for long term planning and for dispatching. While the algorithmic support within the software is similar in both planning phases, the characteristics are different. The long-term planning algorithm focusses on optimality and can schedule large numbers of duties; the dispatching algorithm focusses on feasibility and limited computation time.

The dispatching algorithm can therefore handle a limited amount of duties at the time. Originally, the long-term algorithm was used to reschedule crew one day in advance. However, there were some disadvantages. For example, optimality is not really an issue in this case. The goals of rescheduling were similar

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to dispatching, while the number of duties to reschedule was similar to long term planning. Furthermore, this caused the inconvenient need of an extra data-transfer to the dispatching system. In 2015, this resulted in the implementation of a new customised algorithm in the dispatching software that combined these needs, namely full-scale rescheduling while taking dispatching characteristics into account. Obviously, this also eliminated the extra data-transfer and thus speeding up the process even further.

Advanced O.R. tooling that was used to reschedule crew and rolling stock is vital to successfully implement this timetable

RESULTS AND CONCLUSIONS

Since 2013 the adjusted timetable has been implemented around 20 times.

Table 1 gives an impression of how many scheduling changes were made on one of these days. On days that the adjusted timetable was implemented smooth operation has sometimes been a challenge, due to the actual extreme weather conditions that were the reason to adjust the timetable to begin with. However, real chaos as was seen in the years before

has not occurred anymore. In that sense, the adjusted timetable has been a success.

Advanced O.R. tooling that was used to reschedule crew and rolling stock is vital to successfully implement this timetable.

Without these tools it would certainly be impossible.

However, this approach cancels trains as a precautionary measure.

In the future, NS hopes to develop new O.R. tooling that can dispatch crew and rolling stock in real time in case of an extreme disruption, so preventive cancellations would not be needed anymore. Therefore, new research is being done which aims at early warning detection for major disruptions. The idea is that if a major disruption can be predicted beforehand, pro-active measures can be undertaken rather than reactive dispatching. Furthermore, new dispatching strategies that are customised for large disruptions are being developed, simulated and evaluated.

This article is an updated version of an earlier publication in the Dutch magazine STAtOR (issue June 2013).

Pieter-Jan Fioole is a senior research leader at the Department of Process Quality & Innovation, Netherlands Railways.

Dennis Huisman works as expertise manager logistic processes at the Department of Process Quality & Innovation, Netherlands Railways, and as Professor Public Transport Optimization at the Econometric Institute, Erasmus University Rotterdam.

THE DATA SERIES – EVOLUTION OF PROGRAMMING LANGUAGES Louise Maynard-Atem

This topic is also particularly front of mind for me at the moment, as I’m currently teaching myself Python, so I want to explore the developments that have brought us to this point and speculate on what the future of programming languages looks like.

HOW WE GOT HERE?

I’VE RECENTLY TAKEN UP A NEW ROLE at the credit referencing agency, Experian, where I’m focussing on innovation and data. I’m only four weeks in, but the role has already provided me with inspiration for a series of articles that focus on the asset that underpins operational research: Data. The remit of the team (the Data Exchange) is to develop new value propositions that fuse traditional datasets with alternative and non-traditional data assets in order to generate new insights and revenue. It takes all of the innovation best practice that I’ve developed in previous roles, and concentrates this on taking advantage of the new wealth of data that’s out there.

You’ll no doubt have heard the phrase ‘Data is the new oil’; whilst it may make me cringe ever so slightly, that doesn’t make it any less true. It underpins everything we do, and with the advent of trends like AI and IOT, this is only set to continue. This series of articles will focus, in turn, on all aspects of data; how we use it, how we derive insights from it and how it has come to shape our society.

The first topic I want to cover is programming languages, and how they have evolved over time to their current state today. I’m currently co-located with Experian’s data science team (the Data Lab) here in London, and I get to witness the fundamental nature of programming languages to data science almost every day.

A programming language is defined as a systematic notation by which we can describe computational processes to others. A neat way to discuss the history of programming languages might be if we considered the world in two stages; BC and AC (before the programming language C came into existence, and after). In the times BC, there were three primary languages – FORTRAN (short for FORmula TRANslation, developed by IBM) was proficient at handling numbers and mathematical formulae; COBOL, which resembled English-like grammar and was easier to learn and manipulate; and Pascal, which was largely introduced as a teaching tool to illustrate good programming practices.

Once C was developed in 1972 by Bell Labs, it found lasting use in operating systems and various application software.

It is now viewed as one of the most popular and sustained programming languages in existence.

CURRENT TRENDS

The majority of today’s programming languages can find their origins in the early languages that were developed in the late 60s and 70s. The development and widespread uptake of the internet was another major and defining period in the history of these languages. Java, and subsequently JavaScript, was developed to permit programming in internet applications with the advantage that it was machine independent and could run on any kind of computer.

Figure 1 shows the 20 most popular programming languages of 2017, according to Tiobe, as well as the most in-demand languages from a jobs perspective according to job site Indeed.

I mentioned Python specifically in my introduction to this article; its popularity is increasing and its applicability to both data science and operational research is welldocumented. My view of the relationship between data science and operational research is constantly changing, but currently I see the two disciplines are orthogonal to one another, with a largely overlapping skillset.

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FIGURE 1 THE 20 MOST POPULAR AND MOST IN-DEMAND PROGRAMMING LANGUAGES OF 2017

WHERE NEXT?

The pace of change has been relatively slow in terms of the development of entirely new languages, so it is likely that we will see new iterations based on existing languages developing in the near and medium term. Programming languages have developed in a similar way to human languages: it takes a significant development to bring about major change – and such developments are rare. According to TechBeacon.com, languages to look out for include: • Elm – which is becoming popular within the JavaScript community • Rust – a systems programming language meant to replace a lot of C and C++ development • Kotlin – which has been around for about five years and has now reached a production ready version • Crystal – another language that hopes to bring C like performance into the highly abstracted world of web developers • Elixir – a general purpose functional language that is designed for productivity, scalability and maintainability, and was first introduced in 2012.

Looking further into the future, there remains the question of quantum computing and how that will impact the computing industry. Microsoft hail it as the next big thing, and has already written a language, Q#,

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which joins a list that is increasing in number. Whilst quantum computers remain firmly within the realms of advanced research centres, the opportunities for parallel processing that they open up will be incredibly significant once commercialised. This could easily be the significant development that brings about a new wave of languages that will define how we interact with machines long into the future.

For anyone who is interested, I’m currently using the following resources in my bid to become fluent in Python: • • • •

Learn Python the Hard Way Code Academy: Learn Python Intro to Python for Data Science Learn Python, it’s CAKE

If anyone has any further recommendations on other useful resources, please do let me know.

The next article in this series will be all about democratising data and data access; the pros, the cons and everything in between.

Louise Maynard-Atem is an innovation specialist in the Data Exchange team at Experian. She is the co-founder of the Corporate Innovation Forum and an active member of the OR Society. She is also an advocate for STEM activities, volunteering with the STEMettes and The Access Project.

INSURING BUSINESSES BUSINESSES AGAINST INSURING AGAINST INTERRUPTION RISK is an INTERRUPTION RISK is an increasingly important important consideration increasingly considerationfor for companies in a globalised world, yet the companies in a globalised world, yet the task has often relied more on intuition task has often relied more on intuition than on empiricism. Now the than on empiricism. Now the embracing of ‘big data’ is at last leading embracing of ‘big data’ is at last leading to an approach firmly in keeping with to an approach firmly in keeping with the era of financial technology. the The era of financialservices technology. financial arena has The financial services arena has emerged as one of the most emerged as one of the most fascinating battlegrounds for the clash fascinating for the clash between thebattlegrounds old and the new.

With between old anddestruction the new. With the galesthe of creative the gales of creative destruction blowing ever more fiercely, this is an blowing moreaccelerated fiercely, this is an industryever in which industry in which accelerated

technological reshaping technologicaladvances advancesareare reshaping the landscape at speed. the landscape at speed.

The from Thebreakthroughs breakthroughsarising arising from ‘fintech’ – financial technology – are ‘fintech’ – financial technology – are central to the fight between the central to the fight between the established and the innovative. established and the innovative.

Examples in the world of insurance Examples in the world of insurance include the digitised, fully online, include the digitised, fully online, blockchain-enabled insurance buying blockchain-enabled insurance buying experience.

As in so many spheres, ‘big experience.

As in so many spheres, ‘big data’ is often at the heart of the seismic data’ is often at the heart of the seismic shifts taking place. shifts taking place. the valuable To date, inevitably, To date, inevitably, thefrom valuable insights that can be gained insights that can be gained analytics have benefited somefrom more than analytics have benefited some moreand than others.

Consumer-oriented products others. Consumer-oriented products and

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13 1

1

© Zurich Insurance Company Ltd

NEIL ROBINSON NEIL ROBINSON

© Zurich Insurance Company Ltd

KA AN ND D R E WA R RRIISSK RD D

services have fared particularly well.

Business interruption (BI) insurance, which protects against the losses incurred when disruption to a facility leaves a firm unable to operate normally, is one area that has received little attention.

‘BI insurance has suffered from something of a gap in the literature and in practice for some time,’ says Dr. Kamil Mizgier, of the Swiss Federal Institute of Technology (ETH). ‘This is in many ways surprising, as it offers substantial potential for innovation and digitalisation.’

Researchers from ETH and Zurich Insurance recently demonstrated the benefits of approaching this comparatively neglected issue through the prism of big data. Their work shows how cutting-edge analytics can ‘create a wealth of opportunities for stakeholders’ in an era in which BI insurance is rapidly rising up corporate agendas around the globe.

Business interruption protects against the losses incurred when disruption to a facility leaves a firm unable to operate normally

DISASTER, DISRUPTION AND DATA

Among the costliest man-made disasters of recent years was 2001’s 9/11 terrorist attack on the World Trade Centre, which led to payouts totalling approximately $40 billion. Claims for business interruption formed the biggest proportion of this figure.

BI was also a key element of the likes of the 2003 SARS outbreak, which devastated a variety of service industries in infected areas, and 2010’s Eyjafjallajökull volcanic eruption, whose ash clouds left

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around 10 million airline passengers grounded and cost airports an estimated $128 million in less than a week.

Events such as these forced many companies to reflect on the possible implications of their operations. So, too, did later tragedies such as the Tōhoku earthquake and the subsequent explosions at the Fukushima Daiichi nuclear power plant. With their value chains increasingly defined by international interconnectedness, firms are realising that the scope for potentially substantial BI will only continue to rise – not least in light of the present-day threat of cyber-attacks.

Today, in part because of this complexity, it is not uncommon for a BI policy to pay out more than a property damage (PD) policy. While the latter covers only physical impacts, the former covers the gross profits that a business would have earned in the absence of a BI event. The automotive, chemical, electronics, mining and pharmaceutical industries have proven to be notably prone to sizeable claims.

‘There are a number of reasons why we need a better understanding of the characteristics of BI exposure,’ says study co-author Dr. Otto Kocsis, head of Zurich Insurance’s Technical Centre for Business Resilience. ‘One is to help insurance buyers manage risks along their value chains as effectively as possible. Another is to enable insurers themselves to improve how they assess BI risk by formulating the sort of hazard grades that customarily guide PD risk assessment. Bringing BI into the digital space helps us to achieve these aims.’

The research team began by combining information from several legacy claims databases and Zurich Insurance’s own large claims database (LCD). The final sample consisted of more than 2,000 claims, each of which

was closed between 2002 and 2014 and had a total payout of more than $0.5 million; 45% of these had a BI amount greater than zero.

The first stage of the analysis investigated the importance of BI claims relative to total claims over time. The results supported the idea that globalisation has affected the frequency and severity of BI. The team found that a series of almost step-like increases between 2002 and 2006 was followed by a volatile but consistently raised level of BI share.

Overall, the BI share of total claims almost doubled – from 23% to an average of 45% – during the sample period.

‘The progression of the annual BI share largely follows the progression of the volume of global flows of goods,’ says Mizgier, of ETH’s Department of Management, Technology and Economics. ‘This suggests a link between the increase in international flows and the increase in claims related to supply chains. Similarly, the high volatility of BI’s share of total claims essentially mirrors the high volatility that supply chains have experienced since 2006.’

The next task was to compare the respective characteristics of BI and PD claims. Although only 45% of claims in the LCD had a BI component, the average BI claim was considerably higher than the average PD claim – $8.2 million versus $5.8 million – indicating that BI claims may be less frequent but tend to be larger than their PD counterparts.

Claims size (millions of $)

BI share (%)

>2.5

45

>10

50

>15

52

>20

52

>25

53

>30

55

>50

56

>100

64

10 largest claims

80

TABLE 1 BI SHARE OF CLAIMS BETWEEN 2006 AND 2014 © INFORMS

Further analysis shed even more light on the growing significance of BI. Half of the total amount of all claims of $10 million or more paid out since 2006 was for BI, and the share for the 10 biggest claims of all was 80% (see Table 1). ‘The trend is unmistakably upward,’ says the third member of the research team, ETH’s Professor Stephan Wagner. ‘The larger the claim, the larger the BI share of the total claim. This was the case even before 2006, but the trend has intensified since then.’

The larger the claim, the larger the BI share of the total claim

Faced with such strong evidence of BI’s mounting importance, a crucial challenge for businesses and insurers alike is to comprehend the BI characteristics of individual industries. After all, different sectors have different structures and might therefore be expected to exhibit different reactions when their global supply chains are disrupted.

The team’s findings clearly reinforced the notion that capital-intensive companies are especially at the mercy of BI. For mining, which was revealed as the most vulnerable sector in this regard, BI accounted for 80% of claims; for oil

and gas firms it accounted for 72%; in the automotive sector it accounted for 59%; and for chemical companies it accounted for 58%. At the other end of the scale – for instance, for the likes of real estate, investment banking and municipalities and government – the share was in single figures.

Analysis of causes of loss also highlighted the susceptibility of businesses in capital-intensive industries. Machinery breakdowns, fires and explosions were found to be among the most common sources of losses in general and BI specifically.

‘A big problem for the likes of mining, oil and gas, power and electricity, chemical and pharmaceutical companies is that they often need to invest in specialist or heavy machinery,’ says Mizgier. ‘This can take a lot of time to replace in the wake of disruption. Along similar lines, many companies in technology-driven industries – for example, electronics and automotive – are at risk because they rely on a single supplier for a number of specialised components. In service industries, by contrast, a lack of reliance on physical assets means disruption can usually be overcome quite quickly.’

capital-intensive companies are especially at the mercy of BI

This much was underlined by an analysis of recovery times. Some 215 of the 400 largest claims in the sample featured loss-adjuster reports containing this information. ‘At Zurich we have often encountered situations in which a business’s representatives have expected BI recovery times to be a question of days or weeks,’ says Kocsis. ‘As our research showed, it actually makes sense to think in terms of months. Thinking in terms of weeks isn’t realistic, even for those industries with lower recovery times.’

Recovery times ranged from two to just over 14 months

Recovery times for the sample ranged from two to just over 14 months. Companies in the electronics sector were the slowest of all to overcome disruption (14.1 months), followed by those in aerospace and defence (14 months) and sewage (11.3 months); healthcare businesses were the quickest. The average recovery period across the sample was nine months, although the data also included claims where ‘interruptions’ lasted for three years. Industries with a propensity for fires and explosions were particularly likely to suffer lengthy delays.

The team also investigated the role of alternative production capacities. It was found that in 59% of BI claims a company had no alternatives to compensate for lost turnover. On average, claims were lessened when a firm was able to fall back on one, two or three alternatives. ‘Alternative production capacities can be valuable on two fronts,’ says Wagner. ‘Firstly, they help the insured by enabling a firm to maintain service to its market.

Secondly, by extension, they help the insurer by reducing the BI claim.’

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FIGURE 1 A SIMPLIFIED VERSION OF A SYSTEM OF HAZARD GRADING USED TO ASSESS BI RISK © INFORMS

DEEPER AND DEEPER

One of the reasons why the paucity of research into BI insurance is so surprising is that intuition alone implies that BI risk exposures are more likely to impact on a business than PD risk exposures. In other words, it seems obvious that restoring an end-to-end value-creation process and a company’s market position – both of which are likely to be affected by BI – would be much more difficult than, say, replacing a piece of damaged machinery.

What has been lacking is empirical confirmation. By adopting a ‘big data’ approach, Mizgier, Kocsis and Wagner have begun the process of filling that gap. Now, finally, those who manage global supply chains and those who insure them have the makings of a genuine framework to guide decisions that could ultimately have enormous operational and financial consequences.

Now, those who manage global supply chains and those who insure them have the makings of a genuine framework to

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guide decisions that could ultimately have enormous operational and financial consequences

Zurich Insurance has already drawn on the research to inform how it assesses BI risk. Taking into account factors such as estimated maximum loss (EML), probable maximum loss (PML) and likely recovery times, it is now able to use a system of BI hazard grades similar to that typically used to gauge PD risk (see Figure 1).

Zurich Insurance has already drawn on the research to inform how it assesses BI risk

Assessments are less comprehensive for ‘simple’ customers and become more far-reaching for customers whose organisational complexity leaves them more exposed to BI. Researchers are also confident that analytics will continue to offer everdeeper insights in this field. They

believe the process of managing BI will become increasingly sophisticated – and, in tandem, increasingly effective – for businesses and insurers alike.

‘There’s a wealth of data available,’ says Mizgier, ‘and we need to keep exploring it. This is all about understanding vulnerabilities, improving resilience and developing competitive advantage. This is the way forward.’

For a fuller account of this work see Mizgier, Kamil J., Otto Kocsis and Stephan M. Wagner (2018): Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition.

Interfaces 48(2): 94-107. Table 1 and Figure 1 are reprinted with permission from this article. Copyright 2018, the Institute for Operations Research and the Management Sciences, 5521 Research Park Drive, Suite 200, Catonsville, MD 21228 Neil Robinson is the managing editor of Bulletin Academic, a communications consultancy that specialises in helping academic research have the greatest economic, cultural or social impact.

SHAMIM RAHMAN, DAVID MILLSON, DARREN HOLLAND AND ABDUL KHALED

HOW CAN OPERATIONAL RESEARCH HELP third sector organisations become more efficient? As a team of four Operational Researchers working at the Food Standards Agency, we were keen to volunteer to help a charitable organisation. Through the OR Society’s Pro Bono initiative for UK based third sector organisations, established in 2013, we gave free O.R. consultancy services to a local charity: Action West London (AWL).

© Kiev.Victor/Shutterstock.com

I M P R OV I N G E M P LOY E R E N G AG E M E N T F O R AC T I O N W E S T LO N D O N ACTION WEST LONDON

Action West London’s strategic objectives are to help people in West London into employment, selfemployment, education or training.

AWL’s mission is ‘changing lives through Employment, Education and Enterprise’. They run a number of projects focussing on particular difficulties people have finding employment, such as learning difficulties and/or disabilities, long term

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unemployment, and individuals whose first language is not English. They identify each client’s skills and goals, and help them identify and apply for jobs. AWL also engages with local employers to identify vacancies to which their clients can apply.

The majority of AWL’s projects are funded ‘payment by results’ contracts.

Others are funded through grant applications, and two projects (Acton market and the Doughnut Factory business start-up premises) self-generate income.

WHAT WAS WANTED

AWL asked us to help enhance their ability to get clients into jobs, by suggesting ways to increase the number of job vacancies for their clients. They identified two project strands to achieve this objective. The first would focus on streamlining their processes to allow them to increase the quantity of vacancies on their books (by engaging with employers) and to ensure those vacancies reached their clients (through more efficient internal communication).

The second strand would focus on improving the quality of vacancies on their books by analysing data on past clients and employers to determine what kind of vacancies were most effective for getting clients into work.

PROCESS IMPROVEMENT

To get a more detailed overview of AWL, we held a workshop with a range of staff from management and operations to examine the objectives and strategy of the organisation, and the process currently used to deliver them. We began by asking the staff to draw ‘rich pictures’, see Figure 1 for an example, illustrating how they viewed the charity, its purpose, and its actions.

A common strand in the resulting pictures was a view of AWL from the point of view of the clients’ journeys towards employment, with AWL providing help and input at a number of points along the journey.

FIGURE 1 A RICH PICTURE CREATED BY AWL AT THE WORKSHOP

We then took a more detailed look at the processes by building a purposeful action system (PAS) model.

We asked the staff to write down the

actions that AWL undertakes, and linked these together to form a process diagram of the business. It became clear that the charity’s business model could be broadly divided into a ‘client side’ following the clients’ journey to employment (section A of Figure 2) and an ‘employer engagement side’ ensuring that there are enough vacancies for clients to apply for (section B). The client side motivates the employer engagement side in terms of job types, and the employer engagement side provides vacancies for the client side to support clients into (section C).

This general model was replicated across different projects, reflected in Figure 2 by the three duplicated processes, with vacancies identified in each project generally being restricted to clients of that project. There was some informal flow of vacancies from project to project, but only after the original project determined that its own clients didn’t need the vacancy.

The PAS shown in Figure 2 is the highest level of a multi-level map. Each of the boxes represents a single process that is broken down into sub-processes, some of which can be broken down further. This layered approach to creating the PAS was at the request of AWL, and allowed us to easily examine the high level system seen in Figure 2, without sacrificing the detail that lets

FIGURE 2 THE ORIGINAL PROCESS MAP FOR AWL, DISPLAYING THREE PROJECTS

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individual staff pick out the parts of the system that they work on.

Having developed a PAS model, we held a second workshop with the management team to isolate key issues and elicit solutions. We started with an open brainstorming session, and asked participants to come up with any issues related to employer engagement at AWL. From the list, the group selected two key issues they considered the most important, so we could delve deeper into their root causes. The two chosen issues were around lack of communication, and not getting the right vacancies.

For each of these issues, we facilitated a root cause analysis using Ishikawa causal diagrams (see Figure 3 for an example).

In this cause-and-effect analysis, we asked participants ‘why does this happen?’ repeating the question over and over to investigate all levels of the causes down to the roots. Finally, we split the participants

into two groups, and asked them to suggest solutions for any of the causes of the two original issues. Each group then swapped to look at the other issue, and we asked them to challenge or add to the solutions already suggested by the other group.

In addition to these solutions, we recommended handling the connections between the employer engagement and client sides (section C of Figure 2) more centrally, with one person or team responsible for collating information on vacancies and communicating them to all projects, rather than handling vacancies on a project-by-project basis.

This would not affect the process of front-line employer engagement, but it would allow more efficient use of available vacancies by making all vacancies immediately available to all clients, rather than waiting to see if

the vacancies are of use to clients of one project before opening them up to others. The proposed new process map, shown in Figure 4, was made available to AWL in an interactive and editable tool through the software ‘cMap tools’.

We held a validation meeting with AWL to ensure the PAS model of AWL’s current processes and the suggested solutions to the main problems met requirements and expectations. At this meeting, we refined the proposed solutions and recommendations.

DATA IMPROVEMENT

The aim of this strand was to review existing data held by AWL to see how it could be used more effectively for employer engagement and to make recommendations for future data collection.

FIGURE 3 ISHIKAWA CAUSE-AND-EFFECT DIAGRAM: NOT GETTING THE RIGHT VACANCIES

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FIGURE 4 THE PROPOSED PROCESS MAP FOR ACTION WEST LONDON

We reviewed anonymised data from three main sources. These covered client details (including job preferences), details of employers with whom AWL actively engage, and data detailing successful placement of clients into jobs. Each dataset was cleaned and common fields standardised.

Targeting jobs

Having standardised job titles between the datasets we found that of the 263 different job titles less that 50 of these job titles were used more than twice. The 10 most frequently used accounted for 45% of all placements (Table 1).

Therefore, we recommended employer engagement to be focussed on the jobs where most placements had been achieved. It was a matter of judgement and the resources available as to how far down the list to focus.

RANK JOB TITLE 1 2 3 4 5 6 7 8 9 10

Customer service Cleaner Care Worker Driver Sales Advisor Warehouse Operative Security Guard Manager Store Assistant Administrator

For instance, the top 34 job titles accounted for around two thirds of all placements with each job title having at least four placements.

An alternative would be to concentrate on particular businesses.

However, most of those who have employed over ten clients between 2011 and 2015 recruited in batches and none employed clients every year. This posed the question as to why there hadn’t been a longer-term relationship with these businesses.

A slight caveat to these recommendations was that for the limited number of jobs where we were able to match job preference to job placement, less than a quarter of clients were placed in a job they mentioned.

Therefore, a further recommendation was to investigate whether job preference was sufficiently considered when placing clients and if this ‘hit rate’ was sufficient.

NUMBER OF

% OF ALL

PLACEMENTS PLACEMENTS 72 56 44 42 42 40 26 24 22 21

8.4 6.5 5.1 4.9 4.9 4.6 3.0 2.8 2.6 2.4

CUMULATIVE % 8.4 14.9 20.0 24.9 29.7 34.4 37.4 40.2 42.7 45.2

TABLE 1 10 MOST FREQUENTLY USED JOB TITLES IN DATA SAMPLE

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Seasonality

Looking at the last 4 years of placements there was little evidence of strong seasonality for any time period.

Therefore, we did not suggest focussing employer engagement on any particular months.

Other recommendations

Other recommendation for the data work strand included: • Consistent use of fields between and within datasets to allow the easy production of summary statistics and analysis; • Active use of job readiness codes to enable forward planning. These codes indicate when a client will be ready for work so could be used to anticipate demand for jobs in upcoming months; • How to use the data for evaluating impact.

In addition, specific recommendations were made for each data source on how to improve data quality.

WHAT HAPPENED

We delivered our findings to AWL through a presentation and a report.

They had already made good progress on starting to implement some of our proposed solutions, such as recruiting an employer engagement officer

The project demonstrated to us how charities who constantly struggle for resources can benefit from the enthusiasm, skills and input from the O.R. pro bono consultants.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

00

Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

00

Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

JOURNAL OF SIMULATION

Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

Satis quinquennalis fiducias imputat gulosus agricolae.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

Satis quinquennalis fiducias imputat gulosus agricolae.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

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Shamim Rahman is an Analytics Manager and Principal Operational Researcher at the Department of Health and Social Care in London.

David Millson is an Operational Research Analyst at the Scottish Government.

Darren Holland is Lead Operational Researcher at the Food Standards Agency.

Abdul Khaled is an Information Analyst at the Ipswich Hospital Trust.

For more information about Pro Bono O.R. please contact project manager Amy Hughes at Amy.hughes@ theorsociety.com. Alternatively, please visit http://www.theorsociety.com/ Probono.

VOLUME 00 NUMBER 00 MONTH 00 ISSN: 0960-085X

EJIS

Contents

have taken the recommendations on board. We have appointed specialist recruitment consultants to focus on longer term work with employers who provide the majority of vacancies, we capture data on beneficiaries “job readiness”, preferred employment sectors and jobs secure, and we have improved our database management and built in fields to measure social impact. The project demonstrated to us how charities who constantly struggle for resources can benefit from the enthusiasm, skills and input from the O.R. pro bono consultants.’

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

on a contractual basis, with future plans to make this role permanent.

There were plans in place to use an Information Management system to coordinate client details, their requirements, and vacancy allocations.

Management reports from this system can then be used to evaluate the employer engagement process.

Dr John Blackmore, Chief Executive of Action West London, commented: ‘The work by the O.R. pro bono consultants was really helpful in assisting us to think carefully about the processes involved in securing employment vacancies for the unemployed people we work with who face major barriers to employment. The O.R. consultants enabled us to take a “step back” and review our current processes and practice with insight from their external, objective viewpoints and O.R. knowledge and experience. Since the project we

00 of Simulation (JOS) aims to publish both articles and technical notes from researchers and Journal 00 practitioners active in the field of simulation. In JOS, the field of simulation includes the techniques, tools, methods and technologies of the application and the use of discrete-event simulation, agent00 based modelling and system dynamics. We are also interested in models that are hybrids of these JOS encourages theoretical papers that span the breadth of the simulation process, approaches.

00 including both modelling and analysis methodologies, as well as practical papers from a wide 00 range of simulation applications in domains including, manufacturing, service, defence, health care and general commerce. JOS will particularly seek topics that are not “mainstream” in nature but 00 interesting and evocative to the simulation community as outlined above.

00 Particular interest is paid to significant success in the use of simulation. JOS will publish the 00 methodological and technological advances that represent significant progress toward the application 00 of simulation modelling-related theory and/or practice.

Other streams of interest will be practical applications that highlight insights into the contemporary practice of simulation modelling; articles that are tutorial in nature or that largely review existing literature as a contribution to the field, and articles based on empirical research such as questionnaire surveys, controlled experiments or more qualitative case studies.

THE EUROPEAN JOURNAL OF INFORMATION SYSTEMS

Joint Editors Christine Currie, University of Southampton, UK John Fowler, Arizona State University, USA Loo Hay Lee, National University of Singapore, Dov Te’eniSingapore VOLUME 00

T&F STEM @tandfSTEM

@tandfengineering

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Explore more today… http://bit.ly/2Gg9Zv9 MONTH 2018

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U N I V E R S I T I E S M A K I N G A N I M PAC T EACH YEAR STUDENTS on MSc programmes in analytical subjects at several UK universities spend their last few months undertaking a project, often for an organisation. These projects can make a significant impact. This issue features reports of projects recently carried out at two of our universities: Cardiff and Strathclyde. If you are interested in availing yourself of such an opportunity, please contact the Operational Research Society at email@theorsociety.com

© NHS Wales

© Cardiff University

REDESIGNING ‘STEP UP’ AND HOSPITAL ‘FRONT DOOR’ SERVICES TO MEET THE NEEDS OF OLDER PATIENTS (Sara Heledd Thomas, MSc in Operational Research, Cardiff University)

The NHS Wales Delivery Unit is a focussed team, working alongside Welsh Government, who seek to improve design and the measurement of the successful delivery of safe, effective, dignified and timely care to the Welsh population.

Vastly improved life expectancy, one of the greatest triumphs of the last century, looks likely to be one of the greatest challenges for healthcare providers of this century. A significant body of evidence illustrates that the ways in which older people in health and/or social care crisis are currently managed risks harm and loss of independence for the individual.

Moreover, it can also lead to the wellpublicised ‘blocking’ of hospital beds and the subsequent negative impact on waiting times for emergency and planned care.

Heledd undertook this project with Hywel Dda University Health Board (HDUHB) and its Local Authority partners to support the development of an innovative whole system pathway for older patients that could reduce the strain placed on acute NHS services, support independence and offer more support services closer to home.

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Whilst there is a prevalence of research into the use of modelling techniques in modelling A&E departments in isolation, this project bridged the gap between community health, social care services and hospitalbased clinics, taking the concept of bed capacity planning to the next stage. Statistical analysis, simulation modelling and data mining techniques were all performed, focussing analysis on the largest of HDUHB’s hospitals, Glangwili General Hospital (GGH).

The simulation model developed for the proposed elderly patients’ pathway was built in SIMUL8, to provide NHS Wales with a valuable and commendable model that could be adapted by NHS analysts who have been trained in the software. The model has been programmed in a user-friendly way that enables user inputs to be specified and useful outputs displayed once the model has been run to investigate the effect of varying levels of demand and introducing alternative care packages on the service.

The simulation model is used to identify potential bottlenecks, which will become more important as the proportion of frail

patients in the system is due to increase steadily.

It has been extremely well received by the service, notably by the Assistant Director of Therapies and Health Sciences for HDUHB who commented, ‘This research has busted some myths and provided a different perspective. The findings are interesting and useful, and we are very keen to have the work handed over. The amount of work done here is massive and we very much appreciate these findings and hope to build on these going forward. We have not been told what we already know, but a lot extra’.

The model has demonstrated excellent potential of simulation to help planners more proactively plan and is currently being utilised by the service to support business cases. The main deliverables have been a ‘proof of concept’ simulation model to highlight inefficiencies in current services, the investigation of proposed alternative configurations of care, several valuable recommendations and a change in mind set of hospital staff who now appreciate the need for more robust data collection and Operational Research methods to interrogate available data.

Julian’s project was carried out in collaboration with the Communities Analysis Division (CAD) of the Scottish Government, to investigate the design of the new social security agency contact centre. The project involved collaborating with business analysts and operational staff to map the advice and support contact centre. Julian was then able to develop a discrete event simulation (DES) model of the contact centre, allowing the operational staff to test processes before they come into existence. Simulating social security services is a challenging task, not least because Julian was modelling a service before it existed, meaning there was no existing operational data.

Julian successfully overcame this by researching several useful data sources which he analysed and used to build assumptions. He was able to skilfully communicate how these assumptions were made and the technical capability of the model to the key stakeholders.

Julian considered different options around the implementation of a new contact centre. He built an in-depth and sophisticated simulation model that

ZoranKrstic/Shutterstock.com

© University of Strathclyde

MODELLING THE SOCIAL SECURITY AGENCY CONTACT CENTRE (Julian Venken, University of Strathclyde, MSc Business Analysis and Consulting)

could switch between these different options. Not only did it include advanced technical components to answer the client’s specific question, but Julian was also careful to make a model that could be easily adapted and updated in the future. The use of simulation has been of valuable benefit in this case, as the model can be altered for performing a variety of sensitivity analyses for different types of services. It is also important to note that the choice of simulation is appropriate, as it allows analysis of a yet-to-build system in great detail.

One of the key strengths of the use of a DES approach is that it is equally capable of integrating operational and strategic objectives of the project. The baseline model developed was able to support the whole operation of the contact centre, but the model was also able to offer long-term strategic support for the client. By using DES in Simul8 modelling software, a risk-free method of evaluating the operational system was developed, providing analysts with an opportunity to test different scenarios with stochastic parameters.

This was all accompanied with rich animation and visual display, increasing its usefulness for managers and non-modelling staff in the centres to evaluate suitable scenarios for their required operations.

Of key importance was the active involvement of all key stakeholders, which has ensured acceptance of the approach which will have long-term benefits for the Scottish Government. Julian held several workshops with the key stakeholders to understand their needs and build the model in collaboration with them. The stakeholder analysis carried out confirms that a varied range of people can experiment with the model and bring consensus to the possible implementation scenario. The work creates an opportunity for CAD to prototype their intended services and record measurements against key performance indicators chosen by the stakeholders.

Julian brought technical skill to this project, building a sophisticated model, but also balanced this with careful consideration of stakeholder engagement and effectively communicated a complicated model to a non-technical audience.

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MATHEW DAVIES

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HARTLEY MCMASTER LIMITED – HMcM – is a specialist analytical consultancy, now in its 27th year of existence. The company is based in St. Albans, Hertfordshire and works with customers across the UK and Europe – and occasionally further afield. The company currently has a core team of 12 consultants and a pool of associates that provide additional support or specialist skills when required.

‘Analytical consultancy can mean many things’, says co-founder Martin Slaughter, ‘and can be branded many ways – O.R., OA, Data Science, Analytics, etc. – but if you think of the range of techniques that the members

of the OR Society cover, that is pretty much what we cover too’. The bias is towards the more quantitative aspects of analysis – though not exclusively – and so the consultancy team all have strong numerate qualifications, mainly with an MSc or PhD in O.R., maths or physics.

The company have a long track record for recruiting at the graduate level and developing the skills and experience of their team. 2017 saw the ultimate progression of this when former graduate recruit Tom Dewar returned to the company after work in government and the NHS to become co-owner of the company. ‘I’m not sure that we quite saw this happening when the company

© Courtesy of Hartley McMaster

HARTLEY MCMASTER – SUM PEOPLE

started’, says Martin, ‘but it’s great that developing our team has given us a natural and positive path forward’.

The varied nature of consultancy work means that a wide range of tools are used. The tried and trusted MS Excel/Access/VBA tools are still the workhorses, but increasingly more recent equivalents such as R/Python/ Tableau are used – as well as more specialist packages such as SAS/Xpress MP/Simul8. Martin says, ‘Having the right tools is important, but having the O.R. skills to know how to use them properly is the key’.

Having the right tools is important, but having the O.R. skills to know how to use them properly is the key

forecast trajectories and (where needed) methods to ensure that the contribution of HS2 could be isolated from the impacts of other initiatives and factors. The team also developed a prototype performance reporting tool to support the pilot reporting phase.

Martin described this project as ‘the perfect example of our work, combining analytical methods with consultancy skills. It was also incredibly interesting to work on such a major engineering programme’.

OPTIMISATION

At the applied maths end of the O.R. spectrum, the company have been using optimisation techniques to support the regulatory business planning process in the water industry and asset investment planning in other parts of the utilities sector for over 20 years. The latest incarnation of this is the current work for South Staffordshire Water, supporting their regulatory business planning process. © Courtesy of Dŵr Cymru Welsh Water

Company clients are drawn from across the public, private and third sectors. Central Government, primarily through the Government O.R. Service (GORS) has been a long-term client and the company has worked with many government agencies and local authorities over the years. Private sector clients have tended to be larger organisations – British Airways, Vodafone, Danone, Reuters and Barclaycard, for example and European utility sector organisations like Yorkshire Water, Scottish Water and Tenet Energy.

In the third sector the company has worked with national charities – RNIB and RNLI – and housing associations. The range of projects that HMcM undertake are illustrated with some examples taken from the last couple of years.

reporting systems – usually with the practical constraint that base data needs to be drawn from existing data sources. Over recent years this has been combined with work to develop benefits realisation management (BRM) systems – and a major project for the High Speed 2 rail project was a perfect example of this.

HMcM teamed with a BRM specialist consultancy and the in-house benefits management team to develop a set of metrics and measures that could be used to track progress in achieving the key business benefits of the programme. The team spent a year working with a wide range of HS2 staff and stakeholders in Birmingham and London, developing a set of measures that would provide the benefit owners with a clear picture of how the programme was progressing in terms of the business benefits. This meant that the project combined analytics and data visualisation with the soft skills of facilitation and stakeholder management.

The team developed not only the set of metrics and measures, but also provided baseline data,

DEVELOPING METRICS

Many HMcM projects involve developing metrics and measurement/

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The company have been using optimisation techniques to support the regulatory business planning process in the water industry and asset investment planning in other parts of the utilities sector for over 20 years

STATISTICS AND ANALYTICS

The company often provides statistical and analytical support

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airline. This produces a complex statistical problem.

The work, although statistical and analytical at its core, also draws on the softer consultancy skills, supporting the consultative and iterative process of developing, explaining and agreeing targets with the many stakeholders across the airline.

Martin added, ‘Again, the key to our involvement is that we can offer both the technical skills and the consultancy skills. We routinely work with a wide range of tools – current projects use SAS, R, Tableau and Stata for example – and techniques, but we also look to develop new approaches and tools and have recently developed our own tools for bringing a more quantitative approach to textual analysis’. © Courtesy of Ceri Breeze/Shutterstock.com

The extensive work in asset management and investment planning for utility companies has led to the development of three HMcM software tools; Investment Optimisation Engine (IOE) – a portfolio selection component that users can embed in their own investment selection systems, Investment Optimiser Plus (IO+) – an application that allows users to set up and solve portfolio optimisation problems using IOE to solve the optimisation problem, and IO Solution Manager – which is used to collect asset investment data (performance, costs, etc.) for subsequent processing by IO+.

Almost invariably these tools need to be customised and for South Staffordshire Water the project has been essentially to understand the South Staffordshire Water requirements, and customise and integrate the IO+ and IO Solution Manager into their systems and processes. HMcM have also trained the local users and provided general support during their planning exercise.

‘Our team have strong software development skills across a range of languages – VB, C++, R and Python in particular – but our focus is to use these to build the tools to undertake analysis’ says Martin.

to clients and has, over many years, worked regularly with the BA Research and Insight team.

The R&I team use market research and customer feedback to help plan improvements to services and monitor passenger satisfaction with a wide range of aspects of service (ranging from check-in, through aircraft cleanliness, punctuality and catering to staff performance and likelihood to recommend the airline). HMcM has often helped to set customer satisfaction targets for the next operating period. These targets need to be set at a route level (for the Heathrow to JFK route, for example) and need to recognise planned initiatives – but they also need to be consistent with the highlevel targets set by BA for the whole

RESOURCE AND PROCESS MODELLING

Another major area of work is resource and process modelling to quantify process re-engineering. A good example of this is the process modelling work for the Royal National Institute of Blind People (RNIB) on their Change Programme.

RNIB are undertaking a major programme to change the way it delivers its services across the UK. This includes catering for the increasing preference for digital access to services as well as bringing consistency to service delivery across the UK. A team of HMcM consultants worked with the Change Programme for 10 months to quantify the resource requirements of proposed changes, developing a set of tools that the RNIB team could use to assess and refine their plans as the programme progressed.

The project team built a series of interlinked tools to forecast service demand (an Excel/VBA model to forecast changes in customer demographics and preferences to provide a projection of the volume, type and channel of requests for service), to predict performance in meeting this demand with different resource scenarios (a Simul8 simulation to consider the dynamic aspects – endto-end process times, process choke points, etc. – and an Excel model to calculate resource requirements) and a catchment area model (an Excel/VBA model using public transport travel time data and demographic data to identify the catchment areas round proposed face-to-face service centres across the UK).

Although the HMcM team initially used these tools themselves to support the change programme, the aim was always to train in-house users to take over responsibility for the models.

After nearly 40 years working in O.R., Martin gets quite nostalgic about the use of simulation. ‘I started my O.R. career building process simulations and I am pleased to see that the approach is still just as appropriate today. Modern tools are far more efficient and visual – we have just developed our own set of simulation tools in R, giving us a flexible way to develop licence-free, cloud-based simulations – but the basic approach is the same and is very powerful’.

The company’s experience in analysing data and building models means that it is well placed to review and assure the quality of in-house or third-party analytical projects for our clients. These reviews have taken many forms: • Reviews of the Quality Assurance (QA) procedures and implementation for a project – assessing how well the project has applied QA and whether the way this has been done covers all the key aspects of quality.

• Advising clients to help develop Analytical QA standards and procedures, either for specific projects or for the organisation as a whole.

• Undertaking third-party assurance of analytical models.

HMcM are often commissioned to test models built by client teams or by third-parties for our clients. The company has developed its own set of tools to aid in the testing of models built in Excel (including VBA modules). Testing can range in scope – based on client needs – but usually includes review of the model specification (is the approach adopted sensible?, are the assumptions documented and tenable?, etc.), review of the model implementation (testing and review of the model and data, potentially using the company tools), sensitivity analysis and a review of documentation.

As Martin says, ‘Our close work with GORS means that we often provide assurance for central government models. However, we have also worked with engineering companies and charities over recent months and expect the demand for expert assurance of models to grow’.

THE WAY AHEAD

The potential to use data to create information and insight has never been greater; all sorts of organisations collect vast amounts of data about their customers and their performance and so the opportunity to learn (and profit) from this is everywhere.

Getting this data into a usable form is a major challenge – and one that HMcM is peripherally involved in – but the need will always be there to ensure that data is used appropriately (to know what it doesn’t say as well as what it does), practically and creatively. This won’t change, and HMcM therefore sees its role continuing to be that of bringing O.R. skills and practical experience to bear on real-world problems.

HMcM sees its role continuing to be that of bringing O.R. skills and practical experience to bear on real-world problems

And the company looks forward to one of the current graduate recruits picking up the baton in 20 years’ time!

Mathew Davies has a DPhil in Physics from the University of Oxford and has spent 35 years working in analytical consultancy, including 18 years with Hartley McMaster.

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THE EUROPEAN JOURNAL OF INFORMATION SYSTEMS

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

Satis quinquennalis fiducias imputat gulosus agricolae.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

Satis quinquennalis fiducias imputat gulosus agricolae.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Contents

The European Journal of Information Systems provides a distinctive European perspective on the VOLUME 00 NUMBER 00 MONTH 00 theory and practice of information ISSN: 0960-085X systems for a global audience.

00 We encourage first rate research 00 articles by academics, but also case 00 00 studies and reflective articles by 00 practitioners.

We provide a critical view on technology, development, 00 implementation, strategy, 00 management and policy.

00

00

00 Editor-in-Chief

THE EUROP JOURNAL O INFORMATIO SYSTEMS

Pär Ågerfalk, 00 Uppsala University, Sweden 00

Editors Frantz Rowe, University of Nantes, France Dov Te’eni, Tel Aviv University, Israel VOLUME 00

T&F STEM @tandfSTEM

Dov Te’eni @tandfengineering NUMBER 00

Explore more today… bit.ly/2BWxKtu MONTH 2018

MATTHIAS EHRGOTT

THE MODERN CANCER ‘EPIDEMIC’ means more patients are every day depending on radiotherapy to help save their life. But the treatment can also lead to damaging side effects.

The work described here provides information to clinical staff on how to improve treatment plans. Glyn Shentall at Rosemere Cancer Centre commented: ‘Each completed treatment plan is approved by the patient’s Oncologist and independently checked by a Treatment Planning expert.

However, it is still difficult to know if the proposed plan is just meeting the minimum generic standards, or if it is truly optimal for that patient. Our hope is that the methods proposed by Professor Ehrgott will be able to pick out the patients where an improvement to a treatment plan is feasible. This will

save time and effort for the checkers, and will also ensure that each patient receives the best possible treatment’.

Our hope is that the methods proposed by Professor Ehrgott will be able to pick out the patients where an improvement to a treatment plan is feasible Cancer has been treated with radiotherapy for more than a hundred years. External radiotherapy uses highenergy radiation, such as X-rays or gamma rays to bombard a tumour and destroy the cancer cells. The principle is simple enough: cancerous cells are not as capable as healthy ones to recover from radiation damage. The success of radiotherapy – it

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© Mark_Kostich/Shutterstock.com

OPTIMISING HOPE

contributes to 40% of all cured cancer cases – means that more than half of all cancer patients experience radiotherapy as part of their treatment. In England, just as one example, that means around 130,000 patients each year receive radiotherapy.

The problem is the ways in which the radiation can affect healthy cells in organs around the treated area. While the radiation is targeted to the tumour it inevitably travels through healthy tissue surrounding the tumour and there is always the potential for damage: necrosis (dead tissue and holes), fibrosis (excessive growth of new cells as the body attempts to repair damage), damaged organ function and nerves.

Consequently, radiotherapy has to be planned around meeting conflicting goals. There needs to be a high enough dose of radiation to attack or control the tumour; but also a low enough dose to avoid complications in the normal, healthy tissues. Clinicians have more explicit information on the extent and position of tumours than ever before due to advances in medical imaging and visualisation, but, ultimately, they need to balance benefits with risks and are left with difficult questions of judgement.

Once radiotherapy treatment has been decided upon, Computer Tomography (CT) scans of the patient are taken. These provide anatomical details of the body so that the location, size and shape of the tumour and surrounding organs can be determined. Software is used to firstly ensure a checklist of criteria for safety is met, then to optimise the benefits against potential dangers, and to produce a treatment plan that leads to a dose distribution that achieves the treatment goals. Only if all the criteria are met can a plan be approved for treatment.

For example, a typical prescription for a patient with prostate cancer might be to ‘deliver 74 Gray to the tumour in 37 fractions (the series of daily

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treatments) and such that 99% of the tumour receives at least 70.3Gy (Gray or Gy is the unit of measurement for radiation dose) while at most 80% of the rectum receive 30Gy or more and at most 35% of the bladder receive 70Gy’.

The challenge for clinical decisionmakers is that they are working on a trial and error basis. The software can make sure treatment plans meet the most important criteria, but beyond this, they need to make their own adjustments to try to maximise the benefits of undertaking the radiotherapy. However, they do not know what the trade-offs between successfully destroying the tumour and protecting healthy organs are until after a plan is computed. With the increasing demand for radiotherapy, plans can goahead without taking full advantage of the full potential of the treatment.

PLANNING FOR BETTER

We have been working with the Royal Preston Hospital’s Rosemere Cancer Centre on a way to reduce the level of trial and error involved. In particular, this involved looking at prostate cancer cases – the most common form of cancer in male patients, accounting for a large number of radiotherapy patients. When treating prostate cancer with radiotherapy, damage to cells in the neighbouring bladder and rectum cannot be avoided causing the dilemma of making trade-offs.

We worked with 51 different treatment plans relating to 36 patients, and applied ‘data envelopment analysis’.

It is a management science technique, which compares the performance of a group of different ‘decision-making units’ (treatment plans in our case).

In other words, we tested proposed treatment plans against a dataset of previous plans, looking for evidence of where plans for comparable cases have achieved higher doses of radiation to the tumour accompanied by lower doses of

radiation to the rectum or bladder. This provides information to clinical staff on how to improve treatment plans.

We expect that using this technique will reduce the time needed to identify beneficial treatment plans and ensure the benefits of radiotherapy are available to more patients who need it.

using this technique will reduce the time needed to identify beneficial treatment plans and ensure the benefits of radiotherapy are available to more patients who need it Building on this study, we are now looking at more complex cancer cases affecting the head and neck with Leeds St James Hospital, to build up a stronger body of evidence for the value of the plan evaluation tool and support for treatment planners and oncologists in their efforts to fight cancer using radiotherapy. As more cases are studied and a larger database of treatment plans is built up, the more evidence there is to base improvements on. A virtuous circle of knowledge and testing of knowledge will be created, and a good example of the potential of using big data technologies to improve health treatments for everyone. The aim, over time, will be to make the tool a standard feature of the radiotherapy assessment process in the NHS and among healthcare systems internationally.

This article has been slightly amended from an article in Lancaster University Management School’s magazine Fiftyfourdegrees, with kind permission.

Matthias Ehrgott is Professor of Management Science at Lancaster University. His main research interest is in multi-objective optimisation and applications across medicine, transportation, and manufacturing.

BLENDING AND SOFT SOFT BLENDING HARD HARD AND O.R. CHILDREN’S O.R.TO TO IMPROVE IMPROVE CHILDREN’S HEART HEART SERVICES SERVICES BRIAN CLEGG BRIAN CLEGG

THERE’S JOKE amongst amongst THERE’S AN AN OLD OLD JOKE operational practitionerswhere where operational research research practitioners an party. When Whenshe she anO.R. O.R. analyst analyst is at a party. isisasked what she does for a living, after asked what for a living, after several false false starts, starts, she either several either says says‘I‘I workwith with computers’ computers’ or work or ‘I‘I use usemaths mathstoto solve problems.’

In the early days solve problems.’ early daysofof O.R.,the the answer answer selected selected depended O.R., dependedon on whether the analyst’s O.R. group whether the analyst’s O.R. group considered computers computers aa corrupting considered corrupting influence or the obvious obvious way influence or the way forward. forward.

Now,the thecomputer computer faction won Now, faction hashas won to to theextent extentthat thatthethe answer would the answer would be be ‘I ‘I usecomputers computerstoto maths solve use useuse maths to to solve problems.’However, However, either case, problems.’ in in either case, there that O.R.’s default therewas wasnonodoubt doubt that O.R.’s default image mathematical imagewas wasasasa ‘hard’ a ‘hard’ mathematical science. had a soft side. science.Yet Yetit italways always had a soft side.

Although O.R. applications, Althoughmany many O.R. applications, such or or routing or or suchasasscheduling scheduling routing queueing, have proved highly amenable queueing, have proved highly amenable totoa amathematical approach, the range mathematical approach, the range

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1

of problems operational research tackles has always included human needs, and never more so than when dealing with the National Health Service. Here, the numerical requirements for, say, resource allocation have to be put alongside the patient experience.

INFANTS WITH HEART DISEASE Usually the qualitative side of O. R. – assessed by questionnaires and interviews – has existed separately from the quantitative side. However, a study by the Clinical Operational Research Unit (CORU) at University College London, working with Great Ormond Street Hospital, has combined soft and hard methods to improve the quality of care for infants with congenital heart disease – an approach that may have wider application in the improvement of health care for complex needs.

In medical conditions such as these where care is multifaceted and includes time in hospital as well as care after discharge, the patient is handled by a range of health professionals and organisations. Getting a better understanding of the process to provide opportunities for improvement requires a clear picture of all those involved and how they interact. A combination of this qualitative analysis with the quantitative approach needed to match up patients’ risk to available resources seemed the best way forward.

O.R. input to this project came from Dr. Sonya Crowe, who has a PhD in experimental condensed matter physics. This may seem distant from the practical nature of operational research, yet when the discipline was developed during the Second World War, its instigators were primarily

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physicists. Crowe notes ‘whilst my PhD was fascinating, I wanted to do something that was more directly contributing to society. So, when I finished my PhD, I was looking for things that would enable me to direct my analytical training and skills in “modelling for insight” towards more tangible problems. A friend-of-a-friend was working for the O.R. services within Government (GORS) and when she described it, I thought it sounded really interesting, so I decided to apply.’

Working within GORS, Crowe developed a particular interest in healthcare and in 2009 moved to CORU. Here she won a prestigious Health Foundation Improvement Science Fellowship in 2013, part of which involved the project related to infant congenital heart disease. Crowe: ‘It was during my time at GORS, where I worked on current and sometimes emotive or politically charged topics, that I gained an understanding of the positive role and, importantly, the limitations of O.R. in informing complex decision processes. This continues to inform my work in

academic healthcare O.R. to this day – namely, conducting impactful O.R. focused on making a positive difference to patients, carers, staff and the wider public and conducting research on how to do more effective O.R. for health service improvement.’

Working with health professionals, Crowe explored the services provided for infants with congenital heart disease.

After a hospital procedure, care is delivered by a complex mix of outpatient care at the hospital, local clinics, community and cardiac nurses, GPs and health visitors. There were concerns over mortality rates in these young patients and the project had the key aim of identifying ways to improve care and support after discharge from hospital.

THE RIGHT TECHNIQUES In choosing O.R. tools and techniques to employ, there was a need to deal with both the soft aspect of giving structure to this complex system of interactions and the hard aspect of matching resources to risk levels. For the qualitative side, soft systems

methodology (SSM) was chosen, while the quantitative technique of classification and regression tree (CART) analysis was selected to deal with risk and resources.

SSM is a systems engineering technique, developed at the University of Lancaster. The process involves exploring a problem area from the worldviews of the different participants with the aim of putting together ‘root definitions’ which identify the key systems involved. These are then modelled to explore potential changes that can improve the overall system.

To help with the exploration of the problem area, Crowe and the team assembled a ‘Rich Picture’ – a graphical, cartoon-like portrayal of the people, processes, relationships and issues in the current system (see Figure 1). The first draft of this was based on interviews with parents and healthcare professionals, then augmented with information on risk factors derived from analyses of national health datasets for this patient group. The Rich Picture was then used as a resource in facilitated workshops, first with parents/carers of infants with congenital heart disease and then with professionals to look for opportunities for service improvements.

Although some more mathematically-minded professionals might associate the Rich Picture with a hand waving, non-rigorous approach – Jenni Burt of the General Practice and Primary Care Research Unit at the University of Cambridge, commenting on the study, noted that the term ‘Rich Pictures’ was ‘destined to make my quantitative colleagues shudder’ – there seems no doubt that those involved considered it an eye-opener.

A cardiologist contributing to the project said ‘People saw immediately

FIGURE 1 RICH PICTURE FROM CROWE, S., K. BROWN, J. TREGAY ET AL, (2017).

BMJ QUALITY AND SAFETY 26: 641–652

what it [Rich Picture] was doing and knew which corner of the world that they were in. If I live here I could see that this was only a small section of the territory. Even if you’re just working in improving things here, it absolutely demands that you at least recognise that these other places exist.’ Similarly, the facilitated workshop, bringing together different

disciplines, was considered a powerful benefit. For example, a consultant paediatric cardiologist commented: ‘it was very helpful to have those people [in the workshop], because as a specialist centre worker, you don’t necessarily appreciate some of the stresses and difficulties that the secondary and particularly primary care people have.’

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From the refined Rich Picture, the team developed root definitions for each worldview. The SSM process for this involves identifying a number of factors from customers and ‘actors’ to owners and environmental constraints.

As an example, the root definition for community nursing was ‘A medical support service delivered in the home by community nurses for babies that have difficulties feeding or other medical needs and/or require home monitoring during their inter-stage period (between planned procedures) in order to help them recover from surgery and to spot and respond to any deterioration in their health whilst they are particularly vulnerable.’

The next stage of SSM is to build conceptual models of the systems covered by root definitions. In this case, these were activity diagrams: bubble diagrams of the ordered set of key activities to provide the service, with sub-activities feeding into them. Crowe then used these to generate questions for possible changes to services which were explored with relevant health professionals, leading to an overall picture of potential improvements and recommendations.

PROBLEMS AND SOLUTIONS The problems that emerged were often capable of amalgamation into ‘archetypal service problems’. So, for example, seven different identified issues coming from a range of worldviews were pulled together as ‘Poor access to local support services’, where it was noted that it was difficult for specialist centres to know what local services are available and how to contact them. This was then used to produce solutions, for example, having a named cardiologist, paediatrician,

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specialist nurse and GP at the time of discharge.

This simple graphical presentation of complex underlying data was appreciated by participants as a valuable communication tool

Inevitably, suggestions for improvement require access to scarce resources – and this is where the quantitative part of the project kicked in. The CART analysis was used to identify subgroups of the patients who would benefit from different interventions, based on their risk factors. The outcome was to divide the population into six groups with different combinations of risk factors, such as having other non-cardiac disorders or their length of stay in hospital. Crowe developed a visual representation of the analysis which used a block diagram to show both the relative size of the groups and the risk of problems developing to help with resource allocation.

This simple graphical presentation of complex underlying data was appreciated by participants as a valuable communication tool. The data-set used involved 7,643 patients who survived to being discharged, with many different risk factors being considered, but the approach taken avoided blinding the participants with swathes of numbers. As a health psychologist on the team commented: ‘If you’re not mathematically minded you can still understand [the CART diagram]. I think this really helps people not to feel frightened or to feel that they’re not getting it and, actually, then they can really think about what it means.’

As a result of the combination of the quantitative information and the Rich Picture, a working group of professionals, patient representatives and academics was able to recommend targeted solutions. Feasibility and acceptability primarily came from the qualitative side, while targeting came from the quantitative. For example, multidisciplinary care teams were only recommended for children with long-term complex needs – three out of the six categories – and formal home monitoring was only recommended for babies with particular high-risk cardiac diagnoses (one of the six). The study findings were discussed in detail with National Health Service England and other groups involved in services for congenital heart disease and went on to influence national decision-making about service standards and commissioning in this area. Work is underway to expand this approach to other aspects of improving care for those with congenital heart disease.

A NEW ROLE FOR O.R. Crowe also added a meta-level of research to get a better understanding of the role of O.R. in this process and to see how O.R. methods can be used to mediate knowledge production.

Both this study and the qualitative side of the heart disease study involved lengthy processes of interviews and workshops. Crowe believes that it should be possible to streamline the process to make it more broadly applicable: ‘Streamlining would be important and I’m currently doing some research aimed at this. It includes developing resources with prompts for operational researchers to consider in trying to understand, adapt to or influence the organisational context of a project so as to enhance the potential

impact of their work. This will draw on what I learned in the qualitative part so that not everyone else has to go through the same time-consuming process.’

The study findings went on to influence national decision-making about service standards and commissioning

While there is no question that the mathematical and computer modelling approach that has dominated operational research will continue, Crowe argues that the mixed approach, in which the skills of the O.R. practitioner are brought into play in a mediation role, will have increased value in the future. While there is a considerable amount of hard O.R. in healthcare, its uptake is somewhat limited and the mixed approach could lead to better implementation. ‘I think

operational researchers need to be more critical and reflective about the ways we work and how this influences the impact of what we do. There is much we can learn from other disciplines, but also by returning to our roots and the original ethos of the discipline. And of course, a particular interest of mine is combining the soft and hard elements of O.R. as this can really help to enhance the impact of our work, particularly when trying to support the provision of services that span multiple settings.’

‘What I found in my research is that O.R. also has a lot that it does well and can be of benefit to other disciplines and research endeavours. Specifically, my “knowledge mediation activities” within the multidisciplinary applied health research project supported the generation of accessible, practicerelevant and actionable knowledge. I would argue that incorporating these activities in projects (potentially through an O.R. researcher as mediator)

could help enhance the uptake of research findings into routine healthcare.’

The appeal of O.R. to many practitioners is its combination of a hard, scientific discipline with the opportunity to solve problems that benefit people.

Although Sonya Crowe’s approach is particularly suited to the requirements of the healthcare sector, there seems little doubt that it will find wider application where human need and limited resources require a careful balance.

Brian Clegg is a science journalist and author and who runs the www. popularscience.co.uk and his own www. brianclegg.net websites. After graduating with a Lancaster University MA in Operational Research in 1977, Brian joined the O.R. Department at British Airways, where his work was focussed on computing, as information technology became central to all the O.R. work he did. He left BA in 1994 to set up a creativity training business.

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© VR Group, Tuomas Uusheimo / KEKSI

OPTIMIZING LOCOMOTIVE MAINTENANCE TIMES FOR VR GROUP LTD. IN FINLAND

© VR Group, Tuomas Uusheimo / KEKSI

JOONAS OLLILA AND OTTO SORMUNEN

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VR GROUP LTD. is the main railway operator on Finnish tracks. During the 2010s the company has renewed its operations with more aggressive pricing and significant investments in rolling stock. Both the passenger and cargo operations saw a 5% volume increase in 2017. Simultaneously VR’s personnel has decreased at an average rate of 7% annually over 2013-2017 and the profitability has gone up from 5% in 2013 to 8.8% in 2017, making it one of the top-rated European railroad companies.

IMPACT © 2018 THE AUTHORS

To make operations as efficient as possible, the company has explored and deployed different O.R. solutions such as commissioning multiple M.Sc. theses e.g. for optimizing driver rostering. Recently VR hired several operation researchers with an academic background for optimizing train maintenance with the mission to provide state-of-the-art solutions for mathematical, statistical and supply chain management modelling and optimization challenges. In addition, there has been cooperation with O.R.

consultants such as Weoptit. Weoptit has expertise in O.R. and AI and has earlier optimized locomotive driver work rosters for VR. Now they focused on optimizing the long-term plan of locomotive maintenance operations.

MODELLING LOCOMOTIVE MAINTENANCE OPERATIONS

VR has three types of electric locomotives—Sr1, Sr2 and Sr3. The oldest electric locomotives of type Sr1 were built in the ‘70s and ‘80s. The opening photograph shows an electric locomotive of type Sr1 in maintenance.

Currently they are at a stage in their life cycle where maintenance operations can be well anticipated. Due to increasing traffic and higher personnel costs during night-time and weekends, it is essential to identify low-cost maintenance windows for operations. The locomotives have some natural idle time at maintenance depots between driving tasks. When two or more locomotives are idle simultaneously at the same depot, these idle times can be linked to create maintenance windows of varying length (see Figure 1). In addition, there are some locomotives for which there is no scheduled traffic (extra locomotives) and they can be used for maintenance.

VR also needs the extra locomotives for additional traffic and more timeconsuming upgrades, hence minimizing their use for maintenance is important.

The optimization objective was to minimize maintenance costs. This was achieved by linking idle times together to create suitable maintenance windows during times when maintenance is cheapest. Maintaining rolling stock during night-time and weekends is up to 160% more expensive than during standard work hours. The four maintenance depots had a varying number of tracks, where maintenance can

be carried out. Only one locomotive may occupy a maintenance track at any given time. There were three different sorts of maintenance operations and their lengths were fixed in advance.

relinking the idle times during the optimization and utilizing the extra locomotives most efficiently proved to be very challenging

SOLVING DIFFERENT BUSINESS CASES

VR wanted to assess the maintenance costs and workloads in a range of different scenarios. The scenarios were: 1. Baseline scenario with existing depots and all available extra locomotives (106 locomotives in total) 2. Increased traffic scenario, which means all extra locomotives are tied to traffic tasks 3. Extending an existing depot to also maintain Sr1’s 4. Extending an existing depot and increased traffic

5. Increased traffic and increased maintenance requirement (20% more maintenance) 6. Increased traffic and a different maintenance distribution Weoptit modelled the problem as a mixed integer program. The variables considered were start time of maintenance, finish time of maintenance, whether an operation was assigned to a time window or not and whether a maintenance track is in use during a time window or not. The extra locomotives were modelled as having only idle time and were tied to a single depot, which was determined during the optimization. All operations were required to finish during the planning period. Using a 3-week standard planning period, 15-minute time discretization and all available maintenance tracks the problem comprised several million variables and a similar number of constraints. Especially relinking the idle times during the optimization and utilizing the extra locomotives most efficiently proved to be very challenging. WEOPTIT solved the problem to optimality in all scenarios using the Gurobi solver.

FIGURE 1 CREATING LONGER MAINTENANCE WINDOWS BY LINKING IDLE TIMES

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the avoidance of expensive work time (night-time and weekends) had a profound impact on the total cost

IMPACTING OPERATIONS

The results showed that all maintenance operations fit within the idle times, which frees up locomotive capacity for other tasks. Especially the avoidance of expensive work time (night-time and weekends) had a profound impact on the total cost.

The suggested resourcing distribution is heavily concentrated on day time hours and weekdays (see Figure 2).

Finding suitable maintenance windows by linking idle times would not have been possible without optimization.

The optimized cost is not comparable to actual costs as such, since the 3-week plan does not consider delays in traffic or delayed maintenance operations.

Some buffer time was however included in the model before and after each maintenance task to make results more realistic.

An interesting discovery was that including a new maintenance depot affected the existing depots’ workloads

FIGURE 2 WORKLOAD DISTRIBUTION IN THE OPTIMIZED SCENARIO

very differently. 67% of the work done on the new depot reduced the workload of a single existing depot by the same amount (see Figure 3).

VR continues optimizing its maintenance procedures and research in different O.R. models for improving its operations

In the optimization the longterm locomotive maintenance plan is anonymous in the sense that tasks are

not assigned to a certain individual locomotive. In the short term the driving tasks are interchanged between locomotives when necessary, because of the individual locomotives’ maintenance windows which are dependent on dis­tance driven. For that a dynamic optimi­zation model is necessary, which can assign operations when they are due.

To this end, VR continues optimizing its maintenance procedures and research in different O.R. models for improving its operations. The constructed optimization model offers a good framework for assessing the effect of different traffic scenarios. Furthermore, the results provide analytic support for targeting investments in the maintenance network.

Joonas Ollila is the COO of Weoptit Oy. He received his M.Sc. in Operations Research from Aalto University in 2013. Since then, he has been working on optimization projects both as a practitioner and project owner.

FIGURE 3 DISTRIBUTION OF TOTAL WORKLOAD DECREASE WHEN A NEW DEPOT WAS INCLUDED

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Otto Sormunen is a Data Scientist at VR Group’s maintenance unit. He has a M.Sc. (Econ.) from Hanken School of Economics as well as a M.Sc. (Eng.) and a D.Sc. (Eng.) from Aalto University. His work includes mathematical modelling, statistical analysis and simulation of real-life systems.

R E V E N U E M A N AG E M E N T I N T H E D I G I TA L E CO N O M Y ARNE K. STRAUSS AND NURSEN AYDIN

THE SCIENCE (AND ART) OF MAKING AUTOMATED DECISIONS that influence demand (such as pricing) is traditionally referred to as “revenue management” (RM). The term originates from the airline industry in the late 1970s, when prices were largely fixed and the first automated demand management systems controlled the availability of fare classes with the aim of maximising revenue (given that airlines’ costs are predominantly fixed). The main differentiator to traditional pricing approaches is the in-built automation of decision making that enables large volumes of granular demand management decisions. The latter may relate to pricing, availability control over which product alternatives are being displayed to customers, or possibly even other decisions that impact demand. Due to the success of these systems, many other industries have adopted similar approaches over the decades.

THE DIGITAL ECONOMY

With the advent of the digital economy (DE), new opportunities present themselves due to the rich information on customers available in many DE applications. Furthermore, products and services on offer can be easily customised to customer profiles through digital channels, so overall it is not surprising that RM techniques find a fertile ground in the area of DE applications. For instance, RM

have been recently applied to Internet advertising or attended home delivery.

Furthermore, RM concepts are being applied to admission control by cloud computing providers.

Even traditional RM domains such as the airline industry increasingly seek to embed DE approaches in their business model. For example, the introduction of the New Distribution Capability (NDC) standard by the International Air Transport Association constitutes a major step in this direction. The NDC is a datatransmission standard that may lead to a more interactive sales process connecting travel agents and airlines directly. This should allow airlines to increasingly target customers with personalised offers. Although it will be easier for airlines to gain additional granular customer information thanks to this recent development, it is still a challenge to find the right strategies to use this information.

As Steve Peterson, Global Travel and Transportation Leader at IBM’s Institute for Business Value, stated: “personalisation is not about finding out someone’s maximum willingness to pay, it is about doing something unique for each customer”.

Airlines are seeking ways to harness related big data to offer a more personalised experience to their customers and achieve competitive differentiation

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Providing personalised services requires a customer-centric approach based on an understanding of traveller expectations and desires. In this regard, airlines are seeking ways to harness related big data to offer a more personalised experience to their customers and achieve competitive differentiation. They are testing new methods to capture and to make sense of customer behaviour. The Smile programme of Lufthansa Airlines is one example of personalisation. In order to offer personalised customer service, Lufthansa provides real-time notifications and messages through their mobile application with the use of beacons placed at contracted airports.

By tracking customers in the airport, the application can offer services such as free lounge access, airport discounts and other flight related information depending on the customers’ behaviour and preferences. With the use of this program, Lufthansa can target specific services to relevant customers instead of bombarding them with all of their options. In addition, real-time analytics allows airlines to react to real-time events such as delayed flights and baggage lost.

Moving the focus away from airlines specifically, we observe that personalisation in general is likely to gain significant attention from both academia and industry. Again, this is due to the technical opportunities offered

by DE applications: better information on customers is available, and it is combined with the ability to tailor offers to customers individually (in principle).

However, against the background of new legal frameworks like the General Data Protection Regulation and growing awareness of the potential for misuse of personal data, we believe that the future of personalisation will lie in developing dynamic and learning models of demand

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that “think” in customer types, rather than in individuals. Over the past 15 years or so, a significant body of literature has been produced on RM using models of customer behaviour, in particular with regard to customers’ choices between product alternatives (see Strauss et al. 2018 for a review). This steam of research is currently heading towards personalisation, naturally, within DE applications such as personalised assortments for loyalty programms in retailing.

WHEN IS REVENUE MANAGEMENT APPLICABLE?

In general, revenue management is applicable to any industry that has a fixed capacity, perishable inventory, variable demand and segmentable market. Capacity can be tangible such as number of products or time-based such as reservation times in restaurants.

Time-based products are only available within a certain duration. Different

customers are assumed to have potentially different willingness to pay for the same product and demand is usually variable over time.

WHERE WILL WE SEE REVENUE MANAGEMENT IMPACT IN THE DIGITAL ECONOMY?

The broader use of digital tools has an enormous potential for on-demand services in transportation, hospitality and retail. Today, the consumer trends involve an increasing use of online channels to search and evaluate alternative products before making final purchase decision. The growing popularity of social media to share first-hand experiences allows gathering correlated customer data which then can be used to make informed decisions by companies. There are many new tech companies working mainly in the service industry to analyse public data by mining from online sources

© wavebreakmedia/Shutterstock.com

THE SHIFT TOWARDS PERSONALISATION

© NicoElNino/Istockphoto

and social media. This analysis is used to understand and predict customer behaviour, opening a new gateway to service industry for personalisation and demand management. Qantas Airways is one of the pioneers in this area. By using a data management platform, Qantas combines web, mobile and social data to improve their services.

This database helps them to create new revenue streams by identifying products and featured offers on a specific route.

Other on-demand industries also benefit from the opportunities of this new digital era to expand their market.

By taking advantage of mobile user data, Uber has developed revenue management systems that provide realtime pricing using demand forecasting and customer behaviour analysis.

Over the years, Uber has introduced different service classes such as economy and premium to attract customers from different market segments. Initially, they focussed on traditional black-cab services. Once the business expanded and new opportunities arose, Uber generated new services by segmenting customers

based on demographic, psychographic and behavioural aspects.

Similarly, Airbnb is providing hospitality services without holding any physical assets and without directly employing staff in the accommodation sector. Their business model is to provide data-driven web-based services and again, one of their key features is personalisation. Recently, Airbnb has developed a pricing tool called Smart Pricing for its hosts to personalise their pricing to attract different customer segments. This dynamic pricing tool benefits from the web and social data such as page views, reviews and popular events while making price adjustments.

Real-time analytics allows airlines to react to real-time events such as delayed flights and baggage lost

In online retailing, there is growing pressure to offer attractive delivery options to customers, which normally

means shorter lead times and/or narrow delivery time windows. Given that the fulfilment costs are one of the main cost drivers, there is much interest in ways of increasing the efficiency of the last mile logistics by means of dynamically influencing customers’ decisions on the desired delivery mode. Since customers need to log into their profile before being able to book a delivery, the retailer has detailed information on the customer, other orders booked so far and their destinations, and possibly some information on future expected orders. Combined with a model of customer choice describing the probability of a customer deciding on a particular alternative subject to other available alternatives and their features (such as prices), optimisation models can guide the retailer’s decision of which fulfilment options to show and at what prices in real time.

CONCLUSION

In summary, the digital era brings new ways of obtaining information which drives informed and strategic decision making. Customers have access to vast array of information sources and seek cost effective differentiators in businesses. There is a growing demand for personalisation and product variation. Especially for ondemand businesses, this creates both opportunities to attract customers and challenges due to ever evolving markets necessitating adaptable strategies. Businesses need to find new ways to create difference and become more connected to their customers.

The use of new technologies makes it possible to aggregate and link customer data to other external influences which can then be used to shape the services offered to

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customers. Revenue management has become more strategic and more centralised in this new connected world.

The digital era brings new ways of obtaining information which drives informed and strategic decision making

Dr. Arne K. Strauss is an Associate Professor of Operational Research at Warwick Business School (WBS). He has conducted research on revenue management since 2006, with an emphasis on modelling customer choice behaviour and subsequent choice-based optimisation of demand management

decisions. He has worked with various companies and is currently involved in a number of research projects in this domain with partners in retail, air traffic management and waste collection.

As a concrete example, he worked on dynamically influencing customers’ selections of the time slot for home delivery of goods in real time so as to improve the efficiency of last mile logistics.

He is the Academic Director of WBS’ MSc Business Analytics programme and teaches therein a module on Pricing & Revenue Management.

FOR FURTHER READING Strauss, A. K., R. Klein and C. Steinhardt (2018). A review of choicebased revenue management: Theory and methods. European Journal of Operational Research 271: 375–387.

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Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

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KNOWLEDGE MANAGEMENT RESEARCH AND PRACTICE Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

Satis quinquennalis fiducias imputat gulosus agricolae.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

Perspicax agricolae suffragarit Augustus. Suis vocificat fiducias.

Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

Satis saetosus ossifragi agnascor incredibiliter perspicax apparatus bellis.

Satis quinquennalis fiducias imputat gulosus agricolae.

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

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VOLUME 00 NUMBER 00 MONTH 00 ISSN: 0960-085X

00 To provide an outlet for high-quality, peer-reviewed articles on all aspects of managing knowledge.

00 include not just those focused on the organisational level, but all levels from that of the This will individual to that of the nation or profession. This will include both theoretical and practical aspects, 00 and especially the relationship between the two. There will be a particular emphasis on crossdisciplinary approaches, and on the mixing of “hard” (e.g. technological) and “soft” (e.g. cultural or 00 motivational) issues. Rigorous contributions from both academics and practitioners are welcomed.

00 Articles may be empirical research papers, theoretical papers, conceptual papers, case studies or surveys.

00 KMRP will fill the need for a journal specifically concentrating on knowledge management that 00 maintains the highest standards of rigour, and publishes articles that reflect greater multidisciplinary 00 work and/or conceptual integration than those currently published in existing outlets.

00 A cross-disciplinary focus will also enable articles in the journal to address other important tensions in the field of knowledge management, such as those between:

THE EUROPEAN JOURNAL OF INFORMATION SYSTEMS

• Strategy and operations • People and technology • Short-term and long-term needs

• The organisation and the individual

Editor Giovanni Schiuma, University of Basilicata, Italy Consulting Editor John S. Edwards, Aston University, UK Te’eni Dov VOLUME 00

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NUMBER 00

Explore more today… http://bit.ly/2DYvePT MONTH 2018

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EJIS

Apparatus bellis corrumperet Medusa, quod fiducias amputat verecundus suis.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Contents

Saburre miscere Aquae Sulis. Pessimus tremulus matrimonii insectat Octavius.

Dr. Nursen Aydin is an Assistant Professor in the Operational Research and Management Science group. Before joining Warwick Business School, she worked as a Marie Curie research fellow at Brunel Business School, UK from January 2015 to May 2016. She holds a Ph.D. in Industrial Engineering from Sabanci University, Turkey. During her PhD, she worked as a visiting researcher on airline revenue management at Cornell University, USA. She worked at Pegasus Airlines as an assistant revenue management analyst prior to her graduate studies.

N OT O N LY … . B U T A L S O Geoff Royston

These are very different statements. The first tells the child that if they eat broccoli, that is sufficient for them to get ice cream, but does not exclude the possibility that there might be other ways of getting ice cream! The second makes it clear that it is necessary for the child to eat broccoli to get ice cream, and should protect the parent from a precocious child attempting to negotiate alternative pathways.

Similarly important is recognising the difference between opposites and negation e.g.

• statement: sugar is good for you • opposite: sugar is bad for you • negation: sugar is not good for you (allows the possibility that a small amount of sugar won’t harm you)

Three logicians walk into a bar. The bartender says “Would everyone like a beer?” The first logician says “I don’t know” The second logician says “I don’t know”. The third logician says “Yes”.

Not a comedy sketch from Peter Cook and Dudley Moore, but a favourite joke of the mathematician, concert pianist and nominee for the Royal Society Science Book Prize, Eugenia Cheng.

In her most recent book, The Art of Logic: How to Make Sense in a World that Doesn’t, she makes a bold claim: “there is a lifebelt available to anyone drowning in the illogic of the modern world, and that lifebelt is logic”.

Mathematics is clearly useful in areas where realworld problems can be formulated in the form of, say, equations. But what about all those areas where there is a lot more involved than numbers, equations and computation? In The Art of Logic Cheng sets out a case that the rigorous logical framework for argumentation that is used in mathematics has much wider applicability, as illustrated by the following simple examples from the book.

NECESSARY OR SUFFICIENT?

A parent might say to a child: “if you eat your broccoli you can have ice cream”, or “you can have ice cream only if you eat your broccoli”.

As Cheng says, the opposite of the North Pole is the South Pole, but there is a lot of world in between.

Confusion over negation and between necessary and sufficient is related to a common and troublesome error – thinking that the converse of a true statement must also be true. The truth that “if you are a UK citizen then you can legally live in the UK” does not imply the truth of its converse: “If you can legally live in the UK then you are a UK citizen” – for instance you might have a visa. Indeed, there is no necessary connection at all between the truth of a statement and of its converse.

AND OR

The little words “and” and “or” (which, incidentally, underpin the Boolean logic used in the binary arithmetic circuitry of digital computers) lead to many logical mistakes in argumentation, with real-world consequences.

For example, “I dropped the glass, you didn’t catch it and the floor was hard”. Who or what was to blame for the breakage? Any one of me not dropping the glass, you catching it, or a softer floor would have avoided the smash, but it is the combination that is to blame. Cheng notes that when an (adverse) event has multiple contributory factors, we need to be on guard against the common tendency to look for someone or something to blame just because changing it could have prevented the event. Singling out one thing may not be at all productive, more useful may be understanding the elements and interactions of the whole system.

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Expressing true emotions with false logic can often be unhelpful, and easy to refute: “You never do the washing up!”

“One time I did do the washing up”.

But, of course, the sweeping statement was not meant literally, and the pedantic refutation would certainly not have helped the situation. The complaint probably meant “I feel that you do much less than your fair share of the washing up and so I feel overworked and underappreciated”. It would have been more accurate – and probably would have elicited a more constructive response – to have said just that.

This example also serves as a general warning against making sweeping statements – where even one countercase suffices as disproof. It is, sometimes, safer to preface statements with “sometimes”, or “it seems to me that” or even “there is a sense in which” (the latter can prompt an illuminating search for the sense in which the statement might indeed be true.)

The book moves on to discuss various logical paradoxes and conundrums, such as the Prisoner’s Dilemma and the Tragedy of the Commons - a modern-day version of which is (in)action on climate change – where decisions that are beneficial for a group are not logical for an individual, noting however that this logic can change if trust levels are high.

Such instances take us nearer to another set of issues discussed in The Art of Logic, cases where application of hard logic is difficult or even hazardous; grey areas.

GREY AND FUZZY LOGIC

Logic can make you fat: “It can’t hurt to eat one small piece of cake”.

“And however much cake I have eaten it can’t make much difference to eat just one more small piece”.

This is an obesity-generating logic machine! Imperceptible incremental additions to weight can add up to a very perceptible overall increase. The only way out seems to be to not eat any cake at all, which feels harsh. But Cheng argues that there is no clear, logical, place to draw a strict line between “none” and “as much as you like” for what is a sensible amount of cake to eat. The best approach would seem to be to put a line somewhere in the fuzzy grey area between them, with a position that relates to the severity of the consequences of getting to the extreme positions.

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(Cheng shows how similar arguments can be made for more contentious areas of life such as discrimination or harassment.) Allowing some logical greyness can help avoid arguments descending into extreme black or white positions.

LOGOS, ETHOS, PATHOS

In later sections of the book Cheng discusses more advanced ideas in logic about abstraction, analogies, false equivalences, false dichotomies, and straw man arguments, together with some real-world applications. She notes for example that equating being emotional to necessarily being irrational is a false equivalence and goes on to argue that emotions are never false and cannot be contradicted (if you feel something you are feeling it). Emotions can often play a crucial part in convincing someone of a truth arrived at by logical or evidential means. If people can be made to feel things differently they may then see the logic differently too. Cheng argues that finding an analogy between the logic of a situation you want people to think differently about, say racism, and the logic of one which they already feel strongly about, maybe sexism, can be a powerful way of bridging the logic–emotion gap.

Aristotle used the word logos to describe argument from reason and considered it one of the three modes of persuasion. His other two modes were ethos and pathos – respectively how an audience trusts a speaker and how its emotions are stirred by them. Eugenia Cheng’s plea is for a “true art of logic” that uses “emotions and logic together to think more clearly, communicate more effectively, and achieve a deeper and more compassionate understanding of our fellow human beings. “Not Only …. But Also.”

Image courtesy Profile Books

LOGIC, EMOTION AND TRUST

Dr Geoff Royston is a former president of the O.R. Society and a former chair of the UK Government Operational Research Service. He was head of strategic analysis and operational research in the Department of Health for England, where for almost two decades he was the professional lead for a large group of health analysts.

JOURNAL OF BUSINESS ANALYTICS The mission of the journal is to serve the rapidly growing and emergent community of business analytics both in academics and in industry/ with practitioners. We seek research papers that clearly address a business problem, develop innovative methods/ methodologies and use real-world data to show the how the problem can be solved.

Editors-in-Chief: Dursun Delen, Oklahoma State University, USA dursun.delen@okstate.edu Sudah Ram, Eller College of Management, USA sram@email.arizona.edu

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Beale Lecture 2019

Thursday 21 February 2019 – Entry free

The OR Society’s Beale Medal is awarded each year in memory of the late Martin Beale. It gives formal recognition to a sustained contribution over many years to the theory, practice, or philosophy of OR in the UK.

Lecture: ‘The Future of OR Is Present’ Professor Mike C Jackson OBE Beale Medal Winner 2017

In 1978, at a conference in York, Russ Ackoff argued that the deficiencies of OR could only be overcome by ‘a comprehensive re-conceptualization of the field, its methodology, the way it is practised, and the way students are educated to practise it.’ He saw little chance of this happening and declared ‘the future of OR is past.’ I maintain that the efforts made to broaden the scope of OR will allow us, in 2019, to discern a bright future for the field. In fact, the future of OR is already present.

Opening talk from Dr Itamar Megiddo PhD Winner 2016 for The Most Distinguished Body of Research Leading to the Award of a Doctorate in the Field of OR Dr Itamar Megiddo is a Chancellor’s Fellow, lecturer (assistant professor) at the Department of Management Science at the University of Strathclyde. His research focus is on improving evidence-based decision making and resource allocation in healthcare policy.

32633 OR Beale Lecture 2018 Ad (210x280) stg 1 AW.indd 1

Thursday 21 February 2019 The Royal Society 6-9 Carlton House Terrace London SW1Y 5AG Timings: 14:00 Tea and biscuits 14:30 Lectures starts 16:30 Approximate finish Entry free

Register your place at: www.theorsociety.com/beale Please contact Hilary Wilkes hilary.wilkes@theorsociety.com with any queries.

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