Opssession

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Foreword Warm greetings from Team OIG!!!

This edition of OpsSession brings forward the emerging trends and recent developments in the domain of operations in industry. Detailed explanation of processes and their application in the real time scenario has been presented. Like the previous editions, in this edition too it has been possible because of enthusiastic contribution from IIM Lucknow students.

OIG would like to sincerely thank Professor O.S Vaidya for doing us the favour of critically reviewing the articles by taking time out from his busy schedule.

OIG is thankful to the authors of the articles who have taken the pains of putting so much effort in penning these knowledgeable articles.

OIG would also like to acknowledge Rohit Jharia of PGP-29 for designing the cover page of OpsSession 2013.

We ensure there would be a lot of takeaways from this issue of OpsSession. Happy reading!!!

Team OIG 2013-2014 Operations Interest Group, IIM Lucknow Alok | Anandraj | Hari | Sumit | Tushar | Yokesh Amiya | Chiranjib | Mathew | Sowmya | Ulaganathan

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Contents 1. Articles ................................................................................................................................................ 4 1.1 Operational Intelligence: Bigger, Faster, Stronger Performance .................................................. 4 1.2 Unified Efficiency measurement of Thermal Power Plants using DEA ....................................... 9 1.3 Lean New Product Development ................................................................................................ 15 2. Motivating Stories............................................................................................................................. 19 2.1 Safal ............................................................................................................................................ 20 2.2 Taxi For Sure .............................................................................................................................. 21 3. Measuring Operations Efficiency of Companies .............................................................................. 22 3.1 Gartner Chart of Top performing Companies ............................................................................. 22 3.2 Agriculture Supply Chain: (Facts) .............................................................................................. 24 4. Glossary (A-Z) .................................................................................................................................. 25 5. Solution of crossword ....................................................................................................................... 31

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Rohit Jharia 1. Articles 1.1 Operational Intelligence: Bigger, Faster, Stronger Performance

PGP29179

Information Technology has a major role to play in the current scenario when it comes to applying intelligence in the systems. Operational Intelligence is just another example of the excellent platform that IT provides to the process Industry.

Operational intelligence (OI) is a category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming events and business operations. Operational Intelligence solutions route queries against streaming data feeds and event data to deliver real-time analytic results as operational instructions. It provides organizations the ability to make decisions and immediately act on these analytic insights, through manual or automated actions.

[Street Address] [City, ST ZIP Code] [Telephone] [Web Address] [Dates and Times] [Dates and Times]

In short, Operational intelligence is the ability to answer the question “What is happening in my environment right now?”

Technologies which can be used as Operational Intelligence are as follows: 

 

Real-time monitoring: With real-time monitoring, you can determine what is happening on your system in response to performance issues or problem reports. Real-time monitoring data include statistics that represent the current activity on the system that can help determine usage patterns and resource allocation and identify problem areas.

Correlation of events: It is a technique for making sense of a large number of events and pinpointing the few events that are really important in that mass of information.

Industry-specific dashboards: These are the dashboards that have the details of the various industries listed. The data gets updated at regular intervals, allowing the user to keep a check at other businesses.

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 





Multidimensional analysis: An analysis of many industries under various roles is multidimensional.

For example, analysing all cloth industries under quality, price and distribution basis.

Root cause analysis: It is a collective term that describes a wide range of approaches, tools, and techniques used to uncover causes of problems. The root cause is “the evil at the bottom” that sets in motion the entire cause-and-effect chain causing the problem(s).

Time Series and trending analysis: A time series is a group of observations of well-defined data items obtained through repeated measurements over time. For example, measuring the value of retail sales each month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at equally spaced intervals. Data collected irregularly or only once are not time series. And obtaining a trend through time series would be trending analysis.

Big Data Analytics: Big data analytics is the process of examining great amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information.

Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue.

Comparison of OI with other technologies or solutions: The goal of operational intelligence is to provide business analysts with real-time data related to business processes and activities as they are executed. This real-time information can be used to augment business processes by enabling them to make changes midstream to avert an interruption or avoid a bottleneck.

There are many different types of solutions on the market that attempt to provide business analysts with the same intelligence 1. Business intelligence With business intelligence explanations from vendors such as Cognos or Click view, data is reported on a periodic basis, such as daily, weekly or monthly. The users are given tools to create the parameters for the reports required to make the appropriate business changes. Traditional approaches to business intelligence sit on top of data warehouses or relational data stores, and require a static data model to be defined in order to build the reports for analysis.

In contrast, operational intelligence solutions look at the real-time status of the business process. They are often interactive and enable drill-down competences to enable the analyst to get to the most accurate information at that moment and make appropriate business decisions based on that timely information.

The real-time data provides an incremental judgment of the ever-changing nuances of a business process, and supports greater agility and change.

At the heart of the matter, with business intelligence, analysts are observing at past data, using their conclusions to inform future decisions, but the data and reports are disconnected from the activity stream. With operational intelligence, the data and information are on-going, tied directly to the events and activities that frame a business process, and analysts are proactively tying the decisions with the business processes as they occur.

2. Complex event processing It helps the decision makers in an organization to quickly set up, detect, and examine the information they need to ratify the action they take is timely and pertinent. There is a strong relationship between complex event processing companies and operational intelligence, especially since CEP is regarded by many OI companies as a core component of their OI solutions. CEP companies tend to centre solely on the development of a CEP framework to use it within their organizations as a pure processing engine.

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3. Business activity monitoring (BAM) It is software that aids in monitoring of business processes, as those processes are applied in computer systems. BAM is an enterprise solution primarily intended to provide a real-time summary of business processes to operations managers and upper management. The main difference between BAM and OI appears to be in the implementation details — real-time situation detection appears in BAM and OI and is often implemented by CEP. Furthermore, BAM focuses on high-level process models whereas OI instead relies on correlation to infer a relationship between different events.

S. No

Parameters

Performance by the Solutions O.I. B.I. C.E.P.

1 2 3 4 5 6

Real Time Monitoring of Business Processes Ratification tools Correlation b/w diff processes Interactive with the User Multi-dimensional Analysis Big Data Analysis

High High High High High High

Low High High High Moderate Moderate

High High High Low High Moderate

B.A.M High High Low Low High High

Benefits of Operational Intelligence: One of the major market pressures today in process industries is to cut down on operational costs and drive towards achieving operational excellence. OI aids a tighter coordination between the three pillars of operational excellence, which are production planning and control, manufacturing execution and operational effectiveness of people, processes and assets. The core benefits of OI are: 1. End-to-End Visibility into Operations: It provides a complete transparency across the value chain & reduces costs by cost-effectively and efficiently managing the end-to-end operational processes.

2. Highly Efficient Operations: OI offers an Integrated and standardized operations, automated tasks, alerts, notifications for proactive resolution and a fast access to relevant data from multiple sources.

3. Compliant Operations: OI helps in reducing cost of quality (rejects, reworks, etc.). It aids delivering high-quality products and services with integrated quality standards ensuring regulatory, legal and environmental standards.

4. Industry Leadership OI has an improved customer service because of easy adoption of initiates such as. Lean, Six Sigma, etc. It also supports delivering on promises sustaining superior performance by ensuring continuous improvement of processes and systems.

Thus, OI enhances the process Industry by increasing its productivity, efficiency and Operational effectiveness making the Industry grow on a larger scale, at a faster pace and with stronger performance.

References: 1. http://www.flexeye.com/operational-intelligence.html 2. ‘The next generation of business intelligence: Operational Intelligence’ – Colin White, BI Research 3. ‘Operational Excellence in Process Industries’ – Cognizant

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Interesting Facts:

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Crossword Revisited

1

2

D

3

6

7

Clues Across:

S 5

4

O

M A

L

A 8

B

9

A

C

O

11

15 10

U

12

14

D

5. Mr Y while working with Toyota gave the concept of one the buzzwords of modern day operations management 6. We can drastically reduce the number of iterations and resources required for new product development

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E

1. Mr. X can’t find his car keys without searching his whole house. He should follow _______

C

8. Traffic lights: Road, ______: Lean production

9. These should be removed to reduce waste and to improve efficiency 11. Current status of the quiz 12. This term is used both in production planning and retail shops 13. We can measure efficiency of a service with this 14. An extension of barcode that does not require line of sight Clues Down: 2. The kind of production process where even raw materials are not known before receiving order 3. Vegetarians would not like the other name of this analysis tool 4. If you have this, you can let loose for some time without getting shouted at by your boss 6. This non- Japanese Operations guru became popular while working in Japan 7. This is what Walmart and Big Bazar claim to offer 10. This reduced set-up time drastically 15. This process mapping tool involves all stakeholders in supply chain

Solution on page number 31

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1.2 Unified Efficiency Measurement of Thermal Power Plants Using DEA

Mohan Manivannam PGP29178

Abstract There is significant pressure in protecting the environment especially from excess emission of Greenhouse gases and other harmful pollutants. The major source of the Greenhouse gases and pollutants are the Thermal power plants. So, The Indian government has issued stringent laws for protecting the environment from excess emissions by Thermal Power plants. This study discusses a new DEA (Data Envelopment Analysis) approach to measure the efficiency of Thermal power plants, by including both desirable output (Electrical Energy) and Undesirable output (Co2, SOx, NOx, SPM, RPM, Oil & Grease, Suspended Solids...Etc.). The Proposed approach calculates the Efficiency score for a set of Thermal power plants operating in a region. The output of DEA is validated by carrying out Multiple Discriminant Analysis on the group assigned (Environmentally efficient and In-Efficient) and to determine the Factors which discriminates between the groups and quantify their effect on the Environmental Efficiency score.

Background The paper by Wade D. Cook and Joe Zhu (2006) applied DEA model in comparing the efficiency of set of thermal power plants with desirable outputs. The DEA model was applied where the decision making units are comparable but possess unique circumstances and characteristics. In our current study the same principle of DEA was extended to study the set of power plants within the region along with its environmental effects. Mika Goto et al (2011) devised similar methodology to compute unified efficiency which includes both undesirable and desirable outputs, but their methodology segregates inputs into energy and non-energy input and The model computes directional distance measure for computing the unified efficiency and the study doesn’t determine the major factor contributing to the difference between efficient and In-efficient DMU and doesn’t quantify the effect of each undesirable factor on Environmental efficiency of each power plant.

Divya S PGP28092

The problem setting The usual practice of comparing the efficiency of Thermal power plants by comparing the PLF (Plant Load Factor) is not correct, as, measuring plant load factor considers only availability and computes utilization of plants based on the availability. In the current study, the environmental pollutants are taken as undesirable outputs and a unified efficiency is measured, which includes both undesirable and desirable outputs. This study provides an approach to segregate the environmentally efficient DMU’s and environmentally in-efficient DMU’s, thus enabling the government to reward or penalize accordingly.

Input and output parameter setting The current study analyses the efficiency of 20 power plants with uniform capacity in a region. The core material components such as Coal, Air, Energy, cooling water required for power generation are taken as inputs for all the units. The power generated in MU (million units) and ESP Index are considered as desirable outputs.

The undesirable outputs such as bottom ash, fly ash, SS, oil & grease are also considered in this DEA. The ESP index acts as proxy for measuring the particular matter (SPM and RPM), higher values of ESP index indicates lower level of [Street Address] particulate matters emitted into air. Hence, ESP index was set as desirable outputs in the problem setting.

[City, ST ZIP Code]

[Telephone] 9 [Web Address]

[Dates and Times] [Dates and Times]

Advantage of Our Approach: This approach identifies all the critical factors which differentiate the DMU’s as environmentally efficient and In-efficient. Thus helps in focusing on those factors which are statistically different between the two groups. Those factors which are not statistically differentiating, implies that the environmental limits for those factors are easily attainable by both the groups. n

ESP _ Index   i 1

NOCi 100 FLCi

Where, NOC: Normal Operating Current

FLC: Full Load Current

Programming Model: Objective Function: Min( E )

Input Constraints: Minimizing the input 10

 i  X i,1  E  X1,1 i 1 10

  X i 1

i

i ,2

i 1

i

Output Constraints: Minimizing Undesirable outputs (Pollutants) 10

  X i 1

i ,3

 E  X 1,3

. .

i

i ,6

10

  X i 1

10

  X

 E  X 1,2

where E is the Efficiency Score for a DMUi

i

i ,7

i 1

i

i 1

i ,5

  X i

i ,14

 X 1,14

  X

i ,15

 X 1,15

i 1

 X1,7

10

i 1

  X

  X

10

. .

10

10

 X1,6

Output Constraints: maximizing the output (Desirable output)

i

i ,13

i

 X1,13

 E  X 1,5

Discriminant Analysis: Canonical Tests of Equality of Group Means F

df1 df2 Sig.

Coal

.689

8.127

1

18

.011

Air

.998

.042

1

18

.840

Energy

.999

.020

1

18

.890

Water

.713

7.254

1

18

.015

Bottom_Ash

.630

10.590 1

18

Fly_Ash

.923

1.512

1

SS

.899

2.032

Oil_Grease

.999

Power_Generated

.862 .728

Function

Coefficients

Wilks' Lambda

ESP_Index

Discriminant

Function 1 Coal

.059

Air

-.019

Energy

-.037

Water

.053

.004

Bottom Ash

.100

18

.235

Fly Ash

-.002

1

18

.171

SS

.019

.012

1

18

.915

Oil and Grease

-.005

Power Generated

-.002

2.882

1

18

.107 ESP_Index

-.047

(Constant)

-2.538

6.714

1

18

.018

10

Result: DEA with Undesirable output DMU1

1

Efficient

DMU2

1

Efficient

DMU3

0.9819

In-Efficient

DMU4

0.8103

In-Efficient

DMU5

0.9782

In-Efficient

DMU6

0.8404

In-Efficient

DMU7

0.99

In-Efficient

DMU8

0.9844

In-Efficient

DMU9

0.7898

In-Efficient

DMU10

0.9254

In-Efficient

DMU11

0.9992

In-Efficient

DMU12

1

Efficient

DMU13

1

Efficient

DMU14

0.8497

In-Efficient

DMU15

1

Efficient

DMU16

1

Efficient

DMU17

1

Efficient

DMU18

0.9822

In-Efficient

DMU19

0.8733

In-Efficient

DMU20

1

Efficient

The Efficiency score was calculated using the Linear Programming model mentioned above. The DMU’s efficiency score which equal to 1 are environmentally efficient and those DMU’s whose score are less than 1 are environmentally In-efficient.

The 20 DMU’s are segregated into efficient (8 DMU’s) and In-efficient (12 DMU’s). Multiple Discriminant Analysis was carried out on the groups, the result of the discriminant analysis states that for this sample of power plants, the discriminating factors are Coal & water input, Bottom Ash, ESP Index and Power generated. Since most of the inputs and outputs are correlated because of the constant return to scale characteristics of the power plant. The canonical coefficient quantifies the effect of the statistically significant factors for this sample, on the efficient score. Bottom ash has the greatest effect on efficiency score followed by coal, water and ESP index closely. So, by this analysis we can conclude that minimizing Bottom Ash and maximizing ESP index (SPM & RPM) which are related to environment protection, we can improve the efficiency score.

Conclusion: The DEA analysis and the Multiple Discriminant Analysis has substantiated the effect of environmentally undesirable factor in Efficiency calculation. The current study was limited to small set of decision making units. This study elucidates the link between environmental protection and efficiency. This has a potential to influence major power producers throughout the country to install environmentally preferable technology like super critical and critical boiler, clean coal technology, use of beneficiated/ blended coal.

References 1. Charnes, A., Copper, W., Rhodes, E., 1978. Measuring the efficiency of decision making units.

European Journal of Operational research 2(6), 428-44.

2. Wade, D., Cook, Joe, Z., 2006. Within- Group Common Weights in DEA: An Analysis of Power Plant Efficiency. European Journal of operational research 178(2007) 207-216.

3. Toshiyuki Sueyoshi, Mika Goto, 2011. DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation. Energy Economics 33 (2011) 292–303.

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Appendix:

1.

SS: Suspended Solids

Input

Output Air

Energy Water Bottom Fly Ash Ash

SS1

Oil & Grease Power ESP Index Generated

DMU1 120

640

30

150

9.6

20

100

20

200

80.00

DMU2 135

655

50

160

10.8

47

101

38

210

53.00

DMU3 128

620

40

145

10.24

55

119

22

190

47.00

DMU4

148

675

54

187

11.76

67

122

31

175

55.00

DMU5

145

670

60

165

10.16

46

185

26

205

84.00

DMU6

150

668

55

180

13

67

157

35

180

41.00

DMU7

114

638

25

174

9.12

53

101

24

165

48.00

DMU8

145

645

36

155

15

58

134

32

200

49.00

DMU9

156

665

49

170

18

39

134

35

168

45.00

DMU10

135

684

56

167

14.67

65

124

24

201

35.00

DMU11

140

635

36

154

10.3

39

113

26

200

56

DMU12

125

655

50

135

18

67

98

38

187

53

DMU13

112

690

40

139

10.24

55

136

26

194

42

DMU14

146

695

54

178

14

47

122

38

189

55

DMU15

125

640

60

165

15.3

53

172

15

205

84

DMU16

116

685

55

180

7.9

45

110

35

204

79

DMU17

139

657

30

137

12.89

53

101

15

189

65

DMU18

152

675

50

165

15

63

120

25

210

49

DMU19

128

665

49

189

18

56

134

35

185

45

DMU20

135

684

67

145

14.67

45

102

45

201

80.00

Coal

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Interesting Facts: Ops Gurus

Taiichi Ohno (1912-1990) is a symbol of Japan's manufacturing resurgence after the Second World War. Ohno started working as a production engineer for Toyota towards the end of the Second World War, at a time when its productivity was way below that of America's mighty Detroit industry. Ohno reasoned that there was no other reason than inefficiency and wastefulness for lower productivity of Toyota as compared to that of Detroit. Hence he set out to eradicate inefficiency and eliminate waste in the part of the production process that he was responsible for. This became the core of the so-called Toyota Production System (TPS) that he and others subsequently developed. Several elements of this system became familiar in the West: for example, muda (the elimination of waste), jidoka (the injection of quality) and kanban (the tags used as part of a system of just-in-time stock control).

Genichi Taguchi (1924 - 2012) was an engineer and statistician. Taguchi developed a methodology for applying statistics to improve the quality of manufactured goods. In 1950, he joined the Electrical Communications Laboratory who were engaged in a rivalry with Bell Labs to develop cross bar and telephone switching systems. Taguchi spent twelve years there developing methods for enhancing quality and reliability. Since 1982, Genichi Taguchi has been an advisor to the Japanese Standards Institute. Taguchi has made a very influential contribution to industrial statistics. Key elements of his quality philosophy include 1) Taguchi loss function, used to measure financial loss to society resulting from poor quality; 2) The philosophy of off-line quality control, designing products and processes so that they are insensitive to parameters outside the design engineer’s control.

William Edwards Deming (1900-1993) was an American statistician, professor, author, lecturer, and consultant. He is best known for the "Plan-DoCheck-Act" cycle popularly named after him. In Japan, from 1950 onward, he taught top business managers how to improve design (and thus service), product quality, testing, and sales by various means, including the application of statistical methods. Deming made a significant contribution to Japan's later reputation for innovative, high-quality products, and for its economic power. He is regarded as having had more impact upon Japanese manufacturing and business than any other individual not of Japanese heritage. President Reagan awarded him the National Medal of Technology in 1987. The following year, Deming also received the Distinguished Career in Science award from the National Academy of Sciences.

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Kaoru Ishikawa (1915-1989) was a Japanese organizational theorist, noted for his quality management innovations. He is best known for the Ishikawa or cause and effect diagram (fishbone diagram) that is used in the analysis of industrial process. In 1949, he joined the Japanese Union of Scientists and Engineers quality control research group. After World War II Japan looked to transform its industrial sector. It was his skill at mobilizing large groups of people towards a specific common goal that was largely responsible for Japan's qualityimprovement initiatives. He integrated and expanded the management concepts of Deming and Juran into the Japanese system. He introduced the concept of quality circles. This concept began as an experiment to see what effect the "leading hand� could have on quality. His Contributions to quality are User Friendly Quality Control, Fishbone Cause and Effect Diagram or Ishikawa diagram & implementation of Quality Circles.

Eliyahu Moshe Goldratt (1947 - 2011) was an Israeli physicist who became a management guru. He was the originator of the Optimized Production Technique, the Theory of Constraints, the Thinking Processes, Drum-BufferRope, Critical Chain Project Management. He has authored several business novels, mainly on the application of the theory of constraints. The processes are modeled as resource flows, the constraints representing limits on flows. The plot of the stories, such as in his book "The Goal", revolve around identifying the current limiting constraint and raising it, followed by finding the next limiting constraint. Another theme is that the system being analysed has excess capacity at a number of non-critical points, which contrary to conventional wisdom, is absolutely essential to ensure constant operation of the constrained resource.

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Sudeep Sahu PGP28279 1.3 Lean New Product Development Toyota product development system is often overlooked in the shadow of its production system (TPS), despite being equally innovative and counterinstinctive to conventional engineering management as lean manufacturing is to mass production.

People tend to forget is that no production system is good enough if firm doesn’t have a competent and complimentary product development system in place.

WHY -- The figure 1 below answers the question – close to 95% of cost commitment is done in NPD stages and biggest cost reduction opportunity lies at this stage and not during manufacturing. Since TPS is nothing but a continual exercise in waste elimination, why not start at source itself.

FIGURE 1 EFFECT OF DESIGN ON PRODUCT LIFECYCLE COSTS

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Toyota’s product development process is equally counter-instinctive to conventional engineering management as lean manufacturing is to mass production. Toyota does not follow many practices which are considered critical for traditional TABLE 1 COMPARISON OF DEVELOPMENT TIMES style NPD. Its design team is not collocated. With the exception of chief engineer and his staff, other engineers are not dedicated a particular vehicle program. It doesn’t follow six sigma, reengineering or design automation practices. Toyota Engineers rarely use QFD or Taguchi methods instead they excel at Value engineering. There is nothing exceptional about its CAD / CAE systems. Toyota’s Lean NPD (like TPS) seems wasteful but result in a more efficient development system. Toyota delays decisions and considers a broader range of design options and yet has the fastest and most efficient development record. US National Centre for Manufacturing Sciences report that Toyota NPD projects deploy 150 engineers per project versus 600+ for twice as long at Chrysler. (Table 1) What is Lean NPD? Lean NPS is fundamentally different from Lean Manufacturing and thus tools for the later cannot be used. Manufacturing is a repetitive process for value creation in a sequential and deterministic manner.

Product development on the other hand, is non-repetitive and non-sequential process for knowledge and information creation. For example - while lean manufacturing aims for elimination of variability lean NPD aim for filtering good variability from bad variability and thus require a certain quantum of risk to nurture creativity.

Lean NPD is applying lean principles of waste elimination to product development. Waste in the context of product development are redefined as – 1. Over Production: Too many products / projects, Redundant development (re-use not practiced) 2. Transportation: unproductive flow of information and information sharing, communication, Lack of use of standard parts and / or lack of commonality 3. Waiting: delays due to inessential authorization or testing, Information created too early 4. Inventory: redundant, stoppage in information and data system, unsynchronized processes 5. Motion: erroneous flow of information to people, seeking for superfluous approvals 6. Over processing: superfluous gates due to design of stage gate processes, excessive analysis, and circulation of incorrect decisions and out of place information 7. Defects: failures in tests, erroneous data, and warranty and recall costs.

It is much harder to identify waste in product development because of its non-physical nature— information and knowledge, unlike manufacturing where you can observe waste in the form of rework and inventory.

How does Lean NPD work?

Similar to Lean Manufacturing, Lean NPD is not a collection of best practices but rather a “sub-system” and part of larger Toyota System which can be shown in figure 2. The four major pillars of lean NPD are

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1.

FIGURE 2 TOYOTA: SYSTEM VIEW (4)

Chief Engineer concept paper

Counter intuitive to the concept of traditional product managers, the chief engineer in Toyota is first and foremost a technical expert having a large input in the vehicle’s architecture with loose formal authority despite being responsible for the product from concept to market. Instead he is mostly recognized by his experience, technical and communication skills. He commands a very small team of experienced engineers but all his other resources are in the functional organization. He condenses vision for the vehicle in a “concept paper” which leads into the system design phase.

2. Set based concurrent engineering It is a well-known fact that front loading in NPS realizes significant savings in costs and time. It is seldom achieved in traditional NPD. Reason lies in the different approaches used. Traditional design approach tends to quickly converge on a point in solution space and iteratively refine it to meet objectives. This is effective unless one start with wrong point, refining which can be time consuming and sub optimal. On the other hand SBCE begin by considering a large number of acceptable design FIGURE 3 SBCE METHODOLOGY (7) solutions and gradually narrowing the net to converge. Manufacturing is involved in forming the sets right from the beginning and their consent is required for each step. By frontloading and delaying decisions Toyota actually saves time and costs.

3. Detailed design with standards After the noisy and messy front end Toyota aims for reduction in ‘bad’ variability part of the development process by relying on standardization of skills, processes, and design itself. In line with lean manufacturing principal on the shop floor Toyota uses a number of standardization tools, such as: • • •

Checklists (process checklists and product checklists) Standardized process sheets Common construction sections.

Toyota’s practice of maintaining and sharing its learning continually with young engineers makes sure that wheel in not reinvented every time. These practices make sure that much of the design work is standardized with valuable time saved.

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4. Prototype and Tools with Lean Manufacturing Toyota develops two different series of prototypes, which are not used to test designs (unlike western counterparts) but to choose the different sub-systems and check their integration and identify manufacturing / assembly issues. Beyond this stage no engineering change request is accepted and design is frozen for serial manufacturing. Key differentiator for Toyota is the fact that it uses lean manufacturing tools like checklists right from this stage to construct product prototype and dies. Toyota must also be credited for use of flexible die designs for proto typing which actually save a lot of time and cost while providing unrivalled flexibility.

In summary – Table 2 Lean NPD vs. Traditional NPD

Toyota’s competitive advantage lies in its focus on value creation instead of product, out learning the competition and heavy front loading in the form of detailed discussion of manufacturing issues at the early stages, during which its rivals are mostly concerned with styling and engineering. Toyota invests time and effort in learning early on, to make sure that the end solution is truly the best.

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References 1. Morgan, J. M., & Liker, J. K. (2006). The Toyota product development system. New York: Productivity press.

2. Surly, M., Sopelana, A., Taisch, M., Al-Shaab, A., Keast, J., Flores, M., & Martinez, L. (2010, October). Applying lean thinking concepts to new product development. In APMS 2010 International Conference Advances in Production Management Systems Book of Abstracts (p. 50). PoliScript.

3. NATIONAL CENTER FOR MANUFACTURING SCIENCES, Product Development Process – Methodology &Performance measures, 2000.

4. Ballé, F., & Ballé, M. (2005). Lean development. Business Strategy Review, 16(3), 17-22.

5. Haque, B., & James-Moore, M. (2004). Applying lean thinking to new product introduction. Journal of Engineering Design, 15(1), 1-31.

6. Steven D. Eppinger and Anil R. Chitkara, The New Practice of Global Product Development, SUMMER 2006 VOL.47 NO.4 SMR210, MIT Sloan Management review 7. Raudberget, D. (2010). Practical applications of set-based concurrent engineering in industry. Strojniški vestnik-Journal of Mechanical Engineering, 56(11), 685-695.

8. J. Morgan, “Applying Lean Principles to Product Development”, www.sae.org/topics/leanfeb02.htm, June 20, 2005 9. http://www.designnews.com/document.asp?doc_id=230445&dfpPParams=ind_182,aid_2 30445&dfpLayout=article 10. D. Sobek, A. Ward and J. Liker, “Toyota’s Principles of Set-Based Concurrent Engineering”, sloan Management Review, Winter 1999,vol. 40, no. 2, pp. 67-83 11. Oppenheim, B. W. (2004). Lean product development flow. Systems Engineering, 7(4).

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2. Motivating Stories 2.1 Safal Safal is the largest organised retail network of fruits and vegetables in the Delhi NCR region. Currently it operates over 400 retail outlets in the NCR region. Safal is owned by Mother Dairy, a subsidiary of National Dairy Development Board. Safal was started in 1988 as a Government of India initiative to benefit fruit & vegetable producers and the urban consumers. The task was assigned to National Dairy Development Board as they had similar experience in the related sector of milk with Mother Dairy.

Safal's supply chain covers 16 states, about 50,000 farmers and over 200 farmer associations. The strong point of the company is the back end of its operations which are the farmers. Safal pays the farmers the rates which are provided at the local mandis in addition to picking up the goods at the farmers place thus avoiding transportation costs to the farmers. This model has helped them sustain long term relationships with the farmers. In addition to avoiding the transportation costs, the farmers also get to avoid paying market fee, VAT and commission to local agents. Safal also provides agricultural extension services to the farmers which includes crop planning, advices on types of seeds and fertilizers to be used and tips on good agricultural practices with respect to the amount of pesticides to be used.

However, the recent entry of big names into domestic retail such as Reliance Fresh, Spencer’s and more has caused problems for Safal. Farmers have increasingly started switching their loyalties to the new entrants due to higher prices being offered. Also, retail chains price fresh produce low primarily to increase the footfall. For them, this is not a priority segment and they use profits from other goods to subsidise fresh produce. However, Safal has risen up to the challenge by introducing the concept of open air outlets. Safal operates at least 400 regular outlets selling fresh produce, processed foods, frozen vegetables and ice cream among other food products. In addition to that, they operate makeshift extension counters in the vicinity of bricks-and-mortar stores through a hub-and-spoke model, with the unsold material going back to the main outlet. The extension counters currently contribute about 10% of Safal’s fresh business and the company provides incentives to franchisees to open more such counters. Extension counters are currently operated in locations which are not big enough for operating a full-fledged store. However, the demand for open air outlets has been increasing. Safal is also preparing for the future by identifying localities which could develop in the future and setting up open air outlets in those localities. These outlets can be converted into full-fledged Safal stores as the population grows.

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2.2 Taxi For Sure TaxiForSure, a Bengaluru based cab aggregator, was founded in the year 2011 by long term friends and IIM Ahmedabad grads Raghu and Aprameya. In just two years, the start-up has grown manifold and aims to be the MakeMyTrip of the taxi industry. The domestic cab market is a $6 billion industry, which is largely unorganized and growing at 25-30% year on year. The company works with various taxi operators to offer its technology platform to them. Customers can book local point to point, local packages, outstation and airport transfer taxis, across various car types, by logging on to www.taxiforsure.com or by calling 6060 1010 in Bengaluru, Delhi NCR and Hyderabad. Backed by a VC funding of $5 million, the company manages close to 1900 cabs in Bengaluru and Delhi NCR and around 2000 bookings per day. The Hyderabad operations were started recently in September of this year. What started off as a two man team is today 240 members strong.

An extremely customer centric and operationally challenging business, the biggest challenge lies in ensuring that the taxis booked reaches the customer on time in addition to ensuring a smooth experience.

The company achieves this by aggregating operators and not drivers. This approach is different from a lot of mainstream companies who work with drivers. It provides the company with multiple options hence increasing availability. When a cab request is received, all free cabs in a 5 kilometre radius are alerted. Taxis can bid for a trip and a bid resolution algorithm assigns a taxi to a trip. The resolution is based on parameters like how long has the taxi been waiting, how much it has earned, customer feedback and the distance from the point of pick up. Also the technology cost is much lesser as the back end works on smart phone based applications. The company’s immediate aim is to be in 15 cities by 2015 with Mumbai and Chennai being the next targets. It is also targeting Tier 2 and Tier 3 cities such as Ranchi, Raipur and Nagpur. The company is also planning to enter into corporate bookings.

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3. Measuring Operations Efficiency of Companies 3.1 Gartner Chart of Top performing Companies

Rank Company

Peer Opinion1 (172 voters) (25%)

Gartner Opinion1 (33 voters) (25%)

Three-Year Weighted ROA (25%)

1

Apple

3,203

470

22.3%

82.7

52.5%

9.51

2

McDonald's

1,197

353

15.8%

147.5

5.9%

5.87

3

Amazon.com

3,115

475

1.9%

9.3

33.6%

5.86

4

Unilever

1,469

522

10.5%

6.5

9.0%

5.04

5

Intel

756

515

15.6%

4.2

11.4%

4.97

6

P&G

1,901

493

8.6%

5.8

3.6%

4.91

7

Cisco Systems

1,167

517

8.5%

11.2

7.8%

4.67

8

Samsung Electronics

1,264

298

11.6%

18.5

15.7%

4.35

9

The Coca-Cola 1,779 Company

278

11.7%

5.5

14.0%

4.33

10

ColgatePalmolive

794

324

18.9%

5.2

3.6%

4.27

11

Dell

1,409

342

6.2%

30.7

-0.6%

4.05

12

Inditex

745

221

18.0%

4.2

13.4%

3.85

13

Wal-Mart Stores

1,629

282

8.8%

8.1

4.9%

3.79

14

Nike

955

236

14.1%

4.2

10.6%

3.62

15

Starbucks

808

159

16.5%

4.8

11.5%

3.41

16

PepsiCo

810

314

8.6%

7.8

10.5%

3.41

17

H&M

399

41

28.2%

3.7

6.7%

3.22

18

Caterpillar

714

247

5.8%

2.8

23.4%

2.91

19

3M

999

105

13.3%

4.2

6.9%

2.87

20

Lenovo Group

397

211

2.5%

22.2

29.8%

2.75

21

NestlĂŠ

679

112

13.3%

5.1

-0.6%

2.51

22

Ford Motor

552

231

5.7%

15.1

3.1%

2.51

23

Cummins

74

139

13.3%

5.3

13.5%

2.48

24

Qualcomm

122

45

12.7%

8.5

25.9%

2.37

25

Johnson Johnson

730

144

9.6%

2.9

3.3%

2.35

2

Inventory Turns (15%)

3

Three-Year Weighted Revenue Growth (10%)

4

Composite Score 5

&

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Source: Gartner (May 2013) Supply Chain Top 25 Methodology: The Supply Chain Top 25 ranking comprises two main components: financial and opinion. Public financial data provides a view into how companies have performed in the past, while the opinion component offers an eye to future potential and reflects leadership in the supply chain community. These two components are combined into a total composite score.

We derive a master list of companies from a combination of the Fortune Global 500 and the Forbes Global 2000, with a revenue cut off of $10 billion. We then pare the combined list down to the manufacturing, Retail and distribution sectors, thus eliminating certain industries, such as financial services and insurance (see Table for a full list of excluded industries).

Airlines

Insurance

Banks

Mail, Package and Freight Delivery Software Development

Crude Oil Production

Mining

Steel

Diversified Financials

Petroleum Refining

Telecommunications

Energy

Pipelines

Temporary Help

Engineering/Construction

Railroads

Trading

Entertainment

Shipping

Utilities

Healthcare: Providers

Insurance,

Managed

Care,

Shipbuilding

Services, Services

Source: Gartner (May 2013) A look into top 5 companies: (Apple has topped the rankings for the record 6th time) Apple: As a company traditionally known for its product innovation, Apple now faces formidable competition in the mobile device market. With Tim Cook at the helm, the company known for its focus on simplicity has expanded its product portfolio to a broader array of sizes and price points to address the competition, driving the need for more complexity management in its supply chain. The ex-operations chief has also fostered increased transparency on supplier responsibility, particularly with suppliers and manufacturing partners in China.

McDonald’s: McDonald's stands out with strong new product launch capabilities, excellence in execution consistency coupled with a recent re-emphasis on a strong customer experience, advanced demand sensing and forecasting capabilities across geographies, and an impressive supplier collaboration framework and philosophy that underpins its "never-stock out" mind-set.

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Amazon: Providing fast, free shipping and returns on a truly impressive array of products that it offers through an extensive supplier network, the company has redefined our expectations of service. Now expanding its locker strategy to other venues and aggressively investing in new physical fulfilment centres to support same-day delivery, the company is looking to further differentiate its service. Amazon is also building its digital portfolio of products: From "Kindle Singles" Web services, to "Prime Instant Videos," and now reportedly in development with a set-top box to stream video into homes, Amazon is fast crossing lines into new markets.

Unilever: The company is at the forefront of the supply chain maturity curve, with a wide range of cutting edge practices: end-to-end segmentation, cost to serve, a "perfect store" initiative, a centre-led supplier management program, an advanced ability to flex its factories to take advantage of downstream data, and an impressive ability to design globally and implement locally across every function of its supply chain.

Unilever's supply chain innovations have been a critical component of its ability to retain profitable growth, even in the face of sluggish demand in some of its core markets Intel: Chip giant Intel has made significant investments to enable the broader computing ecosystem.

Downstream, Intel ran an enablement program with PC OEMs, focused on joint product design and marketing for Ultra book products. On the supplier side, it has invested billions of dollars in engineering resources and working capital to significantly increase factory output through larger wafer sizes. The company has also continued its commitment to sustainability and social responsibility in sourcing, having taken a lead role in the issue of conflict minerals.

3.2 Agriculture Supply Chain: (Facts) Some gruesome facts for a wakeup call 1. Agriculture supply chain is completely fragmented 2. Post-harvest losses in fruits and vegetables is a dismal 45% 3. Concept of cold storage is gathering steam in India (currently cold storage penetration stands at 11%) 4. 25000 Refrigerated vehicles (popularly known as reefer trucks) run in India out of which 20,000 vehicles are used for transportation of milk. Refrigerated transportation penetration is 3-4% in India compared to 85% in US 5. A bulk of losses can also be attributed to improper handling and improper packaging

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4. Glossary (A-Z) ABC analysis - Also called Pareto analysis or the rule of 80/20, is a way of categorizing inventory items into different types depending on value and use.

Aggregate plans - Show the overall production planned for families of products, typically by month.

Andon - The visible light or sign that denotes the state of an operation (i.e., on, trouble or off.) The process can be stopped or investigated for quality issues or defects as a result of the status of the lights.

In addition, everyone in the immediate area can see that the problem is being addressed.

Availability - That time or percentage of time that a resource unit or activity centre is ready to process or be activated.

Backlog - The amount of actual demand, orders or contracts that are in the pipeline for future sales.

Can be expressed in units of production time or dollars; e.g. six weeks of firm orders for a plant that can produce $2 million dollars per week would be a $12 million backlog.

Batch - The number of production units in an aggregation of units that can be produced by an activity that produces in batches. A multiple of units in a plant designated for any purpose such as packaging, outside services, etc.

Benchmarking - Benchmarking is defined as a process of continuous comparison of a company’s performance on predetermined measure against that of the best in an industry or a class, considered the standard or the reference.

Bill of material (BoM) - A bill of material is an ordered listing of all the parts in a finished product.

The listing usually includes the part number, how many of each part is required, and a brief word description of the part.

Blockage - Prevention of a processing unit to produce more units because of inadequate storage space or lack of authorization to produce Building to customer order versus Building to forecast - Building to customer order means that at least the final assembly, packaging, and shipping awaits a firm order for the product. Building to forecast means that the product is manufactured to a forecasted demand. Building to customer order means that the product is pulled by customer order rather than pushed by a forecast.

CAD - Computer aided design is a process of generating and manipulating product designs through computer software. The software allows all information of a part to be generated and stored electronically at a computer terminal and transferred to other sites or machines.

CAM - Computer aided manufacturing (often used synonymously with CAD) is a similar process of generating manufacturing processes electronically.

CIM - Computer-integrated-manufacturing. Popular in the 1980s, it implied fully computer-controlled manufacturing processes.

Coefficients of variation - The ratio of the standard deviation to the mean for statistical demands & processes.

Control charts - Statistical charting process that is used to identify sporadic and chronic faults in a process. Mean and variance measurements of a product are charted and acceptable limits are set on these values. An out of control process can be identified and adjustments made to remedy the situation through the use of control charts.

Critical path - That path through a process or activity system that has the longest theoretical flow time.

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Cross-docking - Co-ordinates supply and delivery, typically at a warehouse, so that goods arriving are moved straight away to a loading area ready to be sent to customers DFM - Design for manufacturing. The process by which designs are completed mindful of the cost of manufacturing.

Distribution - This term denotes the process and/or entities that take manufactured products and make them available to the ultimate customer. In the automotive and appliance industries, it is the automobile and appliance dealers.

Decoupling - Implies that through buffers and inventory, processes in a product line can operate relatively independently of the each other.

Downtime - A period when machinery is not being used, either as a result of maintenance or when parts of the machinery have to be adapted to produce a slightly different unit of production Economies of scale - The unit cost reduction that accrues from larger volume production or distribution of similar products or products produced in similar operations.

Economic order quantity (EOQ) - The optimal batch size for an order that minimizes the total period cost, including cost of ordering (setup cost), inventory holding cost, and cost of materials procured. For setup cost S, holding cost H, and throughput R, the optimal batch size Q* is given by Q* = square root (2 x S x R/H) ERP - Enterprise Resource Planning--the latest designation of company-wide computer integrated information system. The term implies that disparate computers, information databases, and communications networks are integrated.

Error proofing (POKA YOKE) - Error proofing seems to be a simple concept, but there are many variations on the primary theme. The basic concept is that a product is prohibited from being taken out of its fixture if it has a quality defect as a result of the machine or operator action. The defect must be corrected prior to release of the product from the fixture.

EVA - Economic Value Added--the amount the profits of a company or entity differs from its cost of capital times its net assets. EVA is increasingly used as a performance measure replacing return on equity and return on investment.

Fill rate - Fraction of total demand satisfied by inventory on hand.

FIVE S's - Toyota defines the fives S (Workplace organisation method): 1. Seiri (sort): Identifying & separating necessary from unnecessary items 2. Seiton (straighten): Ordered placement & identification of all parts & work items 3. Seiso (shine): Maintaining a clean plant 4. Seiketsu (stabilize): Maintaining Seiri, Seiton, & Seison 5. Shitsuke (standardize): Instilling Seiri, Seiton, Seiso, & Seiketsu in workforce FIVE WHYs - The process of repeatedly asking why until the root cause of the problem is found. The purpose is to keep asking why until the root cause of the problem is known and the solution found.

Toyota has found that asking "why" five times usually leads one to the root cause.

Fixtures - Fixtures are what secure tools and components to general-purpose machines. The location and fixture type make a significant difference in the speed with which tools can be changed and in the quality or the part produced. It is best practice to fix a tool at just the right point so that the key 26

characteristics are produced with the most accuracy given the machine and tools being used to make the part.

Flow shop - An operation that produces products at volume in a continuous flow or by a well-defined, connected sequence of activities or processes.

Flow time - The average (actual) time for a unit of production to flow through a process unit or activity including input and output inventories. Theoretical Flow Time is the flow time without inventories.

Forward buying - Buying of materials in advance of need.

Forecast - Usually the prediction of customer sales and the subsequent manufacturing schedule.

Forklift - A general-purpose small truck for lifting and transporting materials and containers in a plant; not conducive to lean operations Heijunka - The Deployment of matched goals throughout the organization Inventory - Goods and products held by a company in the product value stream that are eventually intended for sale to customers on their own or as part of a product system. Inventory includes the material cost of the goods and the value added by the operation to reach its state of manufacture. Raw materials, work in process, and finished goods are three categories of inventory.

Jidoka - The principle of stopping work (or the line) when there is a quality problem--the process for correcting that problem JIT - Just-in-time manufacturing system. In a full JIT system, the only parts that enter a plant or move from process to process in a plant are those identified uniquely with a final product, no more or no less.

Thus, every part being supplied and every part in the plant can be related directly to a bill of material of a product that is either in production or will shortly to be in production.

Job shop - Job shops refer to those operations where each order is more or less unique and where the volumes are small or only one order.

Kaizen - The process whereby teams attack a manufacturing operation to make a series of quick, small steps to improve the process. It is also the process by which such small improvements are continued.

Standardized work is the result of Kaizens.

Kanban - A card that signals the replenishment requirements in a production process. Associated with delivering just the amount of inventory needed at the right time. The heart of a pull system where the process need for inventory is signalled by the placement of a demand card with the supply process.

Lead time - Time that is required to fill an order or meet customer demand.

Lean - A term used to indicate that an operation adheres to the Toyota Production System and has achieved the level of quality, productivity, and customer satisfaction associated with application of that system.

Life cycle costing - Using the full cost of a component or system over its useful life in a financial decision process instead of just original purchase price. For example, life cycle costs brought to present value may justify a higher initial purchase price.

Line balancing - In a production or process operation line with several processes, machines, or operations in sequence, the discipline of balancing the throughput of each operation in the sequence such that production of any one unit in the sequence is equivalent or "balanced" with each of the other units in the sequence.

27

Little’s law - The equation relating Throughput, Inventory, and Flow time for a process. It is: THROUGHPUT = INVENTORY divided by FLOW TIME Logistics - The process of managing materials for operations to meet certain objectives such as delivery speed, low inventories, and high accuracy. Integrated Logistics handle a variety of unrelated components required by a customer or customers.

Management information system (MIS) – System that controls the flow of information throughout an organization and makes sure that everyone has the information they need to work properly Marginal analysis - Analysis of the effect of an action or activity at the limits or at a point in the operation rather than on the average. The marginal cost of an additional unit of production may be lower or higher than the average cost.

Master Production Schedule (MPS) - The schedule of finished goods that are to be manufactured based on actual or forecasted customer demand. Work centres are scheduled to manufacture the products to meet the MPS. Minimum efficient scale - The smallest output that a business can produce while making sure that its average costs are minimised MRO - Those components and parts not associated with the direct material for a product. Tools, gloves, lubricants, machine maintenance parts are part of MRO. MRP - Material requirements planning. MRP systems are used in almost all plants. They coordinate the bill of materials, forecasted demand, long lead-time parts, and the inventory in the plant.

MRP II - Material Resource Planning--an advanced version of MRP that integrates the whole value chain in planning material orders, production, scheduling, and shipments.

Muda - Waste. Reducing waste throughout the enterprise is one of the fundamentals tenets of the Toyota Production System.

OEM - Original Equipment Manufacturer. The term OEM denotes a company or sector that manufacturer’s equipment ready for purchase by the end-use customer. The large automotive companies are referred to as OEMs. Suppliers to such companies supply to the OEMs, they are not OEMs themselves. There is an implication of a distribution entity between an OEM and the ultimate customer.

Operating income - Gross profit less administrative (SG&A) and development (ER&G) expenses.

Opportunity cost - The benefit lost from the next best alternative foregone. For example, if the firm spends money on training then it may not be able to use the money for new machinery -which will be the benefit lost Overhead - In general denotes an allocated cost to a direct operation. It includes all manufacturing costs, other than direct material and direct labour. In addition to indirect material and indirect labour, overhead cost includes utilities, maintenance, depreciation and taxes.

Overtime - Work beyond the federally mandated work period usually a day or a week. Overtime pay regulations are quite specific.

Pick and Place - Equipment that picks up parts from one station on an assembly line and places them on the next. It is usually a pick and place robot.

Pipeline stock - Stock that is currently being moved from one location to another 28

Pooling - That action that combines in parallel previously independent processes to reduce the total variance compared to the variances that would occur when the processes were independent. Generally reduces the variance of the combined processes by the square root of the sum of the squares of the independent processes.

Postponement - That action that delays complexity to later stages in an operation so that product differentiation occurs later in the production cycle.

Productivity - Measure of labour efficiency. Amount of goods produced per unit of labour cost or hours. It can be applied more generally to the efficiency of machines and systems.

Pull system for material control - Equivalent to JIT but most often internal to operations. The pull system means that the "release" for moving material within the plant or from suppliers is signaled by the next process in line that needs the material. The material is moved by the demand from the succeeding process in the production chain or routings not by a central schedule or general release.

Push system for material control - The push system denotes a system whereby material is released for production and movement by a central or local scheduling algorithm and based on forecasted or anticipated needs for that material.

QFD--Quality Function Deployment - The formal process whereby products and services are designed that meet all customer expectations cognizant of costs, competitors, manufacturing, and flexibility.

Quality circles - Group of people who meet to discuss ways of improving product Quality Quality product - A product or service that meets customer' expectations and is therefore "fit for purpose" Rationalisation - Reorganising a business to reduce capacity and increase efficiency Reengineering - The process of redesigning processes or activities to reduce flow times, inventories, and increase throughput. Its primary objective is to reduce costs and increase customer response. It is generally applied to service or support activities in contrast to physical operations. Redesigning or reconfiguring physical processes is usually referred to as lean transformation.

Reorder point - That inventory level where new stock is ordered.

Reverse logistics – Brings materials (defects, spare units, wrong deliveries, packaging, materials for recycling, containers, etc.) back from customers to suppliers Rework - That activity that reprocesses defective parts to make them satisfactory for reuse in the production process.

Scrap - Components or goods to be discarded usually because of poor quality or no demand.

Service level - Probability that customer demand will not exceed inventory for an order cycle.

Service operation - Operations in a service industry or business. Such businesses are generally characterized by direct service to consumers rather than in the supply of manufactured products.

Setup - Denotes the process of changing or fitting tools on general-purpose equipment to produce a particular product.

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Seven zeros - The seven even zeros are an essential part of the TPS. They are: Zero Defects: To avoid delays due to defects (Quality at the source) Zero (Excess) Lot Size: To avoid "waiting inventory" delays Zero Setups: To minimize setup delay and facilitate small lot sizes Zero Breakdowns: To avoid stopping tightly coupled line Zero (Excess) handling: To promote flaw of parts Zero Lead-Time: To ensure rapid replenishment of parts Zero Surging: Necessary in system without work in progress buffers

Shrinkage - The amount inventory is less than what is on the "books". Shrinkage can occur from unrecorded scrapped parts, short shipments by supplier, pilferage, unrecorded over shipments, and damaged parts that are discarded without relieving inventory. Shrinkage reduces pre-tax profits directly.

Single sourcing - Sourcing all the requirements for a particular part to one supplier is called single sourcing.

SPC - Statistical process control involves the implementation of statistical tools (including control charts) that monitor processes in order to identify improvement opportunities. Process faults are identified, a root cause of the fault is isolated, and corrective actions are taken to improve the process.

Supply chain - All the stages in the production process from obtaining raw materials to selling to the consumer from point of origin to point of consumption Sustainability - Production systems that prevent waste by using the minimum of non-renewable resources so that levels of production can be sustained in the future Takt time - The pace at which consumers demand a product--production scheduling at that pace. The line speed in an auto assembly plant (around 1 vehicle per minute) is the Takt time for that plant.

Target cost - A system for utilized in product development where part of the specifications of the product is the cost. The system was developed by the Japanese auto manufactures and has become a concept and system in wide use in business.

Throughput - The production rate of a process or activity measured in units or flow per unit time.

Throughput divided by Capacity is Utilization TQC - Total quality control--a process by which a firm deploys it quality program throughout all functions of the company.

Total Quality Management - (TQM) is an organization-wide efforts to install and make permanent a climate in which an organization continuously improves its ability to deliver high-quality products and services to customers Turns - Commonly thought of as inventory turns or turnover ratio, turns are defined as the ratio of throughput to average inventory. A high number of turns imply that less inventory is kept on hand and/or materials are received in smaller lots and processed quickly.

Utilization - The average fraction of the capacity of a process or activity that is utilized during an operation 30

Work in process (WIP) - Inventory consisting of products that are in a semi-finished state. Work in process is valued at the cost of the purchased material plus the cost of manufacturing up to the stage of completion at the time that the inventory is valued.

5. Solution of crossword Given on page 8

S E I D T S O H I K N V A O W D E A E

T

O N

D E N O S M A L I A N B A N C C G I P K U R

F

I

O A

S M E D

E D L L P S I R P O C

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