View Point PLM and Cloud Computing - Awadhesh Singh Parihar
www.infosys.com
Cloud computing has been revolutionizing the way businesses are run in small and large enterprise alike. The promise of ubiquity and enterprise-wide interoperability provides a solid platform for leveraging social networking in product innovation. The pervasiveness of an „enterprise wide‟ or a „global‟ cloud could be an appealing prospect to alleviate some of the present day technology limitations in effective product collaboration In this paper, the author presents these aspects of cloud computing for possible applications to Product Lifecycle Management. PLM has been examined more as an enterprise application and enactment of business processes related to product, than a package. Hence several aspects, not actively professed in PLM packages (e.g. Analytics) have been discussed with necessary emphasis. Business drivers, opportunities, technology solutions and provisioning related to PLM on Cloud have been explored to present a viable model for innovation beyond the enterprise boundaries.
Why PLM? Term PLM denotes a wide array of concepts, tools and technologies dedicated to R&D and innovations, design, development, testing, launching and sustaining products/services in a typical business endeavor. Conceptually, whether one has to launch a telecom tariff plan or to develop an aircraft, the generic discipline to which the endeavor belongs, is termed as PLM. However it takes its root in complex „engineering‟ enterprises such as those dealing in automotives or aerospace and came as a logical sequel to CAD (Computer Aided Design) class of applications. This history, assumes significance to this discussion, as much of the debate around PLM packages, is around their origins in engineering discipline than to the transactional MIS applications, forefathers of modern day ERP applications.
A typical engineering product lifecycle, so as the audiences relate to the rest of the discussions here, is depicted in fig 1.1. Let‟s examine the aspects of PLM, from business standpoint, which may have potential leverage from the cloud computing technology stack.
Figure 1.1: Typical Product Lifecycle
Manufacturing Process Design, Capacity Planning and Change Management
Design Collaboration through Work ows. Reuse and Validation
Knowledge Reuse, Feasibility Analyses, Product Di erentiation, Requirements Engineering
Production Ramp and Launch
Design and Prototype
Concept
Market Analyses, Customer Needs Management , Portfolio Analyses
Support After Sales Warranty/Support, Feedback for Change
New Product Development
Product Strategy
Reduce time to Market by Knowledge Reuse and Engineering Collaboration
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Product Sustenance
Reinvent
Sustain and Improve
Product Con guration and Variants. Demand creation, ROI
Desupport, Replace and Reuse
Extend Product Market Life and pro tability by e cient ECR process and PPM
Innovation
redundancy of efforts, wasteful iterations and inadequate information available to make critical product decisions.
Product innovation is the core to any enterprise of any reckoning. Some of the most palpable trends today can be seen in consumer electronics in terms of features and product range. However, they have shortened shelf life than their predecessors!
A new feature or a concept so as to drive product demand, open newer markets/demographics, need to go through several phases – Commercial justification, associated core technology research or application of the research in core technologies (Pharma industry for example) and ways to volume produce the feature.
In order to incorporate the new feature/concept in the product, an enterprise leverages several geographically disparate collaborators and resources. Such collaborations leverage internet resources, knowledge base of the enterprise, standards available and regulatory norms. Effectiveness of this collaboration is gauged in terms of „how fast‟ do the product design/development effort converge and „how concurrently‟ do all collaborators get to opine. Product Feature or concept in question will be „productionalized‟ as fast as this collaboration works.
The pervasiveness of an „enterprise wide‟ or a „global‟ cloud could be an appealing prospect to alleviate some of the present day technology limitations in effective product collaboration. Some of the emerging concepts such as „social networking‟, „web 2.0‟, may have profound impact on product innovation, albeit in the long run. However the cloud research orientations need to take cognizance of the leverage to Product Innovation. Academia, Open source communities, enthusiasts, Industry alliances and the „disconnected‟ human resource capital of an enterprise, may provide unprecedented leverage to the cycle of product innovation, from the ubiquitous platform that Cloud Technology provides. However, this is not without challenges, as this entails throwing open the core competitive differentiator of an enterprise (the product IP), to the „creative‟ influences of social forums.
However there could be levels of maturity that an organization can chart, in terms of leveraging social networking in product innovation. Most of the companies, as a fair prediction, would be vary of putting their intellectual capital on to the social networks. The author recommends a gradual adoption curve as shown in fig 1.2 and explained in subsequent sections
More often than not, the collaborations of various agencies to design or develop a new product/feature in it, is mired with
Figure 1.2: „Product Innovation‟ Through Social Networks - Maturity Model
Extraprise social networks
Intraprise social networks
Federation Social Networks
Global Social Continum
Enterprise Social Networks
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Intraprise Social Networks „Limited Private Clouds‟ or Private Clouds implemented in a specific geographical location within an enterprise, may provide the first step to leveraging social networks within the companies specific location, in contributing to product innovation. Given the departmental silos that exit today, within a given location, in an enterprise, there is enough merit in „laying trust‟ on employee‟s social network to fuel the innovation.
Enterprise Social Networks If there be a private cloud (or hybrid cloud) that spans across the whole enterprise, innovation could be thrown open to the employee‟s social networks, and tap on the enormous potential which lies within the „disconnected‟ employees in any organization, to be fuelling the product innovative differentiators
• Technical challenges – While meta information can be put easily on the cloud, making CAX information (i.e. engineering artifacts) available on cloud, requires adequate visualization support, commonly agreed neutral format and ways to link „markups‟ back to the databases. Much of the PLM research needs to look at these topics as well. The technical challenges and architectures are dealt separately in this paper
Design and Development Engineering design and development and the iterations therein, is the core activity in Product Lifecycle parlance. Distributed design teams, working on various aspects of the product, use PLM systems for:
a) Deriving knowledge from previous product development cycles Extraprise Social Networks
b) Global coordination of disparate design activities
If the modern day enterprises critically depend on a tiered network of supply or sourcing chain partners and distribution networks, a extremely beneficial outcome may be orchestrated, if employees in all constituent organizations of the supply chain, be thought of contributing as „social network‟ in fuelling the product innovation
c) Global Analytics, so as to ensure effectiveness of the design activities, with respect to business metrics such as time-to-market, time-to-launch and time-to- volume
Federation Social Networks If there is a regional alliance (commercial or non-commercial) amongst the industries, or organizations/associations, propensity to innovate together amongst the employees of participating companies in such „federations‟ will be very high
Global Collaborative Continuum This is the highest state of evolution in terms of leveraging global communities to innovate together in „social networking‟ paradigm. Several Arguments for and against this exist of which most are in terms of impediments and risks involved. Author presents major risks/limitations as below, so that we devote future research in cloud computing to alleviate/address them, with specific regard to the topic of product innovation
d) Brining all direct/in-direct contributors early on, to facilitate the concept of „concurrent engineering‟. Let‟s examine each of the above, so as to assess why a cloud is not just an add-on, but a necessity, at least in few scenarios. a) Deriving knowledge from previous product development cycles – Typically, a new product is not entirely new, but leverages heavily on previous products (and hence PLM is cyclic not linear). To have a „singular view‟ of each of the products and its constituents developed in the past, from engineering standpoint, is not only beneficial but also essential to development of a new product. However, current design or engineering systems application topology is fairly variegated. Very often, to get the single version of truth, several ad-hoc integrations, expert knowledge and „dealing with redundancy‟ is fairly common. If private clouds act as unifier to such knowledge, so as to provide locational transparency and harmonized schema – it will drastically reduce the effort in locating and making business sense of the past product engineering information
Risks/Concerns associated to innovation on cloud • IP/Patents, the competitive differentiators to any organization, their core product knowledge, cannot be put in „unstructured‟ and „unregulated‟ forum. Slicing the product information as per IP sensitivity and targeting it to specific audiences in the maturity levels discussed earlier, could alleviate some of the risks in this category
• Ownership of the innovation/ideas to be ascertained, would require a broader IPR regime • Incentivize the innovation – If there are commercial interest associated, proper identification/monetization need to be put in place
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b) Global coordination of disparate design activities Disciplines such as PPM (product portfolio management), Design/Partner collaboration, from the point of view of how they cut across during the design activity, comes under this categorization. And in doing so, NPD (new product development) could be a truly cloud worthy/ cloud native application category. The whole NPD cycle, as binder/coordinator of disparate design activity can cut across the locations/applications so as to present a uniform management of design activities
c) Global Analytics – Analytics related to product lifecycle activities can easily be performed if NPD lifecycle (workflow) is cloud native. This could even be linked to product performance post launch so as to provide real time feedback control loop.
d) Concurrent Engineering – Cloud and concurrent engineering could be an interesting subject on its own, but for the purpose of this paper, collaborative tools are native to cloud, in most but trivial cloud implementations. Doing it in a workflow based system, tying back to the NPD cycle, could be an aspect of further exploration
Product Configuration Product configuration, in BTO (Build-to-Order) and mass customization situations have natural propensity to be implemented in cloud as they inherently have „outward‟ focus, interfacing with the structured (sales force) and unstructured (mass customization) audience
Implementation of rules engines, however, will require further exploration as query resolution will require a great deal of engineering calculations, unless it‟s a simplistic hash map of options (rarely the case, in our experience, but not unheard of )
Support and Service a) Engineering interfaces with Product Support function – such as product documentation, service manuals and upkeep of service parts. Will be part of the standard PDM implementation and data store in cloud and can be easily extended to service points
b) MRO – For captial goods, such as seen in ETO scenario, the whole discipline of MRO (such as installed view, service triggers etc.) can be made available as „extraprise‟ on „hybrid‟ cloud by OEM. It would take a few additional steps, for the service providers to be included though
Product Launch Product launch in terms of launch planning, support to campaign and such events though will not fall under PLM purview, but considerations such as „time to volume‟, „demand planning‟, manufacturing planning, where integration (or handover) from PLM to ERP will need to happen, can also benefit from cloud assuming both PLM/ERP are hosted in the same private cloud.
How PLM can be on Cloud? We will delve deeper into the implementation aspects of PLM on the cloud and look at industry segment/size as deployment variables Issues related to enterprise applications on cloud, with specific reference to PLM, I have tried enlisting as below a) Engineering business process on cloud – Workflow implementation on cloud, stages/actors and trigger processing, database interaction, tool interfaces, file integration, are some of the details which need to be worked out. Package vendors/cloud technology vendors need to come together to address various issues related to this. While majority of these are easily implementable in private cloud, hybrid clouds will present significant challenges in terms of work flow state changes and identity management. b) CAX Integration – CAX tools and their heavy payload of CAD files/models will be big performance jolt, if the interaction mandates, that public-private cloud integration needs to happen. Even within private clouds, cloud/non-cloud environment need to interface. HPC (High performance computing) on cloud need to investigated as potential soution to this. Author is reproducing Infosys SETLABS architecture on HPC, with overlays of CAX specific interchange, as a plausible solution to the all important performance issues related to PLM-CAX interchanges. Even in public clouds, suitable adaptation of this HPC architecture will alleviate most of the woes related to heavy CAD data interchange happening in PLM (refer fig 1.3 – source Infosys Research, SETLABS briefing on cloud computing volume 7)
c) PLM Data model - some of the deployment concerns, such as ownership of design artifacts by various collaborator across the globe, subscriptions to the same and expensive replication across multiple sites, has been traditionally achieved using concepts of views, sites, complex ownership rules etc. at data model level. However, cloud native data models are worth exploring, more so given the extent of global collaboration that a design activity (which PLM facilitates) entails. Since cloud native data models (such as implemented in google „big tables‟ [2]or „map reduce‟ etc.) offer high performance and flexibility, true collaborative potential of PLM could be achieved using the same.
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Figure 1.3: HPC Architecture for PLM Cloud Computing Source: Infosys Research, Setlabs Briefing On Cloud Computing Volume 7
tionApplica
tionApplica
tionApplica
tionApplica
Internet
Grid Manager Scheduler and Load Balancer Parallel Framework Library and Middleware Data Grid Application Platform Databases (RDBMS Columnar) Messaging Queues Server and Storage Virtualization Distributed File System and Storage
Location A
Location B
CAD Vaults
Considering the iterative nature of engineering/product design, the following implementation of big table [2] is quite appealing.
Project Context Global Part Identi ed
Rev1
RRev2 R 3Rev3
In the schematic above, the revisions of the part, in project context, at various times (say rev1-t1, rev2-t2 etc.) are stored in a single big table. From the aspect of distribution and load balancing, each logical tablet within big table can reside in a distinct (geographically disparate) node. This is to be seen as big advantage for an extremely federated and complex product design topology, amongst various design/development centers of an enterprise, partners, suppliers and vendors who too collaborate in the product development process. A cloud native PLM data model, in this sense, can be real boon.
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Location C
Compute Servers
Location D
Databases
d) Application Integration – PLM, being the upstream enterprise application (Design preceedes manufacturing or sales/procurement for that matter), needs to draw upon several collaborating applications. For one, it needs to interface with CAD and office automation tools, but more importantly it feeds into ERP applications and derives inputs from SCM/CRM applications. If integrations need to spawn at predetermined business triggers in the PLM workflow, conventional integration practices (read P2P, Adapters etc., EAI etc.) need to give way to more cloud aware integration approaches such as SOA or any other variant of the same.
e) Security – Biggest concern or inhibitor to PLM adoption of cloud, seems to be the information and IP security. Product engineering/related information is the core IP of companies and to have it on the cloud, even the private cloud, poses risks to the information security. Problem is less technical and more related to mind-sets. We will dedicate further explorations to this topic, but as of now, not many plausible solutions exist – Although successful cloud PLM implementation such as Arena PLM exist, but the subscribers mostly are in SMB segment, not the mainstream PLM customers.
f) Identity management – Business processes in PLM, if implemented in cloud, will need to cut across public/ private clouds, or private-private clouds, or cloud-noncloud environments, at least in the near future. To think of a 100%
product roadmap. Open source PLM solutions, haven‟t yet reached the level of maturity, either in terms of industry participation or development community endorsement, whereby ISVs can leverage them in realizing PLM vision, using cloud technology. Given this, the best course of action available to customers is to look at replacing their data centers by private clouds and utilize some of the infrastructure sharing, operational flexibility and performance guarantees provided by cloud. Having truly cloud native or cloud facilitated PLM applications, will stil be subject to industry, SI, and package vendor collaboration, at least in the near term
public or private cloud in PLM domain is far fetched. More than identity management, the user context switching is going to be an issue, which doesn‟t seem to have been addressed adequately. Concepts such as universal identity management and such „independent identity management‟ solutions don‟t exist or exist at a level of maturity which doesn‟t address the needs of large enterprises
g) Vendor Lock-in – PLM functionality, as most of other enterprise functions (such as MRP, Order processing etc.), is realized either through home grown legacy applications or implementing PLM packages. PLM‟s adoption of cloud hence, is by and large constrained by package vendor
How to Make it Work? Firstly, let‟s take a look at benefits that PLM domain aims through adoption of cloud technology. Although touched upon briefly in initial sections, but to summarize, following opportunities justifies exploration of cloud technology in PLM domain: • Collaborative and iterative nature of PLM processes • Need to leverage innovation within the organizations and social networks • Complex engineering analysis requiring the grid like computing infrastructure, but not on perennial basis – such as to run DMU/Analytics • Customer centric product design • Regula tory compliance and green engineering • Leveraging global resources in product development – enacting the paradigm of „design anywhere‟ and „build anywhere‟ •
Seamless integration of PLM to other business functions such as in ERP applications
• Org wide knowledge management Almost all of the above, difficult to achieve otherwise (at the level of efficiencies that businesses want) PLM functions, can leverage cloud technologies and hence the incentive to explore the technology, despite adoption challenges As discussed in the earlier sections, the enterprise cloud (or Private cloud) and private public cloud topology, presents a viable alternative to the present day PLM deployment architecture. We will further elucidate the thought. PLM adoption of cloud and benefits there in are presented in the schematic in fig 1.4
Figure 1.4: PLM Cloud Adoption Options Source: Infosys Setlabs Briefing On Cloud Computing, Volume 7 • • • • • • •
Server resource at cloud vendor Can be created on-the- y Shared server resources (system / database) for IT organization Further reduction in costs SLAs / security concerns due to sharing should be addressed appropriately Vendor provisioned / Third party monitoring tools
• • • • • • • •
Server resources at cloud vendor Can be created on the y Dedicated server resources (system /database) for IT organization Reduction in costs SLAs with cloud vendor Vendor provisioned / Third party monitoring tools
Cloud Vendor / Virtualization Software Vendor Infrastructure Landscape and Control
Organization Infrastructure Landscape and Control
Private Cloud
Virtualized
Standalone
Public Cloud
• • • •
• • • • • •
Virtualizes servers within organization periphery Flexibility in dynamic resource management to certain extent Better visibility to organization server resource management better value of investment than standalone option
PLM Cloud Implementation/ transitions can start here
Standalone servers within organization periphery No virtualization Limited exibility in dynamic resource management Complete visibility to organization on how servers resources are managed. High cost
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Technology Stack of PLM on Cloud Cloud technology, as applied to PLM applications, and its essential ingredients, primarily in hybrid cloud topology, is presented in the following paragraphs. We have purposely left out generic cloud stack discussion , so as to focus on technical viability of implementing PLM on cloud. Figure 1.5: PLM Technology Stack
Client Layer (Rich client, Thin Client)
CAX
ERP Integration Layer DW, others
Messaging Layer
Business Application Layer
LDAP
Messaging Layer
Common Business Layers
Distributed File store
Distributed Databases
Fig 1.5 represents a standard PLM technology stack, which if represented in cloud technology stack is proposed as below Figure 1.6: PLM on Cloud Topology
Client Layer (Rich client, Thin Client)
PLM Work ow Management System (BPEL, OASIS) ERP CAX
Enterprise Messaging Layer DW, others
PLM Reporting and Analytics Collaboration Tools Knowledge Mgmt
PLM Services
Enterprise Infrastructure (Mailing, Directory services, Live Communication, Mobility etc.)
Virtualization
Compute Servers
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Distributed File store
Distributed Databases
Following are the
salient points of the proposed infrastructure for PLM on cloud implementation
PLM Workflows Distributed workflows implementation on cloud, using BPEL, OASIS representations, connection to PLM services using standard XML interfaces/SOA based services. This will allow scalability and service layer seperations across geographically disparate design locations, vendors, suppliers, customers and other collaborators
Enterprise Messaging Layer As Integration backbone (or provider of SOA/web services interchange) PLM Reporting and Analytics in order to facilitate enterprise wide reporting and analytics pertaining to Product Lifecycles, this layer need to be planned in the cloud Collaboration Tools CAD visualizations, online meetings/reviews to be implemented on enterprise collaboration backbone, on the cloud Knowledge Management In order to facilitate enterprise wide design collaborations, PLM knowledge (research, development related data/libraries) management is to be thought as common layer (Across different business entities) on the cloud
PLM Services The core PLM services (Such as BOM Mgmt, Part Mgmt, Digital Mockup etc.) to be implemented in SOA framework and to be using org wide computational abilities, interfacing with distributed CAD stores and meta data. This layer will feed data and services to the other layers on the top
Integration CAX integration, ERP integration etc. will be thought in the context of business processes where they are required.
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As the cloud technology, specifically in enterprise application space, matures, some of the propositions in this paper need to be validated so as to provide viable business alternatives to current PLM deployment across the industry. Our further exploration will focus on following topics:
PLM Data Model
Future Considerations
Experimenting cloud native data models, using Google‟s „big table‟ [2] or other suitable alternatives.
PLM Workflows For PLM process orchestration to be faithfully aligning to the complex design activities cutting across multiple enterprise entities and stakeholders (customers, vendors, suppliers, distributed design centers), newer workflow orchestration paradigm such as distributed workflow engines using BPEL, OASIS specifications/ languages, on the cloud need to be explored.
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Conclusion Various benefits of cloud technology, in specific reference to PLM business functions and deployment concerns were explored in this paper. Technology architecture considerations and limitations of current PLM packages, so as to leverage the full potential of cloud technology stack were discussed. Specifically, technology innovation and rethinking required in PLM data model and workflows were brought forth. Viable cloud stack, such as those presented in hybrid clouds, were thought of as possible PLM cloud deployment alternatives.
Finally future research required in PLM packages with specific reference to cloud technologies was proposed
BIBLIOGRAPHY
1.
Infosys Setlabs Briefing on Cloud Computing – volume 7, No 7 2009
2.
“Big Table - A Distributed Storage System for Structured Data”- Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach,Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber, Google Inc.
About the Author Awadhesh Singh Parihar works as Global PLM Service Line head in Infosys. He has been associated with PLM domain for over a decade and has helped in PLM strategy formulation and implementation for Infosys customers across various discrete and non-discrete industry segments. He holds Masters degree in Industrial Engineering and Operations Research from Indian Institute of Technology Bombay. He has earlier worked as Deputy Manager at Management Services Division of Tata Engineering, a leading Indian Automotive OEM
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