submission for - scpiteam.com award document.pdf · and 1b show the level 2 scor representations of...

Submission for:

Supply Chain CouncilAwards for Excellence in

Supply Chain Operations and Management

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Copyright Gensym 2001

Table of Contents

Section 1: General Information and Project Complexity(1.1) Name of the submitting organization(1.2) Name of the organizational unit(1.3) Mission statement of the organization(1.4) Award category of submission(1.5) Brief description of the supply chain and the processes the submission spans

- Figures 1a and 1b – Level 2 SCOR Representation- Figure 2 – e-SCOR Representation

(1.6) Names and number of people involved from each supply-chain partner organization in theproject

(1.7) Names and number of people involved from each functional organization and category ofeach organization

(1.8) Point of contact for each supply-chain partner

Section 2: Implementation(2.1) Reason that the supply-chain project was undertaken and how it was selected(2.2) Duration of the project. Note if the project was a pilot that is being rolled out. Note if the

project is ongoing/still in process(2.3) Describe, in detail, the process used to complete the project

- Figure 3 – e-SCOR Methodology- Figure 4 – e-SCOR Model Life Cycle

(2.4) Identify significant challenges encountered, the process for resolution, and the solutions.Identify best practices

(2.5) Indicate the metrics used to measure (a) progress and (b) success(2.6) Document and quantify cost and performance improvement benefits

- Figure 5 – ‘As-Is Metrics’- Figure 6 – Lead Constraint Metrics- Figure 7 – Strategy Change- Figure 8 – Shipping Decision- Figure 9 – ‘To-Be’ Metrics- Table – Metrics Summary

(2.7) Outline how the success of this effort supports organizational objectives described in theorganization’s mission statement

Section 3: Knowledge Transfer(3.1) Efforts to share lessons from this effort with other internal organizations(3.2) How these results can be transferred to other organizations, specifying the likely candidates

for transference

SCOR is a registered trademark of PRTM.Disclaimer: This submission does not constitute a product endorsement by Intel Corporation.Gensym, G2 and Intel are registered trademarks of their respective corporations.

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Section 1

General Information and Project Complexity

(1.1) Name of the submitting organization:Gensym Corporation52 Second AvenueBurlington, MA 01803781.265.7100

(1.2) Name of the organizational unit:e-Infrastructure Planning

(1.3) Mission statement of the organization:Gensym Corporation is a leader in adaptive software products for modeling, simulating and managinge-business infrastructure. Gensym helps companies prepare and adapt their IT infrastructure,business-to-business supply network, and manufacturing operations for success in the new world ofe-business.

Gensym is dedicated to developing its powerful real-time reasoning technology into products thatprovide robust solutions to key e-business markets. One of the most important markets that thecompany focuses on is supply-chain design. Gensym has applied the unique capabilities of its coretechnology -- G2� -- and developed it into a unique product for modeling supply-chain designs -- e-SCOR. Gensym is committed to helping all types of businesses manage their rapidly changing supplychains so they can win in the fast-paced world of e-business.

(1.4) Award category of submission:Supply Chain Management Technology Excellence

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Test

(1.5) Brief description of the supply chain and the processes the submissionspans. (Scope) (10 points)

The supply chain consists of a broad cross-section of the Intel semiconductor supply network fromthe fabrication of individual computer chips, assembly, burn-in and test of chip sets and distributionof chip sets to five geographical regions. The project team examined both ‘As-Is’ and ‘To-Be’configurations of the supply chain.

The team separated the overall supply chain into manufacturing and distribution chains. Figures 1aand 1b show the level 2 SCOR representations of the supply chain for the manufacturing anddistribution portions of the supply chain respectively. Figure 2 shows the entire supply chain inGensym’s e-SCOR modeled at SCOR level 1*. The ‘To-Be’ model differed from the ‘As-Is’ model inthat the former included the swim-lane labeled ‘Hubs and X-Docks’. The level 4 implementationdetails differed in the two models, allowing dynamic simulation of build-to-forecast or build-to-backlog strategies and P2/P3 planning cycle changes to examine supply chain responsiveness.

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Figure 1a. The manufacturingportion of the semiconductorsupply chain at SCOR level 2.

Fabrication Assembly Burn-In

Figure 1b. The distributionportion of the semiconductorsupply chain at SCOR level 2.

Factory DC Hubs &X-Docks

Customers

* e-SCOR Level 1 consists of an aggregation of SCOR Level 2 elements. For example, an e-SCOR Level 1 Manufacturer consists ofa combination of source, make, deliver and planning. An e-SCOR Level 1 Distributor does not have a make component.

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(1.6) Names and number of people involved from each supply-chain partnerorganization in the project. (External) (5 points)

Participants from Intel:

Name Position OrganizationGeorge Brown Sr. Staff Architect Worldwide IT Strategy & TechnologyBill Scown Global Transportation Services

ManagerPlanning and Logistics Group

Divyesh Patel Business Development Manager Assembly Test ManufacturingGlen Donelin Delivery Program Manager Logistics Strategic Planning

Figure 2. The semiconductor supply chain at level 1 modeled in e-SCOR.

Proportion of ordersfrom burn-in

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(1.7) Names and number of people involved from each functional organization andcategory of each organization. (Internal) (5 points)

Participants from Gensym:

Name PositionBruce Palmer Product DirectorMike Barnett Director of Consulting ServicesRodrigo Lazarraga Senior ConsultantJennifer Shea ConsultantPhilippe Printz Development ManagerVivian Mulligan TrainerStephen Sellers Account ManagerMark Whitworth VP e-Infrastructure Planning

(1.8) Point of contact for each supply-chain partner:

Gensym CorporationBruce Palmere-SCOR Product [email protected]

Intel CorporationGeorge BrownSr. Staff ArchitectWorldwide IT Strategy & [email protected]

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Section 2

Implementation

(2.1) Reason that the supply chain project was undertaken and how it was selected.(5 points)

Intel is faced with a set of seemingly daunting demands. Customers request and expect greater supplierflexibility and performance in response to marketplace demands. Intel management knows that it mustdrive business processes and more timely data exchanges with third parties and customers to reduceinventories and cycle time in their supply network. They also recognize that the Internet is arequirement for business into the future. In order to keep pace with the complexity of business andthe requests of customers, Intel must:

• Reduce inventory and warehousing requirements• Go to market faster• Increase flexibility• Continue to move towards e-business solutions

Early in 2000, Intel management charged a Logistics/Planning team with the task of recommending acourse of actions to address these needs. The recommendations needed to take into account goals toimprove distribution and goals related to improving manufacturing. These sets of goals are tightlylinked because of dependencies in the supply network.

The team chose to use SCOR to analyze and understand the workings of the supply chain and thedependencies inherent in it. They recognized that management would challenge the team to providedetailed substantiation for whatever plan they recommended. The ability to test the feasibility andpotential impact of changes they were considering needed to be described in terms of the desiredSCOR metrics and processes. The ability to test multiple scenarios and assess the impact of multiplechanges in an already complex system was paramount.

Knowledge of the limitations and strengths of the technologies available led the team to conclude thatmodeling and simulation of their supply chain would be a way to meet the management challenge putto them. The requirements for this model were first and foremost that it represent their supply chainusing SCOR processes and metrics. Knowledge of the marketplace led Intel to evaluate and select e-SCOR. It would enable them to test alternative supply-chain scenarios and to analyze outcomes interms of standard SCOR metrics without a lengthy and costly development effort.

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(2.2) Duration of the project. Note if the project was a pilot that is being rolled out.Note if the project is ongoing/still in process. (5 points)

Due to the significance and potential impact of this project, time was of the essence. Besides divertingkey team members from their regular duties, Intel wanted to make changes as quickly as possible sothat both customer satisfaction and financial goals would be realized. The overall duration of theproject was three months, beginning with the initial training of four Intel engineers in July 2000 andending with a final report to Intel management at the end of September. A proof of concept phasewas conducted prior to training to demonstrate the feasibility of the approach.

This project has been completed and the results led Intel to implement the proposed solution in 2001.Implementation of the proposed solution is ongoing.

(2.3) Describe, in detail, the process used to complete the project. (15 points)

Figure 3 (see below) shows the e-SCOR modeling methodology and Figure 4 (see next page) showsthe life cycle of a typical dynamic simulation model. Together, these served as a roadmap forcompleting the supply-chain improvement project using dynamic simulation. The touch-pointsbetween the two views are indicated in parenthesis below the major steps and modeling phases shownin Figures 3 & 4. Both processes are highly iterative in nature and a provision is made for loop-backsto earlier processes as new information is gathered. Each step in Figure 3 is described in more detail inthe sections below, with references to dynamic model life-cycle phases (Figure 4), where appropriate.

Figure 3. The e-SCOR methodology roadmap.

NoYes

Define the BusinessProblem

Model the Supply Chain(Identification & Refinement)

Analyze Performance & Practices(Refinement & Deployment)

Deploy the Solution(Deployment)

Begin

Step ①①①①

Step ②②②②

Step ③③③③

Step ④④④④

NoYes

Business ProblemDefined?

Model Correct?

End

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(Question 2.3 continued…)

Step 1: Define the Business Problem

Step 1 Goal: A statement of an appropriate business problem expressed in terms of specificSCOR metrics, identification of the important supply chain players or roles, and linkage of theproblem to overall organizational goals and objectives.Problem definition is the most important step in the project. In this step the project team formulatesand reviews the objectives of the project and states the problem in business terms. The team expressestheir key metrics using the SCOR standard.

General business problems often are identified, however, the challenge is to isolate specific issues,including appropriate metrics and organizational policies. This task benefits from development of amodel and dynamic simulation using the model. Thus, this step is often revisited as the projectproceeds in order to refine the problem statement.

Figure 4. The e-SCOR model life cycle.

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Step 2: Model the Supply Chain

Step 2 Goal: A calibrated supply chain model defined by a topology and product specificationthat represents the real supply chain in a way that isolates the defined business problem andpermits examination of the dynamic relationships between business practices, businesssystems and performance metrics.Model definition encompasses the tasks of identification and refinement shown in Figure 4 (seeprevious page). This may include both ‘As-Is’ (current, existing) and ‘To-Be’ (proposed or expected)configurations of the supply chain. The challenge in this step is to collect the information necessary toconfigure a dynamic model that addresses the business problem defined in the first step and eliminatesunnecessary detail that confounds the analysis of the supply chain done in the third step. The essentialtasks in this step are described below.

Product Definition & TopologyThe project team must identify the product family to be investigated and map the supply chain for thedefined product families. This task is completed early in the development cycle in order to frame andlimit the scope of study. If necessary, individual SKUs are combined into categories of product as away to focus on the supply-chain players/product category combinations that are central tounderstanding the supply-chain behavior that impacts the selected performance metrics. SCOR level2 analysis provides the input required for developing the topology. For this project, Gensym’s e-SCOR provided the capability to quickly model the entire supply chain based on the level 2description shown previously in Figure 1.

Data Collection & PreprocessingData determine the quality of the model developed by the project team. These data include modelinputs, such as initial inventories, product forecasts for build-to-forecast scenarios, finished good andraw material orders, transportation times and manufacturing build rates. Data also include values thatare model outputs such as on-time performance data, order fulfillment lead times and days of supplyvalues for finished goods and raw materials. Data inputs are used to structure ‘As-Is’ and ‘To-Be’models and drive their dynamic behavior, whereas data outputs are used as calibration points tounderstand if the ‘As-Is’ model is representative of the actual supply chain. In parallel with thisactivity, competitive or ‘best-in-class’ data are collected for comparison. These data are obtained fromERP systems, marketing departments and, for best-in-class data, external consulting companies or theInternet. Preprocessing includes unit conversion, data reduction, and aggregation to obtain a data setthat matches the input requirements of a dynamic supply-chain model.

Calibration & ValidationCalibration differs from validation, which is more rigorous and follows qualitative calibration of themodel. The usefulness of dynamic simulation results depends on calibration and validation to providea high-fidelity representation of the real supply chain. Numerous interactive simulations, sensitivityanalyses, boundary identification and replication studies are used to confirm that the model is stable,robust and that the results are reproducible. ‘As-Is’ models can be calibrated based on historical dataand these same data can be applied in ‘To-Be’ models, supplemented by hypothetical input data –

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which can easily be changed to evaluate alternatives – for processes that are proposed or expect to beimplemented. Subsequent steps in the methodology cannot commence until a calibration isperformed. This task may be revisited as the model is expanded or new data are obtained.

Step 3: Analyze Performance and Practices

Step 3 Goal: A statement of specific conclusions about how the modification of policies,practices and systems effects the dynamic performance of the organization as expressed inSCOR metrics.Techniques in this step directly supported the scorecard gap analysis applied with the SCORmethodology. In this task the definition of business policies was refined, including source, build anddelivery policies. Some were used in the ‘As-Is’ model and others were used to evaluate businessalternatives in ‘To-Be’ models.

This task consisted of conducting many what-if simulations to explore and understand ‘As-Is’ and ‘To-Be’ model behavior. Two types of understanding took place; model understanding and actual supply-chain understanding. The former was a necessary part of learning the boundaries on the decision spacecovered by the model. This understanding resulted from numerous interactive simulations targeted atdemonstrating the range of behaviors simulated by the calibrated or validated model. The later was thefocus of the supply-chain project and was accelerated when the decision space of the model was fullyunderstood.

The project team used a structured approach to identify key parameters and specific simulationscenarios during what-if analysis. A design-of-experiments approach was employed to develop the setof simulation experiments needed to efficiently isolate important cause-effect relationships. Simulationexperiments were versioned and a scenario manager used to organize simulations into sets, along withraw input data, output data and MS Excel reports and graphics created in the business scenario.

The project team generated output reports in this step. These reports included level 1 and level 2SCOR metrics, time series and charts to support the business case. The final documentation andpresentations were formulated to support the business case and to build consensus in the decisionprocess.

Step 4: Deploy the Solution

Step 4 Goal: The implementation of a supply chain performance enhancement that directlyaddresses the business issues defined in Step 1.In this project, the model demonstrated the business value of a proposed change in the design of theIntel supply chain. The proposed change is currently being implemented.

Modeling extends into the deployment step in two different ways, depending upon whether theapplication of the model is in ongoing design or real-time operations. Design applications employ aconfirmed model and apply simulation to examine alternative parameter combinations to furtherunderstand the real system or proposals for business change. Design activities may motivatedevelopment of new or extended models and examination of new business issues and the modeldevelopment life cycle repeats. Operational applications are used to confirm or diagnose problems inthe supply chain, to explain conditions in a supply chain as they arise and to manage or control supplychains in real time.

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(2.4) Identify significant challenges encountered, the process for resolution, andthe solutions. Identify best practices. (10 points)

The project team committed to a dynamic modeling approach that provided a rich environment forexploring supply-chain transformations. The idea is to extend the simpler static modeling techniquesin order to more closely represent the real world and obtain higher-quality data needed for effectivedecision-making. This approach carries with it challenges, some of which are old or well known andothers that are new.

The challenges that the team directly encountered were:• Mapping the supply chain into SCOR• Defining a common language for discussion• Data collection and reduction• Model abstraction• Understanding time and space changes in the model• Business rule strategy development• Using dynamic modeling effectively

It is difficult to discuss these challenges independently of one another in terms of their resolutionprocess and solution since they are very tightly linked. The interaction between them made it seem, atmany times during the project, that it was one step forward, two steps back.

Like many supply-chain modeling projects, the project team faced challenges in mapping the supplychain to the SCOR standard, defining a common language to describe the supply chain and obtainingdata. SCOR greatly reduces the work required by defining common terms and relating processes todata in a way that is understood by all entities participating in the supply chain. The dynamic modelingtool, e-SCOR adds greater structure by defining data elements exactly and requiring that the projectteam interpret both input and output data in the same way. All members of the project team weretrained in the proper use of the static and dynamic modeling tools, significantly reducing the timerequired to formulate models and communicate issues amongst the team.

A key issue in dynamic modeling is abstracting the problem to a level that eliminates unnecessarydetail and unimportant issues that confound supply-chain analysis. SCOR provides assistance with thisproblem in the definition of processes that aggregate and consolidate lower-levels of detail. Thedynamic simulation tool focuses development energy on defining the appropriate level of model detailand helping to quickly answer questions that arise about how changes in the model structure of inputdata impact the business problem.

Data collection and reduction was one of the most frustrating challenges of the project. Collection ofdata that spanned most of the supply chain was, at least, possible in that data belonged to a singlecorporation. Cross-functional boundaries slowed the collection of data as the data did not always existin the same shape or form. The reduction process was also difficult in that data was not always directlyavailable and had to be inferred or interpreted.

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The project team demonstrated the critical importance of understanding dynamic change in time andacross space in any supply-chain model. Stock-outs, upside and downside flexibility, the impact of realdemand changes and the costs of accumulating inventory are properly understood only in the contextof supply-chain dynamics. There is no substitute for this type of information and designs based onlyon steady-state data can be far from optimum. For example, the project team examined the impact ofdynamically assigning the delivery point for customer orders based on a measure of available-to-promise (ATP) and the average transit time from each warehouse to the customer. Business rulesdefined by the project team and a voting/prioritization scheme enabled efficient shipping of customerorders to meet delivery requirements that resulted in a significantly higher on-time performance. Theimpact of the scheme could only be understood within the context of a dynamic model that permittedevaluation of the scheme with changing ATP and transit times.

The project team learned that dynamic simulation must be highly interactive in order to support bothmodel learning and real-system understanding. The ability to quickly make modifications to the supplychain model and then obtain results from many simulations is critical to obtaining this understanding.Visualization of the results is also helpful and this requires careful aggregation of raw data in a waythat permits development of 2D and 3D graphics that demonstrate key behaviors underlying thelinkage between process changes and SCOR metrics.

Challenge Suggested Best PracticeMapping the supply chain into SCOR Modeling tools that impose structure on the

problem definition

Defining a common language for discussion Alignment with the SCOR model and definitions

Dealing with data collection and datareduction issues

Organize data on the basis of standard parametersand metrics

Abstracting the problem to an appropriatelevel of detail

Highlight/focus on the important issues andeliminate unnecessary, confounding detail. Usemodeling tools to represent and focus on keyissues

Understanding change in the supply chain(stock outs, conditions resulting in stockaccumulation, impact of real, dynamicdemand)

Use dynamic simulation to understand change intime and over space

Development of a strategy (business rules)for dynamic order assignment

Use dynamic simulation to understand thebehavior of policies and practices

Understanding the supply-chain representedas a dynamic supply-chain model

Make simulations interactive and obtainknowledge of results in a timely manner

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(2.5) Indicate the metrics used to measure (a) progress and (b) success. (5 points)

When the project began 12 SCOR metrics were considered as indicators of problem areas and asmeasures of success. These were as varied as on-time performance, inventory days-of-supply, cash-to-cash cycle time, and fill rate. After much discussion, as the project progressed, it was decided to focuson three SCOR metrics against which to measure both progress and success:

• Supplier On-Time Delivery Performance• Inventory Days Of Supply• Order Fulfillment Lead Times

These were selected for their effectiveness at representing a combination of customer satisfaction andother internal goals. Many of the other SCOR metrics that are also considered important to thisproject are linked to these metrics.

(2.6) Document and quantify cost and performance improvement benefits. (15 points)

The project sought to improve order fulfillment time and delivery performance while reducingfinished goods inventory. In the year prior to the project, performance for those key metrics had beenroughly as follows:

Order Fulfillment Time 2 monthsFinished Goods Inventory 1.5 million items ($450 million)On-Time Delivery Performance 90 percent

The specific goals related to order fulfillment were:• To reduce delivery time to less than five days• To increase on-time performance to a target level• To define appropriate business rules to enable efficient order assignment (to regional or

geographical distribution centers)

The specific goals related to inventory were:• To minimize finished goods product investment until needed• To distribute inventory efficiently throughout manufacturing

The ‘As-Is’ ModelA first step in the project was to construct an ‘As-Is’ model that replicated recent historical experience.That was accomplished by using actual demand data and forecasts from the previous year as inputs tothe model, then running simulation to validate order fulfillment and inventory. Results of thevalidation are shown in Figure 5 (see next page). The three bar charts, from left to right, show OrderFulfillment Time, Delivery Performance, and Finished Goods Inventory. The axis from front to backis time, covering roughly one year with the most recent results at the back. Finished Goods Inventory,for example, builds up significantly in the second half of the year.

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Figure 5 - ‘As-Is’ Metrics

Outputs from the simulation runs were consistent with historical data from the ERP system andthus validated the model. The next step for the project team was to try different scenarios for supplychain improvement. A first test was to constrain the model by reducing order lead-time to ten days.As shown in Figure 6, this did have the effect of reducing fulfillment time significantly. However,an undesirable consequence was to reduce delivery performance to 52 percent. That would beunacceptable and proved that management’s goals could not be achieved by simply changing policy.Substantive changes in the supply chain would be required to achieve the results they were seeking.

Figure 6 - Lead Time Constrained Metrics

Order Fulfillment Time Delivery Performance Finished Goods Inventory

Time (1 Year)

56 Days 90%1500K

Order Fulfillment Time Delivery Performance Finished Goods Inventory

10 Days50%

1500K

Time (1 Year)As-Is

Constrained OrderLead Time

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Among the possible changes hypothesized by the team were different combinations of distributioncenters, manufacturing strategies, and planning frequency. They designed different model scenariosto test those changes individually and in combination.

One of the suggested changes that proved worthwhile was to change the production strategy. Theexisting supply chain uses a make-to-stock strategy, pushing finished goods all the way to the pointof distribution. The suggestion was to change to a make-to-order strategy from the point offabrication. Figure 7 illustrates that change in strategy. The effect was to reduce finished goodsinventory throughout the supply chain, but it required demand-forecast information that wouldenable the system to be more responsive.

Figure 7 – Strategy Change

‘As-Is’

Push Pull

‘To-Be’

Push Pull

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Another proposed change was to introduce local distribution centers that would complement theglobal distribution centers to handle those orders with very short lead times. It was recognized,however, that those local distribution centers would entail additional cost and so it was important todefine business rules that would determine the routing for order fulfillment expeditiously. For eachorder, the rules needed to take into account the time to deliver and the availability of product atboth the local and global distribution centers as shown in Figure 8. Furthermore, rules would givepreference to fulfilling orders from one of the global centers whenever possible because it is lessexpensive and preserves greater flexibility within the supply chain. Representing the order fulfillmentrules in e-SCOR enabled the team not only to weigh the benefits of a specific order fulfillmentstrategy, but also to define a set of rules that eventually could be incorporated into logisticsoperations.

Figure 8 – Shipping Decision

After trying multiple scenarios, adopting some hypothesized changes and rejecting others, acombination was found that did meet the team’s targets (Figure 9 – see next page): orderfulfillment was reduced to 4.2 days with projected delivery performance of 97 percent. At the sametime, finished goods inventory could be reduced by two-thirds constituting expected savings ofseveral hundred million dollars.

FactoryDistribution

LocalDistributor

Customer

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Figure 9 – ‘To-Be’ Metrics

Table – Metrics Summary

4 Days

97% 440 K

Time (1 Year)

Scenario

Description

AverageFulfillmentTime

On-timePerformance

AverageInventory

‘As-Is’

CurrentSupplyChain

56 Days

90%

770K

What-Ifs

LowerMfg. Lead

Time

10 Days

50%

770K

What-If 1

---

---

---

---

What-If 7

Distributors&

MTO

4 Days

97%

440K

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(2.7) Outline how the success of this effort supports organizational objectivesdescribed in the organization’s mission statement. (15 points)

For its first 15 years, Intel’s mission was “to be the predominant building block supplier to the PCindustry.” But in a speech last year Chairman Andy Grove noted that, “we have found it necessary toadapt our mission statement…Our current mission statement is to be the building block supplier tothe worldwide Internet economy.” Times change and so do the ways of doing business. Global supplychains and information exchange over the Internet have are now critical concerns for thrivingenterprises.

Last year, too, Intel recognized a need to be much more responsive to their customers anddistributors in order to meet competitive demands in their marketplace. They identified Factory Agilityand Responsiveness (FAR) as a strategic initiative and began a number of projects to make Intel’ssupply chain more efficient and more responsive from end to end. Information flow and managementwere issues of particular attention. The Intel team came to this project with the example of a productsupply chain. They had target objectives for improvement and several ideas about supply chainchanges that might, or might not, improve responsiveness.

Gensym provides enabling technologies precisely in the areas that Intel sought to improve as reflectedin Gensym’s mission statement to help “companies prepare and adapt their IT infrastructure,business-to-business supply chain, and manufacturing operations for success in the new world of e-business.” With e-SCOR, Gensym supported Intel engineers in modeling the supply chain, validatingthe model against historical data, then testing the effects of proposed changes. It was a combinedeffort and a learning experience on both sides as interactions and trends within the supply chainbecame apparent.

This project exemplifies Gensym's commitment to developing software products that use its corereasoning-engine technology to help companies effectively manage their supply chain in today's fast-paced world of e-business. Intel is a leader in applying innovative solutions to its manufacturingprocesses. Consistent with its mission, Gensym developed e-SCOR for exactly the type of supply-chain design and modeling that Intel required. Using e-SCOR, Intel was able to perform what-ifanalyses before they implemented new elements into their supply chain. Gensym is dedicated to bringthat ability to model and test supply-chain designs to all types of businesses in virtually any industryaround the world, so they can successfully manage their rapidly changing supply-chain environmentwith confidence.

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Section 3

Knowledge Transfer

(3.1) Efforts to share lessons from this effort with other internal organizations.(5 points)

Within Intel, teams who have responsibility for strategic planning have expressed interest and takenthe first steps to evaluate this technology to assist in meeting their objectives. They recognize the valuethat simulation of SCOR can provide for making strategic planning decisions, specifically the ability totest alternatives, confirming the benefits of some strategic options while disqualifying others.

(3.2) How these results can be transferred to other organizations, specifying thelikely candidates for transference. (5 points)

Likely candidates for this technology are companies involved in global networks, especially those thatare already practitioners of the Supply Chain Council’s SCOR model. e-SCOR enables companies toderive greater value from SCOR by simulating supply chain models, trying out what-if scenarios forimproved performance, and measuring potential benefits in accordance with SCOR metrics.

A case in point is the Intel-Siemens e-business project. The purpose of the project is to define amethodology and framework that will support the integration of different standards such as SCORmodels and RosettaNet PIPs in an inter-enterprise environment. Such a framework would facilitatecollaborative planning and forecasting to improve supply chains that span multiple enterprises.Participating companies, in addition to Intel Corporation and Siemens, include Fujitsu, Edifecs,Netfish, and IDS Scheer. They are using IDS Scheer’s ARIS software to visualize the structure of thesupply chain. Then by rendering that model in e-SCOR, they will be able to simulate the operation ofthe supply chain and measure its performance on critical SCOR metrics.

Transferring to other organizations what Intel accomplished in this project requires a team withdiverse skills and some specific capabilities. The skills include an understanding of SCORmethodology, broad knowledge of the operations of their supply chain, and understanding how to usesimulation, specifically e-SCOR, to test different supply chain scenarios. The capabilities include e-SCOR software and certain supply chain operations data to configure the model. Gensym offerstraining and consulting to support the use of e-SCOR. The Supply Chain Council offers training in theSCOR methodology. Some management consulting firms with their own expertise, methodology, andbenchmarking data can complement a company’s own expert knowledge to help identify areas ofpotential supply chain improvement and make good use of e-SCOR to test and demonstrate the valueof proposed improvements.

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