chapter 2 the scope of supply chain...

Chapter 2

The Scope of Supply Chain Management

2.1 Collaboration in Supply Chains

2.1.1 Insufficient Collaboration Results in the Bullwhip Effect

The key feature of SCM is close collaboration between two or more business partners. One of the goals aspired to is to smooth processes and to avoid unpredictable ordering behavior of the main customers; more specifically, to avoid the upstream demand amplification already studied in System Dynamics models (Forrester 1961) and popularized as the bullwhip effect (Lee et al., 1997a, b). The first company to report this phenomenon was Procter&Gamble, which it observed in its diaper supply chain. The most prominent model showing the bullwhip effect is the Beer Game (Sterman 1989). Delays in transferring order information and in fulfillment (due to lead times) and the absence of information sharing are main reasons for the bullwhip effect.

To reduce the bullwhip effect, the members of the supply chain may try to improve their information systems and/or their physical systems. Since the speed of data transfer technology has been dramatically improved in recent years, the assumptions prevalent in the Beer Game about the delays in information transfer can only stem from administrative processes in order management. Data is typically not transferred in real-time, and the coordination effort resulting from the using of different systems may also contribute to time-lags. Furthermore, if the demand is static and normally distributed, there is no reason to order distinct volumes at different time points. If the retailer ordered steadily, the other companies would not have to react nervously to unexpected order volumes. Thus, the bullwhip effect is at least partially homemade.

The main implication of studying the demand amplification is that transferring Point-of-Sales (POS) data to the other partners in the supply chain will considerably reduce the bullwhip effect. However, the question arises why a retailer should share its POS data with other members of the supply chain. One argument is that the supply chain is becoming more competitive, by realizing smoother planning, scheduling, and execution processes. The retailer may also agree to provide the POS data if it assumes that this supportive behavior will result in lower purchase prices or, at least, improve its bargaining power. Furthermore, data about capacity, capacity usage, and inventory may also be shared and be beneficial for the down-stream companies. Simulation studies show that the information exchange typically

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is more important for upstream than for downstream companies (Chatfield et al., 2004).

With respect to collaboration, several maturity levels of supply chains have been defined:

Stage 1: Functional Focus: Operating discrete supply chain processes with

Stage 2: Internal Integration: Company-wide aligned and integrated supply chain processes continuously measured and steered to achieve common

Stage 3: External Integration: Collaboration with strategic partners (customers, suppliers, and service providers) including joint objectives, shared plans,

Stage 4: Cross-Enterprise Collaboration: Information Technology and e-business solutions resulting in real-time planning, decision making, and execution of customer requirements (Roussel and Skov 2007).

The data recorded in the course of the survey shows that only a few companies realize collaboration beyond stage 2; thus, today collaboration between inde-pendent legal entities is not very common. However, it should be recognized that the evolution does not necessarily follow this sequence and that some stages (in particular stage 2) may be skipped.

SCM and sourcing decisions are closely related. The number of suppliers may be reduced when a supply chain is designed. In an idealistic view, single sourcing would be appropriate for parts that are offered by supply chain partners. However, risk management may contradict a single sourcing policy. Globalization has a huge impact on achieving supply chain goals. Sometimes offshoring decisions are based on rather myopic views on direct production costs, neglecting such matters as the total cost resulting in the supply chain and the impact on lead times.

2.1.2 Types of Collaboration

2.1.2.1 Information Exchange

Information access and data transfer are highly recommended in SCM systems. Information exchange is bidirectional, while information transfer may be uni-directional. As the company delivering data may not know whether the data trans-ferred or exchanged is relevant for the recipient, the terms data exchange and data transfer would be more suitable. Transfer or exchange of data does not necessarily imply that the recipient is using this data. Therefore, data transfer does not imply that the planning processes of the supply chain partners are based on consistent data. A simplified morphological box distinguishing different types of data ex-change is shown in Table 2.1.

2 The Scope of Supply Chain Management

are well documented and understood.

objectives.

functional management of resources. Supply chain processes and data flows

common processes, and performance metrics.

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Table 2.1 Types of data exchange

Data characteristics

Occurrences

Source of data Last element in supply chain (retailer, OEM)

Tier-1 supplier

Tier-2 supplier …

Recipient of data Next organization upstream

Next but one/two … organizations upstream

Next organization downstream

Next but one/two … organizations downstream

Category of data Actual data Forecast data Planning data Meta data Amount of data All data Selected data,

defined statically

Rule-based selected data

Granularity of data

Elementary data

Aggregated data

Type of provision Data access (pull)

Data transfer (push)

Timeliness Time-point Period Up-to-dateness Real-time

data Delayed data, delay time-based

Delayed data, delay rule-based

Delayed data, delay resolved ad hoc

Actual data may be about (e.g.)

sales volumes at POS, warranties, capacity usages, events, and compliance issues.

Planning data concern (e.g.)

strategies, investments in physical systems and information systems, events such as promotions, announcements of end-of-life products, or of

new product introduction, procurement, production, scheduling, distribution, and financial matters.


inventories,

—

—

—

—

—

—

—

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Meta data may be exchanged to coordinate

quality control, and the use of IS, in particular the

customization of IS, data models, process models, and numbering systems.

Another type of data transfer tries to improve the capabilities of the suppliers,

Collaborative forecasting is based on data exchange or transfer, but does not

a consensus on future data that may be used in local planning or in collaborative planning efforts.

The Delphi method is a well-known procedure for collaborative forecasting of

toward a consensus when those involved are informed about opinions expressed by other experts. However, the result of applying the Delphi method is not a forecast accepted by all concerned. The Delphi method is typically not used in routine forecasting of operative data but in forecasting future trends. Application of the Delphi method can be supported by specific IT systems.

Achieving a common forecast of quantitative data, for example about future demand for certain products or product groups, is a difficult task. Planning typically means considering distinct scenarios that differ in the assumptions and data un-derlying them. A company may look at several scenarios, and the common forecast may be just one of several considered. An agreement to use only a consensus

be enforced.


2.1.2.2 Collaborative Forecasting

forecast may reduce the value of local planning processes considerably and cannot

necessarily result in collaborative planning. This distinction is also emphasized in

future trends. Results show that divergent opinions of experts converge some way

the CPFR model (cf. Section 2.1.2.4). The goal of collaborative forecasting is to find

materials and agricultural best practices. To translate its words into actions, Nestlé employs over 800 agronomists, technical advisers, and field technicians. Their job

world to improve their production quality, as well as their output and efficiency. They do this on a daily basis in as many as 40 countries. This specialist team has pioneered the development of sustainable local fresh milk and coffee production (Nestlé 2006).

•

Mini case: Nestlé supports sustainability in the supply of agricultural raw

•

for example with respect to product quality.

•

is to provide technical assistance to more than 400,000 farmers throughout the

•

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2.1.2.3 Collaborative Planning

Collaborative Planning aims to coordinate the plans of several partners in the supply chain. The associated models can be managed by one or more of the firms involved or by a trusted service provider.

Several types of models may be used. Spreadsheet models and simulation models may be developed to show the consequences of different decisions in certain planning scenarios (“What-If Models”). How-to-Achieve Models change the pers-pective by stipulating target values and determining the corresponding value of an independent variable. Decision models are used to determine the best solution by optimizing algorithms or to find a satisfactory solution by applying heuristics.

Collaborative planning differs from individual planning in several ways (Table 2.2, partially based on Windischer and Grote 2003).

Table 2.2 Comparison of individual planning and collaborative planning

Individual planning Collaborative planning Recognizing the sequential order of events

Communication of anticipated events

Recognizing goals Lateral agreements on goals Recognizing the availability of alternatives

Information exchange about the availability of alternatives

Recognizing the adequacy of plan’s resolving

Recognizing the adequacy of common plans

Monitoring planned actions and diagnosing errors in individual plans

Monitoring and diagnosing errors in common plans

Revising individual plans Coordination of planning and feedback about modifications

Canceling individual plans Common reflection and common decisions to cancel plans

Depending on the amount of information transparency agreed upon, several types of collaborative planning can be distinguished. One of them is Open Book Planning. The collaborating entities deliver data into a common planning model. The semantics of this data (i.e., the definitions used in the data models) must be carefully coordinated. The data and the results obtained by the planning procedure become visible to all participating entities. A very high level of trust is necessary between the partners for this approach to be realized. Even entities belonging to the same group may have objections against (detailed) Open Book Planning. The Open Book may be accessible only to selected members of the supply chain. However, in such a situation it may be even more difficult to make sure that the other entities deliver correct planning data.

Another approach is to install a trusted service-provider as the entity collecting data for the planning model and delivering the planning results to the supply chain partners. In this case the cooperating entities are treated equally with respect to information transparency. However, results of a planning model are usually not


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implemented without further consideration. The purpose of decision models is to provide insight, not numbers. Insight is based on understanding relationships bet-ween input data and output data. It may be difficult to gain insight if the effects of modifying input data cannot be discussed in detail because the input data is clande-stine.

A common planning model may become complex owing to its size and the details considered, and it may be difficult to find appropriate algorithms for deter-mining an optimal solution or even for applying a sound heuristic. Decomposition has been recommended to reduce the complexity of decision models. In this case it is not necessary to exchange all details of the data relevant to the planning model, but only some results obtained from local planning models.

Decomposed decision models are solved in an iterative way. The results of the planning model Pir of entity i in iteration r are used by the collaborating entity j in iteration r + 1. Entity j will consider the effects of Pir on its own situation and decision variables and develop plan Pj, r + 1, which is communicated to entity i. Thus, the planning results of one entity appear as input data in the plan of the other entity.

For obvious reasons only a limited number of such organizational iterations can be realized. The optimal solution, which could be determined by an Open Book model, will typically be missed. However, numerical experiments show that even a small number of organizational iterations may result in solutions that are quite close to the optimum of the Open Book model and, from the perspective of the supply chain, far better than local solutions obtained without collaborative plan-ning (Dudek 2004; Dudek and Stadtler 2005).

2.1.2.4 Collaborative Planning, Forecasting, and Replenishment (CPFR)

Several frameworks for structuring collaboration tasks exist. The best known is the CPFR

CPFR model distinguishes eight collaboration tasks. For collaboration between a retailer and a manufacturer the tasks are exemplified in Table 2.3.

2.1.2.5 Collaborative Scheduling

As scheduling decisions are often short term and taken close to execution, real-time information exchange and contingency management among geographically dispersed entities may be beneficial (Jia et al., 2002; Boyson et al., 2003).

The schedule of transports may determine production schedules, and a need for the exchanging of information between distribution and production schedulers results (Chen and Vairaktarakis 2005). The customer may receive information about successfully finished operations and the time intervals for which remaining operations are scheduled. This could be done via alerting mechanisms (e.g., sending e-mails or messages to a PDA), by providing information on the Web, or even by allowing access to (parts of) the partner’s scheduling system.


Interindustry Commerce Solutions (VICS) Association. Fig. 2.1 shows that the ®® framework. CPFR is a reference model developed by the Voluntary

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Fig. 2.1 Visualization of the CPFR® process (VICS 2004)

Table 2.3 Collaboration tasks between a retailer and a manufacturer (cf. VICS 2004)

Replenishment


Manufacturer Tasks Collaboration Tasks Retailer Tasks

Strategy and Planning

Demand and Supply Management

Execution

Analysis

Account Planning Market Planning

Collaboration Arrangement Joint Business Plan

Vendor Management Category Management

Market Data Analysis Sales Forecasting POS Forecasting

Planning Order Planning/Forecasting Demand Planning

Production and Supply Planning Logistics/Distribution Order Fulfillment

Order Generation

Logistics/Distribution

Store Execution Supplier Scorecard

Exception Management Performance Assessment Customer Scorecard

Execution Monitoring

Buying/Re-buying

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2.1.2.6 Collaborative Execution

resources and the redesign of physical processes. In this case not only information and planning systems are influenced by SCM but also the physical execution systems.

Changes of physical systems have been suggested by such production manage-ment concepts as Just-in-Time (JIT), Lean Production, and Agile Manufacturing. JIT needs close collaboration between the partners, and reducing setup times is an important precondition for the realization of JIT procedures. Cross-docking is a concept intended to minimize handling times at distribution centers by tight co-ordination of inbound and outbound transports. Track&Trace systems show the progress made in bridging the spatial distance between supplier and recipient and allow the recipient to prepare for arrivals, but also to adjust production schedules if an item required should arrive too late. Visibility of real-time data for business partners is regarded as one of the main properties of a “real-time enterprise.” Many SCM systems support the visualization of data.


Mini case: In the chemical industry, changes in the schedule of one plant can affect several other plants, and ripple effects may increase the magnitude of changes in plants downstream. For instance, in the Bayer company the plant schedules are highly interdependent. The results of the nightly centralized scheduling run are broken-down into plant-specific models where decentralized planners use these models for local changes. The local scheduling persons should

be able to work on a smaller model of the facilities they are allowed to schedule but at the same time be able to share data with and view infor-mation from other plants, be able to see the schedule changes of relevant production steps in other plants, make other plants aware of schedule changes, and reduce conflicts and find a mutually agreeable solution for product chains running through multiple plants with the help of a chain planner.

Complex communication mechanisms are set up to achieve these goals. Central coordination mechanisms are combined with complementary information exchange amongst decentralized decision makers between the scheduling runs (Berning et al., 2002).

Mini case: Several companies with basically decentralized organizational struc-tures achieved significant improvements through central coordination of material handling. For instance, the largest Swiss retail company Migros helped to develop an Application Service Providing (ASP) platform for achieving better visibility and

regional warehouses, and its supermarkets (Knolmayer and Dedopoulos 2006). transparency of the associated pallet flows between its suppliers, the central

Collaborative execution may be closely connected with reassignment of tasks and

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2.1.2.7 Collaborative Monitoring and Controlling

Many criteria have been proposed for measuring and evaluating the perfor-mance of a company’s logistical system. Examples are

(differently defined) service levels, response delay, the difference between the delivery day initially requested

by the customer and the negotiated day, lateness, computed from the differences between negotiated delivery day

and actual delivery day, (differently defined) stocks, e.g., work in progress (WIP) as a percentage

of sales, mean and variance of throughput times, and percentages of scrap in production and corrupted inventory.

The Supply Chain Operations Reference (SCOR®) model developed by the Supply-Chain Council (SCC) defines more than 200 Key Performance Metrics at the highest of four levels. SAP SCM™ provides more than 300 KPI that are based

oriented performance attributes are distinguished (cf. Supply Chain Process Im-provement 2007):

Customer-facing performance attributes Reliability

o Delivery performance o Perfect order fulfillment o Fill rates

Responsiveness (Order fulfillment lead times) Flexibility

o Supply chain response time o Production flexibility

Internal-facing Costs

o Costs of goods sold o Total SCM costs o Warranty/returns processing costs

Asset management efficiency o Cash-to-Cash cycle time o Asset turn.


Mini case: In the 1980s, General Motors’ Service Parts Operation used sophisticated Operations Research methods for inventory and transportation management in its relationships with dealers. However, the service to consumers was consistently poorer than the service of most of its competitors, because the dealers’ inventory systems were out of control, resulting in outdated data and metrics and wrong stock-keeping decisions. This illustrates the fact that a supply chain is only as good as its weakest link (Hausman 2004).

on the SCOR® metrics. Three classes of customer-facing and two classes of internally

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The Supply Chain Performance Indicator, which has been defined by The Performance Measurement Group (2007), considers a broad spectrum of business-related metrics which shows the high impact of good SCM practices on business results. With respect to the large number of metrics it is recommended that the most relevant ones be selected. These may be visualized on a dashboard, using Kiviat graphs, spider diagrams, or Balanced Scorecards (Kaplan and Norton 1996).

and a Customer Chain Operations Reference (CCOR) model have been defined by the SCC. These models also define many metrics. A projection of some metrics to Balanced Scorecard Categories is suggested by Bolstorff (2006). Ways of projecting

For supply chains, two different controlling approaches exist. On the one hand, each entity in the supply chain can define its own criteria and eventually present the values achieved in a Balanced Scorecard; however, if this information is passed on to partners, a shared meaning should be accomplished, and this can only be reached when there is agreement upon the definition of data elements and co-ordinated procedures are applied. On the other hand, common metrics for the whole supply chain may be defined and eventually presented in a Supply Chain Scorecard; coordination of meta data becomes even more relevant when this approach is followed (cf. Ackermann 2003; Kleijnen and Smits 2003).

2.1.2.8 Collaborative Reassignment of Tasks

The most far-reaching type of collaboration is a coordinated restructuring of functions and processes, which may result in reassignment of task responsibilities

mediation or disintermediation may be considered when tasks are reallocated; one

Quality control can be moved from the customer to the supplier after a common

by applying Electronic Bill Presentment and Payment (EBPP) systems (SAP 2001) as part of Financial Supply Chain Management.

Vendor Managed Inventory (VMI) is probably the most common reassignment of responsibilities. The customer is no longer placing orders and, therefore, no due dates for delivery are fixed. The vendor is responsible for providing concerted inventory service levels. SAP recommends considering VMI if

key customers constitute a high percentage of the vendor’s sales figures, the products are standardized and requested repeatedly, product growth is not excessive, meaning that the requirement patterns are

stable and the vendor can assume that requirements will not occur spon-taneously, and

the transaction costs for order processing and production planning are high (SAP 2007).


the SCM metrics into terms of income statements, balance sheets, and Economic

®In addition to the SCOR

Value Added indicators have also been suggested (Camerinelli and Cantu´ 2006).

model, a Design Chain Operations Reference (DCOR)

quality management system has been agreed on. Financial flows can be reorganized

example of such an approach is the Fourth-party Logistics Provider (4PL) concept.

from one supply chain partner to another. In redesigning a supply chain, inter-

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Intentia (2001), a former vendor of ERP systems, describes benefits of VMI as follows:

Supplier benefits Visibility of the customer’s POS data simplifies forecasting. Promotions can be more easily incorporated into the inventory plan. Customer ordering errors, which in the past would often lead to a

return, are reduced. Stock level visibility helps identify priorities (replenish stock versus a

stockout). The supplier can see the potential need for an item before the item is

ordered. Customer benefits

Fill rates from the supplier, and to the end consumer, improve. Stockouts and inventory levels often decrease. Planning and ordering costs decrease since the responsibility is shifted

to the supplier.

right time.

Dual benefits

ations.

Both parties strive to offer better service to the end consumer. All parties involved benefit when the correct item is in stock when the end consumer needs it.

customer.

Extremely high benefits are reported from realizing VMI relationships. SAP

pendent third-parties (Table 2.4, cf. SAP 2007).


60 locations in 25 countries, implemented the SAP Inventory Collaboration Hub™ in 2005. The costs of order processes and administration expense were reduced by more than 50%. Many A and B materials are stored via Supplier Managed In-ventory agreements. Capital lock-up was reduced by lower warehouse inventory and safety stocks (Brauchle 2006).

•••

•

The overall service level is improved by having the right product at the

•

The supplier is more focused than ever on providing superior service.

••

Data entry errors are reduced owing to computer-to-computer communic-

•

Overall processing speed is improved.

•

A true collaborative partnership is formed between the supplier and the

•

•

claims very optimistic figures that have been reported by SAP customers or inde-

••

Mini case: Knorr-Bremse, a leading producer of brake systems with more than

•

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Table 2.4 Potential benefits of VMI (SAP 2007)

Business benefits Vendor/customer Value potential Increased revenue/sales Vendor and

customer Lower inventory levels Vendor Increased service levels Vendor Operating costs through full truckloads Vendor Increased service levels Customer Inventory turns Customer Increased service levels Customer

When considering the potential of VMI one has to realize that it is based on the

transfer of detailed data, e.g., POS data and inventory data. Such data transfer may be realized with or without entering on a VMI relationship. An advantage of VMI is that no due dates are fixed by the customer, which makes the vendor flexible with respect to its execution processes. However, the vendor may lack some in-formation which is available only locally at the site of its customer. VMI partnerships should incorporate the obligation to transfer either such local information or at least forecast data based on it. Several simulation studies on VMI systems show significant cost reductions for the entire supply chain (Disney and Towill 2003a, b). As suppliers have access to actual sales and/or inventory data provided by the customers, the Bullwhip Effect can be reduced, resulting in a smaller variability of demand data (Småros et al., 2003). Thus, safety stocks, particularly of suppliers, can be reduced.

Sometimes a distinction is made between Vendor Managed Inventory and Supplier Managed Inventory (SMI). In the CPFR® context four alternatives are distin-guished (Table 2.5). The difference between VMI and SMI is primarily one of viewpoint: VMI involves the management of finished goods inventories outbound from a manufacturer, distributor, or reseller to a retailer, whereas SMI manages the flow of raw materials and component parts inbound to a manufacturing process (Pohlen and Goldsby 2003). IT ownership and IT architectures differ. In the SAP environment there is also a difference in the ownership of the collaborative application system – for VMI the application system is owned by the supplier and for SMI, by the customer.

Table 2.5 Assignment of responsibilities (cf. VICS 2004)

Alternative Sales forecasting Order planning Order generation

Conventional approach Retailer Retailer Retailer Co-managed inventory Retailer Retailer Manufacturer Supplier managed inventory Retailer Manufacturer Manufacturer Vendor managed inventory Manufacturer Manufacturer Manufacturer


100–200%

70% From 89% to 98%

28% From 93% to 99%

27–70% From 93% to 99%

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2.2 Business Architectures for Supply Chain Management

2.2.1 Supply Chain Planning Matrices

systematic frameworks. In the research literature several (slightly different versions

2002; Fleischmann and Meyr 2003; Fleischmann et al., 2005; Meyr et al., 2005). A detailed description of the matrix is given by Fleischmann et al. (2005,

p. 88). In our opinion, Supply Chain Planning Matrices have some disadvantages. The arrows in the top row imply that a certain flow occurs independently of the type of production system. However, for make-to-order production the sequence of the columns “Production,” “Sales,” and “Distribution” should be “Sales,” “Production,” and “Distribution,” and order-specific design activities for make-to-engineer production should appear. Furthermore, the execution and controlling processes are disregarded in the framework and the collaboration with other companies is not visualized in the matrix. We try to improve these shortcomings

Fig. 2.2 A supply chain planning matrix


in our pyramidal representation (cf. Section 2.2.3).

Several models have been followed to arrange the most relevant SCM processes in

of) Supply Chain Planning Matrices (Fig. 2.2) are presented (cf. Neumann et al.,

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2.2.2 The Supply Chain Operations Reference Model

the Supply-Chain Council is attracting a lot of attention. Today the Council counts about 1,000 corporate members worldwide and has established chapters in North

sponsor of its activities.

and system of notation for describing business processes. It is organized into four levels to allow differently detailed views on business processes and focuses on

model to describe the current status of the system (“as-is” situation) or to define a target status (“to-be” situation). Such models are often used in business process

performance of certain process elements. A company may decide to gather this data for internal performance evaluation or also for benchmarking with other companies. The SCC tries to motivate its members to deliver performance data for

“best practices.”

Plan Source Make Deliver Return

at four hierarchical levels. At the uppermost level, the process types are defined as

support companies in making basic strategic decisions regarding its operations in the following, sometimes vaguely formulated areas:

1. Delivery performance, 2. Order fulfillment performance, 3. Fill rate (make-to-stock), 4. Order fulfillment lead time, 5. Perfect order fulfillment, 6. Supply chain response time, 7. Production flexibility, 8. Total SCM cost, 9. Value-added productivity, 10. Warranty cost or returns processing cost,


America, Europe, Greater China, Japan, Australia/New Zealand, South East Asia,

®In practice, the Supply Chain Operations Reference (SCOR ) Model developed by

®The SCOR model defines five process types

public (Supply-Chain Council 2008). SAP AG is a member of the SCC and a main ® Brazil, and Southern Africa. In 2008, Release 9.0 of the SCOR model was made

The SCOR model is a process reference model, proposing a certain terminology ®

®reengineering projects. The SCOR model also defines metrics used to measure the

inter-organizational processes. A company or a supply chain may use the SCOR ®

®the SCOR metrics to support inter-organizational benchmarking and to recognize

shown in Table 2.6. According to the SCC, level 1 of the SCOR model aims to ®

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Definition Plan Processes that balance aggregate demand and supply to develop a

course of action which best meets sourcing, production, and delivery requirements.

Source Processes that procure goods and services to meet planned or actual demand.

Make Processes that transform products to a finished state to meet planned or actual demand.

Deliver Processes that provide finished goods and services to meet planned or actual demand, typically including order management, transportation management, and distribution management.

Return Processes associated with returning or receiving returned products for any reason. These processes extend into post-delivery customer support.

At level 2, e.g., the Make process is refined to

Make-to-stock production, Make-to-order production, and Make-to-engineer production,

whereas the Return process is detailed to

Return defective product, Return Maintenance, Repair, and Overhaul (MRO) product, and Return excess product.

Level 2 also defines some enabling processes. A typical example of an enabling process is to provide the necessary IT infrastructure for process execution.

In 2007, the SCC announced SCORmarkSM, a members-only benchmarking

SCORmarkSM benchmarking

to select the supply chain metrics most critical to its organization, to determine the target performance desired for each supply chain attribute

to enter the relevant data required to calculate the performance for each selected metric into the secure, confidential benchmarking portal.


11. Cash-to-cash cycle time, 12. Inventory days of supply, and 13. Asset turns.

portal based on the SCOR model, in association with APQC (Supply-Chain Council ®2007). As part of the “analyze” phase of the SCOR model, a company may use ®

in the SCOR model, and ®

SCOR® process

Table 2.6 Level 1 Processes, as defined by the Supply-Chain Council

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ARIS™ is a business process management tool developed by IDS Scheer and today offered as part of SAP’s NetWeaver infrastructure. Among other tools, an ARIS EasySCOR Modeler has been developed (IDS Scheer 2007).

are discussed by Huan et al. (2004) and Poluha (2007).

2.2.3 A Supply Chain Pyramid

as a reference framework. With the pyramidal form we reflect the hierarchy of

by Hieber (2002).

zational levels. Strategic, tactical, and operational planning tasks are distinguished

are combined at one level. Source and procurement and make and production are


The data is validated in a seven-step process to produce a report with

an executive scorecard to quickly spotlight on any gaps in the targeted performance levels for each supply chain attribute and

a detailed analysis for each specific metric selected, including best practice information on the drivers of performance and peer group reporting as available.

2001). The SAP Solution Manager is an implementation tool that allows mapping

SAP SCM™ systems automatically deposit the data from ERP and ongoing supply chain transactions into SAP’s Business Intelligence™ applications. These cal-

ERP™ and SAP SCM™ implementations. Once in operation, the SAP ERP™ and

culate the plan-source-make-deliver KPIs and deliver them to SAP’s management

Software vendors included SCOR metrics in their SCM systems (Gassmann ®

Based on the Supply Chain Matrices and the SCOR model, we present a global view ®

Some deficiencies of the SCOR model as seen from an academic point of view ®cockpit for role-based breakdowns of the SCOR model (Gould 2005). ®

of the SCOR model’s “best practices” against what the users want in their SAP ®

model, product design is enclosed in the pyramid to ®avoid the formulation of a separate design model. Furthermore, the SCOR model ®

of the supply chain tasks in the form of a pyramid (Fig. 2.3) and use this pyramid

Compared with the SCOR

visualized in “information pyramids”; cf. Mertens 2007, p. 6). A slightly similar

Fig. 2.3 shows inbound- and outbound-collaboration tasks at various organi-

used synonymously. Execution is explicitly included in the pyramid.

lected CCOR). To emphasize the high importance of selling, we decided to split the

“task reference model of transcorporate logistics” in pyramidal form was proposed

decision rights, planning tasks, and the associated information needs (as often

does not explicity address sales activities (which later became part of the widely neg-

at the horizontal levels. For ease of presentation, operational planning and scheduling

27 2.3 Desirable Features of SCM Systems

Fig.

2.3

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fram

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2 The Scope of Supply Chain Management 28

2.3 Desirable Features of SCM Systems

This section gives a short description of some functionalities that could be helpful

involved in supply chains. Functions and processes in SCM systems have specific characteristics because

be stored. There is a need for special filtering and compression mechanisms before data is fed into common databases, to avoid too great an increase in

single bodies of data often have to be aggregated into larger groups: for instance, equipment into capacity groups, product characteristics into charac-teristics groups, products into product groups;

bigger problems have to be decomposed before they can be treated with optimization algorithms or heuristics; examples are the segmentation of a long planning horizon into several shorter time segments, for which solutions may successively be found although typically the global optimum will be missed.

for SCM. We do not describe functions that are also essential for enterprises not

data of several networked enterprises, not only those of one company, must

the sizes of databases and data warehouses;

In Section 2.3 we formulate desirable properties of SCM systems. In Chapters 3

support by means of IT systems, and there are some uncovered spots on the landscape. Product design activities are outside the scope of SAP’s systems, but there are interfaces to the most relevant CAD systems.

and 4 we discuss how these properties are covered by the SAP SCM™ 5.0 system.

SCM solution map is projected onto the SCM pyramid (Fig. 3.4). As we shall see,

delivery process into distribution and sales already in our Supply Chain Pyramid. We

controlling and support processes. Some strategic planning tasks are difficult to

processes are at least as important as return processes and therefore decided to men-

Supply Chain Visibility Model proposed by the IBM Institute of Business Value

(Butner 2007).

avoid arrows to indicate that sequences between sales and distribution activities de-

tion them explicitly in the SCM pyramid. These modifications are in line with the

The pyramid is refined stepwise in the remainder of the book. First, SAP’s

tactical and operational planning are well covered by the SAP system, as well as

pend on the type of business relationships. Finally, we think that regular after-sales

2.3.

1 D

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form

atio

n A

rch

itec

ture

2.3 Desirable Features of SCM Systems 29

Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

a)

Vis

ualiz

atio

n of

the

com

plet

e su

pply

ch

ain/

netw

ork

at se

vera

l lev

els o

f de

tail.

Pow

erfu

l too

ls fo

r vis

ualiz

atio

n an

d na

viga

tion

(coc

kpit,

leits

tand

) are

requ

ired.

C

f. Se

ctio

n 4.

6.2.

A.

b)

Mod

ifica

tions

of t

he n

etw

ork,

e.g

.,

rega

rdin

g in

term

edia

tion

and

disi

nter

-m

edia

tion

or n

ew a

lloca

tion

of ta

sks

to th

e co

llabo

ratin

g fir

ms.

Whe

n st

ruct

urin

g a

supp

ly c

hain

, dec

isio

ns h

ave

to b

e m

ade

abou

t whi

ch ta

sks s

houl

d be

car

ried

out b

y th

e su

pply

cha

in p

artn

ers t

hem

selv

es a

nd w

hich

are

to b

e ou

tsou

rced

. With

the

impa

ct o

f the

Web

on

the

firm

s’

coop

erat

ion,

new

inte

rmed

iarie

s (e.

g., T

rust

Cen

ters

to

supp

ort f

inan

cial

tran

sact

ions

) are

foun

ded,

whe

reas

so

me

tradi

tiona

l int

erm

edia

ries (

e.g.

, tra

vel a

genc

ies)

ar

e de

clin

ing

in im

porta

nce.

For m

aste

r dat

a re

leva

nt fo

r mod

elin

g a

give

n su

pply

net

wor

k se

e Se

ctio

n 4.

7.3.

SAP

SCM™

doe

s not

pro

vide

func

-tio

nalit

y fo

r dec

isio

n su

ppor

t in

supp

ly

netw

ork

desi

gn.

c)

Con

figur

ator

to fo

rmul

ate

agre

emen

ts

and

cont

ract

s by

com

bini

ng te

xt

mod

ules

(e.g

., su

bcon

tract

ing,

serv

ice

leve

l agr

eem

ents

, per

iods

of n

otic

e,

allo

wan

ce o

f del

ays,

resp

onsi

bilit

y fo

r co

nsig

nmen

t sto

cks)

.

Whe

n th

e st

ruct

ure

of th

e su

pply

cha

in re

mai

ns fi

xed

for a

long

er ti

me

span

dur

ing

whi

ch so

me

partn

ers a

re

repl

aced

, the

con

tract

s with

new

par

tner

s can

be

deriv

ed

from

stan

dard

ized

agr

eem

ents

by

mod

ifyin

g ce

rtain

pa

ram

eter

s. Ex

ampl

es a

re c

ontra

cts b

etw

een

supp

liers

of

stan

dard

ized

pro

duct

s and

big

reta

ilers

.

Not

in sc

ope

of S

AP

SCM™

.

d)

Col

labo

rativ

e in

vest

men

t pla

nnin

g;

nego

tiatio

n of

inve

stm

ent p

ropo

rtion

s. In

supp

ly c

hain

s the

par

tner

s som

etim

es a

gree

to sh

are

larg

e in

vest

men

ts, s

uch

as w

areh

ouse

s nea

r airp

orts

or

proj

ects

to im

prov

e da

ta q

ualit

y, b

y us

ing

RFI

D

tech

niqu

es.

Not

in sc

ope

of S

AP

SCM™

.


D

esira

ble

Feat

ures

C

omm

ents

C

over

age

by S

AP

SCM™

e)

Dec

isio

ns a

nd a

ctio

ns to

avo

id

mul

tiple

qua

lity

chec

ks.

Mul

tiple

qua

lity

chec

ks (a

t the

supp

lier’

s site

bef

ore

ship

ping

and

at t

he c

usto

mer

’s si

te a

fter r

ecei

pt) c

an b

e av

oide

d if

the

partn

ers a

gree

on

a Q

ualit

y M

anag

emen

t sy

stem

: Res

ults

of q

ualit

y co

ntro

l mea

sure

s may

be

exch

ange

d vi

a a

porta

l whe

re th

e cu

stom

er c

an se

e th

em o

r hav

e th

em a

naly

zed

by a

n au

tom

ated

syst

em.

Not

in sc

ope

of S

AP

SCM™

.

f)

Nav

igat

ion

deta

ils o

f the

mas

ter f

iles

by a

ctiv

atin

g no

des a

nd a

rcs s

uch

as

info

rmat

ion

on c

apac

ity a

nd c

osts

of

fact

orie

s, w

areh

ouse

s, tra

nspo

rtatio

n ro

utes

, pro

duct

ion

prog

ram

s of

supp

liers

, tim

e zo

nes,

and

holid

ays a

t th

e lo

catio

ns o

f par

tner

s.

Top-

dow

n na

viga

tion,

ope

ning

win

dow

s, e.

g., t

o vi

sual

ize

such

add

ition

al in

form

atio

n as

cap

acity

co

nstra

ints

of s

uppl

iers

or c

usto

ms r

egul

atio

ns.

Not

in sc

ope

of S

AP

SCM™

.

g)

Man

agem

ent o

f var

iant

s, es

peci

ally

in

the

cont

ext o

f mul

tiple

sour

cing

. Th

e de

cisi

on o

n ho

w m

any

and

wha

t kin

ds o

f var

iant

s ar

e al

low

ed is

diff

icul

t bec

ause

of t

he im

pact

on

sale

s an

d th

e co

sts o

f pro

duct

ion,

inve

ntor

y, lo

gist

ics,

train

ing,

afte

r-sa

les s

ervi

ce, a

nd c

apita

l req

uire

men

ts.

AB

C a

naly

ses a

re re

com

men

ded

for s

uppo

rt of

var

iant

s m

anag

emen

t. W

hen

parts

are

pro

cure

d fr

om d

iffer

ent

supp

liers

, the

se p

arts

may

not

be

stric

tly id

entic

al. T

his

may

be

one

reas

on fo

r var

iant

s.

Not

in sc

ope

of S

AP

SCM™

.


Desi

rabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

h)

Dem

onst

ratin

g th

e di

ffer

ence

s be

twee

n ob

ject

s [pr

oduc

ts, p

rodu

ct

feat

ures

(“pr

oduc

t in

terc

hang

eabi

lity”

), pr

oduc

tion

proc

esse

s] th

at c

an b

e su

bstit

uted

by

each

oth

er a

nd e

xpla

inin

g th

e im

plic

atio

ns, e

.g.,

addi

tiona

l cos

ts

caus

ed b

y a

subs

titut

ion.

Subs

titut

ion

proc

esse

s may

be

indu

ced

by c

hang

es in

th

e co

st o

f obj

ects

or r

esul

t fro

m so

urci

ng o

r pro

duct

ion

bottl

enec

ks.

Subs

titut

ion

is c

onsi

dere

d in

pla

nnin

g;

cf. S

ectio

ns 4

.4.6

and

4.7

.3.

i) Po

sitio

ning

of i

nven

tory

. D

ecis

ions

abo

ut st

orag

e lo

catio

ns o

f raw

mat

eria

ls,

com

pone

nts,

and

prod

ucts

are

fund

amen

tal i

n SC

M.

Crit

eria

incl

ude

cust

omer

dem

and,

serv

ice

leve

l ag

reem

ents

, tra

nspo

rtatio

n tim

e an

d co

sts o

f tra

nspo

rtatio

n be

twee

n si

tes,

valu

e of

the

parts

, the

ne

cess

ary

stor

age

spac

e (d

epen

ding

on

the

volu

me

per

item

), an

d sp

ecia

l tec

hnic

al c

ondi

tions

, suc

h as

coo

ling

or se

curit

y.

Dec

isio

ns o

n w

heth

er o

r not

to st

ock

an e

xist

ing

war

ehou

se c

f. Se

ctio

n 4.

4.2.

A.

Furth

er a

naly

ses a

re n

ot in

scop

e of

SA

P SC

M™

.

j) A

gree

men

ts o

n lo

t siz

es a

nd

prod

uctio

n cy

cles

. Se

t-up

times

and

set-u

p co

sts m

ay d

iffer

con

side

rabl

y be

twee

n su

pply

cha

in p

artn

ers.

The

situ

atio

n w

here

one

pa

rtner

pro

duce

s big

lots

a fe

w ti

mes

per

yea

r whi

le th

e do

wns

tream

par

tner

man

ufac

ture

s sm

all l

ots m

ore

ofte

n sh

ould

be

avoi

ded,

bec

ause

such

unc

oord

inat

ed

deci

sion

s res

ult i

n av

oida

ble

inve

ntor

ies.

Not

in sc

ope

of S

AP

SCM™

.

k)

Man

agem

ent o

f hie

rarc

hies

of t

he

sour

ces,

incl

udin

g se

rvic

e st

atio

ns.

Exam

ple:

Sup

plie

r ->

cen

tral

war

ehou

se ->

regi

onal

w

areh

ouse

-> se

rvic

e st

atio

ns.

Mai

nten

ance

as m

aste

r dat

a; c

f. Se

ctio

n 4.

7.3.

D

ecis

ion

supp

ort f

or su

pply

cha

in

desi

gn is

not

in sc

ope

of S

AP

SCM™

.


D

esira

ble

Feat

ures

C

omm

ents

C

over

age

by S

AP

SCM™

l) C

oord

inat

ion

of d

ata

mod

els i

n da

taba

ses,

data

war

ehou

ses,

and

data

m

arts

of s

uppl

y ch

ain

partn

ers.

Har

mon

ized

dat

a m

odel

s hel

p to

avo

id e

rror

s in

prod

uctio

n an

d co

mpa

rison

of m

anag

emen

t N

ot in

scop

e of

SA

P SC

M™

.

m)

Coo

rdin

atio

n of

pro

cess

and

w

orkf

low

mod

els.

Coo

rdin

ated

wor

kflo

w m

odel

s avo

id re

dund

anci

es,

supp

ort t

empo

ral c

oord

inat

ion,

and

incr

ease

tra

nspa

renc

y in

the

supp

ly c

hain

.

Ale

rts c

an b

e ex

chan

ged;

cf

. Sec

tion

4.6.

2.

n)

Coo

rdin

atio

n of

cus

tom

izin

g pa

ram

eter

s. D

eter

min

ing

the

para

met

ers o

f ER

P sy

stem

s and

oth

er

inte

rdep

ende

ncie

s. Ex

ampl

e: M

inor

mod

ifica

tions

of t

he p

aram

eter

s of t

he

func

tion

“con

sum

ptio

n of

pla

ns”

for f

inal

pro

duct

s may

ha

ve fa

r-re

achi

ng e

ffect

s on

stoc

ks o

f com

pone

nts a

nd

raw

mat

eria

ls.

Not

in sc

ope

of S

AP

SCM™

.

o)

Man

ifold

sim

ulat

ions

to e

valu

ate

alte

rnat

ives

. Si

mul

atio

ns c

ould

supp

ort t

asks

men

tione

d in

item

s g),

i), j)

, and

k) i

n pa

rticu

lar.

Cf.

Sect

ion

4.2.

com

plic

ated

eff

ects

, sid

e ef

fect

s, an

d so

ftwar

e pa

ckag

es is

a d

iffic

ult t

ask

beca

use

of m

any

info

rmat

ion,

e.g

., va

lues

of K

PI.

2.3.

2 C

olla

bo

rati

ve P

rod

uct

Des

ign

(“D

esig

n f

or

SC

M”)


Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

a)

Coo

pera

tion

of th

e su

pply

cha

in

partn

ers i

n pr

oduc

t des

ign,

val

ue

anal

ysis

, and

val

ue e

ngin

eerin

g.

An

SCM

-orie

nted

des

ign

cons

ider

s the

eff

ect o

f des

ign

deci

sion

s on

all p

artn

ers i

n th

e su

pply

cha

in. T

hese

ef

fect

s may

not

be

obvi

ous t

o th

e de

sign

ers o

f a c

erta

in

com

pany

; the

refo

re, c

olla

bora

tion

betw

een

desi

gn a

nd

man

ufac

turin

g ex

perts

of t

he p

artic

ipat

ing

com

pani

es

will

faci

litat

e a

“Des

ign

for S

CM

.”

Dur

ing

the

desi

gn p

roce

ss se

vera

l par

tner

s sho

uld

be

able

to se

e th

e ac

tual

des

ign

resu

lts o

n th

eir s

cree

ns a

nd

to a

dd h

ints

and

sugg

estio

ns. T

he e

valu

atio

n of

al

tern

ativ

e de

sign

s by

the

partn

ers i

n th

e su

pply

cha

in

will

ofte

n di

ffer

. Too

ls, e

.g.,

grou

pwar

e, fo

r bet

ter

help

ful.

Not

in sc

ope

of S

AP

SCM™

.

b)

Proc

ess p

lann

ing

taki

ng th

e ef

fect

s on

SCM

into

acc

ount

. co

ncep

ts o

r req

uire

men

ts o

f RFI

D, s

uch

as a

void

ing

the

pack

ing

or sp

ecia

l pro

tect

ion

need

s of p

rodu

cts (

e.g.

,

trans

port)

.

Not

in sc

ope

of S

AP

SCM™

.

c)

Mea

sure

s to

allo

w si

mpl

e tra

nsfe

r and

ex

chan

ge o

f pro

duct

-def

inin

g da

ta b

y co

ordi

nate

d ap

plic

atio

n of

CA

D,

PDM

, and

PLM

syst

ems.

com

pone

nts m

ay n

ot fi

t tog

ethe

r.

resu

lted

from

usi

ng v

ersi

on 4

in G

erm

an a

nd S

pani

sh

plan

ts w

here

as fa

ctor

ies i

n Fr

ance

and

the

UK

use

d ve

rsio

n 5

(Ste

inke

200

6).

Not

in sc

ope

of S

AP

SCM™

.

avoi

danc

e of

dam

age

thro

ugh

shar

p pr

ofile

s dur

ing

inte

rrup

tion

of R

FID

com

mun

icat

ion

by m

etal

lic

If in

com

patib

le C

AD

tool

s are

use

d, so

me

phys

ical

of th

e C

AD sy

stem

CAT

IA. I

nter

oper

abili

ty p

robl

ems

Exam

ple:

Del

ays i

n th

e de

velo

pmen

t of t

he A

irbu

s Sup

er-

coor

dina

tion

betw

een

supp

ly c

hain

par

tner

s may

be

jum

bo A

380

are

attr

ibut

ed to

the

use

of d

iffer

ent r

elea

ses

Exam

ples

are

the

impl

emen

tatio

n of

pos

tpon

emen

t

2.3.

3 S

ales

an

d D

eman

d P

lan

nin

g

2.3.

3.1

Stra

tegi

c an

d T

actic

al D

eman

d Pl

anni

ng


D

esira

ble

Feat

ures

C

omm

ents

C

over

age

by S

AP

SCM™

d)

Man

ifold

sim

ulat

ions

to e

valu

ate

pr

oduc

t and

pro

cess

alte

rnat

ives

. M

oder

n C

AD

syst

ems a

llow

the

eval

uatio

n of

som

e pr

oper

ties o

f phy

sica

l pro

duct

s and

pro

cess

es w

ithou

t th

e ne

cess

ity fo

r bui

ldin

g a

phys

ical

pro

toty

pe.

Exam

ple:

A su

bass

embl

y de

sign

ed b

y th

e su

pplie

r has

to

be

adju

sted

to su

it th

e to

ols a

vaila

ble

in th

e as

sem

bly

line

of th

e cu

stom

er’s

pla

nt.

Not

in sc

ope

of S

AP

SCM™

.

Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

a)

Porta

ls fo

r col

labo

rativ

e pl

anni

ng

(pla

nnin

g bo

ok).

The

plan

ning

boo

k is

the

mai

n sc

reen

whe

re th

e co

mm

on d

ata

is d

ispl

ayed

, ent

ered

, and

pro

cess

ed a

nd

whe

re in

tera

ctiv

e pl

anni

ng ta

kes p

lace

.

Cf.

Sect

ion

4.2.

3.A

.

b)

Met

hods

of c

alcu

latin

g cu

stom

er

lifet

ime

valu

es.

Estim

atin

g th

e cu

stom

er li

fetim

e va

lue

help

s in

deci

sion

s on

whe

ther

a c

usto

mer

shou

ld b

e in

vite

d to

be

com

e m

embe

r of a

supp

ly c

hain

.

Not

in sc

ope

of S

AP

SCM™

.

c)

Con

side

ratio

n of

the

prod

uct l

ife

cycl

e.

The

life

cycl

e pr

ofile

s hav

e to

be

adap

ted

to ta

ke

acco

unt o

f rec

ent d

evel

opm

ents

. The

par

tner

s sho

uld

prov

ide

rele

vant

dat

a fo

r det

erm

inin

g ty

pica

l pro

files

fo

r SC

M p

urpo

ses b

y us

ing

stat

istic

al m

etho

ds, e

.g.,

fore

cast

ing

met

hods

bas

ed o

n m

arke

t sat

urat

ion

and

sale

s dat

a ag

greg

ated

ove

r the

life

time

of th

e pr

oduc

t.

Cf.

Sect

ions

4.2

.1 a

nd 4

.4.1

.C.


Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

d)

Coo

rdin

ated

inve

ntor

y pl

anni

ng fo

r pr

oduc

ts n

ear t

o th

e en

d of

thei

r life

cy

cle.

The

exch

ange

of p

rodu

ct li

fe c

ycle

dat

a is

of u

tmos

t im

porta

nce

whe

n it

com

es to

inve

ntor

y m

anag

emen

t of

prod

ucts

that

will

soon

be

elim

inat

ed fr

om th

e sa

les

prog

ram

. Car

eful

coo

rdin

atio

n he

lps t

o ke

ep th

e rig

ht

inve

ntor

y in

the

right

war

ehou

ses,

shop

s, an

d se

rvic

e st

atio

ns.

Not

in sc

ope

of S

AP

SCM™

.

e)

Supp

ortin

g C

olla

bora

tive

Plan

ning

, Fo

reca

stin

g, a

nd R

eple

nish

men

t (C

PFR

).

CPF

R is

a th

orou

gh c

oord

inat

ion

of fu

nctio

ns a

nd

proc

esse

s in

the

supp

ly c

hain

with

pot

entia

l for

co

nsid

erab

le b

enef

its to

the

partn

ers.

Exam

ple:

Vol

vo, a

Sw

edis

h ca

r man

ufac

ture

r, im

plem

ente

d co

llabo

rativ

e pr

oces

ses w

ith o

ver 3

50

dom

estic

and

ove

rsea

s ven

dors

and

supp

liers

. One

of

the

mai

n fu

nctio

ns is

the

colla

bora

tive

exch

ange

of

fore

cast

s.

Cf.

Sect

ion

4.2.

3.

f)

Met

hods

of V

MI o

r SM

I. Th

e su

pplie

r con

trols

the

inve

ntor

y of

the

cust

omer

and

re

plen

ishe

s it w

hen

nece

ssar

y.

Exam

ple:

Woo

lwor

th c

alls

up

the

sale

s of a

ll ite

ms i

n ea

ch o

f its

bra

nche

s eve

ry d

ay a

fter c

losi

ng ti

me.

L’

Oré

al, t

he g

loba

l mar

ket l

eade

r in

the

cosm

etic

s in

dust

ry, d

eter

min

es d

eliv

ery

quan

titie

s and

cre

ates

an

optim

al d

eliv

ery

plan

usi

ng th

e SA

P SC

M™

supp

ly

netw

ork

plan

ning

cap

abili

ties b

ased

on

fore

cast

de

man

d, a

ntic

ipat

ed st

ock

mov

emen

ts, o

pen

orde

rs,

and

inve

ntor

y in

form

atio

n.

Cf.

Sect

ions

4.2

.3.B

, 4.3

.1.D

, and

4.

3.2.


D

esira

ble

Feat

ures

C

omm

ents

C

over

age

by S

AP

SCM™

g)

Col

labo

rativ

e fo

reca

stin

g su

ppor

ted

by a

met

hod

bank

with

fore

cast

ing

algo

rithm

s and

syst

ems t

o su

ppor

t the

se

lect

ion

of m

etho

ds, p

aram

eter

co

nfig

urat

ion,

and

the

inte

rpre

tatio

n of

resu

lts.

The

partn

ers m

ay u

se d

iffer

ent f

orec

astin

g pr

oced

ures

, w

hich

, mor

eove

r, m

ay b

e m

odifi

ed d

epen

ding

on

devi

atio

ns b

etw

een

fore

cast

, dem

and,

and

sale

s. If

co

llabo

rativ

e fo

reca

stin

g is

pra

ctic

ed w

ithin

the

supp

ly

chai

n, th

e pa

rtner

s sho

uld

be a

ble

to a

naly

ze th

e de

tails

, in

clud

ing

the

para

met

er se

lect

ions

and

the

resu

lts, b

y us

ing

an e

xpla

natio

n co

mpo

nent

.

Cf.

Sect

ion

4.2.

1.A

.

h)

Adm

inis

tratio

n of

tim

e se

ries a

nd

time

serie

s pat

tern

s, su

ch a

s sal

es

afte

r pro

mot

ions

, e.g

., se

para

ted

for

cust

omer

type

s and

regi

ons (

“glo

bal

fore

cast

ing

prof

iles”

) or p

atte

rns o

f ca

nnib

aliz

atio

n.

Ofte

n pr

omot

ions

are

lim

ited

to a

regi

on, e

.g.,

depe

nden

t on

the

loca

l wea

ther

or l

ocal

hol

iday

s. Pr

omot

ed p

rodu

cts m

ay c

anni

baliz

e ot

hers

. The

de

man

d m

ay b

e sh

ifted

to la

ter p

erio

ds b

ecau

se

cust

omer

s bui

ld u

p st

ocks

of t

he p

rom

oted

par

ts a

nd

buy

less

in th

e fo

llow

ing

perio

ds. F

or th

is re

ason

lo

gist

ic m

anag

ers d

o no

t fav

or p

rom

otio

ns, b

ut th

e ar

gum

ents

of t

he m

arke

ting

spec

ialis

ts o

ften

dom

inat

e,

so th

at th

e pr

oble

ms h

ave

to b

e so

lved

by

SCM

.

Adm

inis

tratio

n of

tim

e se

ries i

s a b

asic

ca

pabi

lity

of D

eman

d Pl

anni

ng a

nd

Fore

cast

ing;

cf.

Sect

ions

4.2

.1 a

nd

4.4.

1.

Prom

otio

n pl

anni

ng is

dis

cuss

ed in

Se

ctio

ns 4

.2.1

.D a

nd 4

.3.2

.C.

i) In

corp

orat

ing

exte

rnal

dat

a.

indi

cato

rs, s

houl

d be

mer

ged

with

inte

rnal

dat

a if

this

m

ight

impr

ove

the

accu

racy

of t

he fo

reca

sts.

Inco

rpor

atio

n of

ext

erna

l dat

a is

a

App

licat

ion

of e

xter

nal d

ata

for

4.2.

1.A

. j)

Stan

dard

ized

ana

lyse

s of t

he a

ccur

acy

of th

e fo

reca

sts a

nd o

f the

repl

enis

h-m

ent p

olic

y (F

orec

astin

g an

d re

plen

ishm

ent a

naly

tics)

.

Com

mon

par

amet

ers o

f the

ana

lysi

s met

hods

allo

w fo

r be

tter d

iagn

osis

of a

ppro

pria

te in

vent

ory

leve

ls in

the

partn

er c

ompa

nies

.

No

stan

dard

repo

rts.

For s

tock

ing

deci

sion

s cf.

Sect

ion

4.4.

2.A

.

SAP

BI™

.

fore

cast

ing

is d

escr

ibed

in S

ectio

n

basi

c co

ncep

t of S

AP

SCM

™ v

ia

Dat

a fr

om e

xter

nal d

atab

ases

, e.g

., bu

sine

ss c

ycle


Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

k)

Var

iabl

e ag

greg

atio

n of

reso

urce

s su

ch a

s wor

kers

(des

crib

ed b

y sk

ill

code

s), m

ater

ials

, pro

duct

ion

faci

litie

s, an

d tra

nspo

rt ve

hicl

es.

Agg

rega

tion

of p

erio

ds, r

egio

ns,

prod

ucts

and

pro

duct

cha

ract

eris

tics

(“ch

arac

teris

tic-d

epen

dent

fo

reca

stin

g”),

aggr

egat

ion

to v

irtua

l pr

oduc

ts (“

phan

tom

s”),

bloc

k pl

anni

ng w

ith b

ucke

ts.

Dis

aggr

egat

ion

of a

ggre

gate

pla

nnin

g

quan

titie

s to

coun

tries

and

fact

orie

s.

The

syst

ems s

houl

d be

abl

e to

avo

id in

form

atio

n ov

erlo

ad b

y pr

ovid

ing

pow

erfu

l agg

rega

tion

proc

edur

es. S

uppl

y ch

ain

partn

ers u

se d

iffer

ent

met

hods

and

par

amet

ers t

o ag

greg

ate,

e.g

., in

ord

er to

de

term

ine

data

abo

ut p

rodu

ct g

roup

s with

an

adeq

uate

st

atis

tical

bas

is. I

n di

ffer

ent c

ount

ries t

he p

rodu

ct

char

acte

ristic

s with

mos

t im

pact

on

the

mar

ket m

ay

vary

. Ex

ampl

e: It

shou

ld b

e po

ssib

le to

clu

ster

all

car m

otor

s w

ith d

efin

ed v

alue

s of t

he C

O2 o

utpu

t or a

ll to

ys m

ade

from

the

sam

e ra

w m

ater

ial.

l) Fo

reca

st a

fter c

onst

rain

ts.

If fo

reca

sts i

ndic

ate

a si

gnifi

cant

gro

wth

in d

eman

d,

info

rmat

ion

on se

rious

cap

acity

con

stra

ints

shou

ld

prev

ent p

lann

ing

with

thes

e sa

les f

orec

asts

.

Cf.

Sect

ion

4.2.

1.J.

m)

Col

labo

rativ

e de

man

d pl

anni

ng.

The

partn

ers i

n th

e su

pply

cha

in sh

ould

try

to d

evel

op

com

mon

scen

ario

s and

to a

chie

ve a

com

mon

est

imat

e of

the

dem

and

resu

lting

in u

se o

f the

se sc

enar

ios a

s the

ba

sis o

f sub

sequ

ent p

lann

ing.

Cf.

Sect

ion

4.2.

3.A

.

n)

Prod

uct m

ix p

lann

ing.

Su

pplie

rs o

ften

have

an

inte

rest

in re

ceiv

ing

orde

rs n

ot

only

for s

ingl

e pr

oduc

ts b

ut fo

r pro

duct

mix

es.

Exam

ple:

A c

hem

ical

com

pany

is in

tere

sted

in se

lling

pr

oduc

t A to

geth

er w

ith p

rodu

ct A

’, w

hich

em

erge

s as

a jo

int p

rodu

ct in

the

prod

uctio

n pr

oces

s.

Cf.

Sect

ion

4.7.

3.

data

, e.g

., of

tota

l pro

duct

ion

Cf.

Sect

ions

4.2

.1, 4

.2.2

.C

(reg

ardi

ng ti

me)

, and

4.2

.2.N

(reg

ardi

ng re

sour

ces)

.


Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

o)

Col

labo

rativ

e de

liver

y sc

hedu

les.

Info

rmat

ion

exch

ange

bef

ore

conf

irmat

ion

of sh

ipm

ent

date

s can

incr

ease

the

prob

abili

ty o

f shi

ppin

g in

tim

e.

Cf.

Sect

ions

4.2

.3.C

and

4.2

.3.D

.

p)

Col

labo

rativ

e pr

omot

ion

plan

ning

. Pr

omot

ions

may

be

trigg

ered

by

inve

ntor

y m

anag

emen

t (h

igh

inve

ntor

y le

vels

hav

e to

be

redu

ced,

add

ition

al

war

ehou

se sp

ace

is n

eede

d), f

rom

spec

ial s

ales

op

portu

nitie

s (e.

g., T

V a

nd v

ideo

sets

bef

ore

big

spor

ting

even

ts),

from

fina

nce

(nee

d to

impr

ove

liqui

dity

), or

by

prod

uct l

ife c

ycle

man

agem

ent (

new

m

odel

s will

soon

repl

ace

old

ones

). Se

vera

l par

tner

s m

ay b

e in

volv

ed, e

.g.,

supp

liers

of s

ervi

ce p

arts

. If

pro

mot

ion

plan

ning

is n

ot su

ffic

ient

ly c

oord

inat

ed,

peak

s in

dem

and

may

resu

lt an

d ra

w m

ater

ials

, co

mpo

nent

s, or

faci

litie

s may

cau

se b

ottle

neck

s. Su

pplie

rs tr

y to

avo

id a

situ

atio

n w

here

seve

ral

cust

omer

s sta

rt pr

omot

ions

at t

he sa

me

time

for t

he

sam

e pr

oduc

t gro

ups.

Cf.

Sect

ions

4.2

.3.A

and

4.3

.2.C

.


Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

Var

iabl

e se

t-up

of p

aram

eter

s di

stin

guis

hing

bet

wee

n fo

reca

st-

cons

umpt

ion

and

plan

-con

sum

ptio

n.

Whe

n an

ord

er a

rriv

es it

is n

eces

sary

to ju

dge

whe

ther

it

is th

e re

aliz

atio

n of

a p

lann

ed o

rder

or n

ot. I

f in

the

first

cas

e th

e or

der e

ntry

dat

e di

ffer

s fro

m th

e pl

anne

d ar

rival

dat

e, it

has

to b

e de

cide

d w

heth

er th

e in

com

ing

orde

r is a

real

izat

ion

of th

e ea

rlier

or t

he la

ter p

lann

ed

orde

r. If

the

inco

min

g or

der i

s allo

cate

d to

the

late

r one

, th

e sy

stem

rega

rds t

he o

rder

pla

nned

for a

pas

t per

iod

as a

n er

ror,

assu

mes

dec

reas

ing

dem

and,

and

ther

efor

e re

duce

s the

new

fore

cast

. If t

he o

rder

is c

lass

ified

as

unpl

anne

d, th

is c

an b

e a

trigg

er to

incr

ease

fore

cast

s an

d/or

dem

and

plan

s. Th

is d

ecis

ion

may

be

auto

mat

ed b

y a

set o

f rul

es. I

n an

SC

M re

latio

nshi

p th

is d

ecis

ion

can

be su

ppor

ted

by

info

rmat

ion

exch

ange

bet

wee

n cu

stom

ers a

nd su

pplie

r in

a m

ore

syst

emat

ic a

nd e

ffic

ient

way

than

in o

ther

bu

sine

ss re

latio

nshi

ps.

Cf.

Sect

ion

4.2.

1.F.

r)

Def

initi

on o

f rul

es fo

r the

leve

l of

safe

ty st

ocks

(e.g

. in

dist

ribut

ion

cent

ers)

and

cap

aciti

es.

The

situ

atio

n w

here

par

tner

s hol

d to

o hi

gh o

r too

low

in

vent

ory

leve

ls sh

ould

be

avoi

ded,

bec

ause

the

chan

ce

to o

ptim

ize

the

tota

l inv

ento

ry in

the

supp

ly c

hain

w

ould

be

redu

ced

if th

ey d

id.

Exam

ple:

If th

e pa

rtne

rs k

now

that

they

can

acc

ess

stoc

ks o

f oth

er p

artn

ers i

f nec

essa

ry th

ey d

o no

t nee

d to

ke

ep h

igh

safe

ty st

ocks

of t

heir

ow

n.

In a

cas

e st

udy

inve

ntor

y po

sitio

ning

was

iden

tifie

d as

th

e by

far m

ost i

mpo

rtan

t dri

ver f

or im

prov

ing

supp

ly

For i

nter

nal c

olla

bora

tion

cf. S

ectio

ns

4.2.

2.A

and

4.4

.2.B

. Col

labo

ratio

n w

ith e

xter

nal p

artn

ers r

egar

ding

safe

ty

stoc

k le

vels

is n

ot c

over

ed b

y SA

P SC

M™

.

chai

n m

etri

cs (S

imch

i-Lev

i et a

l., 2

008)

.

q)


Des

irabl

e Fe

atur

es

Com

men

ts

Cov

erag

e by

SA

P SC

M™

s)

Bac

kord

er p

roce

ssin

g w

ith d

iffer

ent

optio

ns.

If b

acko

rder

s occ

ur, t

he c

onfir

mat

ion

of c

usto

mer

or

ders

may

be

tem

pora

rily

revo

ked

for p

lann

ing

purp

oses

. The

opt

ion

sele

cted

will

influ

ence

the

depl

oym

ent p

roce

ss a

nd a

ffec

t whi

ch o

rder

s are

fu

lfille

d in

-tim

e.

Cf.

Sect

ion

4.2.

5.G

.

t) Fo

rmul

atio

n of

rule

s for

allo

catin

g sc

arce

pro

duct

s to

dist

ribut

ion

cent

ers

and

war

ehou

ses i

n ca

se o

f sho

rtage

s (D

eplo

ymen

t).

The

syst

em sh

ould

supp

ort c

onsi

sten

t beh

avio

r by

prov

idin

g de

ploy

men

t rul

es.

For d

etai

ls o

f dep

loym

ent r

ules

im

plem

ente

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