Decision support system for the management of an agile supply chain

Abstract

A decision support system for managing an agile supply chain including a server side and a client side. The server side includes a decision support system database that interfaces with a model engine that performs analysis of the data to support planning decisions. The server side also includes a server manager that coordinates requests for service and information. The client side includes decision frames that present the various view points available in the system to the users. A frame manager coordinates the requests from the decision support frames to access the needed data and models.

Claims

What is claimed is:

1. A decision support system, comprising:

decision support frames providing a plurality of views into a supply chain, said frames including decision logic;

a model engine comprising modeling processes for analyzing the supply chain, the modeling processes being assembled by the decision logic to address a decision related to one of the plurality of views; and

a frame manager managing the execution of the modeling processes to provide information for said frames.

2. A system as recited in claim 1, further comprising a database management system supplying database information to the modeling processes and said frame manager.

3. A system as recited in claim 1, wherein said system comprises a server mode including said model engine and said frame manager and a client mode comprising said decision frames.

4. A system as recited in claim 2, wherein a database of said database management system comprises an inventory data space, a supply data space and a demand data space.

5. A system as recited in claim 2, wherein a database of said database management system includes a supply chain network including a component supply node, a production node, an inventory node and a demand node.

6. A system as recited in claim 1, wherein said modeling processes comprise a component procurement policy development module, a finished goods distribution network design module, an aggregate production planning module, a finished goods inventory management module, a sales forecasting and planning module, a market data analysis module and a vendor managed replenishment module.

7. A system as recited in claim 1, wherein said manager comprises a system integrator and a functional integrator.

8. A system as recited in claim 1, wherein the supply chain includes sales, inventory and production.

9. A decision support system, comprising:

a plurality of frames which provide viewing information for a supply chain;

a model engine including at least one module capable of analyzing changes on the supply chain; and

a frame manager coupled to the model engine capable of managing execution of the module and providing an output to at least one of the plurality of frames.

Description

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to a system for supporting management decisions associated with manufacturing of service supply chains that span from a point of creation to a point of consumption and, more particularly, is directed to a system that allows the various decision makers in the supply chain to view the supply chain from their own perspective, obtain information and evaluate decisions concerning past, current and future performance with respect to a diverse set of often conflicting goals.

2. Description of the Related Art

Many types of manufacturing database management and inventory control systems exist today. Each of these systems views the process from the narrow viewpoint of the goals of such a system. For example, inventory control processes tend to determine when the inventory of an item is projected to be depleted and when to order goods to prevent such depletion. The inventory control process does not generally take into account the problems associated with availability of materials and machines to satisfy the inventory demand. On the other hand the manufacturing control process considers the availability problem but does not take into account the effect of a sales promotion that will deplete an inventory faster than projected. A marketing department in preparing a sales promotion will often not consider the effect that promotion will have on availability, inventory and profit margin but tends to focus on sales goals. What is needed is a system that will support managers with each of these view points in understanding the effect of the various decisions that can be made on the supply chain as a whole both currently and into the near future.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a system that allows a decision maker in a supply chain to view the chain from their own perspective and understand the effect that their decisions will have on the supply chain as a whole.

It is another object of the present invention to provide a distributed and layered architecture that allows the reuse of processing resources and data in making diverse different view point decisions concerning a supply chain.

It is also an object of the present invention to provide a User Interface that projects a view (a Decision Support Frame) into the supply chain that takes into account the view point of the particular user, such as a plant manager or sales manager.

It is another object of the present invention to provide quantitative models and analytical processes to support the plans prepared and decisions made from the various supply chain view points such that these resources are cost effectively provided and utilized in across the entire supply chain-wide.

It is also an object of the present invention to provide a scenario management system in which Scenarios can be saved, modified and data transferred between view points or frames.

It is a further object of the present invention to allow the user to specify a data domain that limits the data used for a particular view point.

It is an additional object of the present invention to provide a system that will reconcile the demand and supply aspects of a supply chain.

It is still another object of the present invention to allow the creation of an integrated production, sales and inventory (PSI) plan and provide a projection concerning what is feasible in the production, sales and inventory plan.

It is an object of the present invention to allow the manufacturer or vendor to plan the supply of goods and services for a customer that integrates all information about a product, including current, past and projected future sales and inventory, into a feasible replenishment plan.

It is a further object of the present invention to provide a planning process that reconciles top down and bottom up projections.

It is an object of the present invention to provide expert based models that allow forecasts from various view points including a bottom up view point.

It is also an object of the present invention to operate in an interactive and dynamic decision environment, providing decision support to single or a set of users.

It is also another object of the present invention to maintain overall data consistency and provide performance feedback to users reflecting the impact of "local" decisions on global supply chain performance.

The above-identified objects can be attained by a decision support system for the management of agile supply chains that provides an architecture including a server side and a client side. The server side includes a decision support system database that interfaces with one or more model engines that perform analytical processes on the data to determine requirements and make projections. The server side includes a server manager that coordinates requests for service and information. The client side includes Decision Support Frames that present the various view points available in the system to the users. A frame manager that coordinates the requests from Decision Support Frame (view points) is provided by the system to access the needed data and models.

These together with other objects and advantages which will be subsequently apparent, reside in the details of construction and operation as more fully hereinafter described and claimed, reference being had to the accompanying drawings forming a part hereof, wherein like numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 DSS System Architecture in the Context of Manufacturing Supply Chains.

FIG. 2 DSS System Architecture in the Context of Equipment Repair Supply Chains.

FIG. 3 Decision Support Thread.

FIG. 4 The Data Spaces in Supply Chain Management.

FIG. 5 Structural Elements in the DSS Database for the Manufacturing Supply Chain.

FIG. 6 Structural Elements in the DSS Database for the Equipment Repair Supply Chain.

FIG. 7 Specification of Decision Support Frame.

FIG. 8 Decision Processes in the Manufacturing Supply Chain.

FIG. 9 High Level Model of an Equipment Repair Supply Chain and the Decision Processes.

FIG. 10 Process and Data Flow Diagram Legend.

FIG. 11 Data Space Associations of the Demand Management Frame.

FIG. 12 Process Flow of the Demand Management Frame.

FIG. 13 Process Flow of Order Fulfillment.

FIG. 14 Data Flow for the Demand Management Frame.

FIG. 15 Data Space Associations of the Production-Sales-Inventory Planning Frame.

FIG. 16 Process Flow of the Production-Sales-Inventory Planning Frame.

FIG. 17 Data Flow for the Production-Sales-Inventory Planning Frame.

FIG. 18 Data Space Associations of the Supply Management Frame.

FIG. 19 Process Flow of the Supply Management Frame.

FIG. 20 Data Flow for the Supply Management Frame.

FIG. 21 Data Flow for the Supply Management Frame.

FIG. 22 Data Space Associations of the Vendor Managed Replenishment Frame.

FIG. 23 Process Flow of the Vendor Managed Replenishment Frame.

FIG. 24 Data Flow for the Vendor Managed Replenishment Frame.

FIG. 25 Data Space Associations of the Distribution Network Design Frame.

FIG. 26 Process Flow of the Distribution Network Design Frame.

FIG. 27 Data Flow for the Distribution Network Design Frame.

FIG. 28 Seven Modules and Supply Chain Management.

FIG. 29 Pareto Analysis for ABC Classification.

FIG. 30 Scatter Plot for Sales-Volatility Classification.

FIG. 31 Impact of Sales Promotion.

FIG. 32 Curve Fitting for Promotion Effect Analysis.

FIG. 33 System Level Services, the Seven Modules and Model Engine Utilities.

FIG. 34 Supply Chain Frame Manager: High Level Architecture.

FIG. 35 High Level Architecture of the System Integrator.

FIG. 36 High level object representation of the system integrator portion of the frame manager.

FIG. 37 High Level Architecture of the Functional Integrator.

FIG. 38 Data Flow Diagram for the Supply Chain Network Configurator.

FIG. 39 Functionality in the Domain Manager.

FIG. 40 Hierarchical Structure of a Scenario.

FIG. 41 Data Flow Diagram for the Performance Simulator.

FIG. 42 User-DSS Interaction Process.

FIG. 43 Sample Screen from PSI DSS.

FIG. 44 Three-tier Application Development Architecture.

FIG. 45 DSS Development Platform Environment.

FIG. 46 Generic System Architecture.

FIG. 47 System Architecture in View of the DSS Prototype.

FIG. 48 The Logon Dialog Box.

FIG. 49 The Opening Screen of the DSS.

FIG. 50 User Preferences Selection Dialog Box.

FIG. 51 The Select Data Domain Form.

FIG. 52 Edit Data Domain Dialog box.

FIG. 53 Make Product Domain Dialog Box.

FIG. 54 Save Scenario Dialog Box.

FIG. 55 Open Scenario Dialog Box.

FIG. 56 Demand Management, Bottom Up Forecast Screen.

FIG. 57 Demand Management, Top Down Forecast Screen.

FIG. 58 Promotion Calendar Main Display.

FIG. 59 The Promotion Selection Wizard.

FIG. 60 The Main PSI Frame Form.

FIG. 61 The Options menu choices on the PSI screen.

FIG. 62 PSI Reconciliation Order Dialog Box.

FIG. 63 The main capacity checking dialog box--The Options tab.

FIG. 64 The Results tab.

FIG. 65 The Production Resource tab.

FIG. 66 The Key Components tab.

FIG. 67 The Bill of Material tab.

FIG. 68 The Alternative Components Tab.

FIG. 69 The Resource Requirement tab.

FIG. 70 Key Component Selection Dialog Box.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Introduction

Overview of the DSS Conceptual

Architecture

The Decision Support System (DSS) 10 of the present invention (see FIG. 1) relies on quantitative models and data analysis routines to provide decision support. Consider for example the production, sales and inventory (PSI) planning process. An architecture to provide such support in accordance with the present invention comprises a library of models and routines that are logically linked, regularly updated and maintained. To support the PSI planning process for example, one can then employ an appropriate subset of models and routines from the library to represent the underlying supply chain abstraction and provide decision support. The present invention assembles the models and routines in a flexible manner, as needed by a decision making environment, to enable the DSS 10 to provide customized decision support with a readily upgradable and scalable library.

Principal Design Elements

The architecture of the Decision Support System (DSS) 10 for a manufacturing supply chain is shown in FIG. 1 and comprises the principal design elements of: a DSS Database 12 with a Database Management System (DBMS) 14 and Supply Chain Information Systems 15, Decision Support Frames 16 which provides the various supply chain view points, a User Interface 18, a Model Engine 20 including various Model Engine Utilities (processes) 22 and a Supply Chain Frame Manager 24. The architecture of the DSS 10 in the context of an equipment repair supply chain is illustrated in FIG. 2.

Salient Features

The DSS architecture comprises two basic mode divisions: the Client Mode 30 and the Server Mode 32. These modes can be classified with respect to an implementation of the DSS 10 in a supply chain. From a design standpoint the Client Mode 30 is the portion of the DSS architecture that is specific and therefore customizable to provide decision support for any particular decision process and decision maker. The Server Mode 32, on the other hand, is the kernel of the DSS architecture that remains largely the same across different applications of the DSS 10 within a given supply chain. Hence, for a given implementation of the client-server DSS architecture in a supply chain, there will be one Server Mode 32 and a number of Client Modes to provide support for the decision processes.

As shown in FIGS. 1 and 2 the Client Mode 30 comprises the User Interface 18, the Decision Support Frames 16, and the client version of the Supply Chain Frame Manager 24. The Server Mode 32 comprises the Supply Chain Frame Manager 24, the system level services, the Model Engine 20, and the DSS Database 12. The Supply Chain Frame Manager 24, as a participant in both modes, serves to tie the Client and the Server Modes of the architecture together.

The client-server architecture makes the overall design both extensible (new functionality can be added by customizing a frame in the Client Mode 30) and scalable (new models and routines can be added to the Model Engine 20 in the Server Mode 32). Additionally this design is networkable; one can visualize the Server Mode 32 of the DSS 10 hosted by a server workstation in a client-server network while a number of Client Modes 30 of DSS 10 are hosted in the client computers. The layered design of the architecture, in terms of how the principal design elements of the DSS 10 are positioned within the architecture, provides a more resilient and stable backbone for decision support. The high level design architecture is independent of any computing and networking platform and hence applicable to a variety of environments.

The structure maintains the design of the horizontal layers for its key system elements while its functionality is more aligned vertically. The overall DSS 10 interfaces with the Supply Chain Information Systems 15 through the data exchanges between these systems and the DSS Database 12. The DSS 10 has a User Interface 18 to interact with end users through interactive and visual data exchange. Thus, the main body of the DSS 10 includes the following horizontal layers: User Interface 18; Decision Support Frames 16; Supply Chain Frame Manager 24 (Client and Server); Model Engine 20; and DSS Database 12.

As previously mentioned, the above layers of the DSS 10 can be partitioned into client and Server Modes. The system layers within the Client Mode 30 serve an interpretive role between the system data and analytic processing support and the specific end user's decision support needs. Those layers under the Server Mode 32 contain system functionalities common to a diversity of users and decision support problems, and usually require more dedicated, higher performance system processing capabilities.

To capture and process a user's decision support request, the DSS 10 will invoke a vertical decision support thread 40 going through visual objects of a User Interface 18, decision logic and what-if scenario manager in a Decision Support Frame, Supply Chain Frame Managers, models or analysis routines in the Model Engine 20 and appropriate data elements in the DSS Database 12. An example of a decision support thread 40 is shown by the bi-directional arrows in FIG. 3.

Relevant Supply Chains

The detailed discussion and specifications for the Decision Support Frames 16 provided in this document are generic in nature so that the functionality and system features in the resulting DSS 10 can cover decision environments for a diversity of supply chains. The system specifications and descriptions in this document address the two distinctive supply chains previously mentioned: I. Manufacturing: Finished goods are produced from raw materials, components, and subassemblies using resources (e.g., humans and machines) distributed to the demand points, and consumed by the end users. Material flow can be characterized as linear. II. Equipment Repair: Failed items are sent to the repair facilities, repaired using resources (e.g., humans and test equipment), and restored to usable condition. Material flow can be characterized as re-entrant.

Even though a typical manufacturing environment may perform a limited amount of repair operations, such as machine maintenance or product service, its resources are predominantly dedicated to material procurement, finished goods production and distribution. In this view, the distinguishing characteristic between the manufacturing and repair environments is the degree to which the environment focuses on the production of new goods versus the repair of old ones. Compared to the manufacturing supply chain, the equipment repair supply chain has complex reentrant flows, while the manufacturing is characterized by the linear material flows from material procurement to final consumption.

DSS Database

Overview

The DSS Database 12 is internal to the DSS 10 implementation. The main objective of the DSS Database 12 is to support the execution of the decision support functionality of the DSS 10. It contains the synthesized data drawn from a variety of external supply chain information sources and Supply Chain Information Systems 15. It may also maintain a set of data unique to the DSS 10 but not available in any existing Supply Chain Information Systems 15. An example of such unique data is the data derived through the analysis of synthesized data. The DSS Database 12 can be interfaced to the Supply Chain Information Systems 15 to retrieve the required data and provide updated data, as needed.

The Database Management System 14 (DBMS) communicates with the Model Engine 20 to provide the input data, and update the database 12 based on the output of the Model Engine 20. In general the DBMS 14 does not duplicate the transaction-based functionality that is typically featured in the Supply Chain Information Systems 15, unless it is critical for decision support needs.

Data Representation Scheme

Choosing an appropriate data representation is important to ensure data consistency, and reconfigurability. Structural Data Representation.

A structural data representation of the data in various data spaces 50, 52 and 54 (see FIG. 4) is used to model the elements of the supply chain that are relatively static. Specifying these data elements typically specifies the infrastructure of the supply chain. At the highest level of abstraction, four principal nodes (see FIGS. 5 and 6) in the supply chain 58 are identified:

Demand node 59: The customer demands are characterized at this node.

Inventory Node 60: Inventories of key components or finished goods are identified to be at these inventory nodes.

Production Node 61: Production resources (or finished goods supply) are located at production nodes.

Component Supply Node 62: The supply points of key components are characterized at the component supply nodes.

These structural data elements are spatially distributed along the supply chain 58 (see FIG. 5). The relationship between these nodes is preferably modeled in the form of a network. The network characterization is a collection of links that connect the principal nodes. A link establishes a logical relationship between two nodes, which may have several attributes such as distance between the two nodes, transportation lead time, etc.

In greater detail, the structural data elements and their relationship to the principal nodes are described as follows. Key components 63 are supplied by component suppliers 64 tied to specific component supply nodes 62. Production resources 65 produce the various products 66. The production resources can be aggregated into production resource groups 67, and are located at production nodes 61. Products 66 are the end items that are produced and, based on their various attributes, can be grouped into various product groups 67. These products are stocked at various stock locations identified with inventory nodes 60 and inventory headers 68. Customers 69 can be grouped by various criteria into customer groups 70 and demand various products. Customers 69 are located at the demand nodes 59. Markets 71 are defined for a cross-section of customers and products.

A similar structure can be provided for the repair chain as illustrated in FIG. 6.

Such a data representation in the form of a network of nodes and the ability to group the various constituent elements, provide the flexibility to specify and reconfigure a variety of supply chains.

Process Data Representation

In addition to the structural elements, the data associated with the various processes in the supply chain need to be represented. These process data elements are relatively dynamic with respect to time and the associated processes transform data related to the various structural elements. To characterize the process data, we introduce a few additional data structures.

Data Space

A data space is a fundamental domain to characterize basic data elements associated with the supply chain management: demand, supply and inventory data. The data spaces 50, 52 and 54 tie the structural data to the process data. It has three principal dimensions: Product/Component; Time; and Node related structural element. As the node-related structural element can be a customer, an inventory location, or a production resource. The data in each data space can be at any resolution (in terms of level of aggregation) along the three dimensions and can be expressed as a quantity or value. Thus, each point in the data space characterizes the resolution of the product (or component), the time and the node-related structural element. For example, when describing the aggregate production plan data, a product can be at the resolution of product, time at a resolution of week, and the node-related structural element (Production resource in this case) at a resolution of production resource group. On the other hand, in describing bottom-up forecasts, a product can be at the resolution of product, time at a resolution of month, and node related structural element at the resolution of customer.

We identify three principal data spaces associated with supply chain management: Demand Data Space 50; Inventory Data Space 52; and Supply Data Space 54. These data spaces are pictorially represented in FIG. 4 and are explained next.

Demand Data Space 50: This data space 50 has three dimensions: Customer; Product; and Time. Customer can be at a resolution of customer or customer group or demand node. Product can be at the resolution of product or product group. Time can be at a resolution of day, week, bi-week, month, bi-month, quarter or year.

Supply Data Space 52: This data space 52 has the following three dimensions: Production Resource; Product/Component; and Time. Production resource can be at a resolution of production resource, production resource group, or production node. Product/Component can be at the resolution of component, product or product group. Time can be at a resolution of day, week, bi-week, month, bi-month, quarter or year.

Inventory Data Space 54: This data space 54 has the following three dimensions associated with it: Inventory Location; Product/Component; and Time. Inventory location can be at a resolution of stock location or inventory node. Product/Component can be at the resolution of component, product or product group. Time can be at a resolution of day, week, bi-week, month, bi-month, quarter or year.

A decision process relates or transforms data either within a data space or between data spaces. For example, aggregate production planning transforms data from the supply space to inventory space and vice versa. We will employ this data space representation to specify the various decision processes in supply chain management.

The DSS Database 12 comprises structural information (information related to relatively static information such as product groups, market groups, supply chain network, etc.), and process information (dynamic information related to demand, production plan, etc.). FIG. 5, previously discussed, graphically represents the structural data tables for the manufacturing supply chain.

Similarly, the DSS Database 12 for the equipment repair supply chain (see FIG. 6) comprises structural information (information related to relatively static information such as equipment, repair resources, supply chain network, etc.), and process information (dynamic information related to usage, requirements, repair plan, etc.). Again, the structural elements are flexibly collected into different groups to allow users to analyze data at various resolutions. For example, equipment of the same age can be grouped together to analyze the effect of age on repair requirements. FIG. 6 graphically represents the structural data tables for the equipment repair supply chain.

The process data in the DSS Database 12 are contained in two closely related table type data structures (see below): (I) header file that specifies the resolutions and values on the relevant dimensions of product, customer, time, resource, and item, (ii) corresponding data file which contains the actual data at the resolutions and values specified in the header. The process data are preferably stored in these two tables (Table 2, Table 3), rather than one consolidated table (Table 1), to avoid redundancy and updating anomalies. A loss-less join of the header and the data tables will result in the consolidated table (Table 1). The following example demonstrates this point.

                                      TABLE 1
    __________________________________________________________________________
    A single consolidated table with redundancy (not in normal form)
    Orientation
          Customer
               Customer
                    Product
                         Product
                              Time Calendar
                                        Date
                                            Time
      HeaderID Resolution ID Resolution ID Resolution ID Created Period
                                                 Quantity
    __________________________________________________________________________
    1     C    BESTB
                    P    19PR14C
                              M    1    1/1/96
                                            8/1/96
                                                 10
      2 C BESTB P 19PS52C M 1 1/1/96 6/1/96 20
      2 C BESTB P 19PS52C M 1 1/1/96 7/1/96 30
      2 C BESTB P 19PS52C M 1 1/1/96 8/1/96 75
      2 C BESTB P 19PS52C M 1 1/1/96 9/1/96 50
      3 C BESTB P 6P4830W M 1 1/1/96 6/1/96 12
      3 C BESTB P 6P4830W M 1 1/1/96 7/1/96 40
      3 C BESTB P 6P4830W M 1 1/1/96 8/1/96 57
      4 C SEARS P 554528 M 1 1/1/96 9/1/96 50
      4 C SEARS P 554528 M 1 1/1/96 10/1/96   2
      4 C SEARS P 554528 M 1 1/1/96 7/1/96 3
      5 C SEARS P 6P4830W M 1 1/1/96 2/1/96 2
      6 C SEARS P 6P4830W M 1 1/1/96 5/1/96 74
      7 C SEARS P 6P4830W M 1 1/1/96 3/1/96 3
      7 C SEARS P 6P4830W M 1 1/1/96 6/1/96 565
      7 C SEARS P 6P4830W M 1 1/1/96 4/1/96 500
    __________________________________________________________________________


                                  TABLE 2
    __________________________________________________________________________
    Header table that provides the series information
      and the various identifiers
    Orientation
          Customer   Product   Time       Date
      HeaderID Resolution CustomerID Resolution ProductID Resolution CalendarI
                                          D Created
    __________________________________________________________________________
    1     C    BESTB P    19PR14C
                               M    1     1/1/96
      2 C BESTB P 19PS52C M 1 1/1/96
      3 C BESTB P 6P4830W M 1 1/1/96
      4 C SEARS P 5S4528 M 1 1/1/96
      5 C SEARS P 6P4830W M 1 1/1/96
      6 C SEARS P 6P4840W M 1 1/1/96
      7 C SEARS P 6P48S0W M 1 1/1/96
    __________________________________________________________________________


              TABLE 3
    ______________________________________
    Data table that contains references to the header
      table and the time series data
      Orientation      Time
      HeaderID Period Quantity
    ______________________________________
    1              8/1/96   10
      2 6/1/96 20
      2 7/1/96 30
      2 8/1/96 75
      2 9/1/96 50
      3 6/1/96 12
      3 7/1/96 40
      3 8/1/96 57
      4 9/1/96 50
      4 10/1/96  2
      4 7/1/96 3
      5 1/1/96 40
      5 2/1/96 2
      6 5/1/96 74
      7 3/1/96 3
      7 6/1/96 565
      7 4/1/96 500
    ______________________________________

                  TABLE 4
    ______________________________________
    Frame Concept: User's and System Perspective
        User Perspective   System Perspective
    ______________________________________
    An environment to address
                       It is a subsystem that
      a collection of related processes user requests in
      decision problems. the form of Scenarios.
      Always associated with a It consists of a
      decision maker or a group consistent set of process
      of decision makers. Frame and data models.
      exists if and only if
      there are decision
      makers.
      Has a unique perspective It unifies the User
      of the underlying supply Interface, Model Engine,
      chain based on the and DSS Database elements
      responsibility of the that are specific to a
      decision makers decision making process.
      associated with the
      frame.
      Enhances the coordination A convenient client-server
      of decision making architecture that enables
      implicit in the the extension of the DSS
      definition of the frame. functionaiity.
      Enforced through
      organizational
      accountability and
      responsibilities.
    ______________________________________

                  TABLE 5
    ______________________________________
    Analogy and nomenclature between equipment
      repair and manufacturing supply chains
                              Example
       Example Commercial Parallel
      Equipment Defense Equipment with
      Repair Equipment Repair Manufactur
      Supply Repair Supply ing Supply
      Chain Supply Chain Chain Chain
    ______________________________________
    Equipment Aircraft    MRI Machine Customers
      Module LRU Cabinet Product
         Group
      Repair LRU / SRU Circuit Product
      Item  Boards
      Component Bit and IC Component
       piece part
    ______________________________________

                  TABLE 6
    ______________________________________
    Decision Support Frames in the Context
      of Manufacturing and Equipment Repair Supply Chains
         Manufacturing Supply
                          Equipment Repair Supply
      Chain Chain
    ______________________________________
    Demand Management Requirements Management
      PSI Planning Requirements-Supply
       Reconciliation
      Supply Management Supply Management
      Vendor Managed
      Replenishment
      Finished Goods
      Distribution Network
      Design
    ______________________________________