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Chapter 12: Decision-Support Systems for Supply Chain Management

Chapter 12: Decision-Support Systems for Supply Chain Management. CASE: Supply Chain Management Smooths Production Flow. Prepared by Hoon Lee Date on 14 th June, 2007. Contents. 0. Overview 1. Introduction 2. The Challenges of Modeling

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Chapter 12: Decision-Support Systems for Supply Chain Management

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  1. Chapter 12: Decision-Support Systems for Supply Chain Management CASE: Supply Chain Management Smooths Production Flow Prepared by Hoon Lee Date on 14th June, 2007

  2. Contents • 0. Overview • 1. Introduction • 2. The Challenges of Modeling • 3. Structure of Decision-Support Systems • 4. Supply Chain Decision-Support System • 5. Selecting a Supply Chain DSS • 6. Summary

  3. 0. Overview • Aerostructures Corp.’s • A manufacturer of wings and wing components. • Rhythm – A Supply chain management system from i2 Technologies, Inc. • Benefit: Saves $500,000 of inventory costs. • Before and weak • Couldn't schedule any smaller jobs. • Couldn't afford to let unfinished because of 220 operations • 10-year-old MRP-II • By this chapter • Goal of software • What types of decision support tools?

  4. 1. Introduction • Current Problems in Supply chain management system are not so rigid and well defined. • DSS incorporates the knowledge of experts in various fields and suggest possible alternatives. • DSSs are used from strategic problems (logistic network) to tactical problems (assignment of products to warehouse / factory) • DSS uses mathematical tools (Operations Research, Artificial Intelligence) • DSS uses statistical tools (Data mining) * 참고) DSS(Decision Support Systems)

  5. 2. The Challenges of Modeling • Major questions about modeling supply chains • What part of reality should be modeled? • What is the process of modeling? • What level of data and detail is required? • Three important rules of modeling • Model simple, think completed. • Start with a simplified model and add complexity later. • Modeling needs drive data collection, not the other way around.

  6. 3. Structure of Decision-Support Systems • Three major components: • Input database and parameters • Contains the basic information needed for decision making. • Ex) parameters, rules, desired service level, restrictions, constraints • Analytical tools • Involves embedded knowledge of the problem, that the user to fine-tune certain parameters. • Ex) operations research, artificial intelligence, cost calculators, simulation, flow analysis, etc. • Presentation tools • Display the results of DSS analysis. • Ex) GIS, Gantt charts

  7. 3.1 Input Data • Input data is critical to the quality of the analysis. • Model and data validation is essential to ensure that the model an data are accurate enough. • The accuracy of solution depends on the input data. • Refer to examples • [E.12-1] Input data for logistics network design • [E.12-2] Input data for supply chain master planning

  8. 3.2 Analytical Tools • DSS analysis tools and techniques in general: • Queries • to ask specific questions about the data. • Statistical analysis • to determine trends and pattern in the data. • Data mining • to look for “hidden” patterns, trends, and relationship in the data. • On-Line analytical process (OLAP) tools • to view corporate data, typically stored in data warehouses. • Calculators • to facilitate specialized calculations such as accounting costs.

  9. 3.2 Analytical Tools • Simulation • to help decision making in random or stochastic elements of a problem. • Artificial Intelligence • to analysis of DSS input data. • Expert system captures an expert’s knowledge in a database and use it to solve. • Mathematical Models and Algorithms • Exact algorithms find best possible solution. • Heuristics algorithms provide good, but not optimal solution. • Refer to table and example • [T.12-3] Applications and analytical tools

  10. 3.3 Presentation Tools • Geographic Information Systems • GIS is an integrated computer mapping and spatial database management system. • Refer to figure and table • [F.12-1] A typical GIS interface for supply chain management • [T.12-4] Road and Estimated distance • Integrating Algorithm and GIS • Include logistics network design, routing, mode selection, and so forth.

  11. 4. Supply Chain Decision-Support System • Logistics network design • Involves the determination of warehouse and factory locations and the assignment costs. • Refer to example [E.12-4] • Supply chain master planning • Process of coordinating production, distribution strategies, and storage requirements to efficiently allocate supply chain resources. • Operational planning systems • Ranging from demand planning to production and sourcing strategies.

  12. 4. Supply Chain Decision-Support System • Demand planning • Demand forecast: Historical demand data are used to develop long-term estimates of expected demand. • Demand Shaping: the firm determines the impact of various marketing plans • Inventory management • To determine the levels of inventory, safety stock levels, to keep in each location in each period. • DSS apply a heuristic algorithm to generate suggested policies. • Transportation planning • The dispatching of a company's own fleet and decisions regarding selection of commercial carrier on certain routes. • Production scheduling • To purpose manufacturing sequences and schedule.

  13. 4. Supply Chain Decision-Support System • Material requirements planning (MRP) • Use a product’s bill of materials and component lead times to plan when manufacturing of a particular product should begin. • Operational executing systems • Allow executives to run their business efficiently. • Three levels of sophistication • Available to promise (ATP): firm considers finished goods inventory • Capable to promise (CTP); firm considers components/materials • Profitable to promise (PTP): firm considers capability and profitability of completing an order

  14. 5. Selecting a Supply Chain DSS • Considerable issues in evaluating a DSS: • The scope of the problem • The data required by DSS • Analysis requirements – Accuracy, Ability, Desired • The system’s ability to generate a variety of solutions • The presentation requirements • Compatibility and integration with existing systems • H/W and S/W requirements. • The overall price • Complementary systems • Refer to example [E.12-10]

  15. 6. Summary • The major trends, especially and advanced • 1. Integration with and between ERP systems. • Most ERP vendors already boast of supply chain planning functionality. Ex) SAP, Oracle + PeopleSoft • 2. Improved optimization • Many DSSs lack a true optimization capability. • 3. Impact of standards. • Many DSSs are not compatible and difficult to integrate. • Strategic partnering forces the various partners to define standards. • 4. Improved collaboration. • Collaboration can enhance production planning, inventory management, and other supply chain process.

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