1 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

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

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.