1. Chapter 12: Decision-Support Systems for Supply Chain Management
Prepared by Zouxin

2. Contents Case study 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. Case study-Supply chain management smooths production flow
Aerostructures Corp.’s
A manufacturer of wings and wing components.
At present: Rhythm – A Supply chain management system from i2 Technologies, Inc.
Benefit:
level out work flow
Saves $500,000 of inventory costs.
In the past: MRP-II system
Shortage:
Couldn't schedule any smaller jobs.
Couldn't afford to let unfinished products sit around for too long because of 220 operations
Difficult to order materials
By this chapter
Goal of software
What types of decision support tools should be chosen?

4. 1. Introduction
Problems in Supply chain management are not so rigid and well defined that they can delegated entirely to computers.
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) and data warehouses.

5. Framework for SCMS based on planning horizon.
Strategic network design
Supply chain master planning
Operational planning (demand planning, inventory management, production scheduling, transportation planning systems )
Operational execution (enterprise resource planning, customer relationship management, supplier relationship management, supply chain management and transportation systems)

6. 2. The Challenges of Modeling
Model is the heart of any DSSs
Major questions when modeling supply chains
What part of reality should be modeled?
On the one hand, model should include enough detail to represent reality. On the other hand, model should be simply enough to understand, manipulate, and solve.
“Model simple, think complicated”
What is the process of modeling?
“Start with a simplified model and add complexity later ”
What level of data and detail is required?
“Modeling needs drive data collection, not the other way around”

7. 3. Structure of Decision-Support Systems
Three major components:
3.1 Input database and parameters
Input database contains the basic information needed for decision making.
parameters and rules also included, such as desired service level, restrictions, various constraints
3.2 Analytical tools
The data analysis usually Involves embedded knowledge of the problem, while also allowing the user to fine-tune certain parameters.
Analytical tools include operations research, artificial intelligence, cost calculators, simulation, flow analysis, etc.
3.3 Presentation tools
Display the results of DSS analysis.
Ex) GIS, Gantt charts

8. 3.1 Input Data Input data is critical to the quality of the analysis.
Depending on the type of analysis, a DSS may require collecting information from various parts of a company.
Model and data validation is essential to ensure that the model and data are accurate enough.
The decision planning horizon affects the detail of the data required.
examples
[E.12-1] Input data required for logistics network design
[E.12-2] Input data required for supply chain master planning

9. 3.2 Analytical Tools Common DSS analysis tools and techniques :
Queries
simply by asking 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
Provided an intuitive way to view corporate data
OLAP tools aggregate data along common business dimensions and let users navigate through the hierarchies and dimensions by drilling down, up, or across levels.
Calculators
to facilitate specialized calculations such as accounting costs.

10. 3.2 Analytical Tools Simulation Artificial Intelligence
to help decision making in random or stochastic elements of a problem.
Artificial Intelligence
Employed in the analysis of DSS input data.
Expert system captures an expert’s knowledge in a database and use it to solve problems.
Mathematical Models and Algorithms
Exact algorithms find mathematically “the best possible solution” of a particular problem.
Heuristics algorithms provide good, but not optimal solution to the problems.
It is often useful if in addition to the solution, the heuristic provides an estimate of how far the heuristic solution is from the optimal solution.

11. Applications and analytical tools
The analytical tools used in practice are typically a hybrid of many tools.
Applications and analytical tools
problems
Tools used
marketing
Query, statistics, data mining
routing
Heuristics, exact algorithms
Production scheduling
Simulation, heuristics, dispatch rules
Logistics network configuration
Simulation, heuristics, exact algorithms
Mode selection
The table shows a number of problems and analytical tools that are appropriate for them

12. 3.3 Presentation Tools Geographic Information Systems
Integrating Algorithm and GIS
Geographic Information Systems
GIS is an integrated computer mapping and spatial database management system that can provide geographically referenced data.
GIS can be used in many areas, GIS can be applied in supply chain management, such as
Network analysis—transportation, telecommunications
Site selection
Routing
Supply Chain Management

13. A general framework for integrating algorithms and GIS
Integrating Algorithm and GIS
A general framework for integrating algorithms and GIS
Geographic data
Attribute data
GIS engine/map
Network
Solution strategy
Algorithms

14. 4. Supply Chain Decision-Support System
Logistics network design
Supply chain master planning
Operational planning systems
Demand planning
Inventory management
Transportation planning
Production scheduling
Material requirements planning (MRP)
Operational executing systems

15. 4. Supply Chain Decision-Support System
Logistics network design
Involves the determination of warehouse and factory locations and the assignment of retailers to warehouses.
Heuristic or exact algorithms are used to suggest network designs.
Supply chain master planning
Process of coordinating production, distribution strategies, and storage requirements to efficiently allocate supply chain resources.
It is very difficult to do a supply chain master planning manually and an optimization-based decision-support system is needed.

16. The extended supply chain: from manufacturing to order fulfillment
Detailed
production
schedule
Supply chain
Tactical model
and planning
Profit by market
and product
Demand
planning
/Order
fulfillment
Production
Planning
Supply chain
master plan
Demand forest
demand shaping
Feasibility
Cost/profit
Service level
The extended supply chain: from manufacturing to order fulfillment

17. 4. Supply Chain Decision-Support System
Operational planning systems
Includes different types of systems, ranging from demand planning tools to tools that assist with the details of production and sourcing strategies.
Demand planning
Demand planning tools allow supply chain executives to apply two different processes
Demand forecast: long-term estimates of expected demand.
Demand Shaping: A process in which the firm determines the impact of various marketing plans such as promotion, pricing discounts, rebates, new product introduction, and product withdrawal on demand forecasts.
Inventory management
To determine the levels of inventory, safety stock levels, to keep in each location in each period.
In almost all cases, DSS apply a heuristic algorithm to generate suggested policies.

18. 4. Supply Chain Decision-Support System
Transportation planning
Involves the dispatching of a company's own fleet and decisions regarding selection of commercial carrier on certain routes.
Static and dynamic system (for example: telephone repair crews)
Production scheduling
Production scheduling DSSs purpose manufacturing sequences and schedule, given a series of products to make, information about their production processes, and due dates for the product .
Usually use artificial intelligence and mathematical and simulation techniques to develop schedules.
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.

19. Operational executing systems
These are real-time systems that allow executives to run their business efficiently.
DSSs can provide three levels of sophistication
Available to promise (ATP): firm can consider finished goods inventory as well as work in process to make a decision.
Capable to promise (CTP); firm can check components and materials availability to make a decision.
Profitable to promise (PTP): firm considers capability and profitability of completing an order

20. 5. Selecting a Supply Chain DSS
Issues considered in evaluating a particular DSS:
The scope of the problem, including the planning horizon
The data required by DSS
Analysis requirements – optimization, heuristics, simulation, and computational speed needed.
The system’s ability to generate a variety of solutions
The presentation requirements
Compatibility and integration with existing systems
Hardware and software system requirements.
The overall price
Complementary systems

21. 6. Summary The major trends in supply chain DSSs
1. Integration with and between ERP systems.
DSSs will be easier to integrate with ERP systems through standard interfaces.
2. Improved optimization
Many DSSs lack a true optimization capability. Most existing supply chain master planning and MRP systems do not optimize at all and in many cases do not take capacities into account.
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.