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Chapter 12: Decision-Support Systems
for Supply Chain Management
Prepared by Zouxin
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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
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Case study-Supply chain management smoo
ths production flow
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Aerostructures Corp.’s
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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.
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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
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By this chapter
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Goal of software
What types of decision support tools should be chosen?
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1. Introduction
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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.
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Framework for SCMS based on planning horizon.
1.
Strategic network design
2.
Supply chain master planning
3.
Operational planning (demand planning, inventory management,
production scheduling, transportation planning systems )
4.
Operational execution (enterprise resource planning, customer
relationship management, supplier relationship management, supply
chain management and transportation systems)
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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”
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What is the process of modeling?
“Start with a simplified model and add complexity later ”
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What level of data and detail is required?
“Modeling needs drive data collection, not the other way around”
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3. Structure of Decision-Support Systems
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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
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3.1 Input Data
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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
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[E.12-1] Input data required for logistics network design
[E.12-2] Input data required for supply chain master planning
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3.2 Analytical Tools
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Common DSS analysis tools and techniques :
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Queries
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Statistical analysis
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to look for “hidden” patterns, trends, and relationship in the data.
On-Line analytical process (OLAP) tools
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To determine trends and pattern in the data.
Data mining
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simply by asking specific questions about the data.
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
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to facilitate specialized calculations such as accounting costs.
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3.2 Analytical Tools
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Simulation
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Artificial Intelligence
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to help decision making in random or stochastic elements of
a problem.
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
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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.
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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
Heuristics, exact algorithms
The table shows a number of problems and analytical tools that are appropriate 11
for them
3.3 Presentation Tools
Geographic Information Systems
Presentation Tools
Integrating Algorithm and GIS
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Geographic Information Systems
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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
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Integrating Algorithm and GIS
A general framework for integrating algorithms and GIS
Geographic data
Attribute data
GIS engine/map
Network
Solution strategy
Algorithms
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4. Supply Chain Decision-Support System
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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
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4. Supply Chain Decision-Support System
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Logistics network design
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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
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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.
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Detailed
Production
Planning
production
Profit by market
schedule
and product
Supply chain
Supply chain
Tactical model
and planning
master plan
Feasibility
Demand forest
demand shaping
Cost/profit
Demand
planning
/Order
fulfillment
Service level
The extended supply chain: from manufacturing to order fulfillment
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4. Supply Chain Decision-Support System
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Operational planning systems
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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 diff
erent 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 dis
counts, rebates, new product introduction, and product withdraw
al on demand forecasts.
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Inventory management
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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.
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4. Supply Chain Decision-Support System
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Transportation planning
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Production scheduling
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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 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)
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Use a product’s bill of materials and component lead times to
plan when manufacturing of a particular product should begin.
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Operational executing systems
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These are real-time systems that allow executives to
run their business efficiently.
DSSs can provide three levels of sophistication
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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
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5. Selecting a Supply Chain DSS
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Issues considered in evaluating a particular DSS:
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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
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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.
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