Decision Support for Supply Chain

Analysis Subhashini Ganapathy & S. Narayanan

Department of Biomedical, Industrial, and Human Factors Engineering

Wright State UniversityDayton, OH

Contents Supply Chain Systems Research Objectives Problem Domain Decision Support Systems System Architecture Experimental Design Results Discussion

Generic Supply Chain System A set of entities collectively responsible for acquiring,

processing, assembling and distribution of one or many products, by interacting with each other in a network

Suppliers

Storage unit/ Inventory

Production unit

Storage unit/ Inventory

Market centers/ Vendors

Procurement Manufacturing Distribution

Issues Globalization

Decentralization of components Real time interactions Reusable components (generic reference

model) Demand distortion and variance

amplification

Research Objectives Develop a high-fidelity computational

model that emulates real-time interactions in a prototypical supply chain system

Implement a decision support system that aids human decision making

Compare the performance of a prototypical supply chain system with decision support, without decision support, as well as automated

Research Questions

Does a Decision Support System (DSS) enhance the performance of a supply chain system?

In a prototypical supply chain, does the performance of the user differ with changes in system complexity in a model-based decision support system?

Scenario The proposed model represents a sub system

within a supply chain (based on interviews with logisticians) Set of different machines with varying failure

rates in terms of MTBF (e.g., mechanical & electrical components)

Each failure necessitates a specific set of spare parts, supplies, technician, and repair time requirements

Spare parts can be obtained from different suppliers and is based on tradeoff analysis of costs, downtime issues, part priority, etc.

Scenario (contd.)

A set of technicians respond to these failures

Real-time decisions on selecting suppliers, placing orders, and maintaining inventory levels may be done by software agents

The model executes on a server and interconnects between the different entities

Components of the System

Dynamic Data

Updates

Decision Updates

Orderplaced

Response to Order

Executing Model of the Executing Model of the System ( Server)System ( Server)

Executing Model of the Executing Model of the System ( Server)System ( Server)

TechnicianTechnician TechnicianTechnicianMaintenance Maintenance SystemSystem

Maintenance Maintenance SystemSystem

SuppliersSuppliersSuppliersSuppliers

Human Analysts/Decision Human Analysts/Decision Makers/OperatorsMakers/Operators

Human Analysts/Decision Human Analysts/Decision Makers/OperatorsMakers/Operators

Features of the architecture

Shop Floor Components(Machine, Parts are present in the machine

Suppliers

Report Failure

Update machine with repair information

Inform system changes

Basic Simulation Component Interface Component

Update user request

Get quotes

Update inventory status in the interface

Place orderInventory

Control Component

System Features User is presented with information regarding the

parametric conditions of the machine such as the temperature, pressure, voltage and capacitance

The parts fail due to one of the following conditions: Fatigue, Corrosion, Vibration, Connection faults, Cooling system. This results in the change in parametric conditions of the machine

When a machine fails, the user is required to identify the parts that have failed correctly in order to schedule it for repair

Each part has a priority associated with it depending on the usage of the part

For maintaining the inventory the user should order for parts from three suppliers

Decision-making Decision-making in a supply chain is very

demanding, and significantly contributes to the performance of a supply chain

The different stages or echelons of the supply chain are interconnected with the forward flow of materials and backward flow of information. At each of those stages decision-making is involved

Decision Scenarios

Decision Scenarios

Decision Scenarios

Supplier Identification -Analysis of supplier quote-Evaluation of supplier quote-Performing trade-off analysis-Choosing an alternative based on weights

Supplier Identification -Analysis of supplier quote-Evaluation of supplier quote-Performing trade-off analysis-Choosing an alternative based on weights

Part Identification-Evaluation of machine parameter-Identification of failed parts-Notification to the user

Part Identification-Evaluation of machine parameter-Identification of failed parts-Notification to the user

Types of decision making Automated

The tasks are performed based on the choice of the decision support system

DSS Based on multi-attribute utility theory Prescribes the solution to the user User can accept or reject the solution The choice of the supplier is based on a trade- off

analysis of cost, shipping time, and quantity. Without DSS

User has to make the decisions based on the information provided on the interface

Experiment Design

Scenario

Types of System

Without DSS

With DSS Automated System

Simple X X X

Complex X X X

Simple Task Vs Complex Task: No. of Machines & No. Technicians

Dependent Variables Machine down time Number of times parts identified correctly Number of times supplies ordered Number of times DSS used

Prototype Interface

DSS

Results

Comparison of Means (Mean Error Rate)

0

0.5

1

1.5

2

2.5

3

3.5

Automated With DSS Without DSS

Types of System

Mea

n E

rro

r R

ate

Comparison of Means ( Supplies Ordered)

10.8

11.2

11.6

12

12.4

12.8

Automated With DSS Without DSS

Types of System

Mea

n s

up

pli

es o

rder

ed

Comparison of Means (Machine down time)

0

10

20

30

40

50

Automated With DSS Without DSS

Types of system

Mac

hin

e d

ow

n t

ime

(sec

)

Part identification

Supplies ordered

Machine down time

Comparison of mean between different types of system

Results

Comparison of Means (Mean Error Rate)

0

10

20

30

40

50

60

70

Simple Complex

Type of Scenario

Mea

n U

se o

f D

SS

( p

art

iden

tifc

atio

n)

Comparison of Mean (Machine down time)

0

10

20

30

40

50

60

Without DSS With DSS Automated

Type of systems

Mac

hin

e d

ow

n t

ime

(sec

)

Simple

Complex

Comparison of Mean (Mean error rate)

0

0.5

1

1.5

2

2.5

3

3.5

Without DSS With DSS

Types of system

Mea

n e

rro

r ra

teSimple

Complex

Comparison of mean between different types of scenario

Rate of use of DSS

Machine down time

Part identification error rate

Discussion Results show that the performance of the

system is higher for decision support system over system with no decision support as well as automated system.

Future Research Comparison of use interaction with other

software tools such as i2 solutions, Manugistics.

Development of computational platforms to accommodate real-time interactions between distributed agents