Supply Chain Design Problem

Tuukka Puranen

Postgraduate Seminar in Information Technology

Wednesday, March 26, 2009

Contents What is a supply chain Supply chain optimization Supply chain design problem Solving SCDP

What is a Supply Chain A supply chain consists of

Production and storing facilities Transportation lanes Commodities and raw materials Customers

There is a cost associated to each activity Procurement Production Storing Transportation

Supply Chain Optimization Application of processes and tools to ensure

the optimal operation of a manufacturing and distribution supply chain

Mathematical modeling techniques Using computer software To support decision making

Components of a Supply Chain Optimization System

Network design

Integrated planning

MRP andmaterials

management

Production planning

Distribution planning

Scheduling Vehicle routing

Demandmanagement

Ordermanagement

Source: Cordeau, 2008

Why Supply Chain Optimization? Increasing focus on logistics and supply chain

management in large companies Globalization and increasing complexity of

operations Increasing variety of products, on shorter lead

times and of high quality; introducing new technology in processes and materials

Advances in information technology Increased data availability from ERP systems Better software tools for modeling and solving

optimization problems Faster computers

Source: Cordeau, 2008

Optimization Process

Reality

Model Data

MILP

Solution

Decision Support

Data Gathering

Optimization

Processing

Modeling

Analysis

Decision

Data Gathering Aim is to describe the conditions in reality as

accurately as possible Includes, for example,

Costs and capacities for each existing and potential location

Costs, production capacities, demands, and prices for each commodity in each location

Transportation costs, transportation times, and CO2 emissions for each transportation lane

All values must be given for each existing and potential location and lane, and over given set of periods

In practice a tedious task and result in large datasets

Supply Chain Design Problem Aim: determine the structure of the network in

which products will flow from their points of origin to their points of consumption

Main decisions to be made: Number, location, capacity and technology of

facilities Supplier selection Product range assignment Supply channels and transportation modes Product flows (amounts purchased from suppliers,

made in plants, stored in warehouses, transported, etc.)

Source: Cordeau, 2008

Network Formulation

Supplier

Factory

Factory

Supplier

Warehouse

Warehouse

Customer

Factory Customer

Customer

Supplier

Customer

Network Formulation

Supplier

Factory

Factory

Supplier

Warehouse

Warehouse

Customer

Factory Customer

Customer

Supplier

Customer

Mixed Integer Linear Program Formulation

Solving SCDP Mixed Integer Linear Programs can be solved

using commercial solvers; typically based on SIMPLEX algorithm and its variants Branching and cutting used to find integer solutions

A variety of cutting methods Different heuristics in branching Local search

Relaxations Pre- and post-processing

Parameter tuning essential 1,3 million variables and 0,7 million constraints

in 5 min.

A Note on Multi-objective SCDP A Supply Chain Design Problem may have

multiple objectives Minimize cost or maximize profit Minimize lead times (maximize customer

satisfaction) Minimize CO2 emissions Ensure robustness

Three approaches usually used to provide a single objective Assigning weights to different objectives Introducing additional constraints Interactive approach

Additional Extensions Ensuring robustness by optimizing using

multiple scenarios simultaneously Stochastic demand Stochastic costs Pricing decisions Nonlinear demands Nonlinear costs All of the above

Summary A supply chain consists of production facilities,

transportation lanes, commodities and customers Optimization attempts to ensure the optimal

operation of a supply chain Increasing focus on logistics and supply chain

management, increasing complexity Modeled mathematically; data gathering also

challenging Solved using Mixed Integer Linear Programs Used to support decision making Additional extensions continue to provide

computational challenges