or/ms in the networked economy

PwC

OR/MS in the Networked Economy

Melbourne ASOR AGM, March 2001

Jay Horton

Applied Decision Analysis

PricewaterhouseCoopers

This evening’s presentation

• Some opening remarks

• Background to PricewaterhouseCoopers’ - Applied Decision Analysis

• How companies are benefiting from OR/MS

• The emergence of Web based Operations Research


OR/MS in the Networked Economy

Economies of the past:

• “Centralised Allocation” of goods

and services

• Variety of “In-house” capabilities

maintained

• Static planning in a stable

environment

• Local value chain point of view

– Sub-optimal relationships

found

Network Based Economy:

• “Market Based Allocation” of

goods and services

• External services utilised as

needed

• Rapid planning in a dynamic

environment

• Global value chain prospective

– Globally optimal solutions are

feasible


The need for OR in the Networked Economy

• OR techniques can manage risk in an objective way

– these days, the present is a poor guide to the future

• Analytical techniques (eg. Data mining) required to extract useful information from volumes of data

– IT has left us data rich, but information poor

• Modeling business decisions is a fast way to experiment without risking the performance of a company

– companies need to react quickly and avoid failure

Need for OR Consulting over the Web



• Applied Decision Analysis (ADA) is group within PricewaterhouseCoopers providing OR/MS skills to help clients with corporate and financial strategy

• ADA essentially does “Strategic OR”:

– capital and resource allocation

– value chain strategy

– new business initiatives

– market and auction design



• The modelling skills ADA uses:

– Optimisation (LP & MIP), Constraint Programming

– Decision Analysis and Real Option Valuation

– Simulation, Monte-Carlo Analysis

– Game Theory

– Financial Theory


Modelling Online

• Modelling tools can be

made available to Clients

over the Web when needed

• Clients access our models

through pwcdecision.com

PwC

How companies are benefiting from OR/MS


Tactical Planning for the Coal Mining Industry

Block 1 ………Block 2 Block 3Ramp 1Strip &

Mine

Haul

(many more Ramps)

Processor 1

Products

Prod 1

Prod 2

Prod 3Stockpile

Processor 2ROM stockpile

(Many washing options)

..

Block n

Multi-stage process with many choices at each stage


MOPS is an optimisation planning tool for tactical planning

Mine Operations Planning

System (MOPS) is:

• driven by a mixed integer

optimisation model

• a Web based decision support

system

– Allowing multiple users in

various mines

– Administered in Sydney

online


MOPS provides rapid tactical plans for operating a mine

MOPS gives:

• Rapid production of plans

• Reduced operating costs

• Product quality ensured

• Proactive risk management

• “What if” analysis capability


12 plant locations> 20 work centres

Approx. 10,000 products

93 customer locations

Approx. 50 raw materials

Delivering an integrated manufacturing solution for the steel industry• A large producer and distributor of steel, required a strategic

and tactical model to assist in planning production and distribution activities

• ADA developed a Linear Programming model capable of optimising its network of plants, processes and its allocation of products to plants

• Due to the size of the problem, the model needed to be solved using a high powered computer


A major international airline needed a review of its route allocations

• Matching of routes vs aircraft takes account of many variables

– passenger demands, physical airport limitations, maintenance requirements

• Too many types of aircraft

– Inefficient maintenance

– Lack of economies of scale

• Financial problems/performance

– Low prices, high costs on parts of the network


A high-level network optimisation model was developed

Network Value

Advisor

Objective: Maximise Profit

Key Constraints:Must meet demandAircraft - route restrictionsFleet hours available, load factors

Objective: Maximise Profit

Key Constraints:Must meet demandAircraft - route restrictionsFleet hours available, load factors

Key Outputs:

Optimal allocation of fleet to routes

Total costs, revenues and profits

Shadow prices for demand and aircraft hours

Key Inputs:

PAX demand & fares by class

Fleet types & number

Routes by leg

Operating costs


Real Options in 3G Wireless

• A telecommunications firm was considering its future position with the auction of 3G spectrum

• They had a number of decisions to make:

– how much bandwidth to purchase if any

– how to move forward if bandwidth is not purchased

• They also face a number of uncertainties:

– market demand and price

– future developments in technology


Maintain original plan

Expand early when initial indications are that sales potential is

high

SpectrumCost

Sales Potentialin New Market

Success in New Market

BaselineRevenueGrowth

Early Product

ExpansionBelow estimateAt estimate

Above estimate

Minimal

Moderate

High

Unsuccessful

Moderate

Highly Successful

Unsuccessful

Moderate

Highly Successful

Low

Nominal

High

Low

Nominal

High

YesNoYesNoYesNo

YesNoYesNoYesNo

Real Options in 3G Wireless

• ADA used Real Options Valuation to evaluate the various future paths and to guide decision making


Bidder support in spectrum auctions

• There is inherent uncertainty in

the auction

• Risk of serious exposure

problems

– Insufficient spectrum

bandwidth

– Insufficient geographic

scope

• Rules are complex

PwC

The emergence of Web based Decision Support


Why Web based Decision Support?

A Web based approach to decision support:

• Facilitates a Global focus and rapid planning

• Creates economies of scale in:

– expertise

– computing power

• Allows decentralised use, with less hassle

• Allows better support, on-line


The Web facilitates global planning

Industrydatabase

Consultant

Client

Modellibrary

The Web allows global access to the decision support:– On-line models– Consulting advice– Links to industry data

this means planning can be conducted very rapidly


Creating economies of scale in decision support

• Powerful hardware and software is usually required for Decision Support Systems

• Putting a system online allows the centralisation of computing power

• With many users, scale is gained. This:

– lowers the cost to users

– increases the model solving capability

‘Over time, operations research, enterprise

systems, advanced planning and scheduling,

customer relationship management and the

internet will gradually become more and more

blended. This phenomenon is now changing

the meaning of the word, operations research,

itself.’


or/ms in the networked economy

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