OR/MS in the Networked Economy

1. OR/MS in the Networked Economy Melbourne ASOR AGM, March 2001 Jay Horton Applied Decision Analysis

2. 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

3. 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 OR/MS in the Networked Economy

4. 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

5. Applied Decision Analysis • 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

6. Applied Decision Analysis • The modelling skills ADA uses: • Optimisation (LP & MIP), Constraint Programming • Decision Analysis and Real Option Valuation • Simulation, Monte-Carlo Analysis • Game Theory • Financial Theory

7. Modelling Online • Modelling tools can be made available to Clients over the Web when needed • Clients access our models through pwcdecision.com

8. How companies are benefiting from OR/MS

9. Tactical Planning for the Coal Mining Industry ……… Block 1 Block 2 Block 3 Strip & Mine Ramp 1 .. Block n (many more Ramps) Haul Processor 2 ROM stockpile Prod 1 Prod 2 Processor 1 Stockpile Prod 3 (Many washing options) Products Multi-stage process with many choices at each stage

10. 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

11. 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. 12 plant locations > 20 work centres Approx. 50 raw materials Approx. 10,000 products 93 customer locations 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

13. 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

14. Key Inputs: Key Outputs: Optimal allocation of fleet to routes Total costs, revenues and profits Shadow prices for demand and aircraft hours PAX demand & fares by class Fleet types & number Routes by leg Operating costs Network Value Advisor Objective: Maximise Profit Key Constraints: Must meet demand Aircraft - route restrictions Fleet hours available, load factors A high-level network optimisation model was developed

15. 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

16. Early Product Expansion Baseline Revenue Growth Spectrum Cost Success in New Market Below estimate At estimate Unsuccessful Sales Potential in New Market Yes Low No Maintain original plan Yes Moderate Minimal Nominal No Yes High No Highly Successful Above estimate Moderate Unsuccessful Yes Expand early when initial indications are that sales potential is high Low No Yes High Nominal Moderate No Yes High No Highly Successful Real Options in 3G Wireless • ADA used Real Options Valuation to evaluate the various future paths and to guide decision making

17. 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

18. The emergence of Web based Decision Support

19. 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

20. Client Model library Industry data base Consultant The Web facilitates global planning • 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

21. 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

22. ‘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.’ PricewaterhouseCoopers