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Course Title :Operations Research • • • • •

Course Code : MGT 613 Lectures : 3 Tutorials : 1 Credits : 4 Weightage : CA =40 , MTE = 20 , ETE = 40

About the Instructor • Mandeep Singh Hayer • [email protected] Education B.Tech in Agricultural Engineering College of Agri Engg PAU Ludhiana ,1998 MBA in Marketing Management , College of Basic Sciences and Humanities PAU , 2000

About the Instructor Mandeep Singh Hayer Experience Industrial 4 ½ years . Started Career with TATA STEEL as a Senior Sales Officer Academic / Teaching 3 Yrs . Faculty in Deptt of Business Management at PCTE Baddowal Ludhiana

Operations Research Some Definitions • “The science of better” ✦✦✦ “The use of mathematical models, statistics and algorithms to aid in decision-making with the goal of improving or optimizing performance” ✦✦✦ “Research designed to determine the most efficient way to do something” ✦✦✦ “The application of scientific methods to improve the effectiveness of operations, decisions and management”

OR Definitions continued • OR is a scientific method of providing executive departments with quantitative basis for decisions regarding operations under their control . • OR is the application of modern methods of mathematical science to complex problems involving management of large system of men ,machines material and money in industry business government and

Origins of OR • During WWII, OR developed to allocate scarce resources to military operations • After the war, OR introduced into industry • Improvements in techniques • Computer revolution

Necessity of OR in Industry • Complexity : Factors such as customer demand , raw material requirement , equipment capacity , equipment failure , restrictions on manufacturing etc • Scattered responsibility and authority • Uncertainity • Knowledge explosion : OR collects latest info for analysis purpose

Scope /Applications of OR • Accounting : Cash Flow Planning , Credit Policy Analysis • Construction : Allocation of resources to projects , determination of workforce, Project scheduling monitoring and control • Facilities Planning : Factory size and location decision, International logistics systems design , transportation loading & unloading, Warehouse location decision • Finance : Dividend policy making ,

Scope /Applications of OR cont.. • Manufacturing : Inventory control , Production Scheduling and smoothing • Marketing : Advertising Budget Allocation , Product introduction timing , Selection of Product mix • OB : Scheduling of training programmes , Skill balancing , recruitment of employees • Purchasing : Optimal Buying & Material Transfer

Phases / Steps of OR • • • • • • • • • • • •

Problem identified with decision variables How many units to buy/sell... How much time to spend on a task... Measure of performance is the objective function What is the goal? Usually: Max/min profit/cost/time/units A function of the decision variables Restrictions of values of decision variables set in constraints Min acceptable profit Max available resources Parameters are the constants of the objective function and theconstraints

Models of OR • What is a model? • A model is a representation of the structure • of a real-life system (existing, or currently • being built). • In general, models can be classified as follows: • – iconic • – analogue

Models of OR Cont… • Iconic model: Exact replica of the properties of the real-life system, but in smaller scale. Examples are: model airplanes, maps, etc. Miniature Model of a Building • Analogue model: It uses a set of properties to represent the properties of a real-life system. For instance, a hydraulic system can be used as an analogue of electrical, traffic and economic systems. • Symbolic model: It represent the properties of the real-life system through the means of symbols, such as

OR models • Operations Research models are in general symbolic models and they can be classified into two groups based on the nature of environment – deterministic models – stochastic models.

Deterministic models Deterministic models are models which do not contain the element of probability. These are primarily optimization models, such as: – Linear programming – Non-linear programming – Dynamic programming – Simulation techniques.

Deterministic models cont… • Everything is defined , results are certain • For any given input there shall be some ouput • E.g EOQ model we can determine lot size • We can also apply sentivity analysis wher change in the input variable shall change the outcome

Stochastic models • Stochastic models are models which contain the element of probability. Examples are: – Queuing theory – Stochastic processes – Reliability – Simulation techniques.

Stochastic models cont … • Used in case of risk and uncertainty • Input & Output variables take the form of probability distribution • These models reveal the probability of occurrence of an event • They reveal the complexity of the real world • E.g in game theory when saddle points do not exist we apply probabilistic model

Models Other classifications • Static Model: Does not take time into account . Assumes values of variables do not change with time • Dynamic Model : Considers time as one of the variables e.g Dynamic programming , replacement problem • Descripitive Model: One which just describes the situation e.g Poll or a survey

Models Other classifications cont … • Predictive Model : Predicts something based on data e.g election results • Prescriptive Model: Prescribes or suggests a course of action for a problem E.g Any kind of programming ( Linear , Non Linear , Geometric , Dynamic ) • Analytic Model : Exact solution is obtained by mathematical methods • Simulation Model : This reacts in the same manner as realty under a given set of conditions

OR/MS Successes Best cases from the annual INFORMS Edelman Competition 2002: Continental Airlines Survives 9/11 2001: Merrill Lynch Integrated Choice 2001: NBC’s Optimization of Ad Sales 2000: Ford Motor Prototype Vehicle Testing 1996: Procter & Gamble Supply Chain 1991: American Airlines Revolutionizes Pricing Operations Research/Management Science

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Case 1: Continental Airlines Survives 9/11 • Business Problem: Long before September 11, 2001, Continental asked what crises plan it could use to plan recovery from potential disasters such as limited and massive weather delays.

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Continental Airlines

(con’t)

• Strategic Objectives and Requirements are to accommodate: – 1,400 daily flights – 5,000 pilots – 9,000 flight attendants – FAA regulations – Union contracts

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Continental Airlines

(con’t)

• Model Structure: Working with CALEB Technologies, Continental used an optimization model to generate optimal assignments of pilots & crews. The solution offers a system-wide view of the disrupted flight schedule and all available crew information. Operations Research/Management Science

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Continental Airlines

(con’t)

• Project Value: Millions of dollars and thousands of hours saved for the airline and its passengers. After 9/11, Continental was the first airline to resume normal operations.

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Case 2: Merrill Lynch Integrated Choice • Business Problem: How should Merrill Lynch deal with online investment firms without alienating financial advisors, undervaluing its services, or incurring substantial revenue risk?

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Merrill Lynch

(con’t)

• Objectives and Requirements: Evaluate new products and pricing options, and options of online vs. traditional advisor-based services.

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Merrill Lynch

(con’t)

• Model Structure: Merrill Lynch’s Management Science Group simulated client-choice behavior, allowing it to: – Evaluate the total revenue at risk – Assess the impact of various pricing schedules – Analyze the bottom-line impact of introducing different online and Operations offline investment choices Research/Management 27 Science

Merrill Lynch

(con’t)

• Project Value: – Introduced two new products which garnered $83 billion ($22 billion in new assets) and produced $80 million in incremental revenue – Helped management identify and mitigate revenue risk of as much as $1 billion – Reassured financial advisors Operations Research/Management Science

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Case 3: NBC’s Optimization of Ad Sales • Business Problem: NBC sales staff had to manually develop sales plans for advertisers, a long and laborious process to balance the needs of NBC and its clients. The company also sought to improve the pricing of its ad slots as a way of boosting revenue. Operations Research/Management Science

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NBC Ad Sales

(con’t)

• Strategic Objectives and Requirements: Complete intricate sales plans while reducing labor cost and maximizing income.

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NBC Ad Sales (con’t) • Model Structure: NBC used optimization models to reduce labor time and revenue management to improve pricing of its ad spots, which were viewed as a perishable commodity.

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NBC Ad Sales

(con’t)

• Project Value: In its first four years, the systems increased revenues by over $200 million, improved salesforce productivity, and improved customer satisfaction.

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Case 4: Ford Motor Prototype Vehicle Testing • Business Problem: Developing prototypes for new cars and modified products is enormously expensive. Ford sought to reduce costs on these unique, first-of-akind creations.

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Ford Motor

(con’t)

• Strategic Objectives and Requirements: Ford needs to verify the designs of its vehicles and perform all necessary tests. Historically, prototypes sit idle much of the time waiting for various tests, so increasing their usage would have a clear benefit. Operations Research/Management Science

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Ford Motor

(con’t)

• Model Structure: Ford and a team from Wayne State University developed a Prototype Optimization Model (POM) to reduce the number of prototype vehicles. The model determines an optimal set of vehicles that can be shared and used to satisfy all testing needs.

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Ford Motor

(con’t)

• Project Value: Ford reduced annual prototype costs by $250 million.

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Case 5: Procter & Gamble Supply Chain • Business Problem: To ensure smart growth, P&G needed to improve its supply chain, streamline work processes, drive out non-valueadded costs, and eliminate duplication.

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P&G Supply Chain

(con’t)

• Strategic Objectives and Requirements: P&G recognized that there were potentially millions of feasible options for its 30 product-strategy teams to consider. Executives needed sound analytical support to realize P&G’s goal within the tight, one-year objective. Operations Research/Management Science

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P&G Supply Chain

(con’t)

• Model Structure: The P&G operations research department and the University of Cincinnati created decision-making models and software. They followed a modeling strategy of solving two easier-tohandle subproblems: – Distribution/location – Product sourcing Operations Research/Management Science

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P&G Supply Chain

(con’t)

• Project Value: The overall Strengthening Global Effectiveness (SGE) effort saved $200 million a year before tax and allowed P&G to write off $1 billion of assets and transition costs.

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Case 6: American Airlines Revolutionizes Pricing • Business Problem: To compete effectively in a fierce market, the company needed to “sell the right seats to the right customers at the right prices.”

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American Airlines (con’t) • Strategic Objectives and Requirements: Airline seats are a perishable commodity. Their value varies – at times of scarcity they’re worth a premium, after the flight departs, they’re worthless. The new system had to develop an approach to pricing while creating software that could accommodate millions of bookings, cancellations, and corrections.

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American Airlines

(con’t)

• Model Structure: The team developed yield management, also known as revenue management and dynamic pricing. The model broke down the problem into three subproblems: – Overbooking – Discount allocation – Traffic management

The model was adapted to American Airlines computers. Operations Research/Management Science

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American Airlines

(con’t)

• Project Value: In 1991, American Airlines estimated a benefit of $1.4 billion over the previous three years. Since then, yield management was adopted by other airlines, and spread to hotels, car rentals, and cruises, resulting in added profits going into billions of dollars.

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Some Important Points • Differences Between OR/MS and IT • Keys to Success • Conclusion

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Differences Between OR and IT • IT

• OR

– Focuses on data as a corporate resource – Stores, retrieves, formats, displays data – Understands business process and transactions Operations Research/Management Science

– Uses data as input – Provides improved solutions – Gives global focus • Multiple objectives • Multiple criteria

– Evaluates tradeoffs

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Conclusion • Operations Research/Management Science provides analytical tools that help leverage information technology to solve complex business problems involving millions of variables. • OR/MS departments collaborate easily with other departments to achieve goals. • OR/MS can realize savings and benefits in dollars, time, customer satisfaction, and retention. Operations Research/Management Science

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