Operations Research: The Science of Better

1. Operations Research: The Science of Better Operations Research

2. What we do • Our accomplishments • Our mission • Our expertise • Our scope • Our approach • Our services Operations Research

3. Our accomplishments • [name of client/organization] • [business problem] • [O.R. solution] • [value we delivered] Operations Research

4. Our mission • Help you with the challenge of making complex decisions by: • Performing quantitative analysis that provides insight • Providing sensible options and recommending courses of action • Reducing risk • Improving the quality of recurring decisions Operations Research

5. Our mission (con’t) • Have a dramatic, positive impact on a project’s or organization’s value Operations Research

6. Our expertise • Business consulting • Modeling & analysis • Math modeling, simulation, decision support, data mining, optimization, revenue management, supply chain management, logistics, strategical analysis • Technical support, training Operations Research

7. Our scope • Serving any organizational unit, any function, any geographic area • Assistance from strategy through execution Operations Research

8. Our approach • Partner with management to frame and prioritize the issues • Focus on business impact and implementation • Establish a disciplined, consultative approach through teamwork and collaboration Operations Research

9. Our approach (con’t) • Build an objective, quantitative structure for analysis • Transfer technology to your organization so you can take over the project Operations Research

10. Our services • Strategic planning • Supply chain management • Pricing and revenue management • Logistics and site location • Simulation • Marketing research Operations Research

11. Our services (con’t) • Scheduling • Portfolio management • Inventory analysis • Forecasting • Sales analysis • Auctioning • Risk analysis Operations Research

12. A few words about operations research • What is it? • Conventional computing isn’t enough • Combinatorial explosion • The distinct nature of operations research Operations Research

13. O.R.: What is It? Operations research (O.R.) is the discipline of applying advanced analytical methods to help make better decisions Operations Research

14. Conventional computing is not enough • Cannot enumerate alternatives • Combinatorial explosion of viable options Never got outside the box “Few” alternatives Operations Research

15. Combinatorial explosion Example 1 • Problem: Find shortest path through 10 points • Assume a super-powered computer analyzes, quantifies, and compares a million alternatives every second • Answer is found in less than one second • Now find the shortest path through 20 points • Same computer would take over 39,000 years to solve Operations Research

16. Combinatorial explosion Example 2 • Construct a 5 stock portfolio out of 100 possible stocks • Evaluate for risk and return using same computer as Example 1 • Answer found in 1.25 minutes • Now diversify to 9 stocks • Answer found in 220 days • Now diversify to 10 stocks • Answer found in 5.5 years Operations Research

17. Distinct nature of O.R. • Applies leading-edge analysis • Can find the best among many choices, in reasonable time • Can consider and balance multiple objectives • Can help measure, control, and reduce risk Operations Research

18. OR successes Representative cases from the annual INFORMS Edelman Competition Forecasting the Shuttle Disaster at NASA Medicare Saves Billions of Taxpayer Dollars The Operation Desert Storm Airlift New Haven Needle Exchange Fights AIDS A North Carolina School District Improves Planning US Postal Service Automates Delivery Operations Research

19. Case 1: Forecasting the Shuttle Disaster at NASA • The problem • After the Challenger shuttle disaster in 1986, NASA decided to conduct risk analysis on specific systems to identify the greatest threats of a future disaster and prevent them • Consultants at Stanford University and Carnegie Mellon were called in to assess risk to the shuttle tiles Operations Research

20. NASA (con’t) • Objectives and requirements • Identify different possible accident scenarios • Compute the probability of failure • Show how safety could be increased • Prioritize recommended safety measures Operations Research

21. NASA (con’t) • The OR solution • Model was based on a multiple partition of the orbiter's surface • For the tiles in each zone, the OR team examined data to determine the probability of: • Debonding due to debris hits or a poor bond • Losing adjacent tiles once the first is lost • Burn-through • Failure of a critical subsystem under the skin of the orbiter if a burn-through occurs • A risk-criticality scale was designed based on the results of this model Operations Research

22. NASA (con’t) • The value • Found that 15% of the tiles account for about 85% of the risk • Recommended NASA inspect the bond of the most risk critical tiles and reinforce insulation of vulnerable external systems • Computed that such improvements could reduce probability of a shuttle accident from tile failure by 70% • 1994 study quoted extensively in the press after the Columbia, a second shuttle, exploded on reentry in 2003, apparently due to tile failure Operations Research

23. Case 2: Medicare Saves Billions of Taxpayer Dollars • The problem • In the 1980s, the U.S. federal government was already facing rising Medicare costs • A leading operations researcher at Yale was presented with the question: How could expenses be contained? Operations Research

24. Medicare (con’t) • Objectives and requirements • Measure and evaluate the performance of hospitals • Develop a methodology for classifying patients • Use these methods as a basis for • performance measurement • resource management • cost effectiveness • quality care • Define a prospective payment scheme for reimbursing hospitals for Medicare patients Operations Research

25. Medicare (con’t) • The OR solution • Developed the concept of Diagnostic Research Groups (DRGs) • Identified the output of hospitals as classes of patients, each class receiving a similar bundle of goods and services • For each DRG, set a rate considered to be a fair payment to the hospital for diagnosis and treatment of a given illness • Applied full range of industrial management techniques to reimbursement, including flexible budgeting and cost and quality control Operations Research

26. Medicare (con’t) • The value • DRGs were adopted by Medicare in 1983 to serve as a basis for a prospective payment system (PPS) for US hospitals • By 1990, resulted in savings of more than $50 billion in Medicare hospital payments • Extended the solvency of the Medicare Hospital Trust Fund Operations Research

27. Case 3: The Operation Desert Storm Airlift • The problem • In 1991, the Military Air Command (MAC) was charged with scheduling aircraft, crew, and mission support resources to maximize the on-time delivery of cargo and passengers to the Persian Gulf • A typical airlift mission carrying troops and cargo to the Gulf required a three-day round trip, visited 7 or more different airfields, burned almost 1 million pounds of fuel, and cost $280,000 Operations Research

28. Desert Storm (con’t) • Objectives and requirements: • Create a scheduling system • Create a communications system coordinating the schedule among bases in the US and other countries Operations Research

29. Desert Storm (con’t) • The OR solution • MAC worked with the Oak Ridge National Laboratory to develop the Airlift Deployment Analysis System (ADANS) • Within three months, ADANS provided a set of decision support tools to manage: • Information on cargo and passengers • Information on available resources • ADANS also developed tools for: • Scheduling missions • Analyzing the schedule • Distributing the schedule to the MAC worldwide command and control system Operations Research

30. Desert Storm (con’t) • The value • By August 1991, more than 25,000 missions had moved nearly 1 million passengers and 800,000 tons of cargo to and from the Persian Gulf Operations Research

31. Case 4: New Haven Needle Exchange Fights AIDS • The problem • With the advent of HIV and AIDS in the early 1990s, the City of New Haven instituted a needle exchange program as a way of reducing the spread of infection among intravenous drug users • New Haven asked Yale University to determine if the program was actually making progress in the fight against HIV and AIDS Operations Research

32. New Haven (con’t) • Objectives and requirements • Develop a syringe tracking and testing system • Model HIV transmission in New Haven • Estimate model parameters from the data collected in New Haven • Determine if the program is reducing infection rates and saving lives • Recommend continuation or discontinuation of the program. Operations Research

33. New Haven (con’t) • The OR solution • Yale researchers developed: • A syringe tracking and testing system to “interview the needles” rather than rely on addicts’ self-reporting • A Needles That Kill (NTK) model to forecast the incidence of new HIV infections • The modelers made adjustments to determine: • Frequency of shared drug injection • Probability that kits given to addicts for cleaning needles were effective • Departure rate from the population • Infectivity per injection • Per syringe exchange rate • Ratio of drug injectors to needles Operations Research

34. New Haven (con’t) • The value • The researchers were able to determine that needle exchange reduced the HIV infection rate among program clients by 33% • In response, the Connecticut legislature continued funding the program, expanded needle exchange services to Bridgeport and Hartford, and decriminalized syringe possession • New needle exchange programs and legislation were proposed in New York, California, and Massachusetts as a result Operations Research

35. Case 5: A North Carolina School District Improves Planning • The problem • School planning, like public sector land-use planning, takes place within a complex environment, including perceptions of public education, public finance, taxation, politics, and the courts • Johnston County, NC sought to improve school planning while integrating the concerns of participating agencies and community groups • It worked under two constraints: • Inadequate data to support sensitive decisions • Externally imposed constraints on decision-making Operations Research

36. NC School District (con’t) • Objectives and requirements • The school board and administration sought to develop a strong planning culture and a decision-support mechanism that would restore public confidence and win the support of the community’s political leaders • The OR consulting group wanted to fulfill these requests and while creating models that would be effective and portable to other school districts Operations Research

37. NC School District (con’t) • The OR solution • OR/ED Laboratories and the Johnston County schools created a planning system, Integrated Planning for School and Community, to: • Forecast enrollments • Compare enrollment projections to capacity • Find the optimal locations for new school building • Set distance-minimized boundaries for all schools to avoid overcrowding and meet racial balance guidelines Operations Research

38. NC School District (con’t) • The value • Implementing the system has increased the school district’s success in: • Passing bond issues • Reducing pupil-transportation costs • Eliminating frequent adjustments to school-attendance boundaries Operations Research

39. Case 6: US Postal Service Automates Delivery • The problem • In 1988, the US Postal Service foresaw three interlocking problems: • An increase from 166 billion to 261 billion pieces of mail handled a year by the turn of the century • Increased private sector competition • A complexity of operations that would have to be modeled if automation were to respond to the challenges Operations Research

40. US Postal Service (con’t) • Objectives and requirements • Create a decision support tool that could simulate postal operations and quantify the effects of automation alternatives Operations Research

41. US Postal Service (con’t) • The OR solution • Working with two OR consulting groups, the Postal Service developed the Model for Evaluating Technology Alternatives (META) • A simulation model that quantifies the impacts of changes in mail-processing and delivery operations • Blended OR and software tools in a decision support system Operations Research

42. US Postal Service (con’t) • The value • META analysis enabled the Postal Service to release its Corporate Automation Plan, including a cumulative capital investment of $12 billion and labor savings of $4 billion per year • META spawned a family of systems for use at headquarters and field levels, accelerating and enhancing the use of OR throughout the organization Operations Research

43. Some important points • Differences between O.R. and IT • Keys to success • Conclusion Operations Research

44. IT Focuses on data as a corporate resource Stores, retrieves, formats, displays data Understands business processes and transactions O.R. Helps management select the best decision or set of decisions Applies analysis to convert data into useful information Works with management to help gain the deepest insights from analytical results Is typically embedded in an information system to provide recommended decisions or actions Differences between O.R. and IT Operations Research

45. Keys to success • Focuses on business impact and implementation • Improves decision processes while reducing risk • Helps operations become more efficient and effective • Establishes a disciplined, consultative approach • Transfers technology to your department so you can take over the project • Uses the appropriate analytical tools Operations Research

46. Conclusion • Operations research is the discipline of applying advanced analytical methods to help make better decisions • We collaborate easily with organizations like yours to achieve major goals • We can help you realize powerful benefits in dollars, time, productivity, customer satisfaction, market share, and more Operations Research