Quantitative Techniques/Operations Research Successful managers use quantitative techniques in decision making when: 1. The problem is complex. 2. The problem involves many variables. 3. There are data which describe the decision environment. 4. There are data which describe the value or utility of the different possible alternatives. 5. The goals of the decision maker or the organization can be described in quantitative terms. 6. Workable models are available for these situations. Six steps towards making better decisions:

Process Activities Process StepsProcess Output 1. Site visitsObserve the problem Sufficient information Conferencesenvironmentand support to proceed Observation Research 2. Define useAnalyze and defineClear grasp of need for Define objectivesand nature of solution Define limitationsrequested 3. MS/OR toolsDevelop a modelModel that works under Interrelationshipsidentified limitations Mathematical models Known solutions Research 4. Internal/external dataSelect appropriate data Sufficient inputs to Factsinputoperate and test model

Opinions Computer data banks 5. TestingProvide a solution and Solution(s) that support Limitationstest its reasonablenesscurrent organizational Verificationobjectives 6. Behavioral issuesImplement the solution”Ownership” by management “Selling” the ideasufficient to support longer Management involve-run/operation of model ment Explanations Roles of quantitative specialist and managerial generalist/decision maker: Steps in Problem Recognition,Involvement: Quantitative specialist Formulation & Solutionor managerial generalist 1.

Recognize from organizationalManagerial generalist symptoms that a problem exists. 2. Decide what variables areManagerial generalist & quantitative involved; state the problemspecialist in quantitative relationships among the variables. 3. Investigate methods for solvingQuantitative specialist the problem as stated above; determine the appropriate quantitative tools to be used. 4. Attempt solutions to the Quantitative specialist problem; find various solutions; state assumptions underlying these solutions; test alternative solutions. 5.

Determine which solutionManagerial generalist & quantitative is most effective becausespecialist of practical constraints within the organization; decide what the solution means for the organization. 6. Choose the solution to be used. Managerial generalist 7. “Sell” the decision to operatingManagerial generalist & quantitative managers; get their understanding specialist and cooperation Some applications of MS/OR: Accounting: Forecasting cash flows Assigning audit teams effectively Using samples to improve audit accuracy Management of accounts receivables

Deciding which customers to give credit to and how much Improving the effectiveness of cost accounting Resolving transfer pricing problems Establishing costs for byproducts Developing standard costs Finance: Building cash management models Allocating capital among various investment alternatives Managing an investment portfolio Forecasting long range capital needs Building financial planning models Determining optimal times to replace equipment Deciding on the most effective dividend policy Marketing: Determining the best product mix

Effectively allocating advertising among various media Finding the best time to introduce a new product Locating warehouses to minimize distribution cost Planning salespersons’ travel to minimize time and cost Assigning salespeople to customers to maximize sales effectiveness Deciding on the most effective packaging alternative Predicting the loyalty of customers in future periods Determining the appropriate number of accounts for a salesperson Finding the least-cost shipping arrangements from plant to warehouse Determining the best size for a warehouse

Production/Operations: Balancing plant capacity with market demand Leveling a production schedule to minimize hiring and layoffs Mixing chemical ingredients to achieve least cost Smoothing production schedules when demand is seasonal Minimizing in-process inventory Moving products through the manufacturing process in the shortest time Scheduling street sweepers and compactors for large cities Scheduling household garbage collection patterns

Determining landing and takeoff schedules for large airports Scheduling school bus pickups to minimize total travel cost and time Deciding whether to manufacture or purchase components Balancing an assembly line which has many different operations Locating a new plant/branch in the most effective place Allocating R & D budgets most effectively Choosing sites for oil and gas exploration to reduce risk of dry wells Choosing the best size for a new plant Scheduling crews for package delivery airlines

Scheduling police beat assignments in large cities Planning the long-term manufacturing capacity for a company Making quality control more effective Organizational Development/Human Resources: Minimizing the need for temporary help through better scheduling Staffing emergency rooms in hospitals to provide the best level of care Determining how to negotiate in a bargaining situation Coordinating manpower needs in a seasonal business Hiring new employees at the right time and the right rate Deploying a field sales force optimally

Scheduling training programs to maximize skills development and retention Designing organization structure most effectively Choosing pension alternatives to provide greater benefits to employees Opportunities and shortcomings of the quantitative approach Opportunities: 1. MS/OR forces managers to be quite explicit about their objectives, their assumptions and their way of seeing constraints. 2. MS/OR quickly points out gaps in the data required to support workable solutions to problems 3.

MS/OR permits us to examine a situation, change the conditions under which decisions are made, and examine the effects of those changes-all without serious damage or excessive cost. 4. MS/OR forces managers to be very precise about how the variables in a problem interact with each other. 5. MS/OR makes managers consider very carefully just what variables influence decisions. 6. MS/OR lets us find a solution to a complex variable much more quickly than if we had to compute it by hand and often is the only way we can solve large complex problems. . MS/OR lets us model a problem and its solution so that future solutions can be done by a computer, thus freeing management time for decisions that require a more intuitive approach. Shortcomings: 1. Often, MS/OR approaches have to simplify the problem or make simplifying assumptions in order to solve it, and thus produce solutions which have limitations. 2. For problems that a manager must solve only one time, constructing a complex MS/OR model is often too expensive when compared with other less sophisticated approaches. 3.

Sometimes MS/OR specialists become so enamored with the model they have built that they forget it does not represent the real world in which decisions must be made. 4. Sometimes MS/OR specialists forget to counsel managers on the limitations of the model they build, including the fact that many of them have o be combined with judgment and intuition for effective use. 5. Often, managers forget to include an important constraint or assign an incorrect value to a constraint. 6. Many MS/OR solutions are so complex that they are difficult to explain to managers in a way that builds support and confidence. 7.

Many real world problems just don’t have an MS/OR solution. A good MS/OR solution should meet the following requirements: 1. The solution should be technically appropriate. It should produce a solution which works technically, which meets constraints, and which operates in the real problem environment. 2. The solution should be reliable. It should work time after time under the conditions for which it was designed. 3. The solution should be economically viable. It should produce value for the organization in excess of what it costs to develop and it should be seen as a wise investment of MS/OR talent. . The solution should be behaviorally appropriate. It should be viable in its organizational setting, it should have the support of management, and it must work well within the organization on along-term basis Quantitative methods to be discussed 1. Decision Theory is concerned with making sensible decisions both under conditions of complete uncertainty about future outcomes and under conditions such that you can make probability statements about what you think will happen in the future.

Methods will be presented by which probability theory can be coupled with financial data to generate valuable decision algorithms. 2. Linear Programming is of value when a choice must be made from alternatives too numerous to evaluate with conventional methods. It can be used to determine optimal combinations of the resources of a firm to achieve a given objective or to allocate scarce resources optimally. 3. Queuing Theory studies random arrivals at a servicing or processing facility of limited capacity.

Models allow management to calculate the lengths of future waiting lines, the average time spent in line by a person awaiting service, needed facility additions, and the service level or capacity that minimizes the sum of waiting and operating costs. 4. Forecasting is an unavoidable responsibility of management. Faced with uncertainty concerning the future, management looks to past behavior as an indicator of what is to come.