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# APPLICATION OF OPERATIONS RESEARCH IN HRM

APPLICATION OF OPERATIONS RESEARCH IN HRM. GROUP 5. PALLAV SUDHIR PRITY BALA ANUP SARANSH RAJAT HIRNI ANTHONY CHETNA ROGER. History Linear Programming.

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## APPLICATION OF OPERATIONS RESEARCH IN HRM

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1. APPLICATION OF OPERATIONS RESEARCH IN HRM

2. GROUP 5 • PALLAV • SUDHIR • PRITY BALA • ANUP • SARANSH • RAJAT • HIRNI • ANTHONY • CHETNA • ROGER

3. History Linear Programming During the 1940’s and the army needed a way to plan expenditures and returns in order to reduce costs and increase losses for the enemy. George B. Dantzig is the founder of the simplex method of linear programming, but it was kept secret and was not published until 1947 since it was being used as a war-time strategy. But once it was released, many industries also found the method to be highly valuable. Another person who played a key role in the development of linear programming is John von Neumann, who developed the theory of the duality and Leonid Kantorovich, a Russian mathematician who used similar techniques in economics before Dantzig and won the Nobel prize in 1975 in economics.

4. History Linear Programming Dantzig's original example of finding the best assignment of 70 people to 70 jobs emphasizes the practicality of linear programming. The computing power required to test all possible combinations to select the best assignment is quite large. However, it takes only a moment to find the optimum solution by applying the simplex algorithm. The theory behind linear programming is to drastically reduce the number of possible optimal solutions that must be checked. In the years from the time when it was first proposed in 1947 by Dantzig, linear programming and its many forms have come into wide use worldwide. Fourier in 1823 wrote a paper describing today's linear programming methods, but it never made its way into mainstream use. A paper by Hitchcock in 1941 on a transportation problem was also overlooked until the early 1950s. It seems the reason linear programming failed to catch on in the past was lack of interest in optimizing.

5. Linear Programming: A methodology for identifying underutilized resources Identifying underutilized resources is crucial for evaluating the economic feasibility of downsizing. However, identification of these resources is perhaps one of the most difficult and critical aspects of downsizing. A quantitative technique that may be used to identify potentially unproductive resources is linear programming (LP). LP may be used to measure resource utilization and other economic attributes of the firm's operations. For firms not contemplating downsizing, this information may also be potentially useful for making management aware of an organization's underutilized resources as well as their cost to the firm.

6. Linear Programming: A methodology for identifying underutilized resources LP is used to model a firm's goals and its operating constraints. An algorithm is then used to find an allocation of the firm's scarce resources that maximizes the goal specified in the LP model. Diminished product demand is a constraint facing many firms today and is one of the primary reasons firms decide to downsize. LP may also be used to determine departments that are currently understaffed. Identifying these departments should help to prevent work force reductions that would be harmful to the firm. Equally important, identifying understaffed departments represents opportunities for reallocating the unproductive resources of other departments to applications that enhance the firm's financial performance.

7. Linear Programming: A methodology for identifying underutilized resources LP may be used to model a firm's business opportunities and resources. The solution to the resulting set of equations may be used to identify departments with unused resources. Equally important, the LP solution aids in identifying areas within the firm's operations where slack resources may be reallocated to use them productively. Finally, it may be used to measure the profitability and resource utilization from alternative marketing, financing, and production scenarios. Identification of underutilized resources and measurement of their cost is a starting point for evaluating the economic feasibility of downsizing. While LP may be used to measure these variables, downsizing involves much more than the mechanistic computation of the benefits and cost of terminating the firm's employees. It involves the future direction and capabilities of the corporation. However, LP can serve as a useful technique for developing information for making this critical decision.

8. HISTORY CPM/PERT • CPM was developed by M.R.Walker of E.I.Du Pont de Nemours & Co. and J.E.Kelly of Remington Rand, circa 1957,for the UNIVAC-I computer. • first test was made in 1958, when CPM was applied to the construction of a new chemical plant. • In March 1959, the method was applied to a maintenance shut-down at the Du Pont works in Louisville, Kentucky. Unproductive time was reduced from 125 to 93 hours.

9. PERTwas developed primarily to simplify the planning and scheduling of large and complex projects. • It was developed by Bill Pocockof Booz Allen Hamiltonand Gordon Perhsonof the U.S. Navy Special Projects Office in 1957 to support the U.S. Navy's Polaris nuclear submarine project.

10. PERT PERT is a method to analyze the involved tasks in completing a given project, especially the time needed to complete each task, and identifying the minimum time needed to complete the total project.

11. Conventions • A PERT chart is a tool that facilitates decision making; The first draft of a PERT chart will number its events sequentially in 10s (10, 20, 30, etc.) to allow the later insertion of additional events. • Two consecutive events in a PERT chart are linked by activities, which are conventionally represented as arrows (see the diagram above). • The events are presented in a logical sequence and no activity can commence until its immediately preceding event is completed. • The planner decides which milestones should be PERT events and also decides their “proper” sequence. • A PERT chart may have multiple pages with many sub-tasks. • PERT is valuable to manage where multiple tasks are occurring simultaneously to reduce redundancy

12. Uncertainty in project scheduling • During project execution, however, a real-life project will never execute exactly as it was planned due to uncertainty. It can be ambiguity resulting from subjective estimates that are prone to human errors or it can be variability arising from unexpected events or risks. And Project Evaluation and Review Technique (PERT) may provide inaccurate information about the project completion time for main reason uncertainty. This inaccuracy is large enough to render such estimates as not helpful. • One possibility to maximize solution robustness is to include safety in the baseline schedule in order to absorb the anticipated disruptions. This is called proactive scheduling. A pure proactive scheduling is an utopia, incorporating safety in a baseline schedule that allows to cope with every possible disruption would lead to a baseline schedule with a very large make-span. A second approach, reactive scheduling, consists of defining a procedure to react to disruptions that cannot be absorbed by the baseline schedule.

13. APPLICATION • The PERT/cost system was developed to gain tighter control over actual costs of any project. PERT\cost relates actual costs to project costs. Job cost estimates are established from an activity or a group of activities on the basis of a time network. Labor and nonlabor estimates are developed for the network targeting the control of time and costs and identifying potential areas where time and cost can be traded off—all aimed at more effective, efficient project management. • As with all aspects of business, the Internet has become a powerful tool with respect to PERT. Managers can now locate PERT applications on the World Wide Web and apply them directly to the appropriate manufacturing project. In most instances, PERT diagrams are available that eliminate the estimating process and make PERT a more useful and convenient tool

14. CPM • CPM is commonly used with all forms of projects, including construction, aerospace and defense, software development, research projects, product development, engineering, and plant maintenance, among others. Any project with interdependent activities can apply this method of mathematical analysis

15. Basic technique The essential technique for using CPM is to construct a model of the project that includes the following: • A list of all activities required to complete the project (typically categorized within a work breakdown structure), • The time (duration) that each activity will take to completion, and • The dependencies between the activities

16. Using these values, CPM calculates the longest path of planned activities to the end of the project, and the earliest and latest that each activity can start and finish without making the project longer. This process determines which activities are "critical" (i.e., on the longest path) and which have "total float" (i.e., can be delayed without making the project longer). • In project management, a critical path is the sequence of project network activities which add up to the longest overall duration. This determines the shortest time possible to complete the project. Any delay of an activity on the critical path directly impacts the planned project completion date (i.e. there is no float on the critical path). A project can have several, parallel, near critical paths. An additional parallel path through the network with the total durations shorter than the critical path is called a sub-critical or non-critical path.

17. SIMULATION MODELING

18. Overview • Is the process of building a mathematical or logical model of a system or a decision problem, and • experimenting with the model to obtain insight into the system’s behavior or to assist in solving the decision problem.

19. It is an analysis tool used for the purpose of designing planning and control of manufacturing systems. • Simulation modeling may be defined as the concise framework for the analysis and understanding of a system. • It is an abstract framework of a system that facilitates imitating the behavior of the system over a period of time. • In contrast to mathematical models, simulation models do not need explicit mathematical functions to relate variables

20. Simulation modeling techniques are powerful for manipulation of time system inputs, and logic. • They are cost effective for modeling a complex system, and with visual animation capabilities they provide an effective means of learning, experimenting and analyzing real-life complex systems. • They enable the behavior of the system as a whole to be predicted

21. Therefore ,they are suitable for representing complex systems to get a feeling of real system. • One of the greatest advantage of a simulation model is that it can compress or expand time. • Simulation models can also be used to observe a phenomenon that cannot be observed at very small intervals of time. • Simulation can also stops continuity of the experiment.

22. A Brief History of Simulation • Simulation has been around for some time. • Early simulations were event-driven and frequently military applications. • In the 1960’s Geoffrey Gordon developed the transaction (process) based orientation that we are now familiar with. • Gordon’s software was called General Purpose Simulation System (GPSS). • GPSS was originally intended for analyzing time sharing options on mainframe computers. • The software was included as a standard library on IBM 360s and its use was quite widespread.

23. SIMULATION MODEL • Usually, a simulation model is a computer model that imitates a real-life situation. • It is like other mathematical models, but it explicitly incorporates uncertainty in one or more input quantities • When we run simulation, we allow these random quantities to take various values, and we keep track of any resulting output quantities of interest • In this way, we are able to see how the outputs vary as a function of the varying inputs

24. BenefitsLimitations • Does not require simplifying assumptions • Can deal with problems not possible to solve analytically • Provides an experimental laboratory: possible to evaluate decisions/systems without implementing them • Generally easier to understand than analytical models • Building models and simulating is time-consuming for complex systems • Simulation results / simulated systems are always approximations of the real ones • Does not guarantee an optimal solution - lack of precise answers • Should not be used indiscriminately in place of sound analytical models.

25. APPLICATIONS OF SIMULATIONAL MODELING • Simulation enables the study of, and experimentation with, the internal interactions of a complex system, or of a subsystem within a complex system. • Informational, organizational, and environmental changes can be simulated, and the effect of these alterations on the model’s behavior can be observed.

26. Trends • Virtual reality animations. • Advanced statistical functions • Curve fitting for input data. • Automatic detection of warm up • Output analysis modules (including replication). • Bolt on “Optimizers” – Tools to search for optimal settings of parameters.

27. APPLICATIONS IN HRM • With the OB Representation model and simulation system it is possible to simulate the organisational performance under different conditions like varying workload or unexpected critical incidents. • The human resource planning and development can be connected to organisational structure or tasks. It makes possible the performance measurements of employees in an organisations.

28. From the results of the simulation process, the decisions for the personnel planning process could be made. Decision support could be realized through different aspects. • The user of the simulation are able to vary input parameters for identifying critical levels of operation experience, personnel skills or fit between expected staff and necessary competencies.

29. Research and applications of mathematical and statistical models are the core of this program's activities, whether the models represent structural descriptions of human abilities, interests, or temperaments; dynamic simulations of skill acquisition, retention, and performance; or more global models of human systems

30. In addition, it makes possible to develop and evaluate prescriptive models, including models that optimize person-job matching based on aptitudes and interests, or that guide the design of training systems to maximize effectiveness within cost constraints. • An important aspect is the evaluation of modeling and simulation technology for competency mapping & training

31. Simulation Modeling is used in determination of the steady-state manpower situation that would be attained if a certain policy were to be maintained for a prolonged period of time. • It helps a manpower planner accurately identify the age and rank structure that fits the organisational framework required to fulfil the organizational goals.

32. HR FORECASTER • A computer simulation such as HR Forecaster can model a real-life or hypothetical situation on your computer so that you can study how the system works. By changing variables, predictions may be made about the behavior of your workforce.  The software packages for running computer-based simulation modeling makes the process modeling almost effortless.

33. HR Forecaster's simulation methods are especially useful in studying systems with a large number of coupled degrees of freedom and are useful for modeling phenomena with significant uncertainty in inputs, such as the calculation of risk.

34. HR Forecaster uses computational algorithms for simulating the behavior of various workforce variables, either actual or scenario. It is distinguished from other simulation methods by being nondeterministic in some manner – usually by using random numbers (in practice, pseudo-random numbers) – as opposed to deterministic algorithms.

35. Markov Chains

36. Markov AnalysisOverview • Markov analysis is a probabilistic technique. • - It provides information about a decision situation. • - It is a descriptive, not an optimizing technique. • - Specifically applicable to systems that exhibit probabilistic movements from one state (or condition) to another.

37. History Markov analysis analyzes the current behaviour of some variables. This was first used by the Russian mathematician A. Markov to describe and predict the behavior of gas particles in a closed container. In operations research, it has been successfully applied to a wide variety of situations- It has been used in examining and predicting the behaviour of customers in terms of their brand loyalty and their changing from one brand to another. It has also been used to the study the life of newspaper subscriptions. Recently it has been used to study the customer’s account behaviour i.e. to the study the customers as they change from ‘current account’ through ‘one month overdue’ to ‘two months over due’ to ‘bad debt’. In all these applications, future behaviour has been predicted by analyzing the present one.

38. Application in HRM Determining Labor SupplyPredicting Worker Flows and Availabilities • Succession or Replacement Charts Who has been groomed/developed and is ready for promotion right NOW? • Human Resource Information Systems (HRIS) An employee database that can be searched when vacancies occur. • Transition Matrices (Markov Analysis) A chart that lists job categories held in one period and shows the proportion of employees in each of those job categories in a future period. It answers two questions: • “Where did people in each job category go?” • “Where did people now in each job category come from? • Personnel / Yield Ratios How much work will it take to recruit one new accountant?

39. SUCCESSION PLANNING REPLACEMENT CHART FOR EXECUTIVE POSITIONS POSITION REPLACEMENT CARDS FOR EACH INDIVIDUAL POSITION - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - POSITIONWESTERN DIVISION SALES MANAGER DANIEL BEALER Western Division Sales Mgr Outstanding Ready Now PRESENT PROMOTION POSSIBLE CANDIDATESCURRENT POSITIONPERFORMANCEPOTENTIAL SHARON GREEN Western Oregon Sales Manager Outstanding Ready Now GEORGE WEI N. California Sales Manager Outstanding Needs Training HARRY SHOW Idaho/Utah Sales Manager Satisfactory Needs Training TRAVIS WOOD Seattle Area Sales Manager Satisfactory Questionable

40. HUMAN RESOURCE INFORMATION SYSTEMS (HRIS) PERSONAL DATA Age, Gender, Dependents, Marital status, etc EDUCATION & SKILLS Degrees earned, Licenses, Certifications Languages spoken, Specialty skills Ability/knowledge to operate specific machines/equipment/software JOB HISTORY Job Titles held, Location in Company, Time in each position, etc. Performance appraisals, Promotions received, Training & Development MEMBERSHIPS & ACHIEVEMENTS Professional Associations, Recognition and Notable accomplishments PREFERENCES & INTERESTS Career goals, Types of positions sought Geographic preferences CAPACITY FOR GROWTH Potential for advancement, upward mobility and growth in the company

41. Transition MatrixExample for an Auto Parts Manufacturer

42. MARKOV ANALYSIS(STATISTICAL REPLACEMENT ANALYSIS) TO: A TRANSITION MATRIX FROM: TOP MID LOW SKILLED ASSY EXIT TOP .80 .02 .18 MID .10 .76 .04 .10 LOW .06 .78 .01 .15 SKILL .01 .84 .15 ASSY .05 .88 .07

43. MARKOV ANALYSIS – 2(Captures effects of internal transfers) (Start = 3500) A TRANSITION MATRIX FROM/ TO:  TOP MID LOW SKILLED ASSY EXIT TOP 100 .80 .02 .18 MID 200 .10 .76 .04 .10 LOW 600 .06 .78 .01 .15 SKILL 600 .01 .84 .15 ASSY 2000 .05 .88 .07 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - END YR WITH: 100 190 482 610 1760 [358 left] NEED RECRUITS ? 0 10 118 240* 368 tot NEED LAYOFFS ? (10)* (10) tot KEEP STABLE100 200 600 600 2000 = 3500 Tot

44. MARKOV ANALYSIS – 3(Anticipates Changes in Employment Levels) Employment needs are changing. We need a 10% increase in skilled workers (660), and a 15% decrease in assembly workers (1700) by year’s end. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (Start = 3500) A TRANSITION MATRIX FROM/ TO:  TOP MID LOW SKILLED ASSY EXIT TOP 100 .80 .02 .18 MID 200 .10 .76 .04 .10 LOW 600 .06 .78 .01 .15 SKILL 600 .01 .84 .15 ASSY 2000 .05 .88 .07 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - END YR WITH: 100 190 482 610 1760 [358 left] NEED RECRUITS ? 0 10 118 50* NEED LAYOFFS ? (60)* NEW LEVELS100 200 600 600 1700 = 3260 tot

45. Determining Labor Surplus or Shortage • Based on the forecasts for labor demand and supply, the planner can compare the figures to determine whether there will be a shortage or surplus of labor for each job category. • Determining expected shortages and surpluses allows the organization to plan how to address these challenges.

46. PERSONNEL / YIELD RATIOS Past experience has developed these yield ratios for recruiting a Cost Accountant: FOR EVERY 12 APPLICATIONS RECEIVED, ONLY 1 LOOKS PROMISING ENOUGH TO INVITE FOR AN INTERVIEW OF EVERY 5 PERSONS INTERVIEWED, ONLY 1 IS ACTUALLY OFFERED A POSITION IN THE ORGANIZATION OF EVERY 3 JOB OFFERS MADE, ONLY 2 ACCEPT THE POSITION OF EVERY 10 NEW WORKERS WHO BEGIN THE TRAINING PROGRAM, ONLY 9 SUCCESSFULLY COMPLETE THE PROGRAM THUS: 100 APPLICATIONS MUST BE RECEIVED, so that 8.33 JOB INTERVIEWS CAN BE HELD, so that 1.67 JOB OFFERS CAN BE MADE, and 1.11 PEOPLE MUST BE TRAINED, so that we get ONE NEW COST ACCOUNTANT!!!

47. Queuing Systems

48. Queuing theory is the mathematical study of waiting lines, or queues. The theory enables mathematical analysis of several related processes, including arriving at the (back of the) queue, waiting in the queue (essentially a storage process), and being served at the front of the queue.

49. Queuing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide service. It is applicable in a wide variety of situations that may be encountered in business, commerce, industry, healthcare, public service and engineering. • The theory permits the derivation and calculation of several performance measures including the average waiting time in the queue or the system, the expected number waiting or receiving service, and the probability of encountering the system in certain states, such as empty, full, having an available server or having to wait a certain time to be served.

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