# TEACHING AGGREGATE PLANNING IN AN OPERATIONS MANAGEMENT COURSE

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1 TEACHING AGGREGATE PLANNING IN AN OPERATIONS MANAGEMENT COURSE Johnny C. Ho, Turner College of Business, Columbus State University, Columbus, GA David Ang, School of Business, Auburn University Montgomery, Montgomery, AL Tzu-Liang (Bill) Tseng, Dept. of Industrial Engr., University of Texas at El Paso, El Paso, TX ABSTRACT This paper discusses the challenges in teaching the topic of aggregate production planning in operations management. Among these challenges include the need of applying trial-and-error approaches to test various aggregate plans. These approaches involve performing repetitive calculations, which could hinder the possible insights gained from analyzing aggregate planning problems. In our operations management course, we require students to do an aggregate planning team project, which also includes an individual component. The project involves the use of spreadsheet, such as Microsoft Excel, to solve aggregate planning problems with various input datasets, taking factors such as demand pattern and various cost coefficients into consideration. The project also requires each team to write a report discussing how the team derives its final plan and the insights it acquires from the assignment. INTRODUCTION The Aggregate Production Planning (or Aggregate Planning) topic appears in virtually every operations management textbook. It is arguably a must-cover topic in an operations management course, particularly for a higher level undergraduate or a graduate course in operations. Aggregate Planning is about finding the quantity and timing of production for the intermediate future, often from 3 to 18 months ahead depending on the size of the company. As the term aggregate implies, an aggregate plan means combining various resources into general, or overall, terms. That is, the aggregate plan looks at production in the aggregate, not as a model-by-model breakdown. Aggregate planning is part of the hierarchy of production planning decisions (Nahmias 2009) and may be stated below: Given demand forecast, facility capacity, inventory levels, workforce sizes, and related inputs, the planner has to select the rate of output for a facility over the next 3 to 18 months. Generally, the goal of aggregate planning is to meet forecasted demand while minimizing cost over the planning period. However, other strategic issues may be more important than low cost. These strategies may be to smooth employment levels, drive down inventory levels, and/or meet a high level of service. Operations managers try to determine the best way to meet forecasted demand by adjusting production rates, labor levels, inventory levels, overtime work, subcontracting rates, and other controllable variables. When generating an aggregate plan, the operations manager must answer several questions (Heizer and Render, 2011): 1. Should inventories be used to absorb changes in demand during the planning period? 2. Should changes be accommodated by varying the size of the workforce? 3. Should part-timers be used, should overtime and idle time absorb fluctuations? 4. Should subcontractors be used on fluctuating orders so a stable workforce can be maintained? 5. Should prices or other factors be changed to influence demand? Aggregate planning is important for several reasons. First of all, aggregate planning helps stay ahead of the curve as it takes time to carry out options, such as hiring and training new employees. Second, aggregate planning is necessary because it is hard to predict the demand levels of individual models with

2 a high degree of accuracy. Therefore, an aggregate plan serves as an important bridge between long term and short term plans. Finally, aggregate planning is necessary for budgeting process and is important to keep the supply chain synchronized (Stevenson 2012). There are four popular solution approaches that textbooks frequently cover in aggregate planning. These approaches are: 1. Linear Programming; 2. The Transportation Method of Linear Programming; 3. Trial and Error Approaches Graphing. 4. Linear Decision Rule. Linear Programming (LP) is an optimization technique and can be used to solve aggregate planning problems. Depending on the rigor of the treatment, most introductory operations textbook introduce linear programming as an optimization approach for aggregate planning problems but do not give the details of formulating an aggregate planning problem into a linear programming model. A more rigorous treatment involves the formulation of a basic model, frequently along with solution obtained using software. A rigorous treatment for this topic would include various LP models to account for different types of aggregate planning problems. This treatment would only be reserved for the graduate level. The primary disadvantage of the linear programming approach is that students frequently find it difficult to master mathematical formulation, particularly written in abstract notation. Even if the basic model is mastered, most students find it challenging to go beyond the basic model. The special case of the transportation method is generally more restrictive, this is because hiring and layoff options are not available. While subcontract capacity may vary from period to period, regular time and overtime capacity are usually fixed. Backordering is possible but frequently is assumed to be unavailable for simplicity. Moreover, most textbooks do not show how to optimally solve the special case, especially when backordering is allowed. Finally, there is no guarantee that a solution which satisfies the forecast demand over the entire planning horizon exists. Trial-and-error approaches have generally received the most coverage in operations management textbooks, whereas linear programming has the most coverage in advanced operations management texts. Trial-and-error approaches are composed of developing tables and graphs that enable managers to visually compare forecasted demand requirements with current capacity. Graphs are frequently used to guide the development of alternatives. Once alternative plans are identified, they are evaluated in terms of their overall costs. These approaches are common because they are flexible, easy to understand and use. Also, they provide a visual portrayal of a plan. The major disadvantage of the trial-and-error approaches is that they do not necessarily result in the optimal aggregate plan. Other solution techniques include the management coefficients model and linear decision rule; nevertheless, most textbooks only briefly mention these methods. The linear decision rule approach to solving aggregate planning problem has been proposed by Holt et al. (1960). It applies quadratic approximations for all the relevant costs and obtains linear equations for the optimal policies. One of the important limitations of the linear decision rule is that the model can produce solutions that are infeasible, i.e., negative. In this paper, we propose a project that instructors can use as a means that supports processes of learning and teaching of aggregate production planning. The next section describes our aggregate planning project. It is followed by a project example. Finally, we give a summary and share our aggregate planning project experience in the conclusion section.

3 AGGREGATE PLANNING PROJECT Our aggregate planning project consists of two parts the first part is individual, whereas the second part involves teamwork. In Part 1, every student is required to analyze an aggregate planning problem, and determine a level capacity plan and a chase demand plan for the problem. A level strategy maintains a constant output rate or workforce level over the planning horizon, whereas a chase strategy typically attempts to achieve output rates for each period that match the demand forecast for that period. Furthermore, we ask the students to implement their plans with Microsoft Excel. The purpose of this part is to ensure that every student fully understands the relationship of various terms, such as cumulative production used in aggregate planning, in addition to the level capacity and chase demand strategies. Part 1 is also aimed to prepare every student to be an effective contributing team member in Part 2. An example of an aggregate planning problem data is given in Table 1. The aggregate planning problem has an 18-month planning horizon and is quite general in that the firm may vary workforce (via hiring and layoff) and inventory level (including backordering) to satisfy demand. The firm may also use overtime production to meet demand; however, overtime production in a period is restricted to no more than 20% of regular production in that period. Although subcontracting is not considered, this option still can be easily added. It should be noted that a feasible plan requires the firm to assume a non-negative inventory level by the end of the planning horizon. In Part 2, students are assigned into teams of three or four and they are presented with the same aggregate planning problem as in Part 1, plus a number of additional problem scenarios. These scenarios differ from Part 1 by demand requirements and/or cost coefficients such as hiring and layoff costs. Table 2 illustrates two of these scenarios. Scenario 1 differs from Part 1 by demand requirements only, whereas Scenario 2 only differs by cost coefficients. Each team will discuss and analyze each of the problem scenarios, including the base scenario of Part 1. After the discussion and analysis of a problem scenario, each team proposes an initial feasible plan (Plan 1) for the scenario and implements the plan with Excel. As stated previously, a feasible plan requires that the total net demand must be satisfied by the end of planning horizon. At the initial plan, the project is primarily looking for the quality of rationales behind the plan, not so much of the solution quality in terms of total cost. By analyzing various resulting cost components, such as total hiring, total layoff, total holding, total backordering, and total overtime cost, the team prepares for the next plan. This iterative process continues until the team is satisfied with its final plan. For the final plan, say Plan 5 (P5), the team may fine-tune it further in search of the lowest overall total cost solution and label these resulting secondary plans as P5.1, P5.2, P5.3, etc. Finally, the team is required to write a report documenting the rationales it applied to select each of its primary plans, say Plan 1, Plan 2,, Final Plan. It is particularly important that the team explains how it decided to move from one plan to the next. For example, a team comes up with five major plans for a problem scenario, the team is required to explain how it moves from Plan 1 to Plan 2, then to Plans 3, 4, and 5. The quality of the final solution is significant, as well as the logic and insight employed in deriving each of the primary plans. In the report, the team should include a table which summarizes the solution of its major and secondary plans (see Table 3 for an example). Finally, the report should include Excel worksheets showing each the major plans, as well as the fine-tuned secondary plans.

4 TABLE 1: AGGREGATE PLANNING PROBLEM DATA Month Gross Demand Working Days 1 2, , , , , , , , , , , , , , , , , , Total: 40, Beginning inventory = 300 units Ending inventory = 200 units Beginning workforce = 200 workers Cost of hiring = \$2,000/worker Cost of firing = \$3,000/worker Cost of holding = \$80/unit/month Cost of backordering = \$120/unit/month Incremental overtime cost = \$130/unit Overtime production 20% of regular time production In the past: Over 23 working days, with the workforce level constant at 200 workers, the firms produced 2,120 units.

5 TABLE 2: AN EXAMPLE OF AGGREGATE PLANNING PROBLEM Scenario 1 Scenario 2 Month Gross Demand Cost of hiring = \$1,500/worker 1 2,100 Cost of firing = \$3,000/worker 2 2,400 Cost of holding = \$60/unit/month 3 2,100 Cost of backordering = \$100/unit/month 4 2,700 Incremental overtime cost = \$150/unit , , , , , , , , ,600 Total: 41,100 TABLE 3: SUMMARY TABLE Plan Period WF 1 OT 2 WF 1 OT 2 WF 1 OT 2 WF 1 OT 2 WF 1 OT 2 WF 1 OT Tot. Cost \$1,273, Hiring \$62, Firing \$ Holding \$392, Backorder \$818, Overtime \$ Workforce level 2 Overtime production

6 AN EXAMPLE We will present several feasible plans based on the example data given in Table 1. The first plan employs a level strategy; while the second one uses a chase strategy (achieved by hiring and/or laying off). These plans together represent two extremes. Table 4 summarizes the solutions of level capacity (Plan 1) and chase plans (Plan 2). Plan 1 has 285 workers throughout the planning horizon, implying a hiring of 85 workers in period 1. The total cost for the plan consists of two cost components hiring and holding. The total hiring cost is \$170,000, whereas the total holding cost is approximately \$7 million. In Plan 2, the workforce level varies between 148 and 320. The overall total cost for the plan is around \$1.7 million, consisting of approximately \$0.7 million hiring cost, \$1 million firing cost, and \$22,315 holding cost (due to rounding of the workforce). In addition to these two "extreme plans", there are many avenues that students may take to create an initial plan. Since backordering is allowed, students can propose a constant workforce plan using the average number of workers required to satisfy total net demand over the entire planning horizon (called Plan 3), and thereby resulting in backordering in some periods. Sometimes the demand data may suggest that it is advantageous to break the planning horizon into the two, use a constant workforce for the first half and another constant workforce for the second half (called Plan 4). Furthermore, when the cost of hiring and/or firing to the cost of holding and/or backordering is relatively low, along with changing demand pattern, it could be particularly beneficial to break the planning horizon into more, say four, segments, and apply a constant work force in each segment (called Plan 5). Plan 6 is created with a close inspection of demand and cost data and an aid of various graphical tools. Among these are various graphical tools, students can create a line graph of cumulative demand and cumulative production output of an existing plan. When the cumulative demand lies above the cumulative output, excess inventory is built up, and vice versa. This graph provides students insight as to when to increase workforce/overtime or reduce workforce. Another simple graphical tool is the plotting of a bar chart showing inventory level at the end of each period. Table 5 gives a summary of the four plans described above as follows. Plan 3: Constant workforce over the entire planning horizon. Plan 4: Break the planning horizon into two segments, use constant workforce in each. Plan 5: Break the planning horizon into four segments, use constant workforce in each. Plan 6: Analyzing demand and cost data and using graphical tools. Table 5 shows all four plans outperform Plans 1 and 2 significantly. Plan 5 (4 segments) yields the lowest total cost of \$887,244, it is followed by Plan 6 (examining demand and cost data with graphical tools) with a total cost of \$978,365, Plan 3 (1 segment) with a total cost of \$1,273,520, and Plan 4 (2 segments) with a total cost of \$1,346,106. It is important that each team gives rationales to justify all its proposed plans, whichever ones are selected. When a team is satisfied with its final plan, it may further improve the final plan by fine-tuning. Suppose that Plan 3 is concluded as the final plan. By making minor changes to Plan 3 (i.e., constant 231 workers in each period), Plan 3.1 is created. Plan 3.1 utilizes 230 workers from periods 1-17 and 242 in period 18 and its total cost decreases from \$1,273,520 (Plan 3) to \$1,270,449. If it is undesirable to make a large workforce change in any period, a constraint can be introduced to enforce this requirement. Now suppose that Plan 6 is chosen as the final plan. Again by modifying Plan 6 slightly, Plan 6.1 is created. Plan 6.1 is identical to Plan 6 except for periods with 226 (instead of 225) workers and periods with 249 (instead of 250) workers. As shown in Table 6, Plan 6.1 yields a total cost of \$967,277 compared with \$978,365 of Plan 6.

7 TABLE 4: PLAN 1 (LEVEL CAPACITY) AND PLAN 2 (CHASE DEMAND) Period Plan 1 Plan Overtime: \$0 \$0 Hiring: \$170,000 \$770,000 Firing: \$0 \$1,008,000 Holding: \$7,054,330 \$22,315 Backorder: \$0 \$9 Total Cost: \$7,224,330 \$1,800,325 TABLE 5: PLANS 3-6 Period Plan 3 Plan 4 Plan 5 Plan Overtime: \$0 \$0 \$0 \$0 Hiring: \$62,000 \$72,000 \$206,000 \$200,000 Firing: \$0 \$0 \$189,000 \$150,000 Holding: \$392,849 \$290,683 \$193,559 \$302,383 Backorder: \$818,671 \$983,423 \$298,685 \$325,983 Total Cost: \$1,273,520 \$1,346,106 \$887,244 \$978,365

8 TABLE 6: PLANS 7 AND 8 Plan 7 Plan 8 Period # of Workers Overtime Units # of Workers Overtime Units Overtime: \$128,440 \$222,300 Hiring: \$50,000 \$200,000 Firing: \$0 \$180,000 Holding: \$586,637 \$40,758 Backorder: \$295,283 \$195,610 Total Cost: \$1,060,360 \$838,669 Now let s consider the use of overtime production to help reduce overall total cost. Table 6 shows two possible plans Plans 7 and 8. Plan 7 uses a constant workforce level of 225, which is cut by six workers compared with Plan 3. Overtime production is used in periods 1, 2, and 3 to compensate for the output loss. Plan 7 yields an overall total cost of \$1,060,360 compared with \$1,273,520 of Plan 3. Finally, Plan 8 generally employs fewer workforce in most periods compared with Plan 5 and again makes up the difference via overtime production. It yields an overall total cost of \$838,669 compared with \$887,244 of Plan 5. Appendix A illustrates Plan 8 in great detail. CONCLUSIONS This paper discusses the challenges in teaching the aggregate production planning topic in operations management. Among these challenges includes the need to apply trial-and-error approaches to test various aggregate plans. These approaches involve performing repetitive calculations, which could hinder the possible insights acquired from analyzing aggregate planning problems. In this paper, we propose a project that instructors can use as a means that supports processes of learning and teaching of aggregate production planning. In our aggregate planning project, we require students to do a team aggregate planning team project, which also has an individual component. The project involves the use of a spreadsheet, such as Microsoft Excel, to solve aggregate planning problems with various input datasets, taking factors such as demand pattern and various cost coefficients into consideration. The project also

9 requires each team to write a report discussing how the team derives its final plan and the insights it acquires from the assignment. The feedback we have received from our students is very positive. Most students said that the aggregate planning project allows them to learn the topic more effectively than those end-of-chapter problems in the textbook. Among the reasons given are: (1) the project is motivational; (2) it helps fully understand the details of aggregate planning; (3) it allows one to focus on analysis of the problem scenario rather than the repetitive computations; and (4) the Excel spreadsheet and project report are great for my college portfolio. We have found that the project is useful because our students must think critically in order to deliver rationales to each of their plans. REFERENCES [1] Heizer, J. and Render, B., Operations Management, 10th edition. Boston, MA: Prentice Hall, [2] Holt, C.C., Modigliani, F., Muth, J.F., and Simon, H.A., Planning Production, Inventories, and Workforce. Englewood Cliffs, NJ: Prentice Hall, [3] Nahmias, S., Production and Operations Analysis, 6th edition. New York, NY: McGraw-Hill, [4] Stevenson, W.J., Operations Management, 11th edition. New York, NY: McGraw-Hill, 2012.

### ISSN: 2163-9280 Spring, 2015 Volume 14, Number 1

TEACHING AGGREGATE PLANNING IN OPERATIONS MANAGEMENT Johnny C. Ho, Turner College of Business, Columbus State University, Columbus, GA 31907 Francisco J. López, School of Business, Middle Georgia State

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