The entire thrust of aggregate planning and scheduling methods is to employ systems concepts in making key decisions for operations. The results of coordinating decisions about activity levels, work force size, the use of overtime, and inventory levels amply illustrate that these kinds of decisions should be made jointly rather than independently. To make them independently is to sub-optimize. Instead of stopping with the Operations function, better results can be obtained by taking some key operational decisions along with key decisions in marketing and finance.
Management experts have proposed some suggestions like merging marketing strategy selection and production scheduling, price as an independent variable coupled with allocations of compensatory promotion budgets, estimating revenue versus sales curves for each product in each time period with the amount to be sold considered as a decision variable dependent upon price and possibly other parameters. Finally joint decisions in production, marketing, and finance emerged as the most acceptable one. In this model, marketing sector decisions are made with respect to price and promotion expenditures, and finance sector decisions are made with respect to investment in marketable securities and short-term debt incurred or retired. The solution techniques used was a computer search methodology.
The general nature of aggregate planning and scheduling problems in non-manufacturing settings is basically similar to that of manufacturing settings in that an attempt is made to build a cost or profit model in terms of the key decision variables for short term capacity. The degrees of freedom in adjusting short term capacity in non-manufacturing settings are likely to be fewer, because of the absence of inventories and subcontractors as sources of capacity. The result is that the manager is more likely to attempt to control demand through techniques like reservations or to absorb fluctuations in demand rather directly by varying work force size, hours worked, and overtime.
We will not attempt any detailed discussion of aggregate planning in non-manufacturing systems here because separate chapters deal with operations planning and control in large-scale projects and in service systems.
In many realistic situations, there is considerable uncertainty in demand estimates. Usually, the uncertainty is greater for time periods that are further into the future. If actual demand does not turn out to be close to what was expected when the aggregate production plan was formulated, excess inventories or stock-outs could occur. To see this, consider a simple situation where a company is following a level production plan and accumulating inventory to meet the higher seasonal demand. Three scenarios are possible: demand during high season periods turns out to be the same as expected, higher than expected, or lower than expected. In the first scenario, the company will continue to produce as planned. In the second scenario, the company may have to increase its capacity through overtime, subcontracting, or the like, to meet the higher demand. Since the company is following a level production plan, it should have some inventory already accumulated before the high demand season begins and moderate increases in capacity may be sufficient to meet the additional unexpected demand. In the third scenario, however, the company may get stuck with excess inventories if a considerable stock was accumulated in low season to meet a high season demand that did not materialize. This scenario could be especially costly for companies that introduce new products or models frequently and may face a high obsolescence cost.
Now, consider the case of a company that is following a chase production plan in which production rate is kept low in the off-season and is increased just before the high demand season begins. A chase plan is one that follows demand, and is characterized as â€œchasingâ€ demand. Again, three scenarios are possible: demand during high seasons turns out to be as expected, higher than expected, or lower then expected. This company could deal with the first and third scenarios respectively, by keeping the production rate as planned or by decreasing it. In the second scenario however, the company may face a stock out situation. This is because in the chase plan the company is probably using close to its maximum capacity during the high season. Thus, there may not be enough flexibility to increase the capacity further to meet the unexpected higher demand. A firm that has several competitors may be vulnerable in this situation as customers may not wait and sales may be lost.