Aggregate planning is arguably the supply chain area in which information technology has been used the most. The earliest IT supply chain products were aggregate planning modules, often called factory, production, or manufacturing planning. Some of the early modules focused only on obtaining a feasible production plan subject to constraints arising from demand and available capacity. Later modules provided tools that chose an optimal solution from among the feasible production plans, based on objectives such as increased output or lowered cost.
These classic solutions generally formulated the aggregate planning problem as a linear program to get a production schedule of products to be made in each period of time. Today, some planning modules incorporate nonlinear optimization to account for the fact that not all constraints or reasonable objective functions are linear functions. However, given the large amount of data considered in producing aggregate plans—which can render nonlinear problems computationally prohibitive—and the ability to create linear approximations of nonlinear functions, linear programming is often the best way to solve these problems.
Many software vendors (including SAP and Oracle) offer advanced planning systems (APSs) to help firms create aggregate plans. The biggest challenge when using APSs is that the results can be fairly unstable relative to inputs. A small change in an input such as demand can produce a new optimal plan that is quite different from the original plan. If plans become too volatile, the entire supply chain quickly begins to distrust them, effectively rendering them useless. It is thus important to ensure that as new data arrive, plans are modified while trying to ensure some degree of stability.
Data accuracy is critical if APSs are to deliver their full potential. If the APS is using lead times or capacities that are different from reality, the resulting aggregate plan is likely to lead to unhappy customers and high costs. It is thus important to track the accuracy of these parameters and ensure that people are held accountable for these inputs.
Source: Chopra Sunil, Meindl Peter (2014), Supply Chain Management: Strategy, Planning, and Operation, Pearson; 6th edition.
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