Processes Using Aggregate Planning

A simple definition that I have heard for aggregate planning is two- to 12-month capacity plarming. There are three components of this type of planning. The first is the business plan, which determines the strategy for capacity and production. The second is the production plan, which creates the production capacity utilization. The third is the master schedule, which establishes the production schedule for the specific products. 

Ideally, the demand for a product will equal the capacity available. This is not the normal case, so production capacity needs to be adjusted to reflect the real demand. This process can be very easily distorted and complicated by very optimistic forecasts of product demand. Real demand data needs to be available wherever possible so that production capacity can be effectively used. 

There are many factors that need to be considered on both the capacity and the demand side of the process. From the demand side, backorders, pricing, and special sales need to be considered with their impact on capacity. From the capacity side, hiring part-time workers, overtime for existing employees, hiring new workers need to be considered with their impact on 

One of the basic problems that exists in business today is the issue of imeven demand As a result of this, processes need to be put in place that will minimize the costs involved in leveling the demand and effectively using the available capacity. This is the goal of aggregate planning. In the area of operations management, you will find models and simulations that can be run to help optimize this process. 

We have looked at mat r areas of management and developed organizational mission and vision statements that included supply chain behavior in them. The mission and vision statements are the overarching umbrella for the management of the business and should include the supply chain. 

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Since this work deals with the aggregated simulation and planning of chemical production processes, the focus is laid upon methods to determine estimations of the process models. For process control this task is the crucial one as the estimations accuracy determines the accuracy of the whole control process. The task to find an accurate process model is often called process identification. To describe the input-output behaviour of (continuously operated) chemical production plants finite impulse response (FIR) models are widely used. These models can be seen as regression models where the historical records of input/control measures determine the output measure. The term "finite" indicates that a finite number of historical records is used to predict the process outputs. Often, chemical processes show a significant time-dynamic behaviour which is typically reflected in auto-correlated and cross-correlated process measures. However, classic regression models do not incorporate auto-correlation explicitly which in turn leads to a loss in estimation efficiency or, even worse, biased estimates. Therefore, time series methods can be applied to incorporate auto-correlation effects. According to the classification shown in Table 2.1 four basic types of FIR models can be distinguished. 

This structured process should include a design phase, a data collection phase and a data analysis phase. Just to be aware and plan (design) how to use expert knowledge, is an advance compared to how small risk assessment often are done today. A simple mechanical aggregation, if relevant, without behavioral aggregation or weighting, will simplify the analysis phase. 

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