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Planning involves the management of demand and capacity over a longer horizon than for scheduling. The horizon can vary from months to years.
Demand is generally expressed in the form of a combination of forecast and
actual (or "fixed") orders. The forecast can even take the form
of "feature mixes" or "equipment mixes". For example:
- SUV V6 to V8 engines will be in the proportion 80:20.s.
- 25% of Cars require Moon roofs.
In such cases the feature mixes have to be converted into forecast orders before
planning can occur. In a configured product environment, like cars, the generated
forecast orders have to be checked for validity of configuration, by applying
configuration rules like:
- Snow tires and Camper Package are an invalid combination.
- Moon roof units must have Navigation systems.
Optessa has experience in developing systems for forecast order generation for the auto industry.
An optimal plan must consider critical capacity constraints due to material, plant and labor (MPL)
- Production capacities for product lines
- In-house production capacities for critical commodities or components
- External supplier or outsourced capacities for commodities or components
An optimal plan must also consider sales and marketing constraints, such as:
- Regional allocations
- Dealer / distributor allocation and draft rules
- Dealer / distributor priorities assigned to certain types of orders
A wide variety of planning problems exist. Some of the typical planning problems in manufacturing industries are:
Given a set of actual and forecast orders, the planning system has to select a subset of
orders to be assigned to the next few days / weeks / months, while satisfying the MPL and
marketing constraints. An optimal plan would satisfy the constraints and select the highest
possible proportion of actual or high priority orders.
In a multi-plant environment, assign demand to individual plants / lines and daily / weekly /
monthly bins. An optimal plan, again, must satisfy MPL and marketing constraints while ensuring
that high priority / actual orders are planned to be delivered on time.
Effective forecast order generation would combine the forecasted feature mixes with MPL and
marketing constraints, so that demand forecasting rules and MPL and marketing constraints are
concurrently satisfied.
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