Integral Logistics Management — Operations Management and Supply Chain Management Within and Across Companies

10.5.5 Translation of Forecast into Quasi-Deterministic Demand and Administration of the Production or Purchase Schedule

Intended learning outcomes: Identify safety demand. Explain independent demand as total demand including safety demand, taken as a function of the planning period to be covered.

The (stochastic) independent demand to be considered for further planning steps results as the total demand from adding the expected value to the safety demand for the planning period to be covered.

The safety demand is the product of the safety factor and the standard deviation during the planning period to be covered.

Figure shows the total demand to be considered as a function of the planning period to be covered. For products manufactured in-house, this total demand belongs to the production schedule. For purchased items, the independent demand belongs to the purchase schedule for salable products.

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Fig.      Independent demand as total demand, taken as a function of the planning period to be covered.

If the total demand is subdivided into various partial demands later (for example, the annual demand into 12 monthly demands), a larger share of the safety demand needs to be included in the earlier partial demand. The order point technique discussed in Section 11.3 adds the safety demand de facto to the first partial demand.

Note: As presented in connection with Figure, the first step in determining high-cost dependent, but discontinuous or unique demand for an item is to stochastically determine the independent demand belonging to it. After this, the dependent demand is calculated using quasi-determin­istic bill-of-materials explosion. In this way, the dependent demand contains the safety demand needed to produce the safety demand for the independent demand.

For administrating independent demand, an order-like object class fore­cast demand or independent demand is used, with at least the attributes

  • Forecast or independent demand ID (similar to an order ID)
  • Item ID or item family ID
  • Planning date for the demand or its periodicity
  • Forecast quantity (an item issue)
  • Quantity of the forecast already “consumed” by orders (see Section 12.2.2)

A negative forecast demand is also conceivable. It would express receipt of an item, and serves, e.g., as a substitute for a purchase system that is lacking, or to eliminate an over­lap effect on lower structure levels from higher structure levels (see, for example, Section 7.2.1).

There are a number of ways to change or delete a forecast demand:

  • By manual administration.
  • By periodic recalculation, e.g., according to the principle con­­tained in Figure This is particularly important for demand serving as input to subsequent stochastic materials management.
  • With independent demand in the true sense: by successive reduc­tion due to actual demand (e.g., customer orders). If the actual demand reaches the forecast, or if the forecast lapses into the past and is no longer to be considered, the corresponding forecast demand object is automatically deleted. See also Section 12.2.2.

Course section 10.5: Subsections and their intended learning outcomes

  • 10.5 Using Forecasts in Planning

    Intended learning outcomes: Produce an overview on the choice of suitable forecasting technique. Describe consumption distributions and their limits, continuous and discontinuous demand. Explain demand forecasting of variants of a product family. Present safety demand calculation for various planning periods. Disclose the translation of forecast into quasi-deterministic demand.

  • 10.5.1 Comparison of Techniques and Choice of Suitable Forecasting Technique

    Intended learning outcomes: Differentiate between various areas of applicability of forecasting techniques.

  • 10.5.2 Consumption Distributions and Their Limits, Continuous Demand and Discontinuous Demand

    Intended learning outcomes: Identify variables for a consumption distribution. Present expected value and variance of the consumption distribution. Explain distribution function, expected value, and variance of the consumption distribution under the assumption of a Poisson distribution for the frequency of events. Describe the demand filter to handle a discontinuous demand due to infrequent large issues. Disclose effects of length of statistical period on demand fluctuations.

  • 10.5.3 Demand Forecasting of Variants of a Product Family

    Intended learning outcomes: Describe the option percentage. Explain the formulas for forecasting demand for variants.

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