Introduction to Hierarchical Production Planning and (Demand) Forecasting

1
Introduction toHierarchical Production Planning
and (Demand) Forecasting
2
The role of hierarchical production planning in
modern corporations(borrowed from Heizer and
Render)
3
Production Planning through Time-based
Decomposition
Corporate Strategy
Aggregate Planning
Aggregate Unit Demand
(Plan. Hor. ½-2 years, Time Unit 1 month)
Capacity and Aggregate Production Plans
Master Production Scheduling
End Item (SKU) Demand
(Plan. Hor. a few months, Time Unit 1 week)
SKU-level Production Plans
Materials Requirement Planning
Manufacturing and Procurement lead times
(Plan. Hor. a few months, Time Unit 1 week)
Component Production lots and due dates
Shop floor-level Production Control
Part process plans
(Plan. Hor. a day or a shift, Time Unit
real-time)
4
Forecasting

• Def The process of predicting the values of a
  certain quantity, Q, over a certain time horizon,
  T, based on past trends and/or a number of
  relevant factors.
• In the context of OM, the most typically
  forecasted quantity is future demand(s), but the
  need of forecasting arises also with respect to
  other issues, like
• equipment and employee availability
• technological forecasts
• economic forecasts (e.g., inflation rates,
  exchange rates, housing starts, etc.)
• The time horizon depends on
• the nature of the forecasted quantity
• the intended use of the forecast

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Forecasting future demand

• Product/Service demand The pattern of order
  arrivals and order quantities evolving over time.
• Demand forecasting is based on
• extrapolating to the future past trends observed
  in the company sales
• understanding the impact of various factors on
  the company future sales
• market data
• strategic plans of the company
• technology trends
• social/economic/political factors
• environmental factors
• etc
• Rem The longer the forecasting horizon, the more
  crucial the impact of the factors listed above.

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Demand Patterns

• The observed demand is the cumulative result of
• some systematic variation, resulting from the
  (previously) identified factors, and
• a random component, incorporating all the
  remaining unaccounted effects.
• (Demand) forecasting tries to
• identify and characterize the expected systematic
  variation, as a set of trends
• seasonal cyclical patterns related to the
  calendar (e.g., holidays, weather)
• cyclical patterns related to changes of the
  market size, due to, e.g., economics and politics
• business patterns related to changes in the
  company market share, due to e.g., marketing
  activity and competition
• product life cycle patterns reflecting changes
  to the product life
• characterize the variability in the demand
  randomness

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Forecasting Methods

• Qualitative (Subjective) Incorporate factors
  like the forecasters intuition, emotions,
  personal experience, and value system these
  methods include
• Jury of executive opinion
• Sales force composites
• Delphi method
• Consumer market surveys
• Quantitative (Objective) Employ one or more
  mathematical models that rely on historical data
  and/or causal/indicator variables to forecast
  demand major methods include
• time series methods F(t1) f (D(t),
  D(t-1), )
• causal models F(t1) f(X1(t), X2(t), )

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Selecting a Forecasting Method

• It should be based on the following
  considerations
• Forecasting horizon (validity of extrapolating
  past data)
• Availability and quality of data
• Lead Times (time pressures)
• Cost of forecasting (understanding the value of
  forecasting accuracy)
• Forecasting flexibility (amenability of the model
  to revision quite often, a trade-off between
  filtering out noise and the ability of the model
  to respond to abrupt and/or drastic changes)

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Applying a Quantitative Forecasting Method
- Determine functional form - Estimate
parameters - Validate
Update Model Parameters
Yes
No