1. Introduction to Hierarchical 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 Unit
Demand
Aggregate Planning
(Plan. Hor.: ½-2 years, Time Unit: 1 month)
Capacity and Aggregate Production Plans
End Item (SKU)
Demand
Master Production Scheduling
(Plan. Hor.: a few months, Time Unit: 1 week)
SKU-level Production Plans
Manufacturing
and Procurement
lead times
Materials Requirement Planning
(Plan. Hor.: a few months, Time Unit: 1 week)
Component Production lots and due dates
Part process
plans
Shop floor-level Production Control
(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

5. 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.

6. 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

7. Forecasting Methods
Qualitative (Subjective): Incorporate factors like the forecaster’s 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(t+1) = f (D(t), D(t-1), …)
causal models: F(t+1) = f(X1(t), X2(t), …)

8. 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)

9. Applying a Quantitative Forecasting Method
Determine Method
Time Series
Causal Model
Collect data:
<Ind.Vars; Obs. Dem.>
Fit an analytical model
to the data:
F(t+1) = f(X1, X2,…)
Use the model for
forecasting future
demand
Monitor error:
e(t+1) = D(t+1)-F(t+1)
- Determine
functional form
- Estimate parameters
- Validate
Update Model
Parameters
Yes
Model
Valid? No