04_Demand Forecasting Flashcards
purpose of contents of operational planning level
-
- smoothing of seasonal variations of capacity requirements and available capacity
-
determine master production schedule (which products in which quantities)
- procurement planning: which quantities of input materials are needed for master scheduling
chase demand vs level production strategy
pros and cons
**Chase demand: **
- low inventories, but high capacities
Level Production:
- high inventories, low capacities
Demand forecasting
Definion and Types of time series
- prediction of future values of a time series (demand patterns) under uncertainty (based on historical values)
Types of time series: - Level Demand
- Linear Trend
- Seasonal Variation
Analysis of the characteristics of a time series
- long term
- mid-term cyclical variations (eg economic cycles, product lifecycle)
- seasonal fluctuations (eg. annual seasons)
- random variations
Procedure of time series based forecasting
5 Steps
- Analysis of characteristics of time series
- selecting appropriate forecast model
- estimate coefficients of forecast model
- apply forecast model
- monitor and analyse forecast accuracy over time (tracing errors)
Ex ante forecast values
vs
Ex post forecast values
- ex ante: forecast for following period
- ex post: previous forecast
Forecasting Models
- Simple exponential smoothing (level demand time series)
- Exponential smoothing with trend adjustment (linear trend time series)
Simple Exponential Smoothing
forecasting model for level demand time series
- random variations around a constant demand level
- initialization Po has to be determined (often yo is chosen)
- smoothing paramter alpha: determines the strenght of the smoothing effect
- the higher alpha the closer you get to observed demand (naive forecast)
Formula Simple Exponential Smoothing
Pt+1 = alpha x yt + (1-alpha) x Pt
(weighting of previously observed demand and previous forecast adjusted for errors)
yt = observed demand in period t ; Pt = forecast period t
Exponential Smoothing with trend adjustment
forecasting model for trend line time series
- assumed demand process: random variations around linear trend
- simple exponential smoothing causes systematic error
- introdruce trend adjustment (correction factor) to adjust for systematic lag
- Initialization: before first forecast determination adequate initial values a0 and b0 have to be determined, e.g. through linear regressen
Exponential Smoothing with trend adjustment
Step by Step
- update of level demand at end of period t (at)
- update of trend at end of period t (bt)
- Forecast determination