04_Demand Forecasting Flashcards

1
Q

purpose of contents of operational planning level

-

A
  • 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
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2
Q

chase demand vs level production strategy

pros and cons

A

**Chase demand: **
- low inventories, but high capacities
Level Production:
- high inventories, low capacities

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3
Q

Demand forecasting

Definion and Types of time series

A
  • 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
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4
Q

Analysis of the characteristics of a time series

A
  • long term
  • mid-term cyclical variations (eg economic cycles, product lifecycle)
  • seasonal fluctuations (eg. annual seasons)
  • random variations
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5
Q

Procedure of time series based forecasting

5 Steps

A
  1. Analysis of characteristics of time series
  2. selecting appropriate forecast model
  3. estimate coefficients of forecast model
  4. apply forecast model
  5. monitor and analyse forecast accuracy over time (tracing errors)
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6
Q

Ex ante forecast values
vs
Ex post forecast values

A
  • ex ante: forecast for following period
  • ex post: previous forecast
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7
Q

Forecasting Models

A
  • Simple exponential smoothing (level demand time series)
  • Exponential smoothing with trend adjustment (linear trend time series)
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8
Q

Simple Exponential Smoothing

forecasting model for level demand time series

A
  • 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)
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9
Q

Formula Simple Exponential Smoothing

A

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

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10
Q

Exponential Smoothing with trend adjustment

forecasting model for trend line time series

A
  • 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
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11
Q

Exponential Smoothing with trend adjustment

Step by Step

A
  1. update of level demand at end of period t (at)
  2. update of trend at end of period t (bt)
  3. Forecast determination
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