Operations and Supply Chain Management The Core

74 OPERATIONS AND SUPPLY CHAIN MANAGEMENT
∙ Causal relationship forecasting is different from time series (but commonly used) since it
uses data other than past demand in making the forecast.
∙ The quality of a forecast is measured based on its error. Various measures exist, including
the average error, percentage of error, and bias. Bias occurs when a forecast is consistently
higher or lower than actual demand.
Time series analysis A type of forecast in which data relating to past demand are used to
predict future demand.
Moving average A forecast based on average past demand.
Weighted moving average A forecast made with past data where more recent data are given
more significance than older data.
Exponential smoothing A time series forecasting technique in which each increment of past
demand data is decreased by (1 − α).
Smoothing constant alpha (α) The parameter in the exponential smoothing equation that
controls the speed of reaction to differences between forecasts and actual demand.
Smoothing constant delta (δ) An additional parameter used in an exponential smoothing
equation that includes an adjustment for trend.
Linear regression forecasting A forecasting technique that assumes that past data and future
projections fall around a straight line.
Decomposition The process of identifying and separating time series data into fundamental
components such as trend and seasonality.
Forecast error The difference between actual demand and what was forecast.
Mean absolute deviation (MAD) The average forecast error using absolute values of the error
of each past forecast.
Mean absolute percent error (MAPE) The average error measured as a percentage of average
demand.
Tracking signal A measure of whether the forecast average is keeping pace with any genuine
upward or downward changes in demand. This is used to detect forecast bias.
Causal relationship forecasting Forecasting using independent variables other than time to
predict future demand.
Forecasting formulas
​A t−1 + ​A t−2 + ​A t−3​ + ⋯ + ​A t−n
F t = ​ ______________________ [3.1]
n
F t = ​w 1 A t−1 + ​w 2 A t−2 + ⋯ +​ w n A t−n [3.2]
F t = ​F t−1 + α(​A t−1 − ​F t−1 )​[3.3]
F t = ​FIT​ t−1 + α(​A t−1 − ​FIT​ t−1 )​[3.4]
T t = ​T t−1 + δ(​F t − ​FIT​ t−1 )​[3.5]
FIT​ t = ​F t + ​T t [3.6]
Y t = a + bt​[3.7]
​b = ​ Σty − N​ t ¯
__________ ​ ⋅ ​ y ¯ ​
Σ ​t 2 − N t ​ ¯ ​
2 ​ ​[3.8]
​a = ​ ¯ y ​ − b​ ¯ t ​[3.9]