Demand forecasting

Forecasting demand based on expert opinion. Some of the types in this method are,

• Unaided judgment
• Prediction market
• Delphi technique
• Game theory
• Judgmental bootstrapping
• Simulated interaction
• Intentions and expectations survey
• jury of executive method

• Discrete event simulation
• Extrapolation
• Group method of data handling (GMDH)
• Reference class≈ forecasting
• Quantitative analogies
• Rule-based forecasting
• Neural networks
• Data mining
• Conjoint analysis
• Causal models
• Segmentation
• Exponential smoothing models
• Box–Jenkins models
• Hybrid models

Forecast accuracy in the supply chain is typically measured using the Mean Absolute Percent Error or MAPE. Statistically MAPE is defined as the average of percentage errors.

Most practitioners, however, define and use the MAPE as the Mean Absolute Deviation divided by Average Sales, which is just a volume weighted MAPE, also referred to as the MAD/Mean ratio. This is the same as dividing the sum of the absolute deviations by the total sales of all products. This calculation ${\displaystyle {\frac {\sum {|A-F|}}{\sum {A}}}}$, where A is the actual value and F the forecast, is also known as WAPE, Weighted Absolute Percent Error.

Another interesting option is the weighted ${\displaystyle {\text{MAPE}}={\frac {\sum (w\cdot |A-F|)}{\sum (w\cdot A)}}}$. The advantage of this measure is that could weight errors, so you can define how to weight for your relevant business, ex gross profit or ABC. The only problem is that for seasonal products you will create an undefined result when sales = 0 and that is not symmetrical, that means that you can be much more inaccurate if sales are higher than if they are lower than the forecast. So sMAPE is also used to correct this, it is known as symmetric Mean Absolute Percentage Error.

Last but not least, for intermittent demand patterns none of the above are really useful. So you can consider MASE (Mean Absolute Scaled Error) as a good KPI to use in those situations, the problem is that is not as intuitive as the ones mentioned before.[4] Another metric to consider, especially when there are intermittent or lumpy demand patterns at hand, is SPEC (Stock-keeping-oriented Prediction Error Costs).[5] The idea behind this metric is to compare predicted demand and actual demand by computing theoretical incurred costs over the forecast horizon. It assumes, that predicted demand higher than actual demand results in stock-keeping costs, whereas predicted demand lower than actual demand results in opportunity costs. SPEC takes into account temporal shifts (prediction before or after actual demand) or cost-related aspects and allows comparisons between demand forecasts based on business aspects as well.

The forecast error needs to be calculated using actual sales as a base. There are several forms of forecast error calculation methods used, namely Mean Percent Error, Root Mean Squared Error, Tracking Signal and Forecast Bias.