A forecast is an estimate of future demand based on market indicators and/or past performance plus market indicators.   We forecast to reduce the amount of uncertainty in order to prepare for demand.

Who is impacted by a forecast?

Finance

Investment capital

AP forecasting

Operational budgets

Inventory planning – for make to or order to stock environments.

Capacity planning – to ensure forecast does not exceed the ability of the company.

Procurement – to obtain the raw materials or products to support the forecasted sales. The finished goods forecast is broken down further into the materials planning requirements through the bills of materials.

Operations

Investors

Issues you need to consider

Data

Quantitative

Seasonality

Sales history

Record “actual” demand not sales history.

Qualitative

Expert opinion

Customer groups

Market estimates

Supply availability

Supplier or production issues.

Capacity

Available Capital

Current market conditions

Frequency of the forecast

Your planning horizon

Your longest lead time item will determine your planning horizon.

Aggregate forecasts are more accurate.

Determine detail through product families and planning bills.  Below is an example of a product family planning bill:

Planning bills use individual item and family demand history to breakdown an aggregate forecast into individual item requirements.

Measure your forecast performance.

 

 

 

Rate of forecast consumption.

Divide your forecast into smaller periods to track the consumption.  As an example, divide a weekly forecast into daily checks.  If the forecast was 100 for the week you should sell 20 per day if your demand is flat.  Take into consideration any increases or decreases based on your historical demand or known forecast indicators.

If possible adjust the forecast to match the new rate of consumption.

Compare forecast to actual demand.

Track forecast accuracy by period.

APE (Absolute Percentage of Error) – The absolute of ((actual demand – forecasted demand) / actual demand) x 100.

Does not consider the direction of the variance.  Used to calculate the absolute variance for a single period.

MAPE (Mean Absolute Percentage of Error) – The average of the APE across a range of periods.

MAPE (Mean Absolute Percentage of Error) – The average of the APE across a range of periods.

Does not consider the direction of the error.  Used to calculate the absolute variance across a range of periods. Below is an example of the APE and MAPE.

MAD (Mean Absolute Deviation) – The absolute of (actual demand – forecasted demand) / # of periods.

Does not consider the direction of the error.  Used to calculate the absolute variance across a range of periods.

Also is approximately the standard deviation / 1.25.

Standard Deviation – The square root of the absolute of the actual demand – the forecasted demand squared / the number of periods.

Does not consider the direction of the error.  Used to calculate the absolute variance across a range of periods.

Also is approximately the MAD x 1.25.  Below is an example of the standard deviation equation:

Bias – Forecast bias is a consistent variation from the average (mean) in one direction.  The goal is a bias of zero.  This demonstrates that any variance is due to normal fluctuation and is not attributable to the method or data.