## Demand Planning vs Demand Forecasting

Demand Planning is arguably the most important part of Supply Chain Management. It is the first step in the SCM process. Only after the demand planning process is done, people from production can start working on their operations and plan for raw materials, and logistics can focus on details of transportation planning and various warehouse activities.

**demand planning ≠ demand forecasting**. Demand planning is the process of forecasting demand for a product or service

__and__executing an operational strategy across the supply chain in order to meet it. Demand forecasting refers only to the process of predicting demand/sales numbers, and as such is a part of the demand planning process.

## About Demand Forecasting KPIs

You can only improve what we can measure. Demand planners are always on the lookout for THE metric that will help them improve **forecast accuracy **and solve one of the most persistent problems for any supply chain company – increasing the visibility across the supply chain. There are numerous metrics in use in this context, but this post will focus only on demand forecast accuracy KPIs that people most commonly use to **track and improve forecast accuracy**:

*Forecast Accuracy/Error**Bias (Mean Forecast Error)**Mean Absolute Percentage Error (MAPE)**Weighted Mean Absolute Percentage Error (WMAPE)*

*choosing the right forecasting method/model*. There is no “

*one size fits all KPI”*. In other words, you have to be able to make quality assumptions about your business by using the perfect mix of qualitative and quantitative demand forecasting methods. More about those in another post.

### Forecast Error

The most straightforward of all demand forecast accuracy KPIs. It provides insight into the difference between the actual value (Dt) and the forecast value (Ft) for the given period.

How to calculate:

Forecast Error: 1 – [ABS (Dt – Ft) / Dt]

*The actual observation or sales for period t*

*The forecast for period t*

Forecast error numbers range from 0 – 100%.

### Bias (Mean Forecast Error)

Bias is a simple demand forecasting KPI providing the information about the tendency of forecasts to persist in one direction – over or under-forecasting. It is a fairly reliable way to check if your forecasting model works the way it is supposed to. In theory, when the bias is zero, forecasts are not biased. If the bias is greater than 4, for the period of 24 observations, it is safe to say that your forecasting model is on the side of under-forecasting. Naturally, when the bias is less than -4, the model is biased toward over-forecasting.

How to calculate:

**Bias: [∑ (Dt – Ft)] / n**

*The actual observation or sales for period t*

*The forecast for period t*

*The number of forecast errors*

### Mean Absolute Percentage Error (MAPE)

Another great demand forecasting accuracy KPI. You will probably use MAPE most of the time. And for a good reason: it is simple, everyone understands it, and there are only a few accuracy tradeoffs when using it.

How to calculate:

**MAPE: ∑ |Et / Dt |/n * 100**

*Actual observation or sales for period t*

*the forecast error for period t*

*the number of forecast errors*

As you can see, when the sale numbers are lower, MAPE can get deceivingly high. To avoid this, the reccomendation is to to use WMAPE.

__MASE__, or simply ignore these periods, which is my preferred choice.

### Weighted Mean Absolute Percentage Error (WMAPE)

This demand forecasting accuracy KPI – WMAPE values weights more than MAPE does. What this means is that if demand planners know that some products and moments in time are more important to predict properly (for example Monday in the example below), they can attribute them more weight when calculating the error.

How to calculate:

**WMAPE: ∑(|Dt-Ft|) / ∑(Dt)**

*The actual observation for period t*

*the forecast for period t*

In this case, we decided that Monday carries 80% of the importance. Consequently, this made more sense, and made our weighted error more precise – it went from 36.7% down to 9.1%.

## Conclusion

There is no perfect measure to solve every forecasting problem. Choosing the right one depends on your specific situation and use case. Your data will never be perfect, and some information will always be missing, but using the combination of these demand forecasting KPIs and deep understanding of your business processes should help you increase your forecast accuracy. When in doubt, it is best to stick with MAPE, though.

Solving Demand Forecasting and Supply Chain problems is easier with an integrated supply chain planning, forecasting, and reporting software tool that helps automate your forecasts and build complex models in the blink of an eye.