How can you evaluate the accuracy of demand forecasts for manufacturing?

1 What is demand forecast accuracy?

Demand forecast accuracy is the measure of how close your forecasted demand is to the actual demand that occurs in a given period. It is usually expressed as a percentage or a ratio, and it can be calculated at different levels of aggregation, such as product, category, or market. Demand forecast accuracy is important for manufacturing because it affects the performance of your production system, your inventory management, your customer service, and your profitability.

2 How to calculate demand forecast accuracy?

When calculating demand forecast accuracy, there are various methods and metrics to choose from depending on the data and objectives. Mean Absolute Percentage Error (MAPE) is a common metric that averages the absolute values of the percentage errors between the forecasted and actual demand. It is easy to interpret and compare, but can be misled by outliers and zero values. Mean Absolute Deviation (MAD) is another metric that averages the absolute values of the deviations between the forecasted and actual demand. It is simple and robust, but does not account for the scale of the demand. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are two other metrics that average the squared values of the deviations between the forecasted and actual demand. They penalize large errors more than small errors, but they are sensitive to outliers and hard to interpret.

3 How to benchmark demand forecast accuracy?

To evaluate your demand forecast accuracy, you need to have a benchmark or a target to compare it with. This can be based on historical data, such as tracking your accuracy over different time periods such as seasons, quarters, or years. Additionally, you can use industry benchmarks or best practices to compare your accuracy with your competitors or peers. You can also use your business goals or objectives to set your desired level of demand forecast accuracy. For example, you can align your accuracy with customer service level, inventory turnover, or profit margin.

4 How to improve demand forecast accuracy?

Improving your demand forecast accuracy can be done in various ways, depending on your situation and challenges. To begin, it's beneficial to use multiple sources of data, like sales, customer feedback, and market research. Additionally, selecting the right methods and models that fit your data and goals is essential. Trend analysis, regression, or machine learning can all be used to enhance forecast accuracy. Finally, feedback and adjustment mechanisms should be used to monitor accuracy and make corrections or revisions when needed. Forecast error analysis can also help identify sources and causes of forecast errors so that you can take action to reduce them.

5 How to communicate demand forecast accuracy?

Communicating your demand forecast accuracy effectively requires consideration of your audience and purpose. To do this, it's best to use clear and consistent metrics and definitions that your audience understands. Visual and interactive tools, such as charts, graphs, dashboards, or simulations can also be used to present your demand forecast accuracy and its impact. Additionally, context and explanation should be included to put the accuracy in perspective and explain its meaning and significance. Comparison and contrast, such as benchmarks or scenarios, can further highlight strengths and weaknesses or suggest improvements or actions.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?