How can you align your forecast error decomposition with your strategic goals and KPIs?

Strategic Goals: Start by clearly defining your organization's strategic goals. Are you aiming to increase market share, improve profitability, reduce inventory costs, or enhance customer satisfaction? Forecast Objectives: Translate these strategic goals into specific forecast objectives. For example, if your goal is to reduce inventory costs, your forecast objective might be to minimize forecast errors for high-value items. KPIs: Identify the KPIs that will measure the success of your forecast objectives. These could include metrics like Mean Absolute Percentage Error (MAPE), bias, Mean Squared Error (MSE), or specific business-related KPIs like inventory turnover or lost sales.

Choose Metrics that Directly Impact KPIs: Select error metrics that have a clear and measurable impact on your chosen KPIs. Mean Absolute Percentage Error (MAPE): If your goal is to reduce inventory costs, MAPE is a useful metric as it directly reflects the percentage deviation from actual demand, which can translate into excess inventory Bias: To improve customer satisfaction by ensuring product availability, bias is important as it indicates whether you are consistently over- or under-forecasting demand. Mean Squared Error (MSE) or Root Mean Squared Error (RMSE): If your goal is to optimize production planning, MSE or RMSE can be helpful as they penalize larger errors more heavily, which could lead to costly overproduction.

Choose Your Decomposition Method: Mean Absolute Deviation (MAD): A simple method that measures the average absolute difference between actual and forecasted values. Mean Absolute Percentage Error (MAPE): A relative error measure that expresses the average absolute percentage difference between actual and forecasted values. Bias: Measures systematic over- or under-prediction of the forecast. Variance: Measures the random fluctuations in demand that are not captured by the forecast. Once you have chosen your decomposition method, calculate the relevant error components for each forecast period. For example, if you are using the bias-variance decomposition, you would calculate the bias and variance components of the error for each period.

Time Series Plots: Plot each error component (bias, variance, etc.) over time to identify trends, patterns, and outliers. Scatter Plots: Plot error components against relevant variables (e.g., product category, sales channel, region) to identify relationships and potential drivers of error. Histograms: Use histograms to visualize the distribution of error components, revealing if they are normally distributed or skewed. Quantify the Impact: Estimate the impact of each error component on your KPIs. For example, calculate how much excess inventory or lost sales are caused by bias or variance. Prioritize Improvement Efforts: Focus your resources on addressing the error components that have the most significant impact on your KPIs.

Match Method to Error Component: Based on your error analysis, consider switching to a different forecasting method that is better suited to address the dominant error component. For example: High Bias: If you have a high bias, consider using a causal model that explicitly incorporates the factors driving demand, or a judgmental method that allows for expert input. High Variance: If you have high variance, consider using a smoothing technique like exponential smoothing or a machine learning model that can capture complex patterns in the data. Hybrid Approach: You can also combine multiple forecasting methods into a hybrid model to leverage the strengths of each method and mitigate their weaknesses.