Our partners at Enhanced Retail Solutions have published a step-by-step overview for integrating weather-driven demand analytics into retail plans and demand forecasts.
“Estimating future sales is increasingly difficult with so many factors to be taken into consideration. The economy, inconsistencies in stock levels, changes in assortments and consumer shopping patterns to name just a few. Most inventory planners rely on historic sales to create their forecasts. However, that history may need to be adjusted to reflect a more realistic basis. While many forecasts are adjusted for seasonality, more robust adjustments can be made by integrating weather related demand. For product categories that are affected by weather- including temperature or precipitation- fine tuning a forecast based on this feature engineered demand signal significantly improves the predictability of future sales. ERS and Planalytics have partnered together to help companies improve their forecasting accuracy.
Visit the ERS website to read about the following step-by-step process that enables businesses to optimize forecasts by accounting for the weather’s sales impacts, driving improved planning accuracy and increases in sales and profitability.
Step 1: Data Requirements and Technology Platform
Step 2: Collecting the Data
Step 3: Understanding the Basic Logic
Step 4: Adjusting for Lost Sales
Step 5: Fine Tuning with Weather-Driven Demand Analytics
Step 6: Putting the Information Together
“Improving forecast accuracy can have a significant impact on both sales and profit. And because your inventory utilization is better, the cost of capital goes down. If you are currently forecasting in a spreadsheet, you could save a significant amount of time by automating. One of the benefits of the BI tools is seeing exceptions- items with low or high inventory, changes in trends or buys that are needed right away, and accounting for demand fluctuations due to the weather. It serves as an alert system which helps you quickly react to opportunities and address risks.”