Demand Forecasting in Retail: Critical Mistakes to Avoid
Companies in the retail industry rely on demand forecasting to support inventory planning and distribution functions across their sales channel. Accurate demand forecasting requires companies to have a clear understanding of the right data and the repercussions of incorrect demand estimation. If done correctly demand forecasting techniques can help companies across the consumer goods spectrum [...]READ MORE >>
Companies in the retail industry rely on demand forecasting to support inventory planning and distribution functions across their sales channel. Accurate demand forecasting requires companies to have a clear understanding of the right data and the repercussions of incorrect demand estimation. If done correctly demand forecasting techniques can help companies across the consumer goods spectrum to optimize operations and increase value while reducing additional costs. However, the absence of an accurate demand forecasting strategy could result in a faulty business strategy. To avoid this, we uncover some common mistakes made by companies in the retail industry during demand forecasting.
Demand forecasting mistakes in the retail industry
Ignoring store-level demand
Sometimes retail companies tend to build demand forecasting models using a top-down approach in order to speed up and simplify the forecasting process. However, by not using an individual location-level unconstrained demand forecasting can result in significant under-prediction while estimating the actual demand. Furthermore, computing demand forecasts solely against prior sales don’t account for lost sales due to out-of-stocks, causing any forecasts for the future to be artificially depressed, and the cycle to continue.
Planning solely for distribution channels
Sometimes companies in the retail industry make the mistake of using demand planning solely to enhance their distribution channels and make it more efficient. Retailers will be able to churn out more from their investment if they rely on the data to optimize the production process as well. Demand planning, for instance, can be used to determine what manufacturers should be producing.
Overlooking historical patterns
Unless a company has restructured its organization recently, altered their product or service lines significantly, there should be historical data that reflects distinguished demand and sales patterns. These patterns are the best sources to use for demand planning, because while they don’t necessarily consider current changes or possible impacts to the supply chain, they give retailers a baseline against which they can build accurate numbers for demand forecasting.
Optimizing business processes too soon
This is especially true in the case of new businesses or a recently restructured organization. Accurate demand forecasting requires adequate time to gather relevant data. Businesses that tend to undertake demand forecasting based on a small dataset that ranges over a short period are more likely to end up with adverse results.