“It is very hard to predict, especially the future’ – Neils Bohr
Every industry uses forecasting while making decisions.
Accurate forecast helps the various functional units of a business move in lock step with each other. Accurate forecasts are critical in increasing the efficiency of resource utilization by minimizing lost opportunities wastage of resources.
As the complexities of a business increases, the forecasting system needs to be able to account for those complexities.
Today’s forecasting systems need to be able to factor in not only changing consumer preferences, but also the interplay of channels.
Each application has its own specific forecast requirements and care must be taken to deliberate on these requirements before choosing the algorithms and techniques.
Some common questions which can help
you determine the variables and techniques are:
- What is the purpose of the forecast?
- How frequently do you need to forecast?
- What is the level at which you need to forecast?
- How far out should you forecast?
- What are the key drivers of the business variable you are trying to forecast?
What value will one more tool add if I already have data?
|Key considerations||Common Retail Application|
|Buy/Assortment Decision||Allocation||Promotion (Direct Mail)|
|Optimal level||Choice countXStore||SKUXStoreXWeek||SKUXChainXWeek (Assuming chain level promotions)|
|Why is this optimal?||While financial plans (MFPs) are made at a department-chain level, actual buy orders are at a Choice count level.||If the customer’s first choice style or brand is not available, they may buy something else. But if they are not happy with that purchase, there is a risk of losing the customer or that part of their basket forever||Even if promotions are planned at a “promoted product group” level, it is important that the forecast be available at an item level for supply chain to be able to respond correctly. Promotions are typically the same across the chain, so a chain level forecast is appropriate for the business problem. It will also be more accurate than a store level forecast|
|Typical lead time||6-9 months/depending on supply chain agility||Weekly/depending on supply chain agility||3/4 months out/depending on marketing collateral deadlines|
|Why is this typical?||Buying for the next year is typically done 9-12 months before the start of the season. If your buying process is different and requires more/less lead time, ensure that the model choices reflect that||Replenishment from the warehouse to stores is done every week. If your replenishment frequency is higher/lower, ensure that the model choices reflect that.||Typically marketing print formats are frozen 3-4 months before the beginning of big promotional events. If your promotional process is different, ensure that the model choices reflect that.|
|Key Drivers||Attributes, Size profile||Seasonality, lost sales||Promo Price Elasticity, Seasonality|
|Why are these drivers important?||Attributes, Size profile||An understanding of seasonality helps you smoothen out the effect of peaks and troughs in demand. Understanding lost sales is helpful in correcting mistakes in the current decision-making process and errors of one time period are not perpetuated to the next period||Understanding of promo price elasticity and seasonality helps you stay accurate in forecast even when the offer changes from LY. Otherwise you are either left with inaccurate forecasts or same offers as last year.|
|While plans are made at a department level – actual buy orders are at a SKU level||Incorporating lost sales without over/understating it||Same offers are repeated every year; Estimating elasticity of new products|
Factoring in the impact of COVID-19
Retail forecasting was always broken. COVID-19 has just made it worse.
To help you steer around this uncertainty we leverage frequently updated public datasets
Google Mobility Report
Apart from accounting for external factors, some internal factors also need to be considered:
- How is the chain level footfall trending?
- How is overall category level trend?
- How stable is the supply chain?
The final forecast is generated after baking in all the internal and external factors relevant to the business and application.