How to develop an Effective Scientific Retail Demand Forecast?
Purpose of the Forecast
The ability to effectively forecast demand is critical to the success of a retailer. In this hyper competitive environment of ever diminishing margins, every paisa saved or earned is critical. A robust demand forecast engine, can have significant impacts on enhancing both top & bottom lines.
In today’s world, the retailers require forecasts that would be instrumental in directing the organisation through a minefield of capacity constraints, multiple sales geographies and a multi-tier distribution channel.
Demand forecasting helps understand key questions viz. which market would place demands for which specific type of product, which manufacturing unit should cater to which retailer, how many product units are required in a given season etc.? Given the sophisticated tools & techniques available today, all retailers should replace gut based decision making, with scientific forecasts. The benefits, throughout the lifecycle of the analysis will far outweigh the one time set up and ongoing maintenance costs. There is a lot of value in answering these questions through scientific methodologies as compared to educated guesses, or judgmental forecasts.
Scientific forecasting generates demand forecasts which are more realistic, accurate and tailored to specific retail business area. It facilitates optimal decision-making at the headquarters, regional and local levels, leading to much lesser costs, higher revenues, better customer service and loyalty.
Range of Business Users
Traditionally, only the sales department has used forecasts, but in evolved markets the usage of forecasts is now pan organizational. Sales Revenue Forecasting, Marketing & Promotion Planning, Operations Planning, Inventory Management etc. also extensively use sales forecasts. Indian retail needs to imbibe this discipline as their scale of operations grows larger and they are unable to cope with the entrepreneurial style of functioning, which was the key to their success in the start up phase.
Typical Challenges Faced!
Though demand forecasting is an important aspect of a retail business, more often than not, it is laced with multiple challenges. Some of them could be:
Level/Scope of the Forecasts
A large retailer may have thousands of SKUs. A conscious decision has to be made regarding the product hierarchy level at which the forecasts are needed, as it is very challenging to produce forecasts for all existing SKUs, neither does it make sound financial sense in most cases. Other concern would be the number of stores a typical large retailer possesses, and whether a separate forecast is needed for each of the stores.
In order to optimise the cost-benefit, TEG recommends creation of forecasts at the “Store-Cluster” & “SKU-Cluster” levels. The store clusters are created using store characteristics, like past demand patterns and local/ regional demand factors. The SKU clusters are determined by the category type, life cycle etc.
New Product Sales/Demand Forecasts
A retailer typically launches new products every month/season. Using past data to forecast is not feasible, as past data does not exist. TEG, would tackle the situation by considering complementary products, based on their key characteristics like target segment, product category, price level, features etc. A rapidly emerging methodology is the estimation of future demand using Advanced Bayesian Forecasting Models (Fig. 3).
Bizarre/Missing Historic Sales Pattern
The erratic sales figures for many items in the store often pose a lot of issues for scientific methods of forecasting. In these situations, we need to resort to extensive statistical data cleaning exercises.
Non-availability of True Historic Demand
Historic sales are used to estimate the future demand, as it is the only reliable quantitative indicator available about customer...