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. demand forecasting is especially important in the retail industry because it leads to …… lower inventory costs, faster cash turnover cycles, quicker response to trends, etc etc. 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. A robust demand forecast engine will significantly impact both top & bottom lines positively.
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 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 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 demand. However it is possible that sales data end-up with a bias because of the inventory rupture or temporary promotional activities. These situations need correction to sales history to reflect the true demand. Since demand bias is very business specific, such corrections usually require in-depth domain expertise to interpolate/extrapolate the sales figures.
Demand forecasting techniques are broadly divided into two categories: Judgmental and Statistical.
The Scientific (Statistical) Forecast Models
Scientific models are divided into two categories, Extrapolation Models & Causal Models (Fig. 2). The extrapolation models are based exclusively on the past/historic sales data where the trend, seasonality & cyclicity prevalent in the historic sales data are examined to project the sales in future. However it is pretty intuitive that the future sales not only depend on the past sales but also on the other factors viz. economic trends, competitors’ movement, festive events, promotional activities etc. In order to incorporate such external factors in forecasting, a variety of causal models are available. In absence of such external factors’ data, the extrapolation models provide decent forecasts in most of the situations.
Key Comparisons of Various Scientific Models
There are two aspects to forecasting implementation, technical and functional. The challenges in both are different, while the technical challenges are easy to solve given the profusion of tools available in the market today, the functional challenges involve significant business process re-engineering and hence are the most typical point where organizations fail to capture the impact of forecasts.
Technological implementation can be done via modelling tools like SAS, E-views etc. or via forecasting simulators, like TEG’s proprietary FutureWorksTM tool. Given, the forecasting model equation, the tools, would just need the forecasting inputs in order to generate the forecasts. In case of pure time series models, the inputs are simply past figures of the forecasted metric, while in case of causal forecasting models, we need the forecasted values of the input variables as well. This would need multiple models to be created.
Organisationally, the forecasts need to be essential requirements before taking key decisions on supply chain, future media spend, inventory reallocation etc. It should be in the organisation’s DNA, that any of these decisions will not be taken without a study of how these decisions would impact future demand. Traditionally, this has been the hardest part of implementation, as organizations used to operate in a quick, informal, entrepreneurial culture, often fail to see the benefit of the extra discipline and rigor.
TEG Scientific Forecasting Process
TEG follows the CRISP-DM process for all modelling processes, including forecasting.
A TEG Case Study
A leading Indian Sports Goods Retailer wanted to develop a scientific forecasting system to foresee the future sales across various product hierarchical levels irrespective of the supply side constraints to facilitate various short & medium-long term business plans. Additionally, the system could provide an early warning of potential slack across chains/stores to enable full resource utilisation course correction.
Methodology & Results
After setting up the forecasting objective and scope, a list of potential factors (Fig. 5) were considered to build the forecast model across various channel & SKU-cluster combinations. Rigorous data treatment phase followed and various families of statistical models (specified in Fig. 3) were tested for each channel & SKU-cluster combination. A single model was finalized which produced the accuracy at the satisfactory level. Fig. 6 depicts one such model which was used to produce forecasts for 12 weeks in future. As evident, model is doing a good job in anticipating demand for certain types of interventions like, ICC events & seasonal promotions where demand is supposed to shoot upwards.
The deployment of the Scientific Models to their forecasting process helped the Retailer in the following ways:
- Improved Forecasts – The forecasts were improved in the range of ~2-15% across different store-cluster & SKU-cluster combinations.
- Better Stock Management – The key achievement was to accurately pinpoint the slack periods for some of the SKU-clusters which were eating up the rack space in those time periods earlier. The retailer was also able to identify the unfulfilled demand for some of the SKU-clusters which was not getting captured with the traditional judgement forecasting approach. Identification of these gaps helped the retailer to better manage the stocks across different store-clusters by relocating them from low demand stores to high demand stores.
- Early Warning of Lull Periods – The knowledge of low sales regime well in advance (12-24 weeks) helped the retailer to frame the promotion calendar so that the sales could be hiked up to meet the targets.