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Published by
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Data-Driven Business Transformation
What is demand forecasting in Retail
Defining Demand Forecasting
Demand Forecasting in Retail
The Retail System Report (2017) by SAS analyzes that 77% of the winning retailers prioritise demand forecasting which not only helps them become cost-effective but also helps improve overall customer experience.
The Retail System Report (2017) by SAS analyzes that 77% of the winning retailers prioritize demand forecasting, which not only helps them become cost-effective but also helps improve overall customer experience.
How it Helps Retailers
In addition to assortment planning, demand forecasting will ensure that money on supplies is spent, only if needed. If one is not able to achieve their target sales (overpredicted forecasts), they can employ promotion strategies to amp up sales. If they exceed their sales expectations (underpredicted forecasts), they can always ask for more stock to come in or prepare to cross-promote related products.
The best way to increase customer satisfaction and build brand loyalty is to meet their needs at the same moment of that need. Less stock out days ensures this. Once we guarantee the availability of the product, we can spend more focus on improving their overall experience with adequate and well-trained staff, which can assist them and also introduce them to the latest products and other offers.
Risks Associated with Demand Forecasting
Myriad literature available online, most of the challenges associated with demand forecasting are:
- Accuracy of the source data
- Presence of erratic seasonal patterns in sales data
- Forecasting demand for new products without historical data
- Forecasting for short-lived products (e.g. dairy)
- Handling missing values
- Incorporating a geographical aspect to the forecast (store locations etc.)
- Selecting the right hierarchy (store level/product level etc.) and time frame for the forecast (long period or short period forecasts)
Balancing the demand can be taken care of by considering asymmetric loss functions in machine learning which allow the association of user-defined weights to the loss metric. Some asymmetric loss functions are displayed below.
δ = (observed value – predicted value)
a = weight associated with the loss
