With the widespread problems around the availability of raw materials, companies need to reconsider their stock keeping policies and assess whether the ‘just in time’ methodology is right for a post-pandemic world. For FMCG companies, where the loss of sales has a greater P & L impact than the costs of overstocking do, minimum and maximum stock levels need to be re-assessed.
This applies to retailers as well. Until 10-15 years ago, retailers were focused on minimising stock held in their warehouses as consumer sales were predictable based on historical sales patterns. However, with social media and news influencing consumer buying decisions as much as differing weather patterns due to climate change and other events, consumer sales have now become more complex to predict using traditional judgements.
In this scenario, the risk is that FMCG companies and retailers will not be holding enough stock, resulting in lost sales and lost consumer confidence when they encounter empty shelves. To avoid this issue, companies need to adopt data-driven solutions that consider a range of variables to predict sales behaviour, making the decision-making process from the company side far more precise.
“Artificial intelligence and machine learning can be used by both FMCG companies and by retailers to process a large quantum of detailed data to predict consumer demand to within 5% – 10% of actual sales. It ensures that stock levels are optimal at the retailer level, and both stock write-offs and stock-outs can be avoided”, says Veena Giridhar Gopal, co-founder of salesBeat, an AI-driven sales intelligence platform that eliminates out of stocks in supermarkets, convenience stores and in wholesale.
SalesBeat’s algorithm replicates consumption behaviour based on numerous indicators; including but not limited to, information on air pollution levels, weather circumstances, water levels, consumer movements, current affairs, and popular trends. It’s no surprise the company is one of the Top 5 FMCG start-ups to watch in 2022 according to StartUS Insights.
