Winter is now well underway. At this time of the year, it is vital that retailers are able to react quickly to, or better yet predict, what impact certain weather conditions are likely to have on their customers’ buying habits.
Why? Because the weather is the second biggest influence on consumer behaviour after the state of the economy, according to the British Retail Consortium. The sales performance of just about every consumer product can be affected by a particular type of weather condition. Be that the ‘Beast from the East’ that swept across the UK in 2017 – causing retail sales volumes to drop by
Some 1.2& compared with the previous month – or ‘Storm Diana’ which brought with her warnings of flooding, power cuts, traffic chaos and phone outages. While continued consumer demand for products like the humble avocado – which became a prized global commodity in 2018, and annual events such as Christmas or Black Friday, are relatively straightforward to plan for, fluctuations in the weather can cause retailers a major headache.
This is because it is more difficult to prepare accurately for changes in the weather, which will likely vary for each stage of the supply chain. It is, however, essential for retailers to be prepared for severe weather across the globe and anticipate the needs of consumers in the event of severe conditions. Periods of unsettled weather create spikes in demand that pose real threats to retailers, such as the risk of out-of-stocks or overstocks. During a major storm, an excess of supplies may sit redundant in one store while another one lacks the inventory it needs. So, with a force as unpredictable as mother nature, how can retailers better forecast demand to ensure they don’t fall short when it comes to providing for their customers?
Use data to your advantage
There is an abundance of data to help guide retailers as they plan for demand. Aggregating current business and customer data with a new layer of contextual insights, including weather analysis, provide valuable predictive insight that can reduce pressure on manual processes and lead to improved demand forecasting. Automation has become an increasingly important element in demand forecasting, allowing more data to be analysed in less time, producing results with greater accuracy.
However, relying on historical data from previous severe weather events alone will no longer cut it. Today, artificial intelligence (AI) technology can predict sales and drive better forecasting based on internal and external retailer data. By incorporating both historical and predictive weather data into the supply chain, retailers will be better positioned to anticipate shoppers’ needs before, during and after a severe weather period. This is especially important for date-sensitive products such as dairy and baked goods, which are already among the most challenging to manage for daily and intra-daily forecasting.
Demand forecasting technology for supply chains
Better demand forecasting which uses AI-enabled weather analysis makes it easier for retailers to anticipate even the slightest shifts in weather. While small, these changes could impact the entire supply chain – from order quantities, supplier choices, fulfilment execution, logistics, truckload ramifications and pricing, so it’s clearly something that retailers need to stay one step ahead of. Take a patch of volatile weather for instance. A storm wreaking havoc in another region may lead to dangerous road conditions and power cuts. This would require distribution and cool/dry storage methods to be quickly re-evaluated in order for retailers to maintain the level of service their customers in other regions expect. When addressing any such issues affecting one part of the supply chain, retailers must consider these as an element of holistic demand forecasting if they are to ensure timely replenishment and efficient logistics.
The industry has undergone some serious improvements since the days of inventory planners arriving early on hot days to manually push temperature-sensitive items out to stores. Today’s data and technology systems free up retail analysts, allowing them to spend more time planning and less time worrying about manual intervention. With the right demand forecasting technology, retailers can better prepare for any kind of weather event– large or small— and ensure they retain an edge in an increasingly competitive market place.