How IoT is helping retailers eliminate product stock outs
Product stock outs are a huge challenge for retailers. Globally, the average for retail stock outs is 8.3 percent – meaning that for every 20 items on a shopper’s list, at least three are likely to be out of stock. This problem and associated costs for retailers are particularly acute when it comes to products with a short shelf life or that are highly seasonal.
What’s surprising is that in 70-90 percent of cases, stock outs are caused by downstream issues like inadequate replenishment procedures rather than upstream issues such as lack of supply from the manufacturer. In other words, retailers often have adequate stock in a storeroom, but it just hasn’t reached the shelf. CPG analytics have not been applied and executed to the effect they’re capable of.
Many retailers have implemented technologies such as Efficient Consumer Response (ECR) and RFID tags, but still suffer from stock outs primarily because those techniques don’t eliminate the need for people to spot thinning stock and request replenishment. Internet of Things (IoT) technology has great potential to help retailers solve this challenge.
How? An IoT approach combines an array of sensors (pressure, weight and depth), cameras and smart devices such as RFIDs that would constantly monitor shelves for activity. The data generated in this process is relayed to cloud servers, and used to create real-time stock availability scorecards that the store management can use to monitor and make informed decisions. It can also be transmitted via wearable device to stock coordinators, who are then prompted to replenish a shelf. Another possibility: in-store data combined with additional big data sets on macro trends, store traffic and product demand can be used to craft predictive models. This allows for more efficient stocking and replenishment strategies to minimize overages, while ensuring product availability for customers.
Consider the case of a pharmacy that stocks flu vaccines. An IoT-based stock system leveraging big data could detect changes in flu severity in the community and automatically notify replenishment teams, making recommendations on stock planning during flu season. This mechanism ensures enough vaccine to meet anticipated customer demand.
Now consider upstream stock outs. Typically, manufacturing forecasting teams make static predictions at a weekly or monthly level (exponential smoothing models based on historical sales) for a complete year. These forecasts are then shared with different partners in the supply chain who rely on them for the manufacture and distribution of product. Not surprisingly, the forecasts are often quickly out of date. An IoT- and big data-based system could signal the distribution center or the manufacturer for the flow of inventory replenishments at the optimal Return on Investment (ROI) mark. These devices could also talk to the smart systems in the manufacturing plant to adjust the inventory output based on the learnings from the stores groups for larger areas/states, leading to better optimization of resources.
Kroger – one of the world’s largest retailers – recently launched an initiative called Retail Site Intelligence (RSI) that uses wireless mesh networks to integrate sensors, handheld devices and video management to connect and monitor various activities occurring in a retail store. It’s an excellent example of a retailer leveraging IoT and big data to better manage inventory levels.