Typically, manufacturing forecasting teams make static predictions at a daily for line production, weekly or monthly level for a complete year for inventory. These forecasts are then shared with different partners in the supply chain who rely on them for the manufacture and distribution of product. An HARVESTER based system could signal the distribution centre or the manufacturer for the flow of inventory replenishments at the optimal Return on Investment 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 optimisation of resources.
An HARVESTER and AGGREGATOR 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 centralised data servers, and used to create real-time stock availability matrices that the can be used to monitor and make informed decisions. It can also be transmitted to local displays, who are then prompted to replenish a shelf. Another possibility: in-store data combined leading to store traffic and product demand can be used to craft predictive models. This allows for more efficient stocking and replenishment strategies to minimise overages, while ensuring product availability for consumers.