Self-Help for Inventory Shelves with AWS IoT Analytics | HCL Blogs

Self-Help for Inventory Shelves with AWS IoT Analytics

Self-Help for Inventory Shelves with AWS IoT Analytics
November 09, 2020

Empty inventory shelves and abandoned shopping carts are some of the terms enterprises would love to eliminate from their list of alerts while dealing with their store or warehouse inventory. "Stock-Outs cause Walk-Outs," an HBR article points out that as high as 72% of the stock-outs were due to inappropriate inventory replenishment processes. Though the previously mentioned article focuses on the retail stock-out experience, this applies equally for non-retail environments as well – be it manufacturing inventories, parts, and spares of OEM equipment, or critical life-saving devices in hospitals.

Challenges caused by an unpredictable inventory

Imagine the intensity of impact on the customer's goodwill caused by unplanned, unpredictable inventory management, leading to backorders or canceled orders, and losing a customer being the worst-case scenario. The criticality might vary based on the category and end-user consumption of the product concerned. However, the overall impact remains on the opposing side. This includes lost sales due to delayed order fulfillment, higher shipping costs due to the sudden need for stock replenishment, and reduced planned orders from customers.

Role of IoT and Analytics in solving this problem

"Big Data" and "Internet of Things" has created a pathway for firms to go beyond the traditional demand forecast routines by analyzing historical data and provide recommendations. We can now connect the links between data generated at the suppliers' end, warehouse locations, and the POS. With big data and the internet of things, we are now moving toward a centralized monitoring approach that extracts data right from the point of usage and delivers it on a decision-making platter with its imbibed intelligence. By intelligence, we mean understanding the disparity between physical inventory and system inventory management, having the hindsight of items in the warehouse, and having the foresight to predict shelf replenishment items. Leveraging technology, for example, RFID can help track items from the time it enters the suppliers' warehouse to the point where they are available on the shelves for consumers or end-users to consume. In addition to this, sensor data for tracking item parameters like the weight of the consumables (i.e., paints, chemicals, nuts, bolts, etc.) on the shelves can help identify the threshold for reorder points of items. With that data in place, the next one in our checklist should be a cloud-enabled analytics platform on top of this.

Let us take an example of AWS IoT Analytics (AIA) and see how it can help achieve an intelligent replenishment system for inventory in place. Retrofitted for an internet of things (IoT) application, it can automatically store timestamp associated data right from the items in inventory and make time-series analysis easy. The performance analysis of inventory levels can be improvised using the machine learning capabilities of the same. The business logic-based configurations can be set up in the data processing pipelines connected to the integrated AWS IoT Core. For example, we can set up a visual sensor that scans the QR code on an item with a specified expiry date, tie up the business logic to check with the stock in the immediately available stockroom, and send a notification to the sales stakeholders for order replenishment. What is critical here is the humongous amount of unstructured data received from the 'IoT-ized' items that must be processed. AIA helps in cleaning data and enabling ad-hoc/scheduled querying to provide post-analytic inferences and better inventory management recommendations. The inventory manager can get immediate benefits from the same, with prescriptive analytics for planning the order quantity and order placement scheduling activities.

Architecture diagram: AWS IoT Analytics for Smart Shelves

Architecture diagram: AWS IoT Analytics for Smart Shelves

Previously, firms could analyze POS data and IT system inventory data to balance out inventory stock-out problems. Today, we can add sensors, tracking technologies, predictive and prescriptive analytics as an added value enhancer for the firms to consider. While it cannot be a complete full stock-out-proof solution for the firms, IoT enabled inventory replenishment can reduce the worries of losing out on customer loyalty to a great extent.

While it cannot be a complete full stock-out-proof solution for the firms, IoT enabled inventory replenishment can reduce the worries of losing out on customer loyalty to a great extent.

To know more about how this can work for your organization, please contact us at .