Revolutionizing Inventory Management and Delivery Services with IoT

• Functional Applications - Inventory Management Systems
• Sensors - Voltage Sensors

• Automotive
• Buildings

• Procurement
• Warehouse & Inventory Management

• Inventory Management
• Time Sensitive Networking

The customer, an online retailer of office equipment, was facing challenges with their inventory management and delivery services. They were importing and delivering products directly to local distribution centers (DCs), which put each DC at risk of having excess inventory or running out of stock. The company was trying to mitigate these risks by building a replenishment center and delivering products from this center to each DC. However, the accuracy of their received sales plan was low and relied heavily on lagging indicators. Additionally, truck loading planning was done manually, a time-consuming process that often resulted in miscalculations of the required number of trucks. Lastly, the management of the minimum order quantity and complicated ordering conditions, considering the container’s capacity, were managed in Excel, which was not efficient.

The customer is an online retailer that sells office equipment products to medium and small-sized business offices. They provide next-day delivery services, ensuring that their customers receive their orders promptly. The company imports and delivers products directly to local distribution centers (DCs), from where the products are then delivered to the customers. They were facing challenges with inventory management and delivery services, with each DC at risk of having excess inventory or running out of stock. To mitigate these risks, the company was in the process of building a replenishment center to streamline their delivery process.

The company adopted o9, a single integrated, cloud-native platform, to improve their operations. With o9, they were able to improve the accuracy of their sales plan by creating a forecast using “Prophet”, a tool developed by Facebook. This platform also automated the truck loading planning process, estimating the number of trucks needed by considering the loading rate of trucks and the number of pallets. Furthermore, o9 reduced the grunt work by automatically creating order requests considering various ordering conditions, enabling the issuing of an accurate order request. The o9 Enterprise Knowledge Graph was used to build market, demand, and supply-knowledge models, allowing for running all key planning processes for all products and DCs, across all time horizons in a single integrated platform. o9 leveraged its open architecture to incorporate proprietary developed forecasting algorithms, safety stock logic, and truck loading logic in R and Python.

• The implementation of o9 brought about significant operational improvements for the company. The accuracy of their sales plan improved, leading to better inventory management and reduced risk of stockouts. The automation of the truck loading planning process eliminated the time-consuming manual process and reduced the instances of miscalculations. The automatic creation of order requests considering various ordering conditions enabled the issuing of accurate order requests, saving time and improving efficiency. The use of the o9 Enterprise Knowledge Graph allowed for running all key planning processes in a single integrated platform, enhancing the company's overall operational efficiency. The company was also able to incorporate proprietary developed forecasting algorithms, safety stock logic, and truck loading logic in R and Python, further enhancing their operations.

• Reduction of stock at DC and RC, minimizing the risk of excess inventory or stockouts.
• Reduction of lost sales due to improved inventory management and delivery services.
• Time saved on creating accurate purchase orders, increasing operational efficiency.