Revolutionizing Inventory Management in Agriculture with IoT
Technology Category
- Functional Applications - Inventory Management Systems
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Agriculture
- Education
Applicable Functions
- Procurement
- Warehouse & Inventory Management
Use Cases
- Demand Planning & Forecasting
- Inventory Management
Services
- System Integration
- Training
About The Customer
The customer is one of the largest family-owned companies in the world that provides farmers with essentials for the world to thrive. The company markets and distributes products to the agricultural industry, including crop seed, animal feed, specialty chemicals, and ingredients. With 350 branches, the company has a significant presence in the agricultural industry. However, the company was facing challenges with inventory management due to inaccurate forecasting, leading to high costs and an aging inventory. The company's decision-making processes were disconnected due to multiple data sources, disconnected analyses, and an immature planning system.
The Challenge
One of the world's largest family-owned agricultural companies, with 350 branches, was grappling with inventory issues due to inaccurate forecasting. This resulted in high costs and an aging inventory. The company had to place product orders, such as crop seeds, a year in advance for each season. However, the forecasting was poorly done due to reliance on lagging indicators to predict demand, leading to a large write-off of seasonal inventory. Additionally, the company was unable to control total inventory costs due to poor planning and sub-par scenario modeling. The company also lacked planning knowledge, with key decision-making processes being disconnected due to multiple sources of data, disconnected analyses, and an immature planning system.
The Solution
The company partnered with o9 to implement automated forecasting on products with high and low variability using Machine Learning and statistical modeling. The forecast for high variability products used active forecasting to ensure quicker responses to market changes. The company also used o9 to perform capacity optimization planning for warehousing and logistics to help minimize their total inventory costs and improve scenario modeling accuracy. This included automated replenishment and procurement forecasting, which fed back into the ERP system to assist with auto PO creation. Furthermore, o9 helped the company mature its planning capabilities and expertise through training and a single integrated platform to connect functions and running processes. The company used the o9 Enterprise Knowledge Graph to build market, demand, and supply-knowledge models, enabling them to run all key planning processes for all product lines and countries, across all time horizons in a single integrated platform.
Operational Impact
Quantitative Benefit
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