Digital Transformation in Oil and Gas: A Case Study on Inventory Optimization and Forecasting
Technology Category
- Functional Applications - Inventory Management Systems
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Oil & Gas
- Transportation
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Inventory Management
- Picking, Sorting & Positioning
Services
- System Integration
About The Customer
The customer is one of the world's largest publicly traded international oil and gas companies. The company was struggling with data challenges, lack of inventory visibility, and inefficient planning processes. The company's data structures were complex and unorganized, leading to a lack of a single source of truth. The company's forecasting process was predominantly manual, resulting in high inventory levels. The company was more focused on execution rather than planning, leading to a lot of day-to-day volume management and firefighting.
The Challenge
One of the world's largest publicly traded international oil and gas companies was grappling with highly manual and Excel-driven planning processes. The company was facing significant data challenges and a lack of inventory visibility. The data structures were complex and unorganized, making it difficult for the company to maintain an overview of all data and processes. This led to the absence of a single source of truth. Furthermore, the company's forecast accuracy was low, and it relied heavily on manual, lagging indicators in the forecasting process. This resulted in excessively high inventory levels. The company's focus was more on execution rather than on planning, leading to a lot of day-to-day volume management and firefighting.
The Solution
The company partnered with o9 to address these challenges. o9 provided a unique capability to have full visibility over sales, inventory, and supply chain planning on an integrated, single source of truth, cloud-native platform. This helped the company to streamline its data and processes. Additionally, o9 helped the company optimize and reduce inventory levels by incorporating internal and external drivers of demand into o9’s highly differentiated Machine Learning forecasting capabilities. This significantly improved the company's forecast accuracy. Furthermore, all planning processes were managed in the o9 platform, freeing up time for the company to start mid-range planning and focus on other strategic priorities. The o9 Enterprise Knowledge Graph was used to build demand, inventory optimization, and supply-knowledge models. This allowed for running the entire process in a single integrated platform without the need for further integration.
Operational Impact
Quantitative Benefit
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