Technology Category
- Analytics & Modeling - Digital Twin / Simulation
- Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
- Renewable Energy
- Transportation
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Digital Twin
- Manufacturing Process Simulation
Services
- System Integration
About The Customer
The customer is an American multinational renewable energy company with a presence in over 170 countries. The company is committed to delivering reliable green power through blades, hydro, storage, utility-scale solar, and grid solutions. The company was facing significant challenges in managing its supply chain due to an increasing number of complex configurations in its product portfolio and a rapidly expanding customer base. The company's planning processes were disconnected, leading to cost and inventory issues. The company also lacked visibility of constraints and costs from mold capacity planning to installation at customer sites.
The Challenge
The customer, an American multinational renewable energy company with operations in over 170 countries, was grappling with significant supply chain shifts. The company was dealing with an increasing number of complex configurations in its product portfolio and a rapidly expanding customer base. The planning processes for mold capacity planning, blade manufacturing, blade transportation, and blade installation at customer sites were disconnected, leading to cost and inventory issues. The company also lacked visibility of constraints and costs from mold capacity planning to installation at customer sites. Furthermore, due to fragmented business processes and supporting systems, the planning teams were unable to collaborate across multiple functions. The legacy processes and tools resulted in time-consuming planning and reporting efforts by planners, based on snapshots of data. The planning workforce spent the majority of their time number crunching rather than intelligent planning and decision-making.
The Solution
The company adopted o9's platform to address these challenges. The platform provided a digital twin, offering visibility to demand, transportation, blade manufacturing, and mold manufacturing capacities and costs. It also connected all functions and planning processes on a single integrated platform, creating a single source of truth with improved speed and better quality of decision-making. The company no longer needed to manually move data and was able to make decisions and run scenarios based on real-time insights. For instance, the company now has the ability to run fast scenarios on demand upsights and mold-mix changes. The Enterprise Knowledge Graph was used to enable end-to-end planning, including demand, supply, transportation, and IBP. Key capabilities enabled include: what-if scenarios, trade-off evaluation in both units and currency, demand/supply balancing, incremental and interactive planning and a digital twin that provides end-to-end visibility on cost and capacities across the entire network. The system replaced the previous Cognos Planner.
Operational Impact
Quantitative Benefit
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