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 a multinational industrial manufacturer with local operations in over 170 countries. They specialize in manufacturing renewable energy equipment for utilities around the world. The company has a rapidly growing customer base and a product portfolio with increasing numbers of complex configurations. The company's Wind Turbine division was particularly affected by the challenges faced, lacking visibility of constraints and costs from mold capacity planning to installation at customer sites. The company was committed to improving its planning processes and decision-making capabilities to better manage its supply chain and meet the demands of its expanding customer base.
The Challenge
The multinational industrial manufacturer, with operations in over 170 countries, was grappling with significant supply chain shifts. The company's product portfolio was becoming increasingly complex, and its customer base was rapidly expanding. The planning processes for mold capacity, blade manufacturing, transportation, and installation at customer sites were disjointed, leading to cost and inventory issues. The Wind Turbine division, in particular, lacked visibility of constraints and costs from mold capacity planning to installation at customer sites. The planning teams were unable to collaborate across multiple functions due to fragmented business processes and supporting systems. The legacy processes and tools resulted in time-consuming planning and reporting efforts by planners, based on snapshots of data, leaving little time for 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 connected all functions and planning processes on a single integrated platform, creating a single source of truth for improved decision-making. The company no longer needed to manually move data and could make decisions and run scenarios based on real-time insights. The Wind Turbine division used the Enterprise Knowledge Graph to enable end-to-end planning, including demand, supply, transportation, and IBP. Key capabilities enabled included what-if scenarios, trade-off evaluation in both units and currency, demand/supply balancing, incremental and interactive planning, and a digital twin that provided end-to-end visibility on cost and capacities across the entire network. The platform replaced the use of Excel for these processes.
Operational Impact
Quantitative Benefit
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