o9 Solutions, Inc. Case Studies Digital Transformation in Renewable Energy Manufacturing: A Case Study

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	Digital Transformation in Renewable Energy Manufacturing: A Case Study o9 Solutions, Inc.

Digital Transformation in Renewable Energy Manufacturing: A Case Study

o9 Solutions, Inc.

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The implementation of o9's platform led to significant operational improvements for the company. The platform's digital twin provided end-to-end visibility of the supply chain, enabling the company to better manage its resources and respond more quickly to changes in demand or supply. The platform also facilitated collaboration across multiple functions, breaking down silos and improving the speed and quality of decision-making. The company was able to move away from time-consuming manual data movement and instead make decisions based on real-time insights. The use of the Enterprise Knowledge Graph for end-to-end planning also enabled the company to balance demand and supply more effectively, evaluate trade-offs, and plan incrementally and interactively.

Impact #1

The implementation of o9's platform led to significant operational improvements for the company. The platform's digital twin provided end-to-end visibility of the supply chain, enabling the company to better manage its resources and respond more quickly to changes in demand or supply. The platform also facilitated collaboration across multiple functions, breaking down silos and improving the speed and quality of decision-making. The company was able to move away from time-consuming manual data movement and instead make decisions based on real-time insights. The use of the Enterprise Knowledge Graph for end-to-end planning also enabled the company to balance demand and supply more effectively, evaluate trade-offs, and plan incrementally and interactively.

Benefit #1	Improved market share via quicker response to demand or supply changes
Benefit #2	Improved forecast accuracy
Benefit #3	Reduced expedited logistics costs

Benefit #1

Improved market share via quicker response to demand or supply changes

Benefit #2

Improved forecast accuracy

Benefit #3

Reduced expedited logistics costs

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Overview

Digital Transformation in Renewable Energy Manufacturing: A Case Study

Operational Impact

Quantitative Benefit