Westinghouse Electric Company, a global leader in nuclear technology with over 130 years of innovation history, faced significant challenges in coordinating production planning across their complex nuclear fuel manufacturing operations. With five separate sites operating independently using disconnected Excel spreadsheets, the company struggled with lengthy planning cycles and limited visibility across their global supply chain. This case study examines how Westinghouse partnered with MOSIMTEC to implement Simio’s digital twin technology, transforming their production planning and scheduling processes while dramatically reducing response time to changes and improving decision-making capabilities.
Founded in 1886 by George Westinghouse, the company has spent nearly 140 years redefining how the world generates and uses electricity. As the world’s leading supplier of safe, innovative nuclear technology, Westinghouse operates across 21 countries with over 11,000 employees and 90 facilities worldwide. Their technology plays a pivotal role in generating nearly 50% of the world’s nuclear power.
Westinghouse’s nuclear fuel business unit provides complete fuel solutions, handling everything from development and manufacturing to core engineering, safety analysis, and fuel component manufacturing. The company offers four key fuel types: Pressurized Water Reactor (PWR), Boiling Water Reactor (BWR), Water-Water Energetic Reactor (VVER), and Advanced Gas Reactor (AGR). Their nuclear fuel manufacturing facilities are located globally in the United States, United Kingdom, and Sweden.
Before implementing Simio, Westinghouse managed production planning through five separate sites with five different planning groups, all operating independently. As Brad Parker, Director of Global Manufacturing and Material Planning at Westinghouse, explained:
“A majority of it [was] in Excel with no integration, no constraint between manually entered spreadsheets, meaning that when we created the master schedule for 18 months to two years, what would happen then is if there was any scenarios or if there was a change, it would take us up to a week to be able to assess what that change was.”
This fragmented approach created several critical challenges:
For complex changes affecting multiple sites, the impact assessment could take up to a month, particularly when evaluating effects on facilities at the bottom of the vertical supply chain. This lengthy process often resulted in partial assessments that didn’t provide full visibility into the impact of proposed changes.
When evaluating potential solutions, Westinghouse selected Simio for its flexibility and adaptability. According to Brad Parker:
“The flexibility, you know, the ability to add, take away… other models that I looked at seemed to be more the structure of it in terms… it was almost like hard coded, which made amendment difficult. [With Simio] it’s okay, we can be flexible enough to make it fit your process. Not your process has to change to reflect our way of doing it.”
This flexibility was crucial for handling the complexity across five sites with a vertical supply chain. Westinghouse partnered with MOSIMTEC, a consulting services company with over 14 years of experience in applying modeling and simulation across various industries.
MOSIMTEC followed a proven process for deploying simulation models and implementing digital twins at Westinghouse sites:
The implementation integrated data from multiple sources to support capacity planning and scheduling:
An ETL (Extract, Transform, Load) functionality moved data from these sources into Simio, which processed it to produce capacity-feasible planning and scheduling. For rapid implementation, Westinghouse initially used an Excel Power Query-based approach as an interim solution.
The implementation began with developing a detailed functional design specification. As MOSIMTEC explained, “If you can’t clearly describe a system, you can’t model it effectively.” This document outlined both the real-world system and the intended model using diagrams and narrative to convey the necessary level of detail.
The specification was reviewed and approved by Westinghouse before model development began but remained a living document that evolved alongside the model. Westinghouse found this document especially valuable as it provided a single, comprehensive reference capturing the entire process from start to finish.
To build a true digital twin that extracted live data and ran scheduling plans, it was crucial to ensure the right data came from the right sources at the right time. MOSIMTEC developed ETL solutions to clean and integrate raw data, an iterative process involving onsite stakeholders with many adjustments made along the way.
Throughout model development, extensive use of error logs provided feedback to Westinghouse, helping them clean and standardize their data. The models created were fully data-driven, relying on real-time dynamic data inputs. This approach provided users with the flexibility to quickly refresh and generate new plans spanning multiple years.
After each simulation run, users could access several key dashboards:
The implementation of Simio’s digital twin manufacturing solution dramatically reduced the time required for planning and scenario analysis. As Brad Parker noted:
“With the implementation of Simio, what we’ve been able to do is make these decisions a lot quicker. The simulation model can run and we can change parameters, as you know, in Simio based off the tables, based off the demand data.”
What previously took up to two weeks could now be accomplished in just a few hours, enabling faster decision-making and more efficient planning processes.
The digital twin provided an integrated schedule for all areas, eliminating gaps in analysis and improving visibility across the supply chain. This integration was particularly valuable for Westinghouse’s central planning function:
“Being able to see okay, what does this mean, what’s your schedule look like now. Oh, I can see this is going to be light here. Does that impact, what impact, scenario build. It has allowed us an integrated planning function which previously we struggled with.”
The implementation vastly improved Westinghouse’s ability to:
The implementation also highlighted data quality issues and inconsistencies across systems. As Brad Parker explained:
“You wonder why you struggle in planning when you’ve got four systems telling you a different number. It doesn’t work well.”
By bringing data together in the digital twin, Westinghouse could identify and address these inconsistencies, improving data quality and decision-making across the organization.
Westinghouse planners can now update all tables and run plans independently, without requiring intervention from MOSIMTEC. They only contact the consultants when results look strange or when they need new dashboards or changes to existing ones. This self-sufficiency has empowered the planning team to take ownership of the digital twin and use it effectively for day-to-day planning.
One of the biggest challenges was data quality and consistency. As Brad Parker reflected:
“These data, understanding it has been a long journey… a lot of it has been tied to findings on data discrepancy. Most intact with the ETL has helped. So that has allowed us to implement quicker. Obviously recognizing those issues, what I found is that the data gaps slow your Simio implementation when you’re finding the issues in Simio.”
If he could start over, Brad would have done more data mapping upfront to identify discrepancies earlier. However, he wouldn’t have waited for perfect data before implementing, as “you’d never implement if you were waiting for perfect data.”
Another key lesson was the importance of dedicated resources and stakeholder engagement:
“You need to dedicate resources. In effect, at times I was running, I was managing three implementations at three sites on my own. So that slows down the implementation. But if you have dedicated resources, you can manage it.”
Having the right people involved—those who truly understand the processes—was crucial for successful implementation. Without this knowledge, the team would have to go through more iterations to get the model right.
Brad emphasized the importance of ongoing management support:
“I was strongly supported by Westinghouse… one thing you must have when you embark on this journey is not only support up front from senior management, but that has to continue. It’s not going to be perfect overnight. And a lot of people expect, well, we just click a button and it works. It’s unrealistic.”
This sustained support allowed the team to work through challenges and continue refining the implementation over time.
Westinghouse has already rolled out what Brad Parker considers a “basic, simple simulation software” and sees significant opportunities to expand its utilization:
“We have focused on nuclear fuel. However, we are starting to see other parts of the business show interest. eVinci is one example, but how we document and future utilize this with the functionality we’ve not even touched on yet is where we will be trying to take the Westinghouse organization in the utilization of a single solution.”
Future plans include:
The implementation of Simio’s digital twin technology has transformed Westinghouse’s production planning and scheduling processes, providing an integrated view across their complex global supply chain and dramatically reducing the time required for scenario planning and decision-making.
By partnering with MOSIMTEC and following a structured implementation methodology, Westinghouse has established a foundation for continued digital transformation. The success of this implementation demonstrates the power of digital twin manufacturing in complex manufacturing environments and provides valuable lessons for other organizations embarking on similar journeys.
As Brad Parker summarized: “By choosing Simio, businesses can harness the full potential of digital twin technology, mitigate risks, and stay ahead of market demands—all while fostering operational resilience and continuous improvement.”