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Ford Otosan, a leader in Turkey’s automotive industry, is a joint venture between Ford Motor Company and Koç Holding. It is the country’s third-largest exporter and is known as a global hub for commercial vehicle and powertrain engineering R&D.

The company has extensive experience in digital factories and data-driven production processes. In this project, Lanner’s partner in Turkey, CodeArt, supported Ford Otosan to leverage predictive simulation in the design process for a new electric transit vehicle factory.

The challenge: Will the proposed factory layout achieve production targets?

A key benefit of predictive digital twins is the ability to de-risk capital investment projects. And that’s exactly what Ford Otosan was looking to do. They had designed a layout for the newly planned facility, and wanted to determine whether that design would deliver the throughput needed to achieve production targets.

Validating the design required answers to key questions such as:

  • Would station cycle times align with forecasts?
  • How efficiently would robots perform over time?
  • How would breakdowns affect the system?
  • What buffer capacities would be required to maintain required production levels in the event of a breakdown or failure?

The factory design involved complex cyberphysical systems, and Ford Otosan wanted to ensure it was planning its investment in machinery, robotics and operators in the optimal way.

The approach: An ecosystem of predictive digital twins

Ford Otosan designed the facility in 3 sections:

  • Welding and vehicle body production
  • Paint shop
  • Assembly

Each section had a proposed layout with projected robots and operators required to operate the lines. The aim was to validate the layout using predictive digital twins, providing answers to those key questions and determining whether the design would achieve the production targets.

Leveraging more than a decade of experience, CodeArt developed models using Lanner’s WITNESS Horizon predictive simulation software – starting with the welding and vehicle body production lines. First, a model was created for each line to get an in-depth understanding of cycle times, robot and operator performance, downtime and buffer requirements. Then, these detailed models were fed into an integrated model used to test the section’s complete layout.

The predictive digital twin was used to analyze a wide range of scenarios. For example, CodeArt tested many vehicle types to determine the effect of different sequencing on throughput. They looked at the distribution of robots and operators across peaks and troughs in production. Equipment levels were analyzed to see if they could achieve the projected cycle times. And a range of best- and worst-case breakdown scenarios were evaluated to understand the knock-on effects and required buffer capacities.

Importantly, scenarios were run over short, medium and long-term periods to assess the effects over time.

The result: 3% savings – and confidence the layout will achieve its targets

Using the WITNESS models for the welding and vehicle body production lines, Ford Otosan was able to optimize its design, achieving:

  • 15% reduction in cycle time
  • 15% increase in operator efficiency
  • 2% increase in robot efficiency

Savings came from a range of areas, including

  • Previously unidentified bottlenecks that were resolved
  • A smart production algorithm to plan the sequencing of vehicle types
  • Opportunities to achieve robot and operator efficiencies
  • Cycle times that were out of sync with forecasts and could be improved
  • Preventative measures to mitigate the effects of breakdown

Particularly useful was the ability to understand production capacity 12+ months after opening the factory, so Ford Otosan could analyze performance over the long-term in a risk-free way – and adapt its planning and operational approach accordingly.

"Simulations are now indispensable for building high-efficiency lines at the design stage. Our high-tech production lines, where we will produce electric vehicles, will have simulations that are used not only during the design phase, but also after the mass production starts. These simulations will turn into a decision support system by processing real-time production data. We are proud of commissioning a Digital Twin project for the first time at Ford Otosan." Omer Faruk SENOCAK, Data Analytics and Simulation Engineer

Based on the success of this project, Ford Otosan and CodeArt are now using WITNESS to build predictive digital twins of the paint shop and assembly lines, ultimately providing an integrated simulation model of all manufacturing processes for the electric transit factory. This will enable simulation to be a cornerstone of Ford Otosan’s ongoing operational planning, helping them leverage data and Internet of Things investments in line with their digitalization strategy.

"With the help of WITNESS simulation modeling, it is a great advantage to verify the feasibility of new production facility projects and to see opportunities before investment realization. We are also taking the most important step towards the digital twin with WITNESS" M. Nejat TANCA, New Technologies and Smart Factories Leader

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