Driving efficiency through simulation

The challenge

Seeing the road ahead

Nissan Motor Co., Ltd. is an award-winning Japanese automobile manufacturer with operations in the UK. The company sells cars under the Nissan, Infiniti, and Datsun brands – all of which are household names around the world.

Nissan recognises the power of digital technology for identifying new efficiencies in its processes, reducing troublesome bottlenecks, and uncovering potential investment opportunities at its UK site.

The approach

Saying goodbye to bottlenecks

The Nissan team has applied digital twins in a number of cases to create value with their predictive power.

For example, they were used to calculate the optimum investment levels for a new bumper-painting facility, the required capacity of the site’s pre-storage area, and the packaging requirements needed to support engine transportation from Sunderland to Spain. 

Nissan also used a digital twin to identify the cause of congestion in its Painted Body Store – and, more importantly, to find a solution. 

The predictive digital twin showed that simple adjustments to Work in Progress levels, and a new process flow for the slings that carry car bodies through the store, could reduce the bottlenecks in this part of the production process.

After applying these changes in the real world, not only were bottlenecks avoided, but further obstructions later in the production line – specifically in the Trim and Chassis lines – were also removed. 

Another similar project saw Nissan use simulation technologies to improve the manufacturing process control logic in its White Body Store – again reducing bottlenecks in both body shops and paint shops, and bringing new levels of efficiency to its production processes. 

The impact

Significant savings through predictive twins

Digital twins have also helped support cost savings across Nissan’s Sunderland site. 

By twinning the cylinder head machining line, the team was able to run various scenarios for different pallet volumes, analyzing factors such as inputs, tack time and volumes.

The model showed that it needed only 70 pallets – well below the team’s initial estimate of 100 – saving £22,000.

Elsewhere in the factory, the team wanted to assess whether it needed a new paint guard film station to handle the predicted demand for its new Infiniti car model. 

Using a predictive digital twin, it realised that the existing facilities would handle the expected volume – which meant Nissan avoided an unnecessary £25,000 capital expenditure.

Next to these cost and process efficiencies, there have been numerous intangible benefits, too. Nissan’s business leaders have a better understanding of its production processes; it’s easier to communicate new ideas with the support of accurate, timely data; and the ability to simulate new projects and scenarios provides the confidence needed to challenge previously accepted practices.