Modelling the New Normal

The last few months have seen our lives change drastically as COVID-19 has swept across the world. Now, for some of us, we are slowly coming out the other side and into a ‘new normal’.

Since the start of the pandemic, key suppliers and retailers have been operating to keep essential items available to the public, offering a small amount of normality and keeping part of the economy running.

Now, other businesses are getting the green light to reopen, seeing production lines starting up again, supply chains moving and shop doors opening to customers - but only under strict new operating conditions that follow social distancing rules and safety policies. Many are worried that this may have long term effects on their profitability, but immediate action needs to be taken to get back to business.

Understanding the best approach, while preparing for the future, is the challenge facing the business leaders today; what is a ‘new normal’ and what performance can they expect?

It isn’t easy. We have never been in a situation like this before, and we are entering unknown territory. Some businesses have already been lost, and the rest are trying to weather a storm that changes daily, where the wrong move now potentially has serious long-term implications.

With emotions and uncertainty high, decisions need to be based on solid factual information from data that provides clear insight into what move to make next, and what the outcomes to these moves will be.

As discussed in a recent article in Forbes, many see COVID-19:

“the wake-up call manufacturing needs to stop talking-the-talk about Industry 4.0, IIoT or Smart factory and start walking-the-walk.”

This virus has exposed weaknesses and shortcomings of traditional supply chains and manufacturing operations, and made clear the need for more flexible, digitally-enabled solutions.

Factories have felt the impact of COVID-19 heavily, and although production is starting up again, this is with reduced staff, longer lead times and more volatile demand.

Automakers are facing this test now, as General Motors, Ford and Fiat Chrysler in Detroit and JLR in the UK have just begun producing vehicles for the first time since March. Flexibility is key, and predictive digital twins offer a way to quickly test the effects of changes without risk. For example, to better accommodate social distancing, Ford is “rechoreographing” the way in which workers are building vehicles as they move down the line. All four companies have adopted new production profiles as they ramp up, prioritising fast movers (pickup trucks and SUVs in the US) over slower selling models. Most plants are also operating just one shift during this ramp-up phase, rather than the normal two or three per day. Additional uncertainty is being seen through supplier shortages. All of these dynamic variables can be incorporated into a digital twin of the factory, allowing companies to experiment with different ramp-up scenarios as the labor, supplier and demand markets change.

While a small sense of normalcy is slowly beginning to return, companies will need to continue to examine their manufacturing processes and supply chains. As new opportunities are revealed and contingency plans discussed, predictive simulation and digital twins should play a powerful role in testing out new ideas before committing any resources. Doing so ensures that the decisions that are made are made with confidence, and in a way that is sustainable for long-term success.