Could advanced AI-based forecasting and planning techniques be your #1 asset in the struggle for automotive supply chain resilience?

To look at a few examples, the Baltimore bridge collapse in March 2024 may have been a “black swan” event. However, in the past year, we’ve also experienced a lack of water threatening the navigability of the Panama Canal and floods in southern Germany halting automotive companies’ operations. Weather-related disruptions like this are likely to become more frequent in the future as a result of climate change.

Uncertainty within the automotive business environment can be equally disruptive. For example, the current uncertainty surrounding customer demand for electric vehicles (EVs) seriously affects the whole supply chain, along with many other aspects of the industry.

“The automotive industry is going through multiple disruptions in the supply chain space including demand uncertainty in EVs and sustainability conformance challenges. This is definitely the right time for industry CXOs to focus on innovation and data-based insight to build competitive advantage.”

Laurence Noël, Capgemini’s Head of the Global Automotive Industry

With little prospect of a stable business environment, the need for a highly resilient automotive supply chain is more pressing than ever.

Resilience initiatives to date have met with limited success

Of course, automotive companies are only too aware of this need. A recent report from the Capgemini Research Institute describes strenuous efforts that have been made by OEMs and tier 1 suppliers to achieve that resilience and to better manage supply chain risk. Initiatives have included reshoring so that sources of supply are closer to home, and holding extra inventory as a buffer against supply chain interruptions.

Although these initiatives have certainly brought the companies concerned time, they will not achieve the results that are needed in the longer term.

Reshoring is, in principle, a sound approach because a shorter supply chain is less liable to disruption. However, the reality is that reshoring is not happening fast enough, or strategically enough. When reshoring is done tactically rather than strategically, the company may just end up with a few nearshore warehouses, rather than what it really needs – an intelligent approach that continually reshapes the supply chain for maximum resilience. Operations can suffer as a result, especially if supplier relationships deteriorate as a result of reshoring decisions.

Similarly, holding extra inventory “just in case” does avoid interruptions but it ties up working capital, which is expensive, particularly when interest rates are higher, either generally or for particular borrowers (smaller suppliers may fall into this category).

So automotive companies are now looking around for better approaches to improving resilience and risk management.

Time to focus on AI-based forecasting and planning

Better forecasting and planning, leveraging newer technologies such as AI, is on every company’s supply chain agenda.

There are several arguments for prioritizing this capability. A clear view of the future enables a company to operate as efficiently as possible. It also enables it to communicate more transparently with suppliers, sharing accurate projections of parts requirements, which will inspire trust and hence better working relationships along the supply chain.

However, many companies don’t realize quite how crucial their forecasting and planning capabilities are for improving their ability to react to disruption.

Let’s take a quick look at the potential here. If a company has the means to carry out effective, intelligent scenario planning, it can proactively develop robust contingency plans to deal with many of the situations that might arise in the future. And if an unforeseen scenario does emerge later, dynamic planning tools make it easy to replan rapidly, adjusting activities to minimize the effect of disruption.

To summarize the benefits, I’ll quote my colleague, Volker Roelofsen, who is Executive Vice President and Supply Chain Group Offer Leader, Europe at Capgemini. He says, “The right approach to forecasting and planning – leveraging the latest in AI and data analytics – can transform your supply chain from a cost center into a connected, frictionless function that delivers value.”

New approaches to forecasting and planning

The forecasting models of the past tended to work with just a few parameters and to use fairly basic tools such as spreadsheets. To support decision-making in the current challenging environment, the industry needs to adopt far more sophisticated approaches, integrating a variety of analytics techniques and making use of predictive models that can work with large data volumes.

Technologies such as AI are likely to be used, in addition to a full range of statistical methods. KPIs need to be built into forecasts and plans, along with the classic forecast vs. actual comparison.

Here are just a few examples of the many innovative approaches that can transform forecasting:

• Touchless demand forecasting, which uses big data, AI, and machine learning along with statistical modeling to create accurate demand forecasts from historical patterns.
• Order simulation, using similar techniques to predict specific vehicle configurations using historical data plus assumptions about future demand – especially useful in a build-to-order context where part variants can proliferate fast.
• Predicting disruption and its impact on the supply chain using AI-powered models.
• Minimizing delays to vehicle delivery by accurately predicting outbound logistics lead times, again with AI-powered models.
• Demand sensing via social media to supplement other demand forecasting methods (e.g. those based on dealer input).
• Modeling tools to instantly compare different suppliers’ plant availability, proximity, and pricing before ordering parts.
• Dashboards supporting decision-making with an overview of the cost, environmental, and other implications of different choices.

Once forecasting is enhanced with methods like these, planning can become a dynamic activity that happens continuously rather than periodically. You can continuously reassess your production needs, realign plans, and share updated information with suppliers.

Making it happen

Implementing these approaches from scratch is probably unrealistic, and certainly uneconomic, for any one company. We find that collaboration with our ecosystem partners is essential. We work both with mainstream providers of enablers such as data management tools and with innovative startups that offer leading-edge expertise in areas like AI.

Capgemini is already working with major automotive companies to improve their forecasting and planning capabilities. Our help can be accessed in different ways to support individual needs. For example, we undertake collaborative projects – where we work alongside internal management and staff to implement an existing strategy using our automotive-specific solutions and accelerators. But we also offer planning-as-a-service options, where clients get a head start with a solution that is already configured for the automotive industry yet is also customizable.

To find out more, please visit our automotive supply chain solution page. Then get in touch to find out how we can help your company apply advanced forecasting and planning techniques and tools that will improve your supply chain resilience and your ability to manage risk.