The global demand for rubber is increasing. That’s a problem for all automotive manufacturers, but especially for electric vehicle producers.

Being a natural resource, rubber is finite in availability. Yet the production of components that necessitate rubber must increase significantly to meet the rising demand, with EV sales estimated to increase by more than over 100% by 2027. Moreover, EVs need specialized tires with low rolling resistance that are made with specific types of rubber. 

Procurement and production of rubber products are becoming more difficult, especially U.S. and European automotive manufacturers. An overwhelming majority (90%) of rubber is produced in Asia, and as geopolitical tensions subject supply chains to unpredictable disruptions, including trade embargoes and increased tariffs, manufacturers will find it more difficult to source rubber materials and components. 

So how can automotive manufacturers ensure steady production of rubber-based goods? It comes down to being able to predict supply and demand levels proactively and accurately. With smart manufacturing technology, accurate forecasts can be generated with a few clicks. 

Manufacturers have long used algorithms to predict future demand and supply. Historically, they tended to be static – unchanging regardless of market dynamics, past production and industry trends. As a result, manufacturers depending on those algorithms are unable to include contextual information in forecast generation. This creates inaccuracies in forecasts as demand and supply levels change and innovations and new products are brought to market. Today, that’s happening in the auto industry with increasing frequency, as EVs become more advanced and sought-after. 

With the advent of machine learning, the capability to include contextual information in iforecasts makes them more dynamic, comprehensive and, ultimately, accurate. ML-driven algorithms can analyze heaps of historical data, including past production, material delivery delays, fluctuations in material costs, and other relevant details. Possessing a greater understanding of future demand and supply levels improves a manufacturer’s ability to deliver orders on time and in full. 

Using ML to generate industry forecasts is a new technique, so enterprises operating on thin margins might be skeptical of the new approach. Yet measurable benefits are already there. A 1% increase in forecast accuracy results in significant improvements, including a cascading effect on end-to-end supply chain operations. Companies gain a higher level of certainty when ordering resupplies of inventory materials, reducing the risk of overstocking and overspending on procurement and storage. At the same time, they can more confidently meet shifting volume and timeline demands. 

Accurate forecasts allow manufacturers to meet sales targets with less investment in inventory. No longer must they keep surplus inventory on hand in case a large order suddenly comes in, or materials become difficult to procure. ML-driven forecasting considers such information as past orders and material availability, freeing up cash to spend on investment and growth opportunities. 

Most manufacturers don’t have the budget or workforce opportunities to employ a data scientist on staff. Those wishing to take advantage of ML-driven forecasting, therefore, need to deploy systems that are ready to go out of the box, and can realize a quick return on investment.

Manufacturers should explore their vendor options and find a provider that specializes in optimizing operations for their particular industry. For automakers, finding a vendor with experience in that sector should be a priority. A knowledgeable vendor will understand the volumes and types of data already available to manufacturers, and work to implement a forecasting process that blends with pre-existing workflows. This not only ensures that forecasts are accurate and tuned to the business’s unique needs, but it also doesn’t disrupt the flow of existing enterprise processes. 

The ability to accurately and rapidly predict demand and supply for rubber materials is essential to automotive manufacturers’ bottom line. Often they’re working against tight budgets and strict requirements, and the additional funds made available through the adoption of modern forecasting methods can contribute significantly to the competitive value they offer their customers. 

Ara Surenian is vice president product management with Plex, part of Rockwell Automation.