Automotive in a changing world: Factory stock market chart 2024

Geopolitical risks, high energy costs, and the technological shift toward electric vehicles are challenging the automotive industry. To remain competitive, new business models are required, and costs must be reduced. A “stock market chart” for production costs can speed up an automaker’s reactions to disruption.

The wounds are still fresh: Covid-19 disrupted supply chains, made cost-effective transportation routes impossible, and brought manufacturing and deliveries within the production network to a standstill. Since then, a number of other uncertainties have arisen. However, some companies have reacted quickly with new ideas. For example, if specialists cannot quickly travel to a plant to maintain or repair production machinery, or if auditors cannot be on-site, the so-called metaverse can help. In one company, employees within the facility are equipped with smart glasses, which digitally incorporate additional information, and can establish a direct connection to the right specialists whenever necessary. This mix of visible reality with an augmented digital world – the metaverse – avoids bottlenecks by combining remote and on-site maintenance. A prerequisite is that the smart glasses have access to an address book listing the international specialists who can assist, either individually or collectively in a web conference. Access is also required to folders containing, for example, CAD drawings or machine operating instructions. Through additional software, these pieces of information are exported and displayed in the smart glasses through a viewer app or as 3D holograms on the screen. Additional information, such as step-by-step work sequences, is integrated and a direct connection to the right specialists whenever necessary, whether they are in the factory or on the other side of the world, is obtained. This allows the task to be completed quickly without the need for staff to travel to the site.

Four challenges: flexible production, data consistency, artificial intelligence for cost transparency in production, and solid business cases

The example above impressively demonstrates how flexible and innovative the automotive industry can be when necessary. Nevertheless, there are a few things to bear in mind to ensure that companies can cope with the current geopolitical and market-specific conditions:

1. Improving production flexibility

As the concept of remote maintenance in the metaverse has shown, working methods can become more agile and production more flexible through new digital approaches. Digital tools such as smart glasses, representations of machines as 3D digital twins, and virtual prototypes make it possible to simulate solutions in advance and ultimately implement them more efficiently in the factory. Given that many of these solutions are available off the shelf, the investment quickly pays off.

Also on the rise is production planning with automated Advanced Planning and Scheduling (APS) software. With this advanced software, the right worker, the right raw material, and the right machine come together at the right time, no matter how complex the factory. In contrast, traditional production planning software only makes suggestions, so time-consuming manual entry and decision-making are still required.

However, the transformation requires new business models and therefore maximum flexibility in production. If parts for a combustion engine are discontinued, an aluminum piston for a traditional engine, for example, will no longer be in demand. However, the associated manufacturing expertise can be useful in other sectors too – often in the production of entirely different products. Increased product variety and even smaller quantities often deviate from mass orders but can be compensated for with a more flexible production process, as described above.

2. Establishing data consistency utilizing open interfaces

When automated APS software and new digital approaches are used, they are only effective if databases can be accessed readily, and data can flow quickly. Data consistency means that information and data is available throughout the entire life cycle of a product, from its development and manufacture through to sales, invoicing, and delivery. This information is not limited to individual software programs or folders but can be used directly by all the software systems in use. So when a product arrives at the warehouse, it is clear in which plant it was manufactured, who ordered it, and where it is destined for, as the individual software systems for warehousing, production processes, machine maintenance, personnel, quality, logistics, and sales are directly interconnected. Modern platform solutions work with open interfaces that connect all specialized software applications for administration, finance, human resources, procurement, production, intralogistics, quality, and warehouse management in a compatible way.

3. Artificial Intelligence (AI) for cost transparency in production

The energy consumption of production machinery can be monitored seamlessly using sensor technology. To save energy, it should be clear when a machine is needed or should be on standby. A transparent overview of the amount of electricity required annually for base and peak loads allows for better negotiations with the energy provider, and for replacement of power-hungry machines.

Whether machines are kept operational or not depends on many factors, such as order volume, timely delivery of raw materials, and the availability of skilled production personnel. Digital transparency throughout the process enables precise predictions. Based on these, decisions can be made about whether a machine should be temporarily shut down, for example, if a delivery truck is stuck in traffic.

Incorporating global production capacities and transportation routes into planning is no longer a difficult task for intelligent software, especially if it leverages AI. Even if international capacities are included, such software can replan across the entire value stream, almost in real time, to find the most energy-efficient process flow. AI thus creates a kind of stock market chart for production costs, giving a view of the essentials. This is particularly crucial in 2024, when high energy prices in countries such as Germany are one of the industry’s most important challenges.

4. Solid business cases

While companies can quickly respond to skyrocketing production costs with the help of near real-time data, decisions regarding new software technologies and their implementation require the creation of a solid business case stating specific requirements. Business cases including return-on-investment (ROI) calculations are essential for deciding on the most cost-effective new software systems. If the current and desired situations are clearly formulated, tools such as Power BI can provide a forecast and simulation of different variants: For example, the production plants and transportation routes enabling the most cost-effective manufacturing of products can be determined. Particularly in the case of complex software systems such as Manufacturing Execution Systems (MES) for production control, this approach makes it easier to select the right software provider and identify the necessary functional modules. A business case from last year showed that the initial project and ongoing maintenance costs for implementing a new MES system would be amortized after just three years.

Reducing costs and improving sustainability through transparency

The current market environment is challenging. However, there are specific ways and solutions to respond to these challenges. Flexible supply chains, solid business cases, and intelligent systems all make it possible to turn complex information and data into usable insights. More efficient processes not only result in lower costs, but also consume fewer resources.