The supply chain is central to the efficiency of automotive manufacturers, impacting costs, on-time deliveries, production efficiencies, and overall customer satisfaction. The complexity of that chain has deepened with the introduction of hybrids and electric vehicles as well as regulatory mandates and corporate sustainability objectives.
However, new AI technologies have arisen that are digitizing processes and procedures that offer companies new ways to streamline processes throughout the entire supply chain and largely avoid costly disruptions—from outside vendors to line management and to final mile customer deliveries.
Meeting the challenges of an expanding market
According to ABiResearch, global consumer car sales reached more than 70 million in 2022 with a promising forecast that this number would reach about 76.1 million in 2024. While EVs are expected to continue to grow market share and account for more than half of new vehicle sales by 2030, other powertrains (ICE, fuel cell, etc.) still account for 78% of new car deliveries worldwide.
Podcast: AI best practices—Lessons learned at Girtz and Ford
Supply chains are largely in place to handle traditional automobiles, but with EVs comes the need for a distinct group of partners to supply the specialized components that are required for these alternative vehicles. Thus, traditional supply chains will be impacted by new suppliers, new sources of origin and new delivery and transportation methods based on battery charge and safety.
Sourcing parts and components from different parts of the world requires precise planning and coordination. With the complexity of global supply chains and production for multiple markets, the automotive industry faces increasingly complex logistics challenges involving multiple players.
Smarter factories with smarter AI
AI is becoming increasingly powerful at solving problems in areas such as optimization, decision-making, knowledge management, and process automation. This evolution means that AI-driven software will be just as important to businesses as people and hardware are today.
In this scenario, process AI, a rapidly expanding method that analyzes business process data in real-time, looks to improve operational excellence and productivity along with business process optimization and automation.
This means not only implementing AI technology but also training employees to work effectively in the emerging human-machine interface scenario. While machines may take over more complex tasks, humans will continue to have a vital role.
See also: Steel’s gambit: AI, transparency, and the battle against ‘greenwashing’
For example, one global automotive manufacturer took advantage of sophisticated AI technologies in its supply chain management. This particular manufacturer’s parts distribution center in central Europe is focused on three major areas: vehicle imports of its best-selling brand for southeastern Europe; automotive retail; and the provision of automotive financial services.
Spare parts distribution
From one of its key sites, the company manages spare parts distribution for the Austrian and southeast European markets. Every day, the planning team must select specific items, from a catalog of more than a million, to be stored on-site to ensure availability while simultaneously maintaining a appropriate levels of inventory.
The group manager who heads the planning and engineering of spare parts, as well as customer service stated: “The biggest challenges in planning are the wide range of spare parts and the steadily growing catalog. New models are continually released, new brands appear, new combinations, new trim levels, and consequently new spare parts.
We still have to deal with the spare parts of older models, at least until they reach a certain age, which is typically 20 – 30 years in automotive. Additional problems are high seasonal fluctuations and weather dependencies for certain items like windshield wipers, which are difficult to foresee.”
Inconsistent delivery times can be a crucial cost factor
Satellite warehouses are supplied by a central warehouse in Europe. Each item is assigned a parts number, which is used when recording the delivery time. Currently most parts are being shipped by rail with inconsistent delivery times, which emphasis the need for more precise planning to manage inventory and transportation costs.
See also: Episode 2 of (R)Evolutionizing Manufacturing: Data is everything
The ERP system was developed internally and is precisely adapted to the company’s operational business needs. A lack of updates over time had resulted in additional manual processing of 8,000 to 10,000 items a week. The manufacturer had to handle the planning and logistics of more and more items each week and could no longer rely on the forecasts.
This manufacturer hoped to relieve this burden by using intelligent add-on planning software, which would enable a high level of planning automation and inventory optimization. The evaluation process included several test-runs with different optimization software providers, including in-house developed solutions as well.
The expectations of the software were partly focused on its usability, but the most important factor in the selection process was its demand forecasting ability. After a nine-month evaluation phase, they opted for supply chain optimization software that incorporates AI for intelligent decision-making. It is helping to plan and manage their supply chain strategically, collaboratively, and operationally.
The software provider worked hand in hand with the internal IT team, allowing supply chain optimization software to be implemented in the central warehouse in just four months. The implementation of the software in the affiliated warehouse followed shortly after.
See also: Industry 4.0 gains are ahead of the tools to measure them
“We have learned several things from our software provider that we couldn’t have known before. For example, feeding additional information into the system leads to better forecasts. This is the great advantage of working with external providers—you receive a new perspective on the daily business and use of the data. This has resulted in an improved quality of the overall planning and forecasting process,” according to the group manager.
The system calculates forecasts, key demand trends, and order proposals overnight and then transfers them to the ERP system through a seamless integration for placing the subsequent orders the next day. The items are then sorted into groups for either automatic or manual replenishment planning. After a second evaluation which considers specific criteria, such as service and safety stock, the calculation results are then transferred into orders for the planners to process. The previous process only allowed ordering to be carried out once per week, and now occurs daily.
With the use of the software provider’s mathematical knowledge, items that can be easily forecasted are planned automatically. The level of automation now amounts to around 30 percent, which has helped make decisions faster, more precisely, and more intelligently.
Smart AI solves complex business problems
Until now, software could only deliver results based on structured data. This is data that is available in a special form, for example in databases. AI, with its ability to process language, is no longer exclusively dependent on this.
See also: 90% of manufacturers are using AI, but many feel they lag behind competitors
The software of the future will therefore offer seamless transitions between different forms of communication, which amounts to a coexistence of formal and informal forms of digital interaction.
It will therefore be much more closely linked to the real world than before. The fact that AI will be able to understand complex processes and process voice commands makes it intuitive for any user or company.
