Automotive Supply Chain Management 4.0 Trends

The automotive supply chain is different from other sectors owing to a large number of stakeholders involved, new disruptive trends, and the availability of raw materials that necessitate global coordination and strict timelines. The automotive supply chain system is one of the most robust ones due to the industry’s rapidly evolving, lean, and efficient model.

The Covid–19 pandemic completely transformed the automotive supply chain. Manufacturers who had implemented Industry 4.0 practices were better positioned to deal with Covid-19 related challenges. According to a McKinsey survey published last year, 56 percent of respondents who had not implemented Industry 4.0 prior to Covid-19 found their ability to respond to the pandemic, limited. According to a Fortune Business Insights report, the global Industry 4.0 market is expected to grow at a CAGR of 16.4 percent from $116.14 billion in 2021 to $337.10 billion in 2028. This transition to Industry 4.0 technologies has also provided the necessary agility to continue operations in recent months.

The smart factory is the first step towards transitioning current supply chains to an autonomous supply network. Cyber-physical systems monitor physical processes, create a virtual copy of the physical world, and make decentralized decisions within modular structured smart factories. Cyber-physical systems also communicate and collaborate with humans in a synchronous manner and time, both internally and externally, via the Internet of Things (IoT). Some of the Supply Chain Management 4.0 Trends that will completely transform the present supply chain system.

Artificial Intelligence and Machine Learning -Automotive companies have started incorporating AI in supply chains as they have acknowledged the potential of AI to solve the complexities of running a global logistics network. AI also enables businesses to make smarter, more agile decisions and anticipate problems.

AI and Machine Learning (ML) can also be used in obsolete inventory management decision-making. Machine Learning is also ideal for optimization as it can decipher trends and patterns in data from across the supply chain. On the other hand, Predictive forecasting techniques assist supply chains in developing intelligent demands and reducing inventories. A smart warehouse within a smart factory, combined with AR, VR, and AI techniques, will significantly alter how warehouses have been operating till now.

Automated Guided Vehicles - Automated Guided Vehicles play an important role in the material flow in a digitized factory. They reduce manual activities like driving forklift trucks, accelerate transportation, and help in making processes more predictable. The industry is gradually moving towards AGVs.

Smart Sensors - Smart sensors provide digital insights that help generate new value throughout the supply chain. Managers are updated with real-time information about their inventory, machinery, and purchased materials. Smart sensors also provide visibility throughout the supply chain and fuel analytics. This data helps to better understand and predict demand, optimize sourcing, and make high-value manufacturing decisions.

Smart sensors have the potential to improve the supply chain process by increasing operational efficiency, lowering manufacturing costs, and providing critical insights into customer orders. Smart sensors would help simplify predictive maintenance and inventory management. It also aids in increasing labor productivity and accuracy along with updating the management about future product development and after-sales services.