The pandemic’s influence was seen across industries as it sent shockwaves through the global economy. The automotive industry, which was one of the hardest hit, was severely disrupted, emphasizing the need for supply chain transformation.
Production halts and a lack of semiconductors were only the beginning. Rubber, steel, and aluminum, among other essential raw resources, have been in limited supply. Labor shortages, Trump-era tariffs, and the growing frequency of severe weather events were among the factors that compounded the problem, causing longer lead times and disastrous delays.
By late 2021, US vehicle dealerships barely had a fraction of the inventory they had before the pandemic. Meanwhile, health worries about public transportation and government-stimulated consumer spending resulted in a surge in demand that could not be met. While the auto industry’s greatest selling season is usually at the end of the year, enthusiastic buyers found it practically hard to buy a new automobile as 2021 came to a close. In the end, automakers sold fewer than 15 million new vehicles in 2021, significantly below the industry average of 17 million pre-pandemic.
The ongoing health issue has exposed significant vulnerabilities in the car supply chain, providing a critical opportunity for the industry and its suppliers to rethink inventory management. The adoption of technology will be at the vanguard of this transition, with machine learning and artificial intelligence-based systems poised to have the most influence on the organization.
Greater transparency, speed, and predictive ability are three significant benefits of AI in tackling essential challenges at the heart of supply chain breakdowns. Prioritizing the adoption of AI-powered technologies with these goals in mind is likely the single most impactful action carmakers and OEMs can take to mitigate risks and increase supply chain resiliency.
Transparency has improved.
Throughout 2021, supply chain delays were caused by a lack of visibility and access to data. The flow of information was hampered by disparate systems and isolated data silos, compounding disturbances. When a vital component of an auto manufacturer’s assembly line fails and is urgently required, the manufacturer typically lacks reliable information on when the part will arrive. It’s now a question of whether it’ll arrive on time – or at all.
AI-based technologies directly address this problem by simplifying data and making it more accessible, allowing for better-informed decision-making. OEMs and carmakers are now relying on AI to provide complete visibility into the shipping process, including real-time tracking that keeps track of a shipment’s specific location from the minute it leaves the warehouse. All stakeholders are automatically notified of changes or delays if they have access to an AI platform.
AI is also being used by suppliers to acquire rapid visibility into all potential delivery methods, from the fastest to the most cost-effective. The less costly and time-consuming back-and-forth as possible with more information and visibility across the supply chain. AI is allowing greater communication and integration capabilities, as well as assisting industrial decision-makers in responding to changing conditions, through boosting visibility.
Problem-solving Time Reduction
In the face of rapidly changing events, the pandemic highlighted the importance of industrial agility and reactivity. Making quick and well-informed decisions is crucial. However, to move swiftly, stakeholders must have access to data. AI provides unrivaled speed thanks to greater transparency. Technology can improve the decision-making process itself, reducing human error, in addition to offering more data points and insight.
Artificial intelligence (AI) improves efficiency throughout the supply chain. Traditional methods of order coordination rely on phone conversations and e-mails, but future-focused intelligent platforms can optimize every step of the process. AI is already being used by suppliers to provide real-time prices for components and services based on optimal routing and digital tariffs. This can help resume assembly activities as rapidly as feasible in the event of a downed production line.
The delivery procedure is sped up even further with auto-routing and direct dispatch. AI also significantly reduces troubleshooting time by allowing logistics partners to respond instantaneously to traffic, weather delays, and other travel difficulties. Intelligent automation eliminates the need for employees to participate in these decisions, shortening the decision-making process.
Suppliers will need to optimize at unprecedented speeds as the automotive landscape transforms. AI also has significant benefits in this area. AI can learn from its mistakes and improve its performance in a shorter amount of time. In the case of automotive logistics, for example, machine-learning algorithms will penalize a route if it fails, noting the blip and logging the data to prohibit the route from being used again. AI adoption is improving return on investment by allowing more efficiency across the auto supply chain.
Predictions that are more accurate
The industry must increase its ability to predict and plan for future shocks to become more resilient. Most automakers’ capacity to forecast production mishaps is currently limited. Downed lines and costly disruptions reverberate across the supply chain as a result. In a manufacturing line, a single hour of unscheduled downtime can cost hundreds of thousands of dollars in lost revenue.
Automotive success depends on the ability to forecast the future in an unpredictable future. It’s critical to assess the possibility of supply chain failures and to comprehend why they occur. By minimizing human mistakes and integrating previous data into the equation, AI is good at predicting things that people can’t. It can quickly synthesize data on component consumption and maintenance and compare it to past performance and part lifespans to accurately assess the likelihood of supply chain problems.
AI can also predict transportation delays more accurately before they occur. To anticipate the best potential shipping route, machine learning can look at past traffic data, driver performance, on-time departure rates, and weather patterns.
The ongoing health issue exposed flaws in existing car supply chain systems, calling into question the industry’s “just-in-time” (JIT) manufacturing approach. With lean raw material and work-in-process inventories, the JIT approach was designed to cut inventory and minimize surplus, but it swiftly cracked under the strain of pandemic-induced disruptions and rising consumer demand. Cross-continental supply networks and global interdependencies have been brought into question.
The industry is started to examine reshoring manufacturing and supply chains back to the United States to lessen vulnerability to global conditions. It also intends to develop local storage to anticipate the demand for more parts to be stored. However, reshoring manufacturing and localizing operations are only one component of the answer. Automobile manufacturers will continue to strive to cut costs wherever possible, which means that some elements of the supply chain will likely remain in remote but economically favorable areas.
Reorienting priorities around the use of AI technologies that may generate benefits across critical aspects of the supply chain is in the automobile industry’s best interests. Auto production is at a critical juncture; now is the time to innovate and fully employ data and artificial intelligence to streamline and enhance performance. AI-assisted logistics is prepared to deliver on those aims, moving the automobile industry towards a more intelligent and robust future, by providing absolute transparency, rapid problem-solving, and predictive accuracy.
