The Automotive Industry is undoubtedly one of the fastest-growing manufacturing sectors in the global economy. This rapid growth and transformation can be greatly attributed to the fourth industrial revolution (industry 4.0), its pervasive nature, and the continuous digitalization wave experienced worldwide. The fourth Industrial revolution is continuing to pray a critical role in expanding the digital transformation possibilities and increasing its value to the Original Equipment Manufacturers, eradicating structural defects, and improving the manufacturing sector’s quality. Industry 4.0 has made a significant impact in the automotive industry by aiding in harnessing the power within the connected and combined physical and digital technological aspects such as additive manufacturing, artificial intelligence, cloud computing, and robotics, among other aspects.
These physical and digital technological elements have positively affected the automotive industries by improving manufacturing sector quality by increasing flexibility, responsiveness, and efficiency and redefining how the automotive industry engages its customers, how it conducts business, and how delivery is delivered done.
Artificial intelligence in the automotive industry can accomplish a lot of new things besides powering self-driving vehicles. Artificial intelligence improves manufacturing sector quality, ensuring safety in both hardware and software elements of vehicles. This feature has provided artificial intelligence oriented, driving functionality features such as fully autonomous type and driver-assist (Khan and Turowski 2016). The fully autonomous functionality type is the concept behind the upcoming driverless automobiles, first launched in California, United States. The vehicles are controlled by a complex set of computer applications, powered by artificial intelligence, installed in the vehicles. Since driving is more than mere computer applications, two companies, namely Google and Tesla, have invented the right applications, and the outcomes have proved effective in reducing manufacturing defects. These functionality features have been credited with increased safety during driving, as reported by many drivers worldwide.
Additionally, artificial intelligence is useful in forecasting vehicle maintenance patterns by pointing out system errors requiring urgent examination. Artificial intelligence, through cloud marketing, has managed to perfect on individualized marketing concepts. AI-powered cloud platforms have continuously provided ideal solutions that target prospective audiences precisely due to the large data supporting AI( Romero et al., 2016). For instance, through AI, a driver may notice the fuel shortage problem. The system responds by suggesting that the nearest gas station for refilling or a driver’s eating problems may cause the AI-powered vehicle engine to suggest a nearby restaurant to purchase more food. AI-driven systems have also established the necessity for AI automotive insurance. This kind of insurance is appealing to many users because of its incredible ability to forecast the future, a prerequisite for insurance systems. For this reason, the system can fasten the insurance claim process in the event of an accident.
Industry 4.0 has been widely used in improving additive manufacturing in the automotive industry. The fourth industrial revolution has been utilized in making three dimension designs in countries such as India, which are the brand manufacturers of TATA vehicles. Since the coming of the fourth industrial revolution, Indian car manufacturers have ceased drawing car designs on papers, and instead, they are making use of highly sophisticated AI applications that support the newly innovated three-dimension design in reducing structural defects and improving brand quality. More so, the assembly company’s directors have reported being using simulations in car manufacturing now more than ever before. They have further claimed that it has even become easier to test newly assembled vehicles on different terrains, thanks to Artificial Intelligence vehicle simulations.
General Motors, an automotive industry based in Nairobi’s Kenyan capital, has become the latest beneficiary of the fourth industrial revolution execution. A case study on how the company is gradually replacing its conventional systems and adopting the fourth industrial revolution components will provide greater insights into the essence of adopting technology in our workplaces for convenience.
General Motors has embarked on a no-return journey of exploring the fourth industrial revolution’s several tenets, its strengths, opportunities, weaknesses, and threats. The company has then conducted a comprehensive cost-benefit analysis to establish its opportunity costs in the case of incompatibility issues to define the desired organizational outcomes accurately. This case study has undoubtedly helped other industries develop the courage to come out of their comfort zones to adopt this new technology.
The first step taken by General Motors in the successful adoption of the fourth industrial revolution is partnering with Deloitte, an overseas car parts, and assembling industry. Deloitte, being from first world countries, has already adopted the new technology and used it for quite some time, and therefore its vitality is providing constant and continuous mentorship in the adoption process. Fas forward now, General Motors has successfully used additive manufacturing, in the form of 3D printing, to come up with vehicle prototypes in a much faster way than the conventional process, which took longer and produced less efficient prototypes. Furthermore, using the same technology General Motors has been on the frontline in reaping economies of scale through producing cheaper, lightweight, but more effective vehicle structural components ( Romero et al., 2016). Lightweight vehicle spare parts are vital in moving the county closer to fuel consumption regulations’ millennium development goals. Lightweight components are also critical in ensuring balance in moving vehicles, increasing general vehicle safety. These components will go through rigorous trials and tests, proving that the parts will be better than those imported from foreign countries.
General Motors will utilize industry 4.0 in inventing in connectivity, electrification, and fully autonomous abilities. This invention will also involve a massive increase in the number of semiconductor cores in car engines. In the last decade, an average car was reported to have around 5000 semiconductor components, but the number will have to increase to around 8000 cores towards autonomous driving abilities (Qin, Liu & Grosvenor 2016). Because electrification is the number one failure for most cars, increased semiconductors will greatly reduce these failures and improve their reliability in saving costs and improving quality. Also, for eliminating defects in-vehicle systems, General Motors will have to rely on Industry 4.0 to generate new approaches in reducing latent reliability, commonly used for semiconductors whose defects activate if the semiconductors are subjected to extreme weather conditions such extreme heat or chilly conditions heavily. Detecting and testing these semiconductors will certainly necessitate modern approaches, far from traditional methods that deliberately allow these semiconductors to pass through the value chain, causing problems shortly.
Lastly, methods based on machine learning can be favorable for detecting these hidden defects even before the vehicles are released through the value chain. One of the best methods under this category, the Inline Defect Part Average Testing technique, will leverage historical reliability information, inline fault inspection, final tests, and data from power-driven water sorting to develop a classical model capable of detecting possible escapes and reduce flaws (Xu, Xu, &Li 2018). Multiple industries, including General Motors, are reviewing the technique mentioned above and are confident in solving the riddle. This method and others that will probably come up in due course will be crucial in assisting manufacturers of semiconductors in accomplishing the desirable quality levels for reliable and safe vehicle functionality.
Despite the remarkable efforts being taken by local industries such as General Motors in the fourth industrial revolution adoption, there exist numerous hurdles in achieving technological progress, especially in third world countries. One of the major challenges obstructing technological progress is the acute shortage of financial resources. Financial analysts have estimated that technological change is financially consuming that may consume millions of money, which is unavailable to many automotive industries in the country. Therefore, the government may be the only solution to their problems. The government may decide to intervene in these industries to raise their output quality and financially reduce output defects.
Secondly, there is a growing concern for the sharp decline of customer interest regarding AI-powered vehicles. Romero et al. ( 2016 )have shown that the more technology advances, the lesser the vehicle users are to purchase these topnotch machines. The main reason for this disturbing decline is the consumer’s inability to believe in their safety (Xu, Xu &Li 2018). Most consumers are still in disbelief on the current level of technology, and they, therefore, wouldn’t risk trusting a driverless vehicle. However, it seems that manufacturers will have to organize multiple awareness functions to rally vehicle users into trusting their technology.
In summary, both the vehicle users and the AV vehicle manufacturers face an imminent threat of cybersecurity. Cybersecurity is a broad term, and its broadness makes it difficult to impose regulations as it goes beyond geographical boundaries. It is not uncommon to find that someone outside the United States can hack a fully autonomous vehicle. Such problems pose a catastrophic threat because they are unpredictable, and worse, the chances of prosecuting alleged perpetrators are almost zero. Thus, automobile manufacturers will be tasked with the challenging duty of innovating applications that will be difficult to hack for this industry’s prosperity.
References
Khan, A., & Turowski, K. (2016). A survey of current challenges in the manufacturing industry and preparation for industry 4.0. In Proceedings of the First International Scientific Conference, “Intelligent Information Technologies for Industry” (IITI’16) (pp. 15-26). Springer, Cham.
Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å., & Gorecky, D. (2016, October). Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In Proceedings of the international conference on computers and industrial engineering (CIE46), Tianjin, China (pp. 29-31).
Qin, J., Liu, Y., & Grosvenor, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond. Procedia cirp, 52, 173-178.
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.