A new research article published on 2 August 2021 in AI Perspectives, an open-access Springer Nature journal, examines how one corner of that industry — original equipment manufacturers, or OEMs, in the Brazilian Northeast — viewed the role of Industry 4.0 technologies in weathering the disruption and preparing for the years ahead. The study, led by Milton C. Soares, Cristiano V. Ferreira and Thiago B. Murari, offers a detailed snapshot of how manufacturing engineers perceive nine digital technologies across the five phases of automotive product quality planning.
The research team built their investigation around a survey of sixteen manufacturing engineers working in the automotive industry, all experts in manufacturing processes. The survey ran between December 2020 and January 2021, capturing perceptions at a moment when Brazilian automakers had cancelled their quarantine and returned to vehicle production under protocols issued by the Brazilian Association of Automotive Vehicle Manufacturers, ANFAVEA, even with the outbreak still ongoing. Of the surveyed engineers, 87.5 percent worked at two automotive OEMs in the Brazilian Northeast, and the same proportion reported more than ten years of experience in the automotive industry. All participants rated their knowledge of Advanced Product Quality Planning and Control Plan, known as APQP, as moderate to high, while 81.25 percent rated their knowledge of Industry 4.0 technologies in the same range.
APQP itself is a reference manual first released by Ford, Chrysler and General Motors in July 1994, designed to communicate guidelines for product quality planning and control plans to internal and external suppliers. The manual divides the process into five phases: Plan and Define Program; Product Design and Development; Process Design and Development; Product and Process Validation; and Feedback, Assessment and Corrective Action. The survey asked the specialists to rate the current frequency of use of nine Industry 4.0 framework technologies across these phases, and then to forecast their usage five years into the future. The nine technologies assessed were adaptive robotics, cyber-physical systems, additive manufacturing, cloud technologies, virtual and augmented reality, artificial intelligence and data analytics, communication and networking through the Internet of Things, real-time locating systems and radio frequency identification, and cybersecurity.
The results reveal a striking gap between current practice and future expectations for two technologies in particular. Although cybersecurity and artificial intelligence combined with data analytics were not widely used by the OEMs at the time of the survey, between 60 and 80 percent of the experts said these two technologies would be heavily used across all phases of APQP within five years. The authors highlight what they describe as a huge gap between the current and future situation for these technologies, suggesting that specialists anticipate the quantity of data and the complexity of technology in automotive manufacturing will grow substantially. They also note that traditional cybersecurity approaches may not suffice to protect big data, meaning the paired growth of analytics and security could be essential to keeping the supply chain widely connected with the OEM.
A second cluster of technologies emerged as the workhorses of the present moment. Communication and networking through IoT, cloud technologies and adaptive robotics were identified as the most heavily used technologies today in the automotive OEM industry. More than 60 percent of respondents said cloud technologies would be important across all APQP phases over the next five years, with particularly high expected use in the planning, design and process development phases. IoT was perceived as most utilized during the detailed design, product launch and corrective action phases, and more than 60 percent of those interviewed expected it to be highly used throughout the whole APQP process five years ahead. Adaptive robotics drew near-unanimous expectations of heavy future use in the process design and validation phases, as well as in corrective production actions.
The remaining technologies painted a more modest picture. Virtual and augmented reality were currently less used in the initial program definition phase than in later phases, and while the authors expected some rise over five years, they did not foresee an increase as significant as that projected for cybersecurity, artificial intelligence, cloud, IoT or robotics. Additive manufacturing, cyber-physical systems, and real-time locating systems paired with radio frequency identification were considered the least used technologies at the time, with no sharp rise anticipated relative to the leading group. That said, additive manufacturing stood closest to the high-adoption boundary, with specialists considering it important for the five-year scenario, mainly in the process design and validation phases where engineering teams formalize the manufacturing process and complete the Production Part Approval Process. RFID adoption, the authors suggest, is likely to accelerate only when supply chain visibility becomes a widespread reality, since one of its core values is rendering the supply chain visible as a strategic capability.
To synthesize these perceptions, the researchers plotted the average current use of each technology against its average five-year forecast, grouping the results into four quadrants. Cybersecurity and AI with data analytics landed in the quadrant representing the greatest potential for future adoption relative to current usage. Cloud technologies, IoT and adaptive robotics occupied the quadrant of technologies already most used today that should continue rising. The quadrant boundaries were set at approximately 30 percent average current use and 60 percent average forecast use, derived from the arithmetic mean of responses across all technologies and phases.
The study then connected these adoption forecasts to the supply chain challenges that COVID-19 exposed. The authors propose that the technologies expected to rise most sharply could mitigate specific pandemic-driven problems: lack of supply chain flexibility, imbalance between supply and demand, and new consumer behaviors may be addressed through artificial intelligence and data analytics; cloud technologies, IoT and adaptive robotics may feature in solutions for communication issues, lack of security and poor infrastructure; and cybersecurity may reduce access problems by enabling remote access to core systems. The broader context is sobering. Before the pandemic, a slump in global car sales had been projected for 2020, with an estimated 64 million automobiles expected to be sold worldwide against 80 million estimated pre-pandemic. The Brazilian automotive sector, which represents about 5 percent of the country’s gross domestic product and roughly 20 percent of the manufacturing industry’s GDP, employs more than 1.3 million workers across 26 manufacturers, 484 autoparts companies and 5,279 car dealers.
The authors are careful to delineate the limits of their findings. The discussion is restricted to the COVID-19 outbreak, which means the conclusions cannot be extended to other outbreaks or supply chain disruption conditions. Although the evaluation method itself could be applied to assess the automotive industry in any region or country, the results presented reflect only the Brazilian Northeast automotive industry and cannot be considered a snapshot of the national scenario. The surveyed perceptions also come from engineers at just two OEMs, and the authors acknowledge that the literature they reviewed on pandemic supply chain challenges was not composed of in-depth studies specific to the automotive sector, whose supply chain includes more than 900 Tier 1 suppliers and several hundred thousand Tier 2 and Tier 3 suppliers.
Future research, the authors suggest, should evaluate technology adoption challenges in other locations to understand which technologies might raise supply chain resilience worldwide, incorporate the perspectives of design and release engineering experts, and compare results across different OEMs. For now, the study offers Tier 1 and Tier 2 suppliers a data-grounded starting point for planning investments in the digital technologies their largest customers expect to lean on — with cybersecurity and artificial intelligence standing out as the technologies whose moment, in the eyes of the engineers who build cars in the Brazilian Northeast, has only just begun.
Subject of Research: Industry 4.0 technology adoption in the Brazilian Northeast automotive OEM supply chain after COVID-19
Article Title: Supply chain resilience and industry 4.0: a evaluation of the Brazilian northeast automotive OEM scenario post COVID-19
Article References: Soares, M. C., Ferreira, C. V., & Murari, T. B. (2021). Supply chain resilience and industry 4.0: a evaluation of the Brazilian northeast automotive OEM scenario post COVID-19. AI Perspectives, 3(1), Article 3. https://doi.org/10.1186/s42467-021-00010-1
Image Credits: AI Generated
DOI: 10.1186/s42467-021-00010-1
Keywords: Industry 4.0, supply chain resilience, automotive OEM, Brazilian Northeast, COVID-19, APQP, cybersecurity, artificial intelligence, data analytics, IoT, cloud technologies, adaptive robotics
Source: bioengineer.org
