THE MISSING PIECE IN TRANSITIONING AUTOMOTIVE MANUFACTURING
Posted by Zeeshan Zia, PhD
The global automotive industry is in transition. Trends such as electrification, self-driving, shared mobility and connected vehicles, powertrain evolution, global consolidation, and the competition for the Chinese market mean that this sector will look very different in 2030.
The leading players in this industry understand these challenges and have spent the past decade building the business and technology muscle to help them thrive in this new world. On the business strategy front, automakers who previously preferred proprietary technology and in-house engineering have been forming deep collaborations and alliances on autonomy, sharing the burden of investments with other companies. Leading manufacturers are embracing specialization by focusing on particular types of vehicles or on narrower value chain roles to benefit from economies of scale. They have become more zealous in managing costs, investing only in the critical capabilities required to deliver on their strategy while cutting costs in “table stakes” areas.
On the technology front, digitization is the talk of the town, and every single car maker has looked into predictive maintenance, IIoT, automated scheduling and ordering systems, visual part inspection, robots, and augmented reality. Even though projecting ROIs upfront with these projects has been problematic, and a significant fraction never leave the pilot purgatory — in hindsight, the benefits have been clear cut.
However you look at it, these drastic shifts will cause the manufacturing floors to change, and only the most adaptable will survive. The aspect of manufacturing advancement that gets the most press is robotics and automation. Yet, as Apple [1], Boeing [2], Tesla [3] and several leading manufacturers have discovered, robots are not as suited as human workers in adapting to process changes. That’s why robotics investments are best suited to processes which are already cast in stone. Of course, apart from lack of adaptability, state-of-the-art robots also lag behind humans on high dexterity manipulation tasks, which is why humans continue to be ubiquitous on sub-assembly stations.
Unfortunately, human workers come with their own weaknesses. Unlike the early days of assembly lines, factory floor work is no longer seen as a lucrative career by most, which means that retired experts are not getting replaced with younger workers. Meanwhile, cyclical shifts in demand and union pressures means that the majority of the new workforce comprises temps, who are poorly trained and make more mistakes than permanent workers.
A question that several players in discrete manufacturing broadly, and automotive manufacturing specifically have been asking recently is how to make human workers more efficient. Here we discuss several case studies around this question.
Contract manufacturers such as Jabil Circuit [4] have built customized mobile apps to provide “Digital Work Instructions” to help their workers avoid assembly mistakes. Think of these apps as PowerPoints which guide a worker through the steps in an assembly process. While such technologies require upfront investment in terms of redesigning the process around the technology and getting buy-in from workers, they have been shown to improve production yields by 10 percent and reduce manual assembly related quality issues by 60 percent. These tools also help provide visibility into these manual processes by dashboarding cycle times, error rates, and discussions around mistakes – thus “digitizing” the human element of the process. On the flip side, such tools reduce throughput and often annoy workers by requiring a lot of user interaction to help the system track the job status.
There are also some new ideas being explored by companies in the “lot size one” manufacturing space. John Deere [5] has built the JDAAT system (John Deere Assembly Assist Tool) which comprises a smart panel approximately six feet long, equipped with tens of IIoT-enabled tools. The JDAAT system gives the worker a step-by-step sequence of operations on a screen, e.g. “tighten bolt no. 15 and nut no. 15 using gun no. 22.” The gun sensor registers that the desired torque has been reached, and the system advances to the next instruction. John Deere products such as a Combine Harvester cost between $600,000 to $1,000,000 (including attachments) and one factory only produces eight pieces per day. The time window for harvesting is only three months per year, so farmers can’t afford their harvesters to break down. Thus, ensuring the highest quality is really important. In contrast, Tesla produces one car every two minutes, and the workstations there cannot afford to wait for connected powered tools, as even they slow down tasks. That’s why we expect that such solutions are likely an overkill for automotive assembly. In addition, JDAAT cannot track assembly steps that don’t involve power tool usage, e.g. when a worker joins two parts together with her hands which is common in automotive sub-assembly.
Whirlpool as well as manufacturers in the mass customized manufacturing sectors have been exploring overhead projector systems [6] coupled with cameras and basic machine vision technology to help independently train workers by providing them step-by-step guidance as well as live feedback. Fujitsu replaces these bulky projectors with AR technology in Microsoft’s HoloLens [7] to reduce assembly time of the networking equipment from 120 minutes down to 97 minutes (19 percent increase in productivity), and reduce their installation time from 53 minutes to 31 minutes (42 percent improvement). While highly effective, a major challenge is that it takes months to develop these experiences, and installation is often cumbersome in the case of projector setup while the head mounted display is not suitable for wearing all day long.
Denso and a few other Tier 1s have deployed cameras on their assembly workstations to gain visibility into manual processes. These systems analyze human operators performing assembly tasks and allow manufacturing supervisors and industrial engineers to trace mistakes through the line as well as continuously perform time and motion studies. Again, these systems have proven ROI in facilitating Kaizen events given the ubiquitous use of human workers on Tier 1 assembly lines. On the flip side, these solutions also take four to six weeks to customize to a given workstation and cannot keep up with an overnight modification to a process. They are also limited to providing offline analytics, as opposed to live task guidance and feedback to individual workers.
Altogether, human operators will continue to play an important role on automotive factory floors, even more so due to the agile nature of the next decade of developments in this industry. Despite this, most Industry 4.0 investments have focused either on robotics or on adding sensors to “equipment”, and human-powered processes have largely remained in a blind spot. Only in the last 2-3 years, have manufacturers started realizing the importance of bringing the human element into the realm of digitization. We believe that this trend will only accelerate due to the lasting changes brought by COVID-19. We see time to deployment, rapid adaptability, live as well as offline analytics abilities, and integration into existing manufacturing IT infrastructure such as Manufacturing Execution Systems (MES) as direction along which these new tools should grow in. At the same time, we’d love to hear how you are measuring and augmenting the frontline workers on your floors!
Huy takes our product roadmap and figures out what’s achievable with today’s AI advances, and what we need to invent to solve our customers’ pain points.
Huy led teams of scientists and engineers at NEC and invented technologies for driver assistance systems that are part of 500,000+ Honda cars. He is an inventor on more than 20 US patents, and his research papers are cited by every lab from MIT, Stanford, and CMU to Apple, Google, and Baidu.
Michael works closely with our marquis customers to understand their needs, and figures out the best solution that immediately adds dollars to their bottom line.
Michael has almost 20 years of enterprise sales experience. He has held sales leadership roles at pioneering AI and computer vision startups.
Andrey takes inventions and converts them into innovations. He leads our product engineering team to turn our scientific prototypes into robust customer-facing products.
Andrey co-founded and successfully exited a startup to immigrate to the US. He spent his next eight years building the core data science infrastructure for Amazon’s supply chain planning team, and the CV/ML infrastructure for Microsoft’s HoloLens organization.
Zeeshan focuses on delighting our customers by understanding their needs and defining product and strategy to meet and exceed these needs. He works closely with our sales leadership to grow our customer base.
Zeeshan was a scientific leader at Microsoft’s HoloLens division, where he invented AI technologies to assist frontline workers across industries. Earlier, he shipped IoT platforms and visual defect detection software for manufacturers at Siemens and MVTec GmbH.
