Artificial intelligence is among the most fascinating ideas of our time. It has captured the imagination of visionaries, science fiction writers, engineers and wall street analysts alike. In fact, artificial intelligence is in many ways a catalyst for the data revolution something that has disrupted every aspect of modern life. As with all new technologies, some are faster to embrace them, and others are much slower. Is automotive manufacturing one of the faster ones or would it be among the last?
Artificial intelligence (AI) encompasses various technologies including machine learning (ML), deep learning (neural network), computer vision and image processing, natural language processing (NLP), speech recognition, context-aware processing, and predictive APIs. But how much does this impact manufacturing and supply chain operations? Three smarts are worthy of consideration, namely smart machines, smart quality assurance and smart logistics.
The first, smart machines is relevant because improved asset utilisation is one of the greatest opportunities for AI to translate to direct savings. As overall equipment effectiveness (OEE) has been the de-facto standard to compare machine performance, automotive companies are embracing AI and machine learning (ML) algorithms to squeeze every ounce of performance from machines. Typical use cases include bottleneck detection and predictive/prescriptive maintenance. Dynamic bottleneck detection is necessary to efficiently utilise the finite manufacturing resources and to mitigate the short and long-term production constraints. In our case, we developed a neural network-based AI prediction to determine the bottleneck for the future.
A comprehensive AI strategy is vital to the success and competitiveness of automotive manufacturers, regardless of how far-fetched the use cases may seem to executives today
In terms of predictive/prescriptive maintenance, modern manufacturing machine infrastructure is designed with 3Vs for big data: volume, variability and velocity. Harnessing the potential of big data by incorporating machine learning algorithms into the data cloud, provides constant feedback to technicians and managers to ensure zero downtimes. Together with edge computing, machines are provided constant feedback based on output parameters. This leads to smarter machines that autocorrect itself based on individual cycles.
Smart quality assurance is relevant because quality controls such as quality gate are typically performed by workers. The process is often highly subjective and depends on the skill and training level of the operator. Smart assistants based on computer vision and image processing are assisting and, in some cases, taking over the inspection process. Moreover, the AI system constantly improves itself based on feedback.
The third smart is smart logistics. AI adoption in supply chains is taking off as companies realize the potential it could bring to solve their global logistic complexities, and it has a particularly significant role to play in the automotive industry.
Predictive analytics can be used to help with demand forecasting, and AI is helping network planners gain more insights on the demand patterns, resulting in improved forecasting accuracy. The efficiency gained in an accurate forecasting model has a bullwhip effect along the supply chain.
Smart warehouses are inventory systems where the inventory process is partially or entirely automated. This includes interconnected technologies to increase productivity. Smart warehouses use IIOT (Industrial Internet of Things) and AI to connect each process, data is collected at each of the nodes and the smart warehouse continuously learns and optimizes the process.
Most automakers have not taken meaningful steps towards integrating artificial intelligence in their manufacturing operations. Even the projects that do exist are mostly in partnership with universities and companies that offer products that are not customised for automotive applications.
The automotive sector, among other industries, will significantly benefit from robotic process automation (RPA) by transforming various consumer and business applications. In addition to business support functions, RPA can contribute to a number of areas in automotive manufacturing
The first movers have taken a number of initiatives (in series production, not pilot initiatives), including investments in collecting data centrally from their manufacturing operations and supply chains; projects to centrally connect a wide array of sensors to predict maintenance, uptime and other critical information using technologies such as NB-IoT; asset tracking initiatives across the supply chain; advanced predictive technologies for supply chain risks based on supplier reported KPIs and other sourced data; and investments in start-ups for predicting equipment issues.
Automotive manufacturers are often risk averse when it comes to new, unproven technologies, and it is unlikely that AI will find first application in automotive manufacturing due to a number of factors, including return on investment, which is not clear and potentially involves a protracted period; lack of expertise in AI and limited resources to dedicate to this initiative; organisational and process challenges; and availability of non-AI based approaches with satisfactory results.
Automaker manufacturing executives are interested in technology opportunities that have strong, demonstrable pay-off potential, and this is especially true in the case of suppliers. A familiar concept for the industry that has reaped rich rewards over the years is automation and robotics. Ever since the first industrial robot, the Unimate, was installed in a GM factory in 1959, automation has been one of the driving forces for the exponential growth in production and efficiency of the automotive industry. Now with hundreds of robots busy assembling parts on the manufacturing lines, a new type of robot is making waves behind the scenes to prepare for the next automotive industry revolution.
The so called softbots, or digital workforces are programmed software that can help automate many processes that are rules-driven, repetitive and involve overlapping systems. With success in HR, IT and finance, the softbots can work 24/7 on otherwise boring, repetitive manual work that normally would take days for the human workforce to complete. This could result in a significant cost reduction along with a tremendous increase in efficiency. The automotive sector, among other industries, will significantly benefit from robotic process automation (RPA) by transforming various consumer and business applications.
AI adoption in supply chains is taking off as companies realise the potential it could bring to solve their global logistic complexities, and it has a particularly significant role to play in the automotive industry
In addition to business support functions such as HR, IT, and finance, RPA can contribute to a number of areas in automotive manufacturing, including inventory management, production monitoring and balancing, paper document digitization, supplier orders and payment processing, data storage and management, and data analytics and forecasting.
RPA could take over some or most of these processes to reduce resource costs. More importantly, it can integrate with other existing technologies such as object character recognition (OCR), text mining, and nature language processing (NLP) to make more data available from the shop floor for advanced and predictive analytics. The applications can be then developed to detect or predict quality issues much faster and recommend corrective actions based on historical data and expert knowledge.
Beyond manufacturing, RPA is also making an impact in enhancing regulatory compliances such as GDPR or CCPA by helping car companies building systems to auto-process data requests by millions of users.
RPA is the next logical step and a starting point for most automotive companies. Even though RPA is rule-based and does not involve intelligence, it would help to initiate the change in mindset that is required for future AI adoption in automotive environments. In addition, RPA offers relatively quicker ROI by providing benefits in terms of cost reduction and error reduction soon after implementation.
Data-intensive manufacturing leading to data lakes, powerful computing and the availability of efficient algorithms has made it easier to integrate AI into automakers technology roadmaps. Applying AI to current manufacturing operations on a smaller scale does not require massive capital investment. Trainable data is readily available which can facilitate intensive testing and deep learning. Cloud and elastic computing have provided the opportunity to scale computing power as required. It might be beneficial to partner up with AI and ML experts from academic institutions as well as from within automaker product development teams to sustain the digital transformation journey.
Having a comprehensive AI strategy is vital to the success and competitiveness of automotive manufacturers, regardless of how far-fetched the use cases may seem to executives today.
About the authors: Anirudh Ramakrishna is Senior Consultant Industry 4.0 at umlaut; Stephen Xu and Timothy Thoppil are Managing Principals at umlaut
This article is taken from Automotive Worlds December 2019 Special report: how will artificial intelligence help run the automotive industry?,which is available now to download
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Artificial intelligence gets to work in the automotive industry - Automotive World
