Artificial Intelligence in Manufacturing
Artificial intelligence (AI) seems to have travelled from sci-fi fantasy to boardroom hyperbole at warp speed in no time at all. Not only does AI make it possible to have a future working environment that involves robotics, but it is also very relevant in the context of manufacturing and can be of great value to organizations today.
AI will help manufacturers stay competitive, reduce costs, optimize capital employed and provide a better environment for their employees and service to their customers.
Aspects of the supply chain that are being revolutionized by AI
Large manufacturing enterprises are beginning to use AI to make material purchasing and allocation decisions. As the economy has evolved around shorter invention cycles, globalization, and sustainability, and mass personalization, both manufacturers and distributors have to become more agile, which means manufacturing resource planning (MRP) can be a daily process for many companies.
Factories of the future, are taking MRP to the next level. Machine learning models suggest changes to planning parameters, lead times, and inventory stocking levels, and predict quality issues and down chain disruptions to both lead-time and price insulating the end customer and supporting their expectation of immediate gratification.
AI will also help set better expectations, for manufacturers on delivery dates and volumes based on capacity along with planned and unplanned downtime. And AI can help companies decide what to do with spare capacities, like producing seasonal items early that can be wholesaled to retail outlets at a lower cost later in the year.
Industries with the highest AI adoption
Typically, as supervised AI requires good data and good training, most early success will come in industries that use common data. For example, all logistics companies use the same types of the road map, weather, and traffic data. Likewise, virtually all retailers use the same Universal Product Code (UPC) to identify products, which mean AI techniques applied to UPC data benefits a large number of customers, and this will drive AI companies to build solutions for retailers. In other words, industries adopt AI when the solutions begin to emerge. So, industries with high-quality data that is readily available will have AI solutions ready sooner than others.
Impact of AI on the manufacturing workforce
Many modern manufacturing facilities run with very few employees already and this is why manufacturing productivity is actually far higher than most people realize. In the near term, autonomous vehicles may, for example, replace fork-lift drivers. Better computer vision software can make it easier for fewer staff to ensure quality on a mass scale. But these basic automation trends have been in place for some time. The next wave of automation will probably impact roles that analyze data and recommend actions to optimize the business, from design to operations and service. If AI systems can analyze images and correlate data from many sources, then it’s possible for a computer to design the next popular clothing line or at least predict which lines will be successful.
Product design will also be heavily disrupted by AI as we start seeing generative design tools where the problem is defined and all of the problem space is explored computationally, amplifying our cognitive abilities. Augmented collaboration will be the working environment of the future. The human-robot collaboration will be more gesture-based, where workers will be doing the things humans are good at-perception, awareness and decision making-while robots will be doing the things they are currently good at, repetitive tasks with precision.
Emerging trends in AI
AI will impact manufacturing in various ways. The continued improvement in computer vision (CV) has long been used for quality assurance by detecting product defects in real-time. But now that manufacturing involves more data than ever-coupled with the fact that plant managers do not want to pay staff to enter data-AI with computer vision can streamline how data gets captured.
The second area that AI will impact is with the Internet of Things (IoT) or cyber-physical systems. IoT is remarkable in that the basic technology is being deployed rapidly even though the outcomes and security aspects haven’t really been thought through. All in all, IoT is an incoming tsunami of data that AI can use to reason over and evolve. This will help augmented generative design processes where products are reimagined in ways more akin to evolution.
In conclusion, AI is not yet a turn-key solution. You don’t buy it off the shelf and use it, you infuse it in everything you do to augment your business and products and unlock potential for future business growth.