Artificial intelligence (AI) will impact manufacturing in ways one may have not imagined. With the recent advances in AI, Anish Kanaran believes it is imperative for India to view AI as a critical element of its developmental strategy.

 

Several artificial intelligence (AI) experts have commented that ‘everything that humans can do machines can do’. In fact, in a recent study from Oxford and Yale University, researchers predict that in the next ten years, AI will outperform humans in many routine and repetitive activities such as translating languages or driving a truck. Today, AI has already had a profound impact on human life in various ways, such as self-driving cars, Siri on iPhones, weather forecasts, and face recognition on Facebook photos. Indeed, AI seems to have travelled from sci-fi fantasy to board room hyperbole in no time at all. 

 

According to a global report by software giant Adobe, more than 60% of marketers in India believe that new-age technologies are going to impact their workplace practices, and therefore consider it the next big disruptor in the industry. Furthermore, 27% of respondents said that they were extremely concerned about the potential impact of AI or machine learning technologies. Epicor research suggests Indian business leaders are unfamiliar with many common technology terms such as applied AI, advanced machine learning, and intelligent things. More than one-in-ten, for example, admitted to not fully understanding technology trends such as the Internet of Things (12%), 3D printing (19%) and machine learning (17%) and some had never heard of these terms at all.

 

While it’s not surprising that a future working environment with fully autonomous thinking and acting robots is a topic that captures the imagination, it is essential to understand the AI trends and the relevance of AI in today’s manufacturing environment.

 

Understanding AI 

 

AI is a system that can reach the same (or better) conclusions as a human from the same kinds of inputs. Many early AI systems were built to analyse photographs to see if the computer could recognise people or objects, identify what the subjects are doing, and ultimately determine the consequences or next actions. This kind of work has led to the development of self-driving vehicles, which is an advanced form of visual processing. 

 

However, reliably using AI means correctly dealing with unexpected situations, or inputs and the accuracy of an AI system relies on using tremendous amounts of data. This is why many AI (and machine learning) systems are emerging as cloud-based services. It is more feasible to process the millions or billions of visual examples in the cloud necessary to sufficiently train the system. More training data means having the ability to consider more variables and reach more detailed conclusions. It’s safe to say that cloud computing is what has made AI commercially viable today.

 

Benefits of integrating AI in manufacturing

 

Manufacturing has come a long way since the 1900s, when teams of workers pieced together a product. Applying AI to manufacturing requires technology updates and process innovations. Robotics is leading the way when it comes to automating the production line. The same technology being used to help self-driving vehicles navigate or keeping a Roomba vacuum from running over your cat is making its way to the shop floor. Robots aren’t new to manufacturing, but they have traditionally been very expensive to deploy. Some models required magnets embedded in the floor to serve as tracks for guiding the machines, whose routes and tasks all had to be pre programmed. This means any changes to the plant layout, an expected pallet of material in a corridor or new manufacturing processes required reprogramming the robotic staff.

 

But companies like Adept changed the game with robots that sense the plant layout automatically. These robots can walk the plant to discover all of the areas. Once a map has been created, the human staff can provide the names of important areas, which then makes it easy to instruct a robot to fetch material from one place and deliver it to another. If a new obstacle is discovered—like an unexpected pallet or a new copier - the robots find another route and note the change for future tasks. 

 

AI will help manufacturers stay competitive, reduce costs, optimise capital employed and provide a better environment for their employees and service to their customers. Another benefit is safer working environments. Using inexpensive cameras, AI can detect and track workers entering hazardous areas and even ensure protective gear is being worn. AI can also coordinate processes to ensure correct procedures are used.

 

The effects of AI on the supply chain

 

Large manufacturing enterprises are beginning to use AI to make material purchasing and allocation decisions. The classic manufacturing resource planning (MRP) process was invented at a time when the sales forecast, inventory levels, and existing purchase commitments were planned around longer date horizons. But the economy has evolved around shorter invention cycles, globalisation, and sustainability, and mass personalisation (to name a few). So, both manufacturers and distributors have to become more agile, which means planning can be a daily process for many companies. AI can be used to predict micro level lulls or spikes in demand, which in turn can determine the best routing for raw materials. This approach can work for any company, especially those with many product lines and complex manufacturing processes. 

 

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 capacity, like producing seasonal items early that can be wholesaled to retail outlets at lower cost later in the year.

 

Specific industries with highest AI adoption

 

Logistics companies embraced AI once it was clear that freight routing was easy to optimise using the detailed mapping, traffic, shipping, and weather information which has all materialised online in the last few years. But the biggest push recently for AI has been in healthcare. 

 

Typically, as supervised AI requires good data and good training, most early success will come in industries that use common data. For example, logistic companies all use the same types of 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.

 

India is not a big market for artificial intelligence yet. This is due to the lack of access to large data sets and also because the human resource costs are fairly low when compared to Europe and the US.

 

Embracing AI

 

Mid-market enterprises are very receptive to applying new technologies once it’s clear what problems are solved and the solutions are affordable to deploy. For AI, there is an added challenge of building trust in the system to a point where people let go of their long-standing gut instincts which served them well. As it becomes necessary to consider more variables on a continuously basis, using AI-enriched software will eventually become the best way to get accurate decisions made. 

 

AI can help with management decision support and planning, particularly in some service sectors where understanding population centres and demographics is key to maximising growth. It is also being used to help identify where the best placement globally to service customers is by capturing and reasoning over the many complex interdependencies manufacturers deal with along with optimisation 

of facilities. 

 

Impact of AI on the workforce

 

AI is getting quite good at understanding natural language and translating it, which means it will become easier for workers and managers to interact with software more naturally. Software users prefer to search for things rather than navigate a complex menu. In short, AI will help workers get things done in a more intuitive way, which leads to higher productivity and fewer errors. 

 

Current opinions vary about AI replacing jobs within the manufacturing industry. However, many modern manufacturing facilities run with very few employees already and this is why manufacturing productivity is actually far higher than most people realise. 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. These basic automation trends have been in place for some time. 

 

The next wave of automation will probably impact roles that analyse data and recommend actions to optimise the business, from design to operations and service. If AI systems can analyse 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. 

 

People whose primary role is to interact with other people - sales, support, middle management - are today considered safe from being displaced by AI. But that’s mainly because AI hasn’t quite reached the point where it can converse with humans reliably in general cases.

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. 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. 

 

Trends in AI 

 

AI will impact manufacturing in ways we have not yet imagined. However, we can already look at some more obvious examples. 

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. A factory worker should be able to take raw materials stock from the shelf and have the inventory transaction created automatically based on a camera observing the process. This will be the natural user interface, just completing the task at hand not inputting or scanning things into a system. 

 

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. Having detailed operational sensors in finished goods is clearly going to change markets and production tactics. IoT will provide a way to deliver supplies and services to customers who might not realise they are needed. In addition, IoT can send detailed telemetry back to producers and distributors to analyse quality and factors that might drive failures. In short, 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.

When considering where AI fits into the business, bear in mind that AI is not yet a turnkey solution. You don’t buy it off the shelf and use it, you infuse it into what you do to augment your business and products, and therefore unlock potential for future business growth. The best way forward is for businesses to start looking at solutions that help their staff make faster and more accurate decisions. 

 

With the recent advances in AI, it is imperative that India view AI as a critical element of its developmental strategy. AI will become central to the economic growth, revolutionising everything including manufacturing and innovation. The Indian government’s Digital India initiative has also created a favourable regulatory environment for increased use of AI. To prepare India for an AI-based future it is essential to establish AI-based innovation and AI-ready infrastructure.

 

About Aish Kanaran

 

Anish Kanaran is the Channel Director (Middle East, Africa & India) of Epicor Software. He has more than 17 years experience of ERP industry. Prior to joining Epicor in 2011, Kanaran spent three years with Microsoft and was handling new business acquisition for Saudi Arabia. Kanaran has a decade long experience in managing sales, business development & channel management for global ERP solutions such as MS Dynamics, ORION, BaaN, Intuitive & SAGE.