Current Issues

Regular Industry Development Updates, Opinions and Talking Points relating to Manufacturing, the Supply Chain and Logistics.

Beyond the Hype—Artificial Intelligence in Manufacturing

Beyond the Hype—Artificial Intelligence in Manufacturing
Artificial intelligence (AI) seems to have travelled from sci-fi fantasy to board room hyperbole at warp speed, in no time at all.

Whilst it’s not surprising that a future working environment with fully autonomous thinking and acting robots is a topic that captures imaginations, trying to work out the relevance of AI to today’s manufacturing environment is somewhat less clear.

Here, we ask Erik Johnson, chief architect, Epicor Software, to help debunk some common myths around AI and put the hyperbole into some context for manufacturers trying to work out its relevance today.

Debunking the myths
AI is already an over-hyped technology within business. Its important manufacturers understand what it isn’t before coming to a decision about what it is and how it should be used in a manufacturing context. Predictive analytics and machine learning are often confused with AI and whilst they have similar capabilities, they don’t work in the same way. Where predictive analytics uses historical data to predict the future, AI goes further, analysing more variables to provide more detailed conclusions.

Similarly, in relation to machine learning, AI is the broader term for something that constitutes ‘smart’ systems and machine learning is a subset of approaches where machines use data to learn and reason and get better over time.

Quantifying the benefits
When it comes to understanding the practical applications of AI, one quantifiable way AI is being used in manufacturing is via robotics. 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. Examples like this prove how AI will help manufactures stay competitive, reduce costs, optimise capital employed and provide a better environment for their employees and service to their customers.

The Manufacturers Leading the Way
The manufacturers already ensuring they have a competitive advantage 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 is exactly the rationale Merck is using to automate its factories. The same 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.

The Industries Leading the Way
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. One much cited example is the use of IBM’s Watson which can identify very early stage diseases by examining tomography and other data.

One of the industries leading the charge is the logistics industry who embraced AI once it became 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. One much cited example is the use of IBM’s Watson which can identify very early stage diseases by examining tomography and other data.

Typically, as supervised AI requires good data and good training, most early success will come in industries that use common data. For example, logistics 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 means 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.

What lies ahead
But as manufacturing involves more data than ever, we’re going to see AI with computer vision (CV) continue to streamline how all that data gets captured. For example, 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. That is where the future of AI seems to be, or rather, should be, going.

Similarly, AI and IoT are going to continue to become more closely linked. 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.

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. Those unwilling to invest in the technology sooner rather than later, will be the ones watching competitors become more competitive and create a world-class supply chain capable of delivering not only more time efficiency, but both productive and financial efficiencies.