Manufacturing

IBM joins GMIS Mission to change the future of manufacturing

Share this post:

We are delighted to announce that IBM has joined the Global Manufacturing and Industrialisation Summit (GMIS) as a founding partner. The news first broke at Think 2018 – IBM’s landmark conference, and a unique opportunity for IBMers, partners and clients to innovate together. Given our focus on the power of collaboration, it was an apt occasion for the announcement, as the GMIS is the world’s first cross-industry forum. Together, we will transform the future of manufacturing and build stronger societies, communities and businesses through innovation.

Maximizing the potential of Artificial Intelligence

We are particularly excited about this partnership, as it gives us an exceptional opportunity to maximize our leadership in AI technologies. As part of our work with the GMIS, we’ll be continuing to develop new technology applications that will underpin the manufacturing sector and drive Industry 4.0. We are already bringing Artificial Intelligence and machine learning together with our industry expertise, to help manufacturers accelerate their Industry 4.0 journeys. This means that creating a set of purpose-built offerings that support AI-powered manufacturing and enhance asset optimisation. Below, we explore some of these tools and capabilities.

AI-powered data analysis

Data is a crucial tool in helping manufacturers understand how their assets and operations are performing. The challenge is that there is a lot of it, and it comes from many and various sources across the shop floor, such as real-time data from equipment instrumented with connected sensors, digitized manuals, and databases of historical data. Because the data is so disparately generated, getting a clear picture becomes difficult, and can prevent operators from spotting patterns. Here, AI-powered and machine learning solutions come into play, by integrating data from multiple sources to establish a true and complete picture.

Perhaps the simplest example of the potential of AI within manufacturing is around equipment trouble-shooting and repair optimization. When a complex asset in a production environment fails to function as it should, it can be difficult and time-consuming to discover why. Technicians can read the instruction manual to determine how the asset ought to function or they can try reviewing past maintenance logs to understand if the problem is recurring. They can listen out for unusual noises, watch for obvious visual indicators, and, in a pinch, take the asset apart. But these methods are often slow, require tribal knowledge, and, in the case of disassembling an asset, costly. With artificial intelligence, we can speed up the process.

AI-powered diagnostics

AI-powered solutions, such as IBM Acoustic Insights, are able to interpret audio cues to determine the location and nature of a fault. These solutions are quick to train, and manufacturers are already gathering audio data from their machines, equipment and products to advanced data models. As more data is collected, the accuracy and speed of the diagnosis improves.

When you combine this data with information from an operations manual, it becomes easier to identify problems. Not only can AI-powered solutions like these identify problems faster, they can accurately determine the right fix by using machine learning to analyze unstructured data associated with repairs, maintenance, procedures and techniques. The result is enhanced insights and the ability to recommend optimum repair methods and procedures.

The IBM IoT Equipment Advisor is just such a tool. It combines machine learning techniques with Watson’s cognitive methodologies to provide insights for diagnosis and repair. By consuming varied sources of data, can spot faults and recommend appropriate fixes, thereby improving equipment availability and performance.

Watson the problem-solver: a use case from the US Army

In the video below, we hear how Watson was able help the US Army maintain its Stryker fleet. By integrating data from maintenance manuals, previous work orders and on-board sensor data, Watson could pinpoint maintenance problems, predict vehicle breakdown and recommend remedial action. All through the power of AI.

Purpose-built offerings for Industry 4.0

At IBM we are focusing on helping firms optimize both assets and operations, to eliminate unplanned downtime and improve factory key performance indicators. To support this aim, we are building a set of purpose-built applications, bringing our AI together with industry experience, to address challenges related to Industry 4.0. You can learn more about these solutions on our website. To learn more about our partnership with GMIS, take a look at our press release.

More Asset Management stories

Top Takeaways from IoT World 2019

Written by Sarah Dudley | May 20, 2019 | Asset Management, Manufacturing, Platform

The total number of Internet of Things (IoT) devices is expected to hit 10 billion by 2020. With this explosion of growth also comes an uptick in the conversations around how to use these devices and the data they generate to improve our businesses and our lives. At IoT World, North America’s largest IoT event ...read more


And the winner is……Watson IoT at the IoT World Awards 2019!

Written by Sarah Dudley | May 17, 2019 | Asset Management, Platform

It’s been an exciting week as more than 12,000 people converged on the Santa Clara Convention Center for Internet of Things World 2019! Topics have spanned from connected cars and smart buildings to Industry 4.0 and developing an IoT Platform. In the full recap going out in the next few days, we will highlight the ...read more


Do you know the price of poor infrastructure?

Written by Stephen Russo and Bruce D Baron | April 24, 2019 | Asset Management

Over the last 20 years, huge investments have been made in metropolitan areas to understand the immense volume of data collected about the citizens inhabiting these cities. Hundreds of millions of dollars have been spent combining data with emerging mobile, sensor and machine learning technologies. This is all done with the goal to improve infrastructure ...read more