Demystifying AI

Katrina Koffler

21 February 2022

At QiO, we enjoy hearing from our team of industry experts about how they use their industry background and expertise to approach today’s industrial problems. One such expert is our Chief Technology Officer, Gary Chandler.

Gary Chandler has spent the last five years at QiO delivering Internet of Things technologies to large industrials to help them generate value and realize their digital roadmap. Gary has over 30 years of experience in engineering real-time embedded software control systems for safety-critical applications, predominantly aerospace. Gary spent 25 of those years at Rolls Royce, where he served as Chief Engineer for engine control systems and later as Chief Data Architect and Head of Software for the firm.

Gary Chandler has spent the last five years at QiO Technologies delivering Internet of Things technologies to large industrials to help them generate value and realize their digital roadmap. Gary has over 30 years of experience in engineering real-time embedded software control systems for safety-critical applications, predominantly aerospace. Gary spent 25 of those years at Rolls Royce, where he served as Chief Engineer for engine control systems and later as Chief Data Architect and Head of Software for the firm.

Gary recently sat down with Ken Forster, executive director at Momenta and host of their podcast, Digital Thread. Listen to the full podcast episode here or keep reading for some of our favourite insights from Gary.

From Silicon Glen to QiO: Gary’s Journey
Gary comes from an area on the west coast of Scotland, which was known as Silicon Glen, the high-tech industry hub in Scotland. Some of his family members worked at IBM, so from an early age, he was surrounded by developments in digital electronics. Growing up, he was always trying to connect electronics to the IO ports on the early personal computers and create some interaction with the real world. He obtained an electronics degree in ’91. However, he realized early on that he didn’t want to design IT systems or office software; instead, he wanted to work with control systems and was more interested in robotics and automation rather than PCs themselves. After graduation, his first job was working on engine control systems for Rolls Royce jet engines, a mission-critical application that he continued to do for the next 25 years before joining QiO.

While working at Rolls Royce, Gary worked with their famous Power-by-the-Hour business model, which is ubiquitous in the industry today. In this model, the product’s end-user doesn’t ever buy or ever own the product. Instead, they buy the results of using the product by the hour. This is also known as servitization.

To maintain long-term success and profitability with the Power-by-the-Hour model, companies need really robust data to help them set the right price for their services. This depends on a lot of factors, such as being able to predict how each asset will be used, how long it will remain operational, what maintenance it will need, how much the maintenance will cost, and more. This requires a state-of-the-art data center.

“Right from my very first experience with “Power by The Hour,” I was quite inspired by it,” says Gary. “To this day, I still believe servitization is the most eco-friendly business model I’ve seen. If you can reduce the uncertainty using data, you can start to understand incentives across the end-to-end supply chain and make it a win-win for all.”

Around 2013, big data was undergoing a rapid change. Gary took on the Chief Data Architect role to align the data chain across different groups and exploit the rapid developments in IoT technology.

“When I saw the potential of the IoT technologies to transform the day-to-day operations, I was hooked—connected equipment, processing power, compute costs, access to huge data sets,” said Gary. “Suddenly, they all seemed as though they were in touching distance.”

Excited by his new role in software, Gary looked around and made the decision to join QiO Technologies and jump headfirst into the IoT world.

“I was concerned that access to the power of AI in industrials might get locked into a privileged few,” said Gary. “I wanted to try and demystify AI and make it more accessible to industrials who couldn’t necessarily afford to employ the sort of dedicated analytics teams that we had at Rolls Royce.”

Demystifying AI for Industrials
QiO’s mission is to make AI more accessible to industrials and provide key insights to everyone who could benefit from them.

QiO delivers an AI-augmented foresight suite of solutions to accelerate digital transformation, revenue growth, and sustainability for the global industrial sector. QiO solutions essentially act as a coach and a mentor to our clients to make them operate at their best every day. Industrial operations have good days and bad days, but sometimes analyses need to run a few days or weeks before “good” and “bad” can be defined.

The goal is to accelerate digital transformation and help industrials make better decisions faster. We do this by using AI to recommend the best changes and interventions that operators need to make in real-time to keep their systems optimized for energy efficiency and to be the best version of themselves that they can be. Instead of having operators waste valuable time considering multiple outcomes, our AI models recommend the best outcome for them with a clear “do this now” directive.

“I see a lot of great IoT companies out there working at the data and the information layers,” says Gary. “My passion is to use AI to go deeper and generate wisdom from a customer’s data and make this more available to everyone and help accelerate that digital growth, helping companies achieve their sustainability goals and save money. That’s our mission.”

Hear more from Gary
If you’re interested in hearing more insights from Gary about his background and vision for the future of AI and industrials, you can listen to the full podcast interview here. You can also learn more about Gary and the rest of our leadership team on our website.

DEUTSCHE

Die vierte industrielle Revolution, oder Industrie 4.0, ist gekennzeichnet durch das intelligente Vernetzen von Maschinen, Produkten und Mitarbeitern mit cyber-physikalischen Systemen in intelligenten Fabriken. Die Produktion in intelligenten Fabriken wird hochflexibel. Die Optimierung der Wertschöpfungskette erfolgt darin basierend auf Echtzeit-Anforderungen, Mitarbeiter werden von intelligenten Kontrollsystemen profitieren. In der intelligenten Fabrik wird die Kombination aus Material, Lieferanten, Ausrüstung und qualifizierten Mitarbeitern so organisiert, dass sie Einheiten produzieren, die den Kundenanforderungen in Echtzeit genügen. Die Personalisierung auf Kunden- und Marktsegmente wird durch integrierten Bedarfssignale gewährleistet, um skalierbare Geschäfte zu erzielen. Die intelligente Fabrik erfordert eine flexible und agile vernetzte Produktion.

Flexibilität bedeutet, dass die Betriebsmittel in der Lage sein müssen, eine Vielzahl unterschiedlicher Produktionsprozesse durchzuführen. Die Mitarbeiter müssen über ein sehr breites Spektrum an Qualifikationen und Erfahrungen im Umgang mit verschiedenen Systemen und Methoden verfügen.

Darüber hinaus erfordert die Flexibilität eine sehr hohe Verfügbarkeit der Einsatzmittel. Vernetzt bedeutet, dass alle relevanten Informationen für die Herstellung eines Produkts in Echtzeit zwischen allen Systemen und den am Prozess beteiligten Personen ausgetauscht werden. Tritt beispielsweise bei kritischen Anlagen ein Fehler auf, müssen neue Wege durch die Produktion gefunden werden, um Aufträge termingerecht zu erfüllen. Um das zu erzielen müssen alternative Arbeitspläne und die daraus resultierenden Produktionskosten und -zeiten analysiert werden. Neue Produktionseinheiten entstehen durch die neue Kombination von Ressourcen, Ausrüstung und Personal pro Auftrag. Dies erfordert eine enge Interaktion zwischen Ausrüstung, Ressourcen und Mitarbeitern mit einem intelligenten Produktionssystem, das sowohl die alternativen Routen als auch die Produktionskapazitäten verwaltet. Daher ist die Integration von Daten aus verschiedenen Systemen intern und extern unerlässlich.

Ziel dieses Forschungsprojekts ist es, Techniken zur Realisierung schlanker intelligenter Fabriken zu entwickeln, umzusetzen und zu validieren:

Das erste Projektziel ist die Entwicklung von neuen Algorithmen die den aktuellen Zustand der Fabrik bei der Erstellung des Produktionsplans berücksichtigen.

Das zweite Projektziel ist die Entwicklung einer Methodik für das Lösen von Systemen mit multiplen und teilweise probabilistischen Randbedingungen basierend auf reinforcement learning techniques.

Das dritte Projektziel ist es, eine mathematische Darstellung des genauen Zustands und Verhaltens der Produktionsanlagen zu generieren, die QiO den PARCS score nennt.

In diesem Arbeitspaket werden hochdimensionale teilweise diskrete Optimierungsprobleme mit einer Vielzahl von Nebenbedingungen mit modernen mathematischen Verfahren bearbeitet.

ENGLISH

The fourth industrial revolution, or Industry 4.0, is marked by the intelligent networking of machines, products and employees with cyber-physical systems in Smart Factories. Production in Smart Factories will be highly flexible, highly productive and resource efficient. The optimization of the value chain is based on real-time requirements. At the Smart Factory, the combination of materials, suppliers, equipment and skilled employees is organized to produce units that meet customer demands in real time. Personalization to customer and market segments is ensured by integrated demand signals to achieve scalable business.

The Smart Factory requires flexible and agile networked production. Flexibility means that the operating resources must be able to carry out a variety of different production processes. Employees are required to have a very broad range of qualifications and experience in using different systems and methods. Furthermore, flexibility requires a very high availability of operating resources.

Networked means that all the relevant information for the manufacture of a product is shared in real-time between all of the systems and people involved in the process. This requires information about potential machine breakdown, delays to a supplier order, missing parts, personnel availability or available skills.

If a fault occurs to critical piece of equipment, for instance, new routes through production must be found in order to fulfil orders on time. In order to do this, alternative work schedules and the resulting production costs and times must be analysed. New production units arise through the new combination of resources, equipment and personnel per order. This requires close interaction between equipment, resources and the workforce with a master intelligent production system, which manages the alternative routes as well as production capacity.

As such integration of data from diverse systems internal and external is therefore essential. The aim of this research project is to develop, implement and validate techniques for the realization of Smart Factories:

The first project objective is the development of new algorithms that take into account the current state of the factory when creating the production plan.

The second project objective is the development of a methodology for solving systems with multiple and partially probabilistic constraints based on reinforcement learning techniques.

The third goal of the project is to generate a mathematical representation of the exact condition and behaviour of the production facilities, which QiO calls the PARCS™ score. It is designed to enable engineers within software tools such as the QiO Foresight Engine™.

This work package deals with high-dimensional, partially discrete optimization problems with a variety of constraints using modern mathematical methods. The results of the project proposed here go well beyond the general state of the art, especially in the fields:

Artificial intelligence for the dynamic learning and modeling of production processes

Artificial intelligence for production planning and supply chain management

Traditional approaches to modeling and describing production processes assume a well-known problem and seek a solution to a specific problem. This approach is not suitable for solving such problems in the context of an intelligent factory. The boundary conditions that describe the processes change regularly here and they are mostly of a probabilistic nature. We plan to learn the processes dynamically based on the data produced by a production hall. We call such dat, the voice of the production hall.

Such automated learning is important because constructing a graphical model by a human expert can be cumbersome and time-consuming. If, as is often the case, there is a database of examples, learning techniques can do at least part of the work. This approach should lead to an always current and dynamic view on the production processes. It should also form a mathematical basis for dynamic production planning as it will be described in the next.