Leadership Team

Rick Haythornthwaite

Co-Founder & Chairman

Rick Haythornthwaite is chairman of the FTSE-listed Ocado Group plc and chairman of the AA, and an Advisory Partner to Moelis & Co. He is a co-founder and chairman of QiO Technologies and director of the digital consultancy, Globant. He is also an investor in and chairman of ARC International, the global glass tableware manufacturer and an angel investor in a wide range of early-stage technology companies.

Rick was previously the chairman of MasterCard Inc from 2016-2020, CEO of Invensys from 2001-2005 and Blue Circle Industries from 1999-2001, having joined as Director of Asia and Europe in 1997. He spent his early career in BP from 1978-1995 before moving to Premier Oil as Commercial Director from 1995 to 1997. He has served on the boards of Centrica and Network Rail as chairman and Cookson, Lafarge, ICI and Land Securities as non-executive director. In the UK not-for-profit sector, he is the current chair of the Creative Industries Federation and Creative England and former chair of the Southbank Centre and Almeida Theatre. He was educated at MIT (Sloan Fellow) and The Queen’s College, Oxford (MA Geology).

Rajan Pandhare

CEO

Rajan has been an Operating Partner with a leading European £2billion Private Equity group supporting their expansion into technology and tech-enabled services companies. In addition, he has successfully built software services and product companies in the USA and UK, with offshore delivery centres in India. He was Co-founder and Chairman of Irene Healthcare (a chain of secondary care hospitals) and Kalpan Hydro (a renewable energy company), and Vice Chairman of I2India (a joint venture with Imperial College, UK).

Away from the commercial world, Rajan led the expansion of President Clinton’s HIV/AIDS Initiative across Africa and Asia and has been a board member of leading foundations and not for profits (Children’s Investment Fund Foundation, Care International UK and Elton John’s AIDS Foundation Investment Committee). He has also been on the board of GreenInfra and EVI, portfolio companies of private equity groups and remains an active angel investor in early stage technology companies across India and the USA. He holds an MS from Louisiana State University, USA.

Ed Birch

Finance Director

Ed is responsible for all aspects of finance and acts as the performance conscience of the business with end-to-end profitability and cash management accountability. Ed was ‘Big 4’ trained at KPMG with a background in corporate turnaround, having worked in the firm’s Restructuring advisory practice throughout the global financial crisis.

Previously, as Head of FP&A at Experian plc, Ed led the delivery of all financial planning and analysis for the dynamic EMEA region of the FTSE100 software, data and analytics business. He qualified as an accountant whilst at KPMG and is now a Fellow of the Institute of Chartered Accountants in England and Wales.

Gary Chandler

Chief Technology Officer

Gary joined QiO from Rolls-Royce, having spent 25 years developing real-time safety-critical control systems for aero-engines. As a keen strategist and innovator, Gary was a key leader in the adoption of ‘Lean Product Development’ and the ‘Internet of Things’ within Rolls-Royce, deeply engaging with the associated culture change.

Gary is responsible for all commercial aspects of software deployments where he utilises his deep domain expertise to unlock customer value through advanced analytics and AI-infused software.

David McGuirk

Director DC & Telecom

David leads QiO´s global Data Center and Telecoms Team with an approach that creates a close partnership with clients to ensure that business priorities, sustainability and empowerment are at the heart of technology investment and business transformation.

Before joining QiO, David had his own consultancy, which he founded out of a desire to drive sustainability across Hybrid Cloud Environments.

Prior to that, David had a successful 20 year career designing, architecting, building and delivering enterprise-class data centers, previously having cofounded Hawk Systems and was Managing Director at Hive Datacentres. David is a passionate sustainability advocate and thought leader in accelerating GHG emissions reduction associated with digital. David is also QiO´s Head of Sustainability.

Fouad Omri

AI Fellow

Fouad is responsible for the product engineering of AI-infused applications at scale. Managing a global team of engineers to develop AI expertise that provides our customers and partners with self-learning applications that are not limited by static slices of point-in-time views but become smarter with each deployment.

Fouad holds a PhD in computer science and applied mathematics from the Karlsruhe Institute of Technology. He was a Lecturer and Senior Researcher at the Karlsruhe Institute of Technology, specialising in Statistical Modelling, Machine Learning, Big Data, Predictive Analytics, Deep Learning and Reinforcement Learning.

Yassine Houari

Head of Product

Yassine leads the product management function at QiO and is responsible for the development and roadmap of our growing product portfolio.

Prior to joining QiO, Yassine was Product Manager for several products in the Industrial IoT space at Accenture as part of their IndustryX division, a high-growth division developing commercial software products and accelerators for industrial customers across the globe in the manufacturing, chemicals, water utilities and energy space. Yassine has a mechanical engineering background, and a master’s in Environmental Technology from Imperial College London.

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.