Leadership Team

Rajan Pandhare

CEO

Rajan Pandhare responsible for scaling the organization to achieve growth and revenue goalshas 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.

Gary Chandler

Chief Technology Officer

Gary Chandler 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.

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.

Paul Silver

Vice President, Global Sales

Paul Silver is responsible for overseeing global sales and channel programs. His primary responsibilities include driving sales productivity and helping to grow QiO’s revenue and partnerships.

Paul joins QiO with 25+ years of successful Executive sales management experience in privately held and publicly traded technology companies, with a track record of increasing shareholder value.

A startup specialist who excels in building and growing organisations, Paul has played a pivotal role in multiple early-stage organisations that led to successful acquisitions, including Olicom, Entevo, EqualLogic and Tegile.

Tom Millican

Finance Director

Tom Millican leads the company’s finance and operations functions, focussing on strategy, performance optimisation, and value creation to enhance the organisation’s overall performance, productivity, and profitability.

Tom previously held leadership roles at Fnality UK, an ‘on-chain’ payment system for wholesale banking, V-Nova, a forerunner of video compression solutions utilising AI for the media ecosystem and Geniac, an integrated SaaS platform providing specialist business advice to startups.

Tom qualified as a Chartered Accountant (CA) with the Institute of Chartered Accountants Scotland in 2017 and as a CFA Charterholder (CFA) with the Institute of Chartered Financial Analysts in 2018.

Gary Bourton

Director Customer Success

Gary Bourton is responsible for Customer Success & Delivery at QiO. His global team ensures that we meet and exceed the needs of our customers and get them to value in the quickest time possible. He also serves on the Board of Governors at Long Road Sixth Form College, where he sits on the Audit Committee. Additionally, he volunteers weekly for Cambridge City Food Bank.

Gary brings extensive leadership experience from multiple sectors, including IT Services, Building Services, Building Energy Management and most recently in the Industrial sector, where he worked for an Industrial IoT provider as Global SVP for Customer Success and Revenue. His experience also comes from operating in SMEs, Enterprises and Startups.

Gary is a certified Customer Success Manager, Project Manager and Non-Executive Director. He attended Loughborough Business School, where he studied Management & Leadership.

Fouad Omri

Director AI, R&D

Dr Fouad ben Nasr Omri 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.

Dan Stover

Operations and Infrastructure Director

Dan Stover is accountable for contracts and commercial agreements at QiO and coordinates our compliance with ISO 27001.

He has extensive global experience in oil and gas production operations, drilling operations and major project delivery. He has worked in leading international oil companies and development contractors in most oil and gas basins in the world. He has worked at the forefront of technology and in many sensitive environments. He also has substantial expertise outside the petroleum industry in cement manufacture, forestry, legal proceedings and AI technology development.

Dan has a BSc in Petroleum and Natural Gas Engineering, an MSc in Forestry and is a Fellow of the Chartered Institute of Arbitration.

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.