A guide to driving digital sustainability in technical infrastructure

David McGuirk

16 June 2023

When I talk to CSOs and CIOs, it’s apparent that embracing sustainable practices in the digital realm can be challenging and overwhelming. Navigating the regulatory landscape has difficulties, as does setting sustainable goals and gathering and analysing data on your organisation’s digital footprint.

Once a baseline has been set, then what? How does an organisation do more with what they already have? Reducing energy consumption, elongating the life of assets, reducing costs and positively impacting their Scope 2 and 3 emissions along the way.

This step-by-step guide offers suggestions on how to drive digital sustainability, starting with the most important component of your technical infrastructure – the Data Center.

Together, we can drive digital sustainability and create a more environmentally responsible future. I have put together this short step-by-step guide for CSOs, CIOs, Global Systems Integrators, Innovation Teams, and other key personas to navigate the challenges and take meaningful actions towards a sustainable digital transformation. Let’s embark on this journey together, turning pain into progress and fostering a greener digital landscape for generations to come.

Step 1. Regulatory compliance

Ensure regulatory compliance by effectively monitoring and reporting directives-driven metrics across your entire data center fleet.

Did you know?

Under the European Commission’s Energy Efficiency Directive (EED), as of May 15th 2024, companies with data centers over 500kW will need to provide full disclosure and report on the following metrics for the preceding year. Eg this data needs to be tracked from May 15th this year!

Step 2. Baseline

Go inside the rack. Measurement at the device and asset level provides the granularity needed to make a tangible difference.

Did you know?

Via a single pane of glass, emissions data for each individual device (server, network and storage) can be aggregated for a specific client, business unit, application stack, hardware type, putting your teams firmly in control of driving digital sustainability across your organisation.

Step 3. Address inflationary and supply chain pressure

Use a reliability-centred approach for asset replacement and optimise investment timing.

Did you know?

Using Performance, Availability, Reliability, Capacity and Serviceability as the cornerstone metrics of asset replacement strategy, periodic refresh cycles can become a thing of the past.

Step 4. Reduce your IT Energy without impacting performance and throughput

Using a data-centric and predictive approach to CPU Power Management, ITE power consumption can be dramatically reduced without impacting QoS.

Did you know?

Intel has noted a 24% energy reduction with representative workload and 52%energy reduction in idle state with QiO ́s Foresight Optima DC+.

Recently published results from WWT ́s advanced technology center have noted up to 29% saving with 50-90% changeable workload.

Step 5. Optimise your Cooling

Using AI to suggest optimal operating conditions can reduce energy and water consumption by between 5 -15%

Did you know?

Making best use of what you already have is the first rule of sustainability. By reducing ITE load, your data center will benefit from the cascading power saving principle, with further gains achieved by recommending optimised cooling set points.

Step 6. Track progress

Reporting needs to be fully auditable. Real-time data should be integrated into your preferred reporting mechanisms to track progress and achievements.

Did you know?

Automatic and user define report generation will put you firmly in control and at the forefront of digital sustainability.

What I the next step for you to drive digital sustainability?

Contact me at david.mcguirk@qio.io to arrange a demo to see how Foresight Optima DC+™ can help you drive digital sustainability and empower your teams to make a tangible difference to GHG emission reduction.

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.