2 essential cloud success pillars for manufacturers

The manufacturing industry is under pressure to transform. Growing competition, rising costs, tight supply chains and growing customer demands are putting pressure on companies to act fast. You need flexible, scalable platforms to meet these challenges, opening up new prospects for IT to make a real business impact. In the past, IT’s responsibility was mainly limited to providing necessary infrastructure. Now, IT also must increasingly focus on enabling new business ideas and technical innovation.

Public cloud has become the de facto engine to help IT deliver this innovation

Cloud usage and cloud-based innovations top the list of priorities for many of our manufacturing, industrial and automotive customers – who see public cloud as essential to helping them get ahead of the game. 

The rationale is simple: homegrown IT solutions and infrastructure don’t – and can’t – help any company achieve competitive advantage fast enough. They’re inflexible, expensive and time-consuming to build, which means budgets and resources get consumed without really adding value to an immediate business challenge.

From our perspective, AWS, Microsoft Azure and GCP surpassed the average on-premises server/container platforms in aggregate capability many years ago. They offer everything needed to digitalise at scale, out of the box. After all, time is a key aspect of competitive advantage – and public cloud solves it! 

The 2 pillars of cloud success at scale

To get started (and obviously many customers we work with already have started), there are 2 pillars to consider for succeeding with cloud innovation at scale:

  • Horizontal: Infrastructure and development platforms 
  • Vertical: Digital solutions and services 

Cloud is more than just a data centre. We advise manufacturers to use public cloud for digital solutions solving real business problems and to supercharge their software development and developer experience. However, in order to make this scale, the IT governance and automation foundations of that horizontal pillar should be done right.

Horizontal pillar: Tackle IT governance and platform challenges first

Our cloud security community is helping us achieve that. It’s a forum for knowledge sharing, empowerment, skills development and careers advancement.

Having worked with industrial companies from the north of Finland to the south of Germany, we’ve gained a huge experience in how to help organisations fulfill the promise of cloud at scale in the enterprise. 

We’ve learned a key lesson: you have to solve basic horizontal IT challenges in cloud before you can innovate and build digital solutions in a production-ready state. 

Over the years we’ve built internal cloud competency centres and leveraged our partners in AWS, Azure and GCP to help customers operating huge cloud estates in a unified, secure and cost-efficient way. We advise clients to balance control and speed at all times when building these foundations and platforms. You must make sure you’re not compromising the value cloud brings to the business or the developers using it. 

This means avoiding 2 classic cloud adoption pitfalls: 

  • Pushing for cloud-based innovation without fixing the basics: Thereby creating a mess others have to untangle well past the point where digital services go into the production stage
  • Restricting cloud usage so it becomes impossible to innovate with it: Meaning you end up back where you started – with an inaccessible managed hosting farm that doesn’t let you harness hyperscaler tech

You need to strike a balance between these extremes – and we can help. Our aim is to enable cloud value at the absolute highest possible level without compromising the security, compliance or control mechanisms that safeguard your core business and your customers. The very idea of cloud is about allowing engineers to experiment with a vast tooling landscape, with full access to everything hyperscalers have to offer. So to get devs to start making the most of cloud for your business, you need to build platforms, not prisons.

A great example of this is how we’ve helped BMW to build a Public Cloud Platform that enables multi-cloud governance and operations at scale. The platform provides standardised, automated and pre-developed cloud infrastructure so internal teams can use self-service/APIs and develop rapidly while aligning with established rules. For example, users can automatically create new accounts and subscriptions directly in the platform without having to raise a ticket or wait for an internal, manual process. Integrated security features ensure end-to-end monitoring and enforcement of security and regulatory rules.

This gives BMW the ideal balance between speed and control. 

Vertical pillar: Innovate and supercharge your teams with cloud

Cloud technology allows you to scale innovation and speed up time to market. Developer experience and productivity reach new levels when they use hyperscalers’ rich service stacks. But playing with cool stuff is only one part of the story. It’s also important to foster the right type of culture and build the right processes in the horizontal pillar to ensure innovation can happen. As we said: there must be a balance between speed and control. 

Our view is that in order to build sustainably on cloud, developers need production-ready frameworks and tools, together with support teams to take over the application (whether it’s a central function of the IT organisation, a third party or internal staff in their product group). 

The vertical pillar is made up of 3 elements: 

  • Digital products and services 
  • Data platforms
  • Developer experience and culture 

Digital products and services: The primary outcome of using cloud for the business is the creation of products and services that either serve the end customer or optimise production and supply chains. Whatever the business domain, we see the same basic patterns of cloud-native and globally scaled building blocks used to create these solutions. Manufacturing companies thrive when their solutions are available to all clients in the same manner – whether it’s remote monitoring, shop-floor level optimisation and prediction, or aftersales customer interfaces and platforms. 

Data platforms: Data platforms (often referred to as data lakes) underpin most of the digital products and services we’ve built with our manufacturing customers. These offer a secure and controlled environment where engineers and data scientists can access almost any kind of company data – from production to field sensors. Building a reliable and scalable data platform is essential for building digital products that deliver business value. The individual feature teams shouldn’t be burdened with transforming or governing data. 

Developer experience and culture: Those first 2 vertical pillar elements are harder to build when engineers don’t benefit from a great developer experience and culture. Developer experience can be supercharged with cloud. However, as part of the cloud journey, you also need to check your culture and processes associated with developing digital solutions. Teams need autonomy, agility and trust to do the right thing. For example, when working with bearing and seal manufacturer SKF to scale out a cloud-empowered agile innovation culture, we used both coaching and technical tooling. Nordcloud experts were embedded in SKF’s feature teams to make sure every squad could walk the talk. 

Checking your cloud balance

Our #1 tip for manufacturing, automotive and industrial companies is to fast-track a cloud strategy review to ensure you’re striking the right balance with the 2 pillars. Otherwise, you’re destined to hit either wall of innovation-preventing governance or technical debt and security leaks. 

Success with cloud isn’t just about using hyperscaler tools to innovate – it’s about using the right hyperscaler tools at the right time in a secure and sustainable way. Don’t jump on bandwagons or superimpose tech trends on to legacy solutions and ways of working. 

From migrating infrastructure to digitalising manufacturing operations and developing new data-driven customer tools – we’ve helped so many manufacturers get value from cloud fast. 

We’re talking a 30% reduction in deployment costs (Porsche), 2x the mileage from the R&D budget (Kempii) and a new sensor-driven customer app in 8 weeks (Konecranes). The list goes on.

When you take an approach that involves collaboration between your teams, your partner and your hyperscaler – you position the business to thrive in a competitive market. Get in touch to see how we can make that collaboration happen.

Get in Touch.

Let’s discuss how we can help with your cloud journey. Our experts are standing by to talk about your migration, modernisation, development and skills challenges.

    Looking ahead: what’s next for AI in manufacturing?


    BlogTech Community

    AI and manufacturing have been on an exciting journey together. It’s a combination that is fast changing the world of manufacturing: 92 percent of senior manufacturing executives believe that the ‘Smart Factory’ will empower their staff to work smarter and increase productivity.

    How does AI benefit manufacturers?

    Some of the biggest companies are already adopting AI. Why? A big reason is increased uptime and productivity through predictive maintenance. AI enables industrial technology to track its own performance and spot trends and looming problems that humans might miss. This gives the operator a better chance of planning critical downtime and avoiding surprises.

    But what’s the next big thing? Let’s look to the immediate future, to what is on the horizon and a very real possibility for manufacturers.

    Digital twinning

    ‘A digital twin is an evolving digital profile of the historical and current behaviour of a physical object or process that helps optimize business performance.’ – According to Deloitte.

    Digital twinning will be effective in the manufacturing industry because it could replace computer-aided design (CAD). CAD is highly effective in computer-simulated environments and has shown some success in modelling complex environments, yet its limitations lay in the interactions between the components and the full lifecycle processes.

    The power of a digital twin is in its ability to provide a real-time link between the digital and physical world of any given product or system. A digital twin is capable of providing more realistic measurements of unpredictability. The first steps in this direction have been taken by cloud-based building information modelling (BIM), within the AEC industry. It enables a manufacturer to make huge design and process changes ahead of real-life occurrences.

    Predictive maintenance

    Take a wind farm. You’re manufacturing the turbines that will stand in a wind farm for hundreds of years. With the help of a CAD design you might be able to ‘guesstimate’ the long-term wear, tear and stress that those turbines might encounter in different weather conditions. But a digital twin will use predictive machine learning to show the likely effects of varying environmental events, and what impact that will have on the machinery.

    This will then affect future designs and real-time manufacturing changes. The really futuristic aspect will be the incredible increases in accuracy as the AI is ‘trained.’

    Smart factories

    An example of a digital twin in a smart factory setting would be to create a virtual replica of what is happening on the factory floor in (almost) real-time. Using thousands or even millions of sensors to capture real-time performance and data, artificial intelligence can assess (over a period of time) the performance of a process, machine or even a person. Cloud-based AI, such as those technologies offered by Microsoft Azure, have the flexibility and capacity to process this volume of data.

    This would enable the user to uncover unacceptable trends in performance. Decision-making around changes and training will be based on data, not gut feeling. This will enhance productivity and profitability.

    The uses of AI in future manufacturing technologies are varied. Contact us to discuss the possibilities and see how we can help you take the next steps towards the future.

    Get in Touch.

    Let’s discuss how we can help with your cloud journey. Our experts are standing by to talk about your migration, modernisation, development and skills challenges.

      10 examples of AI in manufacturing to inspire your smart factory


      BlogTech Community

      AI in manufacturing promises massive leaps forward in productivity, environmental friendliness and quality of life, but research shows that while 58 percent of manufacturers are actively interested, only 12 percent are implementing it.

      We’ve gathered 10 examples of AI at work in smart factories to bridge the gap between research and implementation, and to give you an idea of some of the ways you might use it in your own manufacturing.

      1. Quality checks

      Factories creating intricate products like microchips and circuit boards are making use of ‘machine vision’, which equips AI with incredibly high-resolution cameras. The technology is able to pick out minute details and defects far more reliably than the human eye. When integrated with a cloud-based data processing framework, defects are instantly flagged and a response is automatically coordinated.

      2. Maintenance

      Smart factories like those operated by LG are making use of Azure Machine Learning to detect and predict defects in their machinery before issues arise. This allows for predictive maintenance that can cut down on unexpected delays, which can cost tens of thousands of pounds.

      3. Faster, more reliable design

      AI is being used by companies like Airbus to create thousands of component designs in the time it takes to enter a few numbers into a computer. Using what’s called ‘generative design’, AI giant Autodesk is able to massively reduce the time it takes for manufacturers to test new ideas.

      4. Reduced environmental impact

      Siemens outfits its gas turbines with hundreds of sensors that feed into an AI-operated data processing system, which adjusts fuel valves in order to keep emissions as low as possible.

      5. Harnessing useful data

      Hitachi has been paying close attention to the productivity and output of its factories using AI. Previously unused data is continuously gathered and processed by their AI, unlocking insights that were too time-consuming to analyse in the past.

      6. Supply chain communication

      The aforementioned data can also be used to communicate with the links in the supply chain, keeping delays to a minimum as real-time updates and requests are instantly available. Fero Labs is a frontrunner in predictive communication using machine learning.

      7. Cutting waste

      Steel industry uses Fero Labs’ technology to cut down on ‘mill scaling’, which results in 3 percent of steel being lost. The AI was able to reduce this by 15 percent, saving millions of dollars in the process.

      8. Integration

      Cloud-based machine learning – like Azure’s Cognitive Services – is allowing manufacturers to streamline communication between their many branches. Data collected on one production line can be interpreted and shared with other branches to automate material provision, maintenance and other previously manual undertakings.

      9. Improved customer service

      Nokia is leading the charge in implementing AI in customer service, creating what it calls a ‘holistic, real-time view of the customer experience’. This allows them to prioritise issues and identify key customers and pain points.

      10. Post-production support

      Finnish elevator and escalator manufacturer KONE is using its ‘24/7 Connected Services’ to monitor how its products are used and to provide this information to its clients. This allows them not only to predict defects, but to show clients how their products are being used in practice.

      AI in manufacturing is reaching a wider and wider level of adoption, and for good reason. McKinsey predicts that ‘smart factories’ will drive $37 trillion in new value by 2025, giving rise to research projects like Reboot Finland IoT Factory, which involves organisations as diverse as Nokia and GE Healthcare. The technology is here and the research is ready – how will AI revolutionise your industry?

      Check out our whitepaper: “Industry 4.0: 7 steps to implement smart manufacturing”


      The uses of AI in future manufacturing technologies are varied. Contact us to discuss the possibilities and see how we can help you take the next steps towards the future.

      Get in Touch.

      Let’s discuss how we can help with your cloud journey. Our experts are standing by to talk about your migration, modernisation, development and skills challenges.

        Nordcloud @ Smart Factory 2018 in Jyväskylä – 20-22.11.2018



        Nordcloud at Smart Factory 2018 Jyväskylä

        Make sure to visit Nordcloud’s booth (C430) at the ‘Smart Factory 2018’ event, which is held in The Congress and Trade Fair Centre Jyväskylän Paviljonki, Jyväskylä between the 20-22.11.2018.


        Smart Factory 2018 is an event focused on how to utilise opportunities offered by digitalisation

        The event gathers together the themes of Industry 4.0 and the related technology, service and expertise offering. Smart Factory 2018 is targeted at all operators who are involved with changes associated with digital transformation in production activities and related new services and concepts. It strongly emphasizes the already known future-building themes, such as automation, machine vision, robotics, industrial internet and cybersecurity.

        You can register for the event here:

        Register to Smart Factory 2018

        See you there!

        Nordcloud at Smart Factory 2018

        Get in Touch.

        Let’s discuss how we can help with your cloud journey. Our experts are standing by to talk about your migration, modernisation, development and skills challenges.