Faster throughput, better predictive maintenance, new value-added products and services – plus happier customers and better profitability. It’s the IIoT and Industry 4.0 dream of harnessing all that machine, solution and sensor data so manufacturing isn’t just smart, it’s genius level.
But let’s be honest – it’s one thing to read the hype, it’s another to untangle the knot of tech and processes to make the dream happen.
But what if the tech/process knot doesn’t need untangling in quite the way you think?
What if you could start using all the trendy data lake/AI/machine learning tech to quickly spin up new products and services that make customers say “Wow!”?
Public cloud lets you do this. And when you take a cloud-native approach, you can develop fast, deploy quickly and give customers yet more reasons to work with you.
So what can you create? Models for smart products and services.
Offering a product as a service
You provide a data platform that powers a network of services available on demand. Data can come from a range of sources across the value chain and is orchestrated in your platform. Customers pay a subscription or by usage, and get access to analytics and dashboards.
Examples include offering intelligent automated maintenance, energy and cost savings recommendations, or even inventory and supply chain management.
Success story: Distribution Innovation
Distribution Innovation (DI) operates in the Norwegian logistics sector. They built a self-service data analytics platform on AWS that provides customers with easy access to data for foresight and prediction.
Customers use the platform to analyse data via dashboards in the DI product. They can run analytical models on their own data plus other sources, such as weather and traffic. DI customers are using it to improve their in sustainability performance and last-mile networks and optimise fixed delivery routes.
“If we can provide quality data and tools, we can help our customers transform their data practices from reporting to insights, prediction and innovation. We see it as a clear differentiator for our business.”
Eirik Lyngvi, Head of Analytics, Distribution Innovation
Enhancing aftercare and support.
You integrate sensors or other IIoT tech into your products. Then you offer a post-sale service where condition and usage data feeds into the customers’ production and maintenance planning. It’s a way of opening new revenue streams and driving loyalty, because you can have a measurable impact in areas like quality improvement, predictive maintenance and automatic replenishment.
Success story: SKF
SKF, a global bearings manufacturer, is using AWS to create a machine learning-powered condition monitoring and analysis service. It equips customer sites with machine alerts and alarms that link back to the data platform and customer portal, enabling smarter, better maintenance planning.
“We wanted to develop a cloud-based predictive maintenance application that would remotely monitor bearing performance, reducing the need for site visits while improving responsiveness for our customers.”
Jens Greiner, Global Manager – Digital Solutions, SKF
From health and safety to net zero, there are opportunities for manufacturers to support customers with their ongoing compliance planning and reporting. Data can be orchestrated to help with everything from more sustainable production design to remote monitoring of energy consumption.
Success story: Konecranes
Konecranes provides lifting equipment and services for industrial companies, shipyards, ports and terminals. When the Security of Life at Sea (SOLAS) regulations were updated, the real weight of all containers loaded on to ships needed to be known and logged.
“We believe our solution was likely the only SOLAS compliant harbor equipment in the harbour. This proved to be a market advantage for us, and the whole investment had a return of only 3 months.”
Timo Harjunen, Director – Digital Platform Development, Konecranes
Konecranes partnered with one of their customers and Nordcloud to create a smart solution in a fast 8-week window. It combines a lift truck, weight sensors and tablet and AWS to make sure all containers’ true masses were known and reliably communicated to harbour operators. Weighing only takes a few seconds (magnitudes faster than traditionals scales), and is more accurate.
What blockers should you be aware of?
And how the cloud overcomes them
Data ownership and access
Data ownership and access to clean data can be real problems. Because there’s so much data out there – when you think about everything across supply chains, production processes, equipment, inventory management and distribution.
You need to be able to do stuff with all that data, in an efficient and cost-effective way. And that requires a robust and scalable data tools and services, which you can get out of the box from hyperscalers.
Availability, scalability and cost-efficiency
To create data-driven products and services, you need access to the right data, at the right time and at a cost that its ultimately profitable for the business. Just because raw data is available real time doesn’t mean customers can do anything with that data as is. The data needs to be analysed in real time, too, so it provides actual value to customers. And that requires network resilience, fast latencies and the ability to scale up and down based on demand. Again, this is baked in when you take a cloud-native approach to smart product and service development, using everything hyperscalers have to offer.
How should you get started?
5 best practices
The first step is to define a clear strategy for how data will be accessed and used. Then, prioritise which applications and platforms need to be modernised or migrated to give you access to the right cloud capabilities. Innovation can be a case of evolution over revolution.
A data lake is used as the single source of truth for the product/service. It’s the central repository that allows you to store data from any source without having to structure the data first. And it allows you to combine with other services to deliver different types of analytics – from dashboards and visualisations to Big Data processing, real-time analytics and machine learning.
Applying artificial intelligence and machine learning to the data in your data lake is the game-changer for identifying new revenue-generating opportunities. It means manufacturers can quickly and easily transform data into those smart products and services, whether it’s serving personalised recommendations to customers, enabling predictive maintenance or monitoring quality.
Developing with a cloud-native approach – harnessing agile and DevOps – is a proven way of enabling teams to bring new products and services to market faster. Plus, the wealth of pre-baked hyperscaler tools and services makes it easier to introduce sophisticated new AI, machine learning and IIoT use cases.
Automation is one of the beautiful things about cloud. And when you take a cloud-native approach, you can maximise your use of automation to make the development, deployment and management process that much faster and more cost-efficient. Your teams can then focus on the fun, value-adding bits – and at the same time, you’re mitigating risks and making it easier to push out new features and updates.
Fundamentally, the speed, agility, scalability and resilience of cloud should mean a strong ROI - check our data modernisation calculator to understand your data money-making opportunities.See Savings