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Four opportunities for machine-builders to increase revenues

04 June, 2021

Leading machine builders estimate that 20% of their revenue will come from digital services in 5 years. What’s your vision and plan?

Creating and selling digital products is one of the best opportunities for machine builders to generate new revenue streams. In 5 years, a minimum of 20% of machine builders’ revenue is expected to come from digital services. Where will you be 5 years from now?

IXON sets out to help machine builders achieve this ambition. Not only by getting the ball rolling with opportunities for new business models, but also by providing concrete ideas and tangible solutions with minimum investment and quick ROI.

In this article we will discuss the challenges of machine builders and introduce new business opportunities that will disrupt existing business models. By implementing one of these new business models, you will be able to generate savings based on intelligent usage of logged data.

Challenges machine builders are facing today

There are a couple of challenges machine builders are facing today regarding their digital transformation journey.

Operating in the extremely competitive machine building industry is hard. Most machine builders are led by the needs of their customers, instead of creating their own path to success, which can be very complex. To produce cost optimised machines you need to determine how to start your digital journey and where to go.

The exploration of digital possibilities to utilise machine optimisation, without locking in to a huge investment, can be challenging. The lack of knowledge, overload of digital technology and potential security risks during the lifecycle of machines makes this even more difficult.

That leads us to the main question: how can you achieve generating 20% of your revenue from digital services in 5 years with a business model from which both you and your customers can benefit, and which your customers are willing to pay for?

New turnover potentials beyond VPN

Making money out of digital services, beyond remote access, all starts with a change in mindset. A critical view on your existing services, and discussing and coordinating your ideas with your customers will lead to new opportunities.

We’ll address four business opportunities based on different machines in a production line. All ideas are driven by data that can drive new revenue growth or save costs to better position your machinery in the market.

  1. Recurring revenue with a consumable strategy;
  2. Monitoring service for wear and tear parts;
  3. Saving on machine parts through machine learnings;
  4. Predictive monitoring service contracts for critical components.

Recurring revenue with a consumable strategy

Out of specification consumables can be a major cause of machine downtime. When consumables are optimised for your machines and in stock, this leads to higher uptime. So why not supply your own consumables, or even better: sell machines in order to sell consumables? Granted, not all machines offer this possibility, but it is worth considering how this fits into your machine’s market.

In the printing and packaging industry, there are plenty of examples on how this strategy is applied successfully. By sending the consumables to your customer before they even know they need them, you completely take away their need to worry about them. This is a win-win, leading to increased uptime and recurring revenue for the machine builder.

Monitoring service for wear and tear parts leading to multiple sources of profit

Certain parts of a machine have a defined lifetime and will wear down at a certain point. Unplanned downtime caused by worn machine parts leads to negative customer experiences and rising costs. For example, you’re happier when your car notifies you when maintenance is needed than when your car suddenly breaks down.

Machine builders can use machine data to determine when wear and tear parts are end of life. They can deploy this as a new service by notifying customers beforehand, for a small fee, which will result in preventing production from stopping, leading to increase of spare part sales, potential service contracts and customer satisfaction.

Saving on machine parts through machine learnings

Implementing machine learning is tricky and difficult to realise, especially for smaller machine builders. But learning from your machines in the field is not. Most machine builders focus on having well functioning machine software with limited bugs and a stable operating system. Changing the software is a no-go and usage of self optimising algorithms and AI is only a possibility in a distant future. 

Imagine some parts of your machines being “too” good. When designing new machines you use certain safety margins, for example 20% on drives and motors. But what if those parts never fail and your safety margins are too high? Mostly, failures lead to improvements via root cause analysis. However, machine builders rarely focus on parts not failing.

What if you could lower the safety margin without running any risk of market failures? By collecting and analysing data you can redesign what’s too good. This will greatly reduce costs and this knowledge can be used in your design fase on future parts and machines to optimise the new generation and gain a competitive advantage.

Predictive monitoring service contracts for critical components

Many machines have critical parts with a long manufacturing and design lifetime, but often these parts will fail during the machine lifetime nevertheless. Breakdown of critical parts frequently leads to long downtime, because most companies don’t have these critical parts in stock. This is devastating for the production process and costs your customers a lot. How much are they set to lose?

What if you could predict such failures based on data available on PLC level? Analysing the data of failed parts and parts close to end of life can potentially be combined with data sciences leading to identifiable patterns. Experienced service engineers can often decide if the machine is running well or not by just listening to the machine or feeling vibrations and identify the cause.

Monitoring the right data and comparing them with known patterns is the link bringing your combined service experience out on each machine 24/7/365. Potentially, you and your customer know a failure is likely to happen prior to it happening. Replacing parts and doing the service before breaking down at a time where the impact on production is limited, can increase the grade and revenue of your service contracts.

Implementing new service strategies with a quick ROI

When it comes to digital transformation and creating value for your customers by implementing new services, the solution is often within reach. But the need for IoT solutions and data to create those types of business models brings new challenges and investments. 

How are you going to claim valuable revenue - and support your customer’s success - with digital services? If you need help on defining your digital strategy and making value out of machine data, feel free to consult one of our industry experts without any obligations.

How condition-based monitoring of wear and tear parts increases revenue

Maximise revenue with a smart consumables business model

Scaling up your IIoT maturity with machine performance analysis

Grow your revenue by predicting critical part breakdown

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