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Asset and service data will be a crucial element of making this transition to a more outcome-based business model. However, at present, surveyed organisations are not accessing the full potential of this data due to their inconsistent use of digital tools and technology. While 98% of respondents report that their organisation uses automated digital tools and technology to aid the collection and utilisation of asset service data, only around half or fewer state that these tools are used in the collection (51%), aggregation (43%) or analysis (52%) stages of the process.

This intermittent use of automated technologies is not only opening the door for inefficiencies but is also directly leading to difficulties with data collection and utilisation. Around four in ten respondents report that when it comes to the management of access to asset service data in real time (40%), aggregating asset service data in a structured way (39%), analysing asset service data (41%), and sharing asset service data analysis with the rest of the business (42%), their organisation either needs huge improvements in these areas, a complete overhaul or that they simply do not do this at all yet.

The difficulties regarding asset and service data are exasperated further by the 59% of respondents who agree that their organisation is held back from the successful analysis of data because the quality of it is usually poor.

Struggles are rife throughout the entire process, right from who is collecting it and how they do this, down to how it is being analysed and shared across the business. How can these organisations possibly expect to make any informed, strategic decisions using the data that is readily available to them if the process is so disjointed, outdated and underdeveloped digitally?

 

Lack of Data Confidence

And these struggles have led to a distinct lack of confidence among surveyed decision makers and their colleagues, with only 50% of respondents reporting that they or other service leaders in their organisation completely trust the asset service data that they have access to. But this will need to change because asset and service data is becoming an ever more integral part of organisations, and this is summed up by the 85% of respondents who agree that service asset data should be central to strategic decision making.

The requirement to boost trust levels is especially pertinent in those organisations where the C-suite is already using asset service data today (39%) or have plans to in the future (34%) because they will need to be able to trust in the data in order to make well-informed decisions for the business.

The use of asset and service data by the C-suite will also serve to set an example for leaders across other departments that this is the best way forward for the organisation.

 

Glaring Skills Gap

However, it is not just these deep-lying trust issues that are a concern for organisations, which is clear from the fact that only 22% of respondents are willing to admit that the IT and field service functions in their organisation work together completely effectively to achieve the goal of better data utilisation.

This lack of collaboration is compounded by a glaring skills gap whereby over three quarters (77%) of surveyed decision makers concede that the pace of data intelligence digitally collected by their organisation’s assets is outpacing the skills of those responsible for actually utilising the data.

Further to this, more than four in ten respondents report that the skills of engineers (45%) and the skills of management (44%) are a cause for concern when it comes to using data produced by advanced technologies (such as a digital twin) meaningfully. This should set alarm bells ringing for organisations because they are struggling with skills among both their employees on the ground and those higher up the organisation as well. It seems that even with the implementation of the appropriate technology for the collection and utilisation of asset and service data, there will still be work to be done in order to extract as much value as possible – this will likely need to be in the form of a rigorous training program.

 

An Appetite for Automation

A lack of collaboration between teams, an ever-increasing skills gap and an inconsistent use of the appropriate technology, leading to trust issues could become a recipe for disaster in these organisations if not addressed quickly.

The need for automated digital tools has rarely been clearer, and respondents recognise this. Only 7% believe that automating the process of collecting and utilising asset service data is not at all required because all data manually entered by service engineers is structured and entirely usable. Whereas over four in ten (43%) report that the automation of this process in their organisation is required to a huge extent, or that it is completely required because manually entered data never/rarely provides value.

Organisations will need to utilise automated digital tools more consistently if they are to progress, but they will also need to upskill their workforce and address any collaboration issues internally. These three areas are crucial if asset and service data is to be utilised to its full potential and this will ultimately underpin the successful transition to an outcome-based business model.

 

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Data is one of the key driving forces underpinning the modern day economy, but asset and service data specifically, is absolutely crucial.

Organisations collect, aggregate and analyse plenty of it, but the way in which they do this can either be the catalyst for soaring above competitors, or the iceberg that sinks the ship.

Equally, outcome-based business models, which could be considered the next generation of the modern economy, might be a “make or break” moment in the lifespan of many global organisations. An outcome-based business model is focused on the outcome for the customer rather than a specific product. Customers receive extensive maintenance and support services after they have purchased the product, helping to nurture their relationship with the supplier.

In addition, customers only pay per outcome.

For example, the ability to only pay for a jet engine while it is in the skies, or an MRI scanner that is fully functioning and actually able to carry out a scan on a patient. Essentially, this type of model ensures that the customer always has the right outcome, enabling them to grow their own business while fostering a healthy client-supplier relationship.

Whether organisations can make an effective transition to this relatively new form of business model will likely be intrinsically linked to their ability to successfully collect and use data derived from their assets and will ultimately decide whether or not they stay afloat.

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Summary of Key Findings:

A new study from Vanson Bourne, sponsored by ServiceMax, from GE Digital, the leading provider of field service management solutions, has found that 75% of IT decision makers believe that machines will receive better, preventative healthcare than human beings by 2020.

Leaders surveyed believe advancements in machines having the ability to predict failure, take preventative measures or self-healing actions are widely viewed as beneficial to a company’s bottom line.

For example:

• 46% of respondents say machines requesting help themselves will help their company better manage their equipment assets.
• 39% of respondents say predictive maintenance would help better manage asset equipment.
• 44% of respondents say digital twin with predictive maintenance and artificial intelligence would help prevent major failures.
• 69% of IT leaders surveyed say they would like their own personal digital twin to help themselves and medical professionals regulate their health in non-invasive ways by taking early action and preventative measures.[/unordered_list]

The new study, “After The Fall: Cost, Causes and Consequences of Unplanned Downtime,”  surveyed 450 field service and IT decision makers in the UK, US, France and Germany across the manufacturing, medical, oil and gas, energy and utilities, telecoms, distribution, logistics and transport sectors, among others.

According to Gartner, by 2020, 10% of emergency field service work will be both triggered and scheduled by artificial intelligence. The new study highlights the impact of new technology like artificial intelligence, analytics, and use of a digital twin on how we monitor industrial machines to predict when a piece of equipment will fail and what preventative service maintenance is required.

“In the same way that organisations want zero unplanned downtime with their equipment assets to avoid expensive loss of production or service, we want to mitigate our own human ‘outages’,” said Mark Homer, Vice President Global Customer Transformation for ServiceMax, from GE Digital. “This holistic view of how something is operating – whether it’s a person, an equipment plant or an individual component in a machine – has historically been disjointed and only visible when something goes wrong.

 

Today, organisations are now acutely aware of the value of a real-time view on the health and performance of their critical assets, as well as predictive analytics on when preventative maintenance or intervention is required, and access to time series data, service history and optimisation demands. The research found that more than half of companies are planning to invest in a digital twin in the next three years.

The value of these digital insights in an industrial context is starting to generate interest in preventative maintenance in a human context.”

A copy of the Vanson Bourne Whitepaper and Executive Summary, can be downloaded here

 

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