Highlighted in a 2018 McKinsey report, many industrial companies are experiencing “pilot purgatory” and continue to be doing so – a phenomenon in which, while they have significant activity underway, they have yet to see any meaningful bottom-line benefits.
Breaking out of this purgatory requires companies to “cross the chasm”, a process observed and described by management consultant Geoffrey A. Moore in the early nineties. Roughly speaking, while many technical IT-related projects will do well at first, with considerable energy behind them, they’ll often struggle to be properly adopted. Moore observed that there’s a significant difference between innovators and early adopters, and the next wave of adopters, whose buy-in is needed to ensure a project will scale. In effect, a reluctance and second-guessing of results from the company’s leadership to proceed to commit to the next stage can leave even successful pilot projects in a state of paralysis.
It’s something you’ll often see with startups. Excited founders develop new technology and become very good at explaining it in technical terms. This helps them garner support from certain types of organisations – the innovators and early adopters – and there’ll be a great deal of initial energy behind the project. But this encouraging start will be followed by a period of silence. This is the chasm they must cross.
It’s not just startups, of course. Vendors can spend time at conferences talking about products in a certain space – such as predictive maintenance – to an audience of innovators and early adopters that, by and large, will understand and be excited by what they hear. The challenge this audience then faces is convincing the ultimate decision-makers within their own companies – the maintenance managers, plant managers, IT, and finance teams – who aren’t going to care about the same things. Technical terms and checklists will simply leave them unimpressed and unconvinced, a gap in capability is matched with a gap in communication.
The chasm can exist within individual organisations as well as in the wider industry. It’s a challenge most commonly faced by those people whose job it is to service a company’s future needs. If they’re unable to create proofs of concept linked to use cases and demonstrate where technology will add value to the business, no one in the business that functions on a day-to-day basis will care that it’s coming.
A company may try a new predictive maintenance solution, for example, but it won’t be able to persuade its maintenance team to adopt it. They’re not interested in technical talk, they’re concerned about that motor that’s running hot, and when it’s going to fail. Predicting the motor’s failure has nothing to do with AI and machine learning as far as they’re concerned – they just want a functional solution that will provide them with some actionable insights.
Many organisations find themselves at the pilot stage and struggling to figure out how to scale. Many others will encounter internal resistance to wider adoption. To improve your chances of crossing the chasm, and escaping pilot purgatory, it’s best to avoid the language of innovators and early adopters and use the language of the early majority instead. The truth is, technical terms such as IoT, AI, machine learning, and proofs of concept are largely meaningless to many people, and will just leave them cold. It’s far more effective to present your new product as a solution, rather than a technology, and describe the business outcomes - the problems it’s going to solve and the benefits it’s going to bring.
A new market, a new message
Senseye faces an additional challenge. We operate in a relatively new market, and if we don’t teach people from the start how our technology works and what business outcomes and benefits it can deliver, we run the risk of our customers becoming laden with misunderstandings and unrealistic expectations. Indeed, in our decades of experience in the predictive maintenance field, we’ve seen how a wide lack of understanding – by manufacturers and vendors alike – has resulted in a huge number of solutions failing to cross the chasm, and become destined to languish in pilot purgatory.
Instead of the plethora of popular buzzwords that include AI, Machine Learning, IoT, and Industry 4.0, predictive maintenance vendors should be talking about business outcomes; what they have achieved, and what they can do not just from the technical point of view but how will they create a measurable impact on the business of their clients. Predictive maintenance is a new paradigm and the benefits are legion:
- Asset lifetime extension through careful monitoring and timing of appropriate replacement
- Over-maintenance elimination by reduction and extension of planned maintenance activities
- Reduction in risky & disruptive physical inspections as all data is monitored remotely and automatically
- Reduced inventory and usage of spare parts as a planned replacement can be extended based upon actual machine condition information
- Reduction in environmental risks present from potential leaks and side-effects of machine failure
- Overall Equipment Effectiveness (or related similar metrics):
- Helping drive quality metrics – healthier machines produce better parts
- Ensuring maximum throughput by being aware of the actual operation and enabling advance maintenance of machines
- Reducing unplanned machine downtime (ensuring availability) by performing maintenance ahead of functional failure
Reduction in business risk with an increase in productivity and sustainability is something that everyone can get behind and the metrics from these projects prove it – these are things that everyone in the early majority can get excited about.
To read more about why the implementation and deployment of predictive maintenance are so misunderstood and littered with failures, and how our experience (good and bad) has informed our own Senseye PdM solution, you can download our white paper “Senseye in Depth – Why is Predictive Maintenance so hard?” here.