January 31, 2018
In volatile market conditions, conservative industrial giants must move faster to survive. And this digital transformation of traditional manufacturing firms cannot — must not — simply be about making existing industrial processes more efficient. They must recognise the opportunity to use digital as a way to create more sustainable and profitable customer relationships, continuously aligning product value to changing customer requirements.
Pulling data off machines is not new
Factories, power stations, logistics companies, and transportation providers have consumed data from their machines for decades. For them, the idea of connecting things is not new. But much of the connectivity was proprietary, and many of the drivers were both local and operational: supervisors had access to some data on how their machines were operating, but there was little aggregation between machines in a factory and almost no visibility at all for managers elsewhere in the business.
As industries digitise to win, serve, and retain customers, that’s simply no longer good enough.
Big providers of industrial machinery, from ABB, Bosch, and General Electric to Hitachi, Schneider, and Siemens, are racing to digitise themselves and their machines. They are investing billions, acquiring companies to inject the software skills they may lack, and building teams to digitise products, processes, and engagements. For these big providers of industrial capability, the need to change is clear. But they understand that they must adapt, and that decades of hard-won excellence in building great machines is no longer enough. They know that they must place those machines in a digitally-enabled ecosystem, involving far closer and more regular interaction with suppliers, partners, and customers. All are investing time, money, and attention in changing. The transition is not simple or smooth, as GE’s very public challenges continue to illustrate. But it is necessary, and it is underway.
Industrial Firms Accelerate Their Investment In Digital To Survive And Grow
Both the challenge and the opportunity are, in many ways, far greater for those smaller companies that supply to or buy from these industrial giants. For them, spending billions on a software company like Mentor Graphics or Pentaho is inconceivable. For them, establishing swanky digital labs in the California sunshine is a pipe dream. But they must change too.
Look beyond operational gains, to transform your business
My latest report, From Grease To Code: Industrial Giants Bet Their Future On Software, explores the shift that’s underway. It looks at the industrial giants and their industrial internet of things (IoT) platforms. But it also looks at the companies that are trying to understand how to consume, integrate, and benefit from the new capabilities being offered to them. Almost everyone fixates on the operational benefits offered by things like IoT-powered predictive maintenance, and I explored this last year with Put Data To Work In The Industrial Internet Of Things. This focus is understandable, as the gains are often easily described and measured, the problems are well understood, and the set of decision makers that must be consulted is relatively small and reasonably aligned. But it’s a huge mistake to simply think about incrementally improving existing processes. It’s far more important to take a broader view, to involve a range of stakeholders and a range of systems, and to grapple with the very real challenge of working out what an IoT-powered, digitally enabled, insights-driven version of your business should look like. A rich digital experience does not replace a well-built premium car, or a dependable and resilient piece of industrial machinery: it augments them.
But getting there is hard, and the report highlights both compelling case studies and broadly applicable lessons. This whole area of research is a big focus for me in 2018 (and beyond!), and I’d welcome vendor Briefings and client Inquiries on this and related topics.
- digital transformation
- internet of things (IoT)
- IoT analytics
- machine learning
- machine-to-machine (M2M)