“Anyone who wants to be 100% sure, will be 100% late.”
I heard this sentence recently at the IoT Forum in Munich. It is a very good summary of the feeling that is emerging in the debate on Industry 4.0. The buzzword battles of the past 48 months seem to be just that; buzzword battles. Stakeholders seem to be stuck waiting for the proposition with guarantees.
Despite the buzzword bonanza, there is a real shortage of strategy papers about industry 4.0, digital transformation or the Internet of Things (IoT). It all started so well. Posts were created, budgets set aside, advisors engaged and orders to innovate passed on to middle managers. And then ... organizations just seem to have stopped, like rabbits in the headlights.
The manufacturing industry, above all its middle management, is a creature of routine. It requires concrete and sustainable use cases before it is prepared to take action. The competitive pressure for resources is so great that anything not fitting this is quietly written off after a year or so. This is how and why industry has been able to manage successful projects for decades.
IoT is not just a project
Of course, the search for viable project approaches is important, but disruptive evolution is much less likely to happen in manufacturing than small, incremental reforms. The challenge is that small one-off use cases do not usually justify the investment required. Improving a demand forecast, reducing a die-cutting machine, optimizing a product's spare parts requirements or logistics routes, are all important and exciting, but often lead to incremental improvements rather than opening up new horizons. You might find that good or bad—but you simply have to accept that it is the situation. Most people within the industry are not disruptive innovators, but just want to make the existing business better.
Properly packaged, IoT initially does not deliver sales, cost savings or new customers. Instead, it provides one thing only: data. And these data have to be evaluated, if they are to deliver beyond easily-copied “if-then rules” to provide sustainable value. This requires a strategy that combines a culture prepared to accept a certain amount of risk and investment with the real insight that tomorrow's likely success stories will be data-driven companies.
If a German company wanted to invent the new Google, it would probably start with a Google search. But if you want to succeed against American-dominated companies and start-ups, you have to think differently, not copy them.
Analytics as the cultural bridge
There is good news, though. From an analytics perspective, data from completely different fields can still be relatively simple. Their evaluation does not differ technologically—and this offers enormous synergies. IoT is therefore an infrastructure business, with many different subprojects. Swisscom concluded, for example, “The first projects have shown that there are no standard recipes for the development and development of IoT applications.”
Companies that provide their middle and project management with a suitable analytical infrastructure and culture will enable the rapid implementation of even the smallest use cases. New approaches will be quickly qualified, models built flexibly and operationalized as required. From a wide range of interesting approaches, we should see the emergence of exciting and, above all, profitable businesses for the industrial base.
How will machine learning advance this cause?
These synergies are likely to be further boosted by the attention enjoyed by Artificial Intelligence (AI). One could argue we are heading for a veritable smorgasbord of hyped terms. Nevertheless, as machine learning (ML) algorithms devour and learn faster from volumes of realtime data from IoT streams, we should see an uptick in organisational interest in IoT.
But to what extent will IoT and ML feed each other’s success? How will other disruptors like blockchain influence adoption?Download research paper: The Key Factors Driving IoT Success