Transforming production operations

The 4^th industrial revolution (4IR) is in the process of transforming that basis of economic life: how we make things.

What’s driving the 4IR is what drove the first three industrial revolutions—tech innovation. But while steam, electricity and the shift to digital drove the first trio, breakthroughs in the Internet of Things (IoT), automation, artificial intelligence, big data, and connectivity are driving the fourth.

Here are some specific ways industry will change as the latest revolution takes hold.

As new models of product provision surface, new processes will complement the manufacturing process, stealing some of its relative importance from it.

Consider how the as-a-service economic model works, and how it differs from the provision model that prevailed until recently. In the old model (to simplify somewhat), producers made products, which were sold to customers, and there the producer-customer relationship essentially ended. Producers had one chance to make their products work: during manufacture. After that, their products were out of their hands.
 

The as-a-service economic model that’s been emerging with the 4IR offers another way. When an enterprise purchases software or IoT-enabled smart lighting “as a service,” according to a subscription model, product lifetime has to be a crucial concern for the producer. After all, the producer still owns the product that it’s licensing out to the customer. Renewals depend on that product’s continued viability.

But that’s understating the matter. In fact, renewals can depend on that product’s becoming even better—in the way that, for example, a “smart” product’s machine-learning capabilities get keener with use, or should. The manufacturer is on the hook for the entire product lifetime, to the benefit not only of the customer, but of innovation itself.

Traditional industry was a heavily bureaucratic affair: all those logistics experts doing all that planning as they ensured that supply chains delivered materials that the enterprise needed, that complementary parts joined up with each other when they needed to, and so on.

Planning is still crucial to manufacturing. What human enterprise could do without it? But it’s a different sort of planning. The advent of big data and of AI applications that can crunch it fast, generating tactical and strategic insights, means that planning will increasingly be integrated into, and identical with, the production process itself. No longer will it represent a stage that ends before production even begins.

The speed and flexibility of the new manufacturing complex that exists at the intersection of data, AI, the IoT, and robotics will make possible situations in which, say, logistical chains are tweaked and improved before a human manager will even know that something was amiss in the first place. Is a bottleneck in China slowing production in Bangladesh? Forget the plan—the new system will adjust the chain in real time. Is more sunlight than usual making it possible to use less electricity in a corporate office? An IoT-enabled “smart” lighting system will adapt to the new reality, without human beings needing to go back to the drawing board.

Lean manufacturing seeks to cut redundancy and waste throughout industrial processes. As might be expected, it relies heavily on employee buy-in and participation. Employees as well as managers need to commit themselves to identifying inefficiencies in their own work and proposing fixes.

As the 4IR transforms production, lean manufacturing is yielding to smart manufacturing. The latter, defined as it is by a number of up-to-the-minute technologies, isn’t “lean” in the accustomed sense. Lean manufacturing is about removing superfluous melodies, and the instruments that play them, from the orchestral score. Smart manufacturing is about writing more complex melodies, for a greater number of more complex instruments.

And yet this “smart” complexity can create an orchestrated whole that’s more efficient than a lean set-up. A “smart” factory that uses an IoT system to minutely trace where components and materials are along a supply chain can make “lean” methodologies like “just-in-time” provisioning work better than they did before. 

New tech will see the imperative to save money downgraded in importance. It will now be more important to devise novel ways to make money. No, managers won’t embrace profligacy. But they will be alive to the possibilities of 4IR tech, and they’ll be wise to cultivate those even as, or before, they cut inefficiencies.

To give one example, the IoT-enabled sensor tech embedded in, say, new turbines will pulse out valuable data testifying to the attrition rates of component parts, to how much energy the machinery is using, and so on. That data opens up for a manufacturer the chance to branch out and provide profitable predictive maintenance services over time. Or else it can sell broader consultative services pertaining to how to optimally exploit such machines throughout their lifetimes.

In another scenario, data testifying to how users are customizing or “hacking” gear for certain uses can give manufacturers ideas for new products to develop.