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The human-powered factory of the future

Jan. 8, 2019

Three steps to get there in 2019.

Much of the talk about digital transformation and Industry 4.0 trends quickly toward

Parsable's Lawrence Whittle

plants that are “lights out” as a result of automation.

The fact is, manufacturers who simply digitize existing processes by replacing human workers with sensors and robots are missing out on the heart of Industry 4.0—the opportunity to remake your processes so that they’re not just digital, but better in every way. It’s about augmenting humans and machines.

The consumer-products factory of the future is more than just sensors and robotics. Far from being a dark future, it is ablaze with machine-assisted human intelligence driving unprecedented process improvements and product innovations.

As we kick off 2019 and think about what the factory of the future looks like over the next decade, what steps can we take now to achieve that vision, one that enables companies to develop new products, grow revenue and engage with consumers in a completely new way?

In 2029: Production delays and line shutdowns largely a thing of the past

You’d think that with machines in a production line far more complex than a decade ago, and automation that has increased the overall complexity and vulnerability of the line, there are more things to go wrong.

One reason is that problems-in-the-making are routinely spotted and avoided. Predictive analytics continuously comb through the data streaming in from the IIoT, including from machine sensors and human actions. These systems spot outliers from norms—a blending machine whose top speed is gradually slowing, a worker putting too little time into a particular cleaning step—and predict likely outcomes. Prescriptive analytics identify preventive actions and trigger alerts, actions and suggestions via mobile to workers, supervisors and/or maintenance teams.

What to do in 2019: As you simultaneously improve and automate your production processes, you capture more and more data from sensors, machines and human work. Combine this data to create the full picture of what’s happening in the plant or factory. It may be easier said than done, but the growing popularity of open APIs—which can connect a myriad of machines and mobile devices to cloud platforms, graph databases and data historians that store and analyze relationships between data from different sources—will surely help.   

With this full picture, manufacturing-excellence programs can drive improvement faster. Companies can also create accurate digital twins to compare existing processes with proposed alternatives and measure simulated results before actually making real-life changes.

In 2029: SKU explosion is just the state of play

The manufacturing unit-of-1 is coming. The multiplication of SKUs is no longer a problem, and the consumer desire for endless choice is a given. Debate about the wisdom of chasing the “longtail” of demand has evaporated, since the longtail now accounts for an increasing share of every manufacturer’s growth and margin.

Line changeovers aren’t considered disruptions to production; they’re seen as its very essence. And a prediction made by PwC in 2015 has come true: “The ability to handle product line complexity cost-effectively can, in itself, become a competitive advantage.”

What to do in 2019: Simplifying the execution of complex work is key to successfully supporting long-term SKU expansion, and right now much of that work is still being done by humans. So, let’s empower them.

Leverage technology that breaks down complicated procedures into clear steps for your workers and enables them to better collaborate, both side-by-side and top to bottom. This is one of the most important early steps you can take toward the consumer-products factory of the future, one that many consider as the foundation of Industry 4.0.

In 2029: Machines + humans=innovation

Machine learning does what it does best—rapidly sorting, classifying and examining relationships between different types of data. With the results of this data-crunching presented in a handful of relevant statistical or visual clues, humans then do what they do best—rapidly reason their way through a problem to a solution.

In 2029, manufacturers recognize that while predictive diagnostic analytics are helpful at pointing in the right direction, humans have an unmatched field-of-vision for complex troubleshooting. Human brains can consider both digital and analog information, follow hunches, and weigh inputs and tradeoffs to hone in on the root cause of problems and develop the best remedy.

What to do in 2019: If you try to fully automate your production processes without having measured and analyzed the human-labor component, you’ll have a huge blind spot. What you can’t see may cause you to approach digitization in suboptimal ways. You might even automate broken processes without realizing you had the opportunity to fix them through digital transformation.

As you establish and refine your long-term digital-transformation vision, don’t forget about the uniquely human aspect of work, which enables a clearer view of the starting point for your journey to the consumer-products factory of the future. You’ll be able to see where straight automation is the way to go. You’ll know where human workers play essential roles. And you’ll understand how to use technology to support their work and augment their capabilities.

Lawrence Whittle is CEO of Parsable.