By Jason Hehman, industrials vertical lead, TXI
Robotics and AI promise major productivity gains in industrial contexts. The biggest challenge, in many cases, is translating those promises into actual utility on the factory floor.
The problem is that there is not (and there never will be) magical technology that works in every industrial context. The good news, however, is that there are basic principles that industrial leaders can use over and over to ensure that their technology investments lead to meaningful improvements for frontline operations.
Those principles: experimentation, user-centered design, and frontline delivery. Here, I’ll explain how industrial leaders can incorporate all three to consistently see positive ROI from tech investments.
Experimentation: Invest in real-world learning
I recently visited a factory that had state-of-the-art technology in place for inventory tracking and management. The tech setup ensured that assembly never slowed because workers were out of a given part.
In addition to the technology that enabled the resupply workflow, this facility also had several drones flying around the warehouse to monitor inventory – especially items stored on shelves several stories high.
At first, I thought the drones were overkill: didn’t they already have inventory management under control? But as I spoke with facility managers, I came to appreciate the investment: by deploying and observing the drones, leaders and frontline workers alike were better able to understand what they could do and envision what they might do.
Essentially, having the tech live let them experiment in real-world conditions.
Most organizations can’t afford to experiment at this scale, but it’s still possible to apply the principle. Testing a single AR headset, for example, might point to exciting efficiency opportunities. Building a rudimentary dashboard to track production can help illuminate what features would be most helpful for various roles.
The bottom line: Each industrial organization is different, and the automation and robotics solutions that will offer the most benefit are also different. Low-cost, quick-turn experiments are an excellent way to identify opportunities worth exploring further.
User-centered design: Ship in slices
One thing experimentation enables is feedback from end users. As industrials progress from experiments to prototypes to scaled implementations, they find the most success when they keep user needs and input at the center of their designs.
In practice, that means showing early mockups and prototypes to frontline users, gathering feedback, and using that feedback to direct further development.
Consider something like a dashboard, for instance. Dashboards can be a great way to unify teams by communicating crucial information from connected robots to floor managers and shift managers. But the utility of a dashboard depends on a number of considerations:
- The data included
- How often data is updated and how close that data is to real time
- How frequently managers can view the dashboard
- What training managers have in interpreting and acting on dashboard data
- … and so on.
The bottom line: The experimental phase is where you test whether something has potential value for the organization. When it comes time to build the actual tech, it’s essential to keep frontline users involved from the beginning. Otherwise, you’ll invest time and resources perfecting something that may or may not work in its intended context.
Frontline delivery: Start with problems, not ‘solutions’
Technology is often billed as a “solution”. But to meaningfully increase productivity, industrial leaders have to start by identifying their most costly problems. Sometimes, the latest and greatest robotics or AI solution solves those problems. Sometimes, it doesn’t.
Starting with a solution rather than a problem can lead to terrible ROI. For example, a manufacturing executive recently told me about a floor manager who wanted a second robot for the floor. He was convinced that the bottleneck his team was experiencing was because of high utilization.
But when they ran cycle-time measurements, they found that the first robot was idle more often than they thought. A second robot would not have fixed the problem (but would have been quite expensive). Instead, the solution lay in staffing and training adjustments, which they made for a fraction of the cost of the new machine.
The bottom line: While AI and robotics are transforming industrial work, those technologies are not always the answer to the problems frontline teams are facing. Starting with these cutting-edge “solutions” can lead to disappointing ROI.
For measurable outcomes, prioritize impact over hype
Today’s industrial leaders have access to some of the most powerful and exciting technologies we’ve ever seen. But if the impact of that technology doesn’t reach the factory floor, the bottom-line boost will be minimal.
To drive the kinds of measurable outcomes that let industrial organizations overcome roadblocks (staffing shortages, skilled labor shortages, shaken supply chains), leaders need solutions that deliver impact – regardless of where they are in the hype cycle.
That happens when organizations embrace experimentation, collect user feedback from the start, and continually focus on pragmatic applications for real-world, frontline workers.
About the author: Jason Hehman is the industrials vertical lead at TXI, a boutique digital consultancy for modern industrial leaders. TXI co-creates intelligent products that reduce risk, activate data, and empower the workforce – delivering outcomes that last. Hehman is also the founder of the Modern Industrialist Xchange (MIX), a curated space where leaders in manufacturing, supply chain, and industrial innovation connect through gatherings and shared insights.
