What People Analytics Can Learn From Motorcycle Manufacturing

"I'll take one mechanic who's a rider over a room full of engineers" ~ (anonymous motorcycle designer)

People Analytics is about as far from building motorcycles as one can get; but after a conversation with employees of a motorcycle manufacturer, I found there are some lessons we leaders in People Analytics may find of value.

It obviously takes a lot to build a motorcycle. The risks of missteps during research and development are high and any flaw in design is capable of collapsing market viability or worse yet endangering safety. I've been passionate about motorcycling since I got my first minibike at 8 years old, so I welcomed the opportunity to sit down with some members of an R&D crew for a discussion with the ambitious intention of drawing parallels to my world of people analytics from their world of cycle manufacturing.

My perceptions of what research and design departments are accountable for were almost immediately dashed. I imagined a room full of engineers and PHD's debating over complex CAD images as to what the impact of moving a part an eighth of an inch would mean to safety, reliability and marketability. Then after great debate and many iterations the design machinists would be tasked with creating the model prototypes and begin real world testing. I was informed quickly that is the way things USED to happen.

The manufacturer found that building a sequential pipeline from the mindsets of engineering to the hands of R&D machinists was resulting in ideas that weren't realistic, executable or just didn't make sense as a priority to a rider. Something that looks tremendous in a drawing doesn't always register on the road. The room full of R&D design machinists were also enthusiasts commonly logging over 20,000 miles a year on their bikes. Given their collective experience, they could often smell a failure far before the first mold had been pressed; but unfortunately after significant ideation and engineering had occurred in a vacuum. It turns out that segregating the "Art of the Possible" from the "Reality of the Road" was causing significant increases in rework and redesign along with delays in getting enhancements to market.

The company recognized the opportunity to bring the groups and processes closer together to limit waste and provide quicker time to market for deliverables. By allowing input by individuals that are creating the deliverable (who in this case also are passionate users of the product) the manufacturer created a less hierarchical and quicker time to market environment with the two groups working as peers with input into prioritization and design. This resulted in limited waste and allowed the collective team to hash out the difference between high value quick deliverables, stretch goals and long term targets.

Which brings us to People Analytics. As a leader in this space, I've often been pushed to segregate reporting from analytics and lean on hiring data scientists over subject matter experts. The idea of thought leaders always entices the C-Suite and interviewees that proclaim a panacea of predictive everything are often well received and hired without being asked HOW they would make that happen. Reporting often isn't sexy, but every analytic relies accurate and clear reporting as a cornerstone. Reporting staff aren't always seen as thought leaders, but often they hold the keys to what works, what doesn't, what is realistic and what isn't and most importantly what people struggle with day to day that keeps them from being more analytical and forward thinking.

The product of Reporting & Analytics should be actionable information. The consumer probably doesn't know if that's a report or an analysis and likely doesn't care. Telling them they have to either go to the "engineers" or "mechanics" just causes confusion and frustration.

By having the reporting and analytics teams either blended or very closely related it allows us to:

• Understand the full breadth of demand for information
• Do the right work in the right places
• Size and Prioritize more effectively
• Help strategists understand execution. Help analysts understand strategy.
• Build out career paths for those in reporting.
• Keep strategies accountable as realistic.
• Provide a balance between delivering forward thinking strategies, iterative improvements and immediate fixes for the maximum benefit to the enterprise.

In the experience I've had standing up teams, I believe the structure of the team should reflect the organization's analytical maturity. Orgs that are having issues getting even basic reporting out, operationalized and trusted should invest on the reporting side and build that foundation before jumping ahead. If I don't trust you to build a bike I'm not trusting you to build a plane. As maturity and trust are established, investing in more thought leadership and advanced technologies become viable for a good ROI. Obviously a strategy to move from the lower rungs of the information maturity spectrum to the higher needs to be had; but investing in each next step has always seemed to be prudent as opposed to announcing we're going to do predictive movement when we can't tell you turnover. I often call it an "uneven parallel" where we invest in each layer of the maturity model; but invest most heavily in the step that we're achieving next.

Any thoughts are always welcome!