Leveraging HR Analytics: Better Productivity From Some Telltale Donut Bags

Apr 21, 2016
This article is part of a series called Editor's Pick.

Editor’s Note: This is an excerpt from the book It’s All About Bob (bie): Strategies For Winning With Your Employees, published by The Workforce Institute at Kronos Incorporated.

By David Creelman

When we hear the phrase “HR analytics” it sounds very, well, very uninteresting. You may also think it’s a technical subject, suitable only for people who find statistics fun.

However, analytics starts with the simple willingness to say “I don’t know. Let’s find out.” You don’t need to be an analytics expert to have that mindset.

Carnegie Mellon’s Denise Rousseau likes to say: “Make decisions based on the best available evidence.” This sounds so reasonable that you wonder why professors would bother writing books about it, but data shows that even in medicine, doctors often base decisions on their personal opinions, not the best available evidence.

The Telltale Donut Bags

Kronos3coverBob was no Sherlock Holmes, but his curiosity was piqued by the piles of donut bags in the trash at the far end of the plant. The nearest donut shop was a 15-minute drive away. A round trip would surely take 45 minutes end-to-end. How did anyone have time to get donuts?

Time was a big deal for him. The team he was directly responsible for was sweating buckets to keep up with the production schedule. They cut and polished parts and they were always rushed at the end of any big job, especially if there was re-work.

Bob had hinted to the factory manager that he needed more staff on this line, but the glare she had given him was enough to send him scurrying back to check on the delivery of some new equipment. There, near the shipping dock, one of the old hands waved him a welcome.

Hey new guy, want to go on a donut run?” Bob answered, “Thanks, but my team is running behind. How is it thatyou guys have time to go for donuts?”

“The enamel’s cooking. We gotta wait until that’s done, no point sitting around staring at the oven.”

Bob saw that there were indeed several workers sitting around; something that never happened in his unit, well, except on the days when they realigned the machines or were between big orders.

A little curiosity always helps

Bob couldn’t go for donuts, but he could ask why it was that in some parts of the plant there was excess labor while in other parts there was too little. When he got back to his end of the plant, he demonstrated a key trait of the analytical manager: curiosity.

He asked one of the other foremen, “Tony, why the heck are there a bunch of guys sitting around at the other end of the plant while we’re running ragged here?”

Tony explained that scheduling workers across the plant was a compli- cated issue; especially since workloads were volatile and not everyone could work in every area.

Tony looked up towards the office perched overlooking the plant. “Man, I remember when I started, the scheduling guy had all these piles of papers and he managed to get it all figured out in one day. He was a bloody genius.”

“Don’t we have computers for scheduling now?” asked Bob. “Yeah, maybe, sure, actually I know we do… heard about it.”

Now it was Bob’s opportunity to go back to the factory manager, “I think I know how we can speed up production.”

Bob was no analytics expert, but he knew that the factory was, as a matter of course, collecting a ton of data in the time and attendance and scheduling system. Surely, given what modern software can do, it should be possible to optimize the shifts so that when some units were oversta ed labor could be shifted to units needing extra hands. The scheduling software would need access to the data on who had the skills and certifications to work in different units, but all that was available.

Scheduling software does the trick

The factory manager, who clearly remembered the guy who could do scheduling with nothing more than a pile of papers, wasn’t sure of the capabilities of their new scheduling software, but a call to the vendor suggested it wouldn’t be hard. In fact, the vendor said it would probably be a simple problem compared to the scheduling they did in hospitals with 24-hour shifts and a wide range of technical specialties.

It turns out, the vendor was right. The scheduling system was not being used to anywhere near its full capabilities. It took some work, but it proved possible to significantly increase throughput by optimizing the scheduling.

Add in some cross-training and the scheduling software was able to easily match talent to need, shift by shift. The only problem was in the donut shop, where the staff found themselves standing around wondering why their customers weren’t coming as often.

What’s the lesson here?

It’s that sometimes, what matters is just an awareness of what analytics can do. Bob never touched the scheduling software, but it was apparent to him that there were underutilized resources, and lots of data about who was working on what for how long. He saw the opportunity for optimization, and made the effort to ask how analytics could help the factory.

Just as the capabilities of phones have increased immeasurably, so too the ability to gather and analyze workforce data has sped ahead of our understanding of what is possible.

Lessons learned

The essence of HR analytics and evidence-based practice is moving away from opinion and argument to simple questions like, “What does the data show?” or, “What is your hypothesis?” or, “What question are we trying to answer?” or, the ever useful, “Let’s find out!”

You don’t need to have a degree in statistics to be an evidence-based manager (or foreman), you just need a scientific mindset. You often do need access to people with deeper analytical skills and analytical tools (like the tools embedded in the advanced scheduling software that Bob used to balance the workload), but those skills and tools are readily available, probably within your own company.

There is something of a myth that analytics will give you the right answer, just like you got the right answer in a 10th grade math test. In business, it’s not like that.

We often can’t get solid proof either way, but we still need to make a decision, so we make it on the balance of the available evidence. If it looks like one cutting tool will be better than another, based on a careful consideration of the available evidence, then that’s the one to buy. That’s a far better way to make a decision than making the choice based on a strong but data-free opinion.

Evidence-based management doesn’t exclude expert opinion as a valuable input, but expert opinion is most convincing if you can tease out the hidden reasons that lead the expert to their conclusion.

Experiments are a key part of evidence-based management and we don’t do them enough. The word “experiment” may sound formal, but it really just means testing something out and learning if it works. However, if you do “try it out” in too casual a way, then the evidence you gather won’t be convincing.

A culture where analytical thinking can flourish

Perhaps another myth that is held by people trained in analytics is that all they need to do is gather and weigh the available evidence and management will happily accept their conclusion. This might work on the planet Vulcan, but it doesn’t happen that way on Earth.

Organizations on this planet are full of humans, and humans don’t react to evidence the way Spock would. They don’t like surprises, they don’t like to be shown to be wrong, and they don’t like decisions to be made without their involvement.

One of the most impressive things about Bob’s organization is hidden from view. This organization had a culture that created the conditions where analytical thinking could flourish.

Employees were engaged and wanted to see improvements. The management was open to listening to employees and changing things. There was enough slack in the system that there was time to do some thinking and some experiments.

Contrast this to companies where employee initiative is not encouraged or where everyone is so insanely busy that they never have time to reject or learn or improve. The science of analytics rests on a base of culture, and you need both for an organization to bene t from the power of evidence-based practice.

Creating a learning organization

Bob is pleased with the wins he has had lately at work as a result of taking a data-driven approach to pushing for changes. The leaders at Bob’s organization are likewise pleased with Bob. Their commitment to creating a learning organization, listening to new ideas, and providing their employees with the opportunity to innovate is paying dividends for everybody.

Bob has decided that he’ll take an online course in LEAN principles and maybe even go for his Six Sigma Black Belt. Maybe he could even be plant manager someday — if he keeps asking the right questions.

Reprinted with permission from It’s All About Bob (bie): Strategies For Winning With Your EmployeesCopyright © 2015 by Kronos Incorporated

This article is part of a series called Editor's Pick.