Making the Case for Quality
February 2009
Driving Higher Workplace Performance:
Using Analytics, Dashboard Metrics,
and Soft Skills to Improve Results
by Bruce Ennis
I was a quality leader employed in the manufacturing sector for 18 years. In the mid-’90s I became fas-
At a Glance . . .
cinated with lean manufacturing and Six Sigma and read voraciously about all facets of both of these
methodologies. In 2003, during my first year after becoming a Six Sigma Black Belt, I led a successful
• A Lean Six Sigma Black project that saved close to $1 million—tops in the company. For my effort, I was flown to New York
Belt was assigned the task to share Six Sigma “success secrets” with the CEOs of several multi-national corporations. I thought to
of improving warehouse
myself, “Six Sigma is easy.”
performance for a Western
Canadian industrial
Then I accepted an offer to apply my expertise in an entirely different field, industrial distribution. I
distribution center.
soon discovered the unique challenges that accompany Six Sigma in a business where human, not
• During the course of
automated, processes frequently predominate.
this project, the Black
Belt rediscovered what
First Project – Warehouse Picking
many business leaders
subconsciously know,
but often forget: Special
My first assignment was to improve the performance of the warehouse “picking” process at our
care must be taken
Western Canadian industrial distribution center. In plain English, a “picker” is an employee that is paid
when tending to human-
to pick, and sometimes pack, customer orders. The job is vitally important: Pickers must select exactly
dependent processes.
what the customer wants, and they must do it in a timely manner. It’s not easy. The distribution center
• This case study relates
warehouse stocks more than 40,000 different products, and we require our pickers to be productive and
the story of a project
manager’s discovery of accurate. A breakdown means that customers either will not receive what they asked for, or they will
the differences between not receive it when they wanted it. Because the pay rate for a picker is modest and the work can be
“human” and “automated”
mundane, the picking function is challenged with high turnover year after year.
business processes.
Economics played a big part in how I approached this project. Within most Six Sigma improvement
initiatives, project leaders are restricted to a tight budget. A costly redesign, meaning process automation,
was not an option. Instead, I was charged with the task of improving the existing process—optimizing
what we already had.
Walk a Mile in My Shoes . . .
Lean Six Sigma project managers are taught to get in the trenches and “walk a mile in the shoes” of
the people who perform the actual work processes to be improved. In my first week of walking a mile
in a picker’s shoes, and of mapping the process, I experienced several “aha” discoveries:
1. Metrics—Our performance measurements were weak and not defensible.
2. Pay structure—Everyone was paid essentially the same. Tenure, not performance, was the
barometer for pay differentiation.
3. Job pride—There appeared to be little dignity in the picking role.
4. Leadership—Active, hands-on leadership was missing.
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Metrics in Place – Now What?
5. Human dependency—Although technical systems (software,
conveyers) were definitely part of the process, the core of our
Research indicated that four critical changes would yield signifi-
warehouse picking function relied on people.
cant improvements to productivity and accuracy:
You Manage What You Measure
1. Transparency – Now that we had data, we needed to
communicate to our employees how they were doing,
There is a business adage that states, “You manage only what
and how their performance compared to others. Creating
you measure.” World class companies measure what’s critical to
this foundation of accountability and expectations in the
their success. These measurements are often referred to as key
workplace would help validate the picking role, sending a
performance indicators (KPIs). Without KPIs—that is, without
clear message that the work matters.
strong leadership—a business is blind, unaware of where it is
2. Utilize dashboards – Dashboards depict statistical
or where it is going, and performance suffers. A job that lacks
information visually. Creating dashboards would make our
clearly defined measurements usually lacks clearly defined goals
key performance measures easy to understand (see Figure 1)
for its employees.
and therefore allow for instant interpretation.
3. Reward excellence – We needed to recognize and reward
As I immersed myself in the world of a picker, my mind flashed
individuals who demonstrated superior performance. The
back to a summer job I had when I was 16. I was required to
current system did not cultivate excellence.
keep a supply yard clean and repair damaged equipment. I
4. Restore pride – Establishing a respectful, collaborative
received very little feedback from my boss. No one seemed to
relationship with the staff would help restore a sense of pride
care what I did, or how much I did. I hated that job.
for all workers. Applying voice of the customer techniques
would help us understand their workplace needs.
In our warehouse I witnessed exceedingly wide variability
among staff—variability in passion, competency, productiv-
Pay for Performance
ity, and accuracy. Our pickers did not appear to feel validated.
The work they did, it seemed, didn’t matter. I recognized very
“Reward your best people, not your worst.”
quickly an enormous improvement opportunity in the optimiza-
—Jack Welch, Former CEO, General Electric
tion of how we managed our human capital.
Rewarding excellence, the third critical change in our list,
Developing a Measurement System
proved to be the most sensitive of the four proposals. Why?
Because I proposed that we reward our line-level staff in a man-
Results of a voice of the customer (VOC) survey showed us
ner never tried before—with money.
that two things mattered with respect to being a good picker:
accuracy and productivity. My challenge was to develop a
From my perspective, the homogenization of worker pay regard-
measurement system that measured both the volume and the
less of contribution cultivates mediocrity in the workplace. At a
accuracy of the work completed in a day.
corporate level, monetary incentives are commonplace, yet on
the ground floor they are almost unheard of. Wouldn’t some-
Numerous managers warned me of the impossibility of apply-
one earning $20,000-$30,000 per year be just as motivated by
ing metrics to warehouse distribution simply because of the
the opportunity to make extra cash as the executive earning a
massive scope of our product offerings and the varying degrees
six-figure income? After polling our warehouse staff, which
of difficulty in picking one item versus another. “How do you
included single parents and young adults trying to make next
compare the productivity of an employee who picks large
month’s rent, the answer was obvious: Yes.
items, such as stepladders, all day, versus an employee who
picks nuts and bolts?” they asked. “Furthermore, how do you The pay-for-performance model I proposed provided an oppor-
quantify accuracy? Some products are very similar and there- tunity for all employees to earn extra cash. As Figure 2 shows,
fore more easily confused than others. We’re not comparing higher bonuses would be paid for higher levels of performance.
apples to apples here.” Despite anxiety regarding the problems that can surface with
floor-level pay-for-performance programs, my employer was
They were right; it wasn’t easy. I began collecting and analyz- immensely supportive.
ing reams of warehouse data, more than 4,000 worker-days of
picker work. I broke the information into warehouse “zones” and Planning the Pilot
analyzed the differences. Using box-plots and a concept called
normalization, I was able to create a system where our company Our CEO approved a four-week pilot that provided carte-blanche
could compare in a fair and equitable manner the accuracy and authority to change the way we administered our picking pro-
productivity of warehouse pickers regardless of what items they cess. After several meetings with our picking staff, the following
picked or where they picked them. changes were authorized:
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Figure 1—Dashboard metrics
Productivity (items picked) Accuracy
Aaron 100
Pat 405
299 Karen
Sandy 100
261 Amy
Maureen 90
253
Shelly Laurie 90
250 Brenda
Sean 80
211 Maureen
Tammy 80
207 Chris
Tanya 70
203
Mel Mel 70
202
Dave Cal 60
193
Laurie Pat 60
169
Harry Cindy 50
150
Don Sandy 50
141
Karen Dave 40
138
Kathy Sean 40
131
Cal Don 30
125
Elaine Shelly 30
102
Cindy Elaine 20
96
Chris Tanya 20
Target
84
Aaron 10
Harry
70
Brenda Tammy 10
69
Amy Kathy 0
0 100 200 300 400 0 25 50 75 100
Performance (productivity × accuracy) Performance (month to date)
72843
24281 Pat
Pat
Maureen 62679
Maureen 20893
Target
Harry 53378
Laurie 17351
Laurie 52053
Sandy 14973
44919
Mel 14228 Sandy
42684
14082 Mel
Karen
Karen 42246
Sean 10000
30000
Aaron 8444 Sean
25344
Dave 8094 Tammy
25332
Cal 7835 Aaron
24282
Shelly 7599 Dave
23505
6748 Cal
Chris
22797
6220 Shelly
Amy
21916 20244
5633 Chris
Brenda
18660
5081 Amy
Cindy
16899
Don 4492 Brenda
15243
4146 Cindy
Tanya
13476
2500 Don
Elaine
12438
2112 Tanya
Tammy
7500
1689 Elaine
Harry
Kathy 0
Kathy 0
0 6,000 12,000 18,000 24,000 0 100,000 200,000 300,000 400,000
Figure 2—Pay for performance—Rewarding excellence
1. We would measure the accuracy and productivity of every
picker, every day. Daily performance—Feb. 28
2. We would post the results in visible locations. 35,000
3. We would reward excellence. Superior performance was Q4
Mark
monetarily rewarded. 30,000
4. I would enable worker involvement, meeting with the Bonus = $200/mo ($10.53/day)
pickers every week to solicit feedback on how we could 25,000 Dave
Melissa
Kathy
improve the process. Q3
Bart Heather
5. Root causes of picking errors or lower productivity would be 20,000 Bonus = $100/mo ($5.26/day)
Sarah
Al
identified, and corrective action, usually additional training, Pat Q2
Liz Sean
would be prescribed. The data would not be used in a 15,000 Bonus = $50/mo ($2.63/day)
Laurie
Colleen
punitive manner. Cal Chris Q1
Ami
10,000 Katie
Did It Work? Standard Pay
5,000
Don
The one-month pilot was a thoroughly rewarding experience. For
0
four weeks I collaborated with 20 pickers, asking them each day,
“How can we make this better?” I received useful feedback on
how to ensure the new picking process was mutually beneficial
to both the staff and the company.
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Absenteeism declined by 47 percent. Workers were frank with
At the end of it all, the picking process, developed jointly by
warehouse employees and a Six Sigma Black Belt, was more me when asked to explain the dramatic reduction in absenteeism.
rewarding and more fun, and performance had improved. Results They identified two primary contributors: (a) they had a chance
are outlined below: each day to make a little more money, and (b) coming to work
was more enjoyable. Morale had clearly improved.
Overtime was eliminated. Prior to project launch, overtime had
Quality improved by 0.2 percent. A previous Six Sigma qual-
been a chronic problem. Pickers were routinely asked to stay late
or work weekends to attack the backlog of work orders. It had ity initiative likely minimized the effect our pilot had on quality.
been years (yes, years) since 100 percent of orders were com- Further improvements to quality would likely require a focus on
pleted the same day they were requested. the physical process, including automation and bar-coding, and
on additional training.
Less than five days into our pilot an amazing thing happened.
Conclusion
We ran out of work! Productivity had increased so dramatically
that the pile of pick tickets was obliterated. Pickers felt an enor-
mous sense of accomplishment as they watched the demoralizing Disciplines such as lean and Six Sigma have dramatically
backlog disappear. improved the health and profitability of scores of businesses.
Irrefutable evidence exists to support this fact. What must
Productivity increased by 25 percent. Our 25-percent increase never be forgotten, however, is the importance of humans, the
in productivity is a conservative value, as on several days we lifeblood of your organization. The ability of a business to moti-
simply ran out of work. Had there been more pick tickets to pro- vate and inspire its work force is a prerequisite to successfully
cess, productivity values would have been higher. improving people-dependent processes.
We celebrated this accomplishment by allowing employees to go For More Information
home early on some days. On other days, we organized 5S teams
around the lean practice that involves cleaning, organizing, and • Contact Bruce Ennis at ennisb@telusplanet.net for more
polishing the workplace. details about this project.
• Access more resources on lean and Six Sigma at
Perhaps the greatest testament to the impact on productivity was www.asq.org/six-sigma in the ASQ Knowledge Center.
that the distribution center maintained a work force of 25 pick-
ers (20 full-time plus 5 part-time). Pilot results showed that a About the author
work force of 16 motivated pickers could accomplish the same
volume of work without compromising quality. Another surpris- Bruce Ennis is a Lean Six Sigma Master
ing discovery: The most productive workers were also the most Black Belt. He currently works for an interna-
accurate. Many had believed an increase in productivity would tional industrial distribution company, leading
lead to a greater number of errors. its North American Lean Six Sigma program.
He has worked as a quality leader for 20 years
and is an ASQ Senior member.
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