Your competitor down the street just raised their wages. NOW WHAT?!!

If you have ever seen John Frehse, a labor strategist, of Ankura Consulting Group present, you know he’s half comedian, half thought leader which always makes for an entertaining and educational session. One of his more recent grabbers is (and I’ll paraphrase here in a much less comedic fashion) describing how search engines are not your friend. Why? They provide access to information in a useful format. This may seem like a great service but in doing so they allow all your employees to see what everyone else makes and often they realize that they are not making as much as they could elsewhere. This creates a wide range of uncomfortable conversations for leadership.

The services provided by Glassdoor and Indeed and search engines such as Google have commoditized wages. Just like the price of pork bellies, a job title/description is tied to a wage and the highest wage advertised gets the most inquiries. These “wage transparency” companies provide a great service and balance some of commoditization with reviews and rankings. But let’s face it the compensation analysis is where everyone goes first.

When a product is commoditized what do producers and service providers do? They work hard to differentiate by bundling in other offerings that haven’t been commoditized. How does that work when it comes to compensation? John comes to the rescue with the thought leadership portion of his presentation. And while this concept isn’t new, John’s years of experience may add a couple of ideas that you hadn’t thought about. These are his top 12 currencies that employees value:

  1. Cash
  2. Fringe Benefits
  3. Feeling Valued*
  4. Work/Life Balance*
  5. Decision Rights*
  6. Access to Information*
  7. Investments in training
  8. Time Off
  9. Structured mentoring*
  10. Equality*
  11. Leadership enabled success*
  12. Merit based promotions

*Indicates categories that can be cost free

What I especially like is that all these wage alternatives benefit the company as well as the employee. Additionally, while these (aside from cash and fringe benefits) are free or low cost at scale, a number of these will take effort at executing. And when I see “effort to execute” I think sustainable differentiated advantage. In other words, most companies will look at that list and say to themselves…isn’t it easier to perform a market compensation analysis and justify higher wages to your executives to combat a retention and recruiting issue? You bet it is, but unless you are the company offering the highest wages, your commodity (e.g. wages) can easily be beaten by just a few cents more an hour. Those that invest in John’s multi-currency strategy will have lower burdened rates, a more engaged workforce and better fiscal performance. Your company becomes stickier to employees when improved work/life balance strategies are employed because it means employees not only have to change their place of employment but that their family will have to change its lifestyle too.

Ok, I get it, that strategy may be worth 10-20% difference in wages but what about when you have those things in place and the gap is still too big?

At KronosWorks this year I had the chance to speak with Jeff Mike, VP, Human Resource Research at Bersin. He had an interesting idea around cash wages. Why not emulate an idea from digital natives such as Uber and Lyft and introduce the concept of surge pay. At first I was a little incredulous…isn’t that similar to overtime? Then Jeff’s brilliance struck me. While it’s a very similar concept to overtime, overtime serves two masters. It’s an excellent tool to flex your workforce from a company perspective but because it is also an FLSA regulatory protection to prevent companies from taking advantage of employees and often used in union agreements to protect their members from the same. It is not a perfect tool for aligning labor with demand. One might also think surge pay is like shift premiums, but shift premiums are a dull axe when a scalpel is required. Surge pay can be a finely tuned compensation tool to make the value proposition more attractive to employees for working during busy times when they might otherwise choose to be doing something else.

How might surge pay work?

Think about a retailer who struggles to entice employees to show up on weekends. As an example of the problem they face, the chart below shows the hours and percent of unplanned absence by day of week at an individual store of a well-run retailer. It’s a version of the same chart I see at most retailers. Why is that? Because many employees don’t want to work weekends when most others are not working.

Absence chart

Ride services such as Uber use surge pay to bring more capacity online (drivers available for rides). In this case surge pay entices employees to work when they otherwise might not want to. This is not simply a weekend premium. What we have learned from the surge fare is that it can be finely tuned to the situation. Maybe a store has great employee availability Saturday mornings but not Saturday afternoons, surge pay can be adjusted for that. Maybe it’s a slower season, the store can stand a little absence, so no surge pay in January and February. How about an unexpected surge in traffic one afternoon and the manager wants a couple of people to stay…Rather than force the manager to ask someone to stay who might otherwise have plans, surge pay automatically increases until someone signs on. This also works with the emerging schedule predictability legislation in several cities. One of the common exceptions to last-minute company driven changes that incur a penalty is when the employee initiates the schedule change to earn the surge pay.

Ride services use historical demand and supply signals such as number of rides delivered, local fares, time of day, traffic, current requests and the available pool of drivers to determine if and how much a surge fare is applied. Similar signals such as employee availability, the predicted customer traffic, special events, acceptance rates when surge pay is used and unplanned absences can determine surge pay timing and rates. Policies could be introduced and automatically applied so that if a higher premium pay is already being earned, stacking of premium and surge wages is not performed.

A common objection from executives would likely be that employees would become accustomed to surge pay and be less engaged or not come to work unless it is offered? One could use that argument that with any type of premium pay. What is unique about surge pay is that because of the many different factors that would be used to calculate it along with historical participation with differing levels of surge pay, it would not be as predictable as current types of premiums. Or it could be used as a carrot…only employees with minimum levels of scheduled hours and high levels of attendance are eligible.

Variable compensation used to shape employee behavior is not new. Surge pay is a new technique to shape behavior that is low risk for employees and for companies all with the intent of providing an improved customer experience.

Digital natives such as Google, Amazon and Uber are using information to create innovative new offerings that are turning the tables on legacy companies. They are using real-time analytics and machine learning to adapt to myriad different situations while delivering new and efficient services. Current compensation techniques are difficult to adapt to a dynamic future. Human Resources and other executives involved in managing a workforce need to be thinking and acting like these innovative companies. If you find yourself competing with the employer down the street while living on tight margins, take a second look at the ideas shared by Jeff and John and start innovating your compensation plan.

What do others think? Let me know.

How a machine learning application led to new KPI’s and simpler dimensional analysis

Every time a timecard or schedule is touched to add a punch, change a shift or edit time, a digital record is generated to record the before and after values. Throughout the course of a year, these records can grow to millions of rows.

Its size makes it difficult to proactively audit through manual methods and it overwhelms spreadsheets. These records are most often used in response to a personnel issue or to respond to the Department of Labor where the specifics of the request make it easy to focus on a small subset. For the majority of time, it is simply considered a record required to be stored by FLSA for two years.

The hidden value in these records is that behaviors are embedded. This is an ideal scenario to use machine learning to identify otherwise hard to identify patterns. Because each company has its own “signature” in terms of behaviors, unsupervised learning using k-means clustering is applied to look for unusual clusters. Below is one example of how its output looks. In this case the purple cluster shows a unique behavior.


As an investigative tool, the machine learning algorithm works wonders identifying unusual patterns within a company’s own set of data. But it’s not the right tool for everyone who is interested in this information. What it did teach us was that while not exactly the same between each company, there were telltale signs that we could identify very quickly that something was not operating correctly. This led us to a much simpler method of creating a KPI that quickly measured specific types of behavior. Using that KPI and dimensional analysis allowed us to roll up and drill down to identify significant changes in distribution or sometimes even root causes.

One example of this is understanding how supervisors are editing timecards in terms of adding or subtracting time from employee’s original punches. To accommodate for this, we created a new kpi that could be applied at any level of the organization – Timecard Skewness. A Timecard Skewness score of 0 means that whenever a timecard edit is made it adds time to an employee’s timecard compared to the original punch. When Timecard Skewness is 100 it means every edit takes time from an employee’s timecard relative to the original punch. In general, we find that companies have company-wide Timecard Skewness ratings in the high 40’s to low 50’s. At a corporate level, this means that edits to timecards are well distributed, they both give and take away small amounts of time which you expect to see in the normal course of business. As always you still need to look at the distribution to understand if this holds true at all levels of the organization. This Timecard Skewness rating can be applied at any level of the organization including the supervisor. Below is an example of supervisors and their timecard edits with a skewness rating. The top of the chart shows the number of edits over the course of a year. The lower chart shows the supervisor’s individual’s skewness rating with the orange line showing a skewness rating of 50. Can you easily tell which supervisors make the most edits and which have abnormally high or abnormally low Timecard Skewness ratings? In this example, most supervisors make very few edits. A handful on the left suggest closer inspection is warranted. Don’t draw conclusions yet! It could simply be that a timeclock is not located properly or that there aren’t enough clocks to accommodate a shift change. In some cases, however, we also find that favoritism or time theft from employees is occurring often from just one or two supervisors among hundreds.


Inspiration struck again when we were helping companies understand how well employees were working to scheduled hours. There are a variety of reasons why employees might deviate, all of which impact employee engagement and business performance. Understanding how well employees are adhering to schedules is tricky. Employees sometimes don’t work when they are scheduled and sometimes they work beyond their scheduled hours. Sometimes schedules are edited to account for this and sometimes they aren’t. In this case we developed a metric called Schedule Adherence. A high Schedule Adherence means employees are working to the schedule and a low value means they are not working to their scheduled hours. We see high performing companies or departments typically scoring in the 80’s. Once again, the score can be applied at any level of the organization. It uncovers a variety of situations. In one case we saw a score in the high 90’s. At face value, we might congratulate the manager for exceptional performance. But it seemed unusual to have such a high score, so we kept exploring. By charting a histogram of schedule edits for this manager, shown below, it shows edits were primarily made after the schedule had been worked, which is not typical nor recommended. By looking at the individual edits, it became apparent what was happening at several locations. Supervisors were changing schedules to match whatever hours employees worked to make it look like employees were following the schedule which was the company’s intended practice. The x-axis in this chart shows how many days before or after the day of work that the schedule is edits. 0 is the day of work.

historical edits

These are two examples that demonstrate how machine learning is providing an initial step in the innovation process that would be very difficult for a data scientist to accomplish through traditional dimensional analysis & visualization techniques. Yet the final outcome is much simpler and economical than the original machine learning process.

What’s a good schedule worth?

I was recently having lunch with a friend, Aram Faghfouri, and we were catching up on a variety of topics when I asked him how he approached business problems when there simply wasn’t much data to analyze.

Knowledgeable as always, he told me that this problem had long been solved and suggested I read How to Measure Anything. This book does a great job of explaining through examples how to use limited data along with different analysis techniques to improve decision-making and reduce risk.

I thought I’d share one of the areas where we applied what we learned. For many of our large retail customers, they have a very digitized process that allows them to forecast labor demand, schedule against rules and employee availability, track actual hours worked and measure resulting sales and productivity.

This allows retailers to follow the entire labor process through data to understand where there might be areas of improvement. For example, they can look at charts like the one below to quickly identify challenges such as not enough employee availability to staff a schedule or does the generated schedule follow the forecast. In the example below it is the roll-up of all of the company’s locations broken out by hour. As the chart shows, every step from forecast to sales is fairly tightly grouped. It’s not surprising as this is a sophisticated specialty retailer that has been honing its processes for years:

retail schedule analysis

For other industries however, scheduling is a much more manual process. Many manufacturers take a production schedule and convert it into either labor budget or hours through a spreadsheet or simply through experience. Supervisors then schedule employees manually based on knowledge of their employees’ skills and production processes.

As a result, there is significantly less data generated to analyze how well this process works. There is decent data in the beginning (production orders) and at the end (labor hours consumed and actual production completed). But understanding if there is even opportunity to improve labor scheduling becomes a traditional industrial engineering approach of inspecting the actual process.

In reading the book, it became clear that rather than throwing up our hands and declaring defeat until we had more data, there was a middle ground. It might be possible to shed a little light on the process and determine whether it was worth more investment.

When we inventoried the data we had, we recognized that most manufacturers put their schedules into a system to measure actual punches against it to determine if employees are following attendance policies. And of course, they have the punches and pay rules to know the actual hours worked.

Our hypothesis was that if any of the curves deviated significantly then we would know that some part of the process before that deviation was not working well and there is opportunity to improve. After some experimentation we generated charts that look like the following:

schedule adherence

What we are looking at above is a single manufacturing plant and each circle represents the hours for one department for one week. The scheduled hours are in the first chart and the actual hours are in the second chart. The horizontal x-axis represents hours of unused capacity and the vertical y-axis represents overtime hours. Capacity here is defined as regular hours (40 and under) that are not worked by a full-time employee but are available to work (e.g. they are not on vacation). The scheduled chart looks pretty good. The departments are heavily clustered near the origin, meaning they have little scheduled overtime and little unused capacity. There are a couple of departments that have significant unused capacity so those would be worth investigating. However, when we look at the hours actually worked in the next chart we see that overtime and unused capacity have both grown and in many cases departments that had neither now have both! This means that some people who were scheduled were sent home or asked not to work and others that were scheduled worked more than their scheduled hours.

Using this analysis, we can now calculate the financial opportunity (Hours of OT that could potentially be converted to regular if worked by someone with capacity). We can also guess that these employees are probably not thrilled because what they thought would be a stable schedule has suddenly changed with some employees working a lot more hours and other employees working a lot fewer hours.

The limited data we do have has told us the financial magnitude of the impact and it has also told us the location of the problem. It lies somewhere in the labor demand calculation or the scheduling process. What we often see is that department supervisors that are performing these processes manually make approximations to simplify the process of creating and staffing a schedule. And now we know what the cost of that manual approximation costs the company in financial terms as well as employee engagement cost. We can communicate the specific area of the problem and financial opportunity to management using simple schedule and time data.

HR benefits too as they can understand the the hard dollar benefit of investing in cross-training.  If retention is an issue, better adherence to schedules providing more stable work hours will reduce turnover.

It’s exciting to see the continued creativity and results of applying even limited data to common business challenges. Especially when we can improve employee’s lives while improving financial outcomes.




Need more productivity? Try utilization cubed

This post is focused on the second of three themes on how companies can successfully thrive through a digital transformation.

It’s what I call Utilization3   (utilization cubed). The successful companies I study have extended beyond traditional means of driving productivity through high utilization of resources. They are innovating and successfully executing new methods with the effect of yielding high levels of productivity for themselves, their partners and their customers.

The first dimension of utilization is the traditional method. It’s basically Lean methodology. Cut wasted efforts out of your processes and your resources will become more productive. Mark Graban writes extensively about this for healthcare and does a great job explaining it in very simple terms. John Frehse specializes in labor strategies at Ankura and I wrote a book on this subject several years ago, called Lean Labor.

The second method of improving utilization is to take full advantage of not only the physical attributes of your workforce but also the skills they bring or have developed on the job. In healthcare, Mark Graban talks about working to full licensure. This idea has been around a long time but not as many company takes advantage of it for two reasons. The first is that often companies simply think of their most expensive resource (usually equipment) as the highest priority to utilize. But this can lead to a sub-optimal customer experience because the overall process is not optimized. Secondly, it’s relatively easy to optimize one resource. Optimizing a process including labor skills means thinking through the entire process, redefining jobs and potentially targeting different segments of a market. This is not easy, but those that get it right wield significant advantage.

Manufacturers have done this historically by training their line operators to perform simple tasks such a adjusting machine parameters such as tooling alignment, temperature, speeds and feeds to keep the line running rather than the traditional method of calling a more expensive maintenance mechanic for every adjustment. The benefit is this frees up the mechanic to spend more time on preventative maintenance.

As I write about in my latest book, Walk-in medical clinics in Pharmacies have redefined a healthcare service by focusing on non-acute patients and hiring nurse practitioners versus trying to create a more efficient primary physician service delivery model.

Southwest Airlines has a fleet of approximately 550 planes in its system. It’s the Boeing 737 with a couple of variations. Is the plane optimal for every route? No, but Southwest has designed its business model and target markets for where it is efficient. And guess what, it’s significantly easier to train mechanics, and move crews throughout their system when everyone can perform their role efficiently on every plane in the fleet.

The last method is the hardest to achieve but is turning industries upside down. This method is crowd-sourcing. Simply put it’s leveraging a network of “part-time resources” in a more efficient way than if a company simply owned/managed the resources full-time. The trick in this technique is at the heart of digital transformation. Companies must first attract and then efficiently co-ordinate those resources to deliver an even better customer experience than if resources were dedicated to that particular task for a single company. This is well explained in the book The Digital Matrix by Professor N. Venkat Venkatraman

The most well-known examples are Uber with cars and drivers and Airbnb with short term housing and hosts. If you are not familiar with how they work (and the controversies they are causing) simply type their names into your browser.

I’m continuing to see interesting examples along these lines with more traditional companies. Walmart has woken up since hiring Marc Lore through its acquisition of and is now innovating like never before. Last June, it announced a method and experiment to improve customer experience and reduce delivery costs by paying their own employees to drop off packages on their way home from the store.

It turns out customers don’t really love assembling their own furniture…but were willing to put up with Ikea’s business model because they loved the furniture and prices…until customers began to find people willing to assemble the furniture for them on Task Rabbit. In order to scale this, Ikea purchased Task Rabbit and can now has more control over the total customer experience.

Airbnb continues to evolve this crowd-sourcing model to extend what it can offer to its customers beyond housing/hosting to include Experiences. It has developed a platform that allows small businesses and individuals to offer a local experience to its customers. This solves two problems…Airbnb’s customers now have more transparency in what activities and tours are available locally as well as reviews. Secondly, local providers have an inexpensive way to market their services to a global, highly targeted audience.

It’s an exciting new world out there and as these companies have demonstrated, it is not simply building robots and developing artificial intelligence. Managing traditional resources, including people, remain squarely in the center of these successful companies’ strategies.


How to achieve the financial performance of a successful technology company: Think like 3M

As I mentioned in my previous blog entry, I’ve identified three themes companies employ to successfully  compete in a digital economy. This is the first: Relentlessly innovating and executing on those ideas.

Everyone loves a founder’s story. They often tell about experiencing some type of everyday problem and through a serendipitous event, a solution is imagined. This solution along with an unbelievable amount of hard work formed the genesis of a now highly successful company.

Sarah Blakely, founder of Spanx tells her story as a one-woman innovation wonder as do the founders of Uber, Airbnb and countless other start-ups. These journeys are driven by the sheer will of individuals who have the passion to see their ideas become successful.

Digital giants continue this tradition even as they have grown. Companies such as Amazon, Google and Tesla generate and experiment with new ideas that rethink their customers’ experience by using the advantages of technology to deliver scale, scope and speed.

For entrenched incumbents who have invested their treasure optimizing processes and raising barriers according to the rules of an outdated supply chain playbook, the future can be worrisome. They are now held captive by the fort that previously protected them.

Fortunately for incumbents, this scenario is not permanent. Just as Amazon and Google have remained innovative and adaptive as they grew, a few companies such as 3M have maintained their spirit of innovation (and outsized financial returns) for decades. By studying 3M’s method of innovation, it becomes clear that it has figured out how to create its own serendipity to generate new ideas as well as emulate at scale the same passion to execute as found in a start-up.

3M’s ability to innovate and execute is well documented. It often ranks highly as an innovative company. I won’t duplicate other’s good work, so you can read what I found to be some of the more interesting articles for yourself. There were two key findings for me. First is that 3M is organized to create and then execute on new products beginning with customer interactions all the way through to revenue conversion. It’s not a single department’s responsibility to innovate, it’s the way the entire company operates. Secondly, 3M has also put the mechanisms in place to stop funding projects and put people back to work on productive projects just as effectively as it begins new projects. Clogging the system with poorly performing ideas hurts performance as much as never funding good ideas.

With the barriers to all types of technology continually falling, a company’s biggest differentiator becomes orchestrating these technologies through its ecosystem to redefine what it delivers to its ultimate consumer. To do this successfully requires the ability to forget the current supply chain rules, have a deep understanding of the processes available to deliver product and services and an intimate understanding of the needs of its customers. That knowledge lies somewhere within your workforce. A leader’s job is to unlock that knowledge and act on it. How close is your company to executing innovation at the level of 3M or Google? How exposed is your company to a new competitor figuring it out first?


Some reading on 3M innovation

Succeed in your digital transformation by focusing on these three areas

When I prepare to speak to a company about the topic of Human Capital, the first thing I do is map out their industry, highlighting how their environment is changing through digital transformation. Each industry is different of course, but in general I follow these themes: New technology that is a part of their industry’s digital transformation, customers that are expanding into their business, complementary companies that have now become competitors and large and small natively digital companies such as Amazon or a startup that is entering this space.

If you are reading this blog, you likely know my discussion will go down the path that a highly engaged workforce will make sense of all this and figure out a path forward.

The feedback I received in my first couple of meetings is generally along these lines: We agree we need a highly engaged workforce and thanks for the ideas to help us achieve/maintain ours. But even with a highly engaged workforce, there are so many moving parts and the pace of each is so different, how do we point our workforce in the right direction…we have limited resources!

To answer this, I went back to the research I performed on the companies highlighted in my book Your Last Differentiator: Human Capital. Each of these companies already has a highly capable, highly engaged workforce. But the workforce isn’t enough on its own. In addition to a great workforce, these companies are successful at executing in three very specific areas.

They all:

  • Innovate relentlessly
  • Focus on what I call utilization^3 (pronounced utilization cubed – sorry, formatting is a little limited in a blog)
  • Deliver a superior customer experience

Each company approaches these in different ways, but each of these elements are present in every company.

My guess is that most understand 1 & 3 but might question the second. Utilization cubed means that these companies have identified ways to unlock underutilized resources in a dramatic way that their competitors have difficulty copying. As a result they themselves are highly productive or they offer a product or service that enables the downstream supply chain including their end customers to be highly productive.

The first dimension of utilization is simply the effective use of a resource whether it is a person, machine or material. All companies understand this dimension and apply methodologies such as Lean to achieve it. The second dimension of utilization is redesigning jobs to ensure that the skills of people are highly utilized. I write about Walgreen’s and Southwest doing this in my book. The final dimension of utilization is the ability to use crowdsourcing to unlock underutilized resources of others through technology. The most obvious of this is Airbnb leveraging underutilized homes, but there are an increasing number of interesting examples around this.

In researching these areas further, I have found a number of creative examples in each of these areas. I’ll share what I consider good practices in future posts to spark your imagination. If you have any you’d like to share, please let me know.



What is the Starbucks experience worth?

I unwittingly walked into the middle of a spontaneous retail experiment yesterday. As I entered the New Orleans convention center on my way to the SHRM conference I saw a long line. At first I figured it was for registration. As I followed the line with my eyes to the beginning I realized it was a snaking line of about 60 people waiting for a Starbucks coffee.

While that is noticeably longer than most lines I see waiting for food, it wasn’t shocking. This is a large conference with about 15,000 attendees so you can expect peak time waits. What caught my attention however was an unbranded coffee and refreshment concession right next to the end of the Starbucks line.  The contrast was stark. In a time when everyone talks about how busy they are, people are willing to wait 20-30 minutes for a Starbucks coffee when a likely reasonable and less expensive cup of coffee was available in less than five minutes. It was worth a picture so I took this panoramic image. On the right is the Starbucks store, in the middle (back) is the line of people waiting for Starbucks. On the left near the back of the line is the unbranded concession with a small line.


When the topic about valuing the brand and a great customer experience comes up, this little experiment shows that at least in the retail coffee market the value is worth about 30 minutes of a customer’s time and probably a 20% price premium even when the switching cost is zero. (Yes, I know, cut me a little slack on sample size)

The lesson can be translated to other markets….bare bones service and low cost offerings have their place in the market, but a larger percentage of consumers place a premium on the overall experience that includes service and product. Is your company doing what it can to deliver this kind of experience to your customers?


So that’s why I like WaWa so much!

In the mid 90’s I was a sales representative for a manufacturing company. My territory was the greater Philadelphia area. In those days, selling was primarily a face to face proposition. This meant that I spent most days in my car traveling to meet with customers in person. I was new to the area, but quickly became acquainted with the local WaWa. For those who are not familiar with WaWa, it is a convenience store. Not just any convenience store. It was the place that started my mouth watering when I thought about it…I LOVED their sub sandwiches. Whenever I had the chance, I would stop there for lunch. I moved to New Hampshire a year or so later and was very disappointed that I could not find a WaWa there. To this day I fondly remember WaWa and recommend it to anyone who is traveling or moving to that area.

Why do I bring this up? A co-worker was reading my new book Your Last Differentiator: Human Capital and started talking about her experience with WaWa. She had also worked with their HR department in the past and knows that they have focused on creating an employee friendly culture for years.

So I did a little research and lo and behold, I had not uncovered some hidden secret in the 90’s, WaWa has been known for their focus on customers and employees for decades. This was not a hold over begun by a paternal founder. WaWa understands that high levels of service differentiate a retail environment and that customers appreciate the small touches like when employees know them by name and what their favorites orders are.

WaWa also practices the servant leadership philosophy and provides a generous list of benefits to its employees, see below for a partial list…

  • Employee Stock Ownership Plan (ESOP)
  • Wawa, Inc. 401(k) Plan with company match
  • Medical Coverage (including Prescription benefits)
  • Dental Plan
  • Vision Plan
  • Disability Coverage (both short and long term)
  • Basic Life & Accidental Death & Dismemberment Insurance
  • Supplemental Life Insurance (including dependent and spousal/domestic partner)
  • Flexible Spending Accounts (Health Care & Dependent Care)
  • Wellness Reimbursement for Weight Management programs & Fitness Center usage
  • Annual Wellness Screenings & Health Coachingcompared
  • Educational Assistance Plan
  • Employee Assistance Program
  • Employee Credit Union
  • PTO (Paid Time Off)
  • Critical Illness and Accident/Injury Benefits

This approach isn’t an Human Resources loss leader strategy vaguely connected to operational benefits. The employee friendly environment results in an industry leading low employee turnover rate of 22% compared to what are often triple digit rates for the industry as a whole. This along with high levels of customer service and unique innovations driven by its employees have also resulted in an extremely loyal customer base. One couple even had their wedding at the WaWa where they met as customers.

If you are struggling to connect employee engagement to improved customer satisfaction and industry leading financial performance, it probably wouldn’t hurt to study how WaWa operates. They are certainly not keeping it a secret. WaWa understands that it’s not the strategy that differentiates them from the competition, it’s the execution of that strategy: Hiring the right employees and then building an environment that brings out their best…that’s not easy for anyone to copy.

Want to learn more? Here’s a couple places to start, there’s even a book about its values:

CCRRC Releases Final Report on C-store Industry Employee Engagement

An Interview with Todd Lombardi, Director of Compensation and Benefits at Wawa, Inc.

Wawa: Rethinking the Convenience Store Experience

Creating the Living Brand


The Wawa Way: How a Funny Name and Six Core Values Revolutionized Convenience

Do you track regular time, absences and overtime? Are you getting this insight from your data?

Let’s start with some background. We were working with a client to help them understand how effectively they were using labor hours in their Distribution Center. This organization’s employees are paid hourly and considered full time.

Similar to most clients, they were tracking Regular, OT and Absence hours by employee. Looking at the data summarized by department across time in the chart below showed nothing unusual. In fact it looks like they have increasing amounts of overtime when they use more overall hours. This is a good method for flexing labor to meet demand. (For a great article on the effective use of OT, check out this The Overtime Lie by John Frehse at Core Practice Partners)

hours by week of year

It appears that they are scheduling effectively too. The chart below shows that in general they are working the hours that are scheduled by department.

scheduled and worked hours

At the summary level many of the details are hidden, It’s only when we get down to the individual employee level that it becomes apparent that there is room for improvement. This is the hardest part of deriving insights from data…organizing the data to start at a summary level and then progressively drilling down towards the right amount of detail to discover something of value.

employee hours

Let me explain the chart above…the x axis shows the number of hours worked in one week.  The y axis shows how many of those hours are overtime hours. Each dot represents the hours worked by an individual employee. The color of the dot represents the range of hours that employee was absent during the week. For example a green dot means all the hours were worked and there was no absence. A yellow dot means that while a person was scheduled for say 30 hours. They were absent between five and ten hours and worked the rest. The legend on the right of the chart shows the complete gradient.

We saw two distinct situations in this data. The first is that for this particular week charted there is a higher rate of absenteeism compared to others. It wasn’t a holiday week or something else that would drive absenteeism. By looking at this chart week by week it became clear that on busy weeks there was higher absenteeism than in slower weeks.

Secondly, we immediately noticed that while everyone is considered full time, many people were not working 40 hours and yet there were many people working significant overtime. So while at a summary level, the OT percentage looked reasonable, the reality was that there was plenty of regular hours capacity to satisfy the demand without the use of overtime. This of course drives costs up, but additionally as we hear in the news frequently, many people are not getting as many hours as they would like and to them, this situation is likely frustrating.

Creative visualization can turn data that companies must collect into valuable insights for improved productivity and increased employee satisfaction. In this case eliminating half the overtime and replacing it with regular hours would have resulted in a ~$35k a week savings. Don’t let your data rest!

Visualizing Schedule “Tradesies”

We recently met with an operations executive that was confronting one of the more vexing issues a manager faces. An employee told her there is a rumor going around that employees are swapping shifts with each other to increase their pay. In this organization, anyone who is not scheduled to work but then works is entitled to premium pay.

One of the well known ways to exploit this type of policy (I explained this as “tradesies” in my book Lean Labor) is to find a couple of buddies and regularly swap shifts with each other. It may not be every shift, but you have to trust your partner enough to know that you can be up or down a shift and when the time comes, they will agree to swap out a shift with you.

This of course drives up costs without increasing output. Not a good outcome for any organization. The executive was pondering what to do. The challenge is that there are many legitimate reasons to swap shifts and the policy is intended to provide flexibility for the workforce but ensure coverage for the workload. A premium may be paid to encourage employees to work hours that they might not otherwise want, thereby providing liquidity to the system.

Addressing this would be tough because it’s difficult to discriminate between legitimate shift swaps and ones that were done purely to increase pay. But the rumor was expanding and if this practice spread it could ultimately lead to lower profits, poor morale and even layoffs of uninvolved people.

Before she acted, she asked my team to take a look at her organization’s scheduling data and see what we could find. This is a fairly challenging exercise because first you have to figure out who actually swapped shifts with who. There is no “marker” other than a premium paycode for one person. After that was resolved, we had a long list of shift swaps. Next we had to figure out a way to visualize that list to help interpret the data.

After a couple of different approaches, the team was excited as they realized this would call for a different type of visual approach. The reason they were excited is that the vast majority of visualizations required are bar, line and scatter charts. These charts do a great job, but we all like some variety!

In this case the team realized they were looking at a networking relationship between the people swapping shifts.

Using a networking diagram, they plotted the employees and who they swapped a shift with. What we wanted to know though was not only who, but how many times shifts were swapped since gaming typically occurs between a small group of people. For that we colored the arrow differently based on the number of swaps made over the time period analyzed.

schedule swap diagram

Below is the result of the effort. As you can see, the majority of the swaps are occasional and with a variety of people. Good news! Most people are swapping shifts as the organization intended. But after applying a filter to remove the occasional swaps there are two clusters of three people that are swapping significantly more times and with the same people. This doesn’t necessarily mean they are gaming the system. It’s possible that they have very specific skills and there is a limited pool of people they can swap with.

This information was illuminating for the executive. Out of thousands of people, she could now focus on six and get to the bottom of it quickly. She could also respond to the rumor with hard facts. Finally, it was peace of mind for her to know that the vast majority of her employees were using the policy as it was intended.