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:
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:
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.
Gregg Gordon oversees the analytics, data science, and big data focused group at Kronos. He is the author of Lean Labor: A Survival Guide for Companies Facing Global Competition and Your Last Differentiator: Human Capital.