3 minute read

Act Three: Moneyball, Sabermetrics and Retail Labor Standards Analysis

Over the past few weeks, we introduced a series of blog posts (see part one and part two) looking at similarities between the 2011 baseball movie Moneyball and the analytics seen in today’s retail world. In the movie, the main characters Billy Beane (portrayed by Brad Pitt) and Peter Brand (portrayed by Jonah Hill) implement a statistical approach called sabermetrics, which helped Beane and Brand better understand baseball player and team performances. In the first post of the series, we outlined how sabermetrics can help put the right baseball players in the right positions to create team success. In retail, there is a parallel benefit to slotting employees in the right role when customers need them. In the second post, we described how sabermetrics uses past data to forecast future team performance. The same goes for retail; accurate forecasting is paramount to retail success!

In today’s final post of this series, we will describe how similar approaches exist between sabermetrics and retail around how retail leaders utilize what-if technology to examine labor changes. Let’s illustrate with an example. In sabermetrics, one helpful approach is to provide a useful function of the player’s donations to his team. When analyzing player data, coaches and analysts are able to understand the player’s contributions and how they add to or detract from the team’s goals. Given that correlation, general managers can sign or release players with certain characteristics that add or detract from the team’s overall mission. There is a scene in Moneyball where Beane is talking with his scouts about potential players. Beane suggests to the scouts that many average players contribute more to the entire team, making the overall team more valuable. This approach to understanding a given player’s characteristics allowed Beane to understand how that impacted the team as a whole.

How it plays out in retail

In retail, a similar approach is often used to understand how certain tasks contribute to an entire labor model. Using engineered standards, workforce management teams build labor models from the ground up. This means that the smallest levels of detail (i.e., specific motions within subprocesses of an overall process) are leveraged to build up the larger picture of required labor (i.e., all labor required within a certain department by day.) Having this level of detail allows retailers to make and model changes and see how that impacts the entire model. These modeling environments are often called what-if environments and offer analysts the capacity to determine how one small change would impact the greater whole. In the past, retailers would use their best non-scientific judgement (and previous trial-and-error) to determine these impacts; today, we estimate them with a low degree of error in real time.

As a recent example, we were part of an analysis where the retailer wanted to understand how much labor would be added to their stores if the requirement to spot-sweep the produce floors was increased from four times to eight times a day. Having engineered standards built at a granular level that included a frequency for sweeping floors allowed us to perform this analysis within the same day and report back to the retailer how much labor that would add to the entire labor model. Without a what-if standards analysis tool, this type of analysis would have taken much longer and likely would have been less accurate. This real-time type of analysis allowed the leadership team to make the decision much faster with a higher level of accuracy, while also allowing the workforce management team to quickly move on to further similar analysis on other labor studies.

In summary, having a labor model that is built on the smallest levels of detail provides retailers the ability to understand and model how certain characteristics contribute to the entire labor model. But, like Beane’s approach with the scouts, it’s also important to have a tool that can perform the analytics to provide such analysis. The tool saves time and ultimately money, making technological solutions with what-if capabilities very handy for retailers who want to increase their profits!

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