Work content key to retail labor accuracy

Moving Beyond Sales to Work Content is Key to Retail Labor Accuracy

Olufemi Olowu
Retail Consultant

In the retail sphere, accurately forecasting business needs and scheduling labor appropriately is a noteworthy dilemma. The common question is: what metric should a retailer base its schedule on? Which is the most accurate tool to use to produce schedules that mirrors customer demands? Historically, companies used sales as the driving factor to predict labor. The foundational paradigm was: the higher the sales, the more labor needed to process the goods moving through the store. This necessitated tracking and monitoring mechanisms to keep hours within agreed-upon limits. Take sales per payroll hour (SPPH) or sales per customer (SPC) for instance; they were and still are used to measure productivity within retail stores. They are utilized to set goals and budgets for each location. So what has been the result of this train of thought? Retailers have experienced overscheduling, wasted labor and incorrect distribution of labor hours between stores, often leading to lost profits and foregone customers. But why? How could so many top-notch retailers and business leaders fall for the same trap?

There are countless possibilities, but it stands to reason that a lack of visibility into the primary variables driving labor hours, as well as few technological solutions that could do anything with such transparency, likely kept retailers in the dark for a long time. Good news is that many retailers have moved from such dark ages into enlightenment, which we refer to as work content-driven labor. Work content-driven labor requires a retailer to understand labor-driving variables like items and customers, as well as spread labor hours appropriately between stores. It is a great way to make sure a retailer satisfies customers and executes its go-to-market strategy. Where retailers once made educated guesses about how to staff their stores, technologists are now providing solutions using state-of-the-art algorithms based on retail expertise. Let us look at an example of how this shift in thinking might play out.

Illustrating with modern trends

One trend seen across many contemporary retailers is that their fresh departments are driving more sales than ever before. This an important shift for some companies, so we will start with a hypothetical example from stores X and Y, particularly their bakery department. So let us say that X does $20,000 more in bakery sales per week than store Y. Traditionally, that meant store X would get more hours due to higher sales. Store X might have a higher sales per item than Store Y; it also has a wider selection of products to buy with an average higher dollar amount per item. So, looking at the traditional distribution of hours, based off using the SPPH method, Store X would get more hours than Store Y. But what data have we missed? Markedly, store Y sells products requiring more effort to produce than Store X. There are a higher number of ingredients and more sub-operations, on average, required to produce the bakery goods that customers in store Y’s region desire. In the past, most leaders did not have access to this information; and if they did, they had underperforming tools to use it.

I’ll go one level deeper in our example. The highest-selling item at store X is a round cake, requiring an average of 8 ingredients and 2 labor hours to assemble. The highest selling item at store Y is a fruit tart, requiring an average of 10 ingredients and 2.5 labor hours to assemble. Store X sells more round cakes than store Y does fruit tarts because store Y is an urban store with customers purchasing individual items on their commute. Looking at SPC, maybe store X has a slightly higher sales per customer metric value, but store Y is utilizing more labor to sell fewer products…and yet, it is what its customers desire. It becomes more convoluted when forecasting and planning metrics look similar across stores but hide visibility into productivity (or a lack thereof). For instance, what if store X’s associates are working less hard because they have been cushioned with extra hours, but store Y is getting more work done in the same amount of time?

From prior experience, I can tell you that this is not surprising. People end up managing their task load according to the time they have to complete it, which impacts efficiency. The psychological reasons for this abound, but one noteworthy reason is called the present bias, which is “our hard-wired tendency to prioritize short-term needs ahead of long-term ones.”1. Present bias can lead to procrastination, and when employees have more time than needed to complete tasks, they rightfully take their time in completing them. That means it is up to retail leadership to ensure the right amount of time is given to each store based on the work they have, not just the sales they produce. Circling back, an important question arises: how can retail leaders determine if employees are managing their time to the amount of hours given versus the amount of work required? Again, the answer is work content-driven labor.

Work content-driven labor is crucial

This starts with measuring the labor required for each process involved in each department. What sub-operations are involved in each task driving labor in a department? With that content, retail engineers can derive a forecast using important metrics (such as items, customers, sales and more) to predict the drivers for the work content, giving retailers the hours required by product or deliverable. These hours are summed up, and using other data, such as employee preferences and customer fluctuations, more accurate staffing schedules can be developed. What’s more? Artificial intelligence (AI) is now allowing technology to learn from each forecast and schedule so the software gets smarter over time. What started as a simple ratio (more sales equals more hours) has become an incredibly intricate system of measuring work, to forecasting metrics, to developing schedules, to automating learning. How incredible!

In closing, it is most important to identify what the true driving components are for accurate labor hours. Fairly distributing labor hours is determined by much more than sales and a simple formula. All metrics, from items to customers, impact working conditions and customer service. Being able to identify and make sound judgement calls concerning the balancing of hours between stores can pay dividends not only in the bakery, but throughout every department in a retail store. A retailer can move from antiquated sales-based metrics to a host of necessary driving factors through work content-driven labor, but it will require effective technology to do so. The business that makes the helpful decision in this matter will likely guarantee their success and longevity in today’s ever-competitive business climate.

References

  1. Lieberman, C. (2019). Why you procrastinate (it has nothing to do with self-control) (Blog post). The New York Times. Retreived from https://www.nytimes.com/2019/03/25/smarter-living/why-you-procrastinate-it-has-nothing-to-do-with-self-control.html