4 minute read
The Case for Superior Retail Staffing Analysis Tools
I’ve often heard the questions, “Why is there a difference between the output of my labor model and the sum of the scheduling requirements that I send to my automated scheduler? Should there be a difference, and why?” These are great questions that merit exploration. There is much to be gained from a solid understanding about the difference between the output of engineered labor standards in a labor model and the staffing requirements needed to drive an organization’s staffing and scheduling strategies.
Understanding engineered labor standards and the function of most labor models
Let’s start by reestablishing some basics. Most labor models are built to quantify workload. All work tasks/operations are identified. Some of these are fixed, and some are variable based on workload drivers (e.g., items, customers, packages, cases, types of cases, etc.) For the variable activities, the workload drivers are identified and mapped to the appropriate time standards. All activities have engineered labor standards, and some of these may have embedded store characteristics for equipment, distances or other points of variation as needed to create accurate, store-specific standards. Labor models will also typically support allowance management for personal fatigue, delay or other ongoing allowances you wish to define. But, in most cases at least, the final output of the labor model is engineered work content time, not necessarily the time needed to schedule that workload.
What’s the difference between engineered time and staffing requirements?
In addition to satisfying the raw engineered time it takes to perform the work, retailers have a variety of rules and conditions that must also be met as the workload is transformed into specific 15-minute scheduling requirements. The most basic of these is minimum (or maximum) coverage requirements. For example, a retailer might stipulate that regardless of how much work needs to be done, a specific job or department—or aggregation of departments—must have a minimum staffing coverage of one associate for a specified time range, such as 8:00 a.m. to 8:00 p.m. If we use a customer service counter as an example, this minimum staffing requirement can be responsible for more hours than the actual standard-based workload of the various operations performed at the service counter.
Along with “min and max” staffing rules, other parameters may be defined in the staffing requirements process. For areas with service queueing, the defined queue parameters can also add time to the engineered workload. While the standards and volume drivers quantify the raw labor, managing the queue experience is often a vital part of customer satisfaction and repeat business. Ignoring this imperative in an effort to push optimization to only the engineered work content can have dire consequences that result in frustrated customers waiting in long lines.
Likewise, certain allowances for new-store learning curves and the introductory ramp-up for new technology or processes, etc. are sometimes best managed as staffing allowances instead of within the labor standards themselves. First, doing so provides greater visibility and easier management of temporary allowances which should be revised as the need goes away. Second, it does not misrepresent the clear work content of what should be expected by employees performing at the standard rate as defined by the organization. It allows the standard to be the standard, not to be morphed by temporary allowances.
I’ve seen retailers that have completely lost the integrity and faith in their standards by trying to meld staffing rules and considerations back into the standards. It is far more manageable to understand the difference and validity of engineered time versus staffing requirements time.
But is staffing time easy to quantify and manage?
The reality is that solutions vary immensely from system to system regarding how easy it is to understand and manage staffing time. It is easy to see these summarily in the difference in weekly engineered hours and weekly schedule requirements. But to see which staffing decisions are causing more hours to be added—and where those hours are getting added across each day in the requirements—is something few systems do well.
What must a better system do?
Ideally, you want to know how much each staffing parameter is contributing to extra hours. You should see this not only in aggregate, but in the individual layers by parameter or by special allowance. The visibility not only must be weekly or daily, but by time of day. Only then can you analyze whether that spend makes sense for your business and assess options. Perhaps the spread of the volume work could be different such that it creates fewer add-on hours due to minimum coverage. Or perhaps that minimum coverage should be challenged for some jobs, or maybe move from job-specific minimums to higher levels that still provide service but reduce a low-value spend.
Whether the insights from such analysis allow you to remove hours for optimization or move hours to higher-value interactions that boost sales and customer retention, these decisions should be well informed and subject to ongoing scrutiny.
With almost every retailer today looking to accomplish more with less, it is imperative to strategically invest and deploy labor resources where they will best drive sales, differentiation and customer loyalty. The elegance of these analysis tools presents a tremendous opportunity. In short, they enable a very small labor team to do far more than what larger labor teams without such tools could ever do to drive better staffing strategies.