When forecasting how much a company will need to spend on labor, precision is vital. If CFOs get the personnel cost forecast wrong, the ripple effect is felt throughout the organization. Nobody feels it more than business-unit managers, who must find a way to get the work done when recruitment and staffing allocations don’t match up with demand.
Knowing the damage that weak personnel forecasting can do, more CFOs today are saying they hope to use predictive analysis to anticipate fluctuations in customer demand, revenue flows, and related costs over time. But how many CFOs can claim a measure of precision at the moment?
An APQC benchmarking study of 996 organizations (all company sizes, industries, and regions are reflected in this month’s chart) found that the ones that most accurately predicted personnel costs missed by 1.5%. These organizations represent the top-performing 25% of all participants in the data test. By comparison, the bottom performers, the 25% that performed the poorest, missed the mark by 2.3%. Is it a problem to misjudge labor cost by, say, 1.8%, which is the median figure in our test? For some CFOs, it is clearly a problem.
Note that the calculation produces an absolute value that reflects budgeted versus actual cost. Personnel cost is the cost associated with personnel compensation and fringe benefits of employees (i.e., those classified as full-time employees, or FTEs, which includes both full-time and salaried/hourly employees).
Addressing Upstream Flaws
To generate more accurate forecasts, finance needs to help front-line managers get better at predicting how many people are needed for next quarter, or for all of next year. One key is looking not just at headcount but at the competencies of the people doing the work. That’s not easy to do with traditional labor-forecasting methods. But a proactive, integrated, technology-enabled approach can make it possible.
APQC studied one national facilities-and-maintenance supply company that has an especially tough personnel forecasting challenge because its staffing needs fluctuate with the changing seasons.
Every summer, people vacate their apartments and dorm rooms, and the facilities teams move in, bringing with them an annual cycle of high demand for paint, flooring, and other maintenance, repair, and operations products and services. To ensure adequate staffing for the summer renovation rush, this company’s labor-cost forecasting has to be precise, or business will be lost.
With thousands of associates, multiple nationwide distribution centers, and hundreds of trucks, getting supplies and people (full-time and contract workers) where they need to be is a serious, labor-intensive business. What’s more, accuracy is critical when contractors’ payment terms require payment, whether the work is there or not. And labor isn’t just a seasonal concern: Finance has year-round responsibility for workforce planning as the overall business grows.
The company used to base its headcount preparations on manual calculation and reconciliation of data, a time-consuming approach that often resulted in unrealistic estimates of future labor demand. Knowing they needed better-quality data, the finance team invested in performance management software that would enable granular planning, forecasting, and reporting of wage costs by type of employee at various intervals. They also created separate planning input schedules and processes for two distinct types of employees: high-turnover hourly associates and more permanent salaried team members.
Now, this finance team can automatically pull data based on job title from the human resources and payroll platforms, producing a monthly headcount analysis based on job title. Instead of forecasting headcount in January based on percentage of overall sales, managers can break down revenue by season or by month, then more accurately plan for the number and type of people needed to meet sales expectations.
Mary Driscoll is a senior research fellow in financial management at APQC, a nonprofit business benchmarking and research firm based in Houston.