Can you answer three key questions about your labor spending?
Posted by Lisa Disselkamp on May 19, 2016
Do you know if you are spending more on payroll than necessary? The question goes beyond conducting an audit to find errors and fraud. The deeper question to ask is this: Is there unnecessary labor expense that is not the result of a mistake or abuse but has become a source of overspending? Many employers don’t know if they are suffering from inflated time-worked reporting or hidden, unproductive paid time. Without oversight, employers are likely to be paying incorrect (i.e., unnecessary, unintended) time correctly, instead of paying the correct time correctly.
Finding—and fixing—the source of this kind of overspending can be an elusive goal. Traditional reports don’t reveal what situations are the most problematic and offer little help in understanding what problems need fixing. Reports can’t explain the real-world context surrounding these problems or guide how to correct them, and analyze what would happen if you applied a different approach. Analytics-driven solutions are different. They work smarter and bring in the necessary context to help answer three important questions about labor costs that organizations often can’t answer: (1) How am I doing?, (2) If there are problems, where and when are they happening specifically?, and (3) Is there a business case for change? If the business case warrants it, analytics can then inform labor cost optimization initiatives to correct practices and situations that inflate payroll costs.
How am I doing?
Employers can’t really answer this question if non-employee and contingent labor are not combined within their view of their costs and labor outputs. Consistently and effectively collecting the time and assigning work is a challenge today.
Small problem areas in time capture and labor scheduling—leaks—often add up to significant, measurable overspend or lost productivity. These leaks may spring from multiple sources: loosely designed systems and policies that people are able to take advantage of (game); unchecked discretion in how hours and payments are assigned, outdated management models and technology; overly complex pay policies; lack of coordination among HR, finance, IT, and operations; and more. Very small gaming manipulations (such as clocking in a few minutes early for a “premium” night or weekend shift) and the incremental migration of systems and practices away from the original intent of the process or policy easily hide in timesheets and schedules. This is where incorrect time creeps in and accuracy, efficiency, and compliance are sacrificed because people are casually inflating their time or are present when it’s no longer necessary to be working. Audits and reports aren’t designed to find leakage.
While payroll registers and other financial reports can show dollar amounts and trend lines, they don’t offer insights into what the numbers actually mean in terms of good or poor performance. Analytics adds in the critical element of context, telling you, for example, whether overtime spending is too much or just right. Analytics allows the user to be right there in the moment to see what was happening and how things were interpreted compared to the real need of the business or the intent of the pay policy.
Where are the specific problems?
Analytical techniques give you clear-cut, multidimensional answers: What day of the week is time editing highest among the groups experiencing excessive premium pay? What business situation is triggering it? Is there a policy behind the scenes that can predict which units will have this problem? The proper data combinations, algorithms, and modeling can reveal the real problem places and people that are increasing expenses.
When spending or schedules jump out as unnecessary within the context of your business, then what? You likely do what employers tend to do in any aspect of business when a defect is detected; you react and work to correct the cause. However, not everything is problematic, even overtime. This is also the beauty of analytics—discerning when and where something is a problem. Data-driven organizations are defining their problem scenarios and setting line-item workforce targets—for example, “Reduce missed meal breaks [a source of overtime] by 20 percent to get to an acceptable rate of x percent missed meals.”
Is there a business case for change?
The most effective analytics tool answers all three questions simultaneously, revealing status, problem areas, and business case. When the tool enables this type of fluid movement through the data and helps users move intuitively through their thought process, analytics lives up to its moniker of being truly analytical and not just visual. Ideally, users should be able to check, focus, identify, validate, quantify, and answer what-if? or what-next? in a few keystrokes.
Correcting the problem: Labor cost optimization
Lisa Disselkamp, CWAM, is a director in the HR Transformation practice of Deloitte Consulting LLP. Her work focuses on workforce management (WFM) business practice and technology design including timekeeping, labor scheduling, leave management, and labor optimization analytics. She has led large and complex multi-state WFM system assessments and deployments. Lisa has authored three books on WFM systems. She is a contributing member of the American National Standards Institute (ANSI) and International Standards Organization (ISO).
As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.