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In the 80’s, the first PCs became available and many of the manual calculations to determine staffing and scheduling needs started to be done in spreadsheet programs such as Excel. However, the focus for labor management did not change. Labor management was designed to improve productivity and reduce labor costs. Additionally, quality assurance concepts came into vogue and organizations, while still focused on productivity improvement, began to look more closely at the quality of the guests’ experience. The staffing guides that were built in the 70’s now had to account for a quality of service delivery aspect. Not only was there an evaluation of work content, but when the work got done, guest expectations needed to be part of the solution. As supply grew and competition increased, quality continued to become more important. And during these periods, labor management and workforce optimization were more closely aligned. Fast forward to the 21st century with PCs in full use and many organizations using spreadsheets as their key labor management tool. Even with more advanced labor management programs having been developed, the focus shifted to using technology to determine staffing needs, in many cases to the exclusion of what the real work content was for the various jobs and tasks. Further, performance measurement was shifted to monthly reviews versus budgets, and the budgeted parameters became the driving force for labor management. Labor Management though, had now diverged from workforce optimization driven to a large extent by simplistic technology under the guise of labor management and productivity improvement. Management expected these PC based approaches to help control and improve labor management. The problem though, was that the guidelines just weren’t accurate and historical performance became codified in the new technologies. 
In order to effect labor optimization compared to just labor management, value engineering of guidelines must be part of the process. And the technology that is going to be most effective needs to support better defined work content-based parameters. Even housekeeping has changed from simply cleaning occupied rooms to the difference between the work required to clean a check-out versus a stayover. And the complexity of the food and beverage business dwarfs those of the rooms business. The number of key staffing drivers multiplies in F&B and therefore the technology must be able to handle these nuances coupled with more accurate assessments of the work impact of the various volume changes. This is a key step towards Workforce Optimization. Accurately understanding and applying better, more refined staffing guidelines to the planning and scheduling process as well as the performance evaluation exercise. Automated scheduling is also part of the equation in today’s environment. It helps managers do a better job by taking into consideration all of the availability issues coupled with the many work rules that need to be accounted for in schedule determination. As the industry progresses into the second decade of the century, human resource management continues to play a broader role in operational management. Studies that have looked at different segments of the working population highlight the different requirements of the various segments. And as these segments became better defined, motivation and engagement of these staff groupings must be understood if one is to optimize labor performance. This information needs to be integrated into the management tools that are being used to determine staff schedules and overall performance. Staff feedback measures have expanded beyond the historic focus on turnover to the effect on customer retention and overall performance of the organization. Work content analysis remains the key component of real labor requirements. Using budgeted parameters and assessing performance by focusing on the P&L statement is no longer going to drive best results. A friend once told me that “using a P&L to guide performance is like driving a car looking in the rear-view mirror”. The challenges that managers face trying to develop a least-cost, quality service schedule are too many to leave to a manual process. The demands of the various employment groups that directly impact engagement, and the flexibility that is expected of an organization, is difficult to keep in one’s head. Therefore, Labor Management needs to evolve from what it is today to Workforce Performance. One’s workforce is a “partner” of the business. It is not just the classical definition of labor as an input to production, especially in the service business and the dynamic nature of customer demand which fluctuates throughout the day and week. Labor Management tools that don’t address these issues are no longer sufficient. Ignoring team engagement, treating it as a step child or having it siloed outside operations won’t work anymore. Technology that incorporates multi-indicator dynamic scheduling based on value engineered labor needs; clear, timely team communications; flexible shifts and real time performance evaluation is what the market demands today. Anything less will deliver less than optimal results and limit the value of the operating business. Hope you enjoy this edition of FocusED. Mark
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Mark Heymann, CEO, UniFocus - Forty years ago, when I started in the productivity business, we built manual systems to manage labor more effectively. We didn’t simply accept the parameters managers used to determine their schedules, but instead set about to measure work content (how much work there was to do as volumes varied), build more accurate staffing guide lines, and develop planning and reporting tools that would apply the new guidelines. Documents were used to help schedule staff and assess productivity results in relation to the new labor requirements (standards). The result of this process was measurable improvement in staff productivity and an improved bottom line. At that time there were no computers, so the real focus of the exercise was to improve staffing levels and therefore, zero based standards were the end-product.