With companies continually streamlining their operations to squeeze out every bit of efficiency and productivity, it is increasingly important to minimize employee idle time as well as regulate overtime. The simple scheduling used in many operations, with its balanced crews and equalized staffing, contains inherent inefficiencies that are no longer acceptable.
A balanced staffing level means that there are times when there is simply not enough work for all the employees who are scheduled and also times when there is just too much work for the number of scheduled employees. Operations are looking for ways to develop and implement schedules that more efficiently match employees to work or to simply put people where the work is.
This is not necessarily an easy task for many reasons. First, predicting the volume and timing of work is often difficult. It can be easier to provide equally distributed coverage and handle any extra work with overtime or by having idle employees in slower periods. The cost of idle time is often overlooked because tracking idle time is more difficult than tracking overtime, but it is often just as costly.
Second, if the most efficient schedules could be developed, it is likely that they would not be closely aligned to the needs and preferences of the employees. Basing a schedule solely on operational requirements without taking into account human physiology and limitations can result in problems with fatigue, low morale and increased accidents and errors.
So, in developing proportional schedules, it is crucial to find solutions that blend the operational, employee and physiological needs of the business. In an effort to better utilize their human resources, managers are increasingly relying on proportional staffing to better match employees to work while still taking into account employee social and physical needs and limitations.
Proportional staffing schedule (varying numbers of employees scheduled at different times) have long been in use in police departments, hospitals and service industries. However, other industries are now finding they can better match their employees to workflow by using proportional staffing.
There are two basic types of proportional scheduling that can be used to identify the most efficient way to schedule employees. The first type, deterministic, is based on known requirements for manpower that stay fairly static over time. The second is known as statistical proportional staffing and is used in environments where the staffing level requirements are less certain and the work can shift much more drastically.
One good example of deterministic schedules can be seen in many police departments. Although the workload, and therefore the man-power requirements, often fluctuates hourly, daily and seasonally, the pattern tends to repeat itself. Most importantly, the requirements are fairly predictable. As a result, the requirements for employees can be determined down to the hour of day and day of week.
Statistical schedules are created by analyzing a combination of historical work activity, known requirements and/or predicted incoming work. Through the analysis, it is possible to identify statistical probabilities of when work will occur and when employees should be scheduled to a high degree of predictability. These schedules must be able to handle wider fluctuations in demand and provide a higher degree of flexibility than would a typical deterministic schedule. As a result, they may utilize on-call shifts or flex shifts. Because these schedules are built around work demands that fluctuate more greatly, it is important to continually measure their efficiency and make adjustments as needed.
A crane operation at a port is a good example of an operation where statistical schedules could be successfully implemented. Because the arrival times of ships can vary greatly from week to week, a standard balanced shift schedule will not provide an efficient match of employees to workload. What is needed is a schedule that can handle not only the regular arrival of ships to the port but also those times when delays and unforeseen events create more demand for employees than was projected. To do this, the entire operation must be viewed and analyzed rather than just the performance of one ship. After this analysis, one can identify patterns in work requirements and use these to develop a set of shifts that, when strung together, form an employee schedule. Work that falls outside these predetermined shifts must be covered by flex shifts or some type of relief or call-in procedure.
The end result of a statistical schedule is a more efficient workforce where the employees are scheduled around the work rather than arbitrarily assigned into balanced crews for equal coverage around the clock. This typically leads to a significant reduction in both idle time and overtime.
In both deterministic and statistical schedules, the number of possible solutions can be almost endless, and identifying the ideal one can be quite a challenge. Tracking the efficiency of the schedules can be a time-consuming and tedious process when done manually. Furthermore, analyzing historical data to identify patterns in workload require-ments is best done in as close to real-time as possible. For these reasons, many companies are turning to software tools for assistance.
Many of the tools that have been developed to deal with variable workload, and thus variable employee demand, focus only on the number of hours of work to be done. Other software tools are able to identify not only the ideal number of employees but also the shifts that they should cover. This approach results in a highly operationally efficient schedule. However, these schedules tend not to incorporate employees social and family needs or preferences and, as a result, can be counterproductive to operational efficiency.
Many of the elements of an ideal scheduling software package already exist either as internal programs within companies or as products available on the open market. But until these functions can work seamlessly, the ideal solution is still just ahead. Experts in the shiftwork and software fields estimate that this type of full service scheduling, forecasting and efficiency-tracking software is only two to three years away.
Todd Dawson is the director of Research and Grants at Circadian Technologies, Inc. E-mail him at firstname.lastname@example.org.