One of the tasks that someone at your company will need to do for payroll and billing is calculating the hours that employees have worked. This usually involves reviewing the employee attendance data and matching it to the shift schedule. The attendance data will contain information about what actually happened: the actual clock-in/out times and the actual breaks that were taken. This helps calculate the hours worked including any overtime that needs to be paid, while also verifying if whether the actual data is consistent with the schedule. Attendance data can be collected in various ways, from traditional methods such as a landline, a smart phone app, mobile or desktop web browser, to more specialized devices and systems, such as time clocks with biometric recognition technologies.
Physical time clock collection devices are available with various features, including RFID or magnetic card readers, and biometric verifications like facial recognition, finger print reader and hand geometry. Time clocks with biometric recognition are used when there is a concern about time theft (buddy punching) or whether or not employees are actually onsite. Physical clocks resolve use cases where multiple employees have to be punched in quickly, or when work is performed in places where network/internet connectivity is not available or is unreliable. The choice of technology depends on the needs of the business, as well as the physical environment your business is located in. With the progress of cognitive computing and face/voice identity recognition technologies, standard punch clocks will likely be replaced by smart camera/microphone devices which will significantly simplify the punch process for the employee, and will produce more accurate attendance data for the workforce management system.
In cases where external time collection systems are used, the attendance data has to be collected in the scheduling system where it can be matched and verified using the planned schedule and attendance rules. In many cases, this is a manual process which can take time and also introduces an element of risk where an error can be made or data can be lost.
Automating this process helps reduce both the time spent and the potential errors that can be made. With an automatch process, matching a single attendance record is automatic (example “clock-in” punch), done with a shift as soon as the attendance record is available. This automatching process not only removes the manual steps someone needs to take to match records, but will also allow for clock alerts to be sent to supervisors, as well as real-time reporting based on shift actual clock-in/out/break times.
In this example, an employee is late clocking-in. The record is sent to Celayix, an automatch is performed and the shift actual-in time is updated. A supervisor alert is also generated informing them of the late clock-in.
This process is also followed for other clock actions.
Celayix will execute the mapping and matching rules in order to find the corresponding shift record. The mapping rules are executed to translate the badge number used on the device to an employee id. The matching rules are executed for “clock-in,” “clock-out,” “break start,” and “break end” attendance records.
One of the challenges with reconciling attendance data from external systems is that it can be inaccurate. For example, an employee can forget to clock-in or out. With an automated process, Celayix can generate alerts notifying a supervisor who will be able to correct shift in/out times. Other incorrect attendance records that can be generated include items like a double clock-in, a clock-out is recorded as a clock-in etc.
An automatch process can detect and generate match exceptions for many anomalies in the attendance data from external devices. With any automatic process, a user interface needs to be provided to help users view records and apply corrections.
With Celayix, the user interface allows users to see the attendance and shift data, review the results of the automatch process, apply corrections by unmatching records, manually match records, re-execute an automatic match for multiple records or ignore bad attendance records.
If the shift is matched, the corresponding shift field is updated. For example, the “actual start time” is updated when a “clock-in” attendance record is received. If there is any concern with how the matching is automatically done, users can remove the automatic match and manually match the records. In addition, if a shift is not matched, a shift exception record is created allowing users to do a manual match.
This provides our users with the best options – strong biometric and location validation from specialized devices and external systems with real time integration with existing schedule and alerts functionality. Reducing the time spent matching information leaves more time for verifying that the information is correct. Automatching also reduces the chance of introducing errors into the matching process and will allow users to spend more time completing other important tasks.