System and method for workforce requirements management
View Patent ↗The present invention provides a workforce requirements management system and method that determines future demand for service related transactions or activity and optimizes the planning of workforce to meet the future demand. Initially, the workforce requirements management system reviews historical data regarding transaction volume and service activity to determine the future demand for such transactions or service activities. The historical data may come from a conventional enterprise resource planning application and/or include demographic data and/or economic indicators. The workforce requirements management system can optimize a forecast by isolating certain variance factors and refining the forecast accordingly. A forecast can also be optimized by estimating transaction times and correlating variance factors with estimated transactions times. Additionally, the workforce requirements management system can optimize the forecast based on the queuing model employed for the particular service. Finally, the workforce requirements management system can create a long term resource plan that identifies the most efficient balance of full time equivalent and part time equivalent staffing levels to meet the long term forecast need at the desired service level at the lowest overall labor cost. Specific outputs generated by the workforce requirements management system include a transaction forecast, a resource forecast, a resource plan, and a resource schedule.
1. A method of operating a resource planning system to transform historical transactions into workforce requirements, wherein the resource planning system comprises a processor and a memory device having program instructions stored thereon readable by the processor, wherein the program instructions, when executed by the processor, direct the processor to perform the method comprising:
analyzing historical transaction data to identify time based variance factors;
analyzing the historical transaction data to identify demographic variance factors;
normalizing the historical transaction data based on the time based variance factors, resulting in normalized historical transaction data;
identifying and selecting at least one demographic variance factor having a statistically significant impact on transaction volume from the identified demographic variance factors;
determining a measurement of variance attributable to the one demographic variance factor;
processing the normalized historical transaction data and the measurement of variance attributable to the one demographic variance factor to generate a transaction volume forecast;
analyzing the demographic variance factors to identify at least one demographic variance factor having a statistically significant impact on transaction time;
determining a transaction time based on the historical transaction data and the at least one demographic variance factor identified having the statistically significant impact on transaction time; and
creating a full time equivalent requirement forecast based on the transaction volume forecast, the transaction time, and a target service level.
2. The method of claim 1 , wherein analyzing the historical transaction data to identify the time based variance factors comprises comparing a fluctuation in the historical transaction data caused by each of the time based variance factors.
3. The method of claim 1 , wherein analyzing the historical transaction data to identify the demographic variance factors comprises comparing a fluctuation in the historical transaction data caused by each of the demographic variance factors.
4. The method of claim 1 , further comprising:
generating a transaction volume per transaction type per time interval per location forecast.
5. The method of claim 4 , further comprising:
determining coefficients based on the one demographic variance factor; and
generating an enhanced transaction volume forecast based on the coefficients and the transaction volume per transaction type per time interval per location forecast.
6. The method of claim 1 , further comprising:
analyzing the time based variance factors to identify at least one time based variance factor having a statistically significant impact on transaction time; and
determining a transaction time based on the historical transaction data and the at least one time based variance factor identified.
7. The method of claim 6 , wherein the time based variance factor comprises a non-cyclical economic factor.
8. The method of claim 7 , further comprising:
optimizing the full time equivalent requirement forecast to include part time equivalent resources; and
providing an optimized workforce schedule comprising full time equivalent resources and the part time equivalent resources.
9. The method of claim 6 , wherein the determining step further comprises:
calculating the transaction time using multivariate regression analysis.
10. The method of claim 6 , further comprising:
generating the transaction volume forecast based on the historical transaction data and the at least one variance factor identified.
11. A resource planning system to transform historical transactions into workforce requirements, the resource planning system comprising:
a processor configured to execute program instructions stored on a memory device, the program instructions comprising a variance isolator module, a forecast module, a correlation module, and a queuing module;
the variance isolator module, when executed by the processor, is configured to direct the processor to receive historical transaction data, analyze the historical transaction data to identify time based variance factors, analyze the historical transaction data to identify demographic variance factors, normalize the historical transaction data based on the time based variance factors, resulting in normalized historical transaction data, identify and select at least one demographic variance factor having a statistically significant impact on transaction volume from the identified demographic variance factors, and determine a measurement of variance attributable to the one demographic variance factor;
the forecast module, when executed by the processor, is configured to direct the processor to process the normalized historical transaction data and the measurement of variance attributable to the one demographic variance factor to generate a transaction volume forecast;
the correlation module, when executed by the processor, is configured to direct the processor to analyze the demographic variance factors to identify at least one demographic variance factor having a statistically significant impact on transaction time, and determine a transaction time based on the historical transaction data and the at least one demographic variance factor identified having the statistically significant impact on transaction time; and
the queuing module, when executed by the processor, is configured to direct the processor to analyze a target service level, the transaction time, and the transaction volume forecast to create a full time equivalent requirement forecast.
12. The system of claim 11 , wherein the variance isolator module, to analyze the historical transaction data to identify the time based variance factors, compares a fluctuation in the historical transaction data caused by each of the time based variance factors.
13. The system of claim 11 , wherein the variance isolator module, to analyze the historical transaction data to identify the demographic variance factors, compares a fluctuation in the historical transaction data caused by each of the demographic variance factors.
14. The system of claim 11 , wherein the forecast module is further configured to generate a transaction volume per transaction type per time interval per location forecast.
15. The system of claim 14 , wherein the variance isolator module is further configured to determine coefficients based on the one demographic variance factor; and wherein the forecast module is further configured to generate an enhanced transaction volume forecast based on the coefficients and the transaction volume per transaction type per time interval per location forecast.
16. The system of claim 11 , further comprising:
the correlation module configured to analyze the time based variance factors to identify at least one time based variance factor having a statistically significant impact on transaction time, and determine a transaction time based on the historical transaction data and the at least one time based variance factor identified.
17. The system of claim 16 , wherein the time based variance factor comprises a non-cyclical economic factor.
18. The system of claim 17 , further comprising:
a resource optimization module configured to optimize the full time equivalent requirement forecast to include part time equivalent resources, and provide an optimized workforce schedule comprising full time equivalent resources and the part time equivalent resources.
19. The system of claim 16 , wherein the correlation module is further configured to calculate the transaction time using multivariate regression analysis.
20. The system of claim 16 , wherein the forecast module is further configured to generate the transaction volume forecast based on the historical transaction data and the at least one variance factor identified.