Sensitivity Analysis of Overtime using Capacity Planning Levers
- December 13, 2018
- Posted by: RSIS
- Category: Management
International Journal of Research and Scientific Innovation (IJRSI) | Volume V, Issue XII, December 2018 | ISSN 2321–2705
Abstract—Demand, Throughput rate, Utilization and Overtime are pillars of Capacity planning. Overtime is a function of Demand, Throughput rate and Utilization. A firm’s objective is to determine the distribution of the Overtime risk estimate using the independent marginal distributions and dependency structure between the capacity levers. This additional operating cost can be managed using capacity levers and cross training efforts in order to help the firm manage labor expenses to meet seasonal demand patterns. If not managed efficiently it can lead to higher than expected costs.Unlike a manufacturing firm the capacity planning for a services firm would vary significantly. Throughput rate for a services firm would vary in parallel with the seasonal demand because of the high dependency on human effort, in high volume period associates complete more transactions in the given time while in a lean period the processing time for a transaction would increase leading to decline in throughput rate. Given this dependency among capacity levers the paper describes the application of t-copula as a joint density estimation technique to model the correlation among Demand, Throughput rate and Utilization. Data is hypothecated for illustration purpose and reflects the business case to model the dependency among capacity levers.
Keywords: Capacity planning, Copula, Overtime, Throughput rate, Utilization
Sensitivity analysis is the study of uncertainty in the dependent variable apportioned to uncertainty in the independent variables. Understanding the variability and predictability of Overtime hours becomes important in the Capacity planning domain. Demand, Throughput & Utilization are key pillars of Capacity planning that are used as levers to meet the incoming demand and thus to effectively manage the overtime cost.