An employee assignment optimization model exploring cross-training and specialization through multiple management strategies

Date

2014-10-15

Journal Title

Journal ISSN

Volume Title

Publisher

Kansas State University

Abstract

Company managers continually face challenges in the market, such as increased demand for their services and variability in the types of service requested. In addition, managers may face internal challenges during periods adjustment such as moving the company forward through a hiring freeze. In these situations, a manager must be able to allocate their scarce resources in a way to continue to perform. For employees, this could mean specializing in tasks or increasing crosstraining to improve work schedule flexibility. The objective of this research is to determine the optimal allocation of employees to tasks, given resource constraints and the need for staff flexibility, to satisfy alternative management strategies. The setting is the service industry, in particular a laboratory setting providing testing and consulting services. An optimization model was developed to incorporate key aspects of a company’s operation, and determine labor allocation among tasks, and for how many hours, to satisfy the manager’s objective. The model estimates the optimal allocation of labor and how much production and net revenues would be generated, with more specialized employees. A sensitivity analysis was employed to determine the impact of cross-training current staff. Results indicate that cross-training affords flexibility; however, the impact on overall production varies depending on the employee trained. The highest benefit is derived from training a lower-producing employee into a high value task at a high productivity rate. Specialization can help to improve productivity in net returns for higher valued tasks, but may limit flexibility, as employees cannot switch between tasks as readily.

Description

Keywords

Labor Management, Specialization, Cross-training, Cross training, Optimization

Graduation Month

December

Degree

Master of Agribusiness

Department

Department of Agricultural Economics

Major Professor

Jason S. Bergtold

Date

2014

Type

Thesis

Citation