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

Master of Agribusiness / Department of Agricultural Economics / Jason Bergtold / 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.

Identiferoai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/18375
Date January 1900
CreatorsWipperfurth, Christy
PublisherKansas State University
Source SetsK-State Research Exchange
Languageen_US
Detected LanguageEnglish
TypeThesis

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