Workforce Planning over the Service Life Cycle

Abstract

Services usually have a limited life under modern conditions of competition. The life cycle phenomenon is characterized by time-varying demand and a learning curve of workforce efficiency, making it difficult to determine staffing requirements. This paper initiates a study of the service life cycle phenomenon, from which the learning curve is found to be manageable through workforce planning. Therefore, optimal control is employed to model workforce planning over a service life cycle. An iterative approach is developed, allowing for an optimal trajectory of staff size that can be efficiently identified. Results from this study suggest that the learning curve should be considered in service workforce planning if the learning process lasts sufficiently long, in comparison to the service's life. Under such conditions, staffing requirements are determined by the dynamics of demand, workforce efficiency, and learning efficiency. Compared to the practice of allowing learning to occur autonomously, the control approach has the ability to strategically turn a small incremental investment in service workers into attractive savings in life cycle costs. Merits of the proposed methodology are also highlighted by its robustness in various life cycle conditions. This paper establishes a foundation for studying advanced problems, including training of a service workforce under life cycle conditions, planning the workforce for a complex service system, and managing the workforce under the uncertainties in service delivery, demand, learning, and turnover. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]