Pursuing Solutions to Service Operation Challenges

After graduating from Xavier University of Louisiana as an undergraduate, I accepted a position with IBM Global Services. I was a part of numerous internal projects and was required to work very closely with clients. I was placed on a project that had a very “hands-on” client. He was a technical guy who really wanted to offer his opinions on every technical aspect of the project. The problem was the fact that the technical aspect was my job. I often found myself having long conversations with him about database queries, coding languages, etc. He would offer suggestions that were often very good. So, was my client actually a resource on the project? This question motivates my research.

My research focuses on developing models for resource planning for service firms in which the client is a direct resource in the service process. Although there are a variety of types of service firms, my research is directed at what I call “complex” services. Complex services are customized to fit a specific customer, have high customer contact, and employ knowledge workers. Examples of complex services are consulting, auditing, I/T development and legal services (e.g., professional services).

This research is based on the idea that the shared creation of value between the service provider and the customer is a distinguishing characteristic of services. Shared creation of value is exhibited when both parties obtain value from the service.

I have published three articles on the topic of client involvement in service systems. The first paper, “A Model for Efficiency-Based Resource Integration in Services,” is published in the European Journal of Operational Research (EJOR) (2012). This paper seeks to answer the question, “How do service firms make resource planning decisions when the customer is an active participant in the process?”

We present a mathematical model of the resource-integration decision for a service process through which the client and the service provider co-produce resource outputs. The primary results of this research are optimal decision rules that provide insights into the optimal levels of client involvement and provider commitment in resource integration.

The second paper, “An Experimentally-Confirmed Resource Planning Model of Services Under Production Function Uncertainties,” is published in the International Journal of Production Economics (IJPE) (2013). The purpose of this study is to offer policy recommendations to service providers and to examine experimentally the sensitivity of estimates of the way in which service firms transform inputs into outputs. We develop a deterministic linear model for the purposes of determining benchmark service input levels for given target output quantities and a stochastic model for resource planning. This paper provides experimental, results-based trade-offs between inefficiencies, risk and costs versus resource allocations; it was accepted into a special issue on service science.

The third paper, “A Review of DEA-Based Resource and Cost Allocation Models: Implications for Services,” has been accepted for publication in the International Journal of Services and Operations Management (IJSOM) (2014). This paper gives a comprehensive review of all previous approaches to efficiency-based resource planning for service operations. Findings of this paper show that existing models predominately apply DEA to mass service industries (e.g., banking), thus revealing the opportunity for researchers to further develop DEA-based resource allocation modeling toward improving the operational efficiencies of other service industries (e.g., professional services).

My research responds to the need for analytic solutions applied to high-value-adding service operations. I identified high-value-adding services as services in which the client is a direct resource in the service process and the provider and the client have shared value in the outcome of the service. Therefore, I introduced the client as a co-generator of the service in my resource-planning models.

Through my research I have found that, just as there are well-established methods and models for planning and scheduling in manufacturing, there is now a need for these types of models for services. The research that I have performed is merely the tip of the iceberg of what can be pursued in terms of analytic solutions to service operation challenges. I hope to continue to be a strong contributor to the body of work in this research area.

Assistant professor Dr. Sheneeta White teaches in UST’s Opus College of Business. 

From Exemplars, a publication of the Grants and Research Office.