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Does One-Size-Fit-All Suffice for Service Delivery Clients?

  • Shivali Agarwal
  • Renuka Sindhgatta
  • Gargi B. Dasgupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8274)

Abstract

The traditional mode of delivering IT services has been through customer-specific teams. A dedicated team is assigned to address all (and only those) requirements that are specific to the customer. However, this way of organizing service delivery leads to inefficiencies due to inability to use expertise and available resources across teams in a flexible manner. To address some of these challenges, in recent times, there has been interest in shared delivery of services, where instead of having customer specific teams working in silos, there are cross-customer teams (shared resource pools) that can potentially service more than one customer. However, this gives rise to the question of what is the best way of grouping the shared resources across customer? Especially, with the large variations in the technical and domain skills required to address customer requirements, what should be the service delivery model for diverse customer workloads? Should it be customer-focused? Business domain focused? Or Technology focused? This paper simulates different delivery models in face of complex customer workload, diverse customer profiles, stringent service contracts, and evolving skills, with the goal of scientifically deriving principles of decision making for a suitable delivery model. Results show that workload arrival pattern, customer work profile combinations and domain skills, all play a significant role in the choice of delivery model. Specifically, the complementary nature of work arrivals and degree of overlapping skill requirements among customers play a crucial role in the choice of models. Interestingly, the impact of skill expertise level of resources is overshadowed by these two factors.

Keywords

Completion Time Service Time Transfer Time Service Request Delivery Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shivali Agarwal
    • 1
  • Renuka Sindhgatta
    • 1
  • Gargi B. Dasgupta
    • 1
  1. 1.IBM ResearchIndia

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