A Benchmarking Model for Management of Knowledge-Intensive Service Delivery Networks

Abstract

Effective management of information technology (IT) and IT-enabled services is becoming increasingly important due to the growing complexity of their context. These services are often delivered by employees who work at widely dispersed locations and interact with each other to constitute knowledge-intensive service delivery networks (KISDNs). This paper contributes to the effective design and management of KISDNs by presenting a mixed-integer programming model that integrates disparate streams of research. This model facilitates analysis and managerial benchmarking of KISDN performance. It captures how the performance of such networks depends on the interaction between workflow decisions, structure of information flow networks (IFNs), and knowledge management decisions. We propose that knowledge about IFNs and worker competence can be effectively used to make workflow decisions. Our results, based on the study of different IFN archetypes, illustrate practices for effective management of KISDNs. Managers can enhance business value by recognizing existing IFNs, increasing randomness in IFNs, nurturing weak or performative ties depending on the archetype, assigning tasks based on effective worker competence, and selectively delaying assignment of tasks to workers. In addition, our results illustrate the impact of training and network density on KISDN performance.

Keywords:

• benchmarking
• IT services
• knowledge-based services
• knowledge management
• OR models
• service delivery
• service science