One of the distinguishing features of the service sector is high emphasis on people interacting with people and serving the customer rather than transforming physical goods in the process. In traditional manufacturing, the machines are characterized by their ability to do only prespecified set of tasks, with quantifiable and predictable productivity rates. These properties makes it is relatively easy to understand, model and analyze the interactions eg One machine of type X can process the output of three machines of type Y. However, People, the analogue of machines in service chains are characterized by,(i) unpredictable productivity rate (ii) ability to become proficient and diversified in skill-set with time. Hence, people to people interaction which is pervasive in services industry provides technical challenges from analysis, diagnostic and optimization purposes. It is evident that analysis of such interactions is an essential aspect of designing effective and efficient services delivery. The results of analysis can be used to handle various aspects, eg, training, team building, risk management etc. Analysis of interaction networks extracted from the service operations is the focus of this tutorial. In many ways, interaction networks are similar to the well-studied social networks. Traditionally, social network analysis has been used to study structural properties of the networks and the positional properties of the individuals. However, from the perspective of interaction networks, it is important to derive information on effectiveness of the interactions and the process of effective team formations. When these objective are taken into account, a rich set of problems emerge, some of which are further generalizations of traditional analysis. Typically, solving these problems involves multidisciplinary approach as in understanding the constraints of the domain, import of mathematical analysis techniques and appropriate interpretation of the results.

Typically, the data needed for the construction of service interaction networks is sensitive in nature and organizations may not readily share the data. Therefore, it is imperative that we build realistic simulations of service interaction networks so as to enable not only the development of novel analyses, but also to validate their