SOCIAL NETWORK ANALYSIS OF TEAM COMMUNICATION AND SUPPLY CHAIN INTERACTIONS IN MANUFACTURING INDUSTRY
Open Access
- Author:
- Thirumurugan, Pragadeesh Selvam
- Graduate Program:
- Industrial Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- July 09, 2018
- Committee Members:
- Ling Rothrock, Thesis Advisor/Co-Advisor
- Keywords:
- Social Network Analysis
Network Analysis
Network Theory
Supply Chain Interactions
Team Communication
Team Work
Team Communication in Manufacturing Industry
Organizational Behavior
Supply Chain Communication
SNA - Abstract:
- Team communication and performance has become a great interest among many researchers and psychologists nowadays. Even industrial organizations started believing that, team communication plays a major role in the improvement of safety, productivity and much more. But, there is no clear picture of communication patterns and behavior of individuals in the manufacturing sectors. This thesis describes, a case study design conducted in a small manufacturing industry that manufactures and assembles roadside safety trucks, to study the team communication, performance and to see how well they are coordinating between them to achieve common goals and tasks. Social networking analysis (SNA) tool was used to investigate the team communication and behavior of the individuals in a manufacturing industry and how the performance of the teams affect the supply chain flows and interactions. This was achieved by observing and recording the frequency of communication between the individuals under various circumstances through a simple three-item questionnaire survey and formed a socio adjacency matrix (Relationship Matrix) from which visual representation of the communication network is displayed. Calculated social networking measures like cohesion, in - degree centrality, out-degree centrality, betweenness centrality, and inferences were drawn. Interaction networks were block modeled using clustering criterions and then, results are discussed in terms to look for the performance of the teams, information flows across various teams which affect supply chain flows. In the future, we suggested using social networking analysis as a tool, on an improvement of supply chain operations like mapping the logistics network, customer-supplier relationships etc.,