Politecnico di Bari, ITA
Ilaria Giannoccaro was born in Bari (Italy) on October 9, 1974. She is an Associate Professor of Supply Chain Management at the Polytechnic University of Bari, Italy. Her principal research interests concern the management of inter-organizational relationships, supply chain management, and behavioural operations. She is author of more than 100 papers mostly published in international books and journals, among which European Journal of Operational Research, International Journal of Production Economics, Industrial Marketing Management, Ecological Economics, Journal of Geographical Systems, Production Planning and Control, Journal of Artificial Societies and Social Simulation, and Emergence: Complexity & Organization.
Assessing and Managing complexity of supply chains
Volume: 20, Issue 4
This paper develops a conceptual framework to assess the complexity of the supply chain. The framework is built on the theory on complex adaptive systems. In particular, extracting the main properties of complex adaptive systems the framework associate to each property a set of supply chain features. The conceptual framework is applied to two case studies in the Made-in-Italy sector. The analysis aims at assessing the complexity of the considered supply chains as well as at formulating theoretical propositions that relate the SCs' features with different level of complexity.
The influence of heterogeneity on knowledge-based agglomeration economies
Volume: 20, Issue 2
We examine the concept of knowledge externalities, namely the benefits that co-located firms receive in terms of knowledge, focusing on the role of interactive learning processes and adopting the single firm perspective, whereas in literature their role has mainly been analyzed adopting the system perspective and focusing on knowledge spillovers. The geographical clustering process is studied as an emerging property of a system made up of independent firms making location choices. The aim of the paper is to analyze how the firm heterogeneity affect the geographical clustering process. In fact, so far literature and empirical evidence do not provide a conclusive answer to this regard. To pursue our aim, an agent-based model of geographical clustering is developed, based on knowledge externalities produced thanks to learning by imitation and learning by interaction and a simulation analysis is then carried out. The main result is that the heterogeneity reduces the willingness of firms to geographically cluster and enhance the development of knowledge.
Assessing the benefits of supply chain trust
Volume: 20, Issue 1
Previous literature has emphasized that developing trust among supply chain (SC) firms is a critical element in achieving SC effectiveness. Since developing trust is an expensive task, however, making an informed decision whether to invest or not in trust requires careful assessment of trust benefits. Therefore, we advance a simulation-based methodology to quantify performance improvements associated with trust in SCs. We develop an NK simulation model of a generic SC that captures the SC dynamics under two alternative scenarios, characterized by the presence and absence of trust respectively. A procedure is then illustrated to quantify the benefits of trust in the SC. We also apply our proposed methodology to a real-world SC. Results show that, when trust is pervasive across the SC, performance increases at both the levels of the overall SC and its leading firm.
How social network features and organizational structure impact team performance in uncertain environments
Volume: 19, Issue 2
Teams are framed as individuals embedded in hierarchical and knowledge networks, who interact among each other with the aim of accomplishing a common task. Social interactions are the means through which team members exert their mutual social influence, change opinions, and converge to a common understanding. In this paper, we investigate how the density and connectivity of the team knowledge network and the team organizational structure relate to team performance. The latter is measured in terms of level of agreement among the team members (consensus outcome). We first develop a theoretical model grounded on social influence theory and then a computational model based on the Ising approach. Successively, we carry out a broad simulation analysis in environments characterized by different levels of uncertainty. Results show that high-density values of the team knowledge network are beneficial in the majority of cases, but may become detrimental, when the uncertainty of the environment is low, the team knowledge network exhibits a random connectivity, and the team organizational structure is characterized by high centralization of the authority and a strong leadership behavior. We also find that scale-free connectivity of the team knowledge network hinders the achievement of consensus, compared to the random connectivity case. Based on the simulation results, we finally identify the best organizational structure that should be adopted to improve the consensus outcome.