An Exploratory Research on Adaptability and Flexibility of a Serious Game in Operations and Supply Chain Management

Giovanni Romagnoli; Matteo Galli; Davide Mezzogori; Francesco Zammori

doi:10.3991/ijoe.v18i14.35083

Authors

Giovanni Romagnoli University of Parma http://orcid.org/0000-0002-9891-0314
Matteo Galli University of Modena and Reggio Emilia, Modena, Italy https://orcid.org/0000-0002-3368-2621
Davide Mezzogori University of Modena and Reggio Emilia, Modena, Italy https://orcid.org/0000-0002-3303-0789
Francesco Zammori University of parma, Parma, italy https://orcid.org/0000-0003-4931-5540

DOI:

https://doi.org/10.3991/ijoe.v18i14.35083

Keywords:

serious game, operations management, supply chain management, flexibility, adaptability

Abstract

Serious games (SGs) in industrial engineering education are an established topic, whose implementations are continuously growing. In particular, they are recognized as effective tools to teach and learn subjects like Operations and Supply Chain Management. The research on SGs, however, is primarily focused on displaying applications and teaching results of particular games to achieve given purposes. In this paper, we provide an exploratory research on the flexibility and adaptability of a specific SG to different target groups and students’ needs in the field of operations and supply chain management. We first provide an overview of the SG and introduce its mechanics. Next, we explain how the mechanics has been implemented, by means of a set of parameters and indicators. We report the results of two different game sessions, played by a class of bachelor’s degree students at different levels of difficulty, which were achieved by altering some specific game parameters. By comparing the Key Performance Indicators (KPIs) in the two sessions, we report and discuss the consequences of the modified game parameters, in terms of impact on the difficulty level of the SG measured by the indicators. Experimental results match with our hypothesis, since the increased level of difficulty of sourcing and delivery times only deteriorates the related subset of indicators in the harder game session, without altering the remaining KPIs.

Author Biographies

Matteo Galli, University of Modena and Reggio Emilia, Modena, Italy

Matteo Galli was born in 1987 in Piacenza (Italy). He obtained his Master Degree in Computer Engineering at the Politecnico di Milano (2013). After his studies, he worked in the private sector as an entrepreneur in a company offering management systems to SMEs; during this time he consolidated his knowledge of industrial processes with multiple projects and dozens of customers. In 2020, at the University of Parma, he has been involved in the DigiLab4U project. Since 2022, he started a Ph.D. at the University of Modena e Reggio Emilia with the goal of optimizing large-scale waste management. He is currently co-author of five scientific publications.

Davide Mezzogori, University of Modena and Reggio Emilia, Modena, Italy

Davide Mezzogori has received his Ph.D. on Industrial Engineering at the University of Parma. He is now research fellow at University of Modena and Reggio Emilia. He is coauthor of more than 20 international scientific publications. He has published articles on the application of Machine Learning and Deep Learning algorithms to Industrial problems, such as demand forecast in the fashion industry, the application of neural networks to WLC systems, as well as articles on optimization algorithms (i.e. metaheuristics) applied to engineering operations management, such as scheduling problems and warehouse allocation problems. Currently, his research effort is towards Green Advanced Planning and Scheduling systems. He has been involved in the DigiLab4U project for the development of the serious game Op&SCM.

Francesco Zammori, University of parma, Parma, italy

Francesco Zammori graduated with distinction in 2004 in Management Engineering and completed his post-graduate studies in 2009, when he received a Ph.D. in Industrial Engineering from the University of Pisa. From 2012 he has been working at the University of Parma with the role of Assistant Professor. Since 2021, he is Associate Professor at the same University, where he teaches Management Accounting Systems, Information Science, Data Bases and Information Systems. His research interests mainly concern: (i) Lean Thinking, (ii) Hybrid Production Planning and Control Systems, (iii) Modeling and Simulation (v) Machine Learning and (iv) Project Management. His research activities led to the publication of more than 50 works, most of which accepted on prestigious International Journals.