Raffaele Secchi

This research explores the application of artificial intelligence (AI) in operations and supply chain management (OSCM) and examines how AI can enhance and strengthen the digital capabilities model (DCM). Artificial intelligence is rapidly transforming supply chain operations. To enhance resilience, sustainability, and efficiency of their supply chains, businesses need to understand how this technology affects digital capabilities. Using VOSviewer software, the study conducts a network analysis of term co-occurrence and identifies twelve clusters reflecting important search topics in artificial intelligence applications for OSCM. The research uses a Systematic Literature Network Analysis (SLNA) technique. The analysis of artificial intelligence applications in OSCM identifies four interconnected macro trends: (1) a move toward more integrated and holistic supply chain visions; (2) a greater emphasis on sustainability; (3) enhanced risk management and resilience; and (4) an increasing significance of data-driven decision making. The paper also shows how AI applications, such as genetic algorithms, deep learning, and machine learning, support and enhance the six core capabilities of the Digital Capabilities Model (DCM). The results highlight numerous opportunities for adopting AI within OSCM, offering valuable insights for practitioners aiming to integrate these technologies into their operations.

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.

The impact of Industry 4.0 and its opportunities are expected to be significant for manufacturers. A lack of empirical studies creates the need for academic contributions on the critical success factors of Industry 4.0 implementations and their resultant improvements for manufacturing businesses. This research uses case studies of eight implementations of Industry 4.0 technologies in Italy to supplement existent literature. An original data set was constructed using a purposely defined research protocol using plant visits and structured interviews. Continuous improvement/lean management emerged as a critical success factor for implementation, together with quality and flexibility-based competition, top management leadership, establishment of inter-functional teams, conducting of preparatory activities, project planning and training activities. Incremental/evolutionary and radical/revolutionary improvements in business model elements are possible outcomes of implementations, while addressing customers' needs emerges as an antecedent to radical/revolutionary improvements. Managers will benefit from understanding how to achieve successful implementations and business improvements.

This study investigates a novel potential barrier to lean implementation: the degree of knowledge codification. Building on extant literature which analyses what affects knowledge transfer effectiveness and efficiency in lean implementation, this study suggests that lean implementations are less likely to fail when they are characterised by the appropriate level of codification, which is contingent upon the following variables: the performance challenge to be addressed, the sense of urgency of the required improvements, the vertical and horizontal articulation of the target organisation, and the organisational units’ absorptive capabilities. The study adopts an abductive approach to case research, combining a process perspective with a quasi-experimental design. It investigates the back-office operations of a financial service provider and is based on data gathered over 15 months.

Using a quasi-experimental research design and an abductive approach, we explore the determinants of lean implementation failure in a financial service provider. Based on quantitative and qualitative data analysis, we show that the analyzed lean implementation was unsuccessful even if undertaken in the absence of the obstacles and barriers suggested by extant lean literature. We also show that lean practices were adopted as a result of the implementation, but such adoption did not translate into operational change and performance improvements. We investigate why this happened conducting thematic analysis of 23 interviews with office managers and lean specialists and abduct that how lean implementation tasks are allocated between lean specialists and office managers (the degree of structural versus contextual ambidexterity built in the implementation process) led to failure. More generally, we discover that how the lean implementation process is organized can generate variation in lean implementation outcomes, and that the conditions for lean implementation failure might be built in the lean implementation process. We develop a testable research proposition that contributes to lean implementation literature, draw some theoretical and managerial implications and suggest directions for future research.

The purpose of this study is to provide insights into the reshoring phenomenon. Although in the past years much emphasis has been placed on providing guidance on make-or-buy and offshoring strategies, reshoring decisions have not been addressed in a holistic, practical, and structured manner. The framework presented in this paper intends to address this gap by providing a graphical representation of why reshoring decisions are made and showing relevant dimensions to be taken into consideration. Moreover, since public case studies about reshoring are limited and not well-documented, an in-depth case study is included to facilitate an understanding of the drivers of reshoring decisions, and to test the operationalization of the framework.