Glenn Charles Parry

Data processing in machine learning and Artificial Intelligence (AI) can be viewed as a conventional supply chain in which data are the primary flowing artefact: they are sourced, generated, collected, stored, and shared across upstream and downstream actors to produce valuable information products and business outcomes. Within AI-data supply chains, we map common training optimisation techniques to explicit extensions of conventional supply chain production technology, focusing on two classes of structural decisions: (i) merger (e.g., ensemble learning, distributed training, and knowledge distillation) and (ii) reconfiguration (e.g., algorithm hardware matching). Although such horizontal mergers and reconfigurations provide a mechanism for productivity growth and improved resilience under disruptions, their implications for overall supply chain productivity remain difficult to forecast. To address this gap, we develop a general framework for evaluating merger and reconfiguration decisions in complex supply chains, with an application to AI-data supply chains. Leveraging key features of the Free Disposal Hull (FDH) model and the free-arrangement assumption in network nonparametric production technologies, we propose a non-radial directional distance function model to support decision-making in merger and reconfiguration-oriented supply chain design. We demonstrate the applicability of the proposed approach using a dataset that represents an AI-data supply chain.

As part of a European Union-funded project, we investigate why SMEs choose to adopt or not adopt blockchain in their operations. The study used the technological-organisational-environmental (TOE) framework to understand the challenges, enablers and barriers faced by small and mid-sized enterprises (SMEs) and their intention to adopt or to not adopt blockchain technology. A qualitative study involved interviews with SMEs and triad consultations session between blockchain technical experts, SMEs and researchers. Our findings suggest that SMEs find blockchain confusing and complex. Consequently, SMEs in our sample adopt the technology only where well-defined use cases can be used to attract larger clients or as a central function of the business model, rather than in addition to existing operations and supply chain uses. We believe this to be important because it means that SMEs will be less able to use blockchain as a disruptive source of competitive advantage against larger players.

Recent decades have seen increased interest in transdisciplinary (TD) research. To deliver on the promise of TD working there has been a call for the expansion of TD education in emerging literature. The challenge with proposed approaches is that they are often difficult to implement requiring significantly changed courses structures, and the coordination of teams of academic and industry experts to deliver. This creates a barrier to the main-streaming of TD education. Our research aims to create a practical approach for Transdisciplinary Engineering (TE) education which can be easily incorporated within existing course designs and in doing so facilitate wider disseminated. This paper presents the design and pilot of a TE session with MRes students from the University of Bath’s, Centre of Doctoral Training in Advanced Automotive Propulsion Systems. The session is evaluated by way of student feedback. The results show broad satisfaction with the session. Six of the eight indicated that they were satisfied with the quality of the session (two students were neutral). All students considered that the course material was presented in a clear and understandable way. All students considered that the course was accessible to their level of understanding. Future work will see the session delivered within additional engineering MSc courses at Bath and internationally with informal agreements in place with Universities in Colombia, Korea and Poland.

This paper examines innovation in product-service systems. Using the lens of the general modular systems theory (Schilling, 2000), the research examines the factors that influence whether a product-service system would benefit from an increasingly modular, or an increasingly synergistic specific (or integrated) state in a servitized context. The paper presents results from an in-depth case study of an OEM of military vehicles. The OEM provided design services to reconfigure military vehicles based on the requirements of the end-user (military personnel), and were based on real-time need from active overseas engagements. The research design uses a mixed-methods approach. Given modularity is a directly observable configuration of structure, design structure matrices (DSMs) were used to inspect the modular structure of vehicles each time a customer-requested design change was integrated. To supplement the DSMs, thematic analysis was conducted on 29 in-depth interviews with the organisation’s employees, as well as on texts, documents and secondary data. In applying the general modular systems theory to the context of an outcome-based product-service system, the research finds four additional factors that push a system toward or away from modularity. These factors arise from the diversity of the customers’ use-contexts that were not included in the original Schilling (2000) framework. These factors include requirements based on contextual variety, emergence, actor agency, urgency in use. The paper contributes to the innovation management and service modularity literature by updating and refining the general modular systems theory, and provides guidance to managers when designing and innovating outcome-based product-service systems.