Abstract
This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.

Abstract
PurposeThe service environment is becoming increasingly turbulent, leading to calls for a systemic understanding of it as a set of dynamic service ecosystems. This paper advances this understanding by developing a typology of service ecosystem dynamics that explains the varying interplay between change and stability within the service environment through distinct behavioral patterns exhibited by service ecosystems over time. Design/methodology/approachThis study builds upon a systematic literature review of service ecosystems literature and uses system dynamics as a method theory to abductively analyze extant literature and develop a typology of service ecosystem dynamics. FindingsThe paper identifies three types of service ecosystem dynamics-behavioral patterns of service ecosystems-and explains how they unfold through self-adjustment processes and changes within different systemic leverage points. The typology of service ecosystem dynamics consists of (1) reproduction (i.e. stable behavioral pattern), (2) reconfiguration (i.e. unstable behavioral pattern) and (3) transition (i.e. disrupting, shifting behavioral pattern). Practical implicationsThe typology enables practitioners to gain a deeper understanding of their service environment by discerning the behavioral patterns exhibited by the constituent service ecosystems. This, in turn, supports them in devising more effective strategies for navigating through it. Originality/valueThe paper provides a precise definition of service ecosystem dynamics and shows how the identified three types of dynamics can be used as a lens to empirically examine change and stability in the service environment. It also offers a set of research directions for tackling service research challenges.

Abstract
Managing supply chain (SC) relationships to deal with challenges posed by contemporary social and business environments is a difficult task that can be facilitated with the use of digital technologies. The growing complexity of supply chains, characterized by over-dependencies on geographically dispersed partners across different regions, increases risks related to managing these relationships and highlights the importance of collaboration and balancing the power dynamics between SC partners. Previous studies have shown that small and medium enterprises (SMEs) can be considered the weakest link in terms of digitization and balance of power. This article aims to analyse how buyer-seller power relations moderate the relationship between the adoption of digital technologies in supply chain management (SCM) processes and innovation performance in the context of SMEs. Data were collected from manufacturing SMEs operating in Portugal. The results support the assumption that the use of digital technologies in processes related to SCM has a positive effect on SMEs innovation performance. The results also suggest that non-mediated power and reward-mediated positively moderate the relationship between the adoption of digital technologies and innovation performance, while the impact of coercive-mediated power was not confirmed. The article contributes to theory and practice by advancing the literature and guiding managers in the challenging task of carrying out digital transformation initiatives, considering their relationship with the power dynamics in the complex context of SMEs.

Abstract
This paper examines the concept of learning factories and their role in addressing contemporary challenges in the production sector. Learning factories integrate learning and production environments, offering hands-on experiences to develop essential competencies for modern manufacturing. Originating from initiatives like the Germany's "Lernfabriken" in the late 1980s and the National Science Foundation's funding in the 1990s, learning factories have gained global prominence. They serve as platforms for research, education, and workforce development, attracting students and workers from diverse sectors. Examples from Europe, the United States, and China illustrate various approaches to leveraging learning factories for industrial advancement and skill development. Overall, learning factories play a vital role in fostering innovation, enhancing competitiveness, and driving economic growth in the manufacturing sector. © 2024 The Authors. Published by Elsevier B.V.

Abstract
This paper emphasizes the need to broaden organizational perspectives through Open X, which promotes sharing and collaboration over selfishness and competition, instead of that industrial intellectual protection through patents can divert resources essential for the growth of organizations. Faced with new realities, organizations need different management approaches with the potential to transform the reindustrialization resulting from deindustrialization into a Neoindustrialization 2.0. It does not mean tearing down or creating new boundaries but an open culture where organizational efforts have social relevance. In the face of economic interests, Open X can make organizational outcomes more plentiful and robust. © 2024 Published by Faculty of Engineering.

Abstract
Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. © 2024 The Author(s). Published by Elsevier B.V.