Industry 4.0—also known as the modern industrial revolution—nurtures close collaboration between various organizations so that they can come together for innovation. While aiming for digital transformation through such innovation, these organizations form certain interdependencies due to the pool of resources and tasks they agree to share to reach both common and independent goals. To understand those interdependencies, we studied a national innovation project in Finland called “Reboot IoT Factory,” which leveraged several resources, processes, and practices to successfully combine modern technologies in manufacturing in a competitive and sustainable way. The participants included in the project were factories, research organizations, and small and medium enterprises (SMEs). An actor dependency model was used to analyze the observed interdependencies through survey and interview data. The results showed strong goal, task, and resource dependencies between the participants. A conventional understanding of advantages and opportunities, such as increased experience sharing and possible long-term synergies, is elaborated; moreover, an analysis of the disadvantages and risks caused by interdependencies, such as delays in tasks and possible inefficiency through unnecessary complexity, is also conducted.

The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.

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For more than two decades, global corporations have governed environmental and social sustainability by imposing ethics codes and sustainability standards on suppliers in the Global South. Yet, despite the growing prevalence of these frameworks, systemic problems persist. Drawing on empirical data from textile suppliers in Pakistan, this study offers a rarely explored perspective on global fashion value chains. Our findings reveal how global brands increasingly shift corporate responsibility along with the financial and environmental burden of sustainability to their suppliers. We conceptualize this phenomenon as buyer‐enforced sustainability management, wherein brands impose rigid sustainability requirements while disregarding the local capacities, constraints, and institutional realities of supplier firms. We argue that these enforcement practices are not isolated but are rather a part of a broader strategy that exploits institutional weaknesses in supplier contexts. We identify local enabling factors such as regulatory voids and reactive compliance that allow this flawed model to continue.

The ongoing Industry 4.0 transformation places significant pressures on how businesses create and capture value. Technological advancements such as next-generation mobile communications are reshaping the business ecosystem of Industry 4.0, resulting in emerging business opportunities for new players, such as local operators, to collaborate and compete with mobile communications companies that are implementing I4.0. These changes raise the need to explore emerging business opportunities concerning the digitalization of future factories. New data and connectivity services are introduced to serve the needs of rapidly increasing machine-type communications that rely on connectivity, primarily through the fifth generation (5G) mobile solutions provided by local operators. Thus, this paper outlines the potential value configurations for data and connectivity solutions by identifying, matching, and bridging the utilizable resources and addressable needs within the factory processes. The research applies an exploratory approach and uses the Gioia method to analyze qualitative data of a single case. The study follows the connectivity-content-context-commerce typology (4C) of Internet business models to structure, analyze, and classify the identified needs and resources in future factories from the perspective of the local operator. The findings show that the content layer of 4C business model typology is the most dominant among data and connectivity-based needs and resources of future factories. The paper contributes by presenting four alternative value configurations for digitalization for local operators in the future factory context: the product, component, platform, and complementary businesses. The results suggest content- and context-specific businesses carries foremost business potential for local operators, however quantitative validation will bring fruitful research avenues. Multiple case studies and different data collection methods may also be considered in future studies.