In today’s highly dynamic and unpredictable project environment, companies need to be able to manage changes quickly and effectively, otherwise, the final product will not be current and will only go to waste. Traditional project management approaches that focus on planning are no longer efficient and companies are forced to adopt new ways of working. As a result, more flexible agile project management (APM) approaches have emerged over the last decades. Originally developed for the software industry, APM is now increasingly recognized and adopted also by other industry sectors. However, due to some discipline-specific differences, the adoption of APM by non-software companies is challenging and requires many adjustments and high financial input. While the larger organizations have sufficient resources to make such a transition, small and medium-sized enterprises (SMEs) generally cannot afford to do so, and therefore need alternative strategies to increase their agility and stay competitive on the global market. In this paper, we present a case study of a Slovenian medium-sized manufacturing company that implemented only certain APM practices separately and not as part of a structured APM methodology, and still managed to achieve significant benefits: improved communication, faster detection of discrepancies, more effective problem-solving and greater flexibility. The results also suggest that APM practices, even when implemented separately, positively impact project success in terms of both efficiency and stakeholder satisfaction, and can thus help in establishing an economically, socially, and environmentally more sustainable workplace.

This study investigates the effects of blockchain technology scalability limitations on the performance of Blockchain-Based Shared Manufacturing (BBSM), an innovative smart-manufacturing paradigm aimed at enhancing the utilization of global manufacturing resources via peer-to-peer (P2P) collaboration of self-organized manufacturing assets. Despite the prevalence of research highlighting blockchain technology’s scalability limitations as the main barrier for adoption, few studies have explored their effects on the operation of blockchain-based systems. The primary goal of the presented research work is to explore the implications of blockchain technology scalability limitations on the BBSM system’s performance and user behavior. To obtain realistic behavior, an experiment is conducted using an online game played by human participants. Analysis of the players’ strategy is used for implementation of a multi-agent simulation model, which is then employed to assess the influence of varying blockchain network configurations on the BBSM concept’s performance. Preliminary experimental findings reveal that a congested blockchain network leads to increased transaction costs and reduced service prices, consequently devaluing the manufacturing role in the BBSM system and causing underutilization of existing maximum production capacities. Moreover, allocating funds to financial activities rather than manufacturing activities yields superior outcomes for system users. Simulation results indicate that the BBSM system’s response to alterations in blockchain network throughput is contingent upon the production function. The findings of this study reveal that the scalability limitations of blockchain technology impair the performance of the BBSM system and affect user behavior in the system, underscoring the necessity for future research to concentrate on incorporating scalable solutions within blockchain-based manufacturing systems.

In order to survive in today’s highly competitive global market, small and medium-sized enterprises (SMEs) have had to transition from sequential to concurrent product development, which significantly shortens development cycles, reduces costs, and ensures high product quality. Despite its many benefits, concurrent product development is still based on detailed upfront planning and cannot address the challenges related to today’s ever-growing uncertainty, constantly changing environment, and unstable requirements. A potential solution to this problem could be in more flexible and value-driven agile project management (APM) approaches, typical of software development. In this paper, we propose a new product development model specifically appropriate for SMEs that combines concurrent product development principles with APM elements. It is designed as a loop of five repetitive steps (macroplan, microplan, iteration activities, review, and retrospective) that are being executed within individual concurrent development loops. The application of the model is presented on a real case example of process development and small batch manufacture of a complex wiring harness. The study reveals many benefits of the proposed model, such as improved communication, faster detection of discrepancies, more effective problem solving, and greater flexibility. A positive impact on project success is also observed.

The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems.