This study investigates the rapid centralization of the Ethereum builder market under the Proposer-Builder Separation (PBS) architecture. We argue that existing research, by focusing predominantly on influential order flows, lacks a comprehensive evaluation of order flow behavioral patterns and economic purposes. To address this gap, we analyze Ethereum transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV) -- the missing components corresponding to these behavioral and economic dimensions, respectively. We introduce a novel exclusivity metric based on Kullback-Leibler divergence and employ supervised learning to identify 75 EOFs and 322 non-atomic MEV flows, which account for 71\% and 23\% of trading-related builder revenue. A longitudinal analysis of builder strategies across these dimensions delineates the market's evolution into four distinct eras, revealing that while EOFs were instrumental in establishing early dominance, incumbents have since decoupled market share from immediate EOF dependency by leveraging entrenched network effects. Ultimately, we conclude that builder centralization is an emergent property of the PBS framework itself, as the architecture systematically violates the fundamental prerequisites of a competitive market.
Customer lifecycle decisions in enterprises are often fragmented across marketing, finance, customer experience, and operations, resulting in inconsistent actions and suboptimal outcomes. This paper introduces Multi-Agent Decision Intelligence (MADI), a framework that models enterprise functions as autonomous yet coordinated agents aligned through shared objectives. Using centralized training with decentralized execution, agents negotiate actions that balance lifetime value growth, churn reduction, customer experience, and cost-to-serve efficiency under operational and regulatory constraints. Experimental results on industrial and semi-synthetic datasets demonstrate consistent improvements over siloed optimization, centralized reinforcement learning, and heuristic baselines. A pilot deployment within a Saudi enterprise further confirms practical feasibility, improved crossfunctional alignment, and reduced decision latency. MADI provides an enterprise-ready blueprint for coordinated AI-driven decisionmaking in complex organizational environments.
Ho Yeol Yu, Kyu-soo Chung, Sam Schelfhout, Anthony D. Pizzo
The esports industry, facing slowing growth and revenue challenges, is actively seeking innovative monetization strategies to revitalize its revenue generation capabilities. This study investigated the adoption of non-fungible tokens (NFTs) as a viable tactic to these challenges. NFTs, unique digital assets secured by blockchain technology, are becoming increasingly integrated into the esports landscape. Guided by a Diffusion of Innovations framework, we analyzed factors influencing the attitudes and purchase intentions toward NFTs of 294 competitive esports gamers. Results revealed that the adoption factors had a significant impact on attitudes toward NFTs, and thus significantly influenced purchase intentions. Notably, investment intentions did not moderate the relationship between attitude and purchase intention, suggesting that the intrinsic value of NFTs drives their appeal, rather than their potential solely as financial instruments. This research underscores the importance of leveraging NFTs’ ability to provide exclusive content and experiences, thereby enhancing fan engagement, diversifying revenue sources, and fostering a more sustainable business model.
This paper addresses critical food safety challenges in modern agricultural supply chain management by proposing an AI-driven quality chain system design. The system integrates five key chains—agricultural product quality, capital flow, logistics, and accountability—into a unified accounting information framework through artificial intelligence and multidimensional accounting theories, achieving "five-chain integration". Centered on "quality accountability", the intelligent open, decentralized, and industry-finance integrated agricultural supply chain management system enhances transparency, traceability, precision, and collaboration within the sector. It plays a vital role in establishing fair market competition, guiding industrial cycles, optimizing resource allocation, and building market confidence while reinforcing social responsibility.
This thesis comprises four chapters. Chapter One provides a “meta-theoretical” agenda by revealing different paradigms or ways of thinking about platform ecosystems. Through the theoretical synthesis of existing research across management fields, Chapter One clarifies the onto-epistemological foundations of each paradigm and helps explain the varying expectations researchers place on the platform ecosystem construct. Chapter Two offers a conceptual framework that theorizes the nature of B2B platforms as meta-organizations. Building on a systematic review of existing literature, it uncovers five dominant B2B platform archetypes (matchmaker, application marketplace, solution enabler, consortium, and decentralized autonomous platforms). Further, it theorizes how the specific design features of B2B platforms shape their governance models. Chapter Three and Four empirically explore the dynamics of establishing B2B platforms. Drawing on a qualitative, single case study of a B2B solution enabler platform, Chapter Three shifts the focus from static platform attributes to the unfolding communication and coordination efforts that underpin the management of B2B platform establishment. Drawing on the framing concept, this chapter foregrounds the role of narratives and their strategic and temporal complexity in establishing a B2B platform, offering insights into the socio-cognitive nature of B2B platform establishment. Chapter Four builds on the same single case study, discusses the paradox of openness, the “black box,” that most platforms face, and shows how firms operationalize strategic openness throughout a platform’s lifecycle. It provides a process model of platform openness, shaped by highlighting how platform identity acts as both a driver and outcome of openness decisions across technical, economic, and socio-cognitive dimensions, indicating that B2B platform establishment is a pathdependent, multi-dimensional, and configurational process.
Hyperledger Fabric (HLF) is an adaptable blockchain platform that enables the requirement-driven construction of cross-organizational distributed ledger networks. While this flexibility offers many advantages, it also introduces challenges in ensuring Fabric networks’ fit-for-purpose nature and extra-functional requirement adherence, e.g., with respect to fault tolerance. This challenge calls for the use of mathematically precise formal analysis techniques, tooling and models. In this paper, we propose a combination of model-driven engineering principles and diverse graph generation to validate Fabric network designs and to facilitate their design for dependability. Specifically, we present a Fabric network meta-model and a set of well-formedness requirements in the Graph-solver-as-aService tool Refinery. Uniquely, Refinery can check conformance on partial models, enabling analysis already during early design.
Mehayrun Nesa Shupti, Niamat Ullah Ibne Hossain, Steven A. Fazio, Jahid Hasan Ashik
Analyzing design principles and supply chain sustainability are critical for organizational success in today's competitive marketplace. The process of evaluating an organization's supply chain design principles and manufacturing sustainability entails incorporating various sources of information, which are typically uncertain, incomplete, and subjective in nature. Particularly when various organizations are confronted with significant principles such as a lack of interoperability, decentralization, virtualization, real-time and capabilities, service orientation, and so on. Using graph theory and a matrix approach (GTMA), this article attempts to analyze the interaction between design principles-related factors with a focus on operational excellence, growth and sustainability in supply chains by considering the business of Industry 4.0. We have considered electrical equipment manufacturing Industry 4.0 as test ground. Based on a literature review and expert opinions, we identified several key principles, and then explored the most significant one using graph theory and a matrix approach. The findings indicate that autonomy has been evaluated as the most significant challenge to manage design principles most effectively for sustainable manufacturing in the Industry 4.0 context. Therefore, organizations must enhance autonomous systems capable of operating and making decisions alone, without relying on external instructions or support, in order to achieve sustainability. This and the other identified principles to achieving corporate supply chain sustainability are ranked in this paper. These findings will be useful for managers and policymakers managing the interaction between people and processes, as well as corporate sustainability management in Industry 4.0-based supply chain organizations. The unique contributions of this paper can aid in the improvement of design principles and the sustainability of emerging economies' Industry 4.0 perspectives.
Yongting Tian, Shouxu Song, Dan Zhou, Ruirui Yang · 5 authors
This article underscores the necessity for sustainable and environmentally friendly manufacturing practices in product family configuration (PFC) projects, which are paramount to the global economy. Nevertheless, conventional approaches often fixate solely on design aspects, overlooking downstream supply chain configuration (SCC) considerations and the corresponding environmental benefits. Consequently, there is an escalating demand for an integrated optimisation approach that encompasses both PFC and SCC to realise economic and environmental advantages. This study delves into a methodology that integrates blockchain smart contracts as binary 0–1 variables with waste recycling and utilisation, yielding a comprehensive multi-objective model. The proposed methodology seamlessly incorporates considerations for both PFC and SCC. Furthermore, a nested leader-follower optimisation algorithm, based on the non-dominated sorting genetic algorithm-II (NSGA-II), has been devised with the objective of achieving triple benefits: augmented profits, maintenance revenue, and diminished environmental emissions. In conclusion, this research contributes to the advancement of sustainable collaborative optimisation through the innovative utilisation of blockchain smart contracts and multi-level modelling. To demonstrate the effectiveness of the proposed methodology, it is applied to a 60 KW DC electric vehicle (EV) charging piles, accompanied by a sensitivity analysis to assess its management implications.
Nikolai Kazantsev, Oleksii Petrovskyi, Julian M. Müller
Rapid market changes call for demand-driven collaborations in manufacturing, which trigger supply chain evolution to more distributed supply structures. This paper explores the system dynamics of the largest European aerospace manufacturer's supply chain. We conceptualise a manufacturing ecosystem by observing the impacts of supplier development, digital platforms, smart contracting, and Industry 4.0 on demand-driven collaborations in time. We contribute to the literature on ecosystem strategy, particularly for regulated industries, by disclosing the role of demand-driven collaborations in supporting the ecosystems' growth. We provide manufacturing firms with an open-access tool to exemplify their ecosystem development and produce initial training datasets for AI/ML algorithms, supporting further analytics.
Purpose The quality liability of prefabricated components (PCs) is a major issue among key stakeholders. The blockchain-based quality tracking systems are supposed to support a more transparent and trusting quality control process. However, many factors affect the stakeholders' willingness toward the adoption of such quality tracking systems. The purpose of this research is to investigate the key factors that influence the stakeholders' adoption decisions toward the application of the quality tracking system in PCs and develop coping strategies. Design/methodology/approach An evolutionary game model is established that includes the manufacturer, constructor and developer. Four scenarios of equilibriums and the game's evolutionary stable strategies are analyzed, and the corresponding stability conditions are then obtained. Based on the tripartite game model, two representative projects are used as case studies to simulate how different factors affect the stakeholders' decisions. Findings First, trade-offs between cost and benefits were the most prominent factor in the adoption decision-making. Second, the advancement of technologies would compensate for their immaturity. Third, subsidy and penalty provision of the developer and high-level trust both incentivize the stakeholders to adopt the quality tracking systems. Originality/value This research investigates the influence of technology, environment and participant related factors on the adoption decisions of the quality tracking system for PCs and discovered that technology maturity and advancement played an essential role. It is expected that the research findings would be of value to policy makers and project management personnel for better quality control of prefabricated construction.
Customers want to experience unique products that suit their individual preferences. Therefore, companies are looking for ways to produce personalized products by moving away from traditional production models. Technologies such as blockchain, IoT, and cyber-physical systems have an important role in adopting the personal custom production model. In this study, a reliable and optimized mass customization framework is proposed. The framework has three main contributions. First, the use of Blockchain provides advantages in terms of data persistence, traceability, transparency, and reliability. Second, the production process is optimized by using artificial intelligence methods. A decision support mechanism is proposed for customers who will place orders. As a combination of the contributions, the proposed mass customization framework covers the processes from ordering to production. In addition to contributions such as facilitated customization and reliable data, energy saving in production is increased with the proposed framework. Simulation results show that energy savings between 10 and 47% are achieved.
Luis A. Estrada-Jimenez, Terrin Pulikottil, Ricardo Silva Peres, Sanaz Nikghadam-Hojjati · 5 authors
The heterogeneity of the components of a Cyber-Physical Production System in addition to the high decentralization and autonomy required in Industry 4.0, introduces new levels of engineering complexity and dynamism that classical reductionists approaches are not able to solve. Within this context, novel solutions that rely on complexity sciences seem to be a good alternative to cope with these underline challenges. In this context, this paper presents a conceptual framework of complexity theory, self-organization and emergence and its subsequent relation to cyber manufacturing systems. Such analysis shows very promising ideas in the further development of complex, robust, adaptive and at least partial autonomous manufacturing systems.
The supply chain plays an essential role in the cost reduction of offshore wind energy. Supply chain complexity is a major driver of end-to-end supply chain costs and at the same time a source of competitive advantage. In this study, a strategic complexity management approach is suggested for analysing and controlling the complexity of the supply chain in offshore wind energy. The adoption of blockchain via the development of software architecture and a discussion of its impact on complexity are provided. A comparative study focused on two UK offshore wind farms based on real industrial data illustrates the complexity analysis and the contribution of blockchain technology to the strategic management of this complexity.
Today, thanks to the developing technology, mass production technology is progressing very fast. With these systems, which aim to meet the increasing customer demands correctly, effectively and efficiently, it is aimed to produce customer-specific products. In this method called mass customization, customers can customize the product they demand and create it according to their personal tastes. In this study, the mass customization concept, which is one of the current production technologies, is modeled by using blockchain. In the proposed system, a blockchain-based smart contract was created to bring together the parties and establish a healthy communication. In addition, it is aimed to produce more effective products with the proposed method. By providing a simulation environment for the products to be produced, it is presented to the users' information and a feedback system is created. Thus, a consensus-based production model is being built.
After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. This book presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), a novel interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic approach, similar to the roundup of a sheep herd, is to let autonomous layout modules interact with each other inside a successively tightened layout zone. Considering various principles of self-organization, remarkable overall solutions can result from the individual, local, selfish actions of the modules. Displaying this fascinating phenomenon of emergence, examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. From an academic point of view, SWARM combines the strengths of procedural generators with the assets of optimization algorithms, thus p...
Design Education and Practice
Product Development and Customization
Systems Engineering Methodologies and Applications
The accelerated growth of technological advancements has triggered the expansion of customer demand leading to highly complex supply chain networks. One viable way original equipment manufacturers (OEMs) can respond to changing purchasing habits is to redesign their strategic and operational activities to build far-reaching information and resource avenues allied with effective marketing policies. These newly implemented policies need to comply with extended producer responsibility (EPR) guidelines that also well align with rising consumer awareness towards green consumption. To achieve this, manufacturers must create efficient end-of-life product (EOLP) return structures and ensure value creation through product recovery operations to dwindle the cascading waste of discarded products. From an environmental viewpoint, retrieving the value embedded in returned items through remanufacturing or recycling has been proven to be effective in reducing the amount of industrial solid waste. EOLP processing operations are heavily reliant on customers' participation in returning outdated devices making product collection a crucial step in point-to-point supply chains. To entice end-users, the OEMs need to design environmentally and economically benign product take-back strategies that would spark the volume of product returns. These constraints dictate two structural challenges: how manufacturers and consumers can become active participants of EOLP treatment activities, and how fast and efficiently OEMs can respond to the changing market and capital needs while preserving their sustainability levels. In terms of active participation, trade-in incentives can help stimulate additional revenue channels for OEMs through product remanufacturing while helping companies comply with the EPR legislations. Trade-in policies are set forth as part of long-term marketing strategies and include incentive programs that aim at enticing current and potential customers to trade-in their used products with newer generations at a discounted price or for instant credit. Within the context of purchasing behavior, trade-in programs positively impact customers' buying decisions by granting buyers the ability to claim the scrap value of their existing devices. Particularly in oversaturated industries such as electronics and automotive, take-back incentives are a pipeline for OEMs to generate significant residual value by reselling remanufactured products on secondary markets. Moreover, offering special discounts or credits in lieu of old devices fuels new product sales by creating an additional revenue stream. Still, in today’s fast-changing market dynamics, inept trade-in practices that fail to eliminate the ambiguity surrounding the prediction of the true quality of returned products bring functional and financial burdens to organizations. The conventional intransigent trade-in schemes fail to address this uncertainty leading to a number of unnecessary inspection, disassembly, and shipment steps resulting in increasing complexity and product recovery cost. Achieving an accurate trade-in scheme is a highly complex multi-dimensional problem requiring novel solutions that traditional manufacturing and supply chain technologies are incapable of offering by design. Such challenging task inevitably necessitates strategic initiatives that stem from the utilization of cutting-edge groundbreaking information technologies for rapid response to customer needs and reduced complexity across all operational layers. Despite the numerous methodologies investigating the potential value gain from remanufacturing and product acquisition pricing policies, there is no study in related literature that incorporates trade-in programs into an intelligent remanufacturing structure. A majority of previous studies propose preventive models with pre-determined and rule-based explicit model parameters hindering the practicability of the substantial volume of data generated by the increased use of technological tools. These models, inevitably, fall short in successfully incorporating long-term manufacturing goals into sustainable business strategies. With this motivation, the architectural framework this dissertation introduces addresses a predictive product recovery model for product returns to enable an autonomous, sensor-embedded, and decentralized disassembly and remanufacturing system. The main objective of this research is to investigate the feasibility of cost- and resource-effective end-of-life product management systems in a smart reverse logistics network where trade-in rebate decisions take place in an autonomous ecosystem. This research, while filling the emerging gap in the utilization of current digital technologies to determine quality-dependent acquisition strategies, also provides a novel quantitative analysis on the efficiency of trade-in policymaking. This model can be employed in manufacturing industries for precise assessment of value creation amid digital advancements in a future-oriented platform. Due to its highly saturated formation, the consumer electronics industry offers a more suitable platform for this study. Therefore, this study examines a trade-in model for a specific technological product, game console, with the help of a case study. First phase of the dissertation evaluates the performance degradation pattern of discarded electronics products in a ubiquitous manner through timestamp data enablers. To handle this highly complex large-volume data, a discrete-event simulation model is developed from the original equipment manufacturer viewpoint. The model aims to examine the behavior of returned devices as well as the expected overall cost of product recovery operations. Following this, a design of experiments study is utilized for the experimentation using Taguchi’s Orthogonal Arrays (OAs). Employing the findings obtained in the first phase, the second phase of the study deals with trade-in policymaking to determine an engaging quotation for varying quality of returned products from the perspectives of all parties involved in the transaction. To achieve this, an initial model for trade-in-to-upgrade incentives is established for discrete sets of quality standards in case where returned products are grouped into three quality classes based on their usage time. The model is then expanded to compare two product acquisition strategies, namely, trade-in-to-upgrade incentives and instant credits. To achieve a realistic strategy, two rebate models are constructed in a simulation-based game setting to mimic the customer behavior and to obtain the resulting payoffs for the OEM in a dynamic ecosystem. To handle the uncertainty in the customer's decision towards the incentive offer, logistic regression analysis is conducted to maximize the likelihood of the acceptance rate. Finally, trade-in policies are compared to obtain favorable strategies augment revenue streams.
Open access
Manufacturing Process and Optimization
Product Development and Customization
Additive Manufacturing and 3D Printing Technologies
This paper presents an application of Decentralised Autonomous Organisation (DAO) in the field of design and AEC industry. The model is applied in the realm of shape grammar proposing the possibility of allowing multiple grammarists to collaborate in the definition of a new grammar within a Blockchain environment that acts as a distributed ledger. DAOs systems and Blockchain are introduced as well as shape grammar and its fundamental rules. The collaborative nature of a DAO with the inner logic of shape grammar, which bases its principle and rules in multiple variations and combinations of simple initial shapes, brings to the problem of recording and validating changes and improvements in the design chain. For this reason, a voting system to govern the process is introduced, based on both quantitative values, i.e. number of votes, and qualitative power, i.e. the reputation of who votes, applying a factor that scales the vote according to the expertise of the voter. An example is provided showing a possible scenario in a design environment along with validation criteria, and predicting future stages applied in an always more BIM-oriented practice.
With increasing product personalization and open innovation, the manufacturing paradigm has been transforming to a more decentralized and socialized one. Social manufacturing was proposed as a new paradigm for industry. It extends the crowdsourcing idea to the manufacturing area. By establishing cyber–physical–social connection via decentralized social media, various communities can be formed as complex, dynamic autonomous systems to co-create customized and personalized products and services. This article presents the concept and characteristics of social manufacturing including distributed, adaptive, and self-organization. It also addresses social intelligence in proactive decision-making for organization of socialized resources and producers in the life-cycle of product.