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.
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.
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.
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.
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 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.