As consumer demand for eco-friendly products continues to grow, manufacturers are increasingly driven to enhance product greenness and disclose this information. Blockchain technology emerges as a pivotal enabler, facilitating credible communication of manufacturersâ sustainability efforts to consumers through retail platforms and influencing supply chain decisions concerning sustainability, pricing , and blockchain adoption. While existing research has extensively examined the positive moderating effect of blockchain technology on consumersâ perceived value of product greenness in retail competition or green supply chain contexts, there remains a significant gap regarding its cross-channel influence in situations of information disclosure asymmetry across retail platforms. To address this gap, we investigate the interactive dynamics of a green supply chain under asymmetric platform competition, where the incumbent platform offers blockchain services while the new platform does not. Our findings indicate that the manufacturerâs decision to adopt blockchain depends significantly on market conditions. Notably, the manufacturerâs inclination towards blockchain adoption widens for a broader range of blockchain costs when the cross-channel influence is pronounced. Moreover, the alignment of the manufacturerâs blockchain adoption strategy with the incumbent platformâs preference is not guaranteed. In scenarios where their interests diverge, joint efforts to reduce blockchain costs can be a viable strategy. Our parametric analysis further reveals that while the cross-channel influence contributes positively to enhancing product greenness and the manufacturerâs profit, it could diminish the profits of both platforms under certain conditions.
Scaling blockchain performance through parallel smart contract execution has gained significant attention, as traditional methods remain constrained by the performance of a single virtual machine (VM), even in multi-chain or Layer-2 systems. Parallel VMs offer a compelling solution by enabling concurrent transaction execution within a single smart contract, using multiple CPU cores. However, Ethereum's sequential, shared-everything model limits the efficiency of existing parallel mechanisms, resulting in frequent rollbacks with optimistic methods and high overhead with pessimistic methods due to state dependency analysis and locking.
Alisha Roushan, Amrit Das, Anirban Dutta, Uttam Kumar Bera
Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters , specifically flash floods triggered by cloudburst events. We propose a multi-objective supply chain model that minimizes both cost and time during emergencies by employing drones for rapid response and delivery to inaccessible areas. The model leverages Dijkstraâs algorithm to identify the shortest emergency routes and integrates Neutrosophic Compromise Programming (NCP) and the Weighted Sum Method (WSM) to optimize drone deployment for cost-effectiveness and timely intervention. Pentagonal Type-2 Fuzzy Variables (PT2FV) manage uncertainty and accurately represent real-world disasters. The study also introduces a smart contract framework to enhance transparency and accountability in logistics and rescue operations. These smart contracts govern the assignment of drone-based delivery tasks, ensuring that supplies are optimally allocated and transported via the most efficient routes. The system verifies task completion and maintains a transparent record of the logistics process . The robustness of the model is validated through sensitivity analysis, while the smart contract system is confirmed through unit testing, demonstrating its reliability under varied conditions. This work aligns with Industry 5.0 , integrating human-centric decision-making, drones, intelligent systems, and blockchain-based smart contracts to automate and effectively manage disaster, facilitating seamless collaboration between humans and machines.
Niels Agatz, Jan C. Fransoo, Elliot Rabinovich, Rui Sousa
Last mile operations (LMO), the processes involved in the critical last stage of delivering goods and services, have widespread relevance across major sectors of the economy, including retail, food services, healthcare, humanitarian services, energy distribution, telecommunications, public services, and others. These operations account for a significant portion of the costs, jobs, and economic output in these sectors. Global economic output involving last mile deliveries alone, for instance, is valued at $165 billion per year and is growing at about 10% per year (InsightAce Analytic 2024). Recent decades have witnessed an acceleration in the rate of evolution of LMO (Agatz et al. 2024; Boutilier and Chan 2022; Boyer and Hult 2005; Dreischerf and Buijs 2022; He and Goh 2022; Lyu and Teo 2022). Technology-driven innovations have catalyzed profound changes in the planning, design, and execution of LMO, with significant implications for the economics of these operations. Extending the last mile to the final user has increased convenience, accessibility, and reliability. Zipline, for example, has introduced drones to safely deliver lifesaving products in remote communities (Ackerman and Koziol 2019). An increasing number of pharmacies in Europe and Africa have been equipped with smart lockers to allow 24/7 access to critical medicines (Gobir et al. 2024). Some innovations leveraging platforms based on smartphone apps have given small corner stores in neighborhoods in cities across Latin America the means to sell and deliver daily groceries and other household staples to local residents (Escamilla et al. 2021). Other innovations, leveraging artificial intelligence, have found applications in vehicle routing tools and warehouse and fulfillment automation (such as Ocado's system (Mason 2019)), track-and-trace systems that provide real-time communications and visibility into delivery processes (such as Instacart and Uber Eats), anticipatory shipping algorithms to move inventories to specific areas ahead of realized demand (Chen and Graves 2021), and integration tools with third-party services (successfully deployed by ClickPost and ShipEngine). However, considerable challenges remain. For example, because of short time frames and high delivery volumes to many dispersed locations, LMO have little room for human error. Yet, since many firms tend to tap into low-skilled, temporary, or crowdsourced labor to provide these services, there is high variability in performance and worker availability. LMO are also expensive, due in part to rising labor costs, delivery failures, more demanding customers, and vehicle and parking restrictions. Although academic research in LMO has a long tradition in Operations Research (see e.g., Agatz et al. (2011), Otto et al. (2018), Boysen et al. (2019) and Reed et al. (2022)), LMO have barely been considered as an operations problem that requires process understanding and management within a sociotechnical system. The need for this is apparent, as increasing evidence points to managerial, economic, and sociotechnical challenges as major determinants of LMO success. Delivery workers have been noted to largely ignore the recommendations by routing algorithms in urban settings (Liu et al. (2023)); working conditions are an increasing societal and corporate concern; and customer experiences are less than satisfactory in many cases. Further, LMO are associated with negative externalities such as emissions, traffic congestion, and the abuse of public parking space. Operational costs are also very highâoften up to a point where LMO are loss-making, such as in grocery home delivery. And, while there have been extensive technological innovations, many seem to fail in scaling at large, which could potentially be due to a poor understanding of the LMO from a process perspective. We need new research to better understand these challenges, as well as to propose new operational practices and business models based on the application of recent innovations. Such research requires a broadening of the phenomenological and theoretical scope of LMO research beyond traditional work in Operations Research. Theories on innovation applied to Operations Management can offer a valuable foundation to study research questions surrounding the scalability of technologies to support new business models in the last mile (Arthur 1994). Similarly, theoretical models examining technology, productivity, and employment can provide a foundation to understand how innovations can change the nature of work in last-mile settings (Autor et al. 2003; Autor 2015). Additional opportunities also exist to use transaction and information cost theories to understand how technological innovations may change organizational boundaries and the nature of organizations in the last mile (Afuah 2003). This confluence of innovations in the field, the multidimensional phenomena that determine performance, and the perspectives from theories from the operations management field provide an opportunity to shape a research program in LMO that will benefit from the Operations Management academic community. This was one of the main goals of our call for papers for the special issue on âInnovations, Technologies, and the Economics of Last-Mile Operations.â Another objective of this special issue was to formalize a research agenda and offer future directions for research to advance our understanding of LMO. To that end, in Section 2, we delve deeper into these operations, their functionalities, distinctive features, and challenges in the context of Operations Management. Then, in Section 3, we expand on research opportunities to tackle the most pressing challenges in LMO and identify knowledge gaps in Operations Management to be addressed in this endeavor. We close in Section 4 with conclusions, recommendations, and potential initiatives to build on the momentum created so far and further advance LMO as a knowledge area within Operations Management. In doing so, we introduce the several papers in the special issue as exemplars of research that can be done in the LMO domain. LMO are made of processes triggered by an agent (e.g., consumer, user, patient, worker, organization) that enable the provision of a service to this agent at the agent's selected location and time (or time period). LMO involve interactions with the agentâwho participates in the process and co-creates valueâand, by definition, comprise different service processes (Sampson and Froehle 2006). These processes are triggered by an agent's request for service and include the preparation and movement of goods and/or tangible resources (people, equipment) required for providing the service to the agent's selected location at the agent's selected time. A key trait of LMO is the fact that agents select the location and time of the provision of the service and that the provision of the service requires at least in part co-location with the agent. We submit that LMO can be classified into two main categories that differ significantly in the nature and extent of the associated customer co-creation activities (Sampson and Froehle 2006): goods-focused and agent-focused. Goods-focused LMO entails the provision of agent access to goods at a selected location and time, involving the preparation and movement of goods (e.g., groceries, meals) and resources (e.g., delivery vans, delivery people) to that location. A typical example would be e-commerce deliveries to consumer homes. Agent inputs are limited, primarily including information about the required goods (product selection and quantities) and delivery (time and location), as well as engaging in minor interactions with the provider during goods reception. The core value added is the movement of the goods to the agent's selected location and time. Goods-focused LMO correspond to âdelivery servicesâ and have received most research attention. Agent-focused LMO entail the provision of more general services to an agent at a selected location and time, involving the preparation and movement of service provision resources (e.g., people, equipment, inventory) to that location. A typical example would be performing repairs of equipment owned by the agent at its selected location, involving the movement of technicians, tools, and inventory (spare parts) to the agent's location. Another example would be an emergency ambulance service, which involves the movement of equipment (vehicle, medical instruments), medical staff, and inventory (medical supplies) to the agent's location. Agent inputs are substantial, including information about the required service, service delivery time and location, and agent's resources, as well as engaging in relevant service co-creation activities at the agent's location. The core value added is the transformation of the agent's inputs (e.g., agent-owned equipment, the agent self). Typically, the level of customization and agent co-creation increases from goods-focused to service-focused LMOs, while the transaction volumes decrease. LMO processes are characterized by a set of distinctive features that raise unique challenges for the management of operations. Based on our conceptualization of LMO and extant literature, we summarize LMO's distinctive features and associated challenges in Table 1. The remainder of the editorial will discuss LMO against this framework and address in more detail several of the distinctive features and challenges. The distinctiveness of LMO processes, their pervasiveness and widespread economic relevance, and the managerial challenges that remain unaddressed jointly motivate the development of a specific research program for LMO within the field of Operations Management. Need to cover very diverse geographical areas, with specific challenges: Reliance on a large number of independent resources (including subcontractors, crowdsourced labor, inventory, contracted or rented equipment, third-party platforms) has the following implications: The features and challenges presented in Section 2 provide a framework for the development of new LMO research that can broaden the scope of LMO subject knowledge, as well as strengthen the theoretical foundations supporting LMO research. This framework also serves as a reference for new research to inform about new technologies and business models in LMO and their implementation and execution. The remainder of this section expands on these research directions. Research on LMO has concentrated on goods-focused LMO, in particular the delivery of goods from a transportation hub or inventory location to an end consumer. 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Abstract Blockchain technology is increasingly used to ensure the authenticity of product information in supply chains. As digital transparency becomes a key factor in modern commerce, the evaluation of blockchainâs value becomes important. In this paper, we model a supply chain with a manufacturer and an online retailer to study the role of blockchain in the marketplace and wholesale price models. Particularly, we take consumer quality preference into account and examine its impact on blockchain adoption. We discuss how blockchain ensures the authenticity of quality information shared between the manufacturer and retailer while improving consumersâ perceived product value. Our findings indicate that blockchain enhances information transparency and reduces the impact of commissions on pricing in the wholesale price model, which is beneficial to both the manufacturer and the retailer. This is significantly advantageous when quality-conscious consumers dominate the marketplace model. We recommend that the manufacturer and retailer should assess consumer preferences for product quality and carefully weigh the cost of implementing blockchain. Blockchain reduces constraints from commission-driven pricing, offering greater flexibility in business model selection. Additionally, transparency improvements are crucial when implementing blockchain in the marketplace model.
Abstract Centrally administrated systems have historically facilitated inter-organizational data exchange in supply chains (SC), relying on the message standard electronic data interchange (EDI). However, the current use of EDI fails to meet information needs, as point-to-point interfaces complicate information sharing among multiple partners and batch processing lacks real-time capabilities. This results in information asymmetries, leading to inefficiencies. Distributed ledger technology (DLT), which offers decentralized communication and data storage, presents a potential solution. In this paper, we present a systematic literature review comparing the centralized architectures utilizing EDI applications with the decentralized architecture of DLT within SCs. We identified the limitations of the current systems and assessed whether DLT offers a solution. The findings show that DLT enhances real-time data exchange, automation potential, and transparency, but also faces shortcomings. Integrating EDI with DLT offers a promising approach to leverage synergies and address the weaknesses of both technologies, e.g., lacking standards for DLT.
Purpose This paper investigates the influence of blockchain technology on trust and transparency within supply chain management. While existing research suggests blockchain has revolutionary potential, real-world evidence remains limited. This study aims to bridge this gap. Design/methodology/approach The research relies on transaction cost analysis and principal-agent theory to develop a conceptual model. The model proposes how blockchain fosters trust and transparency, ultimately leading to a market-based governance model within supply chains. Five different blockchain applications were analyzed in a multi-case study through document reviews and expert interviews to test the modelâs assumptions. Findings The studyâs findings challenge initial assumptions. The complexity of blockchain networks and a reluctance to share information among participants hinder blockchain technologyâs ability to increase trust and transparency. Consequently, the expected reduction in opportunism and uncertainties is not observed, and a market-based governance model fails to materialize. In practice, supply chain partners gravitate toward permissioned blockchains managed by established consortia. Acting as trusted third parties, these consortia assume control over network management, rendering blockchain essentially unnecessary. Originality/value This paper sheds light on the practical limitations of blockchain technology in revolutionizing supply chain management. While blockchain promises much, the findings suggest that established consortia currently play a more critical role in fostering trust and transparency within supply chains.
In recent years, blockchain technology has emerged as a pivotal tool for implementing distributed ledgers. This thesis provides an in-depth exploration of blockchains that rely on one of the most widely adopted consensus mechanisms: Proof-of-Work (PoW). In PoW-based systems, a fundamental tension exists between the operating costs borne by users - such as transaction fees - and the quality of service they receive, measured by transaction confirmation time and the likelihood of confirmation success.
Danilo Rafael de Lima Cabral, Pedro Antonino, Augusto Sampaio
The Ethereum blockchain has a \emph{gas system} that associates operations with a cost in gas units. Two central concepts of this system are the \emph{gas limit} assigned by the issuer of a transaction and the \emph{gas used} by a transaction. The former is a budget that must not be exhausted before the completion of the transaction execution; otherwise, the execution fails. Therefore, it seems rather essential to determine the \emph{minimum gas limit} that ensures the execution of a transaction will not abort due to the lack of gas. Despite its practical relevance, this concept has not been properly addressed. In the literature, gas used and minimum gas limit are conflated. This paper proposes a precise notion of minimum gas limit and how it can differ from gas used by a transaction; this is also demonstrated with a quantitative study on real transactions of the Ethereum blockchain. Another significant contribution is the proposition of a fairly precise estimator for each of the two metrics. Again, the confusion between these concepts has led to the creation of estimators only for the gas used by a transaction. We demonstrate that the minimum gas limit for the state of the Ethereum blockchain (after the block) $t$ can serve as a near-perfect estimation for the execution of the transaction at block $t + Î$, where $Î\leq 11$; the same holds for estimating gas used. These precise estimators can be very valuable in helping the users predict the gas budget of transactions and developers in optimising their smart contracts; over and underestimating gas used and minimum gas limit can lead to a number of practical issues. Overall, this paper serves as an important reference for blockchain developers and users as to how the gas system really works.
Supplier selection is a complex Multi-Criteria Decision-Making (MCDM) problem where decision-maker (DM) preferences heavily influence decision criteria and outcomes. Suitable suppliers capable of meeting performance criteria are central to successful Blockchain Technology (BT) implementation. Numerous qualitative factors influence blockchain adoption within organizations, particularly in the communication between retailers and suppliers via Blockchain, where qualitative uncertainties abound. This study aims to develop a robust system within a probabilistic and fuzzy framework to integrate DMsâ judgments amidst uncertainty effectively. Leveraging the Bayesian best-worst method (BWM), optimal weights for evaluating supplier criteria are determined. This method employs Markov-chain Monte Carlo (MCMC) to calculate the probability of preferring one criterion over another, facilitating confidence level elucidation between criterion pairs and enhancing criteria rankings. Supplier ranking is performed using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The efficacy of the proposed approach is demonstrated through a case study utilizing real data from the railway supply chain. Results indicate the modelâs effectiveness in optimizing supplier selection and enhancing supply chain performance.
Weizhong Wang, Yu Chen, Yi Wang, Muhammet Deveci · 6 authors
Abstract Many attempts have been made to identify barriers to blockchain adoption in supply chain; however, barriers to blockchain adoption in supply chain finance (SCF) are underexplored. This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant. We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants. To determine the barriers, we conducted a literature review. We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy (IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS (ranking sum) methods to determine the barrier weights. To evaluate the barrier level of each alternative participant in SCF, the integrated IVFF-RAFSI (Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval) model is presented to rank the barrier, which uses a power-weighted aggregation operator to fuse expertsâ opinions. A case study demonstrates the practicality of the integrated IVFF-RAFSI model. The results show that uncertain and competitive markets (weighted at 0.0676) are the most significant barriers. This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption. Sensitivity and comparative analyses validate the steadiness and competency of the proposed model. These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.
Customer Lifetime Value (CLV) is an important metric that measures the total value a customer will bring to a business over their lifetime. The Beta Geometric Negative Binomial Distribution (BGNBD) and Gamma Gamma Distribution are two models that can be used to calculate CLV, taking into account both the frequency and value of customer transactions. This article explains the BGNBD and Gamma Gamma Distribution models, and how they can be used to calculate CLV for NFT (Non-Fungible Token) transaction data in a blockchain setting. By estimating the parameters of these models using historical transaction data, businesses can gain insights into the lifetime value of their customers and make data-driven decisions about marketing and customer retention strategies.
To cope with the advancements in blockchain technologies, novel platforms are rapidly evolving. This creates new business and financial opportunities for supply chain networks. Despite the extensive literature on blockchain technologies, few studies have focused on selecting the most suitable platforms for supply chain networks. Furthermore, such decisions may be influenced by the occurrence of future events causing system dynamics. The literature also fails to integrate uncertainty related to such system dynamics into this decision-making process. To address this gap, this study develops a novel and hybrid decision support system using the concept of stratification and multi-preference group decision making. To analyse blockchain technology platforms for a supply chain network, further enhancements are made to the developed model by utilising the principles of complex system behaviour, target-based normalisation, Markov chains and best-worst method. This research is the first to examine how such methods can work together to integrate dynamics of a complex system into the decision-making process. Moreover, the paper analyses a supply chain network blockchain platform technology with complex systems transitions. To validate the reliability of the method, a real-world problem is addressed, which is a blockchain platform technology selection problem in one of largest multinational and professional services networks in New Zealand. The study exposes the efficiency of the proposed approach to address such complex problems.
The thriving live streaming industry is driving manufacturers to invest in green innovation to meet the rising demand for eco-friendly products and to convey their sustainability efforts effectively. While supply chain models in live streaming have been explored in the literature, only a few studies have examined how the spillover effect from live streaming channels influence the investment decisions of manufacturers and live streaming platforms (LSPs) regarding green innovation and blockchain technology. This study considers four models, namely, no investment, manufacturer-led green innovation, joint manufacturer- LSP investments, and information sharing, to investigate how spillover effect, live streamer's effort, and information asymmetry influence investment decisions. Results show that the manufacturer consistently chooses green innovation. For the LSP, blockchain investment is effective when a positive spillover effect exists and when adoption cost remains low. The LSP is willing to invest in blockchain and share information for products with a low commission to benefit the entire supply chain, but investing in blockchain without sharing information is beneficial for products with a high commission. When a negative spillover effect exists, the LSP avoids investing without sharing information. When a positive spillover effect exists, the manufacturer's investment generally yields greater profit gains for the LSP, while the LSP's investment benefits the manufacturer more. A blockchain cost-sharing contract can also achieve supply chain coordination.
The pharmaceutical supply chain is a complex network involving multiple stakeholders and processes, making it susceptible to various inefficiencies and challenges such as counterfeiting, drug expiry, and inefficient inventory management. These challenges may lead to compromised patient safety and financial losses. Blockchain technology is a promising solution to these problems. This study develops a blockchain-enabled mathematical model for pharmaceutical supply chains. A distributed ledger is used to acquire the real-time drug transaction status throughout the supply chain. The study uses real-time data gathered from the distributed ledgers across the supply chain, ensuring optimum inventory with the minimization of expired drugs and transportation costs. By leveraging the proposed model, stakeholders can eliminate counterfeiting, reduce drug expiry, and ultimately ensure the integrity and safety of pharmaceutical products throughout their lifecycle. This model also helps manufacturers in decision making for drug manufacturing based on real-time data. Novelty of the study lies in real-time tracing and managing the drugs across the supply chain.
The increasing pressure on global supply chains to reduce carbon emissions has driven the need for sustainable supply chain network design (SSCND). This paper proposes an innovative framework for SSCND that optimizes facility location and scale decisions under uncertainty using blockchain technology. By incorporating cap-and-trade regulations and carbon trading into a mixed-integer linear programming model, the study addresses both the economic and environmental objectives of supply chains. A two-stage stochastic programming approach is employed to optimize the SSCND. The first stage focuses on facility location decisions and the second stage on production adjustment, transportation, and carbon trading under demand uncertainty. The carbon trading decisions are integrated into the model by assigning a monetary value to carbon dioxide emissions and allowing for dynamic adjustments to real-time environmental impacts. A primal decomposition algorithm is introduced to address the computational challenges involved in solving the two-stage stochastic programming model. Numerical experiments based on data derived from SAIC Motor Corporation's supply chain demonstrate the effectiveness of the model and algorithm. This study provides an efficient approach for integrating environmental sustainability into supply chain management, offering valuable insights for industries aiming to achieve carbon neutrality.