Supply Chain Scenarios Research Articles

Integration of high levels of electrolytic hydrogen production: Impact on power systems planning

The increasing interest in electrolytic hydrogen production using renewable electricity sources will require to adapt power systems to new electrical loads for hydrogen production and supply chains. Such high loads would impact the long-term planning and operation of power systems, as they need to balance the variability of renewable generation sources with electricity demand. This stresses the importance of characterizing the impact of incorporating hydrogen supply chains into power systems and planning accordingly. This study proposes a methodological framework to assess the integration and development of a national power system with electrolytic hydrogen production and supply chain. The framework is based on the well-known optimization tool for energy systems planning, Open Source Energy Modeling System (OSeMOSYS). For a detailed representation, a module was developed where demand side technologies – such as those associated to hydrogen supply chains – can provide operating reserves. As a case study, the integration of the Chilean power system with a hydrogen supply chain for exporting hydrogen between 2018 and 2050 was modeled. Among other findings, results indicate that an on-grid hydrogen supply chain would be more cost-effective than an off-grid one. However, the new electrical loads originated from such hydrogen supply chain would require additional generation capacity — primarily photovoltaic. Other generation capacities such as concentrated solar power, wind, and storage would experience significant variations in their investment requirements, compared to the off-grid hydrogen supply chain scenario. The study revealed that the coordinated operation of electrolyzers is crucial to the operational flexibility of the new electrical system.

Environment and economic analysis of reverse supply chain scenarios for remanufacturing using discrete-event simulation approach.

The study covers the concepts involved in reverse supply chain modeling using the case of a manufacturing company. The purpose of this study is to build a sustainable reverse supply chain model for resource conservation through remanufacturing of stator shafts by using a discrete-event simulation approach. The simulation studies in the reverse supply chain have taken up cases of either plastic or electronic waste remanufacturing, while very limited studies deal with simulation of sustainable reverse supply chains using a manufacturing industry case study from international customers. In this study, reverse supply chain using simulation study in manufacturing sector is carried out using Arena Rockwell simulation software. The simulation model is built using discrete-event simulation for returns from customers of two developed countries, i.e., Germany and the USA to Chennai, India. The study emphasizes full container load and less than container load modes of shipment scenarios and multiple return cases. The comparative analysis suggests that the value-added and non-value-added time of the reverse supply chain is slightly greater in the less container load scenario. The wait time per entity in remanufacturing processes similar for both shipment scenarios varies significantly based on return cases. The cost and carbon emission associated with transportation, in the reverse supply chain inclusive of social carbon cost, have also been estimated. Therefore, the study proposes a possible sustainable reverse supply chain framework that could be adopted by different manufacturing industries and yield opportunities for performance improvement.

Blockchain-enabled supply chain operations and financing: the perspective of expectancy theory

PurposeThis study examines the cognitive factors of adopting blockchain technology in various supply chain scenarios and its role in reframing the distinctive values of supply chain financing. Based on expectancy theory, this study explores the different profiles underlying the components of expectancy, valence and instrumentality.Design/methodology/approachThis is a multiple-case study of four Fintech companies using blockchain technology to promote the performance of supply chain operations and financing.FindingsThe results show that blockchain-enabled supply chain finance (BSCF) can be classified into four scenarios based on the scope and purpose of blockchain technology applications. The success of BSCF depends on the profiles of BSCF expectancy (the recognized purpose and scope of BSCF), instrumentality (identified blockchain attributes and other technology combinations) and valence (the perceived distinctive value of BSCF). Blockchain attributes help solve information asymmetry problems and enhance financing performance in two ways: one is supporting transparency, traceability and verification of transmissions and the other entails facilitating a transformation to new business models.Originality/valueThis research applies a new perspective based on expectancy theory to study how cognitive factors affect Fintech companies' blockchain solutions under a given supply chain operation or financing activity. It explains the behavioral antecedents for applying blockchain technology, the situations appropriate for the different roles of blockchain technology and the profiles for realizing the value of blockchain technology.

Digital twins for selecting the optimal ventilated strawberry packaging based on the unique hygrothermal conditions of a shipment from farm to retailer

Berries are one of the most challenging products to preserve after harvest due to their high perishability and short shelf life. Ventilated packaging plays a key role in maintaining fruit quality along the supply chain. However, every supply chain is composed of different unit operations, and every shipment encounters unique hygrothermal conditions such as air temperature fluctuations over time, sub-optimal humidity conditions, and the risk of condensation. Therefore, every supply chain has an optimal packaging that provides the best hygrothermal climate and ventilation to the fruit. Given the vast space of potential supply chain scenarios and packaging configurations, in-silico studies are an attractive alternative for selecting this optimal packaging. In this study, we developed physics-based digital twins for ventilated packaging of strawberries. We utilized measured air temperature and humidity data from an actual supply chain from the farm to the retail store. With these digital twins, we mimicked in-silico how the strawberries evolve hygrothermally, physiologically, and microbiologically along the supply chain inside 21 different types of ventilated packaging. We predicted actionable metrics of fruit quality and shelf life for these 21 packages. These metrics include total mass loss, risk of putative mold infection due to Botrytis cinerea, retention time of condensate, and remaining shelf life based on respiration, transpiration, and mold growth. In addition, we analyzed the impact of package-related metrics, such as total vent area, degree of filling, pressure drop across the package, and seven-eighths cooling time, on fruit quality metrics. With this approach, we pinpointed the critical quality loss points in the supply chain for every package. We identified the package that performs best in balancing the three-way trade-off between the respiration-driven biochemical shelf life, transpiration-driven physical shelf life, and mold growth-driven microbial shelf life of fruit. Our findings showed that the performance of open trays is comparable to ventilated clamshells, as long as a high humidity is maintained along the supply chain. Flow-wrapped packages presented the highest risk of condensation and microbial growth. We also quantified the spatial heterogeneity in fruit quality within the packages and highlighted the most vulnerable locations for quality loss inside each packaging type. Our study presents a novel, holistic approach to select the optimal ventilated packaging of strawberries from farm to retailer based on its measured hygrothermal fingerprint. This approach can help reduce food loss and contribute towards making supply chains smart and efficient.

SUPPLY CHAINS FOR VALORIZATION OF OLIVE GROVE RESIDUES AND OLIVE OIL INDUSTRY BY-PRODUCTS: A REVIEW ON RESEARCH AND LITERATURE ON OLIVE BASED BIOECONOMY

The residual biomass produced in the olive sector is the result of the large quantity of olive groves and olive oil manufacturers that generate byproducts with a potentially high energy content. Until now, the disposal and management of pruning residues and olive oil by-products has generally represented disposal problems which lead to environmental problems rather than opportunities for additional revenue. In this context, this study aimed to define a roadmap for studies in Turkey to untap the potential of olive grove residues and olive oil industry by-products and residues, (i.e., olive tree prunings, olive leaves, olive pomace) that could be used to produce high-added value products in an integrated biorefinery or in a bioenergy power plant. This paper presents a review on recent research on the valorization of olive by-products in line with the circular economy principles and the bioeconomy strategy and presents the literature which applies to design and management of olive biomass supply chains. Main objective is to provide a conceptual framework for developing and deploying an effective bioeconomy utilizing olive prunings and olive oil industry byproducts conducting a preliminary review of the olive oil chain residues in Europe and Turkey comparatively, assessment of technical potentials, biomass supply costs and allocation of biomass collection sites and energy production facilities with different supply chain scenarios. Biomass resource assessment, biomass feedstock supply chain design and management, and logistics optimization require multidisciplinary and interdisciplinary approaches. They need to be carried out properly in order to execute efficient projects in energy generation from biomass and/or extracting bio-commodities in biorefineries. Turkish researchers have adequate research on valorization methods for olive based biomass resources, but their studies are limited on general biomass supply chain design and particularly no research has been conducted for olive biomass resources. Taking all the aforementioned into account, the author proposes to conduct further studies on a biomass energy project including design of a supply chain network for energy valorization of olive grove residues and olive oil industry by-products in line with the common practice and being inspired by the leading countries like Spain and Italy, which will both contribute to circular business concept development and renewable energy capacity increase.

Fast Counterfeit RFID Tags Detection With Slot Constraints

RFID tags are prone to counterfeit attacks in supply chain scenarios. The attacker often uses counterfeit tags to replace stolen tags to avoid being detected by the inventory process. To defend against such attacks, existing hash-based schemes have to know the hash functions embedded in tags, which are usually unavailable in large scale systems. This letter proposes a novel group-based slot constraint (GSC) scheme for lightweight counterfeit tag detection in RFID-enabled supply-chain systems. GSC can be integrated into the identification process by leveraging elaborately designed group hash functions to authenticate tags based on their slot correlation. That is, the tags in the same group can always map to slots with a fixed offset, which offers evidence for identifying counterfeit tags with an abnormal slot index. We also provide a theoretical analysis of the time slots required to achieve the desired accuracy. The simulation results show that GSC provides reliable accuracy without knowing exact hash functions.

Towards the prediction of carbon consequences in early design decisions

Manufacturing processes and associated supply chains are a recognised source of carbon emissions. In part these emissions are related to the design of the product itself. For example, decisions surrounding the selection of materials have a high impact on emissions and hence the embedded carbon in manufactured products. While there are tools that support the analysis of designs from environmental perspectives, they tend to require near complete design descriptions that are only available later in the design and development process when time and scope for change is limited, e.g., by scheduling and cost constraints. Further, there are often trade-offs to be made. For example, decisions related to the selection of materials impacts the embedded carbon of resources needed to manufacture a product but may also have a knock-on effect on transport and associated carbon emissions.This paper reports early work exploring the feasibility of establishing engineering design tools that are suitable for use early in the design process when the design definition is incomplete and there is scope for exploring a wider range of design options. An illustrative case study was used to consider the quantification of carbon costs associated with material selection and associated transportation. Discrete event simulation models were derived from early design descriptions coupled with a range of alternative material and supply chain scenarios. Early results are promising in that they can be used to compare carbon consequences of alternative design directions.

Assessing the Feasibility of Meeting Tolerance Limits for GM Adventitious Presence in Corn Supply Chain Using Probabilistic Modeling

Highlights Most simulated scenarios showed low probabilities of meeting with 0.9% and 1.5% tolerance limits. 3.0% and 5.0% tolerance limits were achievable at some supply chain stages under specific conditions. Feasible tolerance limits at individual supply chain stages ranged from 2.25% to 6.25%. Seed purity and cross-pollination were key factors affecting the probability of meeting AP tolerance limits. Abstract. Tolerance limits for the adventitious presence (AP) of GM material in non-GM grain, food, and feed vary worldwide from 0.9% to 5.0%. This research analyzed the likelihood of meeting four common trade tolerance limits for AP (0.9%, 1.5%, 3.0%, and 5.0%) in the U.S. commodity corn supply chain. A model was developed to evaluate existing practices and patterns for bulk corn production, handling, and processing in an open-market supply chain that concurrently handles GM and non-GM products. Monte Carlo simulation was used to test 50,000 iterations of supply chain scenarios to determine the likelihood of successfully meeting specified tolerance limits. The model revealed that the supply chain, as it exists today, does not effectively facilitate the concurrent handling of GM and non-GM streams at 0.9% and 1.5% tolerance limits in most cases. At individual supply chain stages, some tolerance limits were reasonably achievable, such as 3.0% and 5.0% at the farm stage. The probabilities of complying with 0.9% and 1.5% tolerance limits at the farm stage were just over 10% and 67%, respectively, while the probabilities of complying with 3.0% and 5.0% tolerance limits were more than 90%. The grain elevator and grain processor could achieve 3.0% and 5.0% tolerance limits with reasonable likelihood. At the feed mill, a 5.0% tolerance limit was achievable but only when bypassing some supply chain stages. The 99% feasible tolerance limits at individual supply chain stages ranged from 2.25% to 6.25%. Significant factors influencing the ability to meet AP tolerances were identified using sensitivity analysis. These factors included seed impurity, cross-pollination, and transportation vehicles. Keywords: Adventitious presence, Corn, Feed, Genetically modified grain, Monte Carlo simulation, Segregation, Supply chain.

Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approach

Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, there is only sparse work to facilitate generalisable technical implementation. We address this gap by investigating multi-agent system approaches for implementing autonomous supply chains, presenting an autonomous economic agent-based technical framework. We illustrate this framework with a prototype, studied in a perishable food supply chain scenario, and discuss possible extensions.

Evaluating supply chain dynamics in the presence of product deletion

Manufacturers and retailers seek product portfolio expansion for strategic reasons. Diverse product portfolio breadth and depth can increase sales and build competitiveness; alternatively, extensive product portfolios may increase resource consumption, operational complexity, and reduce supply chain efficiency and agility. Products may be deleted for many organizational reasons. Few studies have evaluated how product deletion decisions influence supply chain activities and performance. Supply chain flow and supply chains are designed and managed around products and services. Product deletion decisions influence supply chain performance it is a systemic relationship that can change over time. In this study system dynamics is used to evaluate product deletion impact on four core supply chain inter-related systems. A conceptual framework is built upon causal loop and stock-flow diagrams. The framework is tested using simulations indicating how performance develops amongst supply chain systems through product deletion processes. Simulation experiments indicate that sound product deletion will improve market share, cost efficiency, product revenue and profitability. Being the first study that integrates systemic product deletion to the supply chain management domain, the proposed framework and simulation model of this study enable managers to evaluate product deletion decisions in different supply chain scenarios; and hence, rationalize product portfolio variety and manage organizational competitiveness.

Evaluating the Performance of a Safe Insulin Supply Chain Using the AHP-TOPSIS Approach

People with type 1 diabetes require insulin, a lifesaving and essential medication, to maintain their blood sugar levels below dangerous levels. Unfortunately, the insulin industry faces supply and affordability issues, and patients and their families face an enormous burden. As a result of high prices and lack of availability, individuals are turning to other options for purchasing insulin, such as online pharmacies, which may or may not be legitimate. Despite the necessity of safe insulin for diabetics in the legitimate Pharmaceutical Supply Chain (PSC), few researchers have considered implementing strategies to maximize patient safety for purchasing insulin. Therefore, the current research seeks to bridge this gap and provide cohesive information on overcoming this challenge and maximizing insulin safety. This study employs a Multi-Criteria Decision-Making (MCDM) model that combines Supply Chain Operations Reference (SCOR) metrics, Analytic Hierarchy Process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to develop a model that can prioritize and select the best criteria for maximizing insulin safety and achieving the study objective. A comparison of two insulin supply chain scenarios was performed. As a result of this research, adding a traceability technology to the insulin supply chain, specifically blockchain (T42) in scenario 2 provides the best results to the supply chain for maximizing and ensuring the safety of insulin, as compared to scenario 1, where the final score achieved almost 71%. This research provides a useful tool for assessing the safety of other critical goods that customers value in strategic and complex decision-making. Academics, professionals, and decision-makers can benefit from this research using a rigorous scientific decision-support system.

Portable food diagnostic devices and methods: A review

AbstractThe instantaneous detection of food quality is of paramount importance as it can help in taking different decisions in real time. These include the decision on consumption, inventory management, maintaining the storage conditions and even to repurpose the food if it cannot be used for the desired application. In this review, different portable food diagnostic devices and methods are studied and compared as consumers are interested in knowing the food's quality before consumption. The quality of food in real‐time will help in avoiding many problems such as the consumption of unsafe food and taking prompt decisions to avoid wastage in the food supply chain. Portable food diagnostic devices provide advantages such as easy to use, compact, handheld, connectivity with smartphone for rapid diagnosis, as well as these devices have the potential to eliminate traditional method problems such as the requirement of a trained user, bulky equipment, and lengthy diagnosis procedure. In this study, different types of portable food diagnostic devices and methods are compared in terms of their working principle, features, applications, and limitations. These devices are classified in three main categories, as composition analysis, safety, and security based on application. Further these devices are subclassified in single/multiple composition analysis devices, toxic, allergen detecting devices, and methods. Portable food diagnostic devices and methods provide a rapid and on‐site diagnosis. However, the success of these devices depends upon their accuracy, simplicity, and cost‐effectiveness.Practical ApplicationsThe study provides comparative analysis of different portable food diagnostic devices, which help in selecting an appropriate device in terms of its accuracy, size and cost suitable under different supply chain scenarios. This will ultimately help in decision making required for minimizing the food losses. These devices will benefit farmers, suppliers, retailers, and operators in food processing industries. The individual users can also use these devices to manage the food quality. The study also provides the limitations of exiting devices and the need of new research for developing more compact devices measuring multiple constituents in the food in a precise and cost‐effective manner.

Hydrogen fuel supply chains for vehicular emissions mitigation: A feasibility assessment for North American freight transport sector

Freight trucks powered by alternative low-emissions fuel sources such as electricity or hydrogen have gained significant attention as replacements for their fossil fuel-operated counterparts. Even though this shift to alternative fuels can reduce the operational emissions at the vehicle tailpipe, there can be significant emissions throughout their supply chains. A thorough review of the published literature highlights that a comprehensive investigation is lacking on the well-to-wheel life cycle impacts of hydrogen fuel used for freight transportation. Furthermore, a comparison between hydrogen-fueled and electric freight transport is unavailable in the published literature. This study comprehensively investigated the life cycle environmental and economic impacts of alternative fueled freight transport based on vehicle types and relevant supply chains. A life cycle impact database was developed for key electric and hydrogen fuel supply chain scenarios and was ranked based on the economic and environmental performance under different decision-maker perspectives. The life cycle emissions of delivering hydrogen fuel range from 2.2 to 12.6 kgCO2eq./kg of hydrogen whereas electric grid emissions range from 0.005 to 0.315 kgCO2eq./MJ. However, hydrogen systems that use electrolysis to produce hydrogen show better environmental performance over the complete vehicle life cycle compared to electric systems that are highly dependent on nonrenewable energy sources. The cost of ownership of a hydrogen and electric freight truck is approximately 2.15 and 0.54 CAD/km respectively. However, with investments from governments and private institutions to develop hydrogen refueling infrastructure, hydrogen-fueled freight vehicles have the potential for reducing GHG emissions in the road transportation sector.

INTERNET OF THINGS-ENABLED TOURISM ECONOMIC DATA ANALYSIS AND SUPPLY CHAIN MODELING

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenariobased ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM.

Analysis of effects of the hydrogen supply chain on the Korean energy system

The scope of hydrogen energy is being extended in the Republic of Korea as a national innovative growth engine to overcome environmental problems, particularly climate change. The effects of this expansion on the energy system and national greenhouse gas (GHG) emissions are expected to vary greatly depending on the hydrogen energy supply chain scenario. Accordingly, in this study, the energy and environmental effects of hydrogen energy supply chain scenarios on the national energy system were analyzed quantitatively using the TIMES model, a representative bottom-up energy system analysis model. The scenarios were defined in terms of three perspectives: the development level of key technologies, contribution of future renewable energy to the power generation sector, and relative importance of each hydrogen production method portfolio. All scenarios were based on the policies being considered by the Korean government. The results of the scenario analyses show, among others, that green hydrogen, i.e., water electrolysis-oriented hydrogen production, consumes a fairly large amount of electricity. Therefore, from the perspective of the entire national energy system, the transition of the power sector to renewable energy, mainly solar and wind energies, and the advancement of water electrolysis are required to reduce the national GHG emissions.

Cross-Border Supply Chain System Constructed by Complex Computer Blockchain for International Cooperation.

The rapid development of China's local e-commerce and the continuous improvement of its business model have not only pushed the country to the forefront of the globe but also opened up the unprecedented potential for China's cross-border e-commerce. Therefore, it is imperative to build a balanced and sustainable cross-border e-commerce system, and cross-border e-commerce on the Silk Road has become a new highlight of China's e-commerce development. This study proposes a cross-border supply chain model based on the complex computer blockchain for international cooperation scenarios, contrasts and analyzes decision making in two cross-border supply chain scenarios with and without blockchain implementation, and investigates the sufficient conditions for blockchain implementation in the cross-border supply chain from the perspective of various value objectives. The analysis reveals that the cross-border supply chain has sufficient incentive to implement blockchain when the value gain generated by implementing blockchain is high or the value gain is low but the potential market size weakening factor of cross-border products is greater than a certain degree. It demonstrates that the link between cross-border product price elasticity, manufacturer cross-border effort cost, and customer cross-border preference degree would impact the circumstances for adopting blockchain in cross-border supply chains. The model also lays out a plan for the government to improve cross-border e-commerce logistics, strengthen oversight, and create a regional financial service network system to reduce credit risk.4

Pricing Strategy for Reproduction of Worn-out Ball and Gate Valves in Oil and Gas Industry: Using Game Theory on Four Closed-loop Supply Chain Scenarios

Collecting and remanufacturing worn-out products provide significant financial advantages. In this research, we examine how remanufacturing worn-out ball and gate valves, which are important pieces of equipment in the oil and gas industry, could improve the profitability of the closed-loop supply chain. In this regard, four different scenarios for collecting and remanufacturing processes are considered: (1) the manufacturer collects the worn-out product from the consumers and remanufactures them, (2) the retailer collects the worn-out products and both manufacturer and retailer remanufacture them, (3) the third party collects the worn-out products and both manufacturer and third party remanufacture them, and (4) the manufacturer collects the worn-out products without remanufacturing them. To formulate the interactions of the CLSC members under the four different scenarios, we use the Nash and Manufacturer-Stackelberg games. Accordingly, the optimal decision variables i.e., the acquisition price, wholesale price, and retail price are calculated under four scenarios. Then, the optimal solutions are compared in four scenarios. In addition, the optimal profit of the closed-loop supply chain and members are obtained under the different scenarios. The results show that in the Nash game, Scenario 2 is the best scenario. However, in the Stackelberg game, Scenario 3 is the best scenario.

Industrial hemp (Cannabis sativa L.) for phytoremediation: Energy and environmental life cycle assessment of using contaminated biomass as an energy resource

The objectives of this study were to assess the environmental impacts of growing industrial hemp (Cannabis sativa L.) on the phytoremediation of heavy metal-polluted soil and using contaminated biomass as an energy resource. A life cycle assessment approach was adopted to evaluate the cumulative energy demand and climate change impact of using contaminated biomass for energy generation under 4 different scenarios. The industrial hemp supply chain scenarios include analyses of different subsystems (industrial hemp cultivation, seed processing, anaerobic digestion and biomass incineration for electricity generation) and different contaminated products harvested (seeds and straw, dried whole industrial hemp and fresh biomass for silage). As confirmed by a Monte Carlo simulation, the two scenarios that included anaerobic digestion and/or incineration of the contaminated biomass were the most energy-efficient and environmentally sustainable systems and obtained primary energy savings of 25.3 and 36.8 GJ ha−1, respectively, while avoiding emissions of 579 and 641 kg CO2e per reclaimed hectare, respectively. These results demonstrate the great potential of using contaminated areas for bioenergy (electricity) production while decreasing the exploitation of natural resources and pollutant emissions into the environment. Moreover, phytoremediation technologies combined with the energy valorization of biomass are recommended to change unproductive contaminated soils into exploitable and productive areas.

An Effective Credit Evaluation Mechanism with Softmax Regression and Blockchain in Power IoT

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications.

Selection criteria for planning cold food chain traceability technology enabling industry 4.0

Failure to deliver safe and high-quality food not only causes costly food crises and foodborne illnesses, but also impairs consumer confidence and the performance of cold food chains. Industry 4.0 transformation in food traceability systems i.e., the transition from manual paper-based record keeping to automatic identification and sensor technologies, holds the potential to enable improved data transmission and self-monitoring to reduce food quality issues. Selection of these technologies to meet a specific need requires a measurement on how they perform across a range of criteria. An overview of traceability technology performance rating for criteria enabling industry 4.0 in cold food chain is absent in the literature. This article presents a summary of the traceability technology criteria relevant to cold food chain industry 4.0 transformation through iterative literature review and expert validation. The selection criteria are then combined into a quantitative technology performance matrix that can help users to determine which technology is the most beneficial for a given supply chain scenario.