Supply Chain Analytics最新文献

This study triangulates three interrelated and interdependent goals. First, we provide a framework to establish the supply chain (SC) theory within the linchpins of the nine waves of sustainability theory. Second, we provide a real-world example of how blockchain operates to support the premise that hacking a blockchain requires very large capacities of computing power. We also demonstrate a method of detecting the possibility of hacking individual nodes that supply information to the blockchain. Third, we utilize the blockchain and gather thousands of data points and prove statistically that the tokenization of the oil and gas industry will increase market liquidity, market volume, profitability, and sustainability while reducing transaction time. The robotic blockchain is investigated as a mechanism for improving the efficiency of natural gas SC resource use through fuzzy modular function hashing and salting algorithms and entropy density.

The increased frequency of purchases and growing distances between the final distribution points of the perishable products and consumers are contributing to multiple handling, high intermediation, and a greater concentration of outlets selling perishables goods. These in turn have increased logistics costs, and inefficiencies in the supply chain. This study presents a modelling framework for locating perishable goods order fulfilment centers (OFC) near the consumer by using population density as a proxy for demand. The centrality and Borda count measures are used to identify optimal locations in perishable goods supply chain networks. We present a case study to demonstrate the applicability and efficacy of the proposed density-based spatial methodology.

This paper aims to model and minimize transportation costs in collecting tree logs from several regions and delivering them to the nearest collection point. This paper presents agent-based modeling (ABM) that comprehensively encompasses the key elements of the pickup and delivery supply chain model and presents the units as autonomous agents communicating. The modeling combines components such as geographic information systems (GIS) routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. ABM models the entire pickup and delivery operation, and modeling outcomes are presented by time series charts such as the number of trucks in use, facilities inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations and truck numbers and determine the optimal facility location and fleet size.

This study investigates the potential of predictive analytics in improving Key Performance Indicators (KPIs) forecasting by leveraging Lean implementation data in supply chain enterprises. A novel methodology is proposed, incorporating two key enhancements: using Lean maturity assessments as a new data source and developing a hybrid forecasting model combining Logistic regression and Neural Network techniques. The proposed methodology is evaluated through a comprehensive empirical study involving 30 teams in a large supply chain company, revealing notable improvements in forecasting accuracy. Compared to a baseline scenario without process improvement data, the new methodology achieves an enhanced accuracy score by 17% and an improved F1 score by 13 %. These findings highlight the benefits of integrating Lean maturity assessments and adopting a hybrid forecasting model, contributing to the advancement of supply chain analytics. By incorporating lean maturity assessments, the forecasting process is enhanced, providing a deeper comprehension of the underlying Lean framework and the impact of its elements on supply chain performance. Additionally, adopting a hybrid model aligns with current best practices in forecasting, allowing for the utilisation of various techniques to optimise KPI prediction accuracy while leveraging their respective strengths.

Traditional food supply chains are often centralised and global in nature, entailing substantial resource consumption. However, in the face of growing demand for sustainability, this strategy faces significant challenges. Adoption of localised supply chains is deemed a more sustainable option, yet its efficacy requires verification. Supply chain analytics methodologies provide invaluable tools to guide decisions regarding inventory management, demand forecasting and distribution optimisation. These solutions not only enhance facilitate operational efficiency, but also pave the way for cost reduction, further aligning with sustainability objectives. This research introduces a novel decision-making approach anchored in mixed integer linear programming (MILP) and neighbourhood flow models defined in cellular automata to compare the environmental benefits and vulnerability to disruption of these two chain configurations. Additionally, a comprehensive cost analysis is integrated to assess the economic feasibility of incorporating layout changes that enhance supply chain sustainability. The proposed framework is applied on an ice cream supply chain across England over a one-year timeframe. The findings indicate the superiority of the localised configuration in terms of economic benefits, leading to savings exceeding £ 1 million, alongside important reductions in environmental impact. However, in terms of resilience, the traditional configuration remains superior in three out of the four examined scenarios.

This study uses a two-level programming model to present a Stackelberg game. The two-level programming problems consist of two levels of decision-making, each level having its objective function. This model’s first player (leader) includes the supplier and manufacturer, while the second player (follower) includes the distributor, customer, and revival centers. The proposed model is proposed to determine the optimal amount of products and components in each network segment, minimizing the system’s total costs and optimizing transportation in the system. This research (1) considers the environmental factors in the supply chain of wooden products, (2) uses game theory and the Stackelberg game for two players, (3) provides the competition mechanism for two players where the two players do not share their objective functions due to information security. The proposed model is compared with Genetic Algorithm (GA) and Gray Wolf Optimization (GWO) meta-heuristic algorithms. We show the calculation error of the GWO algorithm is less than that of GA. Therefore, it can better predict the behavior of the model in the long term. The results show lower production costs in case of no shortage.

We present a novel variational inequality model (VIM) to capture the complex real decision-making process in multi-tiered supply chain networks (MSCN) without strictly limiting the features of related functions. The VIM is formulated with the equilibrium conditions on links as the optimization goal and the flow conservation condition as the main constraints. We transform the VIM into a series of equivalent Non-Linear Programming Models (NLPMs) to solve. To address this challenge, we propose a novel population-based heuristic algorithm called the Multiscale Model Learning Algorithm (MMLA). The MMLA is inspired by the learning behavior of individuals in a group and can converge to an optimal equilibrium state of the MSCN. The MMLA has two key operations: zooming in on the search field and learning search in a learning stage. The excellent performers, called medalists, are imitated by other learners. With the increase in learning stages, the learning efficiency is improved, and the searching energy is concentrated in a more promising area. We employ sixteen benchmark optimization problems and two supply chain networks to demonstrate the effectiveness of the MMLA and the rationality of the equilibrium models. The results obtained by MMLA for the NLPM show that the MMLA can solve the equilibrium model effectively, and multiple optimal equilibrium states may exist for an MSCN. The flexibility of the NLPM makes it possible to consider more complicated decision-making mechanisms in the model.

Several digital technologies are available to facilitate the transition toward a circular supply chain infrastructure. Small-Medium Enterprises (SMEs) should assess their readiness and measure their performance to select the most appropriate digital technology. This study explores how well-established digital technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), Cloud Manufacturing (CM), and Big Data Analytics (BDA) impact circular supply chain infrastructure in SMEs. Questionnaires have been distributed to collect employees’ preferences concerning the circular supply chain management criteria (profit, innovation, sustainability, and optimization). The responses have been organized into three clusters using Principal Component Analysis (PCA). A fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) technique is adopted to evaluate these technologies since it constitutes a reliable managerial tool when vagueness impacts the smooth operation of the supply chain. Results indicate the ranking order of the investigated digital technologies (CPS>IoT>CM>BDA) as well as the circular benefits and the supply chain attributes imparted upon implementing these technologies. Such benefits and attributes are provided to assess the impact of these digital technologies on a circular economy. Lastly, the perspective of the selection process affected by other factors, such as the enterprise’s extroversion level and its internal structure, are discussed.

A Closed-Loop Supply Chain (CLSC) is a complex network with unique environmental features and attributes that requires specific managerial policies and strategies. Quantitative models can provide a solid basis for these policies and strategies. This study expands the work of Shoaeinaeini et al. (2021) on Green Supply Chain Management. We propose a bi-objective facility location, demand allocation, and pricing model for CLSC networks. The proposed model considers two conflicting objective functions: maximising profits and minimising emissions. We show consumer environmental awareness can predict the products’ rate of return and determine a more suitable price for new products and the acquisition price for used products. The cap-and-trade policy has been implemented at its fullest potential, allowing the trading of carbon quotas. Therefore, companies may decide to produce less to sell more quotas or vice-versa, effectively picking the most profitable option. The model is solved and tested with the commercial solver BARON. The model effectively shows the trade-off between generating profits and emission reduction. Companies are able to turn a profit while abiding by the government’s intention of reducing emissions. The comparison with a single-objective version of the model highlights that the concurrent optimisation of economic and environmental objectives yields better results. The acquisition price of used products is a value worthy of monitoring. The government should focus on policies to assist the reverse flow of used products.

Green practices are no longer a wish but a must in business. Despite that, businesses interested in implementing green supply-chain practices encounter several barriers. This study aims to analyze the barriers and solution proposals related to green supply chain practices in the manufacturing sector. Face-to-face interviews with eleven decision-makers in the manufacturing industry who know about green supply chain practices provided the data for the study. In the analysis phase, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to analyze the barriers that are effective in implementing green supply chain practices. Combining it with fuzzy logic and the Evaluation based on Distance from Average Solution (EDAS) and Complex Proportional Assessment (COPRAS) methods were used to evaluate solution proposals. The Copeland method was used to combine the results and find the final rankings. The three most important barriers are identified as the lack of technological hardware and software infrastructure, fear of failure, and non-adoption of technological improvements. The solutions that can be used in the implementation phase are training employees, collaborating with other businesses, and improving government support and incentives.