Supply Chain Analytics最新文献

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.

Cross-docking is a logistics methodology employed in warehouses to gain a competitive advantage by consolidating and transferring freights directly from an inbound supplier to an outbound client with no or restricted storage. Real-time data processing is required for fast synchronisation of inflows and outflows. This study develops a real-time multi-agent truck scheduling model for single inbound-single outbound cross-docking for fast synchronisation of inflows and outflows. The proposed model exploits the autonomous, reactive, and distributed responsibility characteristics of the multi-agent systems to realise shared computation and respond flexible responses to dynamic events. This type of model is novel in the cross-docking literature for scheduling of both inbound and outbound trucks. The responsiveness of the proposed model is evaluated by employing a combination of different traffic levels based on truck arrival times. Furthermore, various truck-to-door assignment strategies are implemented to achieve the best performance based on key performance indicators such as the average stock level, the number of late pallets, the pallet delay and the outbound truck fill rate. To validate the experimental results, ANOVA (analysis of variance) is performed. The analysis demonstrates that the stock policy (SP) outperforms all the others by sustaining low stock levels and high on-time deliveries and truck fill rates across all traffic levels, while the time-related strategies are adequate for cases where outbound traffic is more elevated than inbound traffic.

Investing in green technologies to increase sustainability in supply chains has become a common practice for two reasons: the first is directly related to the defense of the environment and people’s health to smooth the emissions of pollutants; the second is the increasing consumer awareness of green products. Despite the higher costs of producing with green technologies and processes, there is also a higher markup on the price of products which rewards the former costs. This study proposes a mathematical model for scheduling green investments over time in a two-stage supply chain to minimize the impact of production on the environment and the economic costs deriving from the investment. The resulting bi-objective model has nonlinear constraints and is solved using a commercial solver. Given its complexity, we propose an upper-bound heuristic and a lower-bound model to reduce the optimality gap attained at a given time limit. Tests on synthetic instances have been conducted, and an example demonstrates the applicability and efficacy of the proposed model.

Global warming and climate change are the main hurdles facing humanity. They are caused by various market practices, such as mining natural resources, fossil fuels used for power generation, dumping hazardous waste, massive electricity generation, and much more. Different ideas, such as the use of hybrid cars, solar power, biogas, etc., have arisen to do business without negatively impacting the environment and Green Supply Chain Management (GSCM) is one of them. This paper focuses on the impact of GSCM practices on manufacturing companies’ operational efficiency and overall organizational performance. The study is explanatory and is based on the qualitative model. To analyze the factors influencing organizational performance concerning GSCM, we created questionnaires and utilized structural equation modeling (SEM) for inferential analysis. Our study revealed a mediating role between business performance and GSCM practices. Manufacturing companies need to consider the interaction between internal and external aspects of GSCM to integrate their operations effectively. Additionally, we suggest that examining the relationship between GSCM practices and organizational performance is crucial. This examination addresses the major obstacles in utilizing GSCM practices effectively and proposes a management approach for implementing GSCM in organizations.

This study develops an inventory management system for non-instantaneous deteriorating items in a supplier-retailer-customer supply chain. The proposed model considers carbon emissions during production and applies a carbon tax to regulate the emission. Promotional prices are considered to boost demand. The supplier offers a credit period to the retailer and the retailer to the customers. Imperfect products in the proposed model are separated from the lot using an inspection process performed by the retailer. Finally, a learning process is proposed to spot misclassified products and avoid using misclassification errors. Two models with and without shortages are further developed in this study. The proposed model considers imperfect quality, non-instantaneous deteriorating items based on learning effects, multi-variate demands, and multi-credit periods with the carbon tax. Models with and without shortages are also developed. Numerical examples and sensitivity analysis are provided to verify the applicability and demonstrate the efficacy of the model proposed in this study.

Hierarchical time series demands are often associated with products, time frames, or geographic aggregations. Traditionally, these hierarchies have been forecasted using “top-down,” “bottom-up,” or “middle-out” approaches. This study advocates using child-level forecasts in a hierarchical supply chain to improve parent-level forecasts. Improved forecasts can considerably reduce logistics costs, especially in e-commerce. We propose a novel multi-phase hierarchical approach for independently forecasting each series in a hierarchy using machine learning. We then combine all forecasts to allow a second-phase model estimation at the parent level. Sales data from a logistics solutions provider is used to compare our approach to “bottom-up” and “top-down” methods. Our results demonstrate an 82–90% improvement in forecast accuracy. Using the proposed method, supply chain planners can derive more accurate forecasting results by exploiting the benefit of multivariate data.

Supply chain networks worldwide were disrupted substantially during covid-19 pandemic. More specifically, the supply chain networks for Small and Medium Enterprises (SMEs) were exposed to various risks and disrupted more significantly than large organisations during and after the covid-19 era due to these disruptions and limited resources. This study uses the fuzzy set theory to present a conceptual framework for a comprehensive supply chain risk assessment in SMEs during uncertain times. A case study illustrates the efficacy of the proposed conceptual framework for post-covid-19 risk assessment in SMEs in a developing country. The proposed framework evaluates the overall risk index in SMEs based on seven Supply Chain Risk (SCR) factors and 42 associated attributes. In addition, twenty SCR attributes are identified as the main SCR obstacles according to their fuzzy supply chain risk index.

Mass vaccination programs should employ effective strategies to design a resilient vaccine supply chain for immunizing populations quickly and efficiently. The need for more flexible and responsive vaccine supply chain design is highlighted during the pandemic, where authorities are required to effectively execute vaccine distribution. Our study proposes a scientifically driven approach to identify suitable supply chain strategies for vaccine distribution, enhancing the effectiveness of mass vaccination. We propose a two-stage approach for identifying the best supply chain strategy that supports faster vaccine rollouts, reducing infections and deaths during the pandemic. We optimize the vaccine distribution network under both supply chain strategies using Mixed Integer Programming (MIP) for four disruption scenarios in the first stage. Second, we have used systems dynamics simulation and the Susceptible-Exposed-Infectious-Recovered (SEIR) model for pandemics to identify the impact of vaccination. In all disruption scenarios, vaccine distribution using the Lean strategy is less costly, and the Agile strategy reduces lead time and supports faster vaccine rollout. We show achieving a cost-saving or lead-time saving using either supply chain strategy becomes increasingly difficult when the severity of disruptions at storage increases. Our study suggests a novel methodology that determines the most suitable strategy for vaccine distribution which minimizes infections and deaths under several disruption scenarios. The decision-makers can identify appropriate supply chain strategies for vaccine delivery to densely populated developing regions, using the proposed framework which compares supply chain strategies’ impact on vaccine distribution network design.

With a globalized economy, traditional boundaries are becoming both unclear and uncertain, and it is necessary to analytically measure business globalization to estimate the results of the production activity of resident producer units. The value chains that have bound the world economy are now under new strain. This study presents an analysis of data relating to the activities carried out by a company in multinational territories. We study the distribution of the added value of companies and the relationship with their non-domestic activities for statistical purposes; the type of foreign affiliate known as a branch is considered a quasi-enterprise (Eurostat − Manual on Business Demography Statistics, 2007), resident in one country and controlled by a unit resident in another nation. We use two separate sources of sectoral information for a specific year (2019): Foreign Affiliates Statistics (FATS), covering activities of permanent establishments operating among Italian borders under foreign control, and outward FATS covering the activities of Italian branches abroad. Hence it can be difficult to untangle these complex chains of control; as we detail in this work, the integrated use of archives, statistical, administrative, and tax sources, as well as other information (company sites, profiling of the main multinational groups) allows to select the subset of companies potentially interested in the reality of foreign production a priori, to identify affiliates that are not constituting separate legal entities. This study can be used by public decision maker to highlight fiscal elusive strategies and estimate the real share of domestic and foreign (through stable organizations) production.