Supply Chain Analytics最新文献

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.

Forecasting demand and determining safety stocks are key aspects of supply chain planning. Demand forecasting involves predicting future demand for a product or service using historical data and other external and internal drivers. Stockouts and excess production can be reduced by accurately forecasting demand. This allows companies to plan production, inventory, and logistics more effectively. Companies maintain safety stocks in their inventory to protect against unexpected changes in demand or supply. A company must find the appropriate safety stock level to meet customer demands while avoiding excess inventory and carrying costs. Forecasting demand and determining safety stocks work together to help companies reduce costs, improve customer service, and optimize inventory levels. Key Performance Indicators (KPIs) are commonly used to measure model performance. Classical forecasting models mostly concern themselves with minimizing forecast errors. However, the impact on inventory costs is not directly considered. In this paper, we introduce a Key Performance Indicator to be used in the demand forecasting process that produces more efficient results in terms of inventory costs. We also propose a novel approach to determining the best level for safety stock. This approach considers logistic network supply reliability and seasonality indices identified within historical demand patterns. We use real-life data and show that the proposed method can improve efficiency in forecasting and safety stock levels by reducing the risk of stockouts and excess inventory.

This study proposes a mathematical optimization model for a two-echelon location-routing problem in the last-mile delivery e-commerce environment. The e-commerce firm delivers each customer’s demand at home or through delivery points. Customers could be unavailable when the vehicle arrives at their homes. In this case, the vehicle must visit the allocated delivery points for the unavailable customer. There are several scenarios from all-present to all-absent customers. A mathematical model is proposed with six inequalities to reduce the model’s complexity. In addition, two scenario reduction methods are introduced to deal with the exponential growth of the number of scenarios. We generate twelve numerical instances to evaluate the performance of the model, the scenario reduction methods, and the proposed inequalities. The model produces valid solutions. Also, the scenario reduction methods are helpful for decision-makers in the e-commerce context by reducing the number of scenarios and decreasing the complexity of managing unavailable customer scenarios.

Green Supply Chain Management (GSCM) has emerged as a paramount issue in modern business organizations striving to become environmentally sustainable. Suppliers are pivotal in building a green supply chain. Green supplier selection (GSS) is a complex task involving several steps, from evaluation to final selection. This research aims to select spare parts suppliers of an automotive company based on their GSCM practices. Fourteen critical criteria are extracted from extant literature and refined through a Delphi study. The data was collected through interviews with industry experts using structured questionnaires. This study proposes integrated multi-criteria decision-making (MCDM) and multivariate analysis method with internal consistency checks. The Principal Component Analysis (PCA) is used to calculate criteria weights. A Simple Additive Weighting (SAW) method ranks the suppliers based on weighted criteria. The result shows that “collaboration with suppliers for green purchasing” is the most influential parameter for GSS. The outcome of this research may aid managers in selecting the most suitable green suppliers in the automotive industry by attaining sustainability. The proposed framework can be replicated to select suppliers in other industries.

Inventory control aims to meet customer demands at a given service level while minimizing cost. As a result of market volatility, customer demand is generally changing, and ignoring this uncertainty could lead to under or over-estimation of inventories resulting in shortages or inefficiencies. Inventory managers need batch ordering such that the ordered items arrive before the depletion of stocks due to the lead time between the ordering point and delivery. Therefore, to meet demand while optimizing the cost of the inventory system, firms must forecast future demands to address ordering uncertainties. Traditionally, it was challenging to predict such uncertainties with high accuracy. The availability of high volumes of historical data and big data analytics have made it easier to overcome such a challenge. This study aims to predict future demand in the case of an online retail industry using ensemble deep learning-based forecasting methods with a comparison of their performance. Compared to single-model learning, ensemble learning could improve the accuracy of predictions by combining the best performance of each model. Also, the advantages of deep learning and ensemble learning are combined in ensemble deep learning models, allowing the final model to be more generalizable. Finally, safety stocks are estimated using the forecasted demand distribution, optimizing the inventory system under a cycle service level objective.

This paper presents a systematic review of the literature on Supply Chain Risk (SCR) research, focusing on content-based analysis. The study comprehensively examines the general factors associated with key themes and trends in supply chain risk management, encompassing the identification and assessment of risks, risk mitigation strategies, and the influence of emerging technologies on Supply Chain Risk Management (SCRM). The review provides an overview of current and emerging topics in SCRM, while also introducing categorization frameworks to address research gaps and provide a roadmap for future studies, thereby generating valuable insights in this field. The review highlights the significance of effective SCRM in ensuring business continuity and resilience, emphasizing the need for organizations to adopt a proactive approach to risk management. The paper concludes by identifying areas for future research, including the development of novel risk management frameworks and the integration of emerging technologies into supply chain risk management practices. Additionally, a comprehensive evaluation of each classification is presented, highlighting overlooked aspects and unexplored domains, and offering recommendations for potential next steps in SCRM research.