Supply Chain Analytics最新文献_第2页

A comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains 霍尔特冬季指数平滑法与自回归综合移动平均法在供应链库存优化中的比较评估

Supply Chain Analytics Pub Date : 2024-09-05 DOI: 10.1016/j.sca.2024.100084

Lalji Kumar , Sudhakar Khedlekar , U.K. Khedlekar

{"title":"A comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains","authors":"Lalji Kumar , Sudhakar Khedlekar , U.K. Khedlekar","doi":"10.1016/j.sca.2024.100084","DOIUrl":"10.1016/j.sca.2024.100084","url":null,"abstract":"<div><p>Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their performance in predicting demand amid unpredictable factors and develops robust forecasting algorithms using real-world data. It evaluates HWES and ARIMA in capturing demand fluctuations, considering seasonality, market trends, and cyclic patterns. A comprehensive comparative analysis is conducted under stable and unstable economic conditions. The study also focuses on a dynamic pricing model for limited sale seasons, examining lost sales patterns over time. In the context of supply chain and inventory management, efficient demand forecasting and dynamic pricing are essential for optimizing inventory levels and minimizing costs. Supply chains must adapt quickly to demand fluctuations to avoid overstocking or stockouts, which lead to revenue losses and inefficiencies. The findings reveal that ARIMA consistently outperforms HWES in minimizing lost sales, demonstrating its efficacy in demand forecasting, mitigating stockouts, and reducing revenue losses, particularly in varying economic conditions. This research significantly contributes to current knowledge by developing tailored forecasting algorithms and a dynamic pricing model, enhancing supply chain resilience and performance in uncertain business environments.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294986352400027X/pdfft?md5=98f10ccd1d31fdd03db055c77fb3faa2&pid=1-s2.0-S294986352400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable artificial intelligence model for predictive maintenance and spare parts optimization 用于预测性维护和备件优化的可解释人工智能模型

Supply Chain Analytics Pub Date : 2024-08-26 DOI: 10.1016/j.sca.2024.100078

Ufuk Dereci , Gülfem Tuzkaya

{"title":"An explainable artificial intelligence model for predictive maintenance and spare parts optimization","authors":"Ufuk Dereci , Gülfem Tuzkaya","doi":"10.1016/j.sca.2024.100078","DOIUrl":"10.1016/j.sca.2024.100078","url":null,"abstract":"<div><p>Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000219/pdfft?md5=de370f4dd5787db3d883f746b49da463&pid=1-s2.0-S2949863524000219-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty 双重不确定性下向灾难幸存者发放救济的两阶段优化模型

Supply Chain Analytics Pub Date : 2024-08-23 DOI: 10.1016/j.sca.2024.100079

Palash Sahoo

{"title":"A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty","authors":"Palash Sahoo","doi":"10.1016/j.sca.2024.100079","DOIUrl":"10.1016/j.sca.2024.100079","url":null,"abstract":"<div><p>Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply points, what is needed at destinations, how much transportation capacity there is, and what the routes are like. In this study, we investigate a two-stage multi-item fixed charge four-dimensional transportation problem using the concept of big data theory under the two-fold uncertainties. Here, the model’s parameters such as unit transportation costs, availabilities of items at the suppliers, fixed charges, capacities of conveyances, and demands of the items at the retailers are considered type-2 zigzag uncertain variables. Using big data theory and based on uncertain programming theory, two novel uncertain models are developed such as chance-constrained programming and expected value programming model. These two uncertain models transformed into the deterministic form via uncertainty inverse distribution theory. A critical value based reduction method with three categories (i.e., expected value, pessimistic value, and optimistic value) is applied to reduce the type-2 zigzag uncertain variable to the type-1 zigzag uncertain variable. The genetic algorithm and particle swarm optimization techniques have been proposed to find the optimal solution for the two deterministic models. The efficiency of our proposed approach is demonstrated with a real-life numerical example.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000220/pdfft?md5=1fce1edb90ad4c3e82f399b2092acdb8&pid=1-s2.0-S2949863524000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A home healthcare routing-scheduling optimization model considering time-balancing and outsourcing 考虑时间平衡和外包的家庭医疗路由-调度优化模型

Supply Chain Analytics Pub Date : 2024-08-13 DOI: 10.1016/j.sca.2024.100077

Shabnam Rekabi , Babak Moradi , Farima Salamian , Niloofar Fadavi , Mahsa Zokaee , Amir Aghsami

{"title":"A home healthcare routing-scheduling optimization model considering time-balancing and outsourcing","authors":"Shabnam Rekabi , Babak Moradi , Farima Salamian , Niloofar Fadavi , Mahsa Zokaee , Amir Aghsami","doi":"10.1016/j.sca.2024.100077","DOIUrl":"10.1016/j.sca.2024.100077","url":null,"abstract":"<div><p>Home care services have a significant role in lowering healthcare expenditure. Supply chain management in home healthcare (HHC) ensures efficient delivery of medical supplies and equipment to patients' homes, improving overall quality of care and patient outcomes. This study proposes a routing and scheduling optimization model for HHC by prioritizing patients, developing an effective delivery strategy, and considering home care logistics and services. The model primarily concerns reducing logistics activities’ overall expenses while considering patients’ priorities. A bi-objective optimization model for a multi-period HHC problem is developed by prioritizing patients with urgent critical needs. The best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are used to prioritize patients using a linear programming metric (Lp-metric). The BWM and TOPSIS have been uniquely used in this study for routing and scheduling in HHC. Eventually, the applicability of the proposed method is demonstrated through a real-life case study with a series of numerical examples and sensitivity analysis. For instance, by analyzing privilege, we see patients are carefully matched with caregivers possessing advanced skills, leading to increased patient satisfaction. Based on assigned routes, caregivers prioritize patients with higher weight and emergency conditions at the start of each path, followed by patients with less urgent conditions. This ensures that patients with more severe conditions are serviced first.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100077"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000207/pdfft?md5=2d8a84385cc284572728b28c1022b38c&pid=1-s2.0-S2949863524000207-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking the potential of digital twins in supply chains: A systematic review 释放数字孪生在供应链中的潜力：系统回顾

Supply Chain Analytics Pub Date : 2024-07-29 DOI: 10.1016/j.sca.2024.100075

Syed Adeel Haneef Zaidi , Sharfuddin Ahmed Khan , Amin Chaabane

{"title":"Unlocking the potential of digital twins in supply chains: A systematic review","authors":"Syed Adeel Haneef Zaidi , Sharfuddin Ahmed Khan , Amin Chaabane","doi":"10.1016/j.sca.2024.100075","DOIUrl":"10.1016/j.sca.2024.100075","url":null,"abstract":"<div><p>Digital Twins (DTs) developments are still in the pilot stages of deployment in supply chain management (SCM), and their full integration with real-time synchronization and autonomous decision-making poses many challenges. This paper aims to identify these common challenges and provide a conceptual framework for establishing a Digital Twin (DT) system to improve supply chain management performance. The paper presents a systematic literature review of 129 research papers on DT applications for SCM improvement. The selected papers were reviewed and classified into three categories: manufacturing and production, supply chain, and logistics. The development of digital technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID) devices, cloud computing, cyber-physical systems (CPSs), cybersecurity (CS), and simulation modeling has increased the opportunities to explore the creation of supply chain DTs. However, there are limitations and various challenges due to the complexity of most systems. The results indicate that DT for SCM should include external links (i.e. suppliers, distributors) and internal links (i.e. procurement, production, logistics) to deal with any disruption through data-driven modeling with real-time synchronization. Based on the review findings, this study proposes a three-layered conceptual framework to improve supply chain management performance. The proposed framework provides future directions for DT research in SCM. It provides a holistic and integrated approach to DT implementation, the common DT technologies, and data analytics techniques for improved supply chain performance.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000189/pdfft?md5=4487bd56f93ddc361cd12675e1dc8f76&pid=1-s2.0-S2949863524000189-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of artificial intelligence in reverse logistics: A bibliometric and network analysis 人工智能在逆向物流中的应用：文献计量和网络分析

Supply Chain Analytics Pub Date : 2024-07-27 DOI: 10.1016/j.sca.2024.100076

Oyshik Bhowmik , Sudipta Chowdhury , Jahid Hasan Ashik , GM Iqbal Mahmud , Md Muzahid Khan , Niamat Ullah Ibne Hossain

{"title":"Application of artificial intelligence in reverse logistics: A bibliometric and network analysis","authors":"Oyshik Bhowmik , Sudipta Chowdhury , Jahid Hasan Ashik , GM Iqbal Mahmud , Md Muzahid Khan , Niamat Ullah Ibne Hossain","doi":"10.1016/j.sca.2024.100076","DOIUrl":"10.1016/j.sca.2024.100076","url":null,"abstract":"<div><p>Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000190/pdfft?md5=675a03af106c4975660c495cacb17d46&pid=1-s2.0-S2949863524000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection 用于可解释供应商选择的多标准决策和机器学习混合方法

Supply Chain Analytics Pub Date : 2024-06-29 DOI: 10.1016/j.sca.2024.100074

Ahmad Abdulla , George Baryannis

{"title":"A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection","authors":"Ahmad Abdulla , George Baryannis","doi":"10.1016/j.sca.2024.100074","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100074","url":null,"abstract":"<div><p>Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often applied are unfamiliar to stakeholders and have limited explainability, posing a significant barrier to adopting intelligent approaches in supply chains. To address this issue, we propose a hybrid supplier selection framework that combines interpretable data-driven AI techniques with multi-criteria decision-making (MCDM) approaches: the former aims to reduce the complexity of the supplier selection problem, while the latter ensures familiarity to supply chain stakeholders by retaining MCDM at the heart of the supplier selection process. The framework is validated through two real-world case studies supporting supplier selection decisions in oil, gas, and aerospace manufacturing companies. Preliminary results from our case studies suggest that the framework can achieve comparable performance to approaches utilising only machine learning while offering the added benefits of end-to-end explainability and increased familiarity.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000177/pdfft?md5=5637ab937560ebd25525e4f54ac4c038&pid=1-s2.0-S2949863524000177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supply chain competitiveness through agility and digital technology: A bibliometric analysis 通过敏捷性和数字技术提高供应链竞争力：文献计量分析

Supply Chain Analytics Pub Date : 2024-06-28 DOI: 10.1016/j.sca.2024.100073

Emmanuel Susitha , Amila Jayarathna , H.M.R.P Herath

{"title":"Supply chain competitiveness through agility and digital technology: A bibliometric analysis","authors":"Emmanuel Susitha , Amila Jayarathna , H.M.R.P Herath","doi":"10.1016/j.sca.2024.100073","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100073","url":null,"abstract":"<div><p>Supply chain competitiveness and agility are matured areas in supply chain management. While there is an ongoing evolution in digital technology alongside supply chain competitiveness and agility, the literature appears to have limited bibliometric reviews on how digital technology impacts these aspects. This study examines supply chain competitiveness with bibliometric analysis, focusing on the critical elements of supply chain agility and the impact of rapidly advancing digital technologies. The study bridges the gap between management and technology disciplines. Employing the PRISMA methodology, 147 scholarly articles were meticulously selected and analysed, adopting a multifaceted analytical approach that combines bibliometric and descriptive analyses. This thorough literature synthesis reveals a profound and intricate connection between supply chain agility and digital technology, underscoring their joint significance in fostering competitive advantage within the dynamic business landscape. This investigation contributes to the existing body of knowledge by identifying seven distinct clusters, offering a detailed map of the current research landscape. This study charts a course for future academic inquiries into this critical area and provides valuable insights for practitioners. It underscores the importance of integrating agile supply chain practices and digital technologies to maintain and enhance competitive positioning in today’s fast-paced business environment.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000165/pdfft?md5=ca3444f3e74371f974f42476ebbb8d6f&pid=1-s2.0-S2949863524000165-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of vehicle routing problems and models in multi-echelon distribution networks 多梯队配送网络中的车辆路线问题和模型系统回顾

Supply Chain Analytics Pub Date : 2024-06-18 DOI: 10.1016/j.sca.2024.100072

Peter Nielsen , Mahekha Dahanayaka , H.Niles Perera , Amila Thibbotuwawa , Deniz Kenan Kilic

{"title":"A systematic review of vehicle routing problems and models in multi-echelon distribution networks","authors":"Peter Nielsen , Mahekha Dahanayaka , H.Niles Perera , Amila Thibbotuwawa , Deniz Kenan Kilic","doi":"10.1016/j.sca.2024.100072","DOIUrl":"https://doi.org/10.1016/j.sca.2024.100072","url":null,"abstract":"<div><p>The vehicle routing problem (VRP) is a combinatorial optimization problem that determines optimal routes to enhance distribution efficiency. One of the most popular strategies in freight distribution is multi-echelon distribution. Multi-echelon distribution networks often apply to supply chain management, land transportation, the maritime industry, aviation, etc., and rely on VRP. This comprehensive review systematically analyses 382 papers retrieved through the Scopus database. We use a bibliometric and network analysis tool to complete a systematic literature mapping identifying key interrelationships and research clusters. The analysis depicts five main research clusters: green logistics and decision analysis, scheduling and inventory optimization, VRP for city logistics, mathematical modeling and optimization, and outbound logistics and customer service, identified based on author keywords of the systematically derived paper pool. Each cluster is provided with foundational knowledge, concepts, theories, and employed techniques. Finally, future studies are suggested to explore more comprehensive investigation in highly discussed domains like city logistics problems in e-commerce, vehicle routing problems for sustainable logistics, and technological advancement-based applications.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000153/pdfft?md5=2a68c08fb7b8eef36b3fc18102f86e43&pid=1-s2.0-S2949863524000153-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages 具有威布尔分布劣化、斜坡型需求、碳减排和短缺的综合库存管理模型

Supply Chain Analytics Pub Date : 2024-06-07 DOI: 10.1016/j.sca.2024.100069

Muthusamy Palanivel, Murugesan Venkadesh, Selvaraj Vetriselvi

{"title":"A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages","authors":"Muthusamy Palanivel, Murugesan Venkadesh, Selvaraj Vetriselvi","doi":"10.1016/j.sca.2024.100069","DOIUrl":"10.1016/j.sca.2024.100069","url":null,"abstract":"<div><p>This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100069"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000128/pdfft?md5=f4ac2487f3dd8a8eb4591780d22300f2&pid=1-s2.0-S2949863524000128-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0