Aspects Of Supply Chain Management Research Articles

ІМПАКТ-ІНВЕСТУВАННЯ ЯК СУЧАСНИЙ КОНЦЕПТ СТАЛОГО РОЗВИТКУ ЛОГІСТИЧНИХ СИСТЕМ

The article analyzes the principles of impact investing in the sustainable development of logistics systems, considers trends in logistics provision in the context of the sustainable development paradigm, and identifies the prerequisites, driving forces, and opportunities for ensuringthe sustainability of logistics systems at all levels. In addition, potential trends in their future development was analyzed. The sustaina-bility of a logistics system involves the efficient use of resources, cost reduction and process optimization. The author proposes a conceptual model of sustainable development of a logistics system. It con-tains two contours (strategic development contour and sustainable development contour). These con-tours in their interaction within the logistics system ensure its sustainable development in the long term. The basis is the sustainable development paradigm, and the main goal of the concept is to solve logistics problems not only in the interestsof the current needs of the economy, but also taking into account the right to meet the needs of future generations.The concept of "sustainable development of a logistics system" means the integration of sustain-able practices and principles into the operations and management of logistics systems. This involves balancing the economic, environmental and social aspects of supply chain management to ensure long-term viability and minimize the negative impact on the environment and society. It has been found that a sustainable logistics system should be aimed at reducing environmental impact through the use of environmentally friendly vehicles, energy-efficient technologies, waste man-agement, and reduction of greenhouse gas emissions. Another important aspect is the use of environ-mentally friendly packaging and materials. Consideration of social aspects, such as occupational safety, quality of life of employees, respect for human rights and ethical standards. To achieve these goals, it is important to develop socially responsible labor practices. Aspects of sustainable development require new approaches to the investment processes of logis-tics systems, such as impact investing. The article defines the criteria for impact investing and the di-rections for determining the effectiveness and efficiency of this type of investment, taking into account the specifics of the investment object. When evaluating impact investments in the logistics sector, the authors propose to take into account the following criteria: placeand importance of the logistics system in the supply chain or national economy; resilience of the type of logistics provision to economic down-turn and peculiarities of the system's functioning during stagnation (which allows assessing risks); social significance of the logistics system; provision of the logistics system with its own financial re-sources; level of support for the development of the industry and the degree of control by the State

Intelligent Decision Support Machine Learning Based Optimizing Inventory Management

Inventory management (InvM) is a critical aspect of supply chain management (SCM) and optimizing inventory levels can lead to significant cost savings and improved customer satisfaction. Hence, business organizations have recently worked to increase the value of their operations by utilizing modern and digital technologies as industry 4.0 (Ind 4.0). In the era of Ind 4.0, the technologies as Internet of Every Things (IoET), AI, BDA…etc. Recognizing the significance of InvM in a supply chain (SC), motivated us to volunteer technologies of Ind 4.0 as machine learning (ML) techniques to boosting Decision Making (DM) process to optimize InvM. Subsequently, this study is constructed for providing an intelligent Decision Support ML framework for automating the process of optimizing inventory management. Our constructed framework ensembles powerful ML prediction algorithms for inventory management, such as Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) for building robust sales regressors. The extensive experimentations on a case study of Walmart suggested that the proposed system has the potential to transform inventory management and improve supply chain performance, but further research is needed to address the challenges of data availability and quality.

Research on multi-factory combination optimization based on DOSTAR

With the development of industrial big data, it has become an important research direction to use combinatorial optimization to coordinate multi-objective problems in complex manufacturing scenarios with multiple factories. At present, most of the multi-objective problems are decomposed into single-objective solutions. However, it is difficult to resolve the contradiction between multiple goals. There are many participants in multi-objective problems and complex data types, so there is no suitable research method at present. Based on big data, this paper integrates various aspects of supply chain management of multiple factories, and proposes a DOSTAR combined model. On the one hand, it conducts knowledge discovery based on the fusion of human-cyber-physical ternary data, on the other hand, it conducts multi-objective optimization through knowledge structure. Among them, the most important thing is to establish the six-tuple as the basic model. Then the space weight, time weight and decision weight are obtained through the weight sub-model. Finally, the improved reinforcement learning algorithm is used to extract relevant new knowledge and complete multi-objective coordination. This article takes the supply chain management of Haier water heaters as an example, using the above-mentioned combined model, and the experimental results show that the purpose of improving performance has been achieved. • This article builds a combined optimization model based on knowledge discovery and reinforcement learning. • The combined model mainly includes: six tuples, weight sub-models (AHP and spatiotemporal data), and improved reinforcement learning algorithms. • This research solves the multi-objective optimization problem in the complex manufacturing environment of multi-factories.

Covid-19 Pandemic: Is it a Challenge or Opportunity for Pharmaceutical Industry around the Globe?

In this opinion article, authors emphasized the role of pharmaceutical industries or organizations, the way forward to deal efficiently with pandemics such as covid-19 and various aspects of supply chain management are discussed so as to reach the vaccine to the last end user.

Sustainable Procurement Practices and its Effects on Supply Chain Management

Sustainable procurement is a critical aspect of supply chain management that involves purchasing goods and services in a way that considers their environmental, social, and economic impacts. It extends beyond traditional procurement practices by incorporating sustainability principles into the decision-making process. The role of sustainable procurement in improving supply chain performance is significant. By sourcing from sustainable suppliers, companies can reduce their environmental footprint, enhance their social responsibility, and achieve cost savings, thus contributing to improved supply chain performance.

Optimization Analysis of the Emergency Logistics Identification Method Based on the Deep Learning Model under the Background of Big Data

Vehicle routing, which is effective and efficient, is a dominant aspect of supply chain management in general and deep learning (DL) in particular. It is also a right step towards the fuel conservation and environmental concern in disposing used commodities. Economics of logistics and transportation plays a major role in deciding the competitiveness of the product, either new or used, in the market. With the upward trends of fuel and logistics costs, manufacturing industries have little option other than keeping the cost of transportation the lowest. Many organizations now started implementing lesser expensive and proper transport modes to keep the maintenance of supply chain cost to the minimum. Proper handling of returned commodities to recover value without affecting the environment may need appropriate techniques or methodologies. This paper deals with the routing of vehicles with energy conservation as the agenda in the value recovering method named as repair service work. It is done through a big data-based deep learning model, in a multicommodity environment. Here, the transportation of commodities to repair service facilities is given an in-depth focus to reduce the energy use. The minimization of the distance traveled by the truck fleet reduces the energy consumption by the trucks. This article deals with the optimization of emergency logistic with the assistance of deep learning approach, whereas this approach attains 57.19 km with 6 optimized routes. The process of emergency flow control is attained effectively using deep learning approach.

Pricing-decision analysis of green supply chain with two competitive manufacturers considering horizontal and vertical fairness concerns.

A pricing-decision analysis is a critical aspect of supply chain management since it directly affects manufacturers' and retailers' profits. The members of a supply chain all want to be treated properly during the pricing process, which means that they pay attention to the fairness of the profit distribution. Additionally, consumers are increasingly gravitating to green products as their awareness of green consumption grows. Thus, incorporating consumers' green preferences into a supply chain, this paper investigates pricing decisions with two competitive manufacturers under horizontal and vertical fairness concerns and seeks the optimal degrees of product greenness, prices, profits, and utilities. The game-theoretical models with and without bidirectional fairness concerns are constructed and analyzed to identify the implications on pricing, profits, and utilities of competing manufacturers' bidirectional fairness concerns and consumers' green preferences. Then, we determine the decisional differences between the two designs using comparative analysis and numerical simulation. Finally, propositions, corollaries, and policy implications are derived. The results indicate that consumers' green preferences and competition between manufacturers contribute to increasing the optimal pricing and retailer's profit while harming manufacturers' utilities and the supply chain's profits under some conditions. The findings also demonstrate that horizontal and vertical fairness concerns generate different impacts on the product's greenness degree and pricing. Still, they are detrimental to manufacturers' utilities and supply chain profit while possessing negligible effects on retailer's profit.

Designing Sustainable Recovery Network of End‐of‐Life Product during the COVID‐19 Pandemic: A Real and Applied Case Study

One of the most important aspects of supply chain management (SCM) is the recovery network (RN), which covers all activities associated with return products (such as collection, recovery, repair, recycling, and waste disposal). Our goal in this paper is to provide a new mathematical model of sustainable end‐of‐life management (SEOLM) during the COVID‐19 pandemic for readers. The suggested recovery network model (RNM) can explain the trade‐offs between economic (minimizing total costs), environmental (minimizing bad environmental impacts), and social (minimizing bad social impacts) aspects during the pandemic and the great lockdown. A new RN can be designed with a sustainable and hygienic design when taking environmental, economic, and social considerations into account. It proposes guidelines for managers and scholars on how to address recovery network design (RND) challenges during the pandemic through a mathematical article with a sustainable approach. The scalarization approach of a multi‐objective mixed‐integer programming (MOMIP) problem in this paper is the weighted sum method. The validation of the presented model and the related Pareto frontier has been illustrated by a case study and numerical example. To perform the optimization process, Lingo software is used.

Theoretical and Applied Principles of Supply Chain Management under Crisis Economic Conditions: Strategic Aspect

The article highlights the main methodological and strategic aspects of supply chain management under crisis economic conditions. The authors defined the essence of the concept of «supply chain». In the supply chain structure, it is possible to distinguish complete logistics chains, which, in turn, contain all key links and stages of the reproduction process, starting from raw material sources and ending with final consumers. New tasks related to the formation of rational logistics chains, supply chains and the construction of effective systems of their management have been analyzed. Comparative characteristic of supply chain management models is given. The key characteristic features include structural and conceptual, the level of interconnection with the enterprise strategy, the breadth of the set of activities, intra-organizational connections, inter-organizational connections, value forming factors, information support, etc. A new vision of economic transformation is established in logistics practice. Integration into global production networks will allow countries (especially Ukraine) to reveal their comparative advantages by focusing on specific tasks and industries. Modern production systems, which have become the end result of the dynamism of production factors, distribution and production networks, have been studied. The critical problems regarding the choice of existing corporate strategies by enterprises for their further effective expansion have been highlighted. The strategies available in the global economy have been analyzed, their advantages and disadvantages have been highlighted. In general, to establish the cumulative impact of all business processes on the level of performance of the supply chain, which will allow us to approach the measurement of the level of their synergistic effect, it is necessary to ensure constant monitoring of indicators in two areas: process and system. Under crisis economic conditions (in particular, instability of markets, destruction of well-established supply chains, in particular, due to military actions), there is a need for a quick response, complete modernization, and adaptation of the proposed logistics services throughout the supply chain. It has been proposed to form and offer a market of the latest, perfect, adaptive services to clients, which will allow balancing the unpredictability of the market with the need to obtain a stable, growing level of profitability.

Use of Big Data in Supply Chain Management

Abstract: The rapidly expanding interest in the application of big data analytics (BDA) in supply chain management (SCM) among academics and practitioners has prompted an assessment of current research progress in order to define a new agenda. the use of sophisticated analytics tools to improve supply chain management The apps are divided into three categories: descriptive, predictive, and prescriptive analytics, as well as the supply chain operations reference (SCOR) model domains of plan, source, make, deliver, and return. This review answers to the demand by offering a new classification scheme that provides a comprehensive picture of current literature on where and how BDA has been used in SCM. The classification system is based on Mayring's (2008) content analysis method and addresses four research questions: (1) In which aspects of SCM is BDA used? (2) To what extent is BDA employed in these SCM domains in terms of analytics? (3) What are the different types of BDA models utilised in SCM? (4) How are these models developed using BDA techniques? The consideration of these four topics reveals several research gaps, pointing to future study directions. Purpose - Rapid innovation and globalisation have created a plethora of opportunities and choices for businesses and consumers in the marketplace. Due to competitive pressures, sourcing and manufacturing are now done on a global basis, resulting in a huge increase in product availability. The purpose of this article is to determine whether real-time business intelligence (BI) is required in supply chain analytics. Design/methodology/approach – The paper argues for and analyses the benefits and drawbacks of BI. Findings – The article focuses on the need to review the classic BI notion of integrating and consolidating information in an organisation in order to help service-oriented businesses that want to keep their customers. Using a BI methodology to improve the effectiveness and efficiency of supply chain analytics is a vital component of a company's ability to establish a competitive edge. Originality/value – This study contributes to a better understanding of the difficulties surrounding the usage of business intelligence tools in supply chains. Keywords: Supply chain management, Business analytics, Information systems, Big Data, Big data analysis

The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality

Demand forecasting is the basic aspect of supply chain management. It has important impacts on planning, capacity and inventory control decisions. Seasonality is a common characteristic of most time series demands in practice. Thus, regarding seasons and holidays as important factors of demand forecasting is nontrivial, which contributes to increased forecasting accuracy. In this study, we propose a hybrid approach that integrates Prophet and SVR (support vector regression) models to forecast time series demand in the manufacturing industry with seasonality. In the proposed hybrid PROPHET-SVR approach, Prophet is used to forecast the seasonal fluctuations and determine the input variables of SVR, and SVR is used to capture nonlinear patterns. Therefore, the approach can not only customize the influence of holidays and seasons but also account for the forecasting residual to increase the accuracy. Computational results demonstrate that the hybrid PROPHET-SVR approach outperforms a variety of other prediction methods. This paper also illustrates the application of the new forecasting method in a case of the manufacturing industry in China, and proves the robustness of the method.

Assessment of the Regulatory Aspects of Supply Chain Management of Hazardous Materials HAZMAT with Respect to the Indian Chemical Industry

HAZMAT, defined as materials which can harm people, organisms, and the environment, are a critical part of the industry supply chain as raw material, intermediates, or finished products. A strong and effective regulatory framework for dealing with HAZMAT and its proper management is of utmost importance for ensuring adequate safety and minimizing the risks during different stages of supply chain (manufacturing, storage, transportation). In view of this, the objective of research was to study the prevailing regulatory framework for supply chain management of HAZMAT in the context of Indian chemical industry.

Towards a Supply Chain 4.0 on the post-COVID-19 pandemic: a conceptual and strategic discussion for more resilient supply chains

Purpose This paper aims to present a grounded and strategic discussion regarding Supply Chain 4.0 as a management system orientation for the post-COVID-19 period as well as propose some research directions thereof. Design/methodology/approach For the development of this paper, some theoretical insights were provided based on the literature related to Supply Chain and Industry 4.0. A discussion regarding the constructs of Supply Chain 4.0 on the context of the post-COVID-19 outbreak is developed as well. Findings The discussion, herein, shows that the disruptive technologies might play a crucial role to become supply chains more responsive and resilient to sudden events such as COVID-19. Then a Supply Chain 4.0 is a transformational strategic orientation to be considered on the aspect of supply chain management for the post-pandemic period. Some research questions are proposed at the end of this paper with the aim to further address this subject. Research limitations/implications This paper provides timely insights for researchers and practitioners which might imply on the further research deployments and practical applications although it demands future empirical studies to validate the propositions herein presented. Practical implications Practitioners can be benefited from this paper on having new insights and a strategic direction on regards supply chain management for the post-pandemic period with focus on a technology-driven strategy for supply chains. Originality/value This paper is unique because it brings an unexplored relationship between Supply Chain 4.0 and COVID-19 pandemic. It also significantly contributes to new directions and views for the supply chain management field from these challenging and difficult times of coronavirus global outbreak.

Impact of Supply Chain Management in Globalization

Globalization has had a considerable impact on the logistics industry. The supply chain (SC) is the most significant concept in logistics. Managing SC operations has become more difficult as a result of globalization. Supply chains (SCs) are the pathways between suppliers and purchasers. The role of SCs is to safeguard a product from the moment of its creation till it reaches the hands of a customer. Supply chain management (SCM) is a crucial part of companies. It is the main driver of their success and customer loyalty. Marketing is becoming an increasingly important part of the process, balancing procurement by providing critical demand information and cultivating partnerships that help SC operations run more efficiently. These goods must cross international boundaries to enter as many customers as possible. SCs are important for global markets because, without them, suppliers' goods will not be able to reach global markets efficiently. The paper's objectives are to discuss the aspects of Supply Chain Management and Global Supply Chain.

A Hybrid Mehta-Heuristic algorithm for optimization location-routing problem of facilities in four echelon supply chain

During the last three decades, the concept of integrated decision making in the supply chain becomes one of the essential aspects of supply chain management (SCM). This concept explores the interdependence between facility location, flow allocation between facilities, transportation system structure, and inventory control system. This study presents a new form of the location-routing problem of facilities under uncertainty in a supply chain network for deteriorating items through taking environmental considerations, cost, and procurement time and customer satisfaction into account, to simultaneously minimize total system costs, maximal delivery time and emissions across the entire network and maximize customer satisfaction. The research problem is formulated in a mixed-integer nonlinear multi-objective programming. In order to solve the model, the combination of the two Benders decomposition algorithm and Lagrange multiplier liberalization, as well as the combination of red deer algorithm and annealing simulation, was proposed. For validation, the results of the proposed algorithm in different size examples are compared with the results of the exact method solution by MATLAB software. The mean error of the proposed algorithm for the objective function is less than 4% compared to the exact method in solving the sample problems. Besides, the results of the algorithm performance are investigated based on standard indices. The computational results show the efficiency of the algorithm for a wide range of problems with different sizes. The location decisions are interdependent, and the process of determining the optimal values of these variables interact together, which can lead to an optimal system.

A Challenge in Our Time: Issues of Race in Supply Chain Management

This editorial addresses one of the greatest challenges of our time—racial inequality. Logistics and supply chain management (SCM) factor prominently in people’s everyday experiences, as consumers of products and logistics services as well as through participation in logistics and supply chain workforces. These everyday experiences can be quite disparate depending on one’s race. As academics, we have a responsibility to peel back the layers of how these issues of race and diversity impact and interact with supply chain phenomena. This editorial is intended to bolster conversation around these issues. We identify compelling themes that merit consideration and, perhaps, deeper investigation, including race as an individual difference variable in our research, race in SCM talent and leadership, and race and racism in SCM execution. We also call for more research that addresses racial inclusion and equal access in other aspects of SCM in light of its far‐reaching influence in the realms of business and society. The time has come to address these pervasive issues affecting organizations and individuals. We also introduce the articles appearing in the current issue of the journal.

Strategic Supply Chain Management with the Balanced Scorecard

The strategic management of the supply chain includes the formulation and implementation of a supply chain strategy as well as the assessment of the results. Undoubtedly, the formulation of a supply chain strategy is part of each of the three levels of strategic planning: corporate, business unit, and functional. For successful strategic management of the supply chain, an integrated approach is needed to link the supply chain strategy with the overall strategic management of the company. It is the Balanced Scorecard which seems to be a suitable and effective tool for this purpose. The Balanced Scorecard focuses on formulating a supply chain strategy, linking it to the corporate vision and turning it into particular actions to achieve the strategic objectives. In this article, a methodology for strategic supply chain management is proposed based on the Balanced Scorecard as an instrument for supply chain strategy formulation and implementation. The methodology consists of twelve steps, each of which is commented in more detail. The article aims at filling the gap identified in the literature devoted to strategic aspects of supply chain management.

A Study on the Effect of Packaging Design Considering SCM Aspects on Logistics Efficiency (Focusing on the case of domestic A company)

This study conducted a case study and a questionnaire analysis in parallel. In the case study, a study was conducted on domestic manufacturer A by analyzing pallet loading efficiency of RRP(Retail Ready Packaging) products and pallet loading efficiency of MWC(Membership Wholesale Club) delivered products. As a result of the pallet loading efficiency simulation of 50 RRP products of Manufacturer A, it was 80.0% based on the T-11 type pallet and 84.3% based on the T-12 type pallet. It was found It refers that the route of producing the product from the manufacturer A and delivering it to the MWC A in the form of RRP resulted in the decrease of the pallet loading efficiency through the change of the loading pattern and the adjustment of the number of loads. As a result of analyzing the questionnaire about whether the overall efficiency of the supply chain will be improved if the operation of the packaging system considering the SCM(Supply Chain Management) aspect is χ2 = 178.500, there was a statistically significant difference at the significance level of 0.000. Manufacturers and logistics companies answered “yes” the most, but distributors answered “is average” the most, confirming that the packaging can be constructed with the highest operational efficiency. Therefore, as a result of confirming the impact of packaging design considering the SCM aspect on logistics efficiency, it indicates the importance of closer collaboration between manufacturers and distributors.

Reverse logistics risk management: identification, clustering and risk mitigation strategies

PurposeReverse logistics (RL), an inseparable aspect of supply chain management, returns used products to recovery processes with the aim of reducing waste generation. Enterprises, however, seem reluctant to apply RL due to various types of risks which are perceived as posing an economic threat to businesses. This paper draws on a synthesis of supply chain and risk management literature to identify and cluster RL risk factors and to recommend risk mitigation strategies for reducing the negative impact of risks on RL implementation.Design/methodology/approachThe authors identify and cluster risk factors in RL by using risk management theory. Experts in RL and supply chain risk management validated the risk factors via a questionnaire. An unsupervised data mining method, self-organising map, is utilised to cluster RL risk factors into homogeneous categories.FindingsA total of 41 risk factors in the context of RL were identified and clustered into three different groups: strategic, tactical and operational. Risk mitigation strategies are recommended to mitigate the RL risk factors by drawing on supply chain risk management approaches.Originality/valueThis paper studies risks in RL and recommends risk management strategies to control and mitigate risk factors to implement RL successfully.

A review of supply chain data mining publications

The use of data mining in supply chains is growing, and covers almost all aspects of supply chain management. A framework of supply chain analytics is used to classify data mining publications reported in supply chain management academic literature. Scholarly articles were identified using SCOPUS and EBSCO Business search engines. Articles were classified by supply chain function. Additional papers reflecting technology, to include RFID use and text analysis were separately reviewed. The paper concludes with discussion of potential research issues and outlook for future development.