Browsing by Author "Mangla, Sachin Kumar"

Now showing 1 - 20 of 54

Citation - WoS: 9
Citation - Scopus: 19
A circular business cluster model for sustainable operations management
(TAYLOR & FRANCIS LTD, 2024) Erhan Ada; Muhittin Sagnak; Sachin Kumar Mangla; Yigit Kazancoglu; Ada, Erhan; Sagnak, Muhittin; Mangla, Sachin Kumar; Kazancoglu, Yigit
Ecological pollution scarcity of resources climate change and population growth have forced organisations to transform from a linear to a circular economy. Hence the need arises to redesign value chains and traditional business models. Thus the aim of this study is to develop a new conceptual eco-cluster model called 'Circular Business Cluster Model' to combine the advantages of both clustering and a circular economy. This paper seeks to establish a framework and provide guidelines for policy-makers such as governments local authorities and organised industrial zone administrators. The contribution of this paper is to highlight and emphasise the crucial role of circular economy principles to transform a classical business model into an eco-business model. Circular Business Cluster Model includes the conceptual framework network structure interactions between cluster components and 6R activities. A case study was conducted to check the applicability of the proposed Circular Business Cluster Model. Eleven centres in this model were evaluated. Fuzzy Best-Worst Method (BWM) was used to find the relative weights. Based on the Circular Business Cluster Model theoretical and managerial implications have been presented.
Citation - WoS: 102
Citation - Scopus: 145
A conceptual framework for blockchain-based sustainable supply chain and evaluating implementation barriers: A case of the tea supply chain
(WILEY, 2022) Sachin Kumar Mangla; Yigit Kazancoglu; Abdullah Yildizbasi; Cihat Ozturk; Ahmet Calik; Calik, Ahmet; Yildizbasi, Abdullah; Ozturk, Cihat; Mangla, Sachin Kumar; Kazancoglu, Yigit
The increasing population and income inequality in the last decades have made it necessary to focus on the concept of sustainability. In the changing world order with economic crises instabilities pandemics and social media sustainability awareness differs significantly from past years. In addition developing new technologies and concepts (big data blockchain IoT robotic etc.) plays a crucial role in meeting social awareness in terms of sustainability. Food sustainability is also one of the most important pillars in this concept. The integration of new technologies in agriculture and food chains will enable the current world population to use resources more efficiently and sustainably. Blockchain one of the technologies that emerged with the arrival of Industry 4.0 is a technology that can be used effectively in many sectors. Particularly in supply chain networks it is seen as a technology that supports the sustainability concept due to its features such as decentralization reliability transparency consensus standards and traceability. However since blockchain is an immature and new technology there are some challenges with integrating it into existing conventional systems. This study aims to present a conceptual framework for the integration of blockchain technology to establish a sustainable tea supply chain define possible actions and prioritize the possible risks that may arise in this integration process, this will be done through the Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) approach. In this context initially the current tea supply chain will be considered and the activities carried out will be defined in terms of technological environmental and strategic sustainability. Then the design that includes the integration of all activities with blockchain technology is presented. The proposed design covers the entire tea supply chain from end to end and is considered with regard to all sustainability dimensions. In the proposed framework barriers that may be encountered and risks that may arise at each stage of the tea supply chain process are identified, managerial implications are then presented to eliminate these risks. To enhance the use of the recommendations made risks and barriers are prioritized with SF-AHP management. Thus the problems that need to be solved primarily in the technological transformation process can be evaluated more clearly. The proposed theoretical framework is expected to extend to all agricultural practices and support technological advances throughout the agricultural sector.
Citation - WoS: 37
Citation - Scopus: 39
A decision framework for incorporating the coordination and behavioural issues in sustainable supply chains in digital economy
(Springer, 2023) Patanjal Kumar; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Ali Emrouznejad; Kumar, Patanjal; Emrouznejad, Ali; Mangla, Sachin Kumar; Kazancoglu, Yigit
Global warming climate change and social problems are the worst human-induced sustainability issues that economies across the globe have witnessed. Water pollution greenhouse effect poor working conditions child labour and lack of coordination among channel partners have caused the considerable interruptions in the supply chain network. The purpose of the paper is to identify critical factors affecting behavioural and sustainable supply chain coordination and evaluate strategies for risk reduction in the supply chain coordination in the context of digitization. This study purposes a novel supply chain coordination framework which consists of four themes such as system actor objective and action on which the success or the failure of supply chain can be contingent. Our study integrates multi-criteria decision approach using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to investigate factors that affected the behavioural and sustainable supply chain coordination in the context of digitization. The Fuzzy-AHP method qualified to hierarchically rank the factors based on the relative fuzzy weightage while Fuzzy-DEMATEL established the inter-relationships among the factors and classified them into cause and effect groups. The findings of our study identified the Environmental performance and decarbonization as the most significant factor and the speed to market as the least important factor in developing behavioural and sustainable supply chain coordination in the context of digitization. Our analysis from Fuzzy AHP-DEMATEL approach reveal that the social preferences (power balance reciprocity fairness) is a significant causal factor which can effectively abolish the issues plaguing behavioural and sustainable supply chain coordination in the context of digitization. The results from our study aim to facilitate decision makers in cultivating a sustainable supply chain framework that can boost trust among the channel partners environmental performance social performance and channel efficiency of the supply chain thereby ensuring sustainability and socio welfare of all the supply chain. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 48
Citation - Scopus: 69
A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions
(Elsevier Inc., 2021) Yigit Kazancoglu; Muhittin Saǧnak; Sachin Kumar Kumar Mangla; Muruvvet Deniz Sezer; Melisa Ozbiltekin-Pala; Sezer, Muruvvet Deniz; Pala, Melisa Ozbiltekin; Kazancoglu, Yigit; Sagnak, Muhittin; Mangla, Sachin Kumar
This study determines the potential barriers to achieving circularity in dairy supply chains, it proposes a framework which covers big data driven solutions to deal with the suggested barriers. The main contribution of the study is to propose a framework by making ideal matching and ranking of big data solutions to barriers to circularity in dairy supply chains. This framework further offers a specific roadmap as a practical contribution while investigating companies with restricted resources. In this study the main barriers are classified as ‘economic’ ‘environmental’ ‘social and legal’ ‘technological’ ‘supply chain management’ and ‘strategic’ with twenty-seven sub-barriers. Various big data solutions such as machine learning optimization data mining cloud computing artificial neural network statistical techniques and social network analysis have been suggested. Big data solutions are matched with circularity focused barriers to show which solutions succeed in overcoming barriers. A hybrid decision framework based on the fuzzy ANP and the fuzzy VIKOR is developed to find the weights of the barriers and to rank the big data driven solutions. The results indicate that among the main barriers ‘economic’ was of the highest importance followed by ‘technological’ ‘environmental’ ‘strategic’ ‘supply chain management’ then ‘social and legal barrier’ in dairy supply chains. In order to overcome circularity focused barriers ‘optimization’ is determined to be the most important big data solution. The other solutions to overcoming proposed challenges are ‘data mining’ ‘machine learning’ ‘statistical techniques’ and ‘artificial neural network’ respectively. The suggested big data solutions will be useful for policy makers and managers to deal with potential barriers in implementing circularity in the context of dairy supply chains. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 48
Citation - Scopus: 61
A Green Dual-Channel Closed-Loop Supply Chain Network Design Model
(ELSEVIER SCI LTD, 2022) Yigit Kazancoglu; Damla Yuksel; Muruvvet Deniz Sezer; Sachin Kumar Mangla; Lianlian Hua; Yuksel, Damla; Sezer, Muruvvet Deniz; Hua, Lianlian; Kazancoglu, Yigit; Mangla, Sachin Kumar
Environmental considerations have become a significant issue in the design of supply chain networks due to today's increasing globalization trends. Therefore supply chain network design needs to be managed in an efficient way to deal with the complex networks involved. The aim of this article is to present a multi-objective optimization model for a green dual-channel supply chain network that handles economic and environmental objectives to optimize network flow. A complex mixed-integer linear programming model (MILP) has been proposed in a green dual-channel and closed-loop supply chain (CLSC) network design. The main objective of the generated MILP model is to investigate the optimal selection of echelons and the optimal selection of transportation alternatives between these echelons in a CLSC network that includes an e-commerce channel structure based on economic and environmental considerations. Environmental aims are achieved by decreasing CO2 emissions and by reducing PM (particulate matter) concentration throughout the network. In addition economic aims are also met by minimizing the overall cost. The validity of the presented model is supported by a case study in the home appliances industry. The results indicate that this model provides valuable knowledge and various alternatives to managers and policymakers depending on the different weight combinations.
Citation - Scopus: 1
A Hybrid Decision Model for Balancing the Technological Advancement Human Intervention and Business Sustainability in Industry 5.0 Adoption
(Springer Science and Business Media Deutschland GmbH, 2024) Rahul Sindhwani; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Ayça Maden; Maden, Ayca; Sindhwani, Rahul; Mangla, Sachin Kumar; Kazancoglu, Yigit; Z. Şen , O. Uygun , C. Erden
In Industry 5.0 humans and machines work together using advanced technologies like Artificial Intelligence (AI) the Internet of Things (IoT) and automation to improve efficiency productivity and quality while also supporting sustainable practices and human values. There is a growing interest in learning about the challenges of Industry 5.0 and exploring these technologies to promote sustainability and responsible business practices. We need a hybrid decision model to strike a balance between technical progress human values and sustainable practices as we move toward Industry 5.0 which presents enormous challenges in the areas of technology the environment society and ethics and business and economics. Through a literature analysis guided by the PRISMA technique and the Delphi method the study highlighted challenges in the areas of technology the environment society and ethics and business and economics as well as solution measures to address them. The weightage of the challenges was determined using the Best Worst Method and the ranking of the potential solutions was prioritized using the Elimination and Choice Expressing Reality method. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 7
A machine learning-based hybrid approach for maximizing supply chain reliability in a pharmaceutical supply chain
(PERGAMON-ELSEVIER SCIENCE LTD, 2025) Devesh Kumar; Gunjan Soni; Sachin Kumar Mangla; Yigit Kazancoglu; A. P. S. Rathore; Rathore, A. P. S.; Kumar, Devesh; Soni, Gunjan; Mangla, Sachin Kumar; Kazancoglu, Yigit
In today's interconnected global economy supply chain (SC) reliability is crucial particularly in sectors like the pharmaceutical industry where disruptions can significantly impact public health. SCs have become important to industries due to a customer-driven shift aimed at improving SC reliability especially in terms of delivery performance. It is crucial to define and find the best strategy for reaching the organizational objectives in SC. While designing a SC supplier selection (SS) and order allocation are two decisions that have to be made separately. This study addresses the critical challenges of SS and order allocation within pharmaceutical SCs. It proposes a novel two-phased hybrid approach the first phase integrates machine learning(ML) and multicriteria decision-making(MCDM) method for robust SS. The second phase develops a mathematical model to optimize order allocation while considering SC reliability. This work employs support vector machine (SVM) as the particular ML method in which the training data are historical corporate data that dictate parameters weights. These weights are then used in the measurement of alternatives and ranking according to compromise solution (MARCOS) method to rank the suppliers. A multi- objective mixed integer programming (MOMIP) model is then formulated to identify the right order quantity from the identified suppliers of a pharmaceutical SC in order to minimize SC cost and maximize SC reliability. The results indicate that by optimizing SC reliability and costs orders are directed to high-priority suppliers. This study provides a comprehensive data-driven decision-making framework to assure SC's reliability and cost-efficiency. The implications of the findings are also profound and contribute valuable insights for industry practitioners to improve the performance of SC. To illustrate the proposed methodology an SC example of a pharmaceutical industry is analyzed using the LINGO solver.
Citation - WoS: 18
Citation - Scopus: 19
A proposed framework for multi-tier supplier performance in sustainable supply chains
(TAYLOR & FRANCIS LTD, 2023) Yigit Kazancoglu; Yucel Ozturkoglu; Sachin Kumar Mangla; Melisa Ozbiltekin-Pala; Alessio Ishizaka; Ozbiltekin-Pala, Melisa; Ishizaka, Alessio; Ozturkoglu, Yucel; Kazançoglu, Yigit; Mangla, Sachin Kumar
Over the past few years supply chains have become globalised and multi-tiered. These factors complicate their structure as the focal company is responsible for the problems experienced at each stage in the multi-tier supply chain. The critical issue for focal companies in managing their multi-tier supply chain is to adopt sustainability standards. One of the study's contributions is the role of weight determination in the tiers and evaluation of alternative suppliers in facilitating the effective management of multi-tier supply chains especially in complex industries such as the food industry. The other contribution of this study is its proposition for a multi-stage framework based on sustainability concerns. The study identifies 14 criteria for companies in diffusing sustainability standards throughout multi-tier supply chains. The weights of these criteria are determined for each tier of the food supply chain using the Best Worst Method. Results show that the 'environmental' criteria are most important for supply chain tiers in the food industry. Supply chain visibility/traceability is the most important criteria for the first tier followed by environmental responsibilities for the second and environmental competencies for the third. Finally for the food company PROMETHEE is used to evaluate three alternative suppliers for each tier.
Citation - WoS: 3
Citation - Scopus: 4
Analysing of the territorial competitiveness index in Izmir through dynamic model
(Elsevier Ltd, 2024) Muruvvet Deniz Sezer; Yigit Kazancoglu; Sachin Kumar Kumar Mangla; Sezer, Muruvvet Deniz; Kazancoglu, Yigit; Mangla, Sachin Kumar
Transition towards more sustainable and resilient economies is crucial to provide sustainable economic development by considering environmental protection and social well-being. There is an increasing focus on the concept of territorial competitiveness that encompasses the aspects of environmental social and economic sustainability. Thus this study aims to analyse the factors affecting the territorial competitiveness index in the Izmir town. To facilitate a comprehensive analysis of the factors that impact territorial competitiveness a systematic approach is employed through the use of a system dynamic model. This model enables the exploration of the complex and dynamic relationships between key parameters providing insights into their interdependencies and how they cooperatively influence the territorial competitiveness index. The results show that Gross Domestic Product growth rate renewable energy production and technology innovation index is increased rapidly. This increase caused by development of social well-being in the region. However sustainable land use and waste is decreased and therefore CO2 is increased in the 2010 and 2027. Besides territorial competitiveness index is expected to increase till 2027 since subsidies in energy and material (re)use efficient use of resources (circular economy) achievement of economic development and technological investments. Governments and businesses should adopt sustainable strategies for improving territorial competitiveness index and balancing economic growth with environmental protection and social welfare in the regional context. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 43
Citation - Scopus: 50
Analysing the adoption barriers of low-carbon operations: A step forward for achieving net-zero emissions
(Elsevier Ltd, 2023) Anil Kumar; Sunil Luthra; Sachin Kumar Kumar Mangla; Jose Arturo Garza-Reyes; Yigit Kazancoglu; Garza-Reyes, Jose Arturo; Kumar, Anil; Luthra, Sunil; Mangla, Sachin Kumar; Kazancoglu, Yigit
In November 2021 the 26th United Nations Climate Change Conference (COP26) was held in Glasgow UK the global leaders from nearly 200 countries stressed taking immediate action on the climate issue and how to ensure global net-zero emissions by 2030. It is possible to accelerate the transition to low-carbon energy systems the present study seeks to identify and analyse key barriers to Low Carbon Operations (LCO) in emerging economies. A critical literature review was undertaken to recognise the barriers linked to the adoption of LCO. To validate these barriers an empirical study with a dataset of 127 respondents from the Indian automobile industry was conducted. The validated barriers were analysed using Best Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. BWM is used to determine the priority ranking of barriers while the DEMATEL method is employed to elucidate the cause-effect inter-relationships among the listed barriers. The results suggest that ‘Economic’ is the most influential category of barriers followed by ‘Infrastructure’ and ‘Operational’. The results also show that the barriers ‘Economic’ ‘Environmental’ ‘Infrastructure’ and ‘Organizational Governance’ belong to the cause group. Some significant managerial implications are recommended to overcome these barriers and to assist firms in the successful adoption of LCO and achieving net-zero emissions. The work was carried out in the automotive industry in India but provides findings that may have wider applicability in other developing countries and beyond. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 35
Citation - Scopus: 31
Assessing dairy supply chain vulnerability during the Covid-19 pandemic
(Taylor and Francis Ltd., 2024) Kritika Karwasra; Gunjan Soni; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Karwasra, Kritika; Soni, Gunjan; Mangla, Sachin Kumar; Kazancoglu, Yigit
Frequent occurrence of disruptions in the supply chain due to the Covid-19 pandemic has increased the supply chain vulnerability (SCU) which affects the performance and revenue generation of firms. If the commodity we are dealing with in a supply chain is of a perishable nature then the situation becomes more complicated as such products need restrictive storage and transportation facilities. The dairy supply chain is one such perishable product supply chain. This paper thus proposes a method to identify key drivers of SCU in a dairy SC followed by establishing a model using interpretive structural modeling (ISM) and a graph theory approach (GTA) to calculate the SCU Index. It is important to quantify the SCU for identifying major factors affecting it and then developing techniques to mitigate it. In order to quantify the SCU first the ISM model is used to identify the interrelation between drivers and then an adjacency matrix is formed by using the interdependence thereby adding inheritance of each driver. A variable permanent matrix is formed to calculate the SCU Index for the SC. This proposed approach will help managers in mitigating the adverse effects of COVID-19 on the dairy supply chain. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 10
Barriers to Cement Industry Towards Circular Economy
(Ram Arti Publishers, 2023) Erhan Ada; Yigit Kazancoglu; Sachin Kumar Kumar Mangla; Ugur Aydin; Aydin, Ugur; Ada, Erhan; Kazancoglu, Yigit; Mangla, Sachin Kumar
Cement as the main component of concrete is a crucial industrial product for economic development and civilization. Nevertheless its production is highly energy-intensive environmentally polluting and a source of extreme CO2 emissions. For success in the transition to the circular economy and accelerating sustainable manufacturing in the cement industry understanding and addressing the main barriers are essential. Using the above point of view this study intends to address the challenges and barriers of the cement industry in the transition to a circular economy define the causal relationships between these barriers and determine the necessary practical implications to overcome the barriers. Systematic literature review and focus group study results enable a holistic model that integrates research results and business practical criteria. The DEMATEL method is used for the clarification of causal relations between factors. A total of 18 barriers in 6 clusters have been revealed to be used for managerial implications to speed up the transition to CE applications in the cement business. Out of 18 barriers 6 were effect groups which were the outcomes due to the remaining 12 causing barriers. The top three cause factors are an unstable waste market lack of management competencies and unstable macroeconomic conditions while the leading three effect factors are revealed as giving priority to other issues insufficient organisational structures and deviations in product quality. Although there are many studies on CE in cement they are concentrated on technical and laboratory studies enabling the use of different alternative materials as inputs to the cement process. Studying and revealing the barriers holding back the cement sector in the transition to CE is this study’s core contribution making it novel and unique. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 63
Citation - Scopus: 77
Barriers to organic waste management in a circular economy
(ELSEVIER SCI LTD, 2022) Shristi Kharola; Mangey Ram; Nupur Goyal; Sachin Kumar Mangla; O. P. Nautiyal; Anita Rawat; Yigit Kazancoglu; Durgesh Pant; Kharola, Shristi; Pant, Durgesh; Nautiyal, O.P.; Ram, Mangey; Goyal, Nupur; Rawat, Anita; Mangla, Sachin Kumar
Organic waste disposal methods notably landfilling not only deplete resources but also contribute to environmental challenges. This research looks at potential barriers to organic waste management solutions. The objective of this study is to identify the barriers to organic waste management solutions from an actor's perspective and to explore their causal relationships to overcome the organic waste management problem from a system perspective. Several key challenges were identified regarding organic waste management solution the current intervention overview indicates that promoting and tracking attention towards value to waste would be an effective solution approach. Waste collection fees unethical behavior and a lack of engagement and commitment in activities show a subsequent effect on consumer-household solutions which are currently acting as priority barriers in this research. In order to have a better understanding of this complex issue a detailed knowledge of barriers (leading to organic waste) is discovered and evaluated with the application of fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The data for this research has been taken in the context of a developing economy like India. This work can provide structural support to the managers by knowing the cause (influencing) and effect-group (influencing) barriers to the effective implementation of an organic waste management system in a circular economy context.
Citation - WoS: 10
Citation - Scopus: 13
Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations
(SPRINGER, 2023) Gorkem Sariyer; Mustafa Gokalp Ataman; Sachin Kumar Mangla; Yigit Kazancoglu; Manoj Dora; Ataman, Mustafa Gokalp; Dora, Manoj; Sariyer, Gorkem; Mangla, Sachin Kumar; Kazancoglu, Yigit
Grounded in dynamic capabilities this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients the average daily length of stay (LOS) and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19 and includes data from 238152 patients. Comparing statistics on daily patient volumes average LOS and resource usage both before and during the COVID-19 pandemic we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158347 it decreased to 79805 during-COVID-19. On the other hand while the average daily LOS was 117.53 min before-COVID-19 this value was calculated to be 16503 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies it empirically investigates the impact of different policies on ED operations.
Citation - WoS: 17
Citation - Scopus: 23
Building sustainable resilient supply chain in retail sector under disruption
(Elsevier Ltd, 2024) Esra Ekinci; Muruvvet Deniz Sezer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Sezer, Muruvvet Deniz; Ekinci, Esra; Mangla, Sachin Kumar; Kazancoglu, Yigit
Blockchain technologies have played a crucial role in transforming the retail industry leading to remarkable advancements in recent times. Its pivotal role in managing risky environments by offering preventive and proactive measures cannot be overstated. The research contribution lies in introducing a set of criteria for assessing the adoption of Blockchain technology specifically designed to evaluate the resilience of the retail sector. This study aims to ensure the establishment of a sustainable resilient supply chain across diverse retail categories particularly in the face of uncertain circumstances. A hybrid decision-making approach that combines the Best-Worst Method (BWM) and Fuzzy TODIM has been employed to achieve these objectives. This study encompasses various types of retail companies to assess and compare their resilience levels by adopting Blockchain technology. The results of this study robustly suggest that speciality retailers with well-established long-term partnerships are more predisposed to embrace and leverage the capabilities of Blockchain technologies. Conversely discount retailers in Turkey face various challenges that impede their effective integration of Blockchain technologies. These challenges encompass collaborating with suppliers on short-term agreements and the unavailability of product tracking among other factors. As a result the outcomes of this study offer valuable insights for retailers in the sector suggesting that they should consider modifying their operational strategies to better align with the adoption and integration of Blockchain technologies in the future. © 2023 Elsevier B.V. All rights reserved.
Citation - Scopus: 272
COVID-19 impact on sustainable production and operations management
(KeAi Communications Co., 2020) Aalok Kumar; Sunil Luthra; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Kumar, Aalok; Luthra, Sunil; Mangla, Sachin Kumar; Kazançoğlu, Yiğit
The global production and supply chain system is mostly disrupted due to widespread of the coronavirus pandemic (COVID-19). Most of the industrial managers and policymakers are searching for adequate strategies and policies for revamping production patterns and meet consumer demand. Form global supply chain perspectives the majority of raw materials are imported from China and other Asian developing nations. The COVID-19 pandemic has broken the most of transportation links and distribution mechanisms between suppliers production facilities and customers. Therefore it is imperative to discuss sustainable production and consumption pattern in the post-COVID-19 pandemic era. Most of the prominent economies around the world enforced a total lockdown and the focus has since shifted to surge in demand for essential products and services. This has led to a decline in demand for some nonessential products and services. The production and operations management challenges of the pandemic situations are discussed and adequately proposes policy strategies for improving the resilience and sustainability of the system. This paper also discusses the different operations and supply chain perspectives for handling such disruptions in the future. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 32
Citation - Scopus: 30
Data analytics for quality management in Industry 4.0 from a MSME perspective
(Springer, 2025) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Ceren Ocal Tasar; Sunil Luthra; Sariyer, Gorkem; Tasar, Ceren Ocal; Luthra, Sunil; Mangla, Sachin Kumar; Kazancoglu, Yigit; Ocal Tasar, Ceren
Advances in smart technologies (Industry 4.0) assist managers of Micro Small and Medium Enterprises (MSME) to control quality in manufacturing using sophisticated data-driven techniques. This study presents a 3-stage model that classifies products depending on defects (defects or non-defects) and defect type according to their levels. This article seeks to detect potential errors to ensure superior quality through machine learning and data mining. The proposed model is tested in a medium enterprise—a kitchenware company in Turkey. Using the main features of data set product customer country production line production volume sample quantity and defect code a Multilayer Perceptron algorithm for product quality level classification was developed with 96% accuracy. Once a defect is detected an estimation is made of how many re-works are required. Thus considering the attributes of product production line production volume sample quantity and product quality level a Multilayer Perceptron algorithm for re-work quantity prediction model was developed with 98% performance. From the findings re-work quantity has the highest relation with product quality level where re-work quantities were higher for major defects compared to minor/moderate defects. Finally this work explores the root causes of defects considering production line and product quality level through association rule mining. The top mined rule achieves a confidence level of 80% where assembly and material were identified as main root causes. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 7
Data-driven decision making for modelling covid-19 and its implications: A cross-country study
(Elsevier Inc., 2023) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Vranda Jain; Mustafa Gökalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Jain, Vranda; Mangla, Sachin Kumar; Kazancoglu, Yigit
Grounded in big data analytics capabilities this study aims to model the COVID-19 spread globally by considering various factors such as demographic cultural health system economic technological and policy-based. Classified values on each country's case death and recovery numbers (per 1000000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population median age life expectancy numbers of medical doctors and nursing personnel current health expenditure as a % of GDP international health regulations capacity score continent literacy rate governmental response stringency index testing policy internet usage % human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 38
Citation - Scopus: 50
Detecting fake reviews through topic modelling
(ELSEVIER SCIENCE INC, 2022) Sule Ozturk Birim; Ipek Kazancoglu; Sachin Kumar Mangla; Aysun Kahraman; Satish Kumar; Yigit Kazancoglu; Kumar Mangla, Sachin; Kazancoglu, Ipek; Kazancoglu, Yigit; Birim, Sule Ozturk; Kumar, Satish; Mangla, Sachin Kumar; Kahraman, Aysun
Against the uncertainty caused by the information overload in the online world consumers can benefit greatly by reading online product reviews before making their online purchases. However some of the reviews are written deceptively to manipulate purchasing decisions. The purpose of present study is to determine which feature combination is most effective in fake review detection among the features of sentiment scores topic distributions cluster distributions and bag of words. In this study additional feature combinations to a sentiment analysis are searched to examine the critical problem of fake reviews made to influence the decision-making process using review from amazon.com dataset. Results of the study points that behavior-related features play an important role in fake review classifications when jointly used with text-related features. Verified purchase is the only behavior related feature used comparatively with other text-related features.
Citation - WoS: 1
Citation - Scopus: 1
Editorial note for special issue on “Carbon neutrality through Industry 4.0 based smart manufacturing”
(Elsevier Ltd, 2025) Guo Li; Sachin Kumar Kumar Mangla; Malin Song; Yigit Kazancoglu; Ray Runyang Zhong; Song, Malin; Zhong, Ray Y.; Li, Guo; Mangla, Sachin Kumar; Kazancoglu, Yigit
[No abstract available]