Browsing by Author "Luthra, Sunil"

Now showing 1 - 15 of 15

Citation - WoS: 24
Citation - Scopus: 27
A Framework for Evaluating Information Transparency in Supply Chains
(IGI GLOBAL, 2021) Erhan Ada; Muhittin Sagnak; Yigit Kazancoglu; Sunil Luthra; Anil Kumar; Luthra, Sunil; Kumar, Anil; Ada, Erhan; Sagnak, Muhittin; Kazancoglu, Yigit
Private public profit and non-profit organizations and society as a whole currently face a significant reliable information necessity problem. Especially supply chains need trustworthy information to perform their activities successfully. This study aims to propose a framework and identify how reliability of information can be evaluated and measured through the concept of transparency. In this context dimensions such as comprehensiveness regularity timeliness content scope and user-friendliness are the pillars of the proposed framework. Selected criteria have been used as inputs to develop the information transparency level. The fuzzy analytic network process (ANP) is used to obtain weights of these inputs and data envelopment analysis (DEA) is used for the determination of the efficiency ranking for transparency. Results demonstrated that content scope and comprehensiveness dimensions have 75% impact on the transparency of data. The remaining 25% is affected by timeliness regularity and user-friendliness.
Citation - WoS: 53
Citation - Scopus: 66
An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling
(Springer, 2022) Rohit Agrawal; Vishal A. Wankhede; Anil Kumar; Sunil Luthra; Abhijit Majumdar; Yigit Kazancoglu; Luthra, Sunil; Kumar, Anil; Agrawal, Rohit; Wankhede, Vishal A.; Kazancoglu, Yigit; Majumdar, Abhijit
The world is moving into a situation where resource scarcity leads to an increase in material cost. A possible way to deal with the above challenge is to adopt Circular Economy (CE) concepts to make a close loop of material by eliminating industrial or post-consumer wastes. Integration of emerging technologies such as Artificial Intelligence (AI) machine learning and big data analytics provides significant support in successfully adopting and implementing CE practices. This study aims to explore the applications of AI techniques in enhancing the adoption and implementation of CE practices. A systematic literature review was performed to analyze the existing scenario and the potential research directions of AI in CE. A collection of 220 articles was shortlisted from the SCOPUS database in the field of AI in CE. A text mining approach known as Structural Topic Modeling (STM) was used to generate different thematic topics of AI applications in CE. Each generated topic was then discussed with shortlisted articles. Further a bibliometric study was performed to analyze the research trends in the field of AI applications in CE. A research framework was proposed for AI in CE based on the review conducted which could help industrial practitioners and researchers working in this domain. Further future research propositions on AI in CE were proposed. © 2022 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: 21
Citation - Scopus: 26
Analyzing critical factors of strategic alignment between operational excellence and Industry 4.0 technologies in smart manufacturing
(Emerald Publishing, 2024) Melisa Ozbiltekin-Pala; Yigit Kazancoglu; Anil Kumar; Jose Arturo Garza-Reyes; Sunil Luthra; Ozbiltekin-Pala, Melisa; Garza-Reyes, Jose Arturo; Kumar, Anil; Luthra, Sunil; Kazancoglu, Yigit
Purpose: The manufacturing sector is highly competitive and operationally complex. Therefore the strategic alignment between operational excellence methodologies and Industry 4.0 technologies is one of the issues that need to be addressed. The main aim of the study is to determine the critical factors of strategic alignment between operational excellence methodologies and Industry 4.0 technologies for manufacturing industries and make comparative analyses between automotive food and textile industries in terms of strategic alignment between operational excellence methodologies and Industry 4.0 technologies. Design/methodology/approach: First determining the critical factors based on literature review and expert opinions these criteria are weighted and analytical hierarchy process is run to calculate the weights of these criteria. Afterward the best sector is determined by the grey relational analysis method according to the criteria for the three manufacturing industries selected for the study. Findings: As a result of AHP “Infrastructure for Right Methodology Techniques and Tools is in the first place” Organizational Strategy is in the second place while the third highest critical factor is “Capital Investment”. Moreover based on grey relational analysis (GRA) results the automotive industry is determined as the best alternative in terms of strategic alignment between operational excellence (OPEX) methodologies and I4.0 technologies. Originality/value: This study is unique in that it is primarily possible to obtain the order of importance within the criteria and to make comparisons between three important manufacturing industries that are important for the economies of the world. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 6
Citation - Scopus: 7
Are we really addressing the roadblocks to adoption of renewable and sustainable energy technologies? Total interpretive structural modeling approach
(SPRINGER HEIDELBERG, 2024) Yigit Kazancoglu; Nazlican Gozacan; Sunil Luthra; Anil Kumar; Gozacan, Nazlican; Luthra, Sunil; Kumar, Anil; Kazançoğlu, Yiğit
Urban areas serve as a vital contribution to the global structural change towards renewable and sustainable energy technologies which also influence climate change. The aim of this paper is to identify the adoption roadblocks to renewable and sustainable urban energy technologies. This research has three parts: a mini-systematic literature study was conducted to identify the most prevalent roadblocks. Using total interpretive structural modeling (ISM) the relationships between the roadblocks and the source of causation were then examined. The roadblocks are classified based on their dependence and driving powers using MICMAC analysis in the third part of this research. The principal results and major conclusions demonstrate that all roadblocks are necessary for renewable and sustainable urban energy technologies. The roadblocks at level I are insufficient infrastructure lack of coordination among authorities lack of quality and reliable data and information and competition with non-renewable technologies, roadblocks in level II are lack of skilled and trained personnel limited public participation awareness and consumer interest and lack of standardized technology, roadblock in level III is high initial investment cost, and lastly roadblocks in level IV are lack of subsidies and financial support programs and absence of coherent related policies. Furthermore as a result of the MICMAC analysis none of the aforementioned roadblocks are classified as autonomous variables implying that they are all required. The dependent roadblocks to renewable and sustainable energy technologies are defined as lack of coordination among authorities lack of information and competition with non-renewable technologies. Moreover linkage roadblocks have high dependence and driving powers which are insufficient infrastructure limited awareness and consumer interest and lack of standardized technology. Lastly high initial investment costs lack of subsidies and financial support programs absence of coherent related policies and lack of skilled and trained personnel are the driving roadblocks with high driving power however not dependent.
Citation - WoS: 36
Citation - Scopus: 52
Assessing Supply Chain Innovations for Building Resilient Food Supply Chains: An Emerging Economy Perspective
(MDPI, 2023) Sudhanshu Joshi; Manu Sharma; Banu Y. Ekren; Yigit Kazancoglu; Sunil Luthra; Mukesh Prasad; Luthra, Sunil; Prasad, Mukesh; Joshi, Sudhanshu; Kazancoglu, Yigit; Ekren, Banu Y.; Sharma, Manu
Food waste reduction and security are the main concerns of agri-food supply chains as more than thirty-three percent of global food production is wasted or lost due to mismanagement. The ongoing challenges including resource scarcity climate change waste generation etc. need immediate actions from stakeholders to develop resilient food supply chains. Previous studies explored food supply chains and their challenges barriers enablers etc. Still there needs to be more literature on the innovations in supply chains that can build resilient food chains to last long and compete in the post-pandemic scenario. Thus studies are also required to explore supply chain innovations for the food sector. The current research employed a stepwise weight assessment ratio analysis (SWARA) to assess the supply chain innovations that can develop resilient food supply chains. This study is a pioneer in using the SWARA application to evaluate supply chain innovation and identify the most preferred alternatives. The results from the SWARA show that 'Business strategy innovations' are the most significant innovations that can bring resiliency to the food supply chains followed by 'Technological innovations.' The study provides insights for decision makers to understand the significant supply chain innovations to attain resilience in food chains and help the industry to survive and sustain in the long run.
Citation - WoS: 27
Citation - Scopus: 47
Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector
(MDPI, 2021) Yigit Kazancoglu; Muhittin Sagnak; Cisem Lafci; Sunil Luthra; Anil Kumar; Caner Tacoglu; Luthra, Sunil; Kumar, Anil; Lafcı, Çisem; Taçoğlu, Caner; Kazançoğlu, Yiğit; Sağnak, Muhittin
Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system and to maintain its processes effectively. In the healthcare sector the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials increased energy use and its environmental burden. In this context circularity and sustainability concepts have become essential in healthcare to meliorate the sector's negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector apply big data analytics in healthcare and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation and a proposal for a big data-enabled solutions framework to barriers to circularity using fuzzy best-worst Method (BWM) and fuzzy VIKOR. Based on the findings managerial policy and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.
Citation - WoS: 29
Citation - Scopus: 33
Circular dairy supply chain management through Internet of Things-enabled technologies
(Springer Science and Business Media Deutschland GmbH, 2022) Yigit Kazancoglu; Melisa Ozbiltekin-Pala; Muruvvet Deniz Sezer; Anil Kumar; Sunil Luthra; Ozbiltekin-Pala, Melisa; Sezer, Muruvvet Deniz; Kumar, Anil; Luthra, Sunil; Kazancoglu, Yigit
Internet of Things-enabled technologies help to collect data and make it understandable especially in supply chain processes thus minimizing the problems that may arise in supply chains. It is extremely important to support this process with Internet of Things-enabled technologies especially in supply chains that are vulnerable to disruptions such as the dairy supply chain. Moreover dairy supply chains are the type of supply chains where the most waste is generated, evaluating this waste is very beneficial to the circular economy. Therefore monitoring data in dairy supply chains and using Internet of Things-enabled technologies prevent losses, it is critical to have Internet of Things-enabled circular dairy supply chains in operation. The aim of this study is to determine the success factors of Internet of Things-enabled circular dairy supply chains based on the various stages of these chains, we hope to match each dairy supply chain stage with a success factor of Internet of Things-enabled technology and determine a ranking for these factors. Hence six success factors of Internet of Things-enabled circular supply chains are weighted for each stage of the chain, Internet of Things-enabled digital technologies are then matched with each stage of the chain and the success factor is determined. The ranking of factors can then be drawn up through the integration of Step Wise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference Similar to Ideal Solution (TOPSIS). The outcome of this study will provide managers and policy makers with insights into Internet of Things-enabled circular dairy supply chains. © 2021 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: 68
Citation - Scopus: 79
Drivers of implementing Big Data Analytics in food supply chains for transition to a circular economy and sustainable operations management
(Emerald Publishing, 2025) Yigit Kazancoglu; Melisa Ozbiltekin-Pala; Muruvvet Deniz Sezer; Sunil Luthra; Anil Kumar; Sezer, Muruvvet Deniz; Pala, Melisa Ozbiltekin; Luthra, Sunil; Kumar, Anil; Kazancoglu, Yigit; Ozbiltekin Pala, Melisa
Purpose: The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM). Design/methodology/approach: Ten different BDA drivers in FSC are examined for transition to CE, these are Supply Chains (SC) Visibility Operations Efficiency Information Management and Technology Collaborations between SC partners Data-driven innovation Demand management and Production Planning Talent Management Organizational Commitment Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia. Findings: The results show that Information Management and Technology Governmental Incentive and Management Team Capability drivers are classified as independent factors, Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility Data-driven innovation Demand management and Production Planning Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition Operations Efficiency Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM. Research limitations/implications: The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud implementing laws concerning government incentives reducing food loss and waste increasing tracing and traceability providing training activities to improve knowledge about BDA and focusing more on data analytics. Originality/value: The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 31
Citation - Scopus: 45
Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance
(Emerald Group Holdings Ltd., 2021) Surajit Bag; Sunil Luthra; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Luthra, Sunil; Bag, Surajit; Mangla, Sachin Kumar; Kazancoglu, Yigit
Purpose: The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance. Design/methodology/approach: The primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software. Findings: The results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs) data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance. Practical implications: The theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability. Originality/value: This research explored the relationship between BDA reverse logistics decisions and remanufacturing performance. The study was practice oriented and according to the authors’ knowledge it is the first study to be conducted in the South African context. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 39
Citation - Scopus: 51
Resilient reverse logistics with blockchain technology in sustainable food supply chain management during COVID-19
(John Wiley and Sons Ltd, 2023) Yigit Kazancoglu; Melisa Ozbiltekin-Pala; Muruvvet Deniz Sezer; Sunil Luthra; Anil Kumar; Ozbiltekin-Pala, Melisa; Sezer, Muruvvet Deniz; Luthra, Sunil; Kumar, Anil; Kazancoglu, Yigit
COVID-19 which is a global problem affects the all supply chains throughout the world. One of the supply chains most affected by COVID-19 is food supply chains. Since the sustainable food supply chain processes are complex and vulnerable in terms of product variety it has been negatively affected by the operational effects of COVID-19. While the problems experienced in the supply chain processes and raw material constraints caused stops in production the importance of new business models and production approaches came to the fore. One of the issues of increasing importance is the adoption of reverse logistics activities in sustainable food supply chains and increasing the resilience of food supply chains by integrating blockchain technology into processes. However adapting blockchain technology to increase the resilience of reverse logistics activities in the food supply chain has advantages as well as risks that need to be considered. Therefore it is aimed to determine these risks by using fuzzy synthetic evaluation method for eliminating the risks of blockchain adaptation for flexible reverse logistics in food supply chains to increase resiliency. The novelty of this study is that besides discussing about the benefits of BC-T it is to identify the risks it can create to eliminate these risks and to guide the establishment of resilience in reverse logistics activities of SFSCs. According to results the risks with the highest value among the subrisks are determined as data security risks. Data management risks are calculated as the risk with the highest value. © 2023 Elsevier B.V. All rights reserved.
Role of emerging technologies for empowering resilience and transparency in supply chains
(BMC, 2025) Yigit Kazancoglu; Sachin Kumar Mangla; Sunil Luthra; Mamta Rani Agarwal; Luthra, Sunil; Agarwal, Mamta Rani; Kazancoglu, Yigit; Mangla, Sachin Kumar
Ensuring resilience and transparency in supply chain (SC) operations has become crucial in today's dynamic and complicated global business environment particularly in the Fast-Moving Consumer Goods (FMCG) industry. Disruptions like the COVID-19 pandemic have highlighted the weaknesses of SCs stressing the necessity for proactive initiatives to improve adaptation and visibility. By defining the key success factors (CSFs) that support resilience and transparency in SCs and investigating the role of developing digital technologies most specifically AI in accomplishing these goals this study seeks to solve these issues. This study systematically finds ranks and assesses CSFs and their compatibility with cutting-edge technologies using a hybrid methodology that combines DEMATEL the Best-Worst Method (BWM) and the VIKOR technique. This study highlights AI's unique potential to promote moral decision-making increase accountability and improve predictive and adaptive skills. It also presents AI as a transformational facilitator. AI is distinguished from conventional technology by these qualities which also establish it as a fundamental component of transparent robust and sustainable SCs. This study fills a significant gap in the literature by integrating ethical AI holistically within a larger framework of digital technology. By connecting theoretical understanding with real-world implementations the study adds to the growing conversation around SC transparency and resilience especially in the FMCG sector. Important results show that using AI gives SCs a competitive edge in negotiating ambiguities and disruptions by allowing them to strike a compromise between operational effectiveness and moral concerns. Through the creative application of cutting-edge digital technologies the study offers practical insights for both practitioners and scholars laying the groundwork for more robust and transparent SC ecosystems.
Citation - WoS: 29
Citation - Scopus: 41
The role of agri-food 4.0 in climate-smart farming for controlling climate change-related risks: A business perspective analysis
(John Wiley and Sons Ltd, 2024) Yigit Kazancoglu; Çisem Lafci; Anil Kumar; Sunil Luthra; Jose Arturo Garza-Reyes; Yalcin Berberoglu; Garza-Reyes, Jose Arturo; Luthra, Sunil; Kumar, Anil; Lafci, Cisem; Berberoglu, Yalcin; Kazancoglu, Yigit
The impact of climate change including fires droughts and storms on natural resources and agricultural output is increasing. In addition to these problems resource depletion and greenhouse gas (GHG) emissions agriculture also contributes to global warming. To reduce the dangers of climate change farmers are using sustainable practices. This article aims to link agri-food 4.0 technology with climate-smart agriculture (CSA) to lessen the two-way interaction (both affecting and impacted) between the agricultural sector and global warming as well as dangers related to the agri-food business. In light of this information the research methodology of the paper is twofold. Initially related risks towards climate change and the CSA and agri-food 4.0 technologies to overcome these risks were determined through a literature review. Then risks and technologies are evaluated by adopting the TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) which is used for evaluating the criteria set with the related technologies to overcome climate change-related risks and provide a guiding map for academics and practitioners to eliminate risks associated with these climate change-related factors. According to the study's findings the highest-priority concerns in the agri-food industries that are connected to climate change include energy consumption food safety and GHG emissions. Internet of Things (IoT) bio-innovation and artificial intelligence are thought to be the most promising technological solutions to address these problems. © 2024 Elsevier B.V. All rights reserved.