Supply Chain Management Challenges Research Articles

Addressing Cybersecurity Challenges in Sustainable Supply Chain Management: A Review of Current Practices and Future Directions

Addressing Cybersecurity Challenges in Sustainable Supply Chain Management: A Review of Current Practices and Future Directions

Application of information technology (IT) in Iraq's petroleum industry

The crucial role of information technology (IT) in operating and managing industrial systems is widely recognized, and Iraq's IT capabilities hold great promise for its industrial and other economic sectors. The demand for IT services has risen considerably, particularly for oil and gas companies, which face the challenge of boosting capital asset returns while ensuring safety and sustainability. They need to manage rising demands as well as competition for access to oil reserves while driving improved asset reliability, productivity, and performance. This application show how information technology is used in the petroleum industry. It specifically examines information technology applications in the upstream, midstream, and downstream important segments of the petroleum industry during the drilling process. The increased price volatility's noteworthy effect on the profitability of the energy industry is just one of the current supply chain management challenges. Better supply chain management aids in lowering operational costs, a challenge that many businesses are under demand to address right away. The study's theoretical and practical value is in exposing the characteristics and issues with the infrastructure supporting the Iraqi oil industry and in creating a control algorithm for the evolution of its Regular visual inspections of pipelines, offshore oil rigs, and other infrastructure may be carried out swiftly and safely thanks to technology. Light devices that enable temperature checks using infrared scanning are available in modern applications. Hazards like poisonous substances can be identified by other sensors from a safe distance. Enhancing oil and gas safety with artificial intelligence technologies protects the environment and ensures worker safety

Net zero vs. carbon neutrality: supply chain management challenges and future research agenda

ABSTRACT Climate change endangers human survival, causing heat waves, floods, storms, polar ice loss, ocean acidification and rising sea levels. These effects harm the food supply and living environments and contribute to chronic diseases. Climate agreements like the Paris Agreement and Kyoto Protocol set net-zero emissions and carbon neutrality targets. However, their definitions, differences and challenges need clarification. This paper reviews net zero and carbon neutrality initiatives in supply chain management. CiteSpace analysis of 61 articles on net zero and 44 on carbon neutrality highlights their differences, challenges, practices and research gaps. Net-zero emissions cover more greenhouse gases and emission stages, fitting supply chain management through life-cycle assessment. A life-cycle perspective and strategies like bio-based materials, optimised transportation and electric vehicles are recommended for effective mitigation. Finally, the paper identifies research gaps at global, country, industry and company levels, suggesting research questions for future exploration.

Sustainable and ethical supply chain management: The role of HR in current practices and future directions

This study systematically reviews the integration of Human Resources (HR), sustainability, and ethics within the framework of supply chain management, illuminating the pivotal role of HR practices in enhancing the sustainability and ethical integrity of supply chains. Employing a systematic literature review coupled with content analysis, this research scrutinizes scholarly articles and case studies across various databases, focusing on the period from 2000 to 2024. The methodology hinges on precise inclusion and exclusion criteria to distill relevant literature, which is then subjected to rigorous content analysis to identify prevailing themes and insights. Key findings reveal that strategic HR practices, including talent management, training and development, and leadership cultivation, are central to embedding sustainability and ethical considerations into supply chain operations. These practices not only foster a culture of sustainability and ethics but also drive organizational change towards more sustainable supply chain models. The review identifies a dual challenge for sustainable supply chain management: the complexity of global supply chains and the increasing demands for transparency and ethical conduct, presenting both significant challenges and opportunities for innovation. Finally, the study underscores the critical role of HR in sustainable and ethical supply chain management, highlighting the necessity of integrating sustainability into HR practices. Strategic recommendations for organizations and HR professionals are provided to navigate the evolving landscape of supply chain sustainability. The study calls for further research into the dynamic interplay between HR practices and supply chain sustainability, particularly in the context of emerging technologies and global supply chain complexities.

CHALLENGES IN THE ADOPTION OF A TRACEABILITY SYSTEM IN THE HALAL FOOD SUPPLY CHAIN IN MALAYSIA

Halal and food safety are important issues nowadays. The issue regarding product recalls is not new and still happens, which involves halal and food safety matters. Other than that, the industries also lack awareness, knowledge, and the cost of implementing the traceability system on food premises. Thus, this study aimed to conduct an in-depth analysis of the challenges faced by industry player in adopting the traceability system in the halal food supply chain. The study adopts the qualitative method, where the data was collected through a semi-structured interview with representatives of the supply chain company. The gathered data were analysed using thematic analysis to understand the challenges faced in adopting the traceability system throughout the supply chain. The finding indicated that a traceability system is adopted to assure that information related to halal food and products is available and can be traced along the supply chain. It includes the information pre-production process, such as the sources and origins of the raw materials. As regards halal supply chain management challenges that food establishments faced in implementing halal traceability, its covered aspects of a technical system, logistics, segregation at shop shelves, distribution and retail records, and product recall. It is recommended that the food establishment develop and get acquainted with traceability technology tools as it is more effective and efficient in tracking and tracing the product.

Evaluation of Supply Chain Management Challenges of multinational companies in West Africa under the ECOWAS Trade Liberalization Scheme (ETLS)

This paper examined the Supply Chain Management challenges of multinational companies in West Africa under the ECOWAS trade liberalization scheme. The objective of the study includes to identify the various ECOWAS's regional integration protocols, Assess the level of implementation of ETLS among members state and evaluation of the various negative factors affecting full implementation of ETL, among others. The methodologies adopted includes Qualitative and quantitative method of data collection including literature review, expert interview, survey and case studies over a sample of 150 multinational companies (MNC). The results reveal that Complex and Non-Transparent Regulations exists among the MNC in the West African region, existence of Inadequate Infrastructure and Logistics, Non-Harmonized Trade Facilitation Measures as well as weak institutional co-ordination and enforcements. The study concludes that ETLS presents both opportunities and challenges for MNCs operating in West Africa and recommends among others the Simplification and Harmonization of Trade Regulations, investment in infrastructures and logistics as well as strengthening of institutional co-ordinations and enforcements.

Evaluating the impact of wood flow management software on administrative costs and efficiency for wood suppliers in the Southeastern U.S.: a case study of Timber Resource Analytics and Contract Tracking software

The southeastern U.S. forest industry serves as a crucial source of raw materials for construction and paper production for the global economy. However, this industry faces persistent challenges in wood supply chain management, often centered around logistical inefficiencies and administrative burdens. Additionally, increased operational overhead, due to rising costs, has exerted strain on the business success of wood suppliers. Administrative procedures and record keeping have traditionally been a labor-intensive process for this sector of the wood supply chain. However, technological solutions, such as new wood flow and logistics management software, can enhance efficiency and cost-effectiveness of these procedures. This research investigated the impact of wood flow management software on administrative costs for southeastern U.S. wood suppliers. Utilizing a case study approach, we compared administrative time and cost savings of software users and non-software users, to determine potential financial benefits of software adoption. The research design centered on gathering insight from both user groups, through individual interviews, allowing for a comparative analysis of the software’s efficiency metrics. Results indicated that wood suppliers who utilized software saved an average of 43 hours per week on administrative tasks, resulting in estimated annual savings of approximately $62,000 United States dollars (USD). The greatest reported time and cost savings were attributed to scheduling jobs and organizing information. Adoption of wood flow logistics software may result in additional monetary savings through increased efficiency in administrative procedures and communications. These research findings will be useful to wood suppliers interested in improving operational efficiency and reducing administrative costs.

Assessing Supplier Disruption Risks Using a Modified Pythagorean Fuzzy SWARA–TOPSIS Approach

As the complexity and uncertainty of global supply chains escalate, disruptions have become an increasingly common challenge in supply chain management. Suppliers, who serve as essential connectors for the seamless movement of goods and materials critical to production and distribution, are often at the center of these disruptions, highlighting their significant impact on the overall stability of the supply chain. This study proposes an innovative approach to assessing supplier disruption risks by combining the Pythagorean Fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) with the Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS). By reviewing the literature and consulting with supply chain experts, eight key risk factors were identified. The PF-SWARA method then quantifies the significance of these risks, while a modified PF-TOPSIS technique calculates each supplier’s risk score, facilitating the prioritization of suppliers for targeted improvement. The findings of the study indicate that “natural disasters and geopolitical risks,” “financial instability,” and “delivery delays” emerge as the top three critical disruption risk factors. Suppliers facing higher disruption risks should, therefore, formulate improvement strategies that target these three areas.

Nurturing family business resilience through strategic supply chain management

PurposeThe study aspires to enhance comprehension of the intricate interplay between supply chain management (SCM) and resilience in family businesses, thereby offering valuable insights to managers and policymakers endeavouring to foster resilience in uncertain environments.Design/methodology/approachCommencing from the premise that family businesses (FBs) prioritize the preservation of socio-emotional wealth (SEW) when formulating strategic decisions, this study endeavours to advance understanding of supply chain practices adopted by FBs and their direct impact on resilience during crisis situations or economically challenging periods. Through an exploratory case study of nine FBs, the present research reveals four pivotal strategies in SCM that contribute to their resilience: (i) reorganization of inventory management; (ii) cultivating close relationships with suppliers; (iii) emphasizing product quality and customer retention; and (iv) implementing cost reduction measures to bolster resilience. The aim of the study is to provide an in-depth understanding of the intricate interplay between SCM and resilience in FBs, thereby offering valuable insights to managers and policymakers endeavouring to foster resilience in uncertain environments.FindingsOur approach offers a theoretical framework for SCM aligned with prior research on the interplay between characteristics of family businesses and resilience strategies. Furthermore, this paper illustrates how factors such as the emphasis on high-quality products and services by family businesses contribute to achieving non-economic objectives that owners adopt to reconcile family and business needs, creating intrinsic added value for the company. It reveals various challenges in SCM, including inventory organization changes, supplier closures and the significance of customer retention. Family businesses are implementing product and technology enhancements and leveraging digitization to enhance supply chain processes.Originality/valueThis paper contributes significantly to the field of FBs by highlighting the crucial role of SCM in enhancing business resilience during crises. It empirically examines how the SEW characteristics of FBs influence the reconfiguration of their supply chains to enhance resilience, presenting a theoretical model for this context. Our theoretical framework employs an SEW perspective to elucidate how FBs respond to the challenges posed by the COVID-19 pandemic by adapting their SCM processes to safeguard their social and emotional legitimacy, organizational visibility and reputation. These adaptations gain particular relevance during crises or turbulent conditions, potentially leading to alterations in how FBs formulate their supply chain strategies and manage supply chain-related processes.

LEVERAGING ARTIFICIAL INTELLIGENCE FOR ENHANCED SUPPLY CHAIN OPTIMIZATION: A COMPREHENSIVE REVIEW OF CURRENT PRACTICES AND FUTURE POTENTIALS

The integration of artificial intelligence (AI) technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. This comprehensive review synthesizes current practices and future potentials of leveraging AI for supply chain optimization. Beginning with an overview of traditional supply chain management challenges, the review elucidates how AI solutions address these complexities by enabling predictive analytics, real-time visibility, and intelligent decision-making. The review delves into the diverse applications of AI across different stages of the supply chain, including demand forecasting, inventory management, logistics optimization, and supplier relationship management. Examples of AI-driven technologies such as machine learning, natural language processing, and robotic process automation are analyzed for their role in revolutionizing supply chain operations. Furthermore, the review highlights the transformative impact of AI on supply chain resilience, emphasizing its ability to mitigate disruptions, adapt to dynamic market conditions, and optimize resource allocation. The review also addresses critical considerations such as data privacy, ethical implications, and organizational readiness for AI adoption within supply chain contexts. Lastly, the review discusses future research directions and potential advancements in AI-enabled supply chain management, envisioning intelligent autonomous supply chains characterized by self-learning systems, collaborative ecosystems, and enhanced sustainability practices. In conclusion, this review underscores the pivotal role of AI in driving continuous innovation and competitive advantage within supply chain networks, while also emphasizing the importance of strategic planning and responsible implementation to harness its full potential.
 Keywords: AI, Supply Chain, Optimization, Practices, Review.

A Systematic Review of Strategic Supply Chain Challenges and Teaching Strategies

Background: This study provides a comprehensive overview of current supply chain challenges and how they are taught within university circles or among supply chain professionals to simulate reality. Methods: The study applied a systematic literature review, using bibliometric co-citation and concept-centered content analysis for a comprehensive review of 118 relevant articles, leading to the identification of critical challenges in modern supply chain management. Results: These challenges include supplier selection and quality, supply chain networks, and sustainable supply chains. Supply chain educators are encouraged to use games that mirror real-world scenarios to teach these challenges. Results from this review underscore that existing games covered supply chain concepts such as the bullwhip effect, collaboration, networks, supplier selection, quality management, humanitarian logistics, sustainability, lean supply chain, Supply Chain 4.0, and perishable goods supply. Conclusions: The study’s contribution is to assist in selecting games tailored to the supply chain specific aspects and to guide developers in creating realistic games that address recent challenges in supply chain management. It recommends a holistic approach to enhance new supply chain game development, drawing from methodologies such as problem-based learning and Lego Serious Play. This multifaceted approach imparts practical knowledge and comprehensive skills for addressing supply chain intricacies in modern business settings.

Integration of Lean Six Sigma and Ergonomics in Internal Logistics in the Supply Chain – A Systematic Literature

Today's most significant challenge facing the industry is delivering good quality products quickly, at the lowest possible cost, and flexibly in meeting demand. In terms of timing accuracy, companies must have good internal logistics, one of which is good warehouse management. Productivity in warehouses can be achieved by improving efficiency in warehouse operations, thus resulting in a faster response to customer requests. In improving productivity, it is necessary first to identify unexpected activity. This study demonstrates the importance of applying Lean Six Sigma (LSS) in Supply Chain Management (SCM), particularly internal logistics in the warehouse area, by linking the ergonomics principle with the systematic literature review approach. LSS and ergonomics are mutually complementary methodologies to meet the challenges of SCM in order to have a competitive advantage in business.

Cubic picture fuzzy topological data analysis with integrating blockchain and the metaverse for uncertain supply chain management

This article explores the idea of cubic picture fuzzy sets (CPFSs) for developing blockchain-driven solutions to meet uncertain supply chain management (SCM) and contemporary urban transportation (CUT) challenges. For this objective, the concept of topological structures on CPFSs is explored and the notion of cubic picture fuzzy topology is defined with P-order and R-order. We construct CRITIC-COPRAS method based on cubic picture fuzzy topological data analysis and cubic picture fuzzy fairly aggregation operator for finding solutions to SCM, CUT, electronic toll collection, fare management, micromobility solutions, and car-sharing implementations. We also examine the transformative potential of blockchain technology within the metaverse particularly in the domains of ride-sharing and peer-to-peer mobility platforms. To systematically evaluate and prioritize these integration strategies, we employ a robust multi-criteria decision-making (MCDM) model. This comprehensive assessment covers eight criteria including, transportation efficiency, user experience, technological feasibility, and regulatory compliance. Moreover, it offers promising solutions for revenue management and data security in electronic toll collection and public transportation fare management.

A q-rung orthopair fuzzy decision-making framework considering experts trust relationships and psychological behavior: An application to green supplier selection

The selection of an optimal supplier is a critical and open challenge in supply chain management. While experts' assessments significantly influence the supplier selection process, their subjective interactions can introduce unpredictable uncertainty. Existing methods have limitations in effectively representing and handling this uncertainty. The paper aims to address these challenges by proposing a novel approach that leverages q-rung orthopair fuzzy sets (q-ROFSs). The novelty of the proposed approach lies in its ability to accurately capture experts' preferences through the use of q-ROFSs, which offer membership and non-membership degrees, providing a broader expression space compared to conventional fuzzy sets. Additionally, it incorporates social network analysis (SNA) to effectively consider the trust relationships among experts. The proposed approach is divided into three stages. The first stage, presents a novel method to determine experts' weights by combining SNA, the Bayesian formula, and the maximum entropy principle. This approach allows for a more precise representation of varying levels of expertise and influence among experts, addressing the uncertainty arising from subjective interactions. The second stage introduces a hybrid weight determination method to determine criteria weights within the context of q-ROFSs. Entropy plays a crucial role in capturing fuzziness and uncertainty in q-ROFSs, while the projection measure simultaneously provides information about the distance and angle between alternatives. By employing both objective weights estimated using entropy and normalized projection measure and subjective weights derived using an aggregation operator and a score function, the presented approach achieves a comprehensive assessment of criteria importance. To incorporate both subjective and objective weights effectively, game theory is applied which allows us to align decision-making with both quantitative and qualitative aspects, making the method more versatile and applicable. The third stage redefines the traditional Combined Compromise Solution (CoCoSo) method using Bonferroni mean operators which captures interrelationships among arguments to be aggregated. Furthermore, in recognition of the importance of an expert risk preferences and psychological behaviors, we apply regret theory, ensuring that the chosen solutions align more effectively with their underlying preferences and aspirations. The applicability and effectiveness of the proposed approach are demonstrated through a numerical example of green supplier selection. The comparative analysis illustrates the practicality and real-world relevance while the sensitivity analysis, confirms the stability and robustness of the proposed approach.

An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction

In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.

Towards Blockchain-Integrated Enterprise Resource Planning: A Pre-Implementation Guide

In the face of numerous challenges in supply chain management, new technologies are being implemented to overcome obstacles and improve overall performance. Among these technologies, blockchain, a part of the distributed ledger family, offers several advantages when integrated with ERP systems, such as transparency, traceability, and data security. However, blockchain remains a novel, complex, and costly technology. The purpose of this paper is to guide decision-makers in determining whether integrating blockchain technology with ERP systems is appropriate during the pre-implementation phase. This paper focuses on the literature reviews, theories, and expert opinions to achieve its objectives. It first provides an overview of blockchain technology, then discusses its potential benefits to the supply chain, and finally proposes a framework to assist decision-makers in determining whether blockchain meets the needs of their consortium and whether this integration aligns with available resources. The results highlight the complexity of blockchain, the importance of detailed and in-depth research in deciding whether to integrate blockchain technology into ERP systems, and future research prospects. The findings of this article also present the critical decisions to be made prior to the implementation of blockchain, in the event that decision-makers choose to proceed with blockchain integration. The findings of this article augment the existing literature and can be applied in real-world contexts by stakeholders involved in blockchain integration projects with ERP systems.

ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains.

Navigating Supply Chain Complexity: Empirical Insights from the Developing Country Readymade Garments Industry

Purpose- The study aims to develop an efficient model to handle challenges of supply chain management in readymade garments industry. This paper illustrates the systematic literature reviews and experimental evidence-based study of readymade garments companies of a developing Asian country. The present study also finds the pattern for the healthy and sustained development of clothing enterprises. Design/methodology/approach- This paper illustrates the systematic literature reviews and experimental evidence-based study of readymade garments companies of a developing Asian country. The existing qualitative and quantitative studies were reviewed to find out the challenges in supply chain and the recommendable solutions are provided. The studies undertaken in Asian developing countries were only considered in this study. Findings- The innovation on supply chain management for readymade garments company in developing country will help enterprise to setup legal guidelines for smooth production process. The new innovative supply chain management has two characteristics, a balance of high efficiency and cost saving. Dual-source supply chain management, manufacturing outsourcing, and multi-supplier management are three elements of it. The ICT information management platform is a critical component in ensuring the supply chain flexibility of the clothing industry. Implications- Through the research of application and analysis of the innovative supply chain systems of readymade garment industry of Asian developing countries. It helps to setup guideline, and direction for the practical operation way of modern enterprise with innovative supply chain system knowledge and practice. Originality/value- All this relies on its unique and efficient supply chain management, this paper explains and analyzed innovative supply chain management. This paper also contributes by providing solution to overcome from the challenges of supply chain management in readymade garments industry. Paper Type- Research paper

Improving the availability of antihypertensive drugs in the India Hypertension Control Initiative, India, 2019–2020

Background Antihypertensive drug supply is sometimes inadequate in public sector health facilities in India. One of the core strategies of the India Hypertension Control Initiative (IHCI) is to improve the availability of antihypertensive drugs in primary and secondary care facilities. We quantified the availability of antihypertensive drugs in 2019–20 and described the practices in supply chain management in 22 districts across four states of India. Methods Twenty-two districts from 4 states (Punjab, Madhya Pradesh, Telangana, and Maharashtra) were studied. We described the practices and challenges in supply chain management. We collected data on drug procurement from 2018 to 2020 and drug availability from April 2019 to March 2020. Quantity procured, the proportion of facilities with stockout at the end of each quarter, and availability of drugs in patient days were tabulated. Results All states selected drug- and dose-specific protocols with Amlodipine as the initial drug and shifted to morbidity-based forecasting. The total number of antihypertensive tablets procured for the 22 districts increased from 16 million in 2017–2018 to 160 million in 2019–2020. The proportion of facilities with Amlodipine stock-out was below 5% during the study period. Amlodipine stock was available for at least 60 patient days from the third quarter of 2019 onward in all districts. Conclusions This study demonstrates that including best practices can gradually strengthen the procurement and supply chain for antihypertensives in a low-resource setting. As the program was rapidly growing, there were still gaps in the procurement and distribution system which needed to be addressed to ensure the adequacy of drugs. We recommend that best practices, including choosing a single protocol, basing supply on projected patient load rather than an increment from historical levels, and using simple stock management tools, be replicated in other districts in India to increase and sustain coverage of hypertension treatment.

Improving sporadic demand forecasting using a modified k-nearest neighbor framework

Forecasting sporadic or intermittent demand presents significant challenges in supply chain management, primarily due to the frequent occurrence of zero demand values and the inherent difficulty in capturing underlying hidden patterns in sporadic dataset. Although various parametric and non-parametric methods are available for sporadic demand forecasting, they often fail to detect these hidden patterns, underscoring the need for artificial intelligence (AI) algorithms which are effective in identifying irregular patterns. Among AI algorithms, the k-Nearest Neighbor (k-NN) is particularly good at identifying patterns with limited data. In the past, k-NN based frameworks have been recognized for their ability to effectively forecast sporadic demand, particularly when it is anticipated that patterns will reoccur in subsequent non-zero demand values. However, this assumption does not always hold true in different real-world scenario. In sporadic time series data, zero values often comprise a significant proportion (>30%). To address this, this paper proposes a “zero-inclusive k-NN framework” that leverages both zero and non-zero demand data to identify patterns. The proposed framework offers two significant features: it enables industrial managers to utilize a large number of nearest neighbors and provides adaptability in demand-vector length. Numerical investigations with both synthetic and real datasets affirm the superior forecasting performance of the proposed k-NN method when compared to existing k-NN framework and conventional parametric benchmark methods. The implications of our findings extend to domains where sporadic or intermittent demand forecasting plays a vital role.