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Article
Publication date: 11 April 2023

Mysha Maliha, Md. Abdul Moktadir, Surajit Bag and Alexandros I. Stefanakis

The global resolution of embracing dynamic and intertwined production systems has made it necessary to adopt viable systems like circular economy (CE) to ensure excellency in the…

Abstract

Purpose

The global resolution of embracing dynamic and intertwined production systems has made it necessary to adopt viable systems like circular economy (CE) to ensure excellency in the business. However, in emerging countries, it is challenging to implement the CE practices due to the existing problems in the supply chain network, as well as due to the vulnerable financial condition of the business after the deadly hit of COVID-19. The main aim of this research is to determine the barriers to implementing CE considering the recent pandemic and suggest strategies to organizations to ensure CE for a cleaner environment and greener economy.

Design/methodology/approach

After an extensive literature review and validation from experts, 24 sub-barriers under the class of 6 main barriers are finalized by Pareto analysis, which is further analyzed via the best-worst method to determine the weight and rank of the barriers Further, fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the proposed startegies to overcome the analysed barriers.

Findings

The results identified “unavailability of initial funding capital”, “need long time investment”, “lack of integrating production system using advance technology” and “lack of strategic planning” as the most acute sub-barriers to CE implementation. Further, fuzzy TOPSIS method is used to suggest the best strategy to mitigate the ranked barriers. The results indicated “integrated design facility to CE”, “ensuring large scale funding for CE facility” as the best strategy.

Practical implications

This study will motivate managers to implement CE practices to enjoy proper utilization of the resources, sustainable benefits in business, and gain competitive advantage.

Originality/value

Periodically, a lot of work is done on CE practices but none of them highlighted the issues in the domain of the leather products industry (LPI) and COVID-19 toward achieving sustainability in production and consumption. Thus, some significant barriers and strategies to implement CE for achieving sustainability in LPI are highlighted in this study, which is a unique contribution to the literature.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 28 November 2023

Shikha Singh, Sameer Kumar and Adarsh Kumar

The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the…

Abstract

Purpose

The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the effective functioning of the existing supply chain during COVID-19. The focus is to see the disruptions impacting the energy storage supply chains.

Design/methodology/approach

The procedure entails a thorough analysis of scholarly literature pertaining to various supply chain interruptions, confirmed and verified by experts working in an energy storage company in India. These experts also confirmed the occurrence of more disruptive factors during their interviews and questionnaire survey. Moreover, this process attempts to filter out the relevant causal disruption factors in an energy storage company by using the integrated approach of qualitative and quantitative methodologies.

Findings

The results provide practical insights for the company management in planning and devising new strategies to manage supply chain disruptions. Supply chains for companies in other industry sectors can also benefit from the proposed framework and results in making them more robust to counter future disastrous events.

Originality/value

The study provides an easily adaptable decision framework to different industries by closely examining supply chain disruptions and identifying associated causes for building a robust supply chain focused on the energy storage sector. It examines four disruption dimensions and investigates possible outcomes and impacts of disruptions.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 5 July 2023

Philip Seagraves

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from…

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Abstract

Purpose

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

Design/methodology/approach

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

Findings

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

Practical implications

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

Originality/value

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

Details

Journal of Property Investment & Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 29 January 2024

Girish Prayag, Mesbahuddin Chowdhury and Lucie K. Ozanne

Using dynamic capabilities (DCs) theory, the authors assess whether micro, small and medium-sized enterprises (MSMEs) can leverage DCs to improve operational capabilities (OCs…

Abstract

Purpose

Using dynamic capabilities (DCs) theory, the authors assess whether micro, small and medium-sized enterprises (MSMEs) can leverage DCs to improve operational capabilities (OCs) during the COVID-19 pandemic. The authors also identify whether organizational learning (OL) affects the relationship between DCs and OCs.

Design/methodology/approach

The authors test these propositions on a sample of 419 MSMEs from Australia and New Zealand.

Findings

DCs have no direct effect on OCs, technological or marketing capabilities (TCs or MCs). OL moderates the effect of DCs on both TCs and MCs.

Research limitations/implications

The study assesses only MCs and TCs as OCs and does not explicitly measure pandemic impacts on organizations. However, the results illustrate the importance of OL during crises for recovery purposes.

Practical implications

Managers can use the findings to improve structure, processes and knowledge management emanating from MCs and TCs within organizations impacted by the COVID-19 pandemic.

Originality/value

The authors use a multi-dimensional measure of OL and show that during the pandemic, OL is a critical factor that allows organizations to transform the benefits conferred by DCs into MCs and TCs.

Details

Asia Pacific Journal of Marketing and Logistics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-5855

Keywords

Article
Publication date: 10 August 2022

Mohammad Nasih, Damara Ardelia Kusuma Wardani, Iman Harymawan, Fajar Kristanto Gautama Putra and Adel Sarea

Without a doubt, COVID-19 is a disruptive event that one may not consider before it becomes a global pandemic. This study aims to examine the firm’s risk preference, represented…

Abstract

Purpose

Without a doubt, COVID-19 is a disruptive event that one may not consider before it becomes a global pandemic. This study aims to examine the firm’s risk preference, represented as board characteristics towards COVID-19 exposure in Indonesia.

Design/methodology/approach

This study uses the boardroom’s average value of board age and female proportion to represent board characteristics. Fixed-effect regression based on industry (Industry FE) and year (Year FE) analyses 861 firm-year observations of all firms listed on the Indonesian Stock Exchange in 2019–2020.

Findings

The result shows a positive relationship between the female board and COVID-19 exposure disclosure. Meanwhile, the age proportion does not offer a significant result. The additional analysis document that the directors mainly drove the result and were only relevant during 2020. These results are robust due to coarsened exact matching tests and Heckman’s two-stage regression. This study enriches COVID-19 literature, especially from a quantitative perspective.

Originality/value

The rise of global crises makes the outputs of this study important for non-financial listed firms in Indonesia.

Details

Journal of Financial Reporting and Accounting, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1985-2517

Keywords

Article
Publication date: 12 October 2023

Quanxi Li, Haowei Zhang, Kailing Liu, Zuopeng Justin Zhang and Sajjad M. Jasimuddin

There has been limited research that has explored the connection between digital supply chain (DSC) and SC innovation and SC dynamic capabilities. This paper aims to examine the…

Abstract

Purpose

There has been limited research that has explored the connection between digital supply chain (DSC) and SC innovation and SC dynamic capabilities. This paper aims to examine the mediating effect of SC innovation on the relationship between DSC and SC dynamic capabilities.

Design/methodology/approach

The research model and hypotheses were tested, employing (Statistical Package of Social Sciences) SPSS 25.0 and (Analysis of Moment Structures) AMOS 24.0 on data drawn from the Chinese manufacturing enterprises.

Findings

The study reveals that DSC has a significant positive effect on SC innovation and SC dynamic capabilities. SC innovation also has a significant positive effect on SC dynamic capabilities. Besides, the authors' research illustrates that SC innovation partially mediates the relationship between DSC and SC dynamic capabilities.

Research limitations/implications

Since the results are derived from the data collected from China, it may not, therefore, be generalized to other settings. Moreover, future research could consider other contextual variables such as “environmental uncertainty” and “Government's Reward-Penalty Mechanism,” which may influence SC dynamic capabilities.

Practical implications

The study provides practical insights for senior executives and managers in the manufacturing industry. Managers should emphasize the investment of advanced digital technologies and tools (DTTs) and improvement of SC visibility and collaboration. In the digital age, companies should pay attention to the introduction of advanced technologies, tools and processes and focus on cultivating an innovative spirit to promote SC dynamic capabilities, thereby enhancing competitive advantages.

Originality/value

The paper illustrates that DSC is of great significance to improving SC dynamic capabilities. This study reveals compelling insights for firms to enhance SC innovation and dynamic capabilities by using DSC as an enabler.

Details

The International Journal of Logistics Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0957-4093

Keywords

Article
Publication date: 9 February 2024

Neelesh Kumar Mishra, Poorva Pande Sharma and Shyam Kumar Chaudhary

This paper aims to uncover the key enablers of an agile supply chain in the manufacturing sector amidst disruptions such as pandemics, trade wars and cross-border challenges. The…

Abstract

Purpose

This paper aims to uncover the key enablers of an agile supply chain in the manufacturing sector amidst disruptions such as pandemics, trade wars and cross-border challenges. The study aims to assess the applicability of existing literature to manufacturing and identify additional industry-specific enablers contributing to the field of supply chain management.

Design/methodology/approach

The research methodology is comprehensively described, detailing the utilization of extent literature and semistructured interviews with mid- and top-level executives in a supply chain. The authors ensure the robustness of the data collection process and results interpretation.

Findings

The study identifies six essential dimensions of an agile supply chain: information availability, design robustness, external resource planning, quickness and speed, public policy influencing skills and cash flow management. The study provides valuable insights for industry professionals to develop agile supply chains capable of responding to disruptions in a rapidly changing world.

Research limitations/implications

This study is limited by its focus on the manufacturing sector, and future research may explore the applicability of these findings to other industries. By focusing on these essential dimensions identified in the study, managers can develop strategies to improve the agility and responsiveness of their supply chains. In addition, further research may investigate how these enablers may vary in different regions or contexts.

Practical implications

The COVID-19 pandemic has forced executives to reconsider their sourcing strategies and reduce dependence on suppliers from specific geographies. To ensure business continuity, companies should assess the risk associated with their suppliers and develop a business continuity plan that includes multisourcing their strategic materials. Digital transformation will revolutionize the supply chain industry, allowing for end-to-end visibility, real time insights and seamless integration of business and processes. Companies should also focus on creating a collaborative workforce ecosystem that prioritizes worker health and well-being. Maintaining trust with stakeholders is crucial, and firms must revisit their relationship management strategies. Finally, to maintain business leadership and competitiveness during volatile periods, the product portfolio needs to be diversified and marketing and sales teams must work in tandem with product teams to position new products accordingly.

Social implications

This work contributes substantially to the literature on supply chain agility (SCA) by adding several new factors. The findings result in a more efficient and cost-effective supply chain during a stable situation and high service levels in a volatile situation. A less complex methodology for understanding SCA provides factors with a more straightforward method for identifying well-springs of related drivers. First, the study contributes to reestablish the factors such as quickness, responsiveness, competency, flexibility, proactiveness, collaboration and partnership, customer focus, velocity and speed, visibility, robustness, cost-effectiveness, alertness accessibility to information and decisiveness as applicable factors for SCA. Second, the study suggests a few more factors, such as liquidity management, Vendors’ economic assessment and economic diversity, that are the study’s unique contributions in extending the enablers of SCA. Finally, public policy influencing skills, local administration connects and maintaining capable vendors are the areas that were never considered essential for SCA. These factors have emerged as a vital operational factor during the lockdown, and academicians may consider these factors in the future to assess their applicability.

Originality/value

This study provides new insights for decision-makers looking to enhance the resilience and agility of their supply chains. The identification of unique enablers specific to the manufacturing industry contributes to the existing body of literature on agile supply chains in the face of disruptions.

Details

Journal of Global Operations and Strategic Sourcing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-5364

Keywords

Article
Publication date: 28 December 2023

Seyed Hossein Razavi Hajiagha, Saeed Alaei, Arian Sadraee and Paria Nazmi

Despite the wide research and discussion on international performance, innovation and digital resilience dimensions of enterprises, the investigation and understanding of their…

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Abstract

Purpose

Despite the wide research and discussion on international performance, innovation and digital resilience dimensions of enterprises, the investigation and understanding of their interrelations seem to be limited. The purpose of this study is to identify the influential factors affecting the mentioned dimensions, determine the causal relationships among these identified factors and finally evaluate their importance in an aggregated framework from the viewpoint of small and medium-sized enterprises (SMEs).

Design/methodology/approach

A hybrid methodology is used to achieve the objectives. First, the main factors of international performance, innovation and digital resilience are extracted by an in-depth review of the literature. These factors are then screened by expert opinions to localize them in accordance with the conditions of an emerging economy. Finally, the relationship and the importance of the factors are determined using an uncertain multi-criteria decision-making (MCDM) approach.

Findings

The findings reveal that there is a correlation between digital resilience and innovation, and both factors have an impact on the international performance of SMEs. The cause-or-effect nature of the factors belonging to each dimension is also determined. Among the effect factors, business model innovation (BMI), agility, product and organizational innovation are known as the most important factors. International knowledge, personal drivers and digital transformation are also determined to be the most important cause factors.

Originality/value

This study extends the literature both in methodological and practical directions. Practically, the study aggregates the factors in the mentioned dimensions and provides insights into their cause-and-effect interrelations. Methodologically, the study proposes an uncertain MCDM approach that has been rarely used in previous studies in this field.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 12 October 2023

Xiaoli Su, Lijun Zeng, Bo Shao and Binlong Lin

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production…

Abstract

Purpose

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

Design/methodology/approach

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Findings

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Originality/value

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Open Access
Article
Publication date: 13 August 2020

Mariam AlKandari and Imtiaz Ahmad

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate…

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Abstract

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

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