Niharika Varshney, Srikant Gupta and Aquil Ahmed
This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing…
Abstract
Purpose
This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.
Design/methodology/approach
In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.
Findings
The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.
Research limitations/implications
This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.
Originality/value
This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.
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Keywords
Isma Zaighum, Qaiser Abbas, Kinza Batool, Shehar Bano and Syed Murtaza Sajjad
Intellectual capital (IC) plays a pivotal role in determining corporate risk profiles in the contemporary knowledge era. Consequently, this study aims to analyze the impact of IC…
Abstract
Purpose
Intellectual capital (IC) plays a pivotal role in determining corporate risk profiles in the contemporary knowledge era. Consequently, this study aims to analyze the impact of IC on firm risk (FR) among the manufacturing companies listed on the Pakistan Stock Exchange (PSX).
Design/methodology/approach
The authors have adopted the modified value-added intellectual model which combines human capital efficiency, structural capital efficiency, efficiency of capital employed and relational capital efficiency. FR has been used as the dependent variable, measured as the standard deviation of the daily stock prices. The study has used panel data from a sample of 40 manufacturing companies listed in the KSE-100 Index from 2015 to 2021.
Findings
The results suggest that IC has a significant impact on the FR of manufacturing companies listed on the benchmark index of PSX. Moreover, this relationship is direct; thus, an increase in IC would also increase FR measured by the change in stock prices.
Research limitations/implications
The current study has only used linear techniques. Future researchers may consider investigating the impact of IC at varying levels of FR using nonlinear techniques.
Practical implications
This study provides corporate managers and policymakers valuable insight into the need to strike a balance between investment in IC and their FR, particularly in an emerging market context.
Originality/value
IC is frequently associated with firm performance. However, the relationship between IC and FR has generally been underexplored. This study adds to the strand of limited IC literature by investigating the impact of a modified IC model on FR in an emerging economy.