Research Online at MacEwan :: Browsing by Author "Kang, Parminder Singh"

Browsing by Author "Kang, Parminder Singh"

Now showing 1 - 8 of 8

Bridging the gap: a systematic analysis of circular economy, supply chain management, and digitization for sustainability and resilience
(2024) Kang, Parminder Singh; Bhawna; Sharma, Sanjeev Kumar
The primary objective of this research paper is to conduct a comprehensive and systematic literature review (SLR) focusing on Sustainable Supply Chain Management (SSCM) practices that promote Circular Economy (CE), sustainability, and resilience through adopting emerging digital technologies. A SLR of 130 research articles published between 1991 and 2023 was used to analyze emerging trends in CE, supply chain management (SCM), and digitalization. This study meticulously examined research publication patterns, the intricate themes explored, influential scholars, leading countries, and substantial scientific contributions that have shaped this multifaceted domain. This paper contributed to the collective understanding of how SSCM practices, driven by the principles of CE and empowered by the adoption of digital technologies, foster sustainability, resilience, and innovation within contemporary SCs. The research findings presented herein are primarily based on an analysis of the current literature from only Scopus and Web of Science (WoS) databases, which may restrict the generalizability of implementing these results. Based on this study, organizations and practitioners can assess the maturity of their SCM practices, gauge the resilience and digitalization levels of their SCs, and align them with academic literature trends. This enables practitioners to bridge the gap between scholarly advancements and real-world SCM implementation. Through its systematic review, the study provides a structured literature review that offers a collective understanding of SSCM practices driven by CE principles and empowered by digital technologies. This understanding enables sustainability, resilience, and innovation within contemporary SCs, benefiting academicians and practitioners.
Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation
(2019) Kang, Parminder Singh; Bhatti, Rajbir
Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.
Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem
(2025) Kang, Parminder Singh; Bhawna, Bhawna
Purpose This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature. Design/methodology/approach This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations. Findings The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service. Research limitations/implications The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties. Practical implications The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly. Social implications The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains. Originality/value Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.
The harmonized information-technology and organizational performance model (HI-TOP)
(2024) Enstroem, Rickard; Kang, Parminder Singh ; Bhawna, Bhawna
This study introduces the Harmonized Information-Technology and Organizational Performance Model (HI-TOP), which addresses the need for a holistic framework that integrates technology and human dynamics within organizational settings. This approach aims to enhance organizational productivity and employee well-being by aligning technological advancements with human factors in the context of digital transformation. Employing a two-phased methodology, the HI-TOP model is developed through a literature review and text mining of industry reports. This approach identifies and integrates critical themes related to ICT integration challenges and opportunities within organizations.
Information technology investment and working capital management efficiency: evidence from India survey data
(2022) Gill, Amarjit; Kang, Parminder Singh; Amiraslany, Afshin
Purpose This study aims to test the relationship between information technology investment (IT_INVEST) and working capital management (WCM) efficiency. Design/methodology/approach This study utilized a survey research design to collect data from micro, small and medium enterprises (MSMEs) owners in India. Findings Empirical results show that perceived IT_INVEST plays a role in improving WCM efficiency by decreasing the inventory holding period and reducing the cash conversion cycle (CCC) in India. A three-stage least square model (3SLS) shows that IT_INVEST decreases CCC directly and indirectly through the inventory holding period, accounts receivable period and accounts payable period. The empirical analysis also shows that IT_INVEST decreases the inventory holding period and CCC by 16.80% and 26.40%, respectively, for the examined firms. Research limitations/implications If MSMEs' owners perceive a higher level of IT_INVEST, then the owners perceive a higher WCM efficiency and vice versa. Originality/value This study contributes to the literature on the relationship between IT_INVEST and WCM efficiency. This study may encourage further studies of IT investment and WCM efficiency using data from other industries and countries. MSME owners may find empirical results beneficial to improve WCM efficiency. Moreover, financial management consultants may find results helpful to provide consulting services.
Research recast(ed): S3E13 - Innovation in supply chain risk analysis and machine learning
(2024) Leschyshyn, Brooklyn; Smadis, Natalie; Kang, Parminder Singh
On today’s episode we talk with Dr. Parminder Singh Kang about Alberta and innovations knowledge mobilization workshop and strategic network development grant, as well as his work with his MITACS grants. We discuss how Dr. Parminder Singh Kang’s research is promoting student learning.
Service 4.0: Technology-enabled customer-centric supply chains
(2024) Kang, Parminder Singh; Wang, Xiaojia; Son, Joong Y.; Jat, Mohsin
This book presents a systematic framework for Service 4.0, including service digitization, digitalization, and digital transformation, which is an integral part of Supply Chain 4.0 in coping with complex, dynamic, and interdependent systems. It provides a comprehensive state-of-the-art review of digital technologies to support Service 4.0 and Supply Chain 4.0, and discusses important pillars of customer-centric supply chain models. It then explains the role of big data in customer-centric service-based supply chains and links the different types of data needed to promote end-to-end transparency and value co-creation activities to promote these key pillars. Moreover, the book introduces practical models to support analytics for customer-centric supply chains and sheds light on how the industry practically uses existing models to promote service co-creation. A chapter of a case study on women's clothing e-commerce reviews and demonstrates how various data visualization and text mining methods can be used to uncover meaningful insights within the review data. The book is intended to help students and researchers quickly navigate through various technologies and future research directions in the areas of Service 4.0 and Supply Chain 4.0. It is also a valuable read for practitioners in this field.
Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines
(2017) Aboutaleb, Adam; Kang, Parminder Singh; Hamzaoui, Raouf; Duffy, Alistair
Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets.

Browsing by Author "Kang, Parminder Singh"

Results Per Page

Sort Options