Browsing by Author "Kang, Parminder Singh"
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Item Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation(2019) Kang, Parminder Singh; Bhatti, RajbirPurpose 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.Item Information technology investment and working capital management efficiency: evidence from India survey data(2022) Gill, Amarjit; Kang, Parminder Singh; Amiraslany, AfshinPurpose 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.Item Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines(2017) Aboutaleb, Adam; Kang, Parminder Singh; Hamzaoui, Raouf; Duffy, AlistairFlexible 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.