Applying simulation in lean service to enhance the operational system in Indian postal service industry

Murugesan, Vadivel Sengazhani; Jauhar, Sunil Kumar; Sequeira, A. H.

doi:10.1007/s10479-020-03920-1

Cited by 13 publications

(9 citation statements)

References 25 publications

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“…From an output perspective, additional variables on social and environmental performance like a contribution for local community development, and quantification of toxic emission can be added to study the overall sustainability performance of the firm. More such studies are called for in developing countries which will have significant levels of service industry perspective (Murugesan et al , 2021; Dahmani et al , 2021) and environmental impact (Jauhar et al , 2017) in the future.…”

Section: Discussionmentioning

confidence: 99%

Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

Jauhar

Singh

Rajeev

et al. 2021

BIJ

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PurposeProductivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.Design/methodology/approachAn integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.FindingsAnalysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.Originality/valueProposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

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Section: Discussionmentioning

confidence: 99%

Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

Jauhar

Singh

Rajeev

et al. 2021

BIJ

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“…Following each iteration, a clustered dataset is subdivided into smaller clusters until every object is a cluster or a terminating condition occurs . The bottom-up process begins with collecting individual data objects and forming clusters for each object Murugesan et al, 2021). Knowing the cluster contains similar objects, new objects are added to the cluster when the cluster is not predefined to introduce dynamic behavior (Meilatinova, 2021).…”

Section: Customer Segmentations Using Different Digital Technologiesmentioning

confidence: 99%

Digital transformation technologies to analyze product returns in the e-commerce industry

Jauhar

Chakma

Kamble

et al. 2023

JEIM

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PurposeAs e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.Design/methodology/approachThe authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.FindingsThe authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.Originality/valueThis is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

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“…The DEA approach is widely considered the standard methodology for comparing similar DMUs (Liu et al, 2016). DEA has proven beneficial in a variety of sectors of society, including airports (Adler et al, 2013), banking (Paradi & Zhu, 2013), hotels (Manasakis et al, 2013), railways (Bhanot & Singh, 2014), financial services (Kaffash and Marra, 2016), airlines (Duygun et al, 2016), telecommunications (Masson et al, 2016) and other industries (Dahmani et al, 2021;Jauha & Pant, 2013;Jauhar et al, 2012;Murugesan et al, 2021).…”

Section: Data Envelopment Analysis and Deep Learningmentioning

confidence: 99%

A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

et al. 2022

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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers.

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Applying simulation in lean service to enhance the operational system in Indian postal service industry

Cited by 13 publications

References 25 publications

Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

Digital transformation technologies to analyze product returns in the e-commerce industry

A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

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