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ISSN 1895-2038, e-ISSN:1734-459X

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Issue 3/ 2021, article 9

Rahul S Mor, Arvind Bhardwaj, Sarbjit Singh, Syed Abdul Rehman Khan



Background: Predictive analysis is a vital element to operations management as it facilitates real-time decision making and advanced planning on both strategy and performance. This paper identifies predictors to measure distribution performance in the dairy industry and to establish their importance.

Methods: A distribution model is developed through exploratory structural equation modelling (SEM) techniques. The key performance predictors are marketing and distribution management, quality management, supply chain coordination, and brand management, which account for 71.5% of the variability in distribution performance.

Results and conclusion: The predictors help improving the distribution performance, specifically in quality, order fill rate, and food safety. The outcomes of this research can help dairy professionals in managing their distribution channels, improving traceability, on-time delivery, and shipment accuracy. Consequently, these factors can improve distribution performance. Four predictors are elicited from the data to estimate the distribution performance and the relative importance of predictors is also established.

Keywords: distribution performance; food supply chain, dairy industry, structural equation modelling (SEM), predictive analysis

Full text available in in english in format: Adobe Acrobat pdf article nr 9 - pdf

DOI: 10.17270/J.LOG.2021.609
For citation:

MLA Mor, Rahul S, et al. "Modelling the distribution performance in dairy industry: A predictive analysis." Logforum 17.3 (2021): 9. DOI: 10.17270/J.LOG.2021.609
APA Rahul S Mor1, Arvind Bhardwaj1, Sarbjit Singh1, Syed Abdul Rehman Khan2 (2021). Modelling the distribution performance in dairy industry: A predictive analysis. Logforum 17 (3), 9. DOI: 10.17270/J.LOG.2021.609
ISO 690 MOR, Rahul S, et al. Modelling the distribution performance in dairy industry: A predictive analysis. Logforum, 2021, 17.3: 9. DOI: 10.17270/J.LOG.2021.609
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