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Article . 2022
License: CC BY
Data sources: Datacite
ZENODO
Article . 2022
License: CC BY
Data sources: Datacite
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Hybrid work models and digital transformation in logistics: Enhancing efficiency and workforce adaptation

Authors: Mirzaev, Bobir;

Hybrid work models and digital transformation in logistics: Enhancing efficiency and workforce adaptation

Abstract

This study examines the growing significance of hybrid work models, digital platforms, and employee training in the logistics industry, focusing on their collective impact on operational efficiency and customer satisfaction. In 2022, logistics companies are increasingly adopting hybrid work approaches to enhance flexibility and resilience, while digital tools such as Transportation Management Systems (TMS) and Customer Relationship Management (CRM) platforms optimize resource allocation, streamline processes, and improve customer engagement. Additionally, low-code platforms provide affordable customization options, especially for small and medium-sized enterprises. Employee training, particularly through online and cross-departmental programs, equips workers with necessary skills, supports quick adaptation, and fosters collaboration. This paper highlights these elements as essential in advancing logistics practices and outlines future research directions on their long-term impacts on industry efficiency and client satisfaction.

Keywords

CRM, employee training, customer satisfaction, TMS, digital platforms, logistics efficiency, hybrid work models

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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