A predictive analytics approach to improve the dealers-manufacturer relationship in the after-sales service network; case study in the automotive industry

Ebrahimi, Ahmad; Bakhshizadeh, Pouyan; Varasteh, Reyhaneh

doi:10.1080/17509653.2022.2116733

Cited by 2 publications

(1 citation statement)

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“…Effective dealer management in the automotive industry involves various best practices. Utilizing predictive analytics tools and classification methods can help predict dealers' cooperation based on factors like dealer performance violations, changing dealer principles, and service revenue (Ahmad et al,2022). Implementing decision-making support systems based on network indicators analysis can optimize spare parts deliveries and enhance overall management of the dealer-service network (Makarova et al,2012).…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Tailored Dealer Management System (DMS) for the Automotive Industry

Beken,

Temel,

Abduloğlu

et al. 2024

EJRnD

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This paper presents a thorough investigation into the design and implementation of a Dealer Management System tailored for the automotive industry. The DMS is intended to streamline after-sales processes such as inventory and expense management, service order creation, and financial data monitoring. The system integrates with a variety of other platforms, including ERP, CRM, and payment systems, which improves collaboration between dealers and distributors. The architecture of DMS, known as Copilot Next, is detailed, with a focus on the backend, frontend, and integration components. Sentinet and Dell Boomi are integration tools that allow for seamless connectivity with other systems. The findings show that the DMS effectively meets the needs of multiple vehicle brands, improves part ordering accuracy with AI tools, and ensures data consistency across systems. This study offers useful insights into the design and implementation of a DMS that can significantly improve dealership operations and customer service in the automotive industry.

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Section: Introductionmentioning

confidence: 99%