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Artificial Intelligence as a Catalyst for Supply Chain Resilience: A Qualitative Study Comparing Scania and Volvo in the Construction Equipment Industry
Mälardalen University, School of Business, Society and Engineering.
Mälardalen University, School of Business, Society and Engineering.
2023 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Abstract 

Date: 2023-05-30

Level: Master thesis in Business Administration, 15 cr 

Institution: School of Business, Society and Engineering, Mälardalen University 

Authors: Ramak Amyari Khamneh (84/01/29), Aymen Safi (00/03/27)

Title: Artificial Intelligence as a Catalyst for Supply Chain Resilience: A Qualitative Study Comparing Scania and Volvo in the Construction Equipment Industry

Supervisor: Emre Yildiz

Keywords: Artificial Intelligence (AI), Supply Chain Resilience, Construction Equipment Industry, Disruptions, Agility, Redundancy

Research question: How do Scania and Volvo interpret and implement Artificial Intelligence (AI) technologies to enhance supply chain resilience and mitigate disruptions in the construction equipment industry?

Purpose: The purpose of this master thesis is to investigate how Scania and Volvo interpret and implement AI technologies to enhance supply chain resilience and mitigate disruptions in the construction equipment industry.

Method: Qualitative

Conclusion: The conclusion of the master thesis is that Scania and Volvo have successfully implemented AI technologies to enhance supply chain resilience in the construction equipment industry, despite challenges, and see AI as a critical component for future supply chain strategies.

Place, publisher, year, edition, pages
2023. , p. 68
Keywords [en]
Artificial Intelligence (AI), Supply Chain Resilience, Construction Equipment Industry, Disruptions, Agility, Redundancy
National Category
Social Sciences Economics and Business Business Administration
Identifiers
URN: urn:nbn:se:mdh:diva-62755OAI: oai:DiVA.org:mdh-62755DiVA, id: diva2:1761055
Subject / course
Business Administration
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Available from: 2023-06-14 Created: 2023-05-31 Last updated: 2023-06-14Bibliographically approved

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