The Impact of Artificial Intelligence on Decision-Making in Manufacturing Supply Chain Management

Edisen, Ali (2026) The Impact of Artificial Intelligence on Decision-Making in Manufacturing Supply Chain Management. Doctoral thesis, Birmingham City University.

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Abstract

This thesis examines how and why manufacturers adopt artificial intelligence (AI) in supply chain decision-making, and how adoption improves decision quality, resilience, and performance. The research was operationalised in Europe within the manufacturing industry. A mixed-methods design was used: the survey instrument was designed and developed based on initial expert interviews and the adaptation of scales from valid and established literature. The survey was administered to 371 supply chain professionals in European manufacturing firms and analysed with partial least squares structural equation modelling (PLS-SEM), followed by 23 semi-structured interviews, which explain the mechanisms behind the numbers.

The study makes four main contributions.

First, it identifies what truly drives adoption in practice. Workforce skills and readiness emerged as the strongest predictor, with social and sustainability pressures and security and privacy readiness close behind; organisational culture and ethical–legal governance also had positive effects. By contrast, factors often assumed to be critical, such as data quality/integration and legacy system compatibility, did not predict adoption among the (mostly) adoption-ready firms studied, indicating they operate as threshold preconditions rather than differentiators.

Second, it shows how AI creates value. AI improves supply chain performance mainly by strengthening two capabilities, decision-making effectiveness (faster, more accurate, and more confident decisions) and supply chain resilience (better prediction, real-time visibility, and faster responses). Around two-thirds of AI's total performance impact flows through these indirect capability paths, with a smaller direct effect from operational automation.

Third, it makes a clear theoretical contribution by extending the body of knowledge for Technology-Organisation-Environment (TOE), Diffusion of Innovation (DOI), and Resource-Based View (RBV) theories. The research tests and validates key constructs from these theories within the manufacturing supply chain context in Europe. The findings are generalisable to similar developed economies and industrial sectors with comparable technological maturity and competitive environments.

Fourth, it proposes a practical, evidence-based framework that organises adoption factors hierarchically (preconditions, execution differentiators, strategic drivers) and links AI investments to capability building and measurable outcomes.

For managers, the results suggest five priorities: invest in people and hybrid skills; treat data quality as a baseline to be secured early; build clear governance and security; use sustainability demands to legitimise and accelerate AI cases; and redesign decision processes to embed AI rather than merely bolt it on. For scholars, the work integrates TOE, DOI and RBV perspectives, and demonstrates the value of mixed methods for resolving apparent contradictions between what organisations talk about and what actually predicts adoption and performance.

Item Type:	Thesis (Doctoral)
Dates:	Date Event 2 April 2026 Accepted
Uncontrolled Keywords:	Artificial intelligence; AI adoption; decision-making; manufacturing supply chain management; technology-organisation-environment (TOE); diffusion of innovation (DOI); resource-based view (RBV); PLS-SEM; mixed methods; Industry 4.0; supply chain analytics
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence CAH17 - business and management > CAH17-01 - business and management > CAH17-01-01 - business and management (non-specific) CAH17 - business and management > CAH17-01 - business and management > CAH17-01-09 - others in business and management
Divisions:	Business School > Management, Business and Marketing Doctoral Research College > Doctoral Theses Collection
Depositing User:	Louise Muldowney
Date Deposited:	05 May 2026 12:00
Last Modified:	05 May 2026 12:00
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17025

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