Impact of artificial intelligence, analytics, and procurement strategy on cost reduction

Barrad, Sherif (2020). Impact of artificial intelligence, analytics, and procurement strategy on cost reduction. Thèse. Gatineau, Université du Québec en Outaouais, Département d’informatique et d'ingénierie, 123 p.

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Résumé

Procurement is playing an increasingly important role in helping organizations achieve their savings and profitability objectives. Cost reduction or containment can be referred to as an organizations commitment to identify and capitalize on savings opportunities, ultimately improving shareholder value. While there is evidence that pure cost reduction efforts enable organizations in achieving savings, there have been conflicting research suggesting that cost reduction efforts can also have a reverse effect on long-term savings and goes as far as suggesting that the ‘lowest bid’ approach is not always an effective and sustainable procurement strategy. In this study, we identify the conditions under which emerging AI technologies and analytics (AIA), coupled with more evolutive and “intelligent” procurement strategies, can drive cost reduction. We propose to look specifically at the required organizational context conducive to enhancing the impact of AI and analytics, as opposed to implementing simplistic AI seeking only “lowest cost” rules. We also explored the notion of procurement strategy to highlight the degree of influence generated from strategic sourcing and supplier relationship management activities, as a lower-order dynamic capability, on cost reduction, a higher-order dynamic capability. Our primary hypothesis is that the application of procurement strategies, in an ideal organizational context, coupled with robust and effective AIA technologies, can have a significantly positive effect on cost reduction. This research is empirically validated by surveying procurement executives and guides as to how to prioritize and leverage AIA for cost reduction. A model is tested using the Partial Least Squares (PLS) regression technique and algorithm.

Type de document:	Thèse (Thèse)
Directeur de mémoire/thèse:	Gagnon, Stéphane
Co-directeurs de mémoire/thèse:	Valverde, Raul
Mots-clés libres:	Artificial Intelligence (AI); Analytics; Big Data Analytics (BDA); Cost Reduction; Machine Learning; Partial Least Squares (PLS); Procurement; Structural Equation Modeling (SEM); Strategic Sourcing; Supplier Relationship Management (SRM); System Dynamics
Départements et école, unités de recherche et services:	Informatique et ingénierie
Date de dépôt:	05 mai 2022 15:03
Dernière modification:	05 mai 2022 15:03
URI:	https://di.uqo.ca/id/eprint/1377

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