Enterprise AI Analysis
Artificial intelligence (AI) and machine learning (ML) in procurement and purchasing decision-support (DS): a taxonomic literature review and research opportunities
This research provides a systematic literature review on the intersection of AI, ML, procurement, and decision-support (DS). It investigates the enabler role of AI and ML in procurement and purchasing, offering a process-oriented approach with unique clustering and classification. The study highlights significant potential for AI-ML applications across various sub-processes, revealing benefits like automation, improved visibility, and enhanced decision-making, while also discussing key implementation challenges.
Key Enterprise Impacts & Metrics
A snapshot of the core findings and their implications for modern procurement and purchasing operations.
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Articles Analyzed
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SCI-Expanded Studies
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Case Studies Found
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Peak Research Year
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview of the Topic
This category covers papers that provide a broad understanding of AI and ML's role in procurement, focusing on systematic literature reviews, identifying key benefits, challenges, and general applicability across the procure-to-pay cycle.
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Studies published in 2019-2023, compared to 13 in 2002-2017, highlighting rapidly increasing interest in AI/ML for decision-support.
Generic Procurement Process Supported by AI/ML
Needs Identification
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Identifying Potential Suppliers
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Request for Quotation
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Negotiation, Evaluation, Supplier Selection
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Contract
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Purchase Order Placement
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Receipt, Control, Reconciliation
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Payment
| AI/ML Capabilities | MCDM Enhancement |
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SCM Processes
This section examines how AI and ML enhance various Supply Chain Management (SCM) processes, including inbound logistics, outbound operations, and overall supply chain optimization. Specific applications across the procure-to-pay cycle are highlighted.
Case Study: Supplier Selection via Advanced Matching
Description: AI and ML significantly benefit procurement processes by providing advanced decision support. Automated evaluation of tenders and identification of the best matching between parties are facilitated.
Company/References: Canelas et al. (2013), Rodriques-Aquilar et al. (2004), Siciliani et al. (2023)
Impact: Improved efficiency and accuracy in supplier selection, leading to better strategic partnerships.
SRM
Supplier Relationship Management processes are supported by AI/ML, enabling multi-criteria control and real-time monitoring beyond basic KPIs.
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Sectoral Applications
This category explores the diverse applications of AI and ML in procurement across various industries, including manufacturing, construction, finance, and healthcare, showcasing sector-specific benefits and challenges.
Construction
The construction sector shows dominance in AI-ML applications due to high monetary values and numerous procurement items, justifying specialized DSS.
Case Study: Predicting Green Building Costs
Description: Deep Neural Networks and Random Forests are used to predict green building costs, enhancing accuracy and decision-making for sustainable construction projects.
Company/References: Alshboul et al. (2022b)
Impact: Improved cost estimation for green building projects, contributing to sustainability and financial planning.
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Calculate Your Potential AI-Driven ROI
Estimate the efficiency gains and cost savings AI can bring to your enterprise procurement.
Annual Cost Savings
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Annual Hours Reclaimed
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Addressing Challenges
Understanding and mitigating common hurdles is key to successful AI/ML adoption in procurement.
Technological Hurdles
Addressing systemic data availability issues, accuracy, completeness, and overall data management problems is crucial for effective AI/ML integration. This includes ensuring interoperability and real-time processing.
References: Jawad and Balazs 2024, Bodendorf et al. 2022b, Mhaskey 2024
Integration Complexities
Integrating AI/ML with existing legacy ERP, SRM, and IoT systems requires handling incompatibilities and protocol mismatches, often through middleware and APIs. An immature IS foundation will hinder successful application.
References: Guida et al. 2023, Van Hoek 2024, Narne 2022
Performance & Scalability
Algorithmic complexity, latency, throughput, and scalability issues can affect current performance and limit future growth. These need to be managed for effective enterprise-wide AI/ML adoption.
References: Mhaskey 2024
Data Security & Privacy
Implementing robust data encryption, comprehensive data governance, clear access control, and continuous monitoring is essential to address major concerns regarding data security and privacy.
References: Narne 2022, Mhaskey 2024
Model Acceptance & Confidence
Ensuring the acceptance and confidence in AI/ML model results requires rigorous validation and verification processes, especially when models are integrated into critical enterprise decision-support systems.
References: Bodendorf et al. 2022b
Strategic Implementation Roadmap
A phased approach to integrate AI and ML into your procurement processes for maximum impact.
Phase 1: Strategic Planning & Data Foundation
Define clear AI/ML objectives for procurement, conduct a comprehensive data audit, and establish robust data governance. Ensure data quality, availability, and prepare for integration.
Phase 2: Pilot Implementation & Model Development
Select a high-impact procurement sub-process for a pilot. Develop and train initial AI/ML models, focusing on data integration and validation. Gather user feedback for iterative improvements.
Phase 3: Scaled Deployment & Integration
Expand successful pilot projects across relevant procurement functions. Integrate AI/ML solutions with existing ERP and SCM systems. Implement monitoring and control mechanisms.
Phase 4: Continuous Optimization & Skill Development
Establish a continuous learning environment for AI/ML models. Invest in workforce training and foster a collaborative culture. Regularly review performance and adapt to evolving business needs and technological advancements.
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