Operationalizing AI Readiness in Logistics SMEs
AI & Data
Semester programme:Business Intelligence & Data
Project group members:Kalina Grigorova
Project description
This project investigates how the MLPRALS framework can be translated into practical and actionable guidance for logistics SMEs. While MLPRALS provides structured AI readiness assessments, its outcomes are often difficult to operationalize in practice. Using GreenZone as a case study, the project develops and validates a reusable Proof of Concept (PoC) that supports structured readiness assessment and step-by-step improvement guidance. The research demonstrates how abstract readiness scores can be transformed into concrete recommendations and expected organizational and operational effects, while ensuring transferability to other logistics SMEs.
Context
Logistics SMEs face increasing pressure to adopt AI-driven practices but often lack the organizational, data, and technological readiness required to do so. Limited resources, fragmented IT systems, and reliance on experience-based decision-making hinder effective adoption. Within the Fontys ICT Lectorate AI & Big Data and the Art-IE initiative, the MLPRALS framework was developed to assess AI readiness in logistics SMEs. However, its practical application remains challenging.
This project is situated in the logistics domain and applies MLPRALS at GreenZone, a logistics SME with intuition-driven purchase planning and emerging data infrastructure. While GreenZone serves as the primary case, the project focuses on developing a transferable assessment and guidance approach that can be reused by other logistics SMEs.
Results
The project delivers three key results. First, the MLPRALS framework was applied at GreenZone to produce a structured readiness assessment, gap analysis, and prioritized recommendations. This clarified current limitations in data quality, governance, and decision-making and provided a concrete improvement roadmap.
Second, a reusable Proof of Concept (PoC) was developed that operationalizes the MLPRALS assessment into a digital, assessor-led Readiness Assessment and Steps Suggestion Tool. The PoC implements standardized scoring logic, automated readiness calculations, and structured improvement guidance. Expert-based validation confirmed alignment with the MLPRALS framework, while lab-based evaluation demonstrated correct functioning and usability. The PoC is positioned at approximately TRL 4–5, as a validated prototype tested in a relevant context.
Third, the project generated transferable insights into the organizational and operational effects of adopting MLPRALS-based practices. These include increased transparency in readiness discussions, clearer responsibility for data and analytics activities, and more consistent prioritization of improvement actions. Together, the advisory report and PoC demonstrate both direct value for GreenZone and a transferable mechanism for supporting AI readiness improvement across logistics SMEs.