Επαυξημένη λήψη αποφάσεων στον τομέα της ναυτιλιακής μεταποίησης : ξεπερνώντας τα εμπόδια στην ενσωμάτωση της τεχνητής νοημοσύνης σε εξειδικευμένες ελληνικές ΜμΕ

Παπαναγόπουλος, Ευάγγελος

Augmented decision-making in maritime manufacturing : overcoming barriers to AI integration in specialized Greek SMEs

Master Thesis

Author

Παπαναγόπουλος, Ευάγγελος

Date

2026

Abstract

The digital transformation of manufacturing creates new opportunities for applying Artificial Intelligence (AI) to production planning and decision support. However, the adoption of such technologies remains challenging for small and medium-sized enterprises (SMEs) operating in High-Mix, Low-Volume (HMLV) environments, where operational variability, customised requirements, and reliance on tacit organisational knowledge increase the complexity of production planning. This thesis investigates the conditions shaping production planning and the technological, organisational, and environmental factors shaping the readiness for and barriers to the adoption of Augmented Decision-Making (ADM) systems in specialised Greek maritime manufacturing SMEs. A pragmatist convergent mixed-methods design combines a quantitative investigation (N=16) with a qualitative analysis of three organisational cases through semi-structured interviews. The two datasets are initially analysed separately and subsequently integrated during the interpretation of the findings through the Technology–Organization–Environment (TOE) framework. The results reveal frequent production-schedule changes and a descriptive mismatch between the operational complexity of the examined manufacturing environments and the level of technological support available for production planning. The findings also indicate substantial reliance on tacit organisational knowledge and experience-based heuristics to manage operational disruptions. Within the examined sample, the most frequently reported barriers to AI adoption concern data quality, organisational inertia, and skills shortages, whereas direct financial constraints are reported less frequently. The exploratory exact statistical tests identified no statistically significant relationships; given the limited sample size, these findings require cautious interpretation. The integration of the findings suggests that adaptation to operational variability represents a socio-technical capability combining technological tools, human judgement, and organisational mechanisms. At the theoretical level, the thesis proposes an exploratory Dual-Tier Schedule Nervousness Absorption Model, distinguishing between macro-strategic and micro-operational mechanisms for absorbing variability. From a practical and managerial perspective, the thesis proposes a phased transition framework towards ADM that conceptualises AI adoption as a process of developing socio-technical capabilities and emphasises complementarity between human expertise and algorithmic decision support.

Postgraduate Studies Programme

Βιομηχανική Διοίκηση και Τεχνολογία

Department

Σχολή Ναυτιλίας και Βιομηχανίας. Tμήμα Βιομηχανικής Διοίκησης και Tεχνολογίας

Number of pages

Language

Greek

URI

https://dione.lib.unipi.gr/xmlui/handle/unipi/19863

Collections

Τμήμα Βιομηχανικής Διοίκησης και Τεχνολογίας

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Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα