Artificial Intelligence in the Supply Chain: Great Promise, but… Where Is the Data?

Melina Psarra, 16/10/2025

Artificial Intelligence (AI) has made a powerful entry into the supply chain domain, redefining how demand is forecasted, inventory is managed, transportation is organized, and the flow of goods and information is monitored. Global companies already leverage predictive algorithms, automated procurement processes, and advanced analytics to make faster, more accurate, and strategically aligned decisions.

However, in the Greek business landscape, the transition to this level of digital readiness faces a key obstacle: the absence of reliable, sufficient, and well-structured data. AI cannot function effectively in environments lacking a proper data infrastructure. Unfortunately, in most Greek supply chains, this foundation is missing.

AI as a Catalyst for Supply Chain Transformation

Artificial Intelligence offers exceptional optimization capabilities at every stage of the supply chain:

  • Demand forecasting: Advanced algorithms can predict consumption based on historical data, seasonality, external factors, and even social trends.
  • Inventory optimization: AI can suggest optimal stock levels per warehouse or region, minimizing dead stock and shortages.
  • Logistics automation: From route planning to real-time shipment tracking and delay prevention.
  • Risk and disruption detection: Predictive analytics can alert companies to potential supply chain disruptions before they occur.

In an ideal scenario, a supply chain enhanced with AI operates like a living organism—adapting, predicting, and optimizing in real time. But this presupposes one essential thing: data. Abundant, high-quality, and well-organized data.

Without Data, No AI Can Function

Here lies the challenge for the Greek market. Many companies are interested in AI solutions — and rightly so. They want to run demand forecasts, detect inefficiencies, and automate processes. Yet, they often lack the basics:

  • No unified databases — information is scattered across ERP systems, Excel sheets, emails, and handwritten notes.
  • Data is incomplete or undocumented.
  • No standard data nomenclature — the same products appear under different names or codes depending on the system or even the person.
  • Critical real-time information (e.g., transport delays, order changes) is not recorded.

And most importantly: there is no strategy for data utilization.

AI Is Not Magic — It’s Technology

AI does not “imagine” solutions. It cannot generate useful insights from incorrect, fragmented, or nonexistent data. Even the most advanced machine learning algorithms, when trained on poor-quality or incomplete datasets, merely replicate noise—or worse, produce misleading results and wrong decisions.

Just as a logistics analyst cannot design an effective transport plan without access to full data on inventory, cost, and routes, AI cannot deliver value without proper data pipelines.

How to Approach AI in the Supply Chain

To truly harness the power of Artificial Intelligence in Greek supply chains, we must start from the right foundation:

  1. Data readiness and data quality
    • Integrate data sources (ERP, WMS, TMS).
    • Clean old and unstructured data.
    • Define data ownership and accountability per function.
  2. Investment in data infrastructure
    • Create data warehouses.
    • Develop APIs for real-time information flow across systems.
    • Adopt a cloud-first architecture for flexibility and scalability.
  3. Data governance & KPIs
    • Establish standard data recording methodologies.
    • Continuously monitor data quality.
    • Define business performance indicators.
  4. Collaboration with technology partners
    • Select specialized providers with deep supply chain understanding.
    • Run pilot projects with measurable outcomes.
    • Apply an agile approach with continuous evaluation and adjustment.
  5. Training and a data-driven culture
    • Develop internal analytics and AI literacy skills.
    • Train executives to interpret models and insights.
    • Foster a culture of decision-making based on data, not intuition.

Greece Does Not Lack Talent — It Lacks Organization

The issue is not that Greek companies lack expertise or talent. The issue is that they attempt to deploy advanced technologies in environments that are not yet mature enough for them.

Artificial Intelligence does not replace strategy or organizational structure — it enhances them. It requires people who understand supply chain operations, structured data, and clear objectives.

Conclusion

Artificial Intelligence has the potential to transform the supply chain into an intelligent, agile, and efficient system. But for this transformation to happen, it needs fertile ground to grow — and that ground is data.