Excel built your business. AI can scale it. For Greek retailers, the jump from spreadsheets to predictive analytics doesn’t require a data science team—just cleaner data, simple models, and steady habits. Here is a pragmatic path to lift margins and reduce stockouts within a quarter.
Start with the data you already have
POS exports, supplier deliveries, and returns logs are enough to forecast weekly demand. Standardize product names and units, fix missing values, and tag promotions. Clean data beats fancy algorithms.
Forecast what matters
Use models to predict demand per SKU per store with seasonality and holiday effects. Generate replenishment proposals that respect shelf space and minimum order quantities. Review exceptions rather than every line.
Understand baskets, not just items
Basket analysis reveals products that sell together. Place them closer, bundle them, or co‑promote with small discounts. In Greece, weekend patterns and holiday spikes make these insights especially valuable.
Act with guardrails
Set floors/ceilings for orders and flag risky bets. Use A/B tests for end‑caps and promotions. Keep human override for vendor issues or sudden events.
Measure ROI weekly
Track stockouts, aged inventory, gross margin, and labor hours spent on ordering. Improvements compound; even 1–2% per metric adds up across chains.
Team enablement
Train managers to trust the system with clear explanations and transparent inputs. Start with two categories, then expand. Celebrate quick wins—fewer stockouts before Easter, better sell‑through on seasonal lines.
AI is a force multiplier for retailers who keep it simple and measurable. The sooner your data flows, the sooner your shelves match demand.