Sizing and replenishment decisions sit at the heart of retail profitability — yet they remain among the most underinvested areas in the business.

The gap between what the data makes possible and what most organisations are actually doing is wider than most planning teams would like to admit.

This spotlight cuts through the theory to focus on what practitioners are doing right now: the signals worth trusting, the logic that breaks down at scale, the governance that stops hard-won improvements from drifting, and the organisational reality of getting teams to work differently when the tools change.

No AI fairy dust. No polished case studies.
Just a direct conversation about the decisions that quietly move the P&L, and what it actually takes to get them right.

Angles of discussion:

1. Replenishment is downstream of buying. If the initial size curve set at buy stage is wrong, can any replenishment logic fully recover it? And how must planning and buying work differently together upstream?

2. From article-level to size-level replenishment: why does that shift matter so much, and what does it reveal that product-level performance was hiding?

3. Which signals should actually shape your size curves — and which ones mislead more than they help?

4. Size availability as a leading KPI. Why is this still being overlooked, and what does tracking it properly expose?

5. How should you differentiate replenishment logic across continuity vs seasonal, store tier, and channel? When does a one-size-fits-all approach actively cost you

6. When AI-driven replenishment surfaces previously unknown business inefficiencies, who is responsible for addressing them, and what organisational changes does that require?

7. How do you translate lost sales insights into concrete size curve adjustments without over- or under-correcting?

8. Shifting to size-level logic and AI-driven proposals requires planners to think and work differently. What does it actually take to get teams to trust and adopt new replenishment logic?

9. What does a fast, practical execution loop actually look like? How do you build a rhythm between field feedback, parameter tweaks, and KPI readouts?

10. How do you define clear governance thresholds, specifying when to trust the model and when the planner must intervene?

11. How do you validate that a parameter change actually worked? What does good test-and-learn discipline look like in a replenishment context?