Podcast Episode 55
Stock Smart: Forecasting Without a Data Scientist
Inventory problems quietly erode revenue and stamina: missed sales, tied-up cash, and burned-out teams. In this 30-minute episode we deliver a compact, actionable system—‘Stock Smart’—that combines minimal privacy-safe signals (sales cadence, supplier lead times, simple customer opt-ins, local behavioral proxies) with conservative AI heuristics and explicit human guardrails. The panel presents a step-by-step no-code path (spreadsheets + smartphone signals + Zapier-style automations) plus a lightweight-code option for curious builders. Producers get a micro case study: an independent shop pilot that trimmed stockouts by ~40% and reduced inventory days by ~12% in 30 days. Guests include an indie-founder, an ops lead, and a spreadsheet hacker; segments cover community restock co-ops and non-sales demand proxies (events, weather, loyalty nudges). Listeners leave with a 30-day pilot checklist, resource hub templates, and privacy-first rules to start this week.
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This episode includes a startup-ready operating system: signal definitions, implementation sequence, and a lightweight pilot plan that can be tested in under 30 days.
- - Reporting dashboard template aligned to Stock Smart: Forecasting Without a Data Scientist
- - Pilot KPI tracker (roles, milestones, and owner accountability)
- - Implementation checklist for week 1 and week 2
- - Follow-up prompts for Lyric, Nova, Stryker, and Pulse
