The daily ops dashboard: what to check every morning
Most operational fires are cheap to catch and expensive to miss. The discipline is a fixed 5-minute scan every morning of the few signals that mean something is on fire right now.
The move is one page with red/green thresholds, so a glance tells you whether today is normal, plus a deeper weekly review. AI builds the one-pager and sets the thresholds; you glance at it before anything else.
This is operational health; the business-metrics side lives in the KPI dashboard note.
Fastest path: one prompt, end to end
🤖 AI prompt — paste into ChatGPT / Claude
You are an e-commerce ops lead. Use MY setup only.
Platform + tools: [e.g. Shopify, Gorgias, ShipStation]
My fulfillment SLA + support response target: [e.g. ship in 2 business days, reply in 24h]
Typical daily orders + normal refund rate: [# per day, %]
Do this:
1. Design a 5-minute morning ops scan of 5 signals: unshipped orders past SLA, support tickets over target age, stockouts and low-stock items, refund spikes vs my normal rate, and payment/checkout errors.
2. For each signal, give a red/green threshold based on MY numbers above, so I know at a glance whether to act.
3. Lay it out as one page: what to look at, where it lives (which tool/report), and the single number that matters.
4. Split off a weekly deeper review (trends, root causes, not just today's fires) and tell me what belongs there instead of the daily scan.
Keep this operational health only. For business KPIs (revenue, CAC, LTV) point me to my KPI dashboard note, do not duplicate them here.
Do not invent my thresholds or normal rates. If a number is missing, ask.
Output: one-page daily scan with red/green thresholds + a weekly review checklist.
Or do it in 4 steps
- Fix the 5 signals worth a daily look. Every morning, in order: unshipped orders past your ship-by SLA, support tickets aging past target, stockouts and low-stock items, refund spikes vs your normal rate, and payment/checkout errors. Left a day, these turn into angry customers or lost revenue; everything else waits for the weekly review.
- Set red/green thresholds so it's a glance, not a study. '3 orders past SLA' is green; '30' is red. '2% refunds' is green; '8%' is a fire. Pre-decide the line for each signal so your morning scan is a yes/no, not a judgment call at 8am. Green means move on; red means act now.
- Put it on one page. Whether it's a saved dashboard view, a Shopify report, or a simple sheet, everything lives on one screen so the whole scan takes five minutes. Chasing five numbers across five tabs is how you skip it on a busy day. One page, same order, every morning.
- Split the weekly deeper review. The daily scan catches fires; a weekly session finds patterns: why refunds crept up, which SKU keeps stocking out, whether ship times are drifting. Keep trends and root-cause work out of the daily scan so it stays a fast health check, not a research project.
Worked example (labeled): a store ships in 2 business days and replies to support in 24h.
Their one-page morning scan: unshipped past SLA (green <5, red >15), tickets over 24h (green <8, red >20), low-stock SKUs (red if any bestseller <7 days cover), refunds today (green <3%, red >6%), checkout errors (red if any).
One Monday the refund cell went red at 9%. They caught a broken size chart before it cost a full day of returns. The weekly review then dug into the trend; the daily scan just caught the fire.
Keep the daily scan to operational health with hard red/green lines, and push trends and business KPIs (see the KPI dashboard note) into a separate weekly review.