{"slug":"stock-count-from-photos","name":"Shelf Photo Stock Counter","category":"Operations","customer":"Operations managers at multi-site cafe groups and small supermarket chains","problem":"Stock counts rely on clipboards and guesswork, causing waste and stockouts.","value":"For operations managers at multi-site cafe groups and small supermarket chains, turn shelf photos, POS sales data and supplier catalogues into a draft supplier order with a waste flag list for approval. Address the recurring problem: stock counts rely on clipboards and guesswork, causing waste and stockouts. The value hypothesis is fewer missed deliveries and less waste; the pilot must establish whether that benefit is real.","format":"Evidence review and quality assurance workspace","screens":"Key screens: Capture queue, stock tally, order draft, waste review. Use a photo grid for capture status, a SKU-level count table with confidence indicators, a draft order with line-by-line adjustments, and a waste review panel showing flagged lines with the original photo. Let users approve or edit each line. Display par, usage and discrepancy for every SKU. In this product, the first view is capture queue, followed by stock tally, order draft and waste review.","functionality":"1. Recognise SKUs from label text, barcode, can shape or shelf position. 2. Count visible units per SKU from a single photo. 3. Compare counts against weekday pars and POS depletion rates. 4. Generate a draft supplier order with quantities and suggested lines. 5. Flag discrepancies where stock fell faster than sales explain. 6. Attach the source photo to every flagged line for review.","workflow":"Photograph shelves, confirm zone coverage, review recognition confidence, adjust counts where needed, review draft order, approve flagged waste lines, and send the order to the supplier. Start with shelf photos, POS sales data and supplier catalogue and finish with approved supplier order and waste log.","ai":"Use vision models to detect and count products, and language models to match recognised items to catalogue entries. Keep counts and par values in structured fields. Validate totals and order quantities through deterministic checks. A manager reviews flagged discrepancies and the final order before it sends.","inputs":"Shelf photos, POS sales data, supplier catalogue, weekday pars","deliverables":"Approved supplier order and waste log with photo evidence","admin":"Site ownership, photo timestamps, user roles, order versions, approval states, audit trail of count adjustments, and a record of which manager approved each order.","mvp":"One buyer: operations manager at a three-site cafe group. One use case: dry store counts. First two modules: photo capture and SKU recognition, plus draft order generation. Manual review of all flagged lines and order totals.","expansion":"After the paid pilot, automate POS integration for depletion rates, add cold room and prep bench zones, and introduce automatic retraining when packaging changes are detected.","usp":"A single photo becomes an order and a waste log with photo evidence, cutting count time and guesswork in one pass.","defensibility":"The system learns each site's shelf layout, par behaviour and packaging quirks, so switching costs rise as the recognition model is tuned to the client's catalogue and daily rhythms.","alternatives":"Clipboards, spreadsheets and manual reordering. This differs by turning an existing photo habit into a structured count and order draft with waste flags, rather than adding another data entry step.","revenue":"Test pricing at USD 300 per site per month, with a setup fee of USD 1,500 for catalogue mapping and recognition training.","costs":"Cloud vision API usage, model retraining compute, integration engineering for POS and supplier feeds, and support time for packaging changes.","integrations":"POS systems for sales data, supplier catalogue feeds, and email or API for order submission. Start with a CSV export from the POS and a manual supplier order upload.","dependencies":"A structured supplier catalogue, reliable phone camera capture, and a stable set of SKU pack formats for the first forty items.","pilot":"Run a four-week paid pilot at one site. Baseline: manual count time and weekly waste value. Decision: continue if the draft order is accepted without edits for 80% of lines and waste flags catch at least one real issue per week.","plan30":"Week 1: Build photo capture and upload flow. Week 2: Train recognition on the top forty SKUs from the pilot site. Week 3: Connect par and POS CSV import, generate draft orders. Week 4: Run a live trial with two sites and collect accuracy feedback.","metrics":"Two measurable outcomes: count time reduced by 50% per site per week, and waste value reduced by 15% within the first quarter.","channels":"Industry trade shows, food service distributor partnerships, and targeted LinkedIn outreach to district managers in cafe and supermarket groups.","leadMagnet":"A free shelf photo analysis for one zone, showing the draft order and waste flags for a single day.","message":"Turn a shelf photo into next week's order and waste log before your coffee cools.","retention":"The system learns each site's pars and packaging quirks, so it becomes more accurate over time. Monthly retraining and new SKU onboarding keep it sticky, and the waste log builds a trend report that managers rely on.","controls":"Limit photo capture to authorised staff with timestamps and geotags. Require manager approval before any order sends. Do not auto-order without human sign-off. Keep a full audit trail of count adjustments and approvals. The system must not delete or override par values without an explicit manager action.","crossSector":"Operations; Hospitality and Events","fn":["Recognise SKUs from label text, barcode, can shape or shelf position","Count visible units per SKU from a single photo","Compare counts against weekday pars and POS depletion rates","Generate a draft supplier order with quantities and suggested lines","Flag discrepancies where stock fell faster than sales explain","Attach the source photo to every flagged line for review"],"sc":{"opp":7,"pain":7,"feas":9,"now":8},"phases":[{"name":"MVP","scope":"One buyer segment, one recurring use case; first modules: photo capture and SKU recognition, plus draft order generation. Manual review in the loop.","time":{"days":2,"label":"2 days"},"usd":5500},{"name":"Paid pilot","scope":"Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.","time":{"days":3,"label":"3 days"},"usd":5000},{"name":"Full product","scope":"Self-serve onboarding, billing, monitoring and the wider integration set.","time":{"days":6,"label":"6 days"},"usd":6500}],"running":[{"stage":"MVP and paid pilot","note":"about 3 customers","hosting":[30,60],"ai":[80,160],"total":[110,220]},{"stage":"Full product","note":"about 50 customers","hosting":[110,210],"ai":[880,1750],"total":[990,1960]}],"total":17000,"complexity":0.05,"days":11,"shot":true,"demo":true}