Retail Merchandising Software for Auto-Parts Retailers Across the Philippines

Each outlet type runs its own checklist. A motorcycle and tricycle parts shop needs a different display brief than a four-wheeler multi-brand parts retailer, and a tyre, battery and lubricant outlet is different again. The system tags each shop by type and category mix, and serves the matching checklist the moment the rep checks in.

Every rack, shelf set or signage install runs as a task with a start window, an end window, a GPS-stamped location, and a reference brief. Field reps capture install, mid-cycle, and take-down photos. AI checks that the correct SKUs and branding are in place, and an alert fires if an install runs late or a rival brand starts crowding onto the shelf.

The model is trained on the brand's SKU catalogue together with templates drawn from Filipino parts-shop counters and storefronts. It handles tight shelving, mixed stock, low light and stacked boxes. Any photo the model is unsure about goes to a human reviewer, and new SKUs are added through a short retraining cycle.

The account manager walks into the review with one sheet: shelf-share, display compliance, signage status, stock availability, and an overall execution score, all backed by a timestamped, GPS-stamped photo. If a claim about work done doesn't match the photo record, the record settles it.

Seasonal campaigns are set up once as scheduled drives, each carrying an outlet list, a kit list, install and take-down windows, and reference photos. Status updates in real time, and reports flag outlets where a display never went up or came down early. Trade teams track the whole rollout on one live dashboard rather than chasing updates by phone.

Reps log stock-outs at the SKU and outlet level during the visit itself. The system tags the reason: a shop-side stock issue, a supply gap upstream, a delayed restock, or a delisted part. Daily dashboards let account managers raise the gap with the shop or the distributor the same day instead of waiting for the next scheduled visit.

AI identifies both the brand's own SKUs and competitor SKUs from the same shelf photo. Shelf-share is then calculated as facings per brand against the total category count on display. Reports trend that number by shop, province and category (tyres, batteries, lubricants, filters), so brand teams spot a competitor gaining ground before it shows up in sell-out numbers.

Trade teams follow rollout progress, display compliance, audit pass rates and open action items. Account managers get a shop-level scorecard ahead of each review. Leadership sees the national picture, the province-by-province breakdown, competitor shelf-share, and install progress. Every dashboard drills down from the national view to a single outlet photo.

The app is offline-first. In parts-shop clusters off the main highway and on outer islands where mobile signal drops during typhoon season, reps still capture photos, complete the checklist, log stock-outs, and submit the visit. Once signal returns, the data syncs to the cloud, and conflict handling stops the same visit from being logged twice.

Yes. Retail merchandising runs on the same cloud backend as the SFA and DMS. Each display audit attaches to the rep's visit record, so account managers can trace a visit through to the audit and the resulting order in a single view. 1Voice AI answers plain-language questions on demand, without a separate report request.