Consumer Durables & Electronics Retail Merchandising & Store Execution Software for Malaysian Business

Each chain and format has its own picture-of-success: an electronics chain flagship gets a multi-zone brand corner with demo TVs, fridges, and washing machines, a hypermarket appliance hall gets a simpler gondola-based layout, a small-format store gets a single shelf check. The merchandiser app loads the right check at each store, and reports roll up by chain and format.

Demo units are configured as separate installation tasks with start and end dates, agreed location in the store, and installation photo requirements. Merchandisers capture installation, mid-window check, and dismantle photos. AI image recognition verifies the unit is the correct model. Exception flags fire when installation does not happen, when units get displaced by competitor, or when units get unplugged or stored away.

The AI model is trained on the brand's SKU library plus chain-specific shelf templates. It handles partial occlusion, glare on appliance surfaces, angle variance, and mixed-SKU stacks. For confidence-flagged photos, the system routes to a human reviewer rather than auto-passing. Model retraining cycles incorporate new SKU launches and seasonal POSM variants without long downtime.

The KAM walks into JBP with a chain-specific scorecard: demo unit installation compliance, brand corner setup status, end-cap occupancy, gondola spot fulfilment, share-of-shelf vs competitor, and execution score against the agreed picture-of-success. The data is photo-backed and timestamped. When a chain claims credit for execution that did not actually happen, the platform shows the evidence.

Festive rollouts are scheduled campaigns with chain-specific store lists, POSM kits, installation windows, dismantle dates, and picture-of-success references. The merchandiser app shows the rollout plan per store. Installation status updates in real time. Exception reports flag stores where festive POSM is not up or has been dismantled early. Trade marketing sees the rollout heatmap during the season.

End-cap and gondola windows are configured as separate installation tasks with start and end dates, agreed location, agreed product mix, and agreed POSM elements. Merchandisers capture installation, mid-window check, and dismantle photos. Exception flags fire when installation does not happen, when products are wrong, or when POSM is missing. Trade marketing and finance see actual realisation against the rented window.

Out-of-stock events are logged at the SKU and store level by the merchandiser during the visit. The platform classifies the cause (chain-side stock issue, supply gap, slow replenishment, delisting in progress). Daily OOS dashboards by chain and category let the KAM raise the issue with the chain immediately rather than waiting for the next JBP cycle, especially during festive demand spikes.

Shelf photos go through AI for both own-brand SKU detection and competitor SKU detection. Share-of-shelf is calculated as facings per brand relative to category total. Reports show share-of-shelf trends per chain, per region, per category, and per store. Brand teams see where competitor expansion is happening before it shows up in chain sell-out reports.

Trade marketing sees campaign rollout status, POSM deployment rate, audit pass rate, and corrective action closure. KAMs see chain-wise compliance scorecards ready for JBP. Brand leadership sees national execution score, chain-wise breakdown, competitor share-of-shelf trends, and demo unit installation tracker. Drill-downs go from country to chain to store to individual visit photo.

The merchandiser app is offline-first. Inside the back of an electronics chain store where mobile reception drops, the merchandiser captures photos, completes the audit checklist, logs out-of-stock SKUs, and submits the visit. Once signal returns in the parking lot or front of the store, all data uploads to the cloud. Conflict-handling prevents duplicate visits if a sync happens after a manual entry.