Eye-tracking and cognitive-operational diagnostics across a food-industry picking team
Food-industry producer and distributor of bakery products, with an internal warehouse operation responsible for sorting and picking store orders for daily delivery.
Confidential case study — anonymized. Full details available under NDA upon request.
12 individual operators observed during live picking shifts — attention, engagement, awareness, alertness, search efficiency.
Each profile analyzed on its own terms, separating upstream production delay from genuine attention/search issues.
Integrated team diagnosis translated into prioritized process and standardization recommendations.
| Objective Finding | Measured Value |
|---|---|
| Team-wide attention level (0–1 scale) | ≈ 0.15–0.20 — low |
| Team-wide alertness level (0–1 scale) | ≈ 0.29–0.30 — low |
| Team-wide engagement and awareness (0–1 scale) | ≈ 0.55–0.63 — moderate |
| Team-wide search efficiency (0–1 scale) | ≈ 0.54 average, uneven across operators |
| Time lost to monitor checks, most-affected operator | ≈ 8–10% of task time (up to 25 sec/check cycle) |
| Idle time waiting for production, worst-case operator | ≈ 15 of ≈ 20 minutes observed (≈ 75%) |
| Time lost to a separate monitor check-in step, second operator | ≈ 10% of cycle time |
| Recommendation | Rationale / Expected Impact |
|---|---|
| Process mapping, RACI matrix, and a standardized picking methodology with defined norms | Directly addresses the non-standardized process behind the team’s attention/alertness variance |
| Replace the fixed wall monitor with a handheld/tablet scanner | Removes the walking/context-switching measured at 8–10% of task time |
| Color coding, floor markings, and pre-labeled trays | Reduces reliance on memory — the identified source of tray mix-ups |
| Synchronize upstream production flow with the picking schedule | Targets idle time measured at up to 75% of one operator’s cycle |
| Shift-level KPIs (picks/hour, cart cycle time, error rate, labor cost/order) | Provides the operational visibility that was missing pre-diagnostic |
The data showed that what looked like “twelve operators with inconsistent performance” was, in most cases, one shared root cause — process variability and dependency on upstream production timing — not an individual skills problem. Distinguishing system-caused inefficiency from operator-caused inefficiency meant recommendations could target the process itself, not the people running it, and could be implemented at low or no cost.