Data-driven food design for phygital professional kitchens: A case study of connected cooking appliance interaction traces

Food design increasingly operates in phygital back-of-house professional kitchens where embodied culinary work is mediated by connected cooking appliances, embedded interfaces and cloud data infrastructures. Primary users are professional kitchen staff (chefs, line cooks, kitchen assistants) who operate appliances per cooking cycle within coordinated team workflows. This article examines cloud-hosted interaction event logs as design material for trace-based inference of operational interaction behaviour, captured as sequences, transitions, deviations, interruptions and recovery actions, without researcher presence, task scripting or post hoc self-report. A trace-based case study draws on eighteen months of telemetry from a fleet of 300 connected professional ovens. Analysis combines sequence-oriented metrics with a state-transition representation to characterize recurrent interaction paths, estimate conformance vs. deviation outcomes and localize breakdown-prone transitions across programme-entry flows. Results are presented as traceable evidence (corpus descriptives, device-level conformance distributions and programme-family patterns) and are interpreted as redesign hypotheses targeting programme legibility, alert handling and interruption recovery under time-critical service conditions. Sustainability relevance is framed operationally as reduced rework and fewer aborted cycles, while environmental quantification remains outside the evidential scope of interaction logs alone. Experiential constructs (satisfaction, preference, perceived usability) remain outside trace-only inference and require complementary qualitative inquiry; accordingly, only trace-derived behavioural evidence is presented within everyday kitchen production.