Inspiration
Long wait times and inefficiency.
What it does
Layout (KitFlow) models the facility as stations (office, cold storage, dry storage, assembly bench, box storage, labeling, finished fridge) and the worker’s step-by-step route. It then:
estimates walking distance and walking time per kit, combines that with configurable handling times to estimate total minutes per kit, generates and evaluates two alternatives: Minimal-change layout (small nudges to a few stations), Extensive-change layout (larger rearrangements),
outputs a clear KPI comparison and a recommended option based on lowest cycle time, (optionally) plots layouts and the workflow path.
How we built it
Defined a process model (the route + per-step handling times). Represented the facility as a 2D coordinate layout with bounds. Implemented KPI evaluation: Euclidean distance → walking time → total cycle time. Built a simple hill-climbing optimizer to “search” better layouts: constrained moves for minimal-change, unconstrained moves for extensive-change.
Added optional matplotlib plotting for a quick visual demo.
Challenges we ran into
No real floorplan measurements: we had to start with rough coordinates and assumptions. Handling times vary by worker, batch size, and order complexity; we used placeholders to keep the model flexible. A layout that minimizes distance isn’t always feasible due to refrigeration constraints, safety aisles, and workflow rules—we noted where constraints should be added next. Getting “optimization” to feel meaningful without overengineering (we kept it hackathon-simple).
Accomplishments that we're proud of
Produced quantitative comparisons (distance and minutes/kit) for baseline vs two alternatives. Delivered an end-to-end demo that ties directly to consulting deliverables: two concepts + time analysis + recommendation. Built a framework that can plug in real time-study data later (CSV/parameters). Visual route plots make bottlenecks and backtracking immediately obvious.
What we learned
Even a lightweight model quickly reveals where layout changes matter most (e.g., box storage placement or reducing back-and-forth trips). “Minimal changes” can still yield meaningful gains if you target the right stations. Good ops design needs both process and space; optimizing one without the other misses big opportunities. Clear KPIs (minutes/kit, walking distance) make stakeholder discussions easier and more objective.
What's next for layout
Add real constraints: fixed refrigeration zones, aisle widths, no-go regions, and adjacency requirements. Support batching strategies (building 2–3 kits per trip) and compare policies, not just layouts. Ingest real data: import station coordinates and time-study results from CSV; calibrate against observed cycle times. Add metrics beyond time: congestion score, safety risk proxy, and labor utilization by shift. Generate a one-page auto report (PDF) summarizing KPIs, recommended layout, and assumptions.
