Developer: Sure thing. Keep the local model ultra‑light: just a fixed‑size queue of last N consumption events, maybe 50 per item type. Every new entry updates a running average and a simple linear trend—just add the new value and subtract the oldest. That gives you a rolling mean and slope without storing everything. For caching, use an LRU list on the device, but cap the size at the memory budget; evict the oldest record if you hit the limit. When connectivity returns, push only the delta: the count of each item, the timestamp, and the last trend value. On the server, merge these deltas into a global model that can feed back a more accurate forecast. Use a very small Kalman filter per item type for the predictive part—just a few multiplies and a state update. That’s all you need to stay under 1 MB of RAM and keep latency low. Sync when a Wi‑Fi beacon appears or a scheduled interval hits, but never block the main thread. Simple, fast, and predictable.

Developer: Battery is a big deal. Swap the Kalman for an EWMA; one multiply per item, no state matrix. For the radio, just expose a sync button that dumps the current delta to the receiver. As for the seasonal tweak, keep a tiny lookup table of month‑based multipliers you learn on the ground—just a few bytes. That gives the trend a sanity filter without any extra power.