The project aims to reduce the time spent figuring out where returned clothing belongs, especially after items build up in fitting rooms. A worker can scan, check the best known area, and move on without relying only on memory.

It also creates a shared memory of the store floor. When locations are wrong, repeated "Not There" reports make unreliable records visible, so the worker knows which items need to be rescanned into the correct area.

The benefit is a faster, calmer workflow: fewer repeated searches, cleaner fitting room recovery, less guesswork, and a system small enough to fit the real daily routine of a clothing store.

The goal of this project is to make a practical location memory assistant for retail work. Instead of trying to become a full inventory system, the app focuses on one common problem: a worker has an item, scans or enters its article number, and needs to know the best known area where that item belongs.

Each item is connected to an approximate store location, such as A, B, C, D, or a smaller subzone like B1 or B2. The app stores the best known location, not a guaranteed truth, because clothing store layouts change and items often move during the day.

The primary flow is simple: scan a barcode or enter an article number, then the app returns the stored location, current status, and last updated time. If the item is not actually there, the worker only has one action to take: mark it as "Not There".

Marking an item as "Not There" increases its failure count and makes its stored location less trustworthy. The app can then label the item as valid, suspect, or stale depending on how often the location has failed.

New items, corrections, and relocations all use the same assign-location flow. The worker chooses the current area, enters scan mode, and every scanned item is created or updated with that location. This keeps the interface fast and avoids separate forms for similar tasks.

The main challenge is designing around uncertainty. Store floor data can become wrong quickly, so the system should not pretend that every saved location is always accurate. The status system helps communicate confidence without adding extra work for the user.

Another challenge is keeping the workflow minimal. Retail work is time-sensitive, so the app avoids unnecessary buttons and manual editing screens. Location updates only happen through scanning items into a selected area, which makes the behavior predictable.

The data also needs a lifecycle. Old clothing items should not stay in the active view forever, but deleting them immediately would remove useful history. The planned solution is soft archiving: older inactive records are hidden from default lists but can still be searched and reactivated.