What supply chain visibility data can be captured through barcode labeling on returnable bread trays?

Each barcode scan produces one record: this tray, at this place, at this time, in this kind of event. From a series of those records you can measure how long trays sit at each customer, how long a full cycle takes, how many trays went out and did not come back, and how big the pool needs to be. What barcodes cannot do is see a tray between scans, so the value of the data depends on how reliably each location scans and how long the labels stay readable. A barcode program should be designed around those two limits from the start.

What one scan records, and what a series reveals

A scan event needs at least four fields: tray identity, location, time and the type of event (for example dispatch, delivery, pickup, return, wash or reject), because the same tray at the same dock means different things on the way out and on the way back. With scans at four points in the cycle (dispatch, delivery, empty pickup, return), you can derive:

Measure	From	Use
Dwell at the customer	Delivery to pickup	Find stores that hold trays longest
Cycle time	Dispatch to return	Pool sizing and trend
Outstanding trays	Dispatched, not yet returned	Loss detection and follow-up
Loss pattern	Outstanding trays by store, route or channel	Separate a problem location from scattered loss
Age and use	Count of trips per tray	Plan inspection and replacement

Standard identifiers make the records usable across systems. GS1 describes its Global Returnable Asset Identifier (GRAI) as suited to “the management of reusable transport items” and names “trays, crates, or pallets” as examples of the returnable assets it identifies, by type and, where needed, individually (GS1 Australia on GRAI). Tray-level history, such as trips and repairs per tray, needs the individual form: the same page refers to the GRAI “with serial number” for maintenance and repair records. Using a standard key instead of an in-house number helps when customers or partners scan your trays in their own systems.

Sizing the pool from scan data

Little’s law states that the average number of items in a system equals their arrival rate times the average time they spend in it (Little’s law). For trays:

trays in circulation = trays dispatched per day x average cycle time in days

For example, if scans show 1,000 trays dispatched a day and an average of 2 days at stores, about 2,000 trays are sitting at stores at any moment; the rest of the cycle (transit, wash, staging) adds its own share. Use the average cycle time in this formula. The spread of cycle times, which scans also show, sets the safety buffer; using a slow-case cycle time in the formula and then adding a buffer counts the slow trays twice.

A scan that does not happen leaves no record, and the system cannot tell a missed scan from a missing tray. Three consequences follow:

Missing data looks like loss. A store whose deliveries are not scanned shows trays leaving and never arriving.
Gaps compound across a chain. If missed scans at different points are independent, the share of trays with a complete record is the product of the scan rates. For example, with 95 percent at dispatch, 60 percent at the store and 40 percent at a third-party depot, 0.95 x 0.60 x 0.40 = 0.228, so about 23 percent of trays passing all three have a complete trail. If misses cluster at busy times, the picture can be worse.
Gaps fall where volume is highest. If scans are skipped under time pressure, the missing records come from the busiest periods, when the most trays move.

Measure scan compliance directly: send a counted batch through a node and compare the scans recorded with the count. Do it for each kind of node, repeat it periodically, and use the results to correct loss reports before acting on them. Who scans at each node depends on your delivery model; if drivers scan at the store with handhelds, compliance is in your own hands, and if a third party handles trays, scanning belongs in the contract.

Label survival on washed trays

Trays are washed, abraded and handled many times over their life, and a label that cannot be read creates the same gap as a missed scan.

Label material and attachment. Ask the tray supplier which label methods it offers for washed trays and what wash data supports them; SPF Plastic Group, for example, lists “RFID or bar code label options” on its dolly specification sheet.
Symbology. A one-dimensional barcode needs at least one intact scan line across all of its bars. Two-dimensional symbols such as Data Matrix carry error correction: with ECC 200, the encyclopedia entry on Data Matrix notes that the encoded data can be reconstructed “when the symbol has sustained 30% damage, assuming the matrix can still be accurately located.” That margin matters on worn labels, provided your scanners read 2D symbols.
Placement. Put labels where stacking, nesting and dolly contact do not rub them, and where a scanner can see them on a stacked column.
Replacement. Track no-read rates by tray age; a rise points to labels reaching the end of their life.

Turning the data into action

A useful first report is a list of trays dispatched more than a set number of days ago with no return scan, set from your normal cycle time. Group it by location and channel:

Scattered across many locations: general handling loss; address with training and counting at pickup.
Concentrated at one store, route or partner: a specific process problem; investigate that node first.
Concentrated where scan compliance is low: possibly a data gap rather than a loss; measure compliance there before acting.

Tray loss is a material cost; an American Bakers Association page states that it is estimated that companies in the industry “lose 30 percent of its reusable plastic trays and spend well over $10 million annually in replacement costs” (ABA on tray theft).

Barcode or something more

Barcodes depend on a person scanning. RFID records trays that pass an installed reader without a scan step, but only where readers exist, and read rates have to be measured on loaded columns. How the technologies compare for different network sizes is covered in our guide to identification technologies; color as a fast visual sort is covered in our guide to color coding.

Before investing in analytics, measure scan compliance at every kind of node and fix the worst one; reports built on uneven scanning can point at the wrong places.