Logistics and distribution
Illustrative workflow data package: a 3PL's order exception histories
By SourceX Editorial · Updated
Short answer
A workflow data package for a 3PL links each client order to the exceptions it hit, the actions staff took, the messages exchanged and the final outcome. This illustrative manifest shows the tables, key fields, exception codes and preparation steps for a fictional 3PL, so operations leaders can compare the structure with their own WMS and ticketing records.
Key takeaways
- A workflow data package is organized as linked tables, not a single export, so every exception can be traced from order to outcome.
- Exception, action, communication and outcome tables carry most of the value; the order table is the spine that connects them.
- Consumer, client and associate identities are replaced with stable pseudonyms so patterns survive preparation.
- Known gaps, such as a year lost in a WMS migration, belong in the documentation rather than being papered over.
What does a 3PL workflow data package contain?#
A 3PL workflow data package contains prepared, linked records showing how real warehouse work moved from a trigger to an outcome, delivered with a manifest that explains every table and field. For a 3PL, the natural workflow is order exception handling: an order hits a problem, and staff investigate, act, communicate and close it out.
Everything on this page is illustrative. The company, codes and fields are fictional and simplified, written to show structure rather than describe any real dataset. Field names will differ in your WMS and ticketing tools; the shape is what carries over.
Illustrative: the fictional 3PL behind this package#
Illustrative: the supplier is a fictional multi-client 3PL serving e-commerce brands and retail replenishment clients from several buildings. Orders arrive by EDI and API into a commercial WMS. Exceptions are coded in the WMS, client communication runs through a ticketing tool and a shared inbox, and carrier events come from the TMS.
The 3PL holds years of exception history with one gap: when it replaced its WMS, the old exception notes were archived as flat files and never linked to the new system. The package documents that gap instead of stitching over it. Two clients whose contracts restrict secondary use of their data are excluded entirely.
Manifest overview: the tables in the package#
The manifest lists each table, what one row represents, its key fields and how it links to the rest. Identifiers marked as pseudonymous stay consistent across tables, so a client or associate can be followed through the records without being identified.
| Table | One row is | Key fields | Links to |
|---|---|---|---|
| orders | One client order received by the WMS | order_id, client_ref (pseudonymous), channel, received_at, ship_by, service_level | exceptions, shipments |
| exceptions | One exception event on an order or receipt | exception_id, order_id, exception_code, detected_by_role, detected_at, zone | actions, communications, outcomes |
| actions | One step taken to resolve an exception | action_id, exception_id, action_type, actor_role, actor_ref (pseudonymous), note_redacted | exceptions |
| communications | One message with a client or carrier | message_id, exception_id, direction, channel, body_redacted, sent_at | exceptions |
| outcomes | The final resolution of one exception | exception_id, resolution_code, closed_at, credit_issued, chargeback_flag, recurred | exceptions, orders |
| shipments | One carton or pallet shipment | shipment_id, order_id, carrier_ref, service, shipped_at, delivered_at | orders |
| reference | Code lists and definitions | exception codes, resolution codes, action types, roles | all tables |
Exception codes used in the package#
The exception code list is the backbone of the package because it lets anyone using the records group similar problems and compare how each was handled. The fictional 3PL normalized its codes across both WMS eras into one list and kept the original code in a separate field.
| Code | Meaning | Typical resolution path |
|---|---|---|
| SHORT_PICK | Less stock at the pick location than the WMS expected | Overflow check, reallocation, partial ship or backorder with client approval |
| DAMAGE_HANDLING | Product damaged inside the building | Replacement pick, damage write-off, client notice |
| ADDRESS_INVALID | Carrier rejected or flagged the ship-to address | Address correction with the client, then reship or hold |
| ASN_MISMATCH | Received items or quantities differ from the advance ship notice | Recount, discrepancy report, client inventory adjustment |
| CARRIER_MISSED | A scheduled carrier pickup did not happen | Rebook, service upgrade, client notice |
| ROUTING_NONCOMPLIANT | A shipment broke a retailer's routing guide | Correction before tender, or chargeback dispute afterward |
One exception from start to finish#
A single exception shows how the tables fit together. The fictional record below follows a short pick on a retail replenishment order through every table in the package.
The final field, recurrence, is what turns a log into a learning example. It shows whether the fix held, which is the signal developers of operations agents most often lack.
- orders: a replenishment order arrives by EDI with a firm ship-by date.
- exceptions: the picker scans an empty location and the WMS logs SHORT_PICK in the forward pick zone.
- actions: an inventory lead checks overflow, finds partial stock and records a cycle count adjustment.
- communications: the account manager asks the client whether to ship partial or hold the full order.
- communications: the client replies to ship partial and backorder the balance.
- actions: the lead releases a partial shipment and opens a backorder.
- outcomes: the partial ships by the ship-by date, no chargeback follows, and the same SKU later recurs as a short pick and is flagged for replenishment review.
What preparation removed or changed#
Preparation for this package removed personal and client-identifying details while keeping the structure intact. Every change is recorded in the privacy record, so anyone using the package knows what was altered and the supplier can confirm exactly what left the building.
In this fictional package, automated PII detection made the first pass over notes and messages, and people reviewed samples from every table afterward. Detection tools are candid about that limit: the open-source Presidio project's documentation warns that there is no guarantee it will find all sensitive information, and that additional systems and protections should be employed.
- Consumer names, street addresses, phone numbers and emails removed; ship-to reduced to region.
- Client names and brand references replaced with stable pseudonyms; SKUs that would identify a client generalized to product category.
- Associate names replaced with role plus a pseudonymous ID; badge numbers dropped.
- Free text in notes and messages redacted for names, contact details and references to excluded clients.
- Billing rates and client contract terms removed; credits kept only as yes or no flags.
- Photos and video left out of this package.
What ships with the package, and how SourceX handles it#
The package ships with a SourceX Evidence Packet alongside the data: provenance (which systems and years each table came from), licensing rights (which clients and record families are in scope and why), permitted use, the privacy record and the 3PL's release authorization.
A data dictionary, the exception and resolution code lists, the method used to link tickets to orders and a known-gaps note complete the set. SourceX runs the work through the SourceX five-step transaction: Supply, Rights, Preparation, Approval and Delivery, with the 3PL approving scope at each step. The data itself remains on the 3PL's infrastructure until delivery, and bulky tables travel on encrypted drives if needed.
The documentation can also be mapped to published metadata standards. The Data & Trust Alliance's Data Provenance Standards, for example, group dataset metadata into Source, Provenance and Use, which correspond closely to the provenance, licensing rights and permitted-use parts of the packet.
How to adapt this template to your own records#
Adapting the template starts with your own systems, not with exports. Work through the steps in order and stop at any point where the answer is unclear; unknowns are cheaper to resolve on paper than after files have moved.
- Step 1: list the systems that hold orders, exceptions, client messages and carrier events, with the years of history each still holds.
- Step 2: check whether exceptions and tickets carry an order or receipt reference you can join on.
- Step 3: map your exception codes to one normalized list, keeping the originals.
- Step 4: flag clients whose contracts restrict secondary use and set them aside.
- Step 5: describe in metadata only what a package would contain, and run a fit check before exporting anything.
Frequently asked questions
Is this a real dataset?
No. The company, codes, fields and records described here are fictional and simplified. The example shows how a 3PL's exception history could be structured as a licensed package; a real package follows the supplier's own systems, contracts and preparation decisions.
Do we need all seven tables to have a package?
No. Exceptions, actions and outcomes linked to orders are the core. Communications add a lot when tickets or emails can be matched to exceptions, and shipments help with carrier-related problems. A package built from fewer tables can still work if it is well linked and documented.
Can client-owned order data be included?
Only where client contracts allow it, which is decided in the rights review. Many 3PL agreements limit use of client data to performing the services. Clients with restrictive terms can be excluded, while the 3PL's own operating records for the remaining clients may still form a package.
What file formats are typical?
Delimited text, Parquet or JSON lines are common for tabular records, with a data dictionary describing each field. The format is settled during scoping. Very large tables do not need to be uploaded anywhere; they can be delivered from the 3PL's own storage or handed over on encrypted drives.
Does a buyer see sample records before we approve anything?
No records leave before you approve. The first assessment uses metadata only. If a buyer later needs samples, they go through the same preparation steps and are released only with your authorization and under agreed terms.
Sources
- Presidio's documentation warns that because it uses automated detection mechanisms, there is no guarantee it will find all sensitive information, and that additional systems and protections should be employed. Source
- The Data & Trust Alliance's Data Provenance Standards (version 1.0.0) define dataset metadata in three groups: Source, Provenance and Use. Source
Related resources
See if your company qualifies
A short company assessment. No data uploads are needed.