EnVision 1.4.8 brings your shop floor into EnVision. A new family of processing datasets makes production and cutting data available for dashboards, reports, and EnVision AI — so the same self-service analytics you use for inventory, sales, and purchasing now reaches throughput, yield, scrap, on-time completion, and operator performance.
Ask EnVision AI about production and cutting
EnVision AI now understands the processing datasets. Ask in plain language — for example, "what was the average scrap rate by operator over the last 60 days?" or "which workstations produced the most pounds last week" — and EnVision AI builds the chart or table from your production and cutting data.
New to EnVision AI? EnVision AI is the natural-language analytics companion to EnVision, letting you ask questions in plain English and get back charts, tables, and dashboards generated on your own data. Read the EnVision AI Overview to see what it can do.
If you're an EnVision customer and would like to add EnVision AI to your account, contact customersuccess@enmark.com and we'll help you get set up.
Processing datasets: production and cutting on the shop floor
Nine new datasets cover how material is processed into finished tags. They span the two ways a service center processes material in Eniteo — multi-line production orders worked through the production system (Production Processing, Post-Production, and Production Completion), and material cut directly from inventory in the Cutting application.
Production datasets
Sourced from production orders worked through the production system.
| Production Order Finished Material | Every finished tag a production order produced, traced to its source material and the customer order it fills. |
| Production Order Source Material Utilization and Status | How each source tag was consumed on a production line — scheduled vs. produced quantities, scrap, yield, and the time the job took. |
| Production Workstation Activity | A time-stamped log of every start, pause, stop, and finish at a workstation, with any stop/pause reason. |
| Production Workstation Daily Performance | Daily throughput, scrap, time usage, and per-hour productivity for each workstation. |
| Production Process Daily Performance | The same daily metrics, broken out by process (slit, level, cut, shear, burn). |
| Production User Daily Performance | Daily throughput, scrap, and time per operator — for coaching and productivity comparisons. |
| Orders Requiring Production | Open order lines that still need production work, with scheduling, workstation assignment, and on-time risk (projected days late). |
Note: The performance datasets — Production Workstation Activity, Workstation Daily Performance, Process Daily Performance, and User Daily Performance — are designed for customers using Production Order Processing. Customers who don't use Production Order Processing can still track overall progress and on-time delivery with the Production Order Finished Material and Source Material Utilization datasets.
Cutting datasets
Sourced from material cut directly from inventory in the Cutting application.
| Cutting Batch Finished Material | Every finished tag a cutting batch produced, with its source tag and the customer order it fills. |
| Cutting Batch Source Material Utilization and Status | How each source tag was consumed in a cutting batch — produced quantities, scrap, yield, and scrap cost. |
Want the full detail? The EnVision Dataset Field Reference documents every field in all nine datasets, with plain-language guidance on what each one means.
What you can build with them
- Throughput and yield — pounds produced and yield by workstation, process, operator, or day.
- Scrap in pounds and dollars — scrap rate and scrap cost by workstation, process, or material group.
- On-time completion — days late and projected days late, completion trends, and the orders at risk of being completed late.
- Schedule adherence and open work — how jobs track against their scheduled dates, and open weight still to produce by workstation.
- Operator performance — per-operator throughput, scrap, and time for fair, side-by-side coaching.
- Cutting yield and scrap — yield and scrap cost by source product group, batch, or operator.
Built-in field guidance
Every processing dataset and field carries a plain-language description right where you work — in the Analyze tab when you build a report, and in EnVision AI when it picks fields for you. For the full field-by-field reference, see the EnVision Dataset Field Reference.