Arvion AI
Arvion AI

Data readiness PoC before manufacturing AI rollout

Start with MES, CSV, and Excel exports

Check quality, document, and margin risk before adopting AI

Load MES, CSV, and Excel exports into a client-specific PoC app to summarize LOT/QC connection rates, missing data, COA/HACCP requests, and P&L impact together.

Check with your data View sample outputs
Input data
MES - CSV - Excel
Diagnostic result
LOT/QC connection
Decision material
Actions - PDF report

Problem Fit

When you have data but not a clear AI execution priority, start here.

When MES, ERP, Excel, and quality documents are scattered, a small PoC app can validate traceability, quality, P&L impact, and follow-up actions before a wider rollout.

01

Manufacturing AI action board

Items, work orders, production results, LOTs, and inspection records are reviewed on one screen, with today's quality, document, and P&L risks prioritized.

  • Representative actions and risky LOT summary
  • Supplemental data requests and action status
  • Weekly report Markdown/PDF export
02

LOT traceability and quality validation

Connection keys across raw material LOTs, packaging material LOTs, production LOTs, work orders, and QC results are validated to classify COA/HACCP feasibility and disconnection causes.

  • Raw material LOT to production LOT connection rate
  • Production LOT to QC result connection rate
  • COA and HACCP request list
03

Dataset loading and profile validation

MES snapshots, CSV, and Excel data are loaded against registered profiles, then required columns, row counts, duplicates, missing values, and code standards are validated.

  • Dataset upload/delete flow
  • Required value, duplicate, and format error checks
  • Client column mapping registry
04

P&L and operations support views

LOT quality risk is connected to inventory, quotation, revenue, and P&L operating metrics to define input variables and business impact required for next-stage model development.

  • Margin leakage and value at risk
  • Inventory, LOT, quotation, and revenue support screens
  • AI PoC and six-month roadmap

Validation Dashboard

LOT connection, data quality, and P&L risk are shown on one screen.

Existing MES data is used to separate what can be analyzed now from what needs additional COA, HACCP, and QC detail. Inspection results remain as an action board, API report, and PDF deliverables for next-stage manufacturing AI rollout decisions.

LOT connection rate Required value missing rate Duplicate data rate Risky LOT Margin leakage Weekly report PDF
Manufacturing AI PoC Client-specific instance - pilot line
Example metrics
LOT connection 87%
QC connection 74%
Required missing 9%
Remediation actions 36
Raw LOT
Production LOT
QC result
COA/HACCP

Dashboard screens and figures on this website are illustrative examples. In an actual PoC, calculation evidence and limitations are provided based on the supplied data.

Deliverables

Outputs that can be reviewed directly in decision meetings

Example deliverables include a pilot scope table, dataset quality table, LOT traceability analysis, missing document request, and weekly Markdown/PDF report. The actual package is adjusted to the available data and PoC goals.

Pilot scope table

Product group, production line, validation period, target KPIs, responsible owners, and security standards

Dataset quality table

MES tables, fields, codes, rows and columns, missing required values, duplicates, and error status

LOT traceability analysis

Raw material LOT, production LOT, QC, COA, and HACCP connection rates and disconnection causes

Risky LOT and remediation list

Quality, document, and shipping risk, responsible owners, action status, and revalidation needs

Missing document request

Additional COA, inspection certificate, HACCP/CCP, and QC detail requirements plus impacted revenue

P&L impact summary

Value at risk, document-impacted revenue, quoted margin leakage, and risky LOT P&L linkage

Manufacturing AI weekly report

AI PoC evidence, remediation actions, next-stage modeling input candidates, and Markdown/PDF export

Sample Preview

Sample deliverable preview

The figures and screens below are illustrative examples. In an actual PoC, calculation standards, limitations, and additional record requests are provided with the supplied data.

Sample Deliverable

LOT traceability analysis

Example data basis - not actual client data

Pilot product group A - line 1 LOT traceability and additional-record checks
Illustrative sample
Raw LOT to production LOT 91%
Production LOT to QC result 86%
Disconnected LOTs 18
Additional record requests 7
Raw LOT Production LOT QC result COA/HACCP
Disconnection cause Count Action
LOT notation mismatch 8 Check code mapping table
Missing QC result 6 Add inspection file
COA file not provided 4 Request by raw-material LOT

Example decision: core LOT traceability can be validated after cleansing, and anomaly detection plus defect prediction PoC scope is confirmed after additional COA/HACCP records are secured.

Sample Deliverable

Dataset quality table

Example data basis - not actual client data

MES export sample validation Required column, duplicate, code, date, and quantity format checks
Illustrative sample
Validated rows 12,480
Required missing rate 4.6%
Duplicate rate 1.8%
Code mismatch rate 2.3%
Item code Normal
Work order Needs data
Production quantity Normal
QC judgment Needs data
Check item Metric Data request
Missing required columns 4.6% Confirm column definitions
Code value mismatch 2.3% Request item and process code tables
Date and quantity format error 0.9% Standardize extraction format

Example decision: core tables can be analyzed after cleansing, but work order and QC detail code standards must be confirmed first.

Execution Model

Example implementation process

After confirming business scope and data readiness, the detailed sequence is fixed, with verifiable deliverables and metrics prioritized at each stage.

  1. Kickoff Confirm PoC scope

    Business interview, product group, production line, validation period, and target KPI confirmation

  2. Secure Agree on data and profile

    Agree on MES export, CSV, Excel, DB dump delivery method, column mapping, masking, and retention standards

  3. Load Load datasets

    Register MES snapshots and additional records as datasets, then check rows, columns, and required fields

  4. Validate Run validation

    Repeatedly inspect missing values, duplicates, format errors, code mismatches, and LOT traceability

  5. Connect Validate LOT traceability

    Calculate connection rates across production results, raw material LOTs, production LOTs, QC, COA, and HACCP

  6. Act Organize risky LOTs and remediation actions

    Summarize missing documents, quality issues, responsible owners, action status, and revalidation standards

  7. P&L Connect P&L impact

    Connect value at risk, document-impacted revenue, and margin leakage to operating metrics

  8. Report Export reports and supporting materials

    Generate manufacturing AI reports, missing document requests, and weekly Markdown/PDF outputs

  9. Roadmap Summarize AI applicability and roadmap

    Summarize KPIs, limitations, next-stage manufacturing AI scope, and a six-month execution plan

Contact

Check manufacturing AI PoC feasibility from the data first.

Based on the data you already have in MES export, CSV, Excel, or DB dump form, Arvion AI helps define pilot PoC scope, required additional records, and P&L risk review scope.

Business inquiry [email protected]

First-pass PoC scope can be discussed with samples that exclude or mask sensitive information. The reply will include guidance on data delivery method and the next meeting scope.

Write PoC inquiry email Review deliverables

  • Available data MES export, CSV, Excel, DB dump
  • Pilot scope Product group, production line, analysis period
  • Additional records QC details, COA, HACCP/CCP records
  • Operating metrics Need for inventory, LOT, quotation, revenue, and P&L linkage
  • Protection standards NDA requirements, masking scope, source preservation method