MES - LOT - QC - P&L Manufacturing AI PoC

Manufacturing AI PoC Solution

Before adopting manufacturing AI, load MES, CSV, and Excel files into a dedicated PoC app to validate LOT, QC, COA/HACCP traceability, data quality, and P&L risk together.

Discuss PoC scope Check fit scenarios
Target
Client-specific PoC
Protection
local-only execution - NDA - masking
Outputs
Action board - report - PDF

Before PoC Setup

Agree on execution standards before loading sensitive manufacturing data.

  • NDA and PoC scope agreement Pilot product group, production line, analysis period, and delivery method are confirmed first.
  • Client-specific execution model The default is local-only execution; external sharing is used only after the auth proxy, VPN, or SSO approach is agreed.
  • Source and loaded data separation MES export source files are not changed; loaded app datasets and analysis outputs are kept separate.
  • Evidence-based outputs Connection rate, missing rate, risky LOTs, and P&L impact are recorded with calculation standards.

Fit Check

If this is your current state, a small PoC app is the safer first step.

Before AI model development, MES upgrades, or government support program submissions, verify whether current data actually connects and explains operational metrics so scope and budget can start smaller.

Situation

You have data, but execution priority is unclear

MES, ERP, Excel, PDF, and quality documents are scattered, and risky LOTs are not yet separated from remediation actions.

Starting data

A full database is not required to build PoC screens

Start with a pilot product group, production line, recent MES exports, CSV files, and Excel samples.

First result

Define the next actions, not just a yes or no

Missing rates, connection rates, disconnection causes, document-impacted revenue, and next-stage PoC scope are summarized for decisions.

Trust Signals

Trust is built through verifiable procedures and deliverables.

Instead of promising a large-scale rollout first, Arvion AI leaves evidence that shows which LOTs are risky and which document or operational actions are needed based on data the client already has.

01

Start with pilot scope and KPIs

Product group, production line, validation period, target KPIs, and responsible owners are fixed before the PoC begins.

Output: pilot scope table
02

Inspect with validation run history

Missing values, duplicates, format errors, code mismatches, and disconnected LOT-QC items are recorded as repeatable runs.

Output: validation run
03

Separate datasets and profiles

Source data is not changed. Loaded datasets, column mapping profiles, and analysis outputs are separated for traceability.

Output: dataset load history
04

Summarize with reports and an action board

Before AI modeling, risky LOTs, document-impacted revenue, margin leakage, and API or batch integration candidates are prioritized.

Output: weekly report and PDF
Operating model
Client-specific PoC - founder-led delivery
Initial scope
Product - line - period
Security standard
local-only execution - NDA - masking
Decision basis
KPI - risky LOT - P&L impact

Founder Capability

Manufacturing data is organized into a decision-ready PoC app.

Based on FDE (Forward Deployed Engineer) and DevOps experience, the founder directly reviews MES exports, CSV, Excel, and DB dumps, then organizes LOT-QC traceability, quality metrics, P&L risk, and manufacturing AI applicability into one execution flow.

Experience base
15+ years in FDE (Forward Deployed Engineer) and DevOps
Core role
Data validation - PoC operating model design
Target data
MES - LOT - QC - P&L - COA/HACCP
Credentials
Korean Information Processing Engineer - NAVER Cloud
01

Shop-floor data interpretation

Columns, codes, missing values, duplicates, and connection keys across MES, LOT, QC, item, and material data are organized into an analyzable structure.

02

Validation automation

Repeatable validation runs calculate missing rates, duplicate rates, code mismatch rates, and LOT-QC connection rates.

03

PoC app and API composition

Python, JavaScript, SQL, cloud and on-prem operations, and data pipeline experience are used to compose dataset loading and report API flows.

04

Business impact summary

Anomaly detection, defect prediction candidates, document-impacted revenue, margin leakage, and next-stage manufacturing AI scope are separated.

Before the AI factory rollout

Traceability, quality, and P&L impact should be checked before AI models.

Rather than starting with a full MES rebuild or real-time equipment control, Arvion AI focuses on reducing risk in the next manufacturing AI rollout by loading existing MES exports, CSV, Excel, and DB dump data into a PoC app. Using a pilot product group and production line, the PoC separates usable data, additional materials that need to be collected, and P&L impact that can be explained through operational metrics.

Core Services

Manufacturing AI PoC Execution and Validation Services

Existing MES and shop-floor quality records are used to validate data conditions and operating risks before AI adoption.

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

Scope Boundary

A focused PoC also clarifies what is outside the initial commitment.

Arvion AI does not promise full rollout, real-time control, or AI performance before confirming data condition. PoC results separate what is feasible now from what requires follow-up review.

Included in PoC
  • Dataset loading from MES exports, CSV, Excel, and DB dumps
  • LOT-QC-COA/HACCP connection, missing, and duplicate rates
  • Risky LOT, additional records, and remediation action management
  • P&L impact, weekly reports, and submission support materials
Requires follow-up
  • Full MES rebuild or operating system replacement
  • Real-time equipment control based on PLC, sensors, or OPC-UA
  • Production AI model development and performance guarantees
  • Auth proxy, DB/API connector, OCR/RAG, and ETL batch automation

Validation Criteria

PoC results are explained through four decision criteria.

The output avoids a simple pass/fail answer. It records evidence for which data is sufficient and which materials or operating actions need follow-up.

01

Traceability

Checks whether raw material LOTs, production LOTs, work orders, and QC results connect through the same keys.

Decision: traceable - more data needed - hold
02

Quality

Classifies missing required values, duplicates, date and quantity format errors, and code mismatches.

Decision: validate now - validate after cleansing - new extract required
03

Operating risk

Separates risky LOTs, missing documents, remediation actions, and P&L impact amounts together.

Decision: act now - more data needed - follow-up review
04

AI applicability

Summarizes candidate input variables for defect prediction, anomaly detection, and quality root-cause analysis.

Decision: PoC feasible - feasible after data update - not recommended

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

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.

FAQ

Questions most often checked before consultation

These are the PoC scope, security, and deliverable questions decision makers should confirm before sharing manufacturing data.

Do you directly modify MES source data?

No. Source data is preserved, and loaded app datasets are managed separately from analysis results. Changes to the client's operating system are agreed separately as a later scope.

Do we need to provide the full database from the beginning?

No. Exports, CSV files, and Excel samples for a pilot product group and production line can be enough to build PoC screens, first-pass KPIs, and additional-record request lists.

Do you immediately guarantee AI model performance?

No. LOT, QC, COA/HACCP traceability, quality anomalies, and candidate input variables are validated first, then the feasible PoC scope is decided.

Can the outputs support grant or smart-factory program submissions?

The pilot scope table, LOT traceability analysis, missing document request, and weekly Markdown/PDF report can support manufacturing AI or smart factory scope discussions.

Security & Scope

Manufacturing data security standards and PoC scope are agreed in advance.

The founder directly confirms scope and separates source data from loaded app copies. Personal information inclusion, local-only execution, retention period, and deletion standards are agreed before kickoff.

View privacy and data handling standards
  • Founder-led execution The founder directly handles consultation, data scope review, PoC criteria definition, and deliverable review.
  • Source and loaded copy separation MES exports and app-loaded datasets are separated so change history can be managed.
  • Local execution and masking When needed, first-pass validation uses samples that exclude or mask sensitive information.
  • Retention and deletion agreement Data storage location, retention period, and deletion standards after result delivery are agreed in advance.
  • Pilot scope first Validation starts with a verifiable scope around one product group and one production line.
  • Later-stage expansion design Auth proxy, DB/API connector, ETL batch, OCR/RAG, and HACCP anomaly detection are proposed as later stages.

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