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AI Layer | Arge Bilişim argemas

AI Layer

A decision layer that explains production evidence, proposes a response and acts within an approved boundary

01 / Advanced Technology Solutions

What evidence explains a production deviation before anyone acts?

Decision scope

Define the decision before selecting the model

The argemas AI Layer evaluates manufacturing data exposed through the integration layer together with approved enterprise sources for a defined decision question.

Users can inspect the evidence behind a recommendation. Only authorized roles can initiate work orders, maintenance requests or plan changes through the configured approval path.

01 / Decision question

Ask production questions in natural language

Users can query operational data without searching through multiple reports and dashboards.

  1. Why did productivity fall today?
  2. Which orders are at risk of delay?
  3. Which operation has the most critical bottleneck?
  4. Why did the scrap rate increase?
  5. Which machine fails most often?
  6. Will we meet this month's production target?
  7. What is the most suitable production plan?
  8. Which operators need additional training?

02 / Evaluation

From operational data to a recommended response

Every output is tied to transaction data the user can inspect and to defined sources.

  1. Measures deviation by shift, line and product
  2. Shows the trend in scrap, downtime and cycle time
  3. Forecasts order delivery and the monthly target
  4. Flags delay and breakdown risk with its evidence
  5. Compares planned with actual production
  6. Traces a quality deviation to machine, batch and operation
  7. Compares plan options that relieve the bottleneck
  8. Presents each recommendation with the data behind it

03 / Authorization

Governed workflow actions

Configured roles, data permissions and approval rules determine which actions are available.

  1. Create work orders
  2. Initiate purchase requests
  3. Open maintenance requests
  4. Update production plans
  5. Propose alternative operation assignments
  6. Prepare shift and deviation reports
  7. Notify the responsible role of a deviation
  8. Start approval workflows
  9. Run defined repetitive transactions with an audit record

Control boundary

Critical operations are governed by role-based authorization, configured approval steps and traceable transaction records.

04 / Sources

Grounded in approved enterprise sources

Responses can use authorized, indexed documents and operational history alongside current transaction data.

  1. Grounds the answer in the relevant enterprise document
  2. Matches the work instruction to the operation in question
  3. Shows the quality procedure in the context of the deviation
  4. Retrieves similar past cases and their outcomes
  5. Learns from approved and rejected recommendations
  6. Adds new sources to the index with their access definition

05 / Measurement

Operational measures to track

  1. Time required to reach a decision
  2. Traceability of cited evidence
  3. Deviation detection lead time
  4. Forecast accuracy
  5. Automated and approved transaction count
  6. Planned-versus-actual production variance
  7. Error and rework rate
  8. Time to retrieve enterprise knowledge
  9. Recommendation approval rate

Next decision

Start with one defined production decision

Choose a measurable decision or workflow, identify its data sources and define the approval boundary before selecting the model.

Discuss the use case