Manufacturing

AI for Manufacturing

The lag between a fault alert and a working technician.

INDUSTRYSenior Care LivingAI Agent LayerCloses coordination gaps across your operationsRecordsERP, EHR anddata sourcesSchedulingShifts, tasks andworkflowsBillingFinance, AR andrevenue cycles
01. Why AI Matters

Why AI Matters Here

The day at most manufacturing plants starts with the same questions: what broke overnight, what is running behind, and what is about to cause a problem. Your ERP and MES have the data. What they lack is the ability to connect it to the right person at the right time.

AI sits on top of your existing systems. It monitors continuously, surfaces problems before they compound, and routes the right information to the right person before a shift falls apart.

02. Operational Challenges

Where Staff Time Goes

Unplanned Downtime Response

When a machine fails, the maintenance tech spends 30 to 90 minutes locating the relevant repair history, equipment manuals, and current parts availability before any work begins. That search time adds directly to total downtime on every breakdown.

Production Schedule Disruptions

A material shortage or capacity conflict often surfaces the morning production is supposed to run. The planner rebuilds manually across open work orders, operator availability, and machine capacity, losing 3 to 5 hours of output in the process.

Shift Handover Gaps

What the outgoing shift encountered does not fully transfer through a paper log or a two-minute verbal walkthrough. The incoming shift repeats the same troubleshooting cycle, turning a 2-hour problem into a 10-hour one.

Quality Traceability

When a defect surfaces at final inspection, tracing it to a specific batch, process step, or incoming material requires manual cross-referencing across production records and the QMS. Investigations average 2 to 4 days and often end without a confirmed root cause.

03. Qualification

Who This Is For

Good FitNot a Good Fit
Your production runs on a defined schedule and your team follows documented workflowsYour processes vary significantly by operator or shift and are not consistently followed
You have recurring, identifiable problems: breakdown patterns, scheduling conflicts, or repeat quality issuesYou are mid-implementation of a new MES or ERP system
You are ready to pilot AI on one specific operational problem before any broader rolloutYou expect AI to operate without human review at production-critical decision points
You have an operations lead available to work with Tayana through a 6 to 8 week pilotYou do not have internal capacity to support an active pilot engagement
04. AI Applications

Where AI Agents Work

Production Planning

  • Schedule exception alerts: An agent monitors open work orders against material availability and machine capacity and flags conflicts 48 to 72 hours before the scheduled run.
  • Rush order impact: An agent models how a change order affects the live schedule and surfaces the earliest reliable commit date.

Maintenance

  • Breakdown response: An agent retrieves repair history, relevant manual references, and parts availability for a failing machine and delivers it to the technician within 2 minutes of the fault alert.
  • Failure pattern detection: An agent correlates downtime events with operating conditions and flags equipment showing early deterioration before a breakdown occurs.

Quality

  • Defect traceability: An agent connects a flagged defect to its source batch, process step, and incoming material, cutting investigation time from days to under an hour.
  • Rework queue monitoring: An agent tracks open rework items by work center and escalates jobs aging past the defined threshold.

Shop Floor Operations

  • Shift handover briefs: An agent compiles machine status, open issues, and carry-forward actions from the outgoing shift into a structured briefing for the incoming supervisor.
  • Production status visibility: An agent answers real-time work order status questions so plant managers and customer service teams can respond without calling the floor.
05. Results

What Changes With AI

Machine Breakdown Response

Before: A CNC machine fails mid-shift. The maintenance tech spends 75 minutes locating the prior repair record, a parts list, and the last service log before any work begins. Total downtime runs 2.5 hours.

After: An AI agent delivers repair history, manual references, and parts stock to the tech within 2 minutes of the alert. Productive response begins in under 5 minutes. Total downtime drops below 1 hour.

Figures shown are representative of outcomes in comparable implementations.

Shift Handover Quality

Before: The outgoing supervisor writes a log that covers roughly half the active issues. The incoming shift spends 30 to 45 minutes rebuilding context before production output stabilizes.

After: An AI agent compiles machine status, open work orders, and unresolved issues into a structured 5-minute brief. Recurring problems resolved on the first attempt by the incoming shift increase by 35 to 40 percent.

Figures shown are representative of outcomes in comparable implementations.

06. Agent Screens

What the agents look like

Representative screens showing how AI agents surface data and present decisions to your staff. Click any card to see the full view.

Maintenance

Maintenance Breakdown

Repair history, manual references, and parts availability delivered to the technician within 2 minutes of a fault alert.

Operations

Shift Handover Brief

Machine status, open issues, and carry-forward actions compiled into a structured brief for the incoming supervisor.

Operations

Voice AI Plant Agent

Real-time work order status answered by a voice agent so plant managers can respond without calling the floor.

07. Engagement

How an Engagement Begins

Phase 1: AI Foundation Training (1 to 3 weeks)

Your supervisors, planners, and maintenance leads attend AI Foundation Training before any solution is proposed. They identify the highest-value use cases from inside your operation.

Phase 2: AI Readiness Assessment (2 to 3 weeks)

Tayana evaluates one to three candidate processes for pilot readiness: data availability in your ERP or MES, process definition clarity, and exception frequency. You receive a written recommendation with a clear rationale.

Phase 3: Pilot Deployment (6 to 8 weeks, investment from $10,000)

One process. Your existing production environment. Real data from your systems. You see the agent working before any broader commitment is made.

08. Questions

Common Questions

Can AI agents work with my existing MES and ERP without replacing them?

Yes. Tayana's agents connect to your systems through available APIs and integrations. They read and surface data without touching your underlying configuration or existing workflows.

What manufacturing problems are the best starting point for AI?

Maintenance response, shift handover, and production scheduling exceptions are consistent starting points because they involve defined processes, high repetition, and direct impact on daily output when they fail.

Will an AI agent help reduce unplanned machine downtime?

An AI agent does not predict mechanical failure without sensor data. What it does is eliminate the 30 to 90 minutes of search and coordination that follow a breakdown, so your technician is working on the machine rather than hunting for information.

Do I need new hardware or sensors on the shop floor to get started?

No. Tayana builds pilots that work with the data your ERP and MES already produce. Adding IoT sensors is a separate decision and is not a requirement for a first deployment.

How much does an AI pilot cost for a manufacturing operation?

Pilots start from $10,000. Tayana provides a fixed scope with defined deliverables before any work begins, so you know exactly what you are committing to.

How do I know if a process in my facility is ready for AI?

The indicators are consistent: the process runs frequently, the rules governing it are defined and followed, and the relevant data already exists in your ERP or MES. The readiness assessment confirms this before any build begins.

What happens if the AI agent surfaces incorrect information to a technician or planner?

Every agent includes human-review checkpoints before any action is taken on consequential items. Staff confirm before the agent posts, routes, or escalates. The agent does not act autonomously on production-critical decisions.

How long does it take to deploy an AI agent in a manufacturing environment?

A pilot runs 6 to 8 weeks from engagement start to a working agent in your production environment. The readiness assessment runs before the build and confirms the scope and timeline in advance.

Ready to Take the Next Step

Book a thirty-minute call. We will confirm whether your situation is a fit and what the right starting point is, whether that is the AI Adoption Accelerator, a readiness assessment, or a direct pilot.

Book a Call
Agent screen