Multi-Site Manufacturing

Same product. Same spec.Different yield at every plant.

You have 3 to 15 facilities making the same products, but each one runs differently. Different OEE, different scrap rates, different shift output. We embed with your team, map every plant from receiving dock to shipping bay, and deploy AI specialists that close the gap between your best facility and your worst. Not a consultant report. Real operational change, measured in units per hour.

The Problem

Where the money
is going.

Process

Production Scheduling Variance

Plant A runs 340 units per shift on a product that Plant C gets 280 from. Same spec, same raw material. The difference is changeover sequencing, shift start stagger, and which maintenance windows actually get used. Each plant scheduler builds their own plan in their own spreadsheet. Nobody is comparing what works.

Cost

Unplanned Downtime

Your maintenance teams run reactive. Something breaks, a line stops, second shift loses two hours while someone drives in with a part. The average manufacturer deals with 800 hours of unplanned downtime per year. Your preventive maintenance schedules exist on paper, but the floor runs hot and PMs get pushed. The cost difference between a planned repair and an emergency one is 3 to 5x.

Cost

Fragmented Procurement

Each plant orders its own resin, its own corrugate, its own MRO parts. Plant B pays one price per pound for HDPE. Plant D pays a different price from a different supplier for the same grade. Nobody aggregated the volumes. Nobody compared the contracts. You're leaving 8-15% on the table because purchasing decisions happen at the site level, not the network level.

Risk

Quality Variance Between Facilities

Same product, same incoming material spec, but Plant A runs a 1.2% defect rate and Plant C runs 3.8%. The difference is in setup procedures, operator training depth, and how each plant interprets the same quality standard. First-pass yield gaps like this mean scrap, rework, and customer complaints that trace back to whichever facility happened to fill the order.

How We Work

Three steps. Hands on.

We embed with your team, map your operation, find what no one could see, and deploy specialists that fix it. You get a dedicated team, not a login.

01

Map

We start with a structured discovery across every facility. Our team interviews plant managers, line supervisors, maintenance leads, and shift schedulers at each site. We connect to your ERP, MES, and CMMS systems. The result is your Blueprint: a live, verified map of how each plant actually runs, from raw material receiving through production to finished goods shipping. Not how the SOP says it works. How it actually works.

02

Uncover

We analyze everything we mapped. Cross-plant comparisons surface the scheduling sequences that produce more output, the maintenance patterns that prevent downtime, the procurement overlaps costing you money. We quantify every gap between your best-performing facility and the rest. We validate every finding with your operations team before acting on it.

03

Execute

Every finding comes with a concrete plan and a deploy button. We build AI specialists that handle the fix end to end. Harmonize changeover sequences, consolidate purchase volumes, standardize PM schedules across sites. You approve, they run. We stay with you to make sure they deliver.

Example Findings

What Yield typically finds.

Based on a typical mid-market company with $20M–$50M in annual revenue.

Cost

Procurement Fragmentation Across Facilities

$338K/yr

Cost

Unplanned Downtime from Missed PM Windows

$168K/yr

Process

First-Pass Yield Gap Between Plants

$81K/yr

Process

Cross-Site Scheduling Reconciliation

13 hrs/wk

Risk

Unshared Setup Procedures Across Sites

29 processes

In Practice

See it work.
From day one.

Week 1

Discovery

We walk every plant floor.

AI-led conversations with every plant manager, line supervisor, maintenance lead, and scheduler across all your facilities. Not a survey. Real conversations that capture the workarounds, the tribal knowledge, the shift-to-shift differences no system records.

100%of your team interviewed

Month 1

Blueprint + First Savings

Your Blueprint is live. Agents are saving money.

A complete, verified map of how every facility actually operates, with cross-plant comparisons that show exactly where performance diverges. The first opportunities are identified, and AI specialists are already closing gaps.

30 daysto first value

Ongoing

Continuous Returns

Best practices spread. Every quarter.

Yield keeps finding variance between plants, deploying specialists, and compounding savings. As one facility improves, the benchmark moves for all of them. Procurement consolidation deepens. Maintenance patterns sharpen. The platform pays for itself and keeps going.

10xcost recovered in year one

FAQ

Common questions.

Our five plants run three different ERP systems — can Yield normalize production data across all of them?

Yes. Yield connects to SAP, Epicor, IQMS, and most other ERP and MES platforms through standard APIs. It pulls scheduling, downtime, scrap, and procurement data from each system independently, then normalizes everything into a single cross-plant view. Your Blueprint shows apples-to-apples comparisons even when Plant A runs SAP and Plant D runs a legacy on-prem system.

We ran a kaizen blitz program across three facilities last year and the improvements reverted within two quarters — what prevents that here?

Kaizen events depend on people sustaining new habits after the facilitators leave. When the floor gets busy, the boards stop getting updated and old routines return. Yield deploys AI specialists that enforce the change continuously. The changeover sequence your best plant uses doesn't drift back because the specialist runs it every shift. The procurement consolidation doesn't lapse because the system is placing the orders automatically.

What does measuring ROI look like when each facility starts from a different OEE baseline and different cost structures?

Yield benchmarks each facility against its own historical data first, then against your best-performing plant on the same product lines. Every finding is tied to a specific dollar amount per site. If Plant B's scrap rate on a product is 3.1% and Plant A runs 1.4%, the saving is calculated using Plant B's actual throughput and material cost. You see ROI per facility and network-wide.

Two of our plants are unionized with different work rules around shift scheduling — does that change how Yield deploys specialists?

Yield maps each facility's actual operating constraints, including shift structures, union scheduling rules, and mandatory break windows. Specialists are configured per site. If Plant C has a 30-minute overlap between shifts that Plant E doesn't, the scheduling specialist accounts for that. The goal is closing the gap between your best and worst plant within each facility's real constraints, not imposing a one-size-fits-all template.

See what Yield finds in
your plants.

30 days. Real results. Or walk away.