Dashboard software for advanced manufacturers
Know your true cost per part. Keep machines available.
Advanced manufacturers lose margin when yield, downtime and cost per part live in different systems and no two teams calculate them the same way.
Klipfolio connects MES, ERP, quality and machine data into dashboards, so engineering, operations, finance and leadership see the same numbers and know where to step in.
Who it's for
Mid-sized manufacturers running high-mix, precision or additive production, where production, quality and cost data sit in separate systems and a small operations or engineering team builds the reports. Best fit when MES, ERP and quality data are in different places and no dedicated analytics team exists. Plants whose ERP reports already answer every cost and yield question may not need it yet.
Industries
- Additive manufacturing
- Precision machining
- Electronics manufacturing
- Aerospace and medical components
- Robotics and automation
Roles
- VP Operations
- Plant Manager
- Manufacturing Engineer
- Quality Manager
- Controller
- Maintenance Lead
Company size
- 50 to 1,000 employees
Advanced manufacturers lose margin when yield, downtime and cost per part live in different systems and no two teams calculate them the same way.
Designs move from CAD or PLM into MES, through print farms, CNC or robotic cells, then into post-processing and inspection. Costs shift with material reuse, scrap, energy draw, tool wear and cycle time. The VP of operations reviews it weekly, finance closes it monthly, and quality answers for it to customers.
Each system holds part of the answer. MES knows the jobs, ERP knows the costs, the quality system knows the nonconformances, and the machines know their own temperatures and alarms. Engineering, operations and finance each build their own version of yield and cost per part, usually in spreadsheets. Pasting exports into a generic AI tool doesn't fix it, because the tool doesn't know your routings, your scrap codes or how your plant defines a good part. You get a different answer each week, and someone still has to check it. The result is late surprises, disputed numbers and quotes priced on estimates rather than actuals.
When yield, waste and availability are defined once and refreshed from source, builds, batches, parts and cells roll up to work orders, customers and programs. Engineers see machine health next to quality results. Operations sees the current shift rather than last week's. Finance prices the next quote on what the last one actually cost. Klipfolio connects MES, ERP, quality and machine data into dashboards, so each team sees the same numbers and knows where to step in.
Key jobs and use cases
Live production health by cell
Who: The plant manager owns it. Production supervisors and manufacturing engineers use it. Data: MES and work orders, machine controller logs, production scheduling. Output: A dashboard for each cell or print farm on shop-floor TVs, colour-coded against target when yield or throughput drops. Decision: Where to step in during the current shift, and which jobs are at risk of missing their date.
Key metrics
- First-Pass Yield
- Scrap Rate by machine, material and part
- WIP by stage
- Build or Job Success Rate
- Throughput against plan
Machine utilization and downtime
Who: The maintenance lead owns it. Manufacturing engineering and production planning use it. Data: Machine logs and telemetry, MES, the maintenance system, the production schedule. Output: Condition readings such as vibration, temperature, laser power and pressure, shown beside downtime and throughput. Decision: Which machines need attention before they fail, and whether to schedule maintenance by condition rather than the calendar.
Key metrics
- Overall Equipment Effectiveness (OEE)
- Run, Idle and Changeover Time
- Unplanned Downtime by cause
- Mean Time Between Failures
- Mean Time to Repair
Part-level unit economics
Who: The controller owns it. Engineering, sales and leadership use it. Data: ERP costing, MES actuals, material usage, energy data, quotes. Output: Estimated against actual cost per part, filterable from a margin trend down to the batch, machine and shift behind it. Decision: Which quotes to revise, and which parts, customers or programs to reprice.
Key metrics
- Actual against Estimated Cost per Part
- Material Waste and Reuse Rate
- Post-Processing Hours per Part
- Energy Cost per Build
- Margin by customer, application and program
Quality and root cause
Who: The quality manager owns it. Manufacturing engineering and customer quality contacts use it. Data: Quality management system, inspection results, machine parameters, environmental data. Output: A quality dashboard that lines up defects with the machine settings and conditions behind them, shared with engineering and customer contacts. Decision: Where to focus corrective action, and whether last month's process change reduced escapes.
Key metrics
- Defects per Million
- Rework Rate
- Nonconformances by cause
- Corrective Action Ageing
- Inspection Pass Rate
Delivery and program performance
Who: Operations or program management owns it. Sales, customer service and leadership use it. Data: ERP orders, MES progress, shipping records. Output: A program dashboard sent to leadership and account teams by scheduled email or PDF. Decision: Which customers to warn early, and where to add capacity to protect key programs.
Key metrics
- On-Time Delivery
- Quoted against Actual Lead Time
- Backlog by customer and program
- Schedule Adherence
- Quote-to-Order Conversion
Integrations
Cost per part and yield both cross the digital thread. A single scrapped build touches the design, the machine, the material lot, the inspection and the ledger, so the question "what did that scrap cost us?" needs all of them.
| Production | Machines and sensors | Engineering and quality | Business systems |
|---|---|---|---|
| MES, work orders, production scheduling | Machine controller logs, historians, IoT and telemetry platforms | PLM, quality management, inspection and test results | ERP and costing, purchasing, CRM, supplier data, Excel, MSSQL and other SQL databases |
Machine data usually reaches reporting through a historian, a database or an IoT platform's API rather than directly from the controller. Systems without a prebuilt connector are read through a database connection, a scheduled export or an API, so you don't need to rebuild your stack.
Why Klipfolio for Advanced Manufacturers
One definition of yield and availability
Engineering and finance lose hours reconciling two versions of the same number. Define yield, waste, OEE and cost models once with Excel-like formulas, then apply them across builds, parts, cells and sites.
Numbers as current as the signal
A cell needs fresh numbers during the shift, while cost data only needs to settle daily. Refresh production and machine data every few minutes and cost data daily, so you don't overload source systems.
Floor boards, office detail
Supervisors need to see problems without logging in, and leadership needs a summary without asking for it. Show dashboards on shop-floor TVs, give engineers detailed dashboards on the desktop, and send leadership a scheduled summary by email or PDF.
Cost data stays with the right people
Margin data is sensitive, and operators don't need it. Users, groups and roles keep cost and margin with finance and leadership, while operators and engineers see the production measures they act on.
Klipfolio offers 130+ connectors, 100+ Excel-like functions, 10 distribution methods and refresh intervals from 1 minute to 24 hours.
