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Supply Chain & Operations

Manufacturing Process Capability & Statistical Quality Control (Cpk / Six Sigma)

Six Sigma DMAIC and statistical process control deployment across compound mixing, building and curing CTQ characteristics - structured to meet IATF 16949 process capability requirements.

1.67+

Cpk Requirement

Typical IATF 16949 expectation for special characteristics

DMAIC

Core Methodology

Define-Measure-Analyse-Improve-Control framework adapted to tire process physics

MSA

Gauge R&R

Measurement system analysis verifying real process signal versus measurement noise

RFV·LFV

Final Inspection CTQs

Radial and lateral force variation among key finished-tire quality characteristics

Quality Is Built In, Not Inspected In

Quality in tire manufacturing is not inspected in - it is built in through process control. A tire that fails a uniformity test, exhibits force variation above specification, shows belt edge separation in end-of-life durability testing, or generates a consumer warranty claim for irregular wear has typically failed at a compound mixing, building or curing process step - not at the final inspection stage. The role of Statistical Quality Control (SQC) and process capability analysis is to move quality assurance upstream, to the point where variation is generated, and to bring that variation under statistical control before it propagates into finished product.

Tire manufacturing processes have well-defined critical-to-quality (CTQ) characteristics at every production stage. In compound mixing, CTQ parameters include Mooney viscosity, bound rubber content, dispersion quality of carbon black or silica, and cure scorch time. In tire building, CTQ parameters include component width and gauge, splice overlap, bead seating concentricity and ply turn-up height. In curing, CTQ parameters include cure temperature uniformity across the mold cavity, internal pressure profile during vulcanization and post-cure inflation pressure. At final inspection, CTQ parameters include radial force variation (RFV), lateral force variation (LFV), conicity, balance mass and geometric dimensions.

Six Sigma DMAIC for Tire Manufacturing

Radial Insights deploys Six Sigma DMAIC (Define-Measure-Analyse-Improve-Control) methodology adapted specifically to tire manufacturing process physics. Our engagements begin with a measurement system analysis (MSA / gauge R&R) to verify that measurement equipment and operator variation are not masking real process signal. We then establish baseline process capability (Cpk and Ppk) for all priority CTQ characteristics, identify the sources of variation driving capability shortfalls using designed experiments (DOE) and regression analysis, and implement control plans that sustain capability improvements through statistical process control (SPC) charts, reaction plans and operator standard work.

Measurement System Analysis

Gauge R&R studies verifying that measurement equipment and operator variation are not masking real process signal.

Baseline Process Capability

Cpk and Ppk baseline establishment for all priority critical-to-quality characteristics.

Root Cause & Control Plans

Designed experiments (DOE) and regression analysis to identify variation sources, sustained through SPC charts and operator standard work.

Integrating Inspection Technology

Our quality control work integrates with the inspection technologies present in modern tire plants - Tire Uniformity Machines (TUMs), laser shearography for internal defect detection, X-ray inspection systems for belt and bead inspection, AI-based vision inspection for surface defect classification, and dynamic balancing machines for finished tire balance measurement. We help manufacturers connect the data from these systems into a coherent process control architecture rather than treating each inspection point as an isolated quality gate.

Tire Uniformity & Shearography

Integration of Tire Uniformity Machine (TUM) and laser shearography data into a coherent process control architecture.

X-Ray & AI Vision Inspection

X-ray inspection for belt and bead defects, combined with AI-based vision inspection for surface defect classification.

Dynamic Balance Data Integration

Connecting dynamic balancing machine data for finished tire balance measurement into unified quality control reporting.

Meeting and Exceeding IATF 16949 Expectations

For manufacturers pursuing or maintaining IATF 16949 certification, process capability requirements are explicitly specified for key product and process characteristics. A Cpk of 1.67 or above is the typical expectation for special characteristics, and many OEM customers impose additional requirements above the IATF minimum. Radial Insights brings this automotive quality system context to our SQC engagements, ensuring that capability improvement work is structured to meet OEM customer requirements as well as internal quality cost reduction objectives.

Ready to Improve Your Process Capability?

Our Supply Chain and Operations team brings Six Sigma DMAIC expertise and IATF 16949 quality system context to every SQC engagement.

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