Statistical Process Control (SPC) is an industry-standard methodology for measuring, monitoring, and controlling quality during manufacturing. Formulated under the AIAG Core Tools guidelines and required across IATF 16949 and AS9100D, SPC provides statistical feedback to distinguish between predictable common cause variation and assignable special cause variation.
Traditional quality inspection discovers defects after valuable labor, material, and machine time have already been consumed. SPC shifts operations toward proactive prevention by using real-time control charts to detect process drift, tool wear, and machine degradation before out-of-tolerance parts are produced.
At SriWin, our hands-on workshops and shop-floor consulting enable machine operators, quality engineers, and plant managers to correctly select and plot control charts, accurately calculate capability indices (Cp, Cpk, Pp, Ppk), and implement clear Out-of-Control Action Plans (OCAPs).
Constructing Average and Range (XÌ„-R), XÌ„-S, and Individual Moving Range (I-MR) charts to evaluate continuous parameters like dimensions, torque, and pressure.
Deploying proportion (p), defect count (np), defects per unit (c), and area of opportunity (u) charts for visual inspections and pass/fail attributes.
Applying the 8 standard run tests to identify non-random patterns, trends, cycles, stratifications, and process shifts before parts exceed specification limits.
Calculating short-term potential (Cp) and actual capability (Cpk) under statistical stability to evaluate process centering and tolerance spread against OEM benchmarks (≥ 1.33 / 1.67).
Evaluating long-term overall performance including batch-to-batch, operator-to-operator, and thermal variation for initial sample PPAP submissions.
Establishing clear reaction plans, containment workflows, and root cause correction steps when special cause conditions or rule violations are detected.
Identify process drift and tooling wear early, eliminating scrap and preventing non-conforming parts from reaching downstream stations.
Directly lower Cost of Poor Quality (COPQ) by reducing machining adjustments, line stoppages, and secondary sorting operations.
Demonstrate required process capability indices (Cpk ≥ 1.33 and Ppk ≥ 1.67) mandated by automotive, aerospace, and medical customers.
Replace guesswork with statistical evidence to guide machine maintenance, operator training, and tooling replacements.
Stabilize process parameters to achieve narrower product distributions and consistently meet demanding precision engineering specs.
Provide transparent, live statistical control charts during supplier quality audits and customer technical inspections.
Collaborate with SriWin’s Core Tools consultants to set up live shop-floor control charts, calculate verified process capabilities, and train your operations team.
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