Certification in Quality Control Management

Trace the evolution of quality management from 100% final inspection to process quality control and total quality management, emphasizing quality assurance, cross-functional responsibility, and a top-down philosophy.

Integrate marketing, operations, and management concepts under total quality management to unite direction, meet customer expectations, empower employees, and pursue continuous improvement through facts, analytics, and cross-functional collaboration.

Compare total quality management with traditional views by prioritizing customer expectations and long-term sustainability, while shifting from blame to collective, process-focused problem solving and supplier partnerships.

Discover how total quality management engages all levels to continuously exceed customer expectations, empower employees, and apply statistical concepts, tools, and control charts to monitor variation and prevent defects.

Explore how to plot x-bar and r control charts with a real data set, calculating CL, UCL, LCL, x double bar, and detecting assignable causes in glue-drying time.

Compute p-bar and sigma p from 20 samples of 100 items, with 220 defectives, set upper and lower control limits, and plot p-values on a p chart to monitor defects.

Explore seven quality control tools, including flowcharts, check sheets, histograms, Pareto diagrams, scatter diagrams, control charts, and cause-and-effect diagrams, to diagnose faults, interpret data, and improve processes.

Use flowcharts to visually map every step of a process, including decision points, to identify bottlenecks and danger areas; apply this problem-solving tool to minimize defects, even via automation.

Explore histogram as a visual tool to map the distribution of defects, showing the empirical frequency distribution and revealing which defects are common or rare for quality control.

Explore how scatter diagrams reveal correlations between two variables, distinguish between linear patterns and scattered points, and determine when variables are unrelated for targeted quality control actions.

Learn control charts, including x-bar, R, P, and C charts, using the central line with UCL and LCL to monitor process output and identify assignable causes when limits are exceeded.

Apply acceptance sampling to lots of batches as a form of inspection for inputs, processing, and outputs, and support process quality control and lower external failure costs.

Explore single, double, and multiple sampling plans, set the lot size N from eoq input, select a sample size n, and apply acceptance C to decide the lot.

Use a single sampling plan to draw a random sample from a lot, compare the okay parts to the predefined acceptance number c, and decide whether to accept or reject.

The lecture introduces the double sampling plan with c1 and c2 thresholds, outlining when to accept, reject, or resample, and using c3 to improve decision confidence.

Understand acceptance sampling through the operating characteristic curve, showing how sample size and acceptance limit affect quality acceptance for a given lot, and how the curve guides decisions.

Explore the operating characteristic curve, mapping fraction defective to the probability of lot acceptance, and how n, cql, aql, and lpd shape it, revealing producer and consumer risks.

Explore how improving process capability reduces variation, lowers costs, and enhances customer satisfaction, while examining sampling versus process distributions, central limit theorem, and Six Sigma in quality control.

Assess process capability analysis by comparing variability of a process to design specifications, showing whether outputs stay within upper and lower control limits and thus whether the process is capable.

Improve quality by reducing variation and defects to 3.4 per million using Six Sigma as a business process, driving continuous improvement, cost efficiency, and customer satisfaction across industries.

Six sigma comprises management and technical components, driven by strong leadership, clear performance criteria, project selection, and targeted training to achieve business results, supported by DMAIC and statistical methods.

Explore the philosophy of Six Sigma and how reducing process variability expands the acceptance region to yield 3.4 defects per million (two parts per billion) for economic gains.

Explore the defects per million opportunities (dpmo) as the measurement for six sigma, combining defects, opportunities per unit, units, and a 1,000,000 multiplier into a single calculation.

Calculate dpmo to determine whether a process meets six sigma limits (3.4 ppm) using a 2000-unit example with five opportunities per unit. Learn the six sigma philosophy and dmaic approach.