The 7QC tools are time-tested, excellent problem-solving techniques. The world renown Quality Guru, Mr. Kaoru Ishikawa once said, "More than 95% of the problems in a company can be solved by the 7 QC Tools.

The 7QC course combines Ishikawa's techniques together with the problem-solving model by yet another Quality Guru, Deming, the advocate of the Plan-Do-Check-Act (PDCA) Cycle. These tools use statistical techniques and knowledge to accumulate data and analyse them. They help organise the collected data in a way that is easy to understand. Moreover, by using 7QC tools, specific problems in a process can be identified.

The organisation will be able to :

A Measurement System Analysis (MSA) is a specially designed experiment that seeks to identify the components of variation in measurements. Just as processes that produce a product may vary, the process of obtaining measurements and the data may have variation and produce defects. MSA is used to evaluate the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data used for analysis and to identify any measurement errors.

Common tools and techniques in Measurement System Analysis include: calibration studies, components of variance, Attribute Gage Study, Gage Repeat and Reproducibility (R&R), ANOVA Gage R&R, Destructive Testing Analysis and others. The tool selected is usually determined by characteristics of the measurement system itself. Those implementing ISO/TS 16949 as well as organisations implementing Six Sigma will know of MSA. Improvement teams working on quality issues will likewise find this a useful addition to their quality tool box.

The organisation will be able to:

»	comply to the Automotive Industry Action Group (AIAG)'s requirement on MSA
»	improve on data integrity and in that, make better decisions
»	improve product quality
»	communicate and share the MSA results more easily with customers.

Dr. Walter A. Shewhart of Bell Telephone Laboratories developed the first Control Chart in the early 1920s. Since then, Control Charts have been used by industries worldwide as the means for controlling and monitoring the stability of key process parameters. Statistical process control or SPC for short is a statistically-based family of tools used to monitor, control and improve processes.

In recent years, the demand for control charts as a process improvement tool has been tremendous, and it has become one of the core tools to be deployed in organizations implementing ISO/TS16949 systems. Herein, selecting and setting up the right type of SPC control chart for a given process is crucial to getting the most benefit from this improvement tool.

Failure Mode & Effect Analysis (FMEA) is an established risk assessment methodology for systematically identifying the potential failure of a product or process, assessing the impact of the failure, and developing an action plan to eliminate or minimize the impact of the potential failure. FMEA focuses on prevention at the design and/or manufacturing processes. A well-developed and comprehensive FMEA can greatly reduce the cost of poor quality and failure. As an interactive process, rigorous continual application can help the organization to improve on the quality of its processes and/or products.

FMEA is used in the automotive industry for safety and regulatory considerations, and is used in the manufacturing industries in various phases of the product life cycle and is now increasingly finding use in the food and service industry as well.