Interpretation Of Data For Qc Activities

Published Date: 02 Nov 2017

Quality control is the method of monitoring the process to attain the exact specifications for the product which is required and demanded by the customer.

QC is the process of designing the product before starting the production to confirm the Design of the products or service to meet specs.

Method of inspecting the process to have its production or installation to meet full intent of specs.

Review usage to provide information for revision of specs. if needed the aim should be towards quality improvement[3]

Statistical process control (SPC) is an well known concept centered on using tools to enable continuous process improvement. Which are closely linked to the TQM philosophy, SPC helps firms to improve profitability process; statistical Quality control is a part of Total Quality Management (TQM) in which all the experiments are carried out in a statistical manner.SQC is mainly used for three different reasons in quality management they are as follows [4]

Collection,

Analysis and

Interpretation of data for QC activities

In SQC the two major parts are statistical process control and getting approval for the sample which are mostly attained by monitoring the process with the accurate data’s collection method and by documenting it in systematic way.[3]

American Quality control society views on assurance of quality as the Quality planning tool with systematic activities so that quality requirements for a product or service will be fulfilled by the proper assurance method. From the quality control definition, we can say that quality assurance is the set of procedures developed and activities done before the product or service is manufactured and delivered to the customers as a good quality product. This is a proactive approach. By which we can eliminate the complete dissatisfaction of customers and increase the loyalty and image of the concern as they are the best in market to supply quality products to the customer satisfaction.

All those planned or systematic actions necessary to provide adequate confidence that a product or service will satisfy given requirements for quality

Need systems and procedures to ensure consistency in methods for producing products.[3]

he Quality Management provides data for processing the quotations from the supplier and purchase orders. For example, If there is a small problem in quality of product we are not allowed to place purchase orders for a specific vendor The vendor has to sign the contract which compiles the technical delivery terms and the quality assurance agreements when a quotation is made. Since technical delivery terms are indicated for a material purchase vendor, the long text is printed out which is commonly called as purchase order. When the goods are ordered, you can request that a certificate is required from the vendor of the material, if certificate is not supplied the goods are not allowed to move further without the inspection for product lot. Certificate is must when the purchase order process decision is made. [7]

Quality is Responsible for developing processes and procedures that will produce a quality product like activities include process selection and development, production, planning and support activities sequencing of operations to minimize production difficulties methods study also include design of equipment, inspection devices, and maintenance of product equipment.[7]

Quality is the process for manufacturing quality products meets the required quality at all times. The aim of Quality is to produce the right product at the first time, without any rework. Nowadays all Organizations have a separate department to assure the quality of their products or they may have consultants. Quality is very crucial for the manufacturing industry. Since no manufacturing industry can spend time and money on rework. Every activity in the industry costs money they do not pay for rework. Customers expect more valuable additions for the product when compare to other products in the market at same or lower costs, they move to that company. Hence to assure good quality to customers, quality plays a significant role in manufacturing process. [6]

In Quality Inspection and Testing process the description of the Quality Control activities are performed in accordance to defined quality standard and requirements. It acts as "connecting bridge" between the Quality Assurance and Quality Control. The inspection and test activity will be carried in all phase before after and sustain phase for product realization (i.e.: Manufacturing, Planning, Implementing, Erection), to ensure that the quality level of the product are within standards and requirements. The main goal of inspection for QC is waste of time, money, Manpower. [6]

7. By these workers will fetch ideas and our panel members will plan and analyze, then implementing, checking in regular interval to check whether it’s useful and is it correct path to attain our project and our company objectives if so or not act according to the result in the same cycle again. This is a practice which I got experience from my work experience in QC tools.In this methodology we have four phases but it all in the circular path which indicates that all r are interlinked with each other process. The four phases are as follows [2,10]

Dr. G Taguchi started developing these quality improvement techniques in the 1950's and framed a quote that Variation is the Enemy of Quality. Taguchi's philosophy and methods always support the Japanese QC approach that always states that the higher quality generally results in lower cost. Which is an contradict view of United States QC system that asserts that quality improvement is associated with higher cost."Today the ultimate goal of quality improvement is to design quality into every product and process at all stages from design to final production and sale. An important element is the extensive and innovative uses of statistically designed experiments are to have the six sigma level quality. [7,8]

From the above figure we can clear have an idea that small deviations from the target value result in small losses. These losses, however, increase if target value increase. The function shown above is a simple quadratic graph defin quality that states that quality is conformance to specifications. Of course, under many circumstances a quadratic function is only an approximation.[8]

Taguchi method is that it emphasizes a mean performance characteristic value close to the target value rather than specification limits. Additionally, Taguchi's method for experimental design is straightforward and easy to apply to many engineering situations, making it a powerful yet simple tool. It is mainly used to quickly narrow down the scope of a research project or to identify problems in a manufacturing process from data already in existence. For example, a process with 8 variables, each with 3 states, would require 6561 experiments to test all variables. However using Taguchi's orthogonal arrays, only 18 experiments are necessary, or less than .3% of the original number of experiments. In this way the parameters that have little effect can be ignored. [9, 15]

Taguchi method gives only relative and do not exactly indicate what parameter has the highest effect on the performance characteristic value. Also, since orthogonal arrays do not test all variable combinations, The Taguchi method has been criticized in the literature for difficulty in accounting for interactions between parameters. Another limitation is that the Taguchi methods are offline, Furthermore, since Taguchi methods deal with designing quality in rather than correcting for poor quality, they are applied most effectively at early stages of process development. They require a lot of human energy and resources and it takes a lot of ingenuity, cleverness, and experience to design experiments well. This most often lead to have a considerable artificiality for practice. [9,13,15,19]

Total Quality management is a vast concept relates the improvement of product or service for the betterment of organization, Quality is considered as the backbone for all kind of business. From the study and Taguchi method quality can be achieved with sixteen steps which are as follows.

Step 1: Design of experiments and the Taguchi approach:

A quick understanding of the Taguchi DOE method is very much essential before starting the subject

Step 2: Definition and measurement of improvement:

Consistent performance produces reduced variations. In this step, you learn how population performances are measured and compared.

Step 3: Common experiments and analyses methods:

A more effective method to study and for effective practice are by setting up experiments following the DOE technique.

Step 4: Designing experiments using orthogonal arrays:

A number of standard orthogonal arrays are constructed only for facilitating the designs of experiments. This step is to learn about the different orthogonal arrays and understanding how easy it is to design experiments by using them.

Step 5: Designing experiments with two-level factors:

Experiments of all sizes can be easily designed using these arrays, as long as all factors involved are tested at two levels. Experiments that involve studies of factors with two levels are both simple, common and created specifically for two-level factors. 

Step 6: Designing experiments with three-level and four-level factors:

When nonlinear effects are suspected, more than two levels of the factors are desirable. Although many larger two-level orthogonal arrays can be modified to accommodate three-level and four-level factors, a set of standard arrays are available for this purpose.

Step 7: Analysis of variance (ANOVA):

In this step, you'll learn how all analysis of variance terms are calculated to build your confidence in interpreting the experimental results.

Step 8: Designing experiments to study interactions between factors:

In these steps, you can indeed apply the DOE to solve most production problems whose solutions lie in finding the exact cause and controllable factors, instead of spending time only on some special causes.

Step 9: Experiments with mixed-level factors:

In this step our goal will be to learn the procedure by which columns of an array are modified to upgrade and downgrade the number of levels in creating a new column.

Step 10: Combination designs:

This step will familiarize you with the necessary assumptions that must be made in order to lay out experiments using combination design.

Step 11: Robust design strategy:

This Robot design strategy is promoted by Taguchi to reduces variability without eliminating the cause of variation.

Step 12: Analysis using signal-to-noise (S/N) ratios:

This step will give you an brief idea for calculating MSD for different quality characteristics.

Step 13: Results analysis using multiple evaluation criteria:

This step is to spend your valuable and quant able time in learning the principles involved in formulation for analysis of multiple objectives.

Step 14: Quantification of variation reduction and performance improvement:

This step teaches you'll learn how to estimate the expected savings from the improvement predicted by the experimental results.

Step 15: Effective experiment planning:

The nature of discussions in the planning session is likely to vary from project to project and is best facilitated by one who is expert in this technique.

Step 16: Review of example case studies:

The application knowledge gained in steps 1-15 could be wasted if you don’t practice. One way to build more confidence and extend your application expertise is by familiarizing case studies with complete experiment design.[7,21,22].