**4.1 Requirements for developing intelligent quality management systems**

Intelligent quality requires management commitment in order to provide necessary facilities and capabilities for complying with the requirements and continually improve the effectiveness of the quality system. It should allow quality responsible

to be able to assess the status of quality objectives. However, not only the quality people be involved but all should take part in developing and running the systems. Everybody in manufacturing chain should aim continuous and sustainable quality level for enterprise wide operations and services. This ensures the required level of integration (quality functions with all others). Meaning of this is to communicate quality problems and protect the other systems to be negatively affected while improving quality culture. Having the commitment of all people also try to enable quality systems to support overall business objectives as set forth by the top management.

Another aspect of intelligent quality is to employ a knowledge driven approach for generating and operating the systems as opposed to traditional data driven one. In order to establish required level of intelligence the system should be equipped with respective operational as well as respective quality knowledge. Traditional data based approaches are not sufficient enough to generate intelligent behavior. There is a need for methods and methodologies to dig out knowledge out of available data (this may be called as knowledge mining) for generating self-behavior of intended quality function. Artificial intelligence and machine learning systems provide various alternatives for locating and utilizing quality knowledge.

Due to the nature of intelligent systems, when they are well defined, they provide quality know-how for those which are having difficulty in setting up quality systems. Since they will be equipped with relate domain and quality knowledge, they would prevent;


Moreover, being able to generate a good quality product and services by intelligent quality practices increase customer trust and loyalty. Since the systems will have to self-operate and make recommendations and remedies for the responsible people, they may also find the system attractive for their success.

With this understanding intelligent quality should aim to satisfy following requirements. These may be called as "the baseline requirements" for intelligent quality management systems. Achieving these, verify, validate and assure the effectiveness of the systems employed.

**7**

*Introduction to Intelligent Quality Management DOI: http://dx.doi.org/10.5772/intechopen.94971*

available.

decision making.

infrastructure.

○ Process anomalies

monitoring systems.

○ Employee satisfaction

○ Supplier satisfaction

with quality regulations.

○ Delayed orders

on-line tracking systems.

ever the system is not able to do so.

○ Defective products and scrap rates

○ Downtime due to machine unavailability.

○ Productivity in manufacturing lines

**4.2 Framework for intelligent quality systems**

○ Customer (both internal and external) satisfaction

• Sustaining data security as well as information integrity.

• Minimizing duplication of work and various types of functionalities.

well as expected outputs (defect free deployment)

○ Customer (both internal and external) complaints

• Gathering, updating and storing the real time data and making it timely

• Generating quality knowledge out of data available and using those for self-

• Making accurate predictions about the status of manufacturing equipment as

• Assessing supplier automatically and monitoring their performance through

• Generating alerts for the responsible people to take immediate actions where

• Measuring and sustaining the reduction of the following by means of digital

• Measuring and sustaining the increase of the following by continuous

• Maintaining quality standards as they evolve and being adaptive to cope with the changes without spending too much effort. That is to adaptively comply

Above explanations indicate the importance of intelligent manufacturing in general and intelligent quality in specific. This section provides a general

alternatives for locating and utilizing quality knowledge.

recommend some solutions and remedies.

are new in practicing quality operations

• lack of methods and methodologies to be implemented.

• misleading machine set up and respective inefficiency.

• inaccurate interfaces between processes and operational units.

people, they may also find the system attractive for their success.

effectiveness of the systems employed.

• misleading knowledge and process flow operations.

management.

they would prevent;

capabilities.

to be able to assess the status of quality objectives. However, not only the quality people be involved but all should take part in developing and running the systems. Everybody in manufacturing chain should aim continuous and sustainable quality level for enterprise wide operations and services. This ensures the required level of integration (quality functions with all others). Meaning of this is to communicate quality problems and protect the other systems to be negatively affected while improving quality culture. Having the commitment of all people also try to enable quality systems to support overall business objectives as set forth by the top

Another aspect of intelligent quality is to employ a knowledge driven approach for generating and operating the systems as opposed to traditional data driven one. In order to establish required level of intelligence the system should be equipped with respective operational as well as respective quality knowledge. Traditional data based approaches are not sufficient enough to generate intelligent behavior. There is a need for methods and methodologies to dig out knowledge out of available data (this may be called as knowledge mining) for generating self-behavior of intended quality function. Artificial intelligence and machine learning systems provide various

Due to the nature of intelligent systems, when they are well defined, they provide quality know-how for those which are having difficulty in setting up quality systems. Since they will be equipped with relate domain and quality knowledge,

• lack of quality in process outputs automatically by taking the attention of the operators or other systems to anomalies and defective outputs. They may also

• lack of quality knowledge as well as related process knowledge to some extent.

• lack of experience in handling quality related issues especially for those who

• poor system development through highlighting missing parts and required

Moreover, being able to generate a good quality product and services by intelligent quality practices increase customer trust and loyalty. Since the systems will have to self-operate and make recommendations and remedies for the responsible

With this understanding intelligent quality should aim to satisfy following requirements. These may be called as "the baseline requirements" for intelligent quality management systems. Achieving these, verify, validate and assure the

**6**

	- Customer (both internal and external) complaints
	- Defective products and scrap rates
	- Process anomalies
	- Delayed orders
	- Downtime due to machine unavailability.
	- Productivity in manufacturing lines
	- Employee satisfaction
	- Customer (both internal and external) satisfaction
	- Supplier satisfaction
