Itru-Research Knowledge Based Management

KNOWLEDGE BASED QUALITY MANAGEMENT SYSTEM

Knowledge Based Management and Quality Management System for Textile Mills have been developed through scientific and applied research work carried out in textile mills by Itru Group Ltd-International Textile Research Unit. Application and practicing this system has endeavored to many enhancement in the mills in terms of manufacturing costs, quality ,efficiency and productivity of employees. Establishing of this this research project lasts for 8 months for spinning mills and 18 engineering working week for Itru within this period. In order to understand the conception of Knowledge Based Management in depth we recommend to read our interactive -KNOWLEDGE BASED QUALITY MANAGEMENT SYSTEMS VER 1.0 INTERACTIVE KNOWLEDGE BASED DATA BASED HIGH TECH -TEXTILE REPORT .

As a result of this project following systems are established in spinning mills as outlined in the picture shown below

    -Quality Planning
    -Quality Management
    -Quality Process /Control
    -QCC-Circles
    -Off-Standart Interruptions
    -R&D Mill Unit
    -Process Optimization workings in relation to Knowledge Based Factors (Status,Interference Control and Variable)

Process Optimization workings bases upon Matrix analyses and mathematical models are applied to improve quality and mill performance level in more scientific methods.

Optimum quality / minimum manufacturing costs has become the primary target of the spinning mills in the world. In order to achieve these targets, the 'KNOWLEDGE BASED QUALITY MANAGEMENT SYSTEMS' are carried out as functional and managerial to conduct the process optimization working efficiently in the Textile Mills. Therefore the Quality Management Units are that carry out to provide the knowledge to these working for Process Optimization Working will become productive.

Then R&D unit, which is connected with Quality Management, is established. It is important that there have been sub-structure to get necessary information and direct R&D projects. The basis of Knowledge Based Quality Systems is knowledge. Knowledge is only got from the R&D project and developed. So R&D has to be joined and structured with knowledge based system. Today, it is possible that firms earn more than spending costs for R&D projects. The target of this project is that achieved advantages are proved concrete data by R&D unit in the mill.

Knowledge Based Quality Systems have a lot of different characteristics than the other classic system basically. Our basic behavior is that solving problem instead of talking problems. Therefore basic of this system is getting and analyzing knowledge and eliminating problems. In this way, you could get superiority of competition in the international market with achieving optimum quality, minimum cost and zero defects at the final product. We observed many differences in production levels, product variety, sale, costs, management Organization when we compare the mill that is established this system with others. Their workers develop even system and better results than our expectation are obtained.

The other characteristic of this system is that the mill is managed by knowledge. Also the managers are guider and the number of manager decreases to the lower level. This system gives responsible to everyone.

The Aims of Knowledge Based Quality Management System

  • Optimum Quality
  • Minimum cost
  • Zero default
  • Continuous development

Organization Structure of Knowledge Based System is consisting of three main units. These are,

  • Quality Planning.
  • Quality Management
  • Quality /Process Control /Production Planning and Contr

Organization Structure Of Knowledge Based Quality Management System And Functional Work Flow

Key words: Knowledge Based Quality Management, DPEA, SPC, Quality Planning, Quality Management, Quality Control, Process Control, Pc-programs, Mathematical Models, Matrix Analysis, Problem Analysis / Problem Solving Algorithms, Databank, Database, Intelligent Database, Optimum Quality, Minimum Quality, Zero Default, IIF, System Factors, Mathematical foresight, Neps, Saw-Gin, Roller Gin, Process Optimization, Yarn Process Parameters

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