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Knowledge Management Platform in Advanced Product Quality Planning

-724. Davenport, T. H., Long, D., & Beers, M. C. (1998). Successful knowledge management projects. Sloan Management Review , 43-57. Ford/GM/Chrysler. (1995). Advanced Product Quality Planning and Control Plan. Essex: Carwin Continous. Galliers, R. D., & Newell, S. (2003). Back to the future: from knowledge management. ISeB , 5- Kiku, J., & Lori, L. (2009). From Tacit Knowledge to Organizational Knowledge for Successful KM. In W. R. King, Knowledge Management and Organizational Learning (pp. 27-39). New York

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Conceptualising Quality in Spatial Planning

) illustrate the potentials and limitations of spatial planning as an instrument to discuss future development, to define the role of planners in achieving sustainable spatial development, and to calibrate expectations; (2) communicate planning achievements, make the benefits of high-quality planning visible, and legitimate the actions of planners; (3) integrate the interests and positions of a wide variety of actors and stakeholders; (4) raise awareness of issues related to sustainable spatial development among decision makers and in society; (5) improve legal frameworks

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Problems of use of FMEA method in industrial enterprise

Abstract

Presented paper concentrate on problems connected with FMEA method usage in industrial enterprise. There is in the paper a description of the basic rules of FMEA method and competition between FMEA analysis and gap analysis. The analysis of defects has been done to find recommendations how to eliminate or restrain them. On the basis of conducted research we found that selection of staff to the team is very important factor in the FMEA analysis undertaking process. The staff should have appropriate level of knowledge about FMEA method methodology and other tools which are indispensable in the process of implementing this method within the company.

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The Method for Motivation by Quality Function Deployment (QFD)

References (1) Akao, Y. (1972). New product development and quality assurance – quality deployment system. Standardization and Quality Control, 25 (4), 7-14. (2) Takayanagi, A. (1973). Order production quality control of our company (No. 1) – Concept of Quality Control Activities and Quality Table for Order Production, Quality Control, 24 , May Special Issue, 63-67. (3) Mizuno, S., and Akao, Y. (1978). Quality Function Deployment: The Customer-driven Approach to Quality Planning and Deployment , JUSE. (4) Akao, Y. (1988). Quality Function

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Assessment of the project teams’ communication skills in the automotive industry

REFEENCES Chrysler Corporation, Ford Motor Company, General Motors Corporation. (2008). Advanced Product Quality Planning (APQP) and Control Plan. 2. Southfield : AIAG, 1. Cole, G. A. (1994). Strategie Zarządzania. Londyn: DP Publications. Górniak, A. (2019). Wykorzystanie wybranych środków i sposobów pozyskiwania i przetwarzania danych dla potrzeb wyprzedzającego planowania jakości z uwzględnieniem potrzeb klienta, jako elementu przygotowania produkcji na przykładzie wybranej branży. Praca doktorska, Zabrze: Politechnika Śląska. Górniak

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Risk Analysis of Production Process for Automotive and Electrical Engineering Industry using FMEA

Abstract

A failure mode and effects analysis (FMEA) is the most often used (and required by customers) tool for quality planning. The FMEA examines possible errors with their consequences for customers as well as their possible causes. This analysis can be realized on several levels (structure or process), and thus it may become a subject of various teams, from engineers of whole parts to those dealing with individual units, from workers preparing the manufacturing processes as a whole to those who prepare (or control or even perform) individual operations. The aim of this work is the analysis of risks using the FMEA for a selected production process and proposal of measures to eliminate risks.

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Effect of Non-Fulfillment of Assumptions on Gage Repeatability and Reproducibility Study Evaluation

Abstract

The evaluation of the measurement system quality has already become an integral part of quality planning activities in both the automotive and metallurgical industries. An important assumption for obtaining the most relia ble results is compliance with the basic assumptions for evaluating the variability of the measurement system. The main goal of this paper is to analyze, how the failure to meet the basic assumptions influences the evaluation of the measurement system's statistical properties. This goal is achieved by performing a detailed analysis of the latest developments in the field of measurement systems analysis aimed at verifying the assumptions of normality and uniformity. The evaluation of the effect of non-fulfillment of both assumptions on the values of the most important statistical properties of the measurement system is performed using simulated data. Suitable graphical tools are used for practical verification of both assumptions.

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The I-P-C-V Model For Improving The Quality Management And Its Applications

Abstract

The process of quality management consists of several stages defined by specific verbs. Some experts have named the stages after the following verbs[1,2,3]: to forecast and to plan, to organize, to direct, to control (Fayol), to plan, to do, to check, to act (Deming); to design, to implement, to manufacture, to deliver; to design, to supply, to control, to assess; to develop, to inspect, to control, to improve or to enhance; to establish quality policies and quality objectives, to establish strategies for quality planning, to determine strategies for quality control, to establish quality assurance policies, to establish policies in the field of quality improvement. In this work we will show that a model of improving the quality management process could be defined starting from the following verbs: to identify needs, to program, to construct, to verify. We will also define a new quality indicator in the Oprean-Bucur model. We applied the models for a course of the Faculty of Engineering from Sibiu, Romania.

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Assessment of Human Capability, An Effective Tool to Obtain Confidence in the Visual Inspection Process

Abstract

To consistently produce high quality products, a quality management system must be practically implemented in every organization. One of its core instrument is to ensure the capability of the measurement systems, which are the basis for decisions regarding the behavior of the product critical quality characteristics. Base on requirements of the quality management system, a Measurement System Analysis should be conducted for all measurement system which are mentioned in the organization quality plan. Most problematic measurement system issues come from measuring discrete data, which are usually the result of human judgment (subjective decision) when categorizing products such as good/bad (visual inspection). It was the aim of this paper to address such an issue presenting a case study made in a local company from the Sibiu region, in order to evaluate how capable are the appraisers to visually inspect steel chains. The results were analyzed using MINITAB statistical software with its module called Attribute Agreement Analysis. The conclusion was that the inspection process must be improved by operator training, developing visual aids/boundary samples, establishing standards and set-up procedures.

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Interpretation of Gamma Index for Quality Assurance of Simultaneously Integrated Boost (SIB) IMRT Plans for Head and Neck Carcinoma

Abstract

Objective: The Gamma Index is prerequisite to estimate point-by-point difference between measured and calculated dose distribution in terms of both Distance to Agreement (DTA) and Dose Difference (DD). This study aims to inquire what percentage of pixels passing a certain criteria assure a good quality plan and suggest gamma index as efficient mechanism for dose verification of Simultaneous Integrated Boost Intensity Modulated Radiotherapy plans.

Method: In this study, dose was calculated for 14 head and neck patients and IMRT Quality Assurance was performed with portal dosimetry using the Eclipse treatment planning system. Eclipse software has a Gamma analysis function to compare measured and calculated dose distribution. Plans of this study were deemed acceptable when passing rate was 95% using tolerance for Distance to agreement (DTA) as 3mm and Dose Difference (DD) as 5%.

Result and Conclusion: Thirteen cases pass tolerance criteria of 95% set by our institution. Confidence Limit for DD is 9.3% and for gamma criteria our local CL came out to be 2.0% (i.e., 98.0% passing). Lack of correlation was found between DD and γ passing rate with R2 of 0.0509. Our findings underline the importance of gamma analysis method to predict the quality of dose calculation. Passing rate of 95% is achieved in 93% of cases which is adequate level of accuracy for analyzed plans thus assuring the robustness of SIB IMRT treatment technique. This study can be extended to investigate gamma criteria of 5%/3mm for different tumor localities and to explore confidence limit on target volumes of small extent and simple geometry.

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