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Supply chain quality management of automotive components

Kawalla Claudia, Ligarski Mariusz, Höck Michael

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2019

z. 133

69--83

Inferior automotive component quality might be caused by diverse sources in the supply chain network. To compete in a highly dynamic environment, a supply chain quality management integrating quality issues of the internal and external supply chain is needed. In this paper, a supply chain quality management model for automotive components was developed on the basis of further development of ISO 9001:2015. This way, the quality within the value chain can be managed. To evolve the model, the novel concept of supply chain quality management and its dimensions were reviewed in the literature. Drawing upon the results, intersections concerning common processes of both approaches, quality management and supply chain management, were identified using ISO 9001:2015 and the SCOR model. Based on the findings, a supply chain quality management model for automotive components was developed and will be introduced. The results reflect implications for the management of supply chain quality to be implemented by companies.

Characteristics of strength and structure of PE - HD composite with addition of PE - HD waste

Grzesiczak D., Postawa P.

Acta Innovations

2016

no. 20

13--20

This article presents the possibilities of using the waste material obtained from the vehicle fuel tank (high density polyethylene PE- HD) as an addition to products. The waste material is added to the original polymer in an amount of 10% , 20% and 30%. The following tests have been performed during the research: differential scanning calorimetry DSC, static tensile test, hardness degree determined by pressing a steel ball, and also optical microscopy of obtained breakthroughs in samples made of liquid nitrogen. In addition, there has also been performed an analysis of mixture of original polyethylene that contains 10%, 20 % and 30 % of HD – PE polymer, which has been turned back for recycling from the production line. The aim of the research is to determine the recycling possibilities of polymeric materials obtained from the waste that emerged on the production line and in the fuel tanks of vehicles which were withdrawn from use as materials that during the utilization process have been subjected to a long-term connection with petroleum. Description of the mechanical and structural properties is an attempt to answer the question concerning usefulness of polymeric materials after utilization.

The application of artificial intelligence in optimisation of automotive components for reuse

Wahab D. A., Amelia L., Hooi N. K., Che Haron C. H, Azhari C. H.

Journal of Achievements in Materials and Manufacturing Engineering

2008

Vol. 31, nr 2

595-601

Purpose: Automotive component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Research and development on components design and manufacturing as well as tools and methods to facilitate reuse are under way in many countries. To enable reuse, components have to be assessed and its reliability and life time predicted. This paper presents the development work on an optimisation model for assessing potential automotive components for reuse using artificial intelligence approaches. Design/methodology/approach: As a part of the study, the paper currently focuses on initial study on ease of disassembly design. The model for predicting reliability and durability of reuse components is then developed using Artificial Neural Networks (ANNs) and further optimised for reliability and life cycle cost using Genetic Algorithm (GA). Findings: The proposed model will enable the local automotive industry to effectively assess potential components for reuse in support of further design and manufacturing improvements. Research limitations/implications: This study hopes to contribute to design for reuse by assessing high potential and reliable reuse components at the lowest costs. Originality/value: Artificial intelligence methods, such as artificial neural networks (ANNs) and genetic algorithm (GA), can be applied to solve problem as they can provide satisfactory and acceptable solutions for many complex problems.