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Analytical Quality Control in Shipping Operation Using Six Sigma Principles

Zhuohua, Qu (2015) Analytical Quality Control in Shipping Operation Using Six Sigma Principles. Doctoral thesis, Liverpool John Moores University.

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A large number of benefits achieved through the successful implementation of Six Sigma programmes in different industries have been documented. However, very little research has been conducted on their applications in the shipping sector, especially in the Onshore Service Functions (OSFs) of shipping companies. Literature shows that heavy human involvement in the service industries such as shipping leads to a high volume of uncertainties which are difficult to be correctly and effectively measured or managed by simply using the traditional data analysis and statistical methods in Six Sigma. The aim of this study is to develop new quantitative analytical methodologies to enable the application and implementation of Six Sigma to improve the service quality of OSFs in shipping companies. Intensive investigations on the feasibility and effectiveness of the developed new methods and models through case studies in world leading container ship lines and shipping management companies have been carried out to ensure the achievement of the aim.This study firstly reviews the evolvement of quality control and some typical methods in the area, the development of Six Sigma, its tools and current applications, especially in the service industries. It is followed by a new framework of the Six Sigma implementation in the OSFs of shipping companies which is supported by a few real process excellence projects carried out in a world-leading ship line. In the process of the framework development, various issues and challenges appear largely due to the existence of uncertainties in data such as ambiguity and incompleteness caused by extensive subjective judgements. Advanced methods and models are developed to tackle the above challenges as well as complement the traditional Six Sigma tools so that the new Six Sigma methodologies can be confidently applied in situations where uncertainties in data exist at different levels.A new fuzzy Technique for Order Preference by Similarity to an Ideal Solution ii(TOPSIS) method is developed by combining the traditional TOPSIS, fuzzy numbers and interval approximation sets to facilitate the effective selection of Six Sigma projects and achieve the optimal use of resources towards the company objectives. A revised Failure Mode and Effects Analysis (FMEA) model is proposed in the “Analyse” step in Six Sigma to improve the capability of classical FMEA in failure identification in service industries. The new FMEA model uses the Analytical Hierarchy Process (AHP) and Fuzzy Bayesian Reasoning (FBR) approaches to increase the accuracy of failure identification while not compromising the easiness and visibility of the Risk Priority Number (RPN) method. Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) methods are incorporated with Fuzzy logic and Evidential Reasoning (ER), for the very first time to generate a Key Performance Indicators (KPIs) management method where the weights of indicators are rationally assigned by considering the interdependency among the indicators. Incomplete and fuzzy evaluations of the KPIs are synthesised in a rational way to achieve a compatible and comparable result.It is concluded that the newly developed Six Sigma framework together with its supporting quantitative analytical models has made significant contribution to facilitate the quality control and process improvement in shipping companies. It has been strongly evidenced by the success of the applications of the new models in real cases. The financial gains and continuous benefits produced in the investigated shipping companies have attracted a wider range of interests from different service industries. It is therefore believed that this work will have a high potential to be tailored for a wide range of applications across sectors and industries when the uncertainties in data exceed the ability that the classical Six Sigma tools and methods possess.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Quality control, Six Sigma, Shipping management, Multi-attribute decision making, Uncertainty modelling, Project selection, FMEA, KPI management
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Date Deposited: 19 Oct 2016 09:43
Last Modified: 03 Sep 2021 23:27
DOI or ID number: 10.24377/LJMU.t.00004520
Supervisors: Jenkinson, Ian and Wang, Jin
URI: https://researchonline.ljmu.ac.uk/id/eprint/4520
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