{"id":{"repo_id":"unsw","oai_identifier":"oai:unsworks.library.unsw.edu.au:1959.4/54256"},"canonical_url":"https://search.dev.ndltd.org/etd/unsw/oai:unsworks.library.unsw.edu.au:1959.4/54256","repository":{"repo_id":"unsw","name":"University of New South Wales","base_url":"https://unsworks.unsw.edu.au/oai/provider"},"display":{"title":"Identification and selection of continuous improvement projects","abstract":"Manufacturing organisations must routinely deliver efficiencies in order to remain competitive. Many have embraced continuous improvement methodologies, such as Lean manufacturing and Six Sigma in order to achieve these goals. However their ability to realise sustainable competitive advantage from continuous improvement is hampered by the lack of structured objective approaches for optimal project portfolio selection that link strategy to targeted improvement efforts. As a consequence, scarce resources are inappropriately allocated, opportunities are lost and there is sub-optimisation of the system as a whole. There are three gaps in the extant literature (i) the majority of published methodologies begin with a finite set of explicitly defined alternatives and attempt to maximize the portfolio outcomes without any definition of an optimized future state, (ii), portfolios are limited to choices from an a priori set of alternatives and are therefore unlikely to result in an optimal outcome and (iii) the extant methodologies generally do not include appropriate measurement to judge outcomes. Furthermore, there are significant limitations to the approaches used by industry for project selection and a degree of dissatisfaction with the methodologies employed. The most significant of these is the gap between strategy formulation and portfolio generation. A normative framework that should be used to structure project portfolio methodologies is therefore presented. To resolve these issues, a scalable generic methodology for visualizing and evaluating optimal future states and to evaluate projects and portfolios of projects in the context of those future states is presented. The methodology described employs Multiscale, Object Oriented Modelling and Simulation with Optimal Design of Experiments to create n-dimensional Pareto Frontiers from the set of all feasible production outcomes within given manufacturing configurations and for given strategic scenarios.","abstract_html":"Manufacturing organisations must routinely deliver efficiencies in order to remain competitive. Many have embraced continuous improvement methodologies, such as Lean manufacturing and Six Sigma in order to achieve these goals. However their ability to realise sustainable competitive advantage from continuous improvement is hampered by the lack of structured objective approaches for optimal project portfolio selection that link strategy to targeted improvement efforts. As a consequence, scarce resources are inappropriately allocated, opportunities are lost and there is sub-optimisation of the system as a whole. There are three gaps in the extant literature (i) the majority of published methodologies begin with a finite set of explicitly defined alternatives and attempt to maximize the portfolio outcomes without any definition of an optimized future state, (ii), portfolios are limited to choices from an a priori set of alternatives and are therefore unlikely to result in an optimal outcome and (iii) the extant methodologies generally do not include appropriate measurement to judge outcomes. Furthermore, there are significant limitations to the approaches used by industry for project selection and a degree of dissatisfaction with the methodologies employed. The most significant of these is the gap between strategy formulation and portfolio generation. A normative framework that should be used to structure project portfolio methodologies is therefore presented. To resolve these issues, a scalable generic methodology for visualizing and evaluating optimal future states and to evaluate projects and portfolios of projects in the context of those future states is presented. The methodology described employs Multiscale, Object Oriented Modelling and Simulation with Optimal Design of Experiments to create n-dimensional Pareto Frontiers from the set of all feasible production outcomes within given manufacturing configurations and for given strategic scenarios.","abstract_has_math":false,"creators":["Kornfeld, Bernard"],"institution":"UNSW, Sydney","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-24T05:33:55Z","subjects":["Lean Manufacturing","Continuous Improvement","Project Selection","Six Sigma","Multiobjective Decision Analysis"],"languages":["EN"],"rights":["open access","CC BY-NC-ND 3.0","free_to_read"],"rights_urls":["https://purl.org/coar/access_right/c_abf2","https://creativecommons.org/licenses/by-nc-nd/3.0/au/"],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.26190/unsworks/17392"],"render_values":[{"text":"https://doi.org/10.26190/unsworks/17392","href":"https://doi.org/10.26190/unsworks/17392","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1959.4/54256","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Kornfeld, Bernard"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014"]},{"key":"dc:publisher","label":"Institution","values":["UNSW, Sydney"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis","http://purl.org/coar/resource_type/c_db06"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Lean Manufacturing","Continuous Improvement","Project Selection","Six Sigma","Multiobjective Decision Analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["EN"]},{"key":"dc:rights","label":"Dc Rights","values":["open access","https://purl.org/coar/access_right/c_abf2","CC BY-NC-ND 3.0","https://creativecommons.org/licenses/by-nc-nd/3.0/au/","free_to_read"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/1959.4/54256","https://unsworks.unsw.edu.au/bitstreams/8a74a60f-f254-454f-90fc-c480227b9e67/download","https://doi.org/10.26190/unsworks/17392"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Manufacturing organisations must routinely deliver efficiencies in order to remain competitive. Many have embraced continuous improvement methodologies, such as Lean manufacturing and Six Sigma in order to achieve these goals. However their ability to realise sustainable competitive advantage from continuous improvement is hampered by the lack of structured objective approaches for optimal project portfolio selection that link strategy to targeted improvement efforts. As a consequence, scarce resources are inappropriately allocated, opportunities are lost and there is sub-optimisation of the system as a whole. There are three gaps in the extant literature (i) the majority of published methodologies begin with a finite set of explicitly defined alternatives and attempt to maximize the portfolio outcomes without any definition of an optimized future state, (ii), portfolios are limited to choices from an a priori set of alternatives and are therefore unlikely to result in an optimal outcome and (iii) the extant methodologies generally do not include appropriate measurement to judge outcomes. Furthermore, there are significant limitations to the approaches used by industry for project selection and a degree of dissatisfaction with the methodologies employed. The most significant of these is the gap between strategy formulation and portfolio generation. A normative framework that should be used to structure project portfolio methodologies is therefore presented. To resolve these issues, a scalable generic methodology for visualizing and evaluating optimal future states and to evaluate projects and portfolios of projects in the context of those future states is presented. The methodology described employs Multiscale, Object Oriented Modelling and Simulation with Optimal Design of Experiments to create n-dimensional Pareto Frontiers from the set of all feasible production outcomes within given manufacturing configurations and for given strategic scenarios."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Identification and selection of continuous improvement projects"]}]}],"canonical_facts":{"dc:creator":["Kornfeld, Bernard"],"dc:date":["2014"],"dc:description":["Manufacturing organisations must routinely deliver efficiencies in order to remain competitive. Many have embraced continuous improvement methodologies, such as Lean manufacturing and Six Sigma in order to achieve these goals. However their ability to realise sustainable competitive advantage from continuous improvement is hampered by the lack of structured objective approaches for optimal project portfolio selection that link strategy to targeted improvement efforts. As a consequence, scarce resources are inappropriately allocated, opportunities are lost and there is sub-optimisation of the system as a whole. There are three gaps in the extant literature (i) the majority of published methodologies begin with a finite set of explicitly defined alternatives and attempt to maximize the portfolio outcomes without any definition of an optimized future state, (ii), portfolios are limited to choices from an a priori set of alternatives and are therefore unlikely to result in an optimal outcome and (iii) the extant methodologies generally do not include appropriate measurement to judge outcomes. Furthermore, there are significant limitations to the approaches used by industry for project selection and a degree of dissatisfaction with the methodologies employed. The most significant of these is the gap between strategy formulation and portfolio generation. A normative framework that should be used to structure project portfolio methodologies is therefore presented. To resolve these issues, a scalable generic methodology for visualizing and evaluating optimal future states and to evaluate projects and portfolios of projects in the context of those future states is presented. The methodology described employs Multiscale, Object Oriented Modelling and Simulation with Optimal Design of Experiments to create n-dimensional Pareto Frontiers from the set of all feasible production outcomes within given manufacturing configurations and for given strategic scenarios."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/1959.4/54256","https://unsworks.unsw.edu.au/bitstreams/8a74a60f-f254-454f-90fc-c480227b9e67/download","https://doi.org/10.26190/unsworks/17392"],"dc:language":["EN"],"dc:publisher":["UNSW, Sydney"],"dc:rights":["open access","https://purl.org/coar/access_right/c_abf2","CC BY-NC-ND 3.0","https://creativecommons.org/licenses/by-nc-nd/3.0/au/","free_to_read"],"dc:subject":["Lean Manufacturing","Continuous Improvement","Project Selection","Six Sigma","Multiobjective Decision Analysis"],"dc:title":["Identification and selection of continuous improvement projects"],"dc:type":["doctoral thesis","http://purl.org/coar/resource_type/c_db06"]},"updated_at":"2026-07-24T05:33:55Z"}