{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/368257"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/368257","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Adopting Digital Technology to Drive Resource Efficiency in Manufacturing","abstract":"Amidst growing concerns over depleting natural resources and escalating waste emissions due to increased global consumption, sustainability has become a critical issue. Consequently, the manufacturing industry, a major contributor to global carbon emissions, faces significant pressure to adopt sustainable practices. To reduce carbon emissions and waste, existing literature contends that resource efficiency is paramount. However, the literature often views resource efficiency as a byproduct of digitalization rather than a primary objective to investigate. Hence, this research seeks to address the central question: How can manufacturers adopt digital technologies to enhance resource efficiency? This study utilizes a qualitative research approach, consisting of 2 focus groups, 6 case studies, and 12 semi-structured interviews with industry experts. Focus groups identified impactful digital technologies for resource efficiency, case studies delved into how these technologies can be effectively adopted, and semi-structured interviews corroborated research findings. This methodological triangulation attempts to offer a comprehensive understanding of the interplay between digital technologies and resource efficiency in the manufacturing sector. From a technological perspective, empirical findings revealed that sensors, machine learning, and artificial intelligence are digital technologies that have a substantial impact on resource efficiency, with the availability of high-quality data being essential. These technologies can empower operational staff with critical insights, leading to more effective decision-making. Automation, particularly via industrial control systems, emerged as a key factor in minimizing human error and further enhancing resource efficiency. From an organizational perspective, the primary barriers to adopting these technologies were identified as inadequate knowledge and skills. Hence, effective change management was deemed essential in overcoming these challenges. This study also provides a framework for effectively adopting digital technologies to enhance resource efficiency in manufacturing. The research findings underscore the pivotal role of digital technologies in advancing resource efficiency within the manufacturing industry, contributing new insights into their practical implementation. Additionally, this research contributes to the existing body of knowledge by addressing gaps in the literature and proposing directions for future research.","abstract_html":"Amidst growing concerns over depleting natural resources and escalating waste emissions due to increased global consumption, sustainability has become a critical issue. Consequently, the manufacturing industry, a major contributor to global carbon emissions, faces significant pressure to adopt sustainable practices. To reduce carbon emissions and waste, existing literature contends that resource efficiency is paramount. However, the literature often views resource efficiency as a byproduct of digitalization rather than a primary objective to investigate. Hence, this research seeks to address the central question: How can manufacturers adopt digital technologies to enhance resource efficiency? This study utilizes a qualitative research approach, consisting of 2 focus groups, 6 case studies, and 12 semi-structured interviews with industry experts. Focus groups identified impactful digital technologies for resource efficiency, case studies delved into how these technologies can be effectively adopted, and semi-structured interviews corroborated research findings. This methodological triangulation attempts to offer a comprehensive understanding of the interplay between digital technologies and resource efficiency in the manufacturing sector. From a technological perspective, empirical findings revealed that sensors, machine learning, and artificial intelligence are digital technologies that have a substantial impact on resource efficiency, with the availability of high-quality data being essential. These technologies can empower operational staff with critical insights, leading to more effective decision-making. Automation, particularly via industrial control systems, emerged as a key factor in minimizing human error and further enhancing resource efficiency. From an organizational perspective, the primary barriers to adopting these technologies were identified as inadequate knowledge and skills. Hence, effective change management was deemed essential in overcoming these challenges. This study also provides a framework for effectively adopting digital technologies to enhance resource efficiency in manufacturing. The research findings underscore the pivotal role of digital technologies in advancing resource efficiency within the manufacturing industry, contributing new insights into their practical implementation. Additionally, this research contributes to the existing body of knowledge by addressing gaps in the literature and proposing directions for future research.","abstract_has_math":false,"creators":["Abubakar, Awwal Sanusi"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Evans, Steve"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-18","date_published":"2024-01-18","updated_at":"2026-07-22T22:24:24Z","subjects":["Digital technologies","Digitalization","Manufacturing","Production","Resource efficiency","Sustainability"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/8340c3c9-3586-4088-a78c-b12b52cd5281/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.108569","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Evans, Steve"]},{"key":"dc:creator","label":"Author","values":["Abubakar, Awwal Sanusi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-01-18"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/368257"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Digital technologies","Digitalization","Manufacturing","Production","Resource efficiency","Sustainability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/8340c3c9-3586-4088-a78c-b12b52cd5281/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.108569"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/88f3adae-c91e-4303-9856-a8dffed7e113/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Amidst growing concerns over depleting natural resources and escalating waste emissions due to increased global consumption, sustainability has become a critical issue. 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This methodological triangulation attempts to offer a comprehensive understanding of the interplay between digital technologies and resource efficiency in the manufacturing sector. From a technological perspective, empirical findings revealed that sensors, machine learning, and artificial intelligence are digital technologies that have a substantial impact on resource efficiency, with the availability of high-quality data being essential. These technologies can empower operational staff with critical insights, leading to more effective decision-making. Automation, particularly via industrial control systems, emerged as a key factor in minimizing human error and further enhancing resource efficiency. From an organizational perspective, the primary barriers to adopting these technologies were identified as inadequate knowledge and skills. Hence, effective change management was deemed essential in overcoming these challenges. This study also provides a framework for effectively adopting digital technologies to enhance resource efficiency in manufacturing. The research findings underscore the pivotal role of digital technologies in advancing resource efficiency within the manufacturing industry, contributing new insights into their practical implementation. 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From a technological perspective, empirical findings revealed that sensors, machine learning, and artificial intelligence are digital technologies that have a substantial impact on resource efficiency, with the availability of high-quality data being essential. These technologies can empower operational staff with critical insights, leading to more effective decision-making. Automation, particularly via industrial control systems, emerged as a key factor in minimizing human error and further enhancing resource efficiency. From an organizational perspective, the primary barriers to adopting these technologies were identified as inadequate knowledge and skills. Hence, effective change management was deemed essential in overcoming these challenges. This study also provides a framework for effectively adopting digital technologies to enhance resource efficiency in manufacturing. 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