No abstract

No abstract

No abstract

Purpose A systematic literature review is performed to reveal the state-of-the-art in the implementation of lean principles in the petroleum industry. This paper aims to generate a conceptual framework and reveal research gaps with respect to lean concept application in the petroleum industry. Design/methodology/approach After formulating research questions, the search strategy is generated, followed by data extraction, literature review and synthesis of the results. The search covers any studies in peer-reviewed scientific journals and conference proceedings in the period 1990-2017 that discuss the implementation of the lean concept in the petroleum industry. Findings The lean concept has been used to improve operational and technical aspects, contractor/supplier relationships, team organization and project management practice in the petroleum industry. Based on the literature review, a conceptual framework is generated comprising four main elements: leadership and commitment from management, employee involvement, cooperation and trust with contractors/suppliers and lean project management. These elements are the pillars that are founded on lean philosophy and principles to support technical/operational improvement in the organization. The types of literature identified indicate that the subject of the study is still immature. Research limitations/implications This study focuses only on the upstream sector of the petroleum industry, which restricts the generalizability of the results to midstream and downstream businesses. Practical implications This paper provides knowledge and information regarding the current state of lean implementation in the petroleum industry. The developed conceptual framework provides general guidance for practitioners regarding lean implementation in the petroleum industry, and is also expected to support research on theory building. Originality/value Few studies have discussed the application of the lean concept in the petroleum industry. This paper contributes a platform for researchers and practitioners to comprehend how the lean concept has been applied in the petroleum industry, and provides a foundation for further studies on lean implementation in the petroleum industry.

Purpose Corrosion loop development is an integral part of the risk-based inspection (RBI) methodology. The corrosion loop approach allows a group of piping to be analyzed simultaneously, thus reducing non-value adding activities by eliminating repetitive degradation mechanism assessment for piping with similar operational and design characteristics. However, the development of the corrosion loop requires rigorous process that involves a considerable amount of engineering man-hours. Moreover, corrosion loop development process is a type of knowledge-intensive work that involves engineering judgement and intuition, causing the output to have high variability. The purpose of this paper is to reduce the amount of time and output variability of corrosion loop development process by utilizing machine learning and group technology method. Design/methodology/approach To achieve the research objectives, k-means clustering and non-hierarchical classification model are utilized to construct an algorithm that allows automation and a more effective and efficient corrosion loop development process. A case study is provided to demonstrate the functionality and performance of the corrosion loop development algorithm on an actual piping data set. Findings The results show that corrosion loops generated by the algorithm have lower variability and higher coherence than corrosion loops produced by manual work. Additionally, the utilization of the algorithm simplifies the corrosion loop development workflow, which potentially reduces the amount of time required to complete the development. The application of corrosion loop development algorithm is expected to generate a “leaner” overall RBI assessment process. Research limitations/implications Although the algorithm allows a part of corrosion loop development workflow to be automated, it is still deemed as necessary to allow the incorporation of the engineer’s expertise, experience and intuition into the algorithm outputs in order to capture tacit knowledge and refine insights generated by the algorithm intelligence. Practical implications This study shows that the advancement of Big Data analytics and artificial intelligence can promote the substitution of machines for human labors to conduct highly complex tasks requiring high qualifications and cognitive skills, including inspection and maintenance management area. Originality/value This paper discusses the novel way of developing a corrosion loop. The development of corrosion loop is an integral part of the RBI methodology, but it has less attention among scholars in inspection and maintenance-related subjects.