A novel shipyard performance measurement approach through an integrated Value Engineering and Risk Assessment (VENRA) framework using a hybrid MCDM tool

Abstract

As the ship manufacturer, including new building, repair, and conversion, the shipyard significantly impacts the ship’s quality and performance output. A well-planned ship design that will be built requires skilled shipbuilders who can fulfil quality, timeline, budget, safety, and environmental requirements from shipowners, rules, and regulations. Since diverse and multiple factors influence its efficiency and product output, evaluating the shipyard’s performance is critical for a more impactful and strategic advancement approach. This research aims to apply the novel integrated Value Engineering and Risk Assessment (VENRA) framework, which integrates five main elements: technical, business, external, personnel’s safety, and environment, for measuring shipyard performance. This paper demonstrates the VENRA business element in more detail and applies it to a shipyard case study. Integrated fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Weighted Evaluation Technique (WET) are used to analyse the criteria’s interrelationship by determining the cause-effect group and weight ranking prioritisation. The objective grading system is developed to determine the shipyard’s performance score based on multi-resource qualitative and quantitative data. The shipyard’s case study demonstrates that ‘delivery time’ remains the most critical and influential aspect of the business elements’ performance. In addition, the top three most important factors, ‘delivery time’, ‘financial report condition’, and ‘ship manufacturing cost’, must be taken into account, as they directly influence the shipyard’s performance. Despite being a minor element, ‘innovation and human resources’ is the second most influential factor after ‘delivery time’. The case study’s results demonstrate that the framework can simultaneously identify cause-and-effect criteria groups while prioritising the most critical factors via methodologies.