Asset management life cycle costing model for steel manufacturing industry in Saudi Arabia

Abstract

Despite numerous studies have been conducted in the field of Life Cycle Costing (LCC), there are limited holistic and practical models have been introduced to the industry. When commencing a design and implementation project, Engineering Asset Management (EAM) must consider the overall life cycle of physical assets, including commissioning, operational and end-of-life phases. Life Cycle Costing was recently suggested by researchers to be utilized to optimize the selection and operation of engineering assets to achieve optimum asset selection and utilization. This research developed an LCC Decision Making Model to enable executives and business owners to make informed decisions for their new asset selection, current asset expansion or replacement options. The study reviews the literature on generic LCC frameworks and models, and utilizes the results of the review together with the professional inputs from the steel fabrication and manufacturing industry through a survey and semi structured interviews in Saudi Arabia in order to identify the problem and develop the conceptual framework. The review indicates the lack of a holistic Model, that considers the strategic and operational life cycle asset cost activities, and addresses the variables impacting those costs such as uncertainty and discounting that can aid organizations to achieve the optimum selection of their plants, and assist in managing their performance. Present models in literature mainly focus on the maintenance, but still dearth to address major components of the life cycle, neglect detailed uncertainty and discounting factors consideration, or do not consider sensitivity analysis. These factors resulted on models that lack practicality, accuracy and applicability in industry. Hence, a conceptual framework and its respective Cost Breakdown Structure (CBS) were developed. This conceptual framework and CBS provided the cost variables that are used for a case-study cost data collection from a plant in the steel fabrication industry. The conceptual framework and its CBS were validated by four industry experts. The historical costs were modelled, and the forecasted costs were derived using a model that includes Artificial Neural Network (ANN) methods and stochastic modelling. The developed mathematical model considered uncertainty and discounting factors, and simulated different weightages from probabilities derived from the industry. The prediction model derived high accuracy performance measures of operational and tactical dimensions such as annual revenues and income forecasted values, that aid in decision making for performance management. The model also regarded for performance measures including Return on Investment (ROI) and Pay Back Period (PB) for strategic dimensions that aid for comparisons for asset selection. The model provided a more accurate representation of long-term plant costs since it enables the quantification of risks anticipated during the plant’s operations, and thus forecasts are based on a sounder approach than what was previously used in the industry. The developed model was then validated using industry data in a case study. Industry professionals confirmed that no solid forecasting tool is currently being utilized at the industry, which makes the proposed novel model ideal for aiding in the decision of selection of assets from existing options, and in the performance management of the assets for guiding decision makers on expansion and replacement decisions.