Models for Life Cycle Cost Estimation of Spare and Wear Parts for Urban Gondola Lift Systems : A Case Study

Abstract: Urban gondola lift systems are becoming a regular sight rather than a rarity throughout the globe. Authors attribute their increasing popularity to factors such as environmental sustainability, operational reliability and cost efficiency compared to other right-of-way transit solutions. Replacing conventional modes of transit with urban gondolas cannot however be achieved without tackling several operational challenges. As potential new operators often lack the human resources and knowledge base required to successfully man, operate and maintain systems, they turn to manufacturers for increased after-sale support. Companies of the Doppelmayr Garaventa Group, the world’s largest manufacturer of gondola lifts, responded to these demand patterns by offering complete operations & maintenance contracts which, among other services, include the delivery and installation of reserve components. Calculating the total cost of such components for the life cycle of a system however still proves to be demanding and requires new computational models to increase its efficiency. The applicative purpose of this paper was thus set to formulate a model that is capable of performing life cycle cost calculations for components of urban gondola lift systems, according to a set of criterion defined by industrial entities. Its research aim is accordingly to answer questions about how concurrent instruments are set up, what models does contemporary research regard as efficient in similar industries and whether these models are able to enhance life cycle cost calculation capability within the urban gondola lift market. These aims were achieved through an analysis of current company practices, followed by the formulation of two new model alternatives based on a review of contemporary scientific literature, and concluded by an iterative process wherein the two alternatives were compared to each other in terms of performance and then merged to combine the best performing features of each version. Through a second iteration, the merged model was then compared to current instruments and established as the superior choice, using industry criteria. The paper concludes by resolving the research questions it set out to answer and making further recommendations for the direction of future research and studies.