Critical spare parts ordering decisions using conditional reliability and stochastic lead time

The decision-making processes have become crucial for organizations immersed on an intensified competitiveness situation. Because of current industrial reality, a continuous improvement of the ability for adding value and enhancing profitability of decisions is needed by firms (Pascual et al, 2009). Companies are frequently required for reducing production costs and increasing asset utilization; consequently, stress on machines is generated. This situation affects reliability and, most important, system throughput. Due to the demanding use of equipment, the stressful effect tends to be critical for the performance of asset intensive industries, e.g.: mining operations, aeronautic, nuclear industry, or defence. One way to improve the system performance is to allocate inventory resources. Stocks can represent about one third of all assets of a typical company (Dfaz & Fu, 1997). Of these assets, spare parts have special relevance for asset intensive industries since extensive system downtime could be produced by the insufficiency of spare part stocks (Louit 2006). As an example, in the airlines business, spares sum up above US$50 billion (Kilpi & Vepsalainen, 2004). The management of spare part stocks requires a balance between shortage costs (costs of shutting down the operations) and overstock costs (financial costs associated with holding a safety stock) (Sarker & Haque, 2000) and ordering costs. Insufficient stocks affect overall performance of physical assets; on the other hand, oversized stocks lead to an inefficient use of resources and can imply high investment costs. Therefore, decisions about spare-stocking policies can become essential in the cost structure of companies. In order to provide an efficient spare management performance, a suitable ordering strategy can be relevant. This situation provides an opportunity to improve decision making methods. A spare part classification scheme becomes necessary in order to set optimal policies for those spares that may affect the system the most and at the least effort. We proposed an ordering decision-aid method, in order to secure the spare management performance into an operational environment that needs continuity to compete into a demanding business context.