Development of a multi-scenario simulation model for spare parts inventory optimization in mining operations

2021-8
Şenses, Sena
The growing market competition compels many industries to change their operational structures at strategic and operational levels dramatically. It has been recognized that inventory management requires continuous monitoring and improvement and it is vital for businesses to ensure smooth operations by avoiding production loss and reducing the overall cost of inventory. For various production companies operating in different industries, inventory is considered as one of the most expensive assets. Among different inventory types, the spare parts inventory is of great importance for production management in ensuring high equipment availability at a minimized operating cost. In the mining industry, mass and continuous production should be sustained with an incontrovertible contribution of high-capacity equipment. Due to the high operational loads, complexity, and cost of the mining machines, unplanned downtimes resulting from spare parts' unavailability may cause a great financial loss. Among different types and classifications of spare parts available in a mining spare parts warehouse, tires are significant for operations where wheel machineries are used. In open-pit operations where trucks with varying capacities are extensively employed for material hauling operations, tires can account for up to 20% of the total operating costs. In addition, any tire shortage in a warehouse may lead to tremendous production loss. In this regard, the current study intends to develop a discrete-event simulation algorithm for optimizing spare parts inventory problems in various inventory systems, giving the cost-wise best output among all the scenarios by utilizing Arena® Software. Within the scope of generating these scenarios, different parameter combinations of four inventory policies, namely (s, Q), (s, S), (R, Q), and (R, S), are utilized. The proposed algorithm is implemented for the tires of a truck fleet, which covers seven identical trucks with six tires each and operates in a surface coal mine in Turkey. In this study, four well-known inventory policies are utilized to evaluate both continuous and periodic inventory review approaches. For the continuous reviewing approach, (s, Q) and (s, S) policies are identified, while (R, Q) and (R, S) policies are discussed for the periodic reviewing approach. On this basis, a total of 637 scenarios are generated from the inventory policies built based on different reviewing and triggering mechanisms, and each scenario is simulated for five years. The results show that the most optimal scenario for continuous review inventory policy is observed to be (s=9, S=49), where spare parts are ordered up to an inventory level of 49 whenever the number of components in the inventory drops to 9. Similarly, the most optimal scenario for periodic review policy is observed to be (R=6480, Q=45), where the fixed batch size of 45 is ordered every 6,480h. Accordingly, the annual system cost is observed as $2,604,032 and $2,608,617 for the best-case scenarios of continuous and periodic review policies, respectively. Besides, it was observed from both policies that the model is capable of ensuring the balance between the cost items by allowing stock-out to a certain extent in the best-case scenarios.
Citation Formats
S. Şenses, “Development of a multi-scenario simulation model for spare parts inventory optimization in mining operations,” M.S. - Master of Science, Middle East Technical University, 2021.