Ever needed to know how to stay on top of your Parts Inventory Management? Getting the right Analytic understanding might be a step in the right direction. We are excited to share with you the benefits, analytical approaches and real life examples of Parts Inventory Management.



Organizations maintain spare parts inventory across service shops and warehouses to provide for the material consumption during the planned and unplanned maintenance. The cost of capital tied to inventory levels is one of the key drivers of maintenance costs. Higher inventory level can provide better service level, but at a higher cost. Optimum quantity of spare parts across the service supply chain frees unproductive capital locked in inventory, while maintaining the required optimal service levels.


Analytical Approaches

Advanced analytical techniques are used for spare part consumption forecasting and subsequent optimization, across the supply chain. Time series and hierarchical forecasting techniques are used for determining the demand for spare parts. The forecasts are made at a shop level and then aggregated to arrive at warehouse level forecasts. The multi-echelon inventory optimization model takes into account the forecasting results and supply chain cost parameters to determine the optimum stocking levels at each location.


Real Life Examples

For a large airline cost of parts in the inventory may exceed a billion dollars. They store the critical parts such as engines at strategic locations such as hubs from where they can transport them to where they are needed quickly. They also may have agreements for pooling such parts with other airlines at a given location.
A large repair facility for locomotives may be shared by multiple parties, generally there are tracks dedicated to each party. Each party maintains their own parts inventory. However, there may be agreements similar to airlines that allow them to borrow parts in case of an inventory stock-out. Parts inventory management does not get as much focus in the railcar and trucking industries compared to their airline and locomotive counterparts due to the fact that the assets are less expensive and generally have other assets available to take the place of failed asset to maintain the service levels.


We hope to provide relevant insights and a fresh perspective on how analytics can make a profound impact on asset-intensive organizations.  Get ready for our post next week about Warranty Analytics.