Inventory management best practices for aircraft servicing industry in India

Inventory Management is one of the key areas within an Aircraft Servicing and Maintenance organization. The management of aircraft spares, Line Replacement Units (LRUs), Rotables, special hardware items and consumables of an aircraft or aircraft servicing, maintenance, repair and overhaul (MRO) facility includes several key areas starting from demand management, modelling and forecasting, material management, order fulfilment and tracking of movements, warehouse management and physical inventory control.

In a traditional hierarchical inventory system, direct orders are the only information for inventory management that is exchanged between the firms in the Supply Chain. But due to the rapid development of modern information technology, it becomes possible for the firms to share more information in real time, e.g., demand and inventory status data along with inventory information with integrated Supply Chain links. As such Indian Aircraft Industry (IAI) aimed at such information sharing with the supply chain because IAI has recognised this as an important source of competitive advantage.

In order to achieve objective of optimizing the inventory holding level at Indian Aircraft Industries, especially at the aircraft servicing centers, various Selective Inventory Control models have been used during the past fifteen years which have yielded results to bring down the level of inventory to its minimum, have been elaborated upon in this paper. Selective control is based on the principle “Take maximum care of the most important thing”. This is the prudent approach of any management. Similarly in inventory management classification of inventory and grouping of inventory is resorted-to, so that major portion of managerial time is spent effectively on materials which are important. There are many methods for this selective control. The methods followed in Indian Aircraft Industries are reviewed in this paper with suggestions for improvement apart from utilization of Inventory Modelling and neural networking methods.