How Multi-Indenture, Multi-Echelon Inventory Optimization Affects Airline Expansion Costs
Written by: Vipul Agrawal
Asia’s air service demand is booming. According to Boeing’s Current Market Outlook 2015-2034 report, the region will grow by more than 100 million passengers per year for the foreseeable future. To meet this demand, airlines operating in Asia will procure more than 14,000 new aircraft.

One of the obvious costs of expanding air service to a new market is establishing maintenance, repair, and overhaul capabilities. There are a few spare parts planning techniques airlines can employ to allay these costs, one of which is a method known as multi-indenture, multi-echelon (MIME) inventory optimization.

What is Multi-Indenture, Multi-Echelon Optimization?

When expanding your market presence, one of the objectives is to minimize the costs associated with constructing new maintenance depots or renovating existing ones. MIME addresses these concerns by correlating the aircraft availability targets you specify (95% in China, 80% in Cambodia, etc.) with:

  • Effective lead time (ELT)
  • Repair lead time (RLT)
  • Probable failure
  • Part cost and criticality

Essentially, MIME identifies where to stock line-replaceable units (LRUs), sub-replaceable units (SRUs), and sub-SRUs based on their criticality, the time required to transport them across the spare parts network, and how often those components fail under certain conditions.

A consideration: Your organization needs a reliable planning tool. MRO and other execution systems provide much of the data MIME optimization algorithms use, as Major General (Ret. Air Force) H. Brent Baker mentioned in his webinar on MRO and service parts management integration. Assuming that data is accurate, which there are many ways to accomplish this aspect, MIME will generate exceptional inventory recommendations; based on the availability, and budget requirements of your organization.

How MIME tempers MRO expansion costs 

Suppose you have a maintenance depot in Osaka, Japan, and a regional spare parts warehouse in Tokyo. Your MRO department deduces you’ll have to expand the depot’s spare parts inventory capacity by 4,000 square meters (M²) to support Osaka’s growing flight activity. The cost of such a project, based on the average cost per M² in Tokyo, according to Compass International, would be $3.4 million.

Probable failure, ELT, and RLT vary depending on the LRU, SRU, or part you wish to stock, but for the sake of argument, suppose you want to know how many annular combustion chambers you’ll have to store in Osaka.

MIME first considers which SRUs and sub-SRUs often cause annular combustion chambers to fail by referencing service bills of materials (sBOMs). If the sBOMs denote fuel manifolds cause combustion chamber failures 80% of the time, the algorithm would recommend that you store more fuel manifolds than other combustion chamber SRUs.

To identify optimal stocking locations, MIME considers the strategic placement of each warehouse, Osaka’s aircraft availability target (90%), and the available capacity of Tokyo and Osaka’s inventories.

It may conclude that your organization lacks the budget to stock eight fuel manifolds at Osaka, but you could stock six in Tokyo and two in Osaka. As the ELT between Osaka and Tokyo is approximately eight hours, and the RLT is about three hours, a grounded aircraft would be up and running the same day it is brought in for maintenance.

Applying MIME across spare parts planning

The example above only considers the tradeoffs associated with storing one LRU. If applied across all spare part numbers, MIME minimizes location expansion needs.

Depending on the scale of your expansion, new facilities may be necessary anyway, but not as many as you would need without MIME. Even if you only applied multi-echelon optimization, you’d likely decrease your spares inventory by between 15% and 35%.

Qantas Airlines actually used MIME inventory optimization to not only reduce its spare parts stock but also increase each part's availability rate to 95%. You can read more about the project below:

PTC Service Cast Study

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Tags: CAD Aerospace and Defense Service Lifecycle Management (SLM)
About the Author Vipul Agrawal

Dr. Vipul Agrawal is the Technical Vice President of PTC's Servigistics Business Unit. He has an extensive command of the technical aspects of service parts optimization. In 1999 he co-founded MCA Technologies with Morris Cohen, and together they developed the first commercial multi-echelon optimization algorithms. Vipul joined Servigistics and then PTC through acquisition, and has contributed to the innovation that has distinguished Servigistics as the industry-leading service parts optimization solution. Vipul published the article “Winning in the Aftermarket” in Harvard Business Review with co-authors Morris Cohen and Narendra Agrawal. In his current role, Vipul is focused on supporting PTC’s Servigistics Business Unit and helping service organizations orchestrate world-class service parts optimization (including service parts management and service parts pricing). He is part of the team leading rapid innovation with connected service parts management, leveraging ThingWorx to improve forecasting and optimization using equipment data.