Spare Parts Planning in Defense and Aerospace: Repair Cycle, Criticality, and Obsolescence

In brief: An aircraft waiting on the ground because of a single missing part, or a platform unable to go on a mission, clearly shows the distinctive nature of spare parts planning. In defense and aerospace, this planning is based on the usage and failure behavior of installed systems. Some parts are repaired and reused, lead times are measured in months, and the absence of a single part can take the entire system out of service. This article covers the distinction between repairable and consumable parts, criticality classification, calculating repair pipeline stock, and obsolescence risk.

Why Is Spare Parts Demand Different?

• It depends on the installed base: Demand is derived from the number of systems in the field, usage hours, and part failure rate.
• It is intermittent: Many parts see no demand for months and are then requested a few units at a time (see Intermittent Demand in Time Series Analysis).
• It includes a repair cycle: A repairable part that fails is replaced from the warehouse with a new one; the failed part is sent for repair and joins stock when it returns.
• The outcome is system availability: The main goal is the share of systems ready for mission, rather than a part-level service level.

How Are Parts Classified?

How Is Repair Pipeline Stock Calculated?

For a repairable part, the required stock depends on the average number of parts that remain "missing" in the system while repair is under way. The subject can be explained with an example (figures are for illustration purposes). Suppose there are 50 systems in the field, each operating 60 hours per month, with an hourly failure rate of 0.002. The expected number of failures per month is calculated as 50 × 60 × 0.002 = 6. If the repair turnaround time is 2 months, there are on average 6 × 2 = 12 parts in the repair pipeline.

Failures are assumed to follow a Poisson distribution. To raise the probability of having no outstanding demand at any moment above 95%, a stock of 18 units is needed (with 17 units the probability is about 93.7%; with 18 units about 96.3%). When the repair turnaround time is reduced to 1 month, the average falls to 6 and the same target is reached with 10 units. Therefore, speeding up repair is in many cases a more effective method than purchasing new parts.

For repairable parts, the stock level depends on the repair turnaround time
as much as on purchasing decisions.

Where Should Stock Be Held?

The same total stock gives quite different results depending on where it is placed. In a multi-echelon structure consisting of a central warehouse, regional warehouses, and the field, as a general rule it is recommended to pool expensive and rarely failing parts at the center and to keep cheap and frequently failing parts close to the field. The distribution that reaches the system availability target with the lowest inventory investment can be calculated with multi-echelon inventory models (e.g., the METRIC family).

How Is Obsolescence Risk Managed?

While a system serves for decades, the electronic components and special parts inside it may be discontinued long before. In managing this risk, suppliers' end-of-life notifications are tracked regularly, the last-time buy quantity for critical parts is calculated according to the demand expected over the remaining life of the system, and alternative part or redesign options are prepared in advance. The last-time buy quantity also requires a balance. Excess purchase leads to obsolete stock, while insufficient purchase creates the risk that the part cannot be found later.

Which Metrics Should Be Monitored?

Technology and Digitalization: Predictive Maintenance and Configuration Management

Although spare parts demand appears random at first glance, in defense and aerospace it depends largely on the usage intensity of the equipment and the maintenance it has undergone. When operating hours and failure records are collected digitally, a significant share of demand considered "random" becomes plannable.

Enterprise Resource Planning (ERP) is the enterprise transaction system that records orders, inventory, purchasing, production, and finance transactions in a common database; it is the master data and transaction source for planning. It keeps records of parts, repair work orders, serial numbers, and lot tracking, and makes it possible to monitor repair pipeline stock.

Product Lifecycle Management (PLM) is the system that manages design, product configuration, and engineering changes; it shows which part is valid in which configuration. It shows the configurations in which parts are valid and engineering changes. In this way, obsolescence and change risk can be seen at an early stage.

Computerized Maintenance Management System (CMMS) is the system that records maintenance work orders, equipment history, and the parts used in maintenance. Maintenance history and the parts used are recorded in this system; it is the source of time-between-failure and part consumption data.

Internet of Things (IoT) is the infrastructure in which sensors on machines, vehicles, warehouses, and products transmit data automatically over a network; location, temperature, status, and consumption data are collected without human input. Data such as operating hours, vibration, and temperature are collected from platform and equipment sensors. This data forms the input of usage-based demand forecasting.

Artificial Intelligence (AI) and Machine Learning (ML) are methods that learn patterns from data to produce forecasts, classifications, and recommendations; machine learning is the sub-field of artificial intelligence that learns from data. They classify sparse spare parts demand and forecast repair turnaround times and supply delays. They also generate risk warnings for critical parts.

Predictive Maintenance is an approach that uses sensor and failure data to anticipate when equipment will fail and plans maintenance accordingly. Failure probability is predicted with artificial intelligence models; thus, part requirements can be planned before the failure occurs.

Digital Twin is a virtual model of a factory, line, or supply network that is updated with data; it allows scenarios to be tested without affecting the physical system. The effect of different usage intensities on maintenance and parts requirements is tested on the platform's virtual model.

Effectiveness of predictive maintenance. Predictive maintenance supported by artificial intelligence is one of the effective methods for reducing spare parts uncertainty. However, the result obtained depends on the quality of sensor data, the completeness of maintenance records, and the maintenance recommended by the model actually being carried out in the field. In environments with high security and confidentiality requirements, such as defense, where the data will be processed and who can access it must also be determined separately. It is recommended that this issue be designed at the start of the project.

Timely supply of a part does not depend solely on the model that predicts the failure;
it requires discipline in records, repair, and maintenance.

Implementation: Checklist

1. Classify the parts list by repairable/consumable, criticality, demand behavior, and value dimensions.
2. Collect installed base, usage hours, and failure rate data by part.
3. Measure repair turnaround times; evaluate repair capacity for parts with long durations.
4. Define the stock target through the system readiness rate rather than by part.
5. Set up end-of-life notification tracking and a last-time buy procedure for parts with obsolescence risk.
6. Link usage and maintenance data (operating hours, failure records) to spare parts planning; manage serial number and configuration tracking digitally.

Related Articles

• Time Series Analysis for Demand Forecasting: Stationarity, Trend, and Seasonality
• 5 Common Mistakes in Safety Stock Calculation and the Correct Approach
• Total Cost of Ownership (TCO) in Purchasing: Supplier Selection and Green Procurement

Frequently Asked Questions

Can spare parts demand be forecast?

This varies with the nature of the part. For regularly used consumable parts, forecasts can be made from past consumption. For repairable parts and parts with intermittent demand, approaches derived from installed base, usage, and failure rates, together with intermittent demand methods, give much more reliable results.

What is VED analysis?

VED analysis is the classification of parts as vital, essential, and desirable according to their effect on the operation of the system. When used together with ABC analysis, stock targets can be set on the basis of both value and criticality.

How is the last-time buy quantity determined?

The demand expected over the remaining life of the system, possible repairs, and the scrap rate are taken into account. Then the inventory cost of excess purchase is compared with the cost of not being able to find the part later.

Why does stock for a repairable part depend on repair time?

While a failed part is in repair, a part is issued from stock in its place. As repair takes longer, the number of parts waiting in repair at the same time increases, and more stock is needed to fill this gap.

How does predictive maintenance affect spare parts inventory?

Being able to anticipate failure turns demand from random into plannable. For critical parts, it may be possible to reach the same service level with less safety stock. The gain obtained depends on the quality of sensor data and on the maintenance process actually being carried out.

References

• Sherbrooke, C. C. (2004). Optimal Inventory Modeling of Systems: Multi-Echelon Techniques (2nd ed.). Kluwer.
• Muckstadt, J. A. (2005). Analysis and Algorithms for Service Parts Supply Chains. Springer.
• Silver, E. A., Pyke, D. F., & Thomas, D. J. (2017). Inventory and Production Management in Supply Chains (4th ed.). CRC Press.

Related Optimum Planlama Trainings

Institutions We Work With in Defense and Aerospace

Some of the institutions to which we provide training and/or consulting services in the defense and aerospace sector are listed below. For all sectors, you can visit our References page.

Alp Havacılık, 2021

Ekinoks-AG, 2022

Erve Savunma, 2020

Meteksan, 2016

PFW Havacılık, 2017, 2018

Promotech, 2023

SAHA İstanbul, 2019

Sarsılmaz Silah, 2023, 2024

System Defence, 2022

TAI-Tusaş, 2015, 2018

TEI-Tusaş, 2019

Telsaş, 2010

Tusaş, 2010–2019

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