The Proposal of Demand Estimation of Repairable Items for the Weapon Systems During the Initial Provisioning Period: F-16 Case Study

Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 56)

Abstract

Every country that has military systems such as aircraft, radar, warship or tank has to meet the operational needs of those military systems in order to keep them ready for military operations . Logistics support needs are defined basically either before or after acquisition. If they are defined before acquisition, they are called “initial support requirement” , otherwise called “sustainment support requirement”. Our study is focused on the initial support requirement phase since the new weapon systems will be added into the Turkish military inventory . In addition to that, it is focused on repairable items since most of the material acquisition cost within the initial support budget is allocated to repairable items. The flight hour parameter is used for computing the initial support requirements of repairable items in the United States of Air Force (USAF) whereas the usage parameter is used in that of repairable items in Turkey. Based on these calculations, a new parameter called SORTIE, which is the one cycle of take-off and landing, is generated. Taking the consideration of flight hour, usage and SORTIE parameters, 24 scenarios (eight for each parameter) have been created by using real data set of F-16 with a quantity of 894 repairable items. Each scenario is named according to the usage of past data in years. The data set which covers the last 11 years (44 quarters) is divided into two parts: the first 8 years data is used for running the scenarios, and the last 3 years data is used for comparing the results of scenarios with the actual values. While evaluating the effectiveness of scenarios, parameters of mean absolute error and mean percentage error are used. In addition to the traditional approach that tries to find the best parameter common for all data, two new approaches are formed up. The first approach requires grouping the repairable items according to the supply group corresponding to the first two digits of NATO Stock Number (NSN). The other approach treats each NSN independent from each other. Each scenario is run under the three approaches including the traditional one and results are recorded. The Friedman and Wilcoxon Sign Test are applied for determining whether the results are significantly different from each other at the confidence level of 95 %. The approaches that decrease mean absolute error down to the level of 55 % can provide significant cost savings. On the other hand, repairable items whose mean absolute error and standard error values are higher than 3 and 5 respectively are recommended for future detailed studies.

Keywords

Inventory Weapon systems Repairable parts Initial support requirement Item based approach Flight-hour based approach SORTIE-based approach 

Abbrevations

A/C

Aircraft

APPL PCT

Application Percentage

DDR

Daily Demand Rate

DOTMPLF

Doctrine, Organization, Training, Material, Personnel, Leadership and Facilities

EF

Expected Failures

FPS

Failures per sortie

IBA

Item Based Approach

METRIC

Multi-Echelon Technique for Recoverable Item Control

NRTS

Not Repairable at This Station

NSG

NATO Supply Group

NSN

NATO Stock Number

OIM ANN DEM

OIM Annual Demand

OIM

Organizational or Intermediate Maintenance

PFP

Program Forecast Period

QPEI

Quantity Per End Item

RDS

Requirement Distribution System

RTS

Repairable at This Station

TOIMDR

Total Organizational or Intermediate Maintenance Demand Rate

TURAF

Turkish Air Force

USAF

United States of Air Force

References

  1. AECMA Spec 2000M (Association Européenne des Constructeurs de Matériel Aérospatial) : Specification 2000M Issue 3.0 (2000)Google Scholar
  2. Air Force Military Command Instruction (AFMCI): 23-106 Initial Requirement Determination (1997)Google Scholar
  3. Air Transportation Association (ATA): e-Business for Material Management SPEC2000 (2002)Google Scholar
  4. Anderson, B. (Lt. Col).: LOGM-570 Principles of Inventory Management Lecture Notes, Air Force Institute of Technology (AFIT) (2009)Google Scholar
  5. Guide, Daniel V.R. Jr., Srivastava, R.: Repairable inventory theory: Models and applications. Eur. J. Oper. Res. 102, 1–20 (1997)CrossRefGoogle Scholar
  6. Fortuin, L.: Initial supply and re-order level of new service parts. Eur. J. Oper. Res. 15(3):310–319 (1984)CrossRefGoogle Scholar
  7. Gümüş, A.T., Güneri, A.F.: Multi-echelon inventory management in supply chains with uncertain demand and lead times: Literature review from an operational research perspective. J. Eng. Manuf. 221, 1553–1570 (2007) (Part B)CrossRefGoogle Scholar
  8. Hillstad, R.J.: Dyna-METRIC: Dynamic multi-echelon technique for recoverable item control. R-2785-AF, RAND Corporation (1982)Google Scholar
  9. Joint Strike Fighter Cost and Operational Performance Trade Process (JSF COPT): Concept to Baseline (2000)Google Scholar
  10. MIL-HDBK-502: Military Handbook Acquisition Logistics (1997)Google Scholar
  11. MIL-STD-1388-1A: Military Standard Logistics Support Analysis (LSA) (1983)Google Scholar
  12. MIL-STD-1388-2B: Military standard DOD requirements for a logistic support analysis record (LSAR) (1991)Google Scholar
  13. Muckstadt, J.A.: A model for a multi-item, multi-echelon, multi-indenture (MOD-METRIC) inventory system. Manage. Sci. 20(4), 472–481 (1973)CrossRefGoogle Scholar
  14. Muckstadt, J.A.: Analysis and Algorithms for Service Parts Supply Chains. Springer, New York (2005)Google Scholar
  15. NATO Logistics Handbook NSN web page http://www.nato.int/structur/ac/135/ncs_guide/english/e_1-6-1.htm (2007)
  16. Paterson, C., Kiesmuller, G., Teunter, K.: Inventory models with lateral transshipments: A review. Eur. J. Oper. Res. 210, 125–136 (2011)CrossRefGoogle Scholar
  17. Sherbrooke, C.: METRIC: A multi-echelon technique for recoverable item control. Oper. Res. 16, 122–141 (1968)CrossRefGoogle Scholar
  18. Sherbrooke, C.: VARI-METRIC: Improved approximations for multi-indenture, multi-echelon availability models. Oper. Res. 34, 311–319 (1986)CrossRefGoogle Scholar
  19. Sherbrooke, C.: Optimal Inventory Modeling of Systems. Wiley, New York (2004)Google Scholar
  20. Slay, F.M., Bachman, T.C., Kline, R, C., O’Malley, T.J., Eichorn, F.L., King, R.M.: Optimizing spares support: The Aircraft Sustainability Model (ASM). AF501MR1, Logistics Management Institute, Virginia (1996)Google Scholar
  21. SPSS Statistics Base 17.0 User GuideGoogle Scholar
  22. Turkish Air Force Requirement Distribution System (RDS): User Handbook, Turkish Air Force Command Press, Ankara (1997)Google Scholar
  23. Wong, H., Houtum, G.J., Oudheusden, D.Van.: Multi-item spare parts systems with lateral transshipments and waiting time constraints. Eur. J. Oper. Res. 171, 1071–1093 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Hv.K.K.ligi Karargahi Harekat BaskanligiBakanliklar-AnkaraTurkey
  2. 2.Gazi Universitesi Endustri Muhendisligi BolumuAnkaraTurkey

Personalised recommendations