Long-term support of legacy electronic systems is challenging due to mismatches between the system support life and the procurement lives of the systems’ constituent components. Legacy electronic systems that are used in safety, mission, and infrastructure critical applications that must be supported for 20+ yr are threatened with diminishing manufacturing sources and material shortages (DMSMS)-type obsolescence, and their effective system support lives may be governed by existing nonreplenishable inventories of spare parts. This paper describes the development of the end of maintenance (EOM) model, which uses a stochastic discrete-event simulation that follows the life history of the population of parts in a system using time-to-failure distributions and other forecasted demands. The model determines the support life of the system based on existing inventories of spare parts and cards, and optionally harvesting parts from existing cards to extend the support life of the system. The model includes: part inventory degradation, periodic inventory inspections, and design refresh planning for selected cards. A case study using a real legacy system comprised of 117,000 instances of 70 unique cards and 4.5 × 106 unique parts is presented. The case study was used to evaluate the support life of a system with various future failure assumptions, including with and without the use of part harvesting. The case study also includes sensitivity analyses for selected design refreshes to maximize potential system life-cycle capabilities, and optional design refresh planning required to sustain the system to a specific date.

References

References
1.
Alford
,
L.
,
2001
, “
The Problem With Aviation COTS
,”
IEEE Aerospace Electron. Syst. Mag.
,
16
(
2
), pp.
33
37
.10.1109/62.904242
2.
Hall
,
J.
, and
Naff
,
R.
,
2001
, “
The Cost of COTS
,”
IEEE Aerospace Electron. Syst. Mag.
,
16
(
8
), pp.
20
24
.10.1109/62.942215
3.
Sandborn
,
P.
,
2008
, “
Trapped on Technology’s Trailing Edge
,”
IEEE Spectrum
,
45
(
4
), pp.
42
58
.10.1109/MSPEC.2008.4476445
4.
Sandborn
,
P.
, and
Myers
,
J.
,
2008
, “
Designing Engineering Systems for Sustainment
,”
Handbook of Performability Engineering
,
K. B.
Misra
, ed.,
Springer
,
London
, pp.
81
103
.
5.
Stogdill
,
R.
,
1999
, “
Dealing With Obsolete Parts
,”
IEEE Des. Test Comput.
,
16
(
2
), pp.
17
25
.10.1109/54.765200
6.
Tomczykowski
,
W. J.
,
2003
, “
A Study on Component Obsolescence Mitigation Strategies and Their Impact on R & M
,”
Proceedings of the Annual Reliability and Maintainability Symposium (RAMS)
, Tampa, FL, pp.
332
338
.
7.
Cruchley
,
I.
,
Dam
,
R.
,
Gold
,
R.
, and
Ferguson
,
B.
,
2010
, “
Bruce Power Aging Obsolescence Program
,”
Proceedings of the ASME International Conference on Nuclear Engineering
, Xian, China, pp.
347
356
.
8.
Singh
,
P.
, and
Sandborn
,
P.
,
2006
, “
Obsolescence Driven Design Refresh Planning for Sustainment-Dominated Systems
,”
Eng. Econom.
,
51
(
2
), pp.
115
139
.10.1080/00137910600695643
9.
Zheng
,
L.
,
Terpenny
,
J.
,
Sandborn
,
P.
, and
Nelson
, III,
R.
,
2012
, “
Design Refresh Planning Models for Managing Obsolescence
,”
Proceedings ASME International Design Engineering Conferences & Computers and Information in Engineering Conference
, Chicago, IL.
10.
Shaffer
,
G.
, and
McPherson
,
G.
,
2010
, “
FAA COTS Risk Mitigation Guide: Practical Methods for Effective COTS Acquisition and Lifecycle Support Revision 3.2
,”
Federal Aviation Administration
.
11.
Wilson
,
R. H.
,
1934
, “
A Scientific Routine for Stock Control
,”
Harvard Business Rev.
,
13
(
1
), pp.
116
128
.
12.
Kennedy
,
W. J.
,
Patterson
,
J. W.
, and
Fredendall
,
L. D.
,
2002
, “
An Overview of Recent Literature on Spare Parts Inventories
,”
Int. J. Product. Econ
,
76
, pp.
201
215
.10.1016/S0925-5273(01)00174-8
13.
Guide
,
V. D. R.
, and
Srivastava
,
R.
,
1997
, “
Repairable Inventory Theory: Models and Applications
,”
Eur. J. Operat. Res.
,
103
, pp.
1
20
.10.1016/S0377-2217(97)00155-0
14.
Sheikh
,
A. K.
, and
Younas
,
M.
,
2000
, “
Chapter 4: Reliability Based Spare Parts Forecasting and Procurement Strategies
,”
Maintenance, Modeling, and Optimization
,
M.
Ben-Daya
,
S. O.
Duffuaa
, and
A.
Raouf
, eds.,
Springer
,
London
, pp.
81
110
.
15.
Bharadway
,
U. R.
,
Silberschmidt
,
V. V.
,
Wintle
,
J. B.
, and
Speck
,
J. B.
,
2008
, “
A Risk Based Methodology for Spare Parts Inventory Optimisation
,”
Proceedings of the ASME International Mechanics Engineering Congress and Exposition
, Boston MA.
16.
Chen
,
F.-L.
, and
Chen
,
Y.-C.
,
2009
, “
An Investigation of Forecasting Critical Spare Parts Requirement
,”
Proceedings of the World Congress on Computer Science and Information Engineering
, Los Angeles, CA, pp.
225
230
.
17.
Louit
,
D.
,
Pascual
,
R.
,
Banjevic
,
D.
, and
Jardine
,
A. K. S.
,
2011
, “
Optimization Models for Critical Spare Parts Inventories—A Reliability Approach
,”
J. Operat. Res. Soc.
,
62
(
6
), pp.
992
1004
.10.1057/jors.2010.49
18.
Croston
,
J. D.
,
1972
, “
Forecasting and Stock Control for Intermittent Demands
,”
Operat. Res. Quart.
,
23
(
3
), pp.
289
303
.10.1057/jors.1972.50
19.
Hong
,
J. S.
,
Koo
,
H.-Y.
,
Lee
,
C.-S.
, and
Ahn
,
J.
,
2008
, “
Forecasting Service Parts Demand for a Discontinued Product
,”
IIE Trans.
,
40
(
7
), pp.
640
649
.10.1080/07408170701745337
20.
Eppen
,
G. D.
, and
Martin
,
R. K.
,
1988
, “
Determining Safety Stock in the Presence of Stochastic Lead Time and Demand
,”
Manage. Sci.
,
34
(
11
), pp.
1380
1390
.10.1287/mnsc.34.11.1380
21.
Sandmann
,
W.
, and
Bober
,
O.
,
2009
, “
Stochastic Models for Intermittent Demands Forecasting and Stock Control
,”
Proceedings of the Vienna Conference on Mathematical Modelling
, MATHMOD, Vienna, Austria.
22.
Solomon
,
R.
,
Sandborn
,
P.
, and
Pecht
,
M.
,
2000
, “
Electronic Part Life Cycle Concepts and Obsolescence Forecasting
,”
IEEE Trans. Comp. Packag. Technol.
,
23
(
4
) pp.
707
713
.10.1109/6144.888857
23.
Josias
,
C.
, and
Terpenny
,
J.
,
2004
, “
Component Obsolescence Risk Assessment
,”
Proceedings Series, Industrial Engineering Research Conference (IERC)
, Houston, TX.
24.
Sandborn
,
P.
,
Prabhakar
,
V.
, and
Ahmad
,
O.
,
2011
, “
Forecasting Technology Procurement Lifetimes for Use in Managing DMSMS Obsolescence
,”
Microelectron. Reliab.
,
51
pp.
392
399
.10.1016/j.microrel.2010.08.005
You do not currently have access to this content.