The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Electronic part life cycle concepts and obsolescence forecasting
Abstract
Obsolescence of electronic parts is a major contributor to the life cycle cost of long-field life systems such as avionics. A methodology to forecast life cycles of electronic parts is presented, in which both years to obsolescence and life cycle stages are predicted. The methodology embeds both market and technology factors based on the dynamic assessment of sales data. The predictions enabled from the models developed in this paper allow engineers to effectively manage the introduction and on-going use of long field-life products based on the projected life cycle of the parts incorporated into the products. Application of the methodology to integrated circuits is discussed and obsolescence predictions for dynamic random access memories (DRAMs) are demonstrated. The goal is to significantly reduce design iterations, inventory expenses, sustainment costs, and overall life cycle product costs.
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“The premature aging process through calculating and planning when various circuit-breakers in the product shall fail, reliably renders the product useless or less useful after a certain predetermined date, even if often only one aspect or component of the product truly breaks (Solomon et al., 2000).”
From Designed to break: planned obsolescence as corporate environmental crime (2022) · cites this paper as Solomon et al., 2000
In the citing paper
“For many products, the availability of parts is limited in time or so expensive that repairs are not cost-effective (Solomon et al., 2000).”
From Designed to break: planned obsolescence as corporate environmental crime (2022) · cites this paper as Solomon et al., 2000
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Cites
EXPLOIT THE PRODUCT LIFE CYCLE
Levitt, T. (1965) · Cited by 772
Stogdill, R. C. (1999) · IEEE Design & Test of Computers · doi:10.1109/54.765200 · Cited by 62
PECHT, M. G., & Das, D. (2000) · IEEE Transactions on Components and Packaging Technologies · doi:10.1109/6144.833060 · Cited by 41
Self-citationStatus : a report on the integrated circuit industry
Strauss, W. I., McClean, W. J., & Pitcher, H. I. (1984) · Medical Entomology and Zoology · Cited by 36
Uprating electronic components for use outside their temperature specification limits
Wright, M. B. S. O., Humphrey, D., & McCluskey, F. P. (1997) · IEEE Transactions on Components Packaging and Manufacturing Technology Part A · doi:10.1109/95.588581 · Cited by 27
A risk-informed methodology for parts selection and management
Jackson, M. M., Sandborn, P. A., PECHT, M. G., Hemens‐Davis, C., & Audette, P. (1999) · Quality and Reliability Engineering International · doi:10.1002/(sici)1099-1638(199907/08)15:4 · Cited by 21
Self-citationWhy a new parts selection and management program?
Foucher, B., Kennedy, R., Kelkar, N., Ranade, Y., Govind, A., Blake, W., Mathur, A., & Solomon, R. (1998) · IEEE Transactions on Components Packaging and Manufacturing Technology Part A · doi:10.1109/95.705487 · Cited by 20
Self-citation
Research works printed but not linked: 5
- texas instruments, 1999, 1999 Standard linear and logic product withdrawal device list
- national semiconductor, 2000, Product Folder LF11331
- sherwood, 2000, Product life cycle (PLC) code definitions and applications
- 1997, Product Life Cycle Data Model
- bumbalough, 1999, usaf manufacturing technology's initiative on electronics parts obsolescence management, Proc 44th Int SAMPE Symp, 2044
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Erkoyuncu, J. A., & Roy, R. (2014) · Decision engineering · doi:10.1007/978-3-319-12111-6_17 · Cited by 3
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Hu, G., Wang, L., Bidanda, B., & Wang, Y. (2007) · Journal of International Crisis and Risk Communication Research · Cited by 3
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Neumann, P., Houser, J., Pospíšilík, M., Skočík, P., & Adámek, M. (2016) · Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium · doi:10.2507/26th.daaam.proceedings.003 · Cited by 2
Digital excitation systems—Growing obsolescence of aging systems
Schaefer, C. (2017) · IEEE Conference record of annual Pulp and Paper Industry Technical Conference · doi:10.1109/ppic.2017.8003867 · Cited by 2
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Liu, Y., & Zolghadri, M. (2026) · Research in Engineering Design · doi:10.1007/s00163-026-00470-8 · Cited by 1
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Wu, K.-H., Rau, H., & Chien, Y.-C. (2013) · Springer eBooks · doi:10.1007/978-981-4451-98-7_167 · Cited by 1
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Soares, M., Brown, M., & Rea, R. (2012) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2012-01-2198 · Cited by 1
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Pierucci, A., & Dell’Osso, G. R. (2013) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-04166-7_9 · Cited by 1
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Sandborn, P. A., & Terpenny, J. P. (2006) · Cited by 1
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Wee, H., Wu, K. H., & Daryanto, Y. (2017) · International Conference on Management Science and Management Innovation (MSMI) · doi:10.2991/msmi-17.2017.35 · Cited by 1
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Electronic Part Total Cost Of Ownership And Sourcing Decisions For Long Life Cycle Products
Prabhakar, V. J. (2011) · University Libraries (University of Maryland) · Cited by 1
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Wilkinson, C., PECHT, M. G., Wasson, J., & Condra, L. W. (2002) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2002-01-2983 · Cited by 1
Self-citationSchroeter, M., & Spengler, T. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.515287 · Cited by 1
Wu, J., Chien, C., Huang, Y.-S., & Huang, H.-Y. (2010) · International Conference on Computers and Industrial Engineering · doi:10.1109/iccie.2010.5668429 · Cited by 1
Life cycle prediction for spare parts at IBM spare parts operations
Bensing, L. (2012) · Cited by 1
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Feitosa, F. C. (2019) · doi:10.31414/ee.2019.d.130986 · Cited by 1
Parallel reconfigurable hardware architectures for video processing applications
Ali, K. M. A. (2018) · theses.fr (ABES) · doi:10.70675/d7388d6czb24fz476az802az93b81127a017 · Cited by 1
Sabet, E. (2012) · Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) · Cited by 1
Maintenance Consideration for Long Life Cycle Embedded System
Meng, X. (2012) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
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Saleh, J. H. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546136.007
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Lee, A., & Ziediņš, I. (2012) · Asia Pacific Journal of Operational Research · doi:10.1142/s0217595912400179
Multistage semiconductor memory inventory model based on survival analysis
Wu, J., Chien, C., & Tsou, Y.-C. (2014) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2014.6899391
Deisz, D. (2010) · IEEE Transactions on Components and Packaging Technologies · doi:10.1109/tcapt.2010.2051598
2000 In de x IEEE Trans ac tions on Com po nents and Pack aging Technologie s
Johnson, B., King, P., & Jun, T.-C. (2000)
Performance and area evaluations of processor-based benchmarks on FPGA devices
Kao, J. P. (2014) · Loughborough University Research Repository (Loughborough University)
Mulholland, K., Pitt, M., & Mclennan, P. (2016) · Journal of Facilities Management · doi:10.1108/jfm-12-2015-0036
Csuzi, I., & István, C. B. (2015) · DOAJ (DOAJ: Directory of Open Access Journals)
Long-term reliability of lead-free electronic systems
Pandian, G. P., Ramaswami, G. K., Hodkiewicz, M. R., Cripps, E. J., & PECHT, M. G. (2016) · IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) · doi:10.1109/ipfa.2016.7564306
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Balan, S., Mathew, J., Lajitha, C. S., Sreedhanya, L. R., & Rao, V. B. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929559
Rukmi, H. S., & Almamalik, L. (2020) · International Journal of Innovation in Enterprise System · doi:10.25124/ijies.v4i02.59
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Chittamvanich, S. (2007) · doi:10.31274/rtd-180813-16726
Bartels, B., Ermel, U., PECHT, M. G., & Sandborn, P. A. (2012) · doi:10.1002/9781118275474.refs
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Özkan, B. E., & Bulkan, S. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-9029-4.ch025
Seabridge, A. G., & Moir, I. (2019) · doi:10.1002/9781119611479.ch4
Moir, I., & Seabridge, A. G. (2012) · doi:10.1002/9781118469156.ch4
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Clay, W. S., McQuay, W. K., Wolff, F., & Papachistou, C. (2025) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon65708.2025.11235440
Environmental Impact Characterization of Obsolescence Remediation Solutions in Long-Life Product
Mokraoui, S., Affonso, R. C., Besbes, M., Zolghadri, M., & Baron, C. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-09700-2_43
Optimizing design refresh decisions for long life systems production
Uhles, C., Medal, H., Sherwin, M. D., Bean, J., Rosson, D., Stageberg, K.-A., & Shuchat, S. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111723
Sherwin, M. D., Rosson, D., Sadler, B., & Martin, S. (2026) · Journal of Engineering Design · doi:10.1080/09544828.2026.2639937
Sierra-Fontalvo, L., Ruiz‐Pastor, L., Gonzalez-Quiroga, A., & Mesa, J. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-43761-9
Predictive Obsolescence Management
Dharmananyala, R., Munirathnam, K., Marokeyfrancis, J., Sadashivaiah, N., & Kondamari, H. (2026) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2026-26-0780
Technology overload? macroeconomic implications of accelerated obsolescence
Basihos, S. (2026) · Journal of economic growth (Boston) · doi:10.1007/s10887-026-09270-0
Entscheidungshilfe für End-of-Life-Strategien bei Intralogistiksystemen
Eisenacher, S., Böning, C., Stonis, M., & Nyhuis, P. (2026) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2026-1119
Metadata
- Authors
- Pecht, M. G., Sandborn, P. A., & Solomon, R.
- Affiliations
- University of Maryland, College Park (US)
- Venue
- IEEE Transactions on Components and Packaging Technologies
- Published
- 2000
- Volume
- 23
- Issue
- 4
- Pages
- 707-717
- ISSN
- 1521-3331 (ISSN-L)
- OpenAlex
- W2119785455
- Cited by
- 194
- References
- 7
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 4 · 2023: 2 · 2022: 10 · 2021: 19 · 2020: 5 · 2019: 14 · 2018: 9 · 2017: 9 · 2016: 10 · 2015: 9 · 2014: 11 · 2013: 8 · 2012: 16
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Components and Packaging Technologies (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Transportation Systems and Infrastructure
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Technology Assessment and Management
- Keywords
- life cycle cost, avionics, integrated circuits, dynamic random access memory, DRAM, sales analysis, technology forecasting, inventory management
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