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Frontiers of Engineering Management

Front. Eng    2020, Vol. 7 Issue (2) : 172-181     https://doi.org/10.1007/s42524-019-0032-5
RESEARCH ARTICLE
Managing obsolescence of embedded hardware and software in secure and trusted systems
Zachary A. COLLIER1, James H. LAMBERT2()
1. Collier Research Systems, Barboursville, VA 22923, USA
2. Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22903, USA
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Abstract

Obsolescence of integrated systems which contain hardware and software is a problem that affects multiple industries and can occur for many reasons, including technological, economic, organizational, and social factors. It is especially acute in products and systems that have long life cycles, where a high rate of technological innovation of the subcomponents result in a mismatch in life cycles between the components and the systems. While several approaches for obsolescence forecasting exist, they often require data that may not be available. This paper describes an approach using non-probabilistic scenarios coupled with decision analysis to investigate how particular scenarios influence priority setting for products and systems. Scenarios are generated from a list of emergent and future conditions related to obsolescence. The key result is an identification of the most and least disruptive scenarios to the decision maker’s priorities. An example is presented related to the selection of technologies for energy islanding, which demonstrates the methodology using six obsolescence scenarios. The paper should be of broad interest to scholars and practitioners engaged with enterprise risk management and similar challenges of large-scale systems.

Keywords enterprise risk management      diminishing manufacturing sources and material shortages      scenario-based preferences      systems engineering      deep uncertainty      product life cycle     
Corresponding Author(s): James H. LAMBERT   
Just Accepted Date: 12 April 2019   Online First Date: 14 May 2019    Issue Date: 27 May 2020
 Cite this article:   
Zachary A. COLLIER,James H. LAMBERT. Managing obsolescence of embedded hardware and software in secure and trusted systems[J]. Front. Eng, 2020, 7(2): 172-181.
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http://journal.hep.com.cn/fem/EN/10.1007/s42524-019-0032-5
http://journal.hep.com.cn/fem/EN/Y2020/V7/I2/172
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Zachary A. COLLIER
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Criterion Baseline weight
C01 12%
C02 27%
C03 12%
C04 27%
C05 12%
C06 5%
C07 5%
Tab.1  Criteria for energy technology selection
Criterion A01 A02 A03 A04 A05 A06
C01 Somewhat agree Disagree or N/A Agree Strongly agree Disagree or N/A Disagree or N/A
C02 Disagree or N/A Somewhat agree Agree Strongly agree Agree Disagree or N/A
C03 Disagree or N/A Somewhat agree Strongly agree Strongly agree Somewhat agree Somewhat agree
C04 Disagree or N/A Disagree or N/A Agree Strongly agree Strongly agree Somewhat agree
C05 Disagree or N/A Agree Agree Somewhat agree Strongly agree Somewhat agree
C06 Disagree or N/A Somewhat agree Agree Agree Strongly agree Disagree or N/A
C07 Disagree or N/A Somewhat agree Agree Strongly agree Disagree or N/A Somewhat agree
Tab.2  Responses to whether each of 7 criteria is addressed by 6 alternatives. Qualitative responses were converted into numerical value scores
Emergent and future conditions Source
EC01. Drop in customer demand Song and Zipkin, 1996
EC02. Industries with high rates of tech innovation Song and Zipkin, 1996
EC03. Industries with frequent shifts in consumer taste Song and Zipkin, 1996
EC04. Import competition Song and Zipkin, 1996
EC05. Safety hazards Song and Zipkin, 1996
EC06. Current level of technology Song and Zipkin, 1996
EC07. Strength of competing products Song and Zipkin, 1996
EC08. Relatively sudden shift in competitor’s market penetration Song and Zipkin, 1996
EC09. Competitor is trying to develop a better product Song and Zipkin, 1996
EC10. Competitor announces positive research results Song and Zipkin, 1996
EC11. Competitor distributes test samples Song and Zipkin, 1996
EC12. Introduction of several competing products at once Song and Zipkin, 1996
EC13. Demand has dropped to low levels Solomon et al., 2000
EC14. Production materials and technology no longer available Solomon et al., 2000
EC15. Longer life span of systems Solomon et al., 2000
EC16. Public’s demand for longer warranties Solomon et al., 2000
EC17. Long periods of manufacturing Solomon et al., 2000
EC18. High cost system qualification and certification Solomon et al., 2000
EC19. Introduction of a superior competing part Solomon et al., 2000
EC20. Improvement of a competing part Solomon et al., 2000
EC21. Identification of a problem associated with the part Solomon et al., 2000
EC22. Failure to reach the critical mass that allows economies of scale Solomon et al., 2000
EC23. Lack of a unique and compelling application for the part Solomon et al., 2000
EC24. High cost/lead times for technology insertion and design refresh Singh and Sandborn, 2006
EC25. Low or no control over the part supply chain Singh and Sandborn, 2006
EC26. The system the software executes changes Singh and Sandborn, 2006
EC27. The vendor terminates support Singh and Sandborn, 2006
EC28. System hardware changes make software obsolete Sandborn, 2007
EC29. System requirements changes make software obsolete Sandborn, 2007
EC30. System software changes make software obsolete Sandborn, 2007
EC31. Original supplier no longer sells the software as new (end-of-sale) Sandborn, 2007
EC32. Inability to expand or renew licensing agreements (legally unprocurable) Sandborn, 2007
EC33. Original supplier and/or third parties no longer support the software (end-of-support) Sandborn, 2007
EC34. Digital media obsolescence, formatting, or degradation Sandborn, 2007
EC35. Software upgrades that will not execute correctly on the hardware Sandborn, 2007
EC36. More technologically advanced hardware is available Sandborn, 2007
EC37. Owner/operators can no longer procure a part Sandborn, 2007
EC38. Security patches for software terminate Sandborn, 2007
EC39. Inability to obtain the necessary software licenses Sandborn, 2007
EC40. New environmental regulations render materials obsolete Romero Rojo et al., 2010
EC41. Cannot obtain small quantities due to high minimum order quantities (MOQ) Romero Rojo et al., 2010
EC42. Obsolescence of a manufacturing process prevents manufacture of a material Romero Rojo et al., 2010
EC43. Incompatibility between new and old systems Romero Rojo et al., 2010
EC44. Losing skilled and knowledgeable workers Romero Rojo et al., 2010
EC45. Obsolescence of tooling and testing equipment Romero Rojo et al., 2010
EC46. Physical deterioration based on age and/or wear and tear Reilly, 2013
EC47. No longer performs the function as well as it did when new Reilly, 2013
EC48. Intended function has become obsolete over time Reilly, 2013
EC49. Locational obsolescence—changes in neighborhood conditions near the property Reilly, 2013
EC50. Operations no longer earn a profitable rate of return Reilly, 2013
EC51. High volatility and quick evolution of requirements Wnuk et al., 2013
EC52. Scope creep, requirements creep and requirements leakage Wnuk et al., 2013
Tab.3  The emergent and future conditions that are related to obsolescence
Scenario Emergent and future conditions
S1. Parts unavailable EC37
S2. Software incompatible EC35
S3. Difficult to upgrade EC18, EC24
S4. Support terminated EC27
S5. Flaw in key part EC21
S6. Workforce leaving EC44
Tab.4  Obsolescence scenarios consisting of emergent and future conditions
Criterion S1 S2 S3 S4 S5 S6
C01 Decreases somewhat ? Increases ? Decreases Decreases somewhat
C02 ? Increases somewhat ? ? Increases somewhat ?
C03 Increases somewhat Increases ? Increases somewhat Increases ?
C04 ? ? Increases somewhat Increases somewhat Increases somewhat Increases somewhat
C05 ? ? Increases ? Increases Increases
C06 Increases somewhat Increases ? Increases somewhat Increases Increases
C07 ? Increases somewhat ? ? ? ?
Tab.5  Assessment of the effects of scenarios on preferences
Criterion S0 S1 S2 S3 S4 S5 S6
C01 12.00% 1.14% 3.17% 23.82% 3.75% 0.27% 0.58%
C02 27.00% 15.43% 42.74% 6.70% 8.44% 28.80% 7.85%
C03 12.00% 41.14% 25.33% 2.98% 22.50% 17.07% 3.49%
C04 27.00% 15.43% 7.12% 40.20% 50.63% 28.80% 47.09%
C05 12.00% 6.86% 3.17% 23.82% 3.75% 17.07% 27.91%
C06 5.00% 17.14% 10.55% 1.24% 9.38% 7.11% 11.63%
C07 5.00% 2.86% 7.92% 1.24% 1.56% 0.89% 1.45%
Tab.6  Criteria weights updated across all scenarios
Fig.1  Ranking of energy alternatives, where the triangles represent the rank in the baseline scenario, and the bars represent the highest and lowest rank across scenarios. A01: no action; A02: bury power line; A03: microturbine with trigeneration for critical load; A04: microturbine with trigeneration for base load; A05: microgrid of backup; A06: solar PV generators.
Scores S0 S1 S2 S3 S4 S5 S6
A01 0.0396 0.2409 0.7008 0.9026 0.6578 0.1848 0.0396
A02 0.0038 0.2979 0.7999 0.8958 0.6319 0.2187 0.0038
A03 0.0104 0.3065 0.7461 0.9429 0.5741 0.1437 0.0104
A04 0.0786 0.1973 0.6701 0.8362 0.7067 0.2252 0.0786
A05 0.0124 0.1629 0.7365 0.9430 0.7674 0.2588 0.0124
A06 0.0009 0.2904 0.7180 0.8615 0.7762 0.2106 0.0009
Tab.7  Value scores of each energy alternative across obsolescence scenarios
Scenario Kendall’s Tau Disruptiveness rank
S1 1.000000 5
S2 1.000000 5
S3 0.733333 1
S4 0.733333 1
S5 0.866667 4
S6 0.733333 1
Tab.8  Scenario disruptiveness
Category Result
Most disruptive scenarios S3, S4, S6
Least disruptive scenarios S1, S2
Best performing alternatives A03, A04, A05
Worst performing alternatives A01, A02, A06
Tab.9  Summary insights
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