This document discusses advanced techniques for forecasting part obsolescence to support strategic management of diminishing manufacturing sources and material shortages (DMSMS) problems. It describes existing ordinal scale and data mining approaches to obsolescence forecasting and their limitations. The objective is to develop a new data mining approach that mines part databases to identify trends and forecast obsolescence dates, even for parts without clear parametric drivers. Examples are provided of forecasts for linear regulators and other part types.

1. &
Advanced Part Obsolescence Forecasting
as an Enabler for Strategic Management of DMSMS Problems

2. &
• The value of forecasting:
– To supplement initial part selection activities
– To support pro-active DMSMS management
– To enable strategic life cycle planning solutions
• Most existing commercial forecasting tools are good at
articulating the current state of a part’s availability and
identifying alternatives, but limited in their capability to
forecast future obsolescence dates.
Why Forecast Obsolescence?
It’s hard to make predictions -
especially about the future.
- Yogi Berra
“
“

3. & Existing Obs. Forecasting Approaches
Ordinal scale
approaches
Data mining
approaches
Two general methods for forecasting obsolescence exist

4. &
• Ordinal scale approaches – weighted accumulation of
“scores” assigned to a set of predetermined part type,
technology and supply chain attributes.
– Accuracy increases as you get closer to the
obsolescence event
– Historical basis for the forecast is subjective
– Confidence levels and uncertainties are not generally
evaluable
Existing: Ordinal scale approaches

5. &
• Data mining approaches – mapping known part
obsolescence dates to the life cycle curve of the part
type to build vendor-specific (and vendor independent)
forecasting algorithms.
– Used for parts with clearly identifiable parametric drivers,
e.g., memory
– Based on the historical record - Produces accurate part-
type and vendor-specific forecasts
– Forecasts include confidence levels
Existing: Data mining approaches

6. & Obsolescence Forecasting Strategy
Part primary
attribute driven
forecasts
• Historical data driven
• Most accurate
forecasts available for
applicable parts
• Only forecasting
approach that provides
uncertainties or
confidence levels
Procurement
lifetime forecasts
• Used if primary
attributes can’t be
identified
• Historical data driven
• Worst case, vendor
specific, part type
specific, obsolescence
forecast
Short-term
forecasts based
on distributor
inventory levels
• … source counting
and other vendor
provided information
supersede the long-
term forecasts near the
end of a parts
procurement life
THIS PAPER

7. &
• The large electronic part databases are treasure troves
of data for predicting obsolescence, the challenge to
figuring out how to mine the data to find the significant
trends.
• Previously developed data mining approaches work
very well for parts with clear parametric evolutionary
drivers (e.g., memory, microprocessors), but they do
not work for part types that lack these drivers
Objective of this Work

8. &
• Several have postulated that the “age” of electronic
parts is not a factor in determining what gets obsoleted.
– J. Carbone, “Where are the parts,” Purchasing, pp. 44-
47, Dec. 11, 2003.
– S. Clay, “Material Risk Index (MRI) and Methods for
Calculating MRI for Electronic Components,” to be
published IEEE Trans. on Components and Packaging
Technologies, 2009.
• Not so fast! Age appears to play a role in the
obsolescence of many (not all) part types …
The “Age” Effect

9. &
Procurement Lifetime Data Mining
Approach
Procurement Lifetime = Obsolescence Date – Introduction Date
Obsolescence Date = Procurement Lifetime + Introduction Date
Introduction Date
ProcurementLifetime
Procurement Lifetime = Age

10. &
Example ‐ EPROMs Have a
Clear Parametric Driver
Obsolescence is not “age” dependent

11. &
Example ‐ Linear Regulators Do Not
Have a Clear Parametric Driver
347 obsolete linear regulators from 33 vendors

12. &
A group of parts introduced on various dates, all discontinued on or
about the same date – common practice
Introduction Date
ProcurementLifetime
Understanding the Graph
Slope = -1
Discontinuance date 1
(longest life parts)
Discontinuance date 2
Top boundary of the wedge

13. &
A group of parts introduced on various dates all having identical
procurement lifetimes, i.e., everything is procurable for exactly y years
Introduction Date
ProcurementLifetime
Understanding the Graph
Slope = 0
y
xx-y
No data points after this
introduction date
Analysis date = x

14. &
If the introduction dates were wrong, e.g., they were all the same
database record creation date d, where d is some point in time after
the parts were introduced.
Introduction Date
ProcurementLifetime
Understanding the Graph
Slope = ∞
(all parts have the same introduction date)
d

15. & Understanding the Graph
Known
wedge
2008
If the data set is complete up to 2008,
nothing could ever fall in this area
Parts that were
introduced in the past
but are not obsolete
yet (note, the top of
the historical record
data need not
correspond to the
boundary of the green
area (it could be
below it). The two will
correspond only if
parts are discontinued
in the analysis year,
e.g., 2008 – lower
green boundary
moves up every year

16. &
The bottom of the wedge is where the critical information is
(not the top).
Introduction Date
ProcurementLifetime
Understanding the Graph
Bottom boundary of the wedge
There is an “age” effect
No “age” effect
Approximate first
part introduction

17. &
Parts with primary parametric evolutionary drivers do not show the
“age” effect. These parts include: memory, microprocessors.
Age Effect Examples
0
4
8
12
16
20
24
28
32
36
1969 1974 1979 1984 1989 1994 1999 2004
Introduction Year
ProcurementLifetime(years)
Flash Memory
Op Amps
No age effect
Flat
Age effect
Not Flat!
Strong parametric evolutionary driver: memory size

18. & Example ‐ Linear Regulators
Worst case forecast for linear regulators
If Introduction Date < 1997.67
Procurement Life > -2.095(introduction date) + 4188.5
If Introduction Date > 1997.67
Procurement Life > -0.1014(introduction date) + 206.77
Obsolescence date = Introduction Date + Procurement Life

19. & Another Look at the Data
One-year slice (1997-1998)
Distribution of procurement
lives for the entire range

20. & Another Look at the Data
Censored = non-obsolete
parts not considered
Uncensored = included 495
non-obsolete parts

21. &
Example ‐
Vendor Specific Linear Regulators
National Semiconductor

22. & Key Part Attributes: 5V Bias Logic Parts
Procurement Life
Decreasing before 1999
Procurement Life of 5V
Logic Parts Increasing
after 1999?

23. &
• Worst case, and median vendor specific, part type
specific, obsolescence forecast
– Worst case = no known parts of this type or from this
vendor have had smaller procurement lifetimes
– Vendor specific = the upper limit on the band is the
vendor’s worst case, the lower limit is the part type’s
worst case
– Part type specific = specific to the part type or group of
part types used to create the forecast
What do we really have?

24. &
• Note, the above statement says “part type specific”
NOT “part specific”
– If you give me a specific Fairchild xxxxxx linear regulator,
I can forecast the worst case obsolescence date based
on Fairchild’s history of supporting linear regulators, but I
cannot tell you anything about Fairchild’s specific plans
for the xxxxxx linear regulator
• This methodology is applicable to long-term forecasting
(pro-active and strategic management value).
– Long-term means > 1 year from obsolescence
– Short-term (< 1 year from obsolescence), other factors
kick in
What do we really have?

25. & SiliconExpert Screens
SiliconExpert BOM Manager – End of Life Data

26. & SiliconExpert Screens
SiliconExpert Parts Detail – Risk Analysis

27. & SiliconExpert Screens
SiliconExpert Parts Detail – Forecast Graph

28. &
• Ordinal Scale Based Obsolescence Forecasting:
– A.L. Henke and S. Lai, “Automated Parts Obsolescence
Prediction,” Proceedings of the DMSMS Conference, 1997.
– C. Josias and J.P. Terpenny, “Component obsolescence risk
assessment,” Proceedings of the 2004 Industrial Engineering
Research Conference (IERC), 2004.
• Data Mining Based Obsolescence Forecasting:
– P. Sandborn, F. Mauro, and R. Knox, "A Data Mining Based
Approach to Electronic Part Obsolescence Forecasting," IEEE
Trans. on Components and Packaging Technologies, Vol. 30,
No. 3, pp. 397-401, September 2007.
http://www.enme.umd.edu/ESCML/Papers/ObsForecastingSe
pt07.pdf
References