&

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

&

• 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“

“

& Existing Obs. Forecasting Approaches

Ordinal scale approaches

Data mining approaches

Two general methods for forecasting obsolescence exist

&

• 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

&

• 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

& 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

&

• 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

&

• 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

&Procurement Lifetime Data Mining Approach

Procurement Lifetime = Obsolescence Date – Introduction Date

Obsolescence Date = Procurement Lifetime + Introduction Date

Introduction Date

Proc

urem

ent L

ifetim

e

Procurement Lifetime = Age

&Example ‐ EPROMs Have a Clear Parametric Driver

Obsolescence is not “age” dependent

&Example ‐ Linear Regulators Do NotHave a Clear Parametric Driver

347 obsolete linear regulators from 33 vendors

&

A group of parts introduced on various dates, all discontinued on or about the same date – common practice

Introduction Date

Proc

urem

ent L

ifetim

e

Understanding the Graph

Slope = -1

Discontinuance date 1(longest life parts)

Discontinuance date 2

Top boundary of the wedge

&

A group of parts introduced on various dates all having identical procurement lifetimes, i.e., everything is procurable for exactly y years

Introduction Date

Proc

urem

ent L

ifetim

e

Understanding the Graph

Slope = 0y

xx-y

No data points after this introduction date

Analysis date = x

&

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

Proc

urem

ent L

ifetim

e

Understanding the Graph

Slope = ∞(all parts have the same introduction date)

d

& 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

&

The bottom of the wedge is where the critical information is (not the top).

Introduction Date

Proc

urem

ent L

ifetim

e

Understanding the Graph

Bottom boundary of the wedge

There is an “age” effect

No “age” effect

Approximate first part introduction

&

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 YearPr

ocur

emen

t Life

time

(yea

rs)

Flash Memory Op Amps

No age effect

Flat

Age effect

Not Flat!

Strong parametric evolutionary driver: memory size

& Example ‐ Linear Regulators

Worst case forecast for linear regulators

If Introduction Date < 1997.67Procurement Life > -2.095(introduction date) + 4188.5

If Introduction Date > 1997.67Procurement Life > -0.1014(introduction date) + 206.77

Obsolescence date = Introduction Date + Procurement Life

& Another Look at the Data

One-year slice (1997-1998)

Distribution of procurement lives for the entire range

& Another Look at the Data

Censored = non-obsolete parts not considered

Uncensored = included 495 non-obsolete parts

&Example ‐Vendor Specific Linear Regulators

National Semiconductor

& Key Part Attributes: 5V Bias Logic Parts

Procurement Life Decreasing before 1999

Procurement Life of 5V Logic Parts Increasing after 1999?

&

• 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?

&

• 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?

& SiliconExpert Screens

SiliconExpert BOM Manager – End of Life Data

& SiliconExpert Screens

SiliconExpert Parts Detail – Risk Analysis

& SiliconExpert Screens

SiliconExpert Parts Detail – Forecast Graph

&

• 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/ObsForecastingSept07.pdf

References