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Harvard Business School
9-190-061
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Analog Devices: The Half-Life System
A problem with management information systems is that they are strongly biased toward
reporting financial information to stockholders and government agencies. Unless quality
improvement and other more fundamental performance measures are elevated to the same
level of importance as financial measures, when conflicts arise, financial considerations win
out. To address this issue, we designed a division scorecard that reports only the barest of
financial information and places greater emphasis on quality improvement goals.
—Ray Stata, chairman and president, Analog Devices, Inc.1
Company Background
Analog Devices, Inc. (ADI), headquartered in Norwood, Massachusetts, produced integrated
circuits and systems for the high-end data acquisition market. The devices converted between
physical and digital data in equipment such as high performance computer disc drives, aircraft
sensors, medical instruments, and sophisticated consumer electronics (compact disc players, digital
audio tape players and high definition television). As a senior ADI executive remarked, “The real
world is not digital; it’s analog. Someone has to measure temperatures, pressures, and velocity and
convert these data into digital form.”
ADI, with 5400 employees and seven manufacturing sites worldwide, had 1988 sales divided
among the United States (56%), Europe (28%), and Asia (16%, principally Japan and Korea). Its
customers were in the military/avionics, telecommunications, computer, instrument, and industrial
market segments. Summary financial data appear in Exhibit 1.
Recently, the company had dedicated itself to an ongoing Quality Improvement Program.
Ray Stata described the motivation for the effort:
For more than fifteen years, Analog Devices grew consistently at a rate of about 25
percent per year. Then for the first time, between 1982 and 1987, we missed our fiveyear goals—and by a country mile. Like other semiconductor companies we were
affected by the malaise in the U.S. electronics industry and by the strong dollar. But
1Comments by Ray Stata throughout this case were extracted from his article, "Organizational Learning—The
Key to Management Innovation," Sloan Management Review (Spring 1989): 63-74.
This case was prepared by Robert S. Kaplan as the basis for class discussion rather than to illustrate either effective or
ineffective handling of an administrative situation.
Copyright © 1990 by the President and Fellows of Harvard College. To order copies or request permission to
reproduce materials, call 1-800-545-7685 or write Harvard Business School Publishing, Boston, MA 02163. No
part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in
any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the
permission of Harvard Business School.
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Analog Devices: The Half-Life System
the external environment was only part of the problem: something was also wrong
internally, and it had to be fixed.
But what was the problem? We had the largest share of our niche market in highperformance linear integrated circuits. We had the best designers and technologists
in our business. We had excellent relations with a highly motivated workforce. We
were not guilty of underinvestment, nor of managing for short-term profits. The only
conclusion was that there was something about the way we were managing the
company that was not good enough.
Motivated by systems dynamics concepts articulated decades earlier by Jay Forrester at
Massachusetts Institute of Technology (M.I.T.), Stata came to focus on organizational learning as the
key management concept: “I would argue that the rate at which individuals and organizations learn may
become the only sustainable competitive advantage, especially in knowledge-intensive industries.”
Improved performance in customer service, product quality, yield, and cost were becoming
key strategic goals for ADI. Historically, the company had played a niche strategy, focusing its
attention on being the first to the market with new products whose unique performance enabled it to
earn substantial margins. These applications usually required only modest production volumes. But
several of the newer applications for ADI’s products had developed substantial high-volume
potential. For high-volume applications, customers were demanding lower prices and better delivery
performance. ADI decided to concentrate on penetrating the higher-volume markets developing in
computers, communications networks, and consumer products and to use the lower-cost structure
from serving these markets to maintain and increase penetration in its traditional lower-volume
industrial and military markets.
Stata recalled what he believed was necessary in order to penetrate the high-volume markets:
We decided to focus our attention on product quality, on-time delivery, lead time,
yields, and new-product time to market. We went to seminars, read books, gave
speeches, and introduced information systems to measure our performance. But
three years into the mission we were not getting very far very fast.
We knew all about error detection and correction and about doing it right the first
time. But we did not have any notion of what rate of improvement was satisfactory
or what we could do to accelerate the improvement process.
The Quality Half-Life Concept
ADI believed strongly in operating with a small corporate staff, and that line managers had to
take the lead in improving quality. But frustration with the slow rate of improvement led to the
hiring of Art Schneiderman as vice president for quality and productivity improvement.
Schneiderman, with mechanical engineering and management degrees from M.I.T., had worked for
many years as a consultant for Bain & Co. His consulting experience had focused on case histories of
successful quality improvement programs. Schneiderman was a follower of the Juran philosophy2
that believed quality goals had to be incorporated into incentive and reward systems. But to be part
of the reward system, Schneiderman knew that the quality goals needed to be realistic:
The basic flaw in current goal setting is that specific goals should be set based on
knowledge of the means that will be used to achieve them. Yet the means are rarely
known at the time goals are set. The usual result is that if the goal is too low, we will
2 J.M. Juran, Quality Control Handbook, 3rd Ed. (New York: McGraw Hill, 1979).
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underachieve relative to our potential. If the goal is too high, we will underperform
relative to others’ expectations. What’s really needed to set rational goals is a means
of predicting what is achievable if some sort of standard means for improvement
were used.3
Schneiderman had recently made an important discovery:
I had inadvertently made a transformation of some data provided by Yokogawa
Hewlett Packard (HP) [see Exhibit 2] that suggested a simple model for the results of
Quality Improvement Process (QIP) activity. Any defect level, subjected to legitimate
QIP, decreases at a constant rate so that when plotted on semilog paper against time,
it falls on a straight line. At the Japanese HP plant, the continuous improvement
process produced a 50% reduction in the failure rate every 3.6 months over a twoyear period. After reducing defects by a factor of more than 250, the process
eventually slowed down probably due to inherent equipment limitations.
After this initial discovery, I gathered data on every quality improvement program
reported in the literature. The reports came from a wide variety of sources including
my experience, various publications, presentations from the Juran Institute and the
American Society for Quality Control, and a wide variety of textbooks on quality
improvement.
In analyzing this data, I used the word “defect” in its most general sense: any
measurable quantity that is in need of improvement.
Among the “defects” studied by Schneiderman were errors, rework, yield loss, unnecessary
reports, cycle times in manufacturing, design and administrative processes, unscheduled downtime,
inventory, employee turnover, absenteeism, lateness, unrealized human potential, accidents, late
deliveries, order lead time, setup time, cost of poor quality, and warranty costs. Exhibit 3 shows the
graphs for five of the quality improvement programs, and Exhibit 4 presents a summary of the data
for the 64 observations.
In each QIP, Schneiderman measured the half-life for improvement. For each increment of
time that equals this half-life, the defect level drops by 50%. For example, if the initial defect level
was 10%, and the defect half-life was six months, then after the first six months, the defect level
would be down to 5%; after the next six months, 2.5%; and so on. Schneiderman proceeded to test his
half-life concept at ADI:
One of the key corporate goals was to reduce the percentage of orders shipped late.
We assembled a team from various organizations involved with customer service to
analyze the causes of lateness. For each late shipment, we determined the cause, and
then we plotted their distribution. We found that a relatively small number of causes
was responsible for 50% of the problems.
Next we assembled problem-solving teams to attack these major causes of lateness.
When the cycle was completed, we repeated the process by prioritizing the causes for
50% of the remaining problems and then eliminating those causes. This cycle was
repeated again and again; each time the most important remaining problems were
identified, and resources were focused on solving them.
3 Comments from Schneiderman in the case were obtained both from direct interviews and from his article,
"Setting Quality Goals," Quality Progress (April 1988): 51-57.
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Under Schneiderman’s leadership, the company had implemented a total QIP that stressed
continual problem solving. The process was described as the PDCA cycle (see Exhibit 5): Plan, Do,
Check, Act. Exhibit 6 shows the PDCA cycle as different points along a clock face. This formulation
emphasized that a quality program is not something an organization does for a year or two to correct
some problems and then moves on to something else. Rather, the QIP embodied a continual problemsolving commitment in which the half-life method served as the speedometer for measuring how fast
the organization was traveling around the PDCA cycle (see also Exhibit 7).
Schneiderman commented on factors that influenced the improvement half-life:
The slope of the learning curve seems to be determined by how long it takes to
identify and prioritize the causes of the problem and to eliminate those causes. The
required time for each cycle of improvement is largely a function of the complexity
and bureaucracy of the organization [see Exhibit 8]. Ray Stata likes to rephrase this
by saying that the half-life is determined by the rate of organizational learning.
Implementing the Half-Life Concept at ADI
The ADI five-year plan for fiscal year (FY) 1987 to FY 1992 called for:
Sales Growth
Operating Profits
Profit After Tax
Return on Capital
20% to 25% per year
17% of Sales
9.4% of Sales
15%
The sales growth target was particularly challenging since the worldwide projected growth
rates for electronic equipment was only 11% per year and for semiconductor sales, 13% per year. ADI
would have to grow profitably at a rate considerably higher than its competitors if it were to meet its
objectives. Stata felt this could only be achieved if ADI was thought by its customers to be number
one in terms of total value delivered. Rather than allow individual department and division
managers to establish their own metrics and goals for customer performance, the senior executives of
ADI established a top-down performance measurement system. In addition to continuing to turn out
high-performance products that met customers’ functional needs, specific targets were established for
on-time delivery, defect levels, and lead time:
On-Time Delivery
Outgoing Defect Level
Lead Time
1987
85%
500 PPM
10 wks.
1992
>99.8%
< 10 PPM
< 3 wks.
Half-Life
(months)
9
9
9
The 1992 goals were derived by asking key customers what kind of performance they
expected from their number one supplier. Frequently, customers’ purchasing people specified only
modest improvements from 1987 performance. Customers’ operations people were much more
demanding, since they compared ADI to all their suppliers, not just to linear IC manufacturers. Some
goals specified from the toughest customers were too demanding; ADI felt it could not reasonably
expect to meet them. It then looked to see what its best competitor was currently doing and could be
expected to be doing in 1992; and this became the target. Only if ADI could not meet its toughest
4
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Analog Devices: The Half-Life System
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competitor with its currently projected half-life improvement rate was the 1992 target specified by
extrapolating from current half-lives.4
Schneiderman knew that these ambitious goals for customer service could be met in only two
ways. One way involved building and holding inventory and using lots of inspection to meet the
delivery, quality, and lead time goals. Schneiderman felt, however, that this way led to bankruptcy.
The second way was to make continuing, fundamental improvement in manufacturing processes.
Four measures of internal performance were established for every division and five-year goals for
improvement of these measures specified:
Manufacturing Cycle Time
Process Defect Level
Yield
Time to Market
1987
15 wks.
5000 PPM
20 %
36 months
1992
4-5 wks.
<10 PPM
>50 %
6 months
Half-Life
(months)
9
6
9
24
The half-lives for improvement of both the external and internal measures had been
estimated from Schneiderman’s data base of 64 improvement examples (in Exhibit 4). Schneiderman
also pointed out that while the detailed tracking of improvement measures depended on making a
quantity (such as % late deliveries) smaller, for motivational purposes the company executives liked
to emphasize the positive aspects such as the on-time delivery percentage increasing from 60% to
greater than 95%.
Stata recalled the introduction of the new performance measurement targets:
The challenge of making continuous improvements with nine-to-twelve month halflives over an extended period is awesome. The first reaction of our organization was
to recoil from what looked like unrealistic objectives. But if a company really gets its
quality improvement act together, there is no fundamental reason why these goals
cannot be achieved. There were companies in Japan already operating at these levels
on some of these measures.
Schneiderman designed a quarterly scorecard (see Exhibit 9) so that the predicted
performance of each division on the external and internal metrics could be conveniently displayed:
Each year, I fill in the scorecard for the next year with benchmarks based on half-life
improvement rates. Then the division managers come up with their bottom-up
targets. We negotiate differences, usually ending up in the middle between our
proposals.
The Corporate Scorecard [Exhibit 9] is divided into five panels. The top panel,
Financial Performance, presents information of interest to stockholders. The second
panel, QIP indicators, presents data on how we look to our customers and
employees. The measures, such as lead time, on-time delivery, and employee
turnover, indicate what’s important and what we need to improve. The third and
fourth panels present measures of internal manufacturing performance. These
measures are what we believe drive the external measures shown in the first two
panels. The fifth panel shows how well we are doing in introducing new products
and achieving the strategic goals specified in our five-year plan.
4 Some in ADI felt that if the performance of its best competitor could not be met, then perhaps ADI should
consider exiting that line of business.
5
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Analog Devices: The Half-Life System
During the year, the trends on all key indicators are reported monthly and quarterly (see
Exhibit 10 as a sample report for on-time delivery performance of the seven ADI divisions). Stata
emphasized the importance of the reporting process:
How information is displayed makes an incredible difference. The simple summary
of on-time delivery [Exhibit 10] replaces pages of information that used to be
circulated to managers. With all these pages, the most crucial information—the halflife trend—was missing. For management purposes, displaying all divisions together
on a single page has great motivational value. A high level of internal competition
exists to generate the fastest learning curve; it is obvious and embarrassing when you
are not performing.
Schneiderman reviewed the trend performance of each division quarterly:
By statistical analysis of the improvement curve, I can estimate upper and lower
control limits for each observation [see Exhibit 11]. I circle any major variance, a red
circle for an unfavorable variance and a green circle for a favorable one. When a
“green variance” occurs, managers are asked to share their insights which led to a
more rapid rate of improvement than had been historically achieved. For “red
variances,” managers must explain what was controllable versus noncontrollable,
and give suggestions about how to make further improvements.
Over time, managers have learned that there are right answers and wrong answers
for explaining unfavorable variances. The wrong answer is to claim that lots of little
reasons combined to produce the bad outcome. The right answer is to identify the
two or three key problems that contributed to 80% to 90% of the bad performance
and to describe the program that will lead to reducing the impact of these problems
in the future.
The QIP Quarterly Scorecard soon became accepted at ADI, but a new unforeseen conflict
had developed. Operating managers continued to receive extensive financial summaries of their
monthly performance. Frequently, the QIP Scorecard and the Financial Performance summaries
pointed in opposite directions; a manager might be performing well by one scorecard and performing
poorly with the other scorecard. Gene Hornsby, director of product assurance, described the
problem:
We were trying to get managers to keep inventory down, improve quality, and match
production to customer deliveries. But with the monthly financial reports, the
operating people got stroked when volume was high and beat up when volume was
low. They’re not unresponsive. They soon figured out that getting stroked felt better
than getting beat up. The accounting system seems to be a barrier to our attempts to
implement just-in-time and short cycle times.
Active discussions and debates ensued attempting to reconcile the conflicting signals.
Monthly Financial System
The ADI financial system was a traditional process-oriented system that tracked expenses to
each of the major production cost centers and allocated cost center costs to products. Each production
center had a direct cost center (for materials and labor) and a fixed cost center. Most of the indirect
expenses were considered fixed. Material, labor, and variable overhead costs were assigned to each
batch of wafers started through the fabrication process. Fixed expenses were allocated to products
only when preparing external financial statements to conform to generally accepted accounting
practices.
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The key determinant of the cost of an individual die was the wafer yield, defined as the ratio
of good die produced on a wafer to the total number of die that can be printed and produced on the
wafer. All the wafer fabrication costs were assigned to the good die based on estimated yield
percentages. Currently, yield was averaging about 35%.
Labor and overhead expenses were assigned to wafers based on estimates of machine
utilization and efficiency. Russ Brennan, semiconductor divisional controller at ADI, remarked:
It’s been normal to have unfavorable variances equal to 20% to 25% of standard
direct cost. The standards overstate the efficiency of machines and their utilization.
For example, a machine may only be working for two hours per day, but the
standard for the machine could be six hours of operation each day. Also, we
frequently find there’s a big gap between what we thought a machine’s performance
would be and what it is actually delivering.
But the big issue is yield. Each quarter, planners make assumptions about yield, and
these assumptions drive the production schedule. The planners’ estimates of yield
can be different from the existing standard and also different from recent actual
experience. They are evaluated by customer-service levels, not inventory levels, so
that they have been reluctant to change production schedules based on short-term
improvements in yield.
For example, as some of the continuous improvement activities took effect recently,
yields increased dramatically. A planner eventually noticed the large buildup of die
inventory and started to slow down the number of wafer starts. The slowdown
caused overhead to be underabsorbed. Yield variances were favorable, but our
absorption variances were unfavorable, since labor and overhead rates had been
based on a higher number of wafer starts.
Weekly financial reports summarized new bookings, shipments (referred to as billings), and
the key semiconductor industry ratio—”bookings to billings.” A book-to-bill ratio larger than one
signaled sales growth and a ratio less than one indicated a potential industry slowdown. Also
reported weekly were scrap dollars, yield percentages, yield variances from standard and plan, and
detailed inventory positions by product type and production stage.
A monthly income statement (see Exhibit 12) reported bookings, backlog, sales, and several
levels of profit margins. Managers focused especially on the contribution margin line, which
represented gross margin less fixed manufacturing and divisional fixed expenses (marketing,
engineering, and general and administrative expenses). Monthly spending summaries, by detailed
account type, were prepared for each department. The monthly report also summarized absorption
variances of labor and variable overhead, purchase and usage variances for materials, and efficiency
and rate variances for labor and variable overhead (see Exhibit 13). Brennan acknowledged:
We use a pretty conventional costing system that was designed more for inventory
measurement and valuation than for performance measurement. Line managers look
mostly at yield and more at percentages than the dollar variances. They also keep
track of completed goods each period. As the company moves forward with its
continuous improvement activities, we will need to decide when we have enough
confidence in the recent operating results to incorporate them into the financial
numbers and the production build plan. Historically, our product volumes have
been small, and process yields can change quickly when products are not produced
in a smooth learning environment.
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The Impact on Operating Managers
Goodloe Suttler, formerly a product-line manager at the Wilmington facility, had recently
been appointed general manager of the Semiconductor Division. Suttler was skeptical about the
value of financial information for semiconductor manufacturing:
Two years ago, we were producing one product for a very large computer
manufacturer. Every day, I was looking at the die yields from the test and probe cost
center. It was the best predictor for process efficiency and for meeting the customer’s
delivery schedule. A handful of key results, not the accounting system, tell you what
you need to know to operate in real time. With timely and accurate local indicators
at critical control points, we obtain orders-of-magnitude improvements in our ability
to control. You can never get the official accounting system to provide the necessary
timely, relevant information.
Suttler was asked his opinion of the recently introduced half-life system:
The half-life measurements provide the context for long-term problem solving. It
made us realize that we had several chronic problems that we had learned to live
with for a long time without fixing. With thousands of problems arising in our
complex production environment, we can’t address the most serious ones without a
comprehensive measurement system. The metrics now being used will be much
more useful than installing a better cost accounting system.
In the past, the ADI culture was design and marketing; manufacturing was a
necessary evil in order to get customers to part with their money. Cost-reduction
programs never seemed to work; they were boring, not meaningful. They didn’t
address how we were adding value for customers.
The QIP changes the emphasis from boring cost reduction to making improvements
in measures that people can get really excited about. We are now following a three
stage program. First, identify what matters to customers. This sets the objectives for
our efforts. Second, we develop metrics for these objectives, and third, we analyze
the metrics to develop problem-solving activities. We try to determine what
problems exist that affect our ability to improve performance along each metric and
form teams to address the problems with the highest priorities.
Suttler recalled the recent incident described by Brennan, when yields had increased substantially.
The production planners were determining wafer starts based on historic average
yields, and they didn’t slow down starts initially. They had seen ups (and downs) in
yields many times before, but this time we had really fixed some problems and yields
kept increasing. A lot of extra good die started piling up in inventory, but no realtime system was tracking it. The wafer fab manager figured out that the inventory
must be building up somewhere, but he kept quiet for a while because he didn’t want
the unfavorable volume variances and pressure to idle workers that would result
from slowing down starts. Eventually, he reported the buildup to me, but I waited
another month to verify the buildup so that we produced even more inventory before
finally ordering a major cutback on starts. We could have been producing the wrong
mix for inventory and risking considerable obsolescence. Now we look weekly at our
yields and schedule starts based on the most recent data. But until we worked off the
inventory we had built, we had lower cost absorption. Wall Street analysts called us
about the reductions in our short-term margins, not about the long-term
improvement in die yield from wafer production.
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An unexpected problem from the improvement in yields was a decreased demand for
workers. ADI did not want to lay off workers because of the improvement in its production processes.
Suttler speculated that perhaps the future QIP improvement targets should be matched with attrition
and hiring rates to avoid the pressure for layoffs. Suttler recognized, however, that a big benefit from
improved yield was to increase the effective capacity of the facility and therefore to defer, perhaps
indefinitely, capital additions. Suttler was asked to describe occasions when financial measurements
and operating improvements conflicted:
We always knew that manufacturing cycle time was a critical factor for improving
customer service. A few years ago, however, our cycle times increased from 22 to 24
weeks to 30 weeks. When we investigated, we learned that we were using
inventories to generate rapid earnings growth. We have a cost system with only a
few inventory recognition points. Any material that starts into production is treated
as WIP (assumed to be about halfway finished). At the end of one quarter, we had
started lots of wafers into production, and they all got valued at the midway WIP
point; this really helps earnings. In a high sales growth situation, you, in effect, pull
third-quarter results into the first quarter. But all the wafers in WIP sat in front of
new orders and had to be processed before we could get any of the new orders
through the system.
Art Schneiderman described a second conflict:
There’s an obvious tradeoff between OTD [on-time-delivery] and short-term financial
performance. In the past, we have occasionally delayed many low revenue
shipments that were near completion at the end of a quarter and substituted high
revenue shipments that were due the next month. So the daily OTD deteriorated
sharply; about a third of our late deliveries were caused by this revenue acceleration
effect. And at the beginning of the next quarter, we had to go out and find all the
small orders that had been delayed and reschedule them. The OTD statistics were
lousy for another two weeks. Many companies, not just ADI, seem to go through this
hockey-stick shipment phenomenon at the end of months or quarters. We have to
decide whether we’re going to be revenue-recognition driven or OTD-performance
driven.
Lou Fiore, an operations manager in the semiconductor division, commented on his disdain
of periodic financial summaries:
Cost variances are useless to me. I don’t want to ever have to look at a cost variance,
monthly or weekly. Once you’ve decided to run a product, you don’t have many
choices left. Resources are already committed regardless of how the cost system
computes costs among alternative processes.
Asked about what information he finds useful to look at, Fiore responded:
Daily, I look at sales dollars, bookings, and OTD—the percentage of orders on time.
For OTD, a late order is counted only once, on the day it was not shipped on time.
Weekly, I look at a variety of quality reports including the outgoing QC report on
items passing the final test before shipment to the customer, in-process quality, and
yields. Yield is a good surrogate for cost and quality.
Monthly, I do look at the financial reports. I want to see the bottom line P&L for the
period and the actual direct margin percentages. I look closely at my fixed expenses
and compare these to the budgets, especially on discretionary items like travel and
maintenance. I also watch head count. But the financial systems still don’t tell me
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Analog Devices: The Half-Life System
where I am wasting money. I expect that if I make operating improvements, costs
should go down, but I don’t worry about the linkage too much. The organizational
dynamics make it difficult to link precisely cause and effect.
The biggest challenge is OTD. We work on this continually. We meet once a week
and discuss the data on missed deliveries. We develop a Pareto diagram for the
reasons and decide whether there’s anything important that we’re not yet focusing
on or were the problems already being addressed by actions underway but not yet
completed.
The hot button we’re working on now is cycle time. If we can continually reduce
cycle time, the efficiency of the whole operation will increase. The previous
production manager liked to run large lots because the system told him this reduced
costs. My approach is exactly the opposite. We reorganized the test area from a jobshop functional layout to 30 production cells that match most of our routings. Out of
the 30 groups, only 3 are really constraints. I focus on these cells, closely monitoring
their efficiencies and making sure they don’t go out of service.
I also try to size production lots so that they can be completed in eight hours on the
slowest (gating) operation in the process. This enables us to finish a lot in one shift
and makes production scheduling a lot easier since we can schedule work in one shift
units. By reducing the lot size, we reduced both cycle time and the variance of cycle
times. This led to dramatic decreases in WIP inventory while greatly improving our
ability to spot quality problems. Production now matches customer demands more
closely, and the number of expedited and late orders has decreased.
Russ Brennan, the divisional controller, defended the role for financial measurements. He felt
that when conflicts between QIP and financial measures occurred, the financial measurements were
not always at fault.
For much of last year, overall chute yields were increasing, and yield variances were
favorable, so the two signals were consistent with each other.5 Then in the last
quarter, actual chute yield continued to increase, but we reported an unfavorable
yield variance. This caused considerable confusion among the operating people.
After investigating, we found that the mix of our business had changed in the last
quarter. We were building less low-volume, high ASP [average selling price]
products and much more high-volume, low ASP products. The standard yields on
the low ASP business are higher than on the high ASP products, so the reported
improvement in chute yield was due more to the favorable mix change than to
fundamental improvements in the production processes. Actual yields did not
increase as much as they should have, an effect only revealed by our financial
measure, the yield variance.
Current Developments
The QIP program was proceeding under a three-phase program. Phase I, worked on in 1989,
concentrated on on-time delivery performance (OTD), measured relative to ADI’s committed
shipping date.
5 Chute yield equals the ratio of good die released from final testing to the number of total die started into
production.
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Phase II, beginning in 1990, would add two new measures of customer responsiveness: (1)
the Percentage of Time ADI met customers’ lead-time requests, and, when customer lead-time
requests were not met, (2) Weeks of Excess Lead Time, measured as the difference between the lead
time requested by the customer and the lead time committed to by the factory. The Phase II
measurements (see Exhibit 14) would be added to the division scorecards. The causes of missedOTD were also broken down by responsibility:
Source
Factory
Warehouse
Credit
Customer
Possible Cause
No product available
Handling error
Customer on credit hold
Closed for holiday
Requested shipment hold
In Phase III, ADI hoped to use lead time as a strategic weapon. Schneiderman wanted to be able to
offer lead times even below those requested by customers.
ADI had recently introduced an additional incentive bonus plan to increase attention paid to
its operating indicators. It retained a corporatewide performance bonus tied to meeting ADI’s
financial goals. The plan was supplemented for divisional personnel with a bonus tied to surpassing
a Divisional Net Income Benchmark while achieving an OTD percentage of 90% or better and
operating with defect rates of less than 500 PPM (these target levels were to be raised each year).
Annual performance reviews of professional staff highlighted setting and achieving goals that were
linked to departmental or organizational objectives in support of the Benchmark and Five-Year Plan.
11
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
190-061
Exhibit 1
Analog Devices: The Half-Life System
Selected Financial Statistics for Analog Devices, Inc., from 1979 to 1988
Selected Financial Statistics
($000,000)
1988
Sales
1987
1986
1985
1984
1983
1982
1981
1980
1979
$439
$370
$334
$322
$313
$214
$174
$156
$136
$100
Net Income
38
19
23
30
37
18
10
5
9
7
Total Assets
449
397
369
348
296
223
163
145
126
84
49
43
37
69
58
19
19
16
20
8
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Gross Margin
54%
54%
55%
53%
57%
54%
52%
48%
52%
50%
R&D
14
15
13
12
9
9
9
8
7
6
Operating Income
13
9
12
14
18
15
13
11
16
15
9
5
7
9
12
9
6
3
7
7
0.09
0.13
0.14
0.23
0.17
0.21
0.65
0.76
1.11
0.96
Capital Expend.
Sales
Return on Sales
Debt-to-Equity
12
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
Analog Devices: The Half-Life System
Exhibit 2
190-061
Yokogawa Hewlett-Packard: Dip Soldering Failures
13
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
190-061
Exhibit 3
Analog Devices: The Half-Life System
Five Quality Improvement Programs
14
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
Analog Devices: The Half-Life System
Exhibit 4
190-061
Observed QIP Half-Lives
15
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
190-061
Exhibit 5
Analog Devices: The Half-Life System
The Deming Cycle
16
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
Analog Devices: The Half-Life System
Exhibit 6
190-061
The PDCA Cycle and The 7 Steps
17
This document is authorized for use only by Teemu Malmi in 2015.
Exhibit 7
The "Half-Life"—A Metric of Continuous Improvement
190-061
-18-
For the exclusive use of T. Malmi, 2015.
This document is authorized for use only by Teemu Malmi in 2015.
Exhibit 8
Target Half-Lives
190-061
-19-
For the exclusive use of T. Malmi, 2015.
This document is authorized for use only by Teemu Malmi in 2015.
Exhibit 9
FY 1990 Corporate Scorecard
190-061
-20-
For the exclusive use of T. Malmi, 2015.
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
Analog Devices: The Half-Life System
Exhibit 10
190-061
Analog Devices, Inc., On-Time Customer Service Performance
21
This document is authorized for use only by Teemu Malmi in 2015.
Exhibit 11
Percent of Scheduled Lines Due But Not Shipped On Time: Customer Service Performance (July 1988 through June 1989)
190-061
-22-
For the exclusive use of T. Malmi, 2015.
This document is authorized for use only by Teemu Malmi in 2015.
80,257
79.3%
(1,007)
78,450
77.5%
53,134
52.5%
44,936
44.4%
57,507
56.8%
20,863
20.6%
15.3%
(810)
21,673
21.4%
Variances
Direct Margin
% of Sales
Gross Margin
% Sales
Division Fixed
% Sales
Total Fixed
% Sales
Contr Margin
% Sales
ROA
Deferred OH &
Other Income
Operating Prof.
% Sales
101,207
Sales-Total
Standard Margin
% of Sales
103,479
1Q88
This document is authorized for use only by Teemu Malmi in 2015.
25,694
23.4%
(877)
19.9%
24,817
22.6%
60,613
55.3%
46,912
42.8%
59,617
54.4%
85,430
77.9%
(1,818)
87,248
79.6%
109,608
115,486
2Q88
25,672
22.7%
(906)
20.2%
24,766
21.9%
62,646
55.3%
48,486
42.8%
62,906
55.5%
87,412
77.2%
(1,629)
89,041
78.6%
113,284
114,583
3Q88
24,731
21.5%
(921)
18.8%
23,810
20.7%
65,381
56.8%
50,992
44.3%
61,007
53.0%
89,191
77.5%
(1,974)
91,185
79.2%
115,107
111,567
4Q88
Analog Devices, Inc., Income Statement, June 1989 (QTD)
Bookings-Trade
& Affil.
Exhibit 12
97,770
22.3%
(3,514)
18.6%
94,256
21.5%
246,227
56.1%
191,326
43.6%
236,843
53.9%
340,483
77.5%
(7,220)
347,711
79.2%
439,206
438,206
YEAR
1988
25,419
22.3%
(913)
19.6%
24,506
21.5%
65,405
57.3%
51,137
44.8%
60,497
53.0%
89,911
78.8%
(1,519)
91,430
80.1%
114,145
113,182
1Q89
24,013
20.9%
(920)
17.4%
23,093
20.1%
69,117
60.1%
54,741
47.6%
62,677
54.5%
92,209
80.2%
(463)
92,692
80.6%
115,003
117,336
2Q89
11,725
15.7%
(598)
13.2%
11,126
14.9%
48,246
64.5%
38,896
52.0%
38,597
51.6%
59,372
79.4%
(19)
59,391
79.4%
74,800
70,027
2 MOS
QTD
(2,753)
20
(2,732)
(5,368)
(5,368)
(134)
2,636
979
1,657
2,542
(765)
VAR
ROLL
-23-
23,976
20.4%
(939)
17.2%
23,036
19.6%
69,160
58.9%
54,483
46.4%
62,937
53.6%
92,197
78.5%
(1,622)
93,819
79.9%
117,420
115,021
ROLL
3Q89
190-061
For the exclusive use of T. Malmi, 2015.
For the exclusive use of T. Malmi, 2015.
190-061
Exhibit 13
Analog Devices: The Half-Life System
Variances
24
This document is authorized for use only by Teemu Malmi in 2015.
Exhibit 13
(Continued)
190-061
-25-
For the exclusive use of T. Malmi, 2015.
This document is authorized for use only by Teemu Malmi in 2015.
For the exclusive use of T. Malmi, 2015.
190-061
Exhibit 14
Analog Devices: The Half-Life System
Phase II Measurements
26
This document is authorized for use only by Teemu Malmi in 2015.