Download THE KNOWLEDGE OF LIFE CYCLE

THE KNOWLEDGE OF CYCLES
Α. Kokkosis, S. Tsitomeneas, C. Κokkonis, A Charitopoulos
ΤΕΙ of Piraeus, Dept. of Electronics, Thivon & P. Ralli, Athens -12244
[email protected], [email protected], [email protected], [email protected]
ABSTRACT
The knowledge is industry’s most valuable resource. The competitive value of
knowledge erodes over time, so the advantage is not sustainable. Therefore industrial
success depends on finding ever-faster ways of learning and doing. But a very
important parameter of knowledge process is the rate at which we learn and then we do
will become the last sustainable source of competitive advantage. In order to be able to
give a satisfactory answer we must seriously take in account the life cycle. We must
explain what life cycle is, and why it matters so much. What’s happening to life cycle in
the information age, what accelerates cycles, and what retards them.
This paper describes about how all of us can drive cycles of learning and doing, not be
driven by them.
Keywords: Knowledge, Cycles, Globalisation, Learning
1. INTRODUCTION
There is nothing at all complicated about cycles. They are just complete sets or series
of regularly recurring events. But they are not that simple. Because although cycles can
be observed, and to an extent anticipated, their outcomes are not always predictable.
Why cycles matter it is precisely because cycles can generate discontinuities that
understanding them and exploiting them matters so much. Discontinuity means
opportunity. Opportunity not seized turns into risk. And the shorter the cycles, the
greater is the opportunity - and the risk. It’s true for all kinds of cycles, in all fields of
endeavour. They all have the potential to bring about material change.
Take the seemingly mundane production cycles in a factory. Every manufacturing
engineer knows that the more you make of a product, the more opportunities there are
to learn how to make that product more efficiently. It’s a well studied relationship,
capable of precise mathematical description, and usually presented graphically as a
‘learning curve’. In some parts of the electronics industry, for instance, so-called ‘70%
learning curves’ are not uncommon. That means that for every doubling in the number
of production cycles, the cumulative average time of each production cycle is expected
to fall to 70% of its previous level. So for the first of the competing manufacturers of a
new type of product to make a hundred, or a thousand, or ten thousand, or a hundred
thousand, or a million units, there is an opportunity to prosper. The others are left with a
risk to their prosperity, and perhaps to their survival. With some kinds of cycle, repetition
hundreds and thousands of times over is not necessary for an equally dramatic effect.
One cycle can be all that it takes. Consider the progression from the telegraph to the
telephone. We think of it as a process of evolutionary change. But was it? Well, no. The
telephone changed the nature of telecommunications so fundamentally that many
companies became trapped in the telegraphic age, lost their way, and were never heard
of again.
Figure 1. Learning curve.
Perhaps it was an easy mistake to make then. But not now. Not after we have seen that
first, second and third generation mobile telephony was no more an evolution of landline
telephony than landline telephony was an evolution of telegraphy. And when we expect
fourth generation mobile telephony to be much more than just a stage in the evolution of
mobile phones.
Figure 2. Mobile phones.
The telegraph, to the telephone, to the mobile phone. Each change was profound and
discontinuous. And each discontinuity was generated by just one cycle of innovation.
2. MEASURING CYCLES TIMES
Therefore cycles matter. So measuring cycle times must matter too. At the micro-level,
it’s not difficult, because cycles are usually easy to define - one production cycle, one
sales cycle, one financial cycle, one publishing cycle, one teaching cycle, and so on. At
the macro-level, it’s often more difficult, because some cycles seem never really to end.
Take mobile phones for instance. The cycle for one of the second generation standards,
GSM, began in Europe in 1982. Five years later, in 1987, the standards engineers
made a critical decision - to go digital. The first GSM networks opened in 1991, starting
a market life cycle. By 2002, there were a billion mobile phones worldwide, and 700
million of them were GSM. And that number is growing still. The cycle has not ended,
and there is no end in sight. So for macro-level cycles we have to choose waypoints
with which to measure cycle times. Waypoints like the first million users of a technology
Figure 3. Years to reach one million users.
When television was launched, it was 20 years before there were a million viewers. The
second cycle, to colour television, was twice as fast - 10 years. Video cassette
recorders - 8 years. Personal computers - 5 years. Mobile phones - just 3 years. And in
the connected world in which we now live, we have to change the scale of our
waypoints, to provide meaningful measures of cycle time.
3. CYCLE TIMES IN THE INFORMATION AGE
Semiconductors power the Information Age. So let’s look at cycle times in that industry.
It’s an industry which has grown enormously since the transistor was invented in 1947.
Figure 5. Semiconductor cycle times
The integrated circuit - many transistors and other components on a single chip - came
11 years after the invention of the transistor, in 1958. The type of integrated circuit
called a micro-processor arrived 13 years afterwards, in 1971. In the years that
followed, there were a great many important developments in semiconductor
technology. But it was not until 2001 - three decades later - that one of the most
significant turning points was reached: the combination of silicon and gallium arsenide
on a single chip. Unexciting though it may sound, combining silicon and gallium
arsenide removes a barrier which is holding back many advanced semiconductor
applications - the size and speed of semiconductor devices. Moore’s Law states that the
number of transistors per unit area of silicon will double every 18 months. Moore made
his famous prediction in 1965, and he said it would hold good until 1975. Through
advances in technology, it has held for much longer. But there is a limit. And continually
scaling silicon will soon become too expensive to be economically viable. Joining silicon
and gallium arsenide in a single microchip changes the rules, by enabling us to make
devices 40 times faster. A new learning curve has begun. Eleven years. Then thirteen
years. Then thirty years. The cycle times for technology breakthroughs seem to be
getting longer, not shorter! And they are measured not in months; not even in years; but
in decades! Can this be so? From the perspective of a semiconductor manufacturer,
maybe. But not from the perspective of a semiconductor research engineer, who is
painfully aware that these ‘headline’ breakthroughs are the result of many, many
research threads being drawn together. And that the cycle-times within each thread are
getting shorter, not longer. The more threads that are required to achieve a major
technology breakthrough, or to bring a complex product package to market, the longer
the cycle time can seem to be. Because the headline has to wait until the whole story
has been composed. This ‘bunching’ effect can distort our perception of cycle time, and
make us think that the pace of innovation is slower than it actually is. So semiconductor
research engineers are not deceived by bunching. They know that cycle times are
shortening, not lengthening. The shorter are the cycles, the more evident it is when they
overlap. And by the way, there are no prizes for guessing the biggest engineering
challenge facing those engineers - system speed! The cycle time challenge is allpervasive. It challenges us to create faster machines faster. The engineer-industrialists,
whose task it is to engage the markets, have yet another perspective. Their very
mission is to shorten cycle times. Because they know that, often, the race is to the swift.
A study in 2003 found that turnover expands six times faster in companies with a higher
than average percentage of sales from new products, than it does in competitors with a
lower than average percentage of sales from new products. How are those engineerindustrialists succeeding? We can turn again to the mobile phone. This time to texting.
To the use of the mobile phone not for the primary purpose for which it was engineered
- talking and listening. But for sending text messages from the keys of one mobile
phone to the display.How are those engineer-industrialists succeeding? We can turn
again to the mobile phone. This time to texting. To the use of the mobile phone not for
the primary purpose for which it was engineered - talking and listening. But for sending
text messages from the keys of one mobile phone to the display screen of another. Who
would have thought that a method of communication based on codes and abbreviations
- a method which pre-dates telecommunications - would be in vogue today? And that
the average annual profit for Mobile Companies in Greece from SMS is about 36%
during the last year. In the process, it fuelled innovation all along its value chain, from
manufacturers to content-providers. Add the economic dimension to the human
dimension, and it’s likely that the historians of the future will regard texting as a defining
phenomenon of telecommunications at the start of the 21st century. It was a
phenomenon created by ever-shortening cycle times. So, whether cycle times are
lengthening or shortening is a matter of perspective. The closer is your viewpoint, the
shorter you perceive cycles to be. Until you get so close that you are part of the cycle.
Then it’s no longer a matter of perception. It’s reality. And the reality is that with every
cycle the pace quickens.
The quickening pace brings with it change that can be invigorating. But change can be
wearying too. Yet there is no enduring respite from change. There never has been. And
there never can be. So it behaves us to try to understand what drives cycles. And to use
that understanding to shape cycles that vitalize us. The list of factors to which cycle time
is sensitive is endless, of course.
4. GLOBALISATION
One of the biggest changes in the industrial landscape in recent decades has been
globalisation. It’s a new word not existing in the dictionaries published before 1990. A
word fabricated to embrace the diverse consequences of the increasing economic
interdependence between countries, which is resulting from the liberalisation of
markets; the emergence of developing economies; the lowering of trade barriers; and
the quickening pace of technological change. Globalisation is manifest not only in the
cross-border flows of goods, services and capital, but in the cross-border flows of
knowledge too. Globalisation is placing more knowledge within the reach of more
people, more quickly. So things should happen faster. Cycle times should reduce.
Usually they do. But it’s not always that straightforward. Globalisation offers the
possibility of global markets. In the industries in which our profession is engaged, global
markets often depend on global standards. And the cycle time for developing and
harmonising global standards can be long. So companies may be faced with a dilemma.
Is it better to enter the market with a leading-edge standard which has not yet achieved
global acceptance? And take the risk that it may be beaten in the global standards race
by another, competing standard? Or is it better to wait until the race is over, and a
global standard is acclaimed? Global leadership, or global compromise? Global
leadership starts you on a learning curve ahead of your competitors, but it may turn out
to be the wrong learning curve. And a high standards churn rate can erode public
confidence and limit the market growth for everyone. Global compromise can enable a
huge, relatively stable market, as it did with GSM mobile phones. But the price may be a
long cycle time. Global leadership versus global compromise is a dilemma which can
often be resolved by collaboration. That way, leading-edge standards can gain global
acceptance remarkably quickly.
5. LEARNING
We can begin by learning rigorous methods of analysing processes and modeling the
effects of re-engineering them. Such methods lie at the heart of the six sigma approach
to cycle time reduction and quality improvement. They are indispensable. But they are
not enough to gain sustainable competitive advantage. Because they are based on a
‘learn and then do’ model of the learning process. Really rapid learning happens when
we ‘learn’ and ‘do’ at the same time. And now, in the Information Age, we can engage in
simultaneous learning and doing on an unprecedented scale. Indeed, the distinction
between learning and doing is becoming increasingly difficult to discern. For instance,
the customary way in which engineers topped-up their knowledge was to go on a
course, or to a conference, or to a seminar, or to a library, or to other places. Then,
armed with their new knowledge, they would return to their places of work to put into
practice what they had learned. But things are changing. Instead of going to the library
to find the latest published thinking in our field, we can search the a database online.
We can do it from our desks. And we can do it on the spur of the moment, as often as
we like. A world of knowledge just a few keystrokes away. And we can put that
knowledge to immediate use. Is that learning? Or is that doing?
Reading published materials cannot substitute for dialogue with fellow professionals in
our chosen field, of course. That’s why we organise conferences and seminars. But
attendances at the best-organised conferences and seminars are limited by the
practical considerations of place and time. The arrangements won’t work for everyone.
Indeed, they won’t work for most people. And for the fortunate few who can attend, the
learning experience lacks immediacy. So simultaneous learning and doing is an
arrangement from which everyone can gain. But there is a trap set for the unwary.
Simultaneous learning and doing reduces cycle time only when it generates new
understanding. And uses that understanding to effect process change. Otherwise the
simultaneous learning and doing may be said to be ‘practising’. General speaking
‘Practice is the best of all instructors’. Well, only if the process being practiced is as
good as it possibly can be. And of which processes can that be said? Certainly none in
the engineering profession, and none in the industries. So practising - learning and
doing without changing that which is being learned - runs the risk of grouping inefficient,
error-prone processes and inducing complacency. That way lies uncompetitiveness.
Learning and doing in a way which changes that which is being learned offers
opportunities to do things faster, and with fewer opportunities for error.
If those opportunities are seized, then every cycle of learning and doing builds on the
one before, accelerating the rate of process change; reducing cycle times; increasing
competitive advantage.
5. CONCLUSION
Ultimately, measuring cycle times, Globalisation. Collaboration, and Learning are some
of the four basic drivers of cycle time. Four keys to learning and doing more quickly, if
we decide to use them. We should, because the decision to reduce cycle time is one of
the most effective, least-risk decisions we can make. It is rarely, if ever, a wrong
decision. And sometimes it can be an extraordinarily high-yielding decision. Because,
quite simply, cycles create choices. So, the more cycles, the more choices are created.
And, better still, cycles don’t just create choices. They prompt us to make choices too.
Because the start of every new cycle is predicated on choice.
6. REFERENCES
[1] Jason Westland, ‘The Project Management Life Cycle’, Kogan Page; Bk&CD-Rom
edition (March 1, 2006).
[2] ‘Product Life Cycle Data Model’, American Standard ANSI/EIA-724, September
19,1977.
[3] Guido Sonnemann, Francesc Castells, and Marta Schuhmacher, ‘Integrated LifeCycle and Risk Assessment for Industrial Processes’, CRC (November 24, 2003).
[4] M., Pecht, and D., Das, ‘The Electronic Part Life Cycle’, IEE Trans. On Components
and Packaging Technologies, vol 23, no 1, pp. 190-193, March 2000.
[5] E., Sherwood, Proposal to EIA, ‘Product Life Cycle (PLC) Code definition and
Applications’, Motorola, Inc, February 7, 2000.
[6] Karls, J., Dickens, H., and Sharon-Roy, L., Status 2001, ‘A report on the Integrated
Circuit Industry ICE’, Scottsdale Arizona 2001.
[7] Jeroen B. Guinée, ‘Handbook on Life Cycle Assessment’, Springer; 1 edition (May
31, 2002)