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Automation

Abstract

Automation is the conversion of a work process, a procedure, or equipment to automatic rather than human operation or control. Automation does not simply transfer human functions to machines, but involves a deep reorganization of the work process, during which both the human and the machine functions are redefined. Early automation relied on mechanical and electromechanical control devices; during the last 40 years, however, the computer gradually became the leading vehicle of automation. Modern automation is usually associated with computerization.
122
AUTOMATION
Planware has several types of knowledge,
all
encoded
through parameterized theories. The first is knowledge
of the scheduling domain, including the constraints
on use of the different types of resources, such as reus-
able or sharable resources. Another type of knowledge
is
algorithm knowledge,
such as generate-and-test,
branch-and-bound, divide-and-conquer, dynamic pro-
gramming, and hill-climbing
(see
ALGORITHMS,
DESIGN
AND
CLASSIFICATION
OF).
By codifying them as para-
meterized theories, algorithms can be automatically
derived for a given very-high-level problem specifi-
cation, given appropriate domain axioms.
A
third type
of
knowledge is
implementation knowledge,
which
defines how higher-level constructs such as sets can be
encoded as more implementation-level constructs such
as lists or bit-vectors.
All
of these tools use advanced knowledge representa-
tion and automated reasoning capabilities. Although
research tools today, they represent the degree of
programming automation that may become commer-
cially available within a decade.
Bibliogvaphy
1956. Newell,
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on
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IT-2,
3
(March), 61-79.
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H.
A.
“Experiments with a Heuristic Compiler,”
1982. Martin, J.
Application Development without
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D.,
Balzer,
R.,
Cheatham, T., and
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Rich, C. “Report on
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Knowledge-based Software Assistant,”
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Readings in Artificial Intelligence and Software
Engineering
(eds. C. Rich and R. C. Waters). San Francisco:
Morgan Kaufmann.
1990. Rich, C., and Waters, R. C.
The Programmer’s Apprentice.
Reading, MA: Addison-Wesley.
1991. Lowry, M. R., and McCartney, R. D. (eds.)
Automating
Software Design.
Cambridge, MA: MIT Press.
1993. Kant,
E.
“Synthesis of Mathematical Modeling Software,”
IEEE
Software,
10,
3
(May), 30-41.
1995. Flener, P.
Logic Program Synthesis from Incomplete
Information.
Boston: Kluwer Academic Publishers.
1996. Smith, D. R., Parra, E.
A,,
and Westfold,
S.
J.
“Synthesis
of Planning and Scheduling Software,” in
Advanced Planning
Technology
(ed.
A.
Tate), 226-234. Menlo Park, CA: AAAI
Press.
1997. Browne,
T.,
Davila, D., Rugaber,
S.,
and Stirewalt,
K.
“Using Declarative Descriptions to Model User Interfaces with
MASTERMIND,” in
Formal Methods in Human Computer
Interaction
(eds.
F.
Paterno and P. Palanque). New York:
Springer-Verlag.
for Applications.
Boston: Kluwer Academic Press.
1998. Bibel, W., and Schmitt, P.
Automated Deduction.
A
Basis
Websites
Amphion,http://ic-www.arc.nasa.gov/ic/projects/
Mastermind.
http://ww.cc.gatech.edu/gvu/
amphion/.
user-interfaces/Mastermind/.
Planware,http://www.kestrel.edu/HTML/projects/
SciNapse.
http://www.scicomp.com/about/
arpa-plan2/.
technology.htm1.
Michael
R.
Lowry
AUTOMATION
Automation
is the conversion of a work process, a pro-
cedure, or equipment to automatic rather than human
operation or control. Automation does not simply
transfer human functions to machines, but involves a
deep reorganization of the work process, during which
both the human and the machine functions are
redefined. Early automation relied on mechanical and
electromechanical control devices; during the last
40
years, however, the computer gradually became the
leading vehicle of automation. Modern automation
is
usually associated with computerization.
This article examines the major phases of historical
development and social and economic aspects of in-
dustrial automation, focusing
on
the computeriza-
tion of production, engineering, and managerial tasks.
Other areas of computer-based automation include
administrative applications
(4.
v.),
communication via
electronic mail
(q.v.),
banking applications, medical
applications
(q.v.),
and library automation
(see
DIGITAL
LIBRARIES).
Phase
I:
Mechanization and
Rationalization
of
Labor
The mechanization of machine tools for production be-
gan during the Industrial Revolution at the end of the
18th century with the introduction
of
the Watt steam
engine, the Jacquard loom, the lathe, and the screw
machine. Mechanization replaced human or animal
power with machine power; those mechanisms, how-
ever, were not automatic but controlled by factory
workers. The factory system, with its large-volume,
standardized production, and division of labor, re-
placed the old work organization, where broadly
skilled craftsmen and artisans produced small quan-
tities of diverse products. In the late 19th century
Frederick W. Taylor rationalized the factory system by
introducing the principles of “scientific management.
He viewed the body of each worker as a machine
whose movements had to be optimized in order to
minimize time required to complete each task and thus
increase overall productivity. “Scientific management”
strictly separated mental work from manual labor:
AUTOMATION
123
workers were not to think but to follow detailed
instructions prepared for them by managers. The
rationalized factory system gave birth to a new man-
agerial class and large clerical bureaucracies. The
Taylorist principles served as a basis for Henry Ford’s
system of mass production. In 1913 the Ford Motor
Company introduced a moving assembly line, drasti-
cally cutting assembly time. The assembly line imposed
a strict order on production by forcing workers to keep
pace with the motion of the conveyor belt. Mass
production relied on the standardization of compo-
nents and final products and routinization of manu-
facturing and assembly jobs. The Ford assembly line
became a symbol of efficiency of American manufac-
turing; for workers and social critics, however, it
epitomized the monotony and relentless pressure of
mechanized work.
Phase
II:
Automation
of
Production
In 1947 the Ford Company brought the term “auto-
mation” into wide circulation by establishing the first
Automation Department, charged with designing
electromechanical, hydraulic, and pneumatic parts-
handling, work-feeding, and work-removing mechan-
isms to connect standalone machines and increase the
rate of production. In 1950 Ford put into operation the
first “automated” engine plant. Although early auto-
mation was “hard,” or fixed in the hardware, and did
not involve automatic feedback control, this concept
provoked great public enthusiasm for “unmanned fac-
tories” controlled by “buttons that push themselves,
as well as causing growing concern about the prospects
of mass unemployment.
To meet
US
Air Force demands for a high-performance
fighter aircraft whose complex structural members
could not be manufactured by traditional machining
methods, a technology of Numerical Control
(NC)
of
machine tools was developed in the early 1950s.
NC
laid foundation for programmable, or
“soft,”
automa-
tion, in which the sequence of processing operations
was not fixed but could be changed for each new prod-
uct style. Commercial
NC
machines for batch produc-
tion appeared in the
mid-1950s.
Designed to military
specifications, early
NC
equipment proved too com-
plex and therefore unreliable, as well as prohibitively
expensive, and was applied mostly in the state-sub-
sidized aircraft industry.
The abstract, formal approach of
NC,
based on mathe-
matical modeling of the machining process, superseded
the record-playback technique of direct machine imita-
tion of workers’ actions. While the record-playback
approach relied on the skill and discretion of the
worker,
NC
technology allowed engineers and man-
agers to exercise greater control over the production
process.
Phase
Ill:
Computer-Aided Manufacturing
(CAM)
The first industrial applications of digital computers
occurred in the electrical power, dairy, chemical, and
petroleum refinery industries for automatic process
control. In 1959, TRW installed the first digital com-
puter designed specifically for plant process control at
Texaco’s Port Arthur refinery. Early applications were
open-loop control systems: gathering data from mea-
suring devices and sensors throughout the plant, the
computers monitored technological processes, per-
formed calculations, and printed out “operator guides”;
subsequent adjustments were made by human opera-
tors. In the 1960s closed-loop feedback control systems
appeared. These computers were connected directly to
servo-control valves and made adjustments automati-
cally
(see
CYBERNETICS).
In the late 1960s, with the development of time
sharing
(4.v.)
on large mainframe computers
(q.~.),
standalone
NC
machines were brought under Direct
Numerical Control (DNC) of a central computer. DNC
systems proved vulnerable to frequent failures due to
malfunctioning of the central computer and the inter-
ference of factory power cables with the data trans-
mission cables of the
DNC
system.
With the introduction of microprocessors
(q.v.)
in the
1970s, centralized
DNC
systems in manufacturing
were largely replaced by Computer Numerical Con-
trol
(CNC)
systems with distributed control, in which
each
NC
machine was controlled by its own micro-
computer. This blending of information and produc-
tion technologies produced a new breed of machinist-
programmer who could operate
CNC
equipment by
generating and debugging
NC
programs, thus break-
ing down the traditional distinction between white-
collar and blue-collar jobs.
Robotics combined the techniques of
NC
and remote
control
to
replace human workers with numerically
controlled mechanical manipulators. The first com-
mercial robots appeared in the early 1960s. Robots
proved very efficient in performing specialized tasks
that demanded high precision or had to be done in
hazardous environments. To approach the human level
of flexibility, robots were supplied with sophisticated
techniques of feedback, vision and tactile sensors, rea-
soning capabilities, and adaptive control. In the 1980s
industrial applications of robots slowed down, as their
increasing complexity resulted in growing costs and
insufficient reliability.
Hierarchical Numerical Control Systems combined
DNC and
CNC
features: they linked each standalone
computer controller to a central computer that main-
tained a large library of
CNC
programs and monitored
124
AUTOMATION
production. This approach aspired to replace the
human operator’s expertise by engineering knowledge
formalized in
CNC
programs. In such systems, human
operators generally no longer programmed
CNC
equipment on the shop floor, and production was
brought under remote supervision of a central manage-
ment-controlled computer.
Flexible Manufacturing Systems (FMS) combined
DNC
equipment with machines for automated loading,
unloading, and transfer of workpieces. These systems
permitted varying process routes and sequences of
operations, allowing automatic machining of different
products in small batches in the same system. Cen-
tralized FMS have often proved too complex, however,
and they are increasingly subdivided into smaller
flexible manufacturing cells (FMC) that include several
CNC
machines, robots, and transfer devices controlled
by a single computer, the “cell controller.”
Phase
IV:
Automated Engineering
In the 1960s large aerospace manufacturers, such as
McDonnell-Douglas and Boeing, developed proprietary
computer-aided design
(CAD)
systems, which provided
computer graphics
(q.v.)
tools for drafting, analyzing,
and modifying aircraft designs. In 1970 Computer-
Vision Corporation introduced the first complete turn-
key commercial
CAD
system for industrial designers,
which provided all the necessary hardware and
soft-
ware in one package. In the 1970s, combined
CAD/
CAM
systems emerged which used the parameters of
a geometrical model created with the help of
CAD
to
generate programs for
CNC
machine tools and develop
manufacturing plans and schedules. While
CAD
systems are often packaged and standardized,
CAM
(Computer-Aided Manufacturing) applications tend to
be industry-specific and proprietary. With the introduc-
tion of Computer-Aided Engineering (CAE) systems for
standard techniques of engineering analysis, the whole
range of engineering tasks-from conceptual design to
analysis to detailed design to drafting and documenta-
tion to manufacturing design-became automated. The
distinction between blue-collar and white-collar jobs
was further blurred, as engineers, clerks, and managers
became integrated in an automated office.
Phase
V:
Automated Management
Among the earliest applications of information tech-
nology was the automation of information-processing
tasks. The first stored-program digital computer pur-
chased by a nongovernment customer was UNIVAC
(q.v.),
installed by
GE
in 1954 to automate basic trans-
action processing: payroll, inventory control and mate-
rial scheduling, billing and order service, and general
cost accounting. Large clerical bureaucracies, which
processed huge amounts of data generated in mass
production and mass marketing, became a primary tar-
get of automation and job reduction in the 1960s and
1970s. By 1970 the profession of bookkeeper was
almost completely eliminated in the USA. In the mid-
1960s the first management-information systems
(MIS)
appeared, providing management with data, models of
analysis, and algorithms for decision-making; even-
tually they became a standard tool for operation con-
trol, management control, and strategic planning.
Phase
VI:
Computer-Integrated
Manufacturing (CIM)
In the late 1980s an integration of the automated factory
and the electronic office
(4.v.)
began. CIM combines
flexible automation (robots, numerically controlled
machines, and flexible manufacturing systems),
CAD/
CAM
systems, and management-information systems
to build integrated production systems that cover the
complete operations of a manufacturing firm, including
purchasing, logistics, maintenance, engineering, and
business operations. CIM emphasizes horizontal links
between different organizational units of a firm and
provides the possibility of sharing data and computing
resources, making it possible to break the traditional
institutional barriers between departments and create
flexible functional groups to perform tasks more speed-
ily and efficiently.
Social and Economic Dimensions
of Automation
Views of automation range between two extremes-
unabashed optimism and utmost pessimism. The opti-
mists believe in a technological utopia, an imagined
bright future in which machines will relieve people of
all hard work and bring prosperity to humankind. The
pessimists view machines as instruments of subjuga-
tion and control by a ruling elite, argue that automa-
tion leads to the degradation of human beings, and
depict the future as a grim technological dystopia. Both
sides view automatic technology as an autonomous
force determining the direction of human history.
Automation itself, however, is a social process shaped
by various social and economic forces. This process
may take various directions and may have diverse
consequences depending on the socioeconomic and
organizational choices made during automation.
The Productivity Paradox
While productivity in major industries in the USA rose
sharply during production automation in the 1950s
and 60s, its growth has slowed significantly since the
1970s,
precisely at the time of widespread computer-
ization of the factory and the office. The link between
AUTOMATION
125
computerization and productivity remains problem-
atic. The advantages most commonly associated
with computer-aided manufacturing include increased
production rates, better product quality, more efficient
use of materials, shorter lead times, reduced work
hours, and improved work safety-all factors leading
to higher productivity. Among its main disadvantages,
analysts usually cite the high cost of designing, build-
ing, and maintaining computerized equipment; vulner-
ability to downtime; relatively low flexibility compared
with humans; and worker displacement and emotional
stress-all leading to lower productivity. It is particu-
larly difficult to compare directly productivity before
and after computerization, since it brings with it not
merely technological, but also organizational change
which transforms the entire nature of production and
brings with it the most benefits and losses.
As manufacturers who introduced computer-aided
manufacturing systems affirm, the largest payoff
from computerization comes not from speeding up
old operations but from making work organization
more flexible and efficient. On the other hand, if
computers are used to conserve old inefficient organi-
zation, computerization can only accelerate negative
trends. As John Bessant has remarked, “When you put
a computer into a chaotic factory the only thing you get
is computerized chaos” (quoted in Ayres, 199 1-1 992,
Vol.
4,
p. 94). Most successful manufacturers stream-
line operations before computerization, following the
dictum, “Simplify, then automate!” Efficient compu-
terization takes far more than merely installing a
computer: it requires changes in the entire workstyle.
Worker Displacement, Skill, and Working
Conditions
A
leading concern among workers, labor leaders, and
social critics has been the issue of worker displace-
ment-a
loss
of work, transfer to a different job, or
geographic dislocation-due to automation. Such cate-
gories as welders, carpenters, insulators, machinists,
and clerical staff have been most heavily affected. At
the same time, automation creates new highly-skilled
jobs in programming, operating, and maintaining com-
puterized production machinery. Workers need exten-
sive retraining programs, however, to prepare for such
jobs.
Another risk is the danger of employees losing essential
working skills as work becomes increasingly mediated
by the computer. With automation, the worker has
gone through a series of transformations-from a di-
rect producer of goods and services to the operator
of
production equipment to the programmer of the
computer that operates and controls that equipment.
Engineering changed from hands-on tinkering with
machinery to the use of standard design and analysis
procedures that tell the computer how to design and
build a needed part. Management evolved from direct
supervision of labor to “management by numbers,”
based on numerical data reports and pre-programmed
computer algorithms for decision-making. When
operators must step in and take control in case of an
emergency at an automatically controlled nuclear
power plant, would they possess the necessary skills
if their training and daily experience mainly concerned
work with a computerized control system?
Because of the high cost of downtime, efficient main-
tenance and fast repairs become crucial in automated
production, which places a great burden of responsi-
bility and tight time constraints on maintenance and
repair crews. Computerized equipment can be used to
enhance the flexibility of work organization, leaving
one in charge of planning one’s work time, but it may
also be used to impose a strict and inflexible work
regime on factory and office workers by closely
monitoring their performance. As a result, automation
can make work either easier or more exhausting and
stressful, depending on the type of work organization.
Technocentric
vs.
Human-Centered
Approaches
Historically the predominant approach to automa-
tion has been technocentric: a goal of automation is to
reduce and ultimately entirely eliminate human par-
ticipation in production and eventually arrive at an
unmanned factory. From this standpoint, workers are
seen as a source
of
potential errors, disturbance, and
unreliability; on the other hand, automatic machinery
is viewed as inherently more precise, reliable, and
controllable. The technocentric approach extends the
principles of Taylorist work organization to modern
information-processing and production systems. It is
based on further subdivision of labor, with more com-
plex and intelligent tasks trusted to flexible computer
systems and simpler tasks left to low-skilled workers
who assume a residual role. Skill gradually passes from
people to machines, and control functions are also
transferred in the same direction.
The technocentric approach faces a fundamental para-
dox: it aspires to replace human skill with highly flex-
ible computerized machinery, but this machinery
requires even more human skill to operate, maintain,
and repair it. Instead of “freeing” production from
the “human element,” automation only increases the
importance of highly qualified, versatile, and motivated
workers. Accidents at the nuclear power plants at Three
Mile Island and Chernobyl testify that automation does
not eliminate the possibility of human error; it only
makes this error more costly.
126
AUTOMATION
The Taylorist logic of seeking productivity by accel-
erating the pace of work may not apply in a compu-
terized workplace. With computerization, companies
do not simply automate, but “informate” their opera-
tions. Computer-based control of production becomes
an information-processing task; workers turn into
analyzers of information rather than simple machine
minders. Improving the quality of this analysis, instead
of speeding up workers’ movements, becomes a crucial
problem of automation.
An alternative approach aspires to change the work-
force from being part of the manufacturing problem
into part of the solution. Instead of taking skills,
responsibility, and control away from the worker and
absorbing them into the machine, human-centered
CIM systems mobilize the intellectual resources of
all
employees. Leading Japanese companies, such as
Matsushita and Toyota, achieved much greater pro-
ductivity gains from automation than their American
competitors by decentralizing control and reorganizing
the factory layout into production islands controlled by
semi-autonomous multi-skilled teams responsible for
all
operations. Reversing the Taylorist trend of subdivi-
sion of labor, the human-centered approach integrates
functions and skills in flexible teams, where workers
can rotate jobs and choose the optimal order and pace
of work. Instead of being forced to follow instructions
handed to them from above, workers are motivated to
play a greater role in decision-making by programming
CNC
equipment on the shop floor. In the late 1960s and
early 1970s only a handful of American companies,
such as Procter
&
Gamble, Cummins Engine, and
Gaines Foods, realized that greater productivity did not
come automatically with more sophisticated equip-
ment but required profound organizational change.
In 1974 Volvo built a highly productive plant at
Kalmar, Sweden, which implemented the “sociotech-
nical systems” approach, elaborated in Britain. Based
on group assembly instead of a conventional assembly
line, this new design gave workers more initia-
tive, flexibility, and control over product quality. In the
1980s
major American manufacturers began experi-
menting with worker involvement in decision-making,
a recent example being
GM’s
Saturn project. The
human-centered approach finds a source of productiv-
ity in more efficient utilization of human abilities, rather
than in the utopian
efforts
to eliminate people from
production.
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Article
In this article, we employed a systematic mapping methodology to examine the existing literature at the intersection of technology, gender and organizations. While much has been written about gender in organizations, the research has not consistently considered that modern organizations are increasingly technology-driven – in technology may lie an underexplored lever that could help expand our understanding of gender issues at the workplace. By analyzing a final sample of 168 research papers, we found that two main forms of conceptualizing technology emerged: technology as culture and technology as tools. Papers in the first category are concerned with environments in which technology drives a large part of what is produced, and, therefore, heavily influences culture; authors employ this framing to study technology companies, roles, and entire economic sectors under a gender perspective. The second approach corresponds to the understanding of technology as tools that individuals can use to perform their tasks. A tool can be physical, based on software, or even combine hardware, software, procedures and people; authors employ this framing to study gendered use, or adoption, of technologies to work. We synthesized all the extracted data to obtain a mapping of the literature and conclude with suggestions for future research at the intersection of technology, gender and organizations.