Epistemic Implications of Engineering Rhetoric
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Louis Bucciarelli, “The epistemic implications of engineering
rhetoric,” Synthese 168, no. 3 (June 1, 2009): 333-356.
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The Epistemic Implications of Engineering Rhetoric
Louis L. Bucciarelli
Abstract
The texts (and talk) of engineers take different forms. In this essay, I present and critique several texts written
for different purposes and audiences but all intended to convey to the reader the technical details of whatever
they are about - whether a textbook passage describing the fundamental behavior of an electrical component,
a journal article about a mathematical technique intended for use in design optimization, a memo to co-workers within a firm about a heat transfer analysis of a remotely sited building, or a general introduction to the
field of ‘ergonomics’. My aim is to explore how the ways in which engineers describe and document their
problems and projects frame what they accept, display and profess as useful knowledge. In this I am particularly interested in how engineers envision the 'users' of, or participants in, their productions.
Like science, engineering texts are written as if they were timeless and untainted by socio-cultural features. A
technical treatise is not devoid of metaphor or creative rendering of events; there is always a narrative within
which worldly data and instrumental logic is embedded - but it is a story in which the passive voice prevails,
history is irrelevant, and the human actor or agent is painted in quantitative parameters fitting the occasion.
Whether this rhetoric can be sustained in the face of challenges to traditional ways of doing engineering is an
open question.
I am concerned with the way engineers, primarily academics, express themselves in their text books, journal
articles, reports and memos - and how this relates to engineering knowledge as trusted, deployed, and
enlarged upon in a research project, in the classroom, in the design of a product. “Rhetoric” is to be understood in an honorable sense, as the crafting and presentation of argument intended to explain and persuade not simply or crassly, as “ornamental writing”. My interest, though, is not in “persuasion” per se - as exemplified in an engineering faculty proposal to NSF seeking funds to support her research program - but in more
ordinary, mundane texts that are written for, and used in, the classroom, the laboratory, the product design and
development process1. My thesis is, first, that the way engineers write about, describe and document their
problems and projects frames (reflects and constrains) what they accept, display and profess as useful knowledge. Second, this rhetoric is in need of revision in the light of the new demands placed upon the engineering
practitioner2.
I have selected texts from different contexts and different engineering domains. The first, drawn from a university level electronics textbook, is about the behavior of a diode; the next presents excerpts from a journal
article about a computational algorithm for optimizing the design of complex systems; then follows an analysis of a company memo about energy requirements for a guard house located at an oil field in the Arabian
peninsular; a final textbook selection is concerned with “human factors” in the design of manufacturing processes. I also include as a preliminary, and in the closest I come to persuasion, a brief selection from a draft of
a report prepared by an interdisciplinary faculty committee at MIT describing a new engineering course on
“engineering method”. Most of this writing is narrowly focused and strictly instrumental -or so it seems at
first reading; the challenge is to try to wring out of the text certain implications about engineering knowledge
- what it is, what it is not - and how the rhetoric enables and constrains engineering thought and practice.
1. My purpose is not to critique, none the less fully explain the text’s technical argument; the analyses one finds in
textbooks and journal articles have been through the peer review and most likely are rigorous and watertight. Unless
there is some glaring error, I presume that the author has gotten it right.
2. Ahearn, Alison L., “Words Fail Us: the Pragmatic Need for Rhetoric in Engineering Communication”, Global J.
of Eng. Educ., vol. 4, no. 1, 2000, pp 57-63.
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Of particular interest is the way engineers write about human behavior -and this in two ways. In their work,
engineers must take into account the behavior of the potential users of their products and systems. We shall
see that the way they characterize the user will depend upon their technical interests and responsibilities in
design. But they also write about the “things” they shape and design as if they alive and kicking. This too is
of interest.
Engineering knowledge is for solving problems
Before presenting and analyzing representative texts used in the engineering curriculum, a word about what
faculty perceive engineers do with the knowledge these texts are meant to provoke and the nature of the tasks
to which such knowledge is considered essential. What do engineers need to know to do what they need to
do?
Most faculty agree that the design and development of new products and systems generally requires the coordination of a team or group of individuals from different specialties who work on different features of the system. Each participant in design will have different responsibilities and more often than not, the creations,
findings, claims and proposals of one individual will be at variance with those of another. While they all share
a common goal at some level, at another level their interests will conflict. As a result, negotiation and “tradeoffs” are required to bring their efforts into coherence.
I enhance this picture, claiming that each participant in the task inhabits his or her own world of professional
practice - a world situated with respect to a particular infrastructure with its own unique instruments, reference texts, prototypical bits of hardware, special tools, suppliers’ catalogues, codes, and regulations. The
world contains particular devices, recommends certain methods, and employs specialized modes of representation. There are exemplars, standard models of the way things work from the disciplinary perspective of the
particular world, unwritten rules, and particular metaphors which enlighten and enliven the efforts of inhabitants. There are specialized computational algorithms, specialized ways of picturing states and processes.
Each participant works with a particular system of units and with variables of particular dimensions - certain
ranges of values perhaps. Dynamic processes, if that is their concern, unfold with respect to a particular time
scale - for someone’s world it may be milliseconds, in another’s, hours or days. Within each of these worlds
one “speaks” a different dialect - a “proper” language, all neat and tidy, precise1. I say that different participants work within different object worlds A structural engineer inhabits a different world from the electronics
engineer working on the same project.
It is for work within these object worlds, these specialized domains where instrumental rationality reigns
supreme, that engineering curriculum are intended. Major programs in engineering focus, at their core, on a
subset of paradigmatic sciences and problems and exercises that are amenable to analysis by the concepts,
principles and methods peculiar to the phenomena they explain. Most all of the texts I analyze are the product
of, or intended for, object-world reading and application. Engineering faculty see object-world knowledge as
the hard core of engineering knowledge. It is knowledge of a powerful sort - the kind that can solve problems.
Solving problems is a persistent theme running through texts addressing what engineers do.
Here, for example, is an excerpt from a well-know textbook in engineering mechanics.
The main objective of a basic mechanics course should be to develop in the engineering student
the ability to analyze a given problem in a simple and logical manner and to apply to its solution
a few fundamental and well-understood principles2.
The mechanics problem is given - not to be formulated by the student; it demands a simple and logical analysis - not a conjectural, inferential thinking up and about; and is to be solved using a few fundamental and wellunderstood principles - not by trying several, alternative, perhaps conflicting, approaches and perspectives.
The work-life of an engineering student, hence graduate, is neat, well posed, deductive and principled. If we
1. Bucciarelli, L.L., “Between thought and object in engineering design”, Design Studies, Vol. 23, No. 3, May 2002.
2. Beer, F. P., Johnston, E. R. Jr., & DeWolf, J. T., Mechanics of Materials, McGraw-Hill, 4th ed., 2006, p. xiii
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were to ask what engineers do with the specialized knowledge of object worlds, irrespective of specialty, the
texts say problem solving.
A claim made by members of MIT’s, School of Engineering Council for Undergraduate Education charged
with the task of defining the objectives of a course to be required of all MIT students - a course intended to
teach the Engineering Method - provides another example.
“...despite the myriad disciplines and domains where engineering is developed and applied, there
is a common theme, a unified approach and foundational knowledge that embodies what engineering is: The major common themes of this engineering method [are]: (a) an integrated, interdisciplinary view of problem solving. (b) the concept of abstraction; (c) development of larger
abstractions and model; and (d) design and synthesis...”1
What is common to all fields of engineering, then, is method for problem solving. Knowledge is knowledge
of how to solve a (given) problem. Abstraction and reduction, based in the mathematical sciences, are key to
this universal method. In an elaboration of the concept of “abstraction”, simplification is stressed and quantification deemed essential:
[Abstraction requires] Simplification of a complex problem by breaking it down into manageable components. Specifically modeling in quantitative terms critical aspects of the physical and
human world, and necessarily simplifying or eliminating [my emphasis] less important elements
for the sake of problem analysis and design...2
Here we find a further qualification of what constitutes legitimate engineering knowledge: For a problem to
be treated as an engineering problem it must be expressed in quantitative terms. Only factors, aspects and feature of this big world that can be construed as measurable and quantified matter. Numerical measures of
inputs, outputs, parameters, variables, behavior and performance, costs and benefits are the essential ingredients of a problem. One might wonder what criteria are used in eliminating, or deforming, more qualitative
elements for the sake of problem analysis and design. Is it perhaps the case that only those “elements” that
can be quantified are considered at all? Anything that can’t be measured is, ipso facto, irrelevant, not of interest or significance? (Hence not complex?) McCloskey, in a provocative critique of the rhetoric of economics,
identifies this sort of behavior with that of the person who, having lost his keys, searches only around the base
of the lamp post because that’s where the light shines3.
Even engineering design can be cast as problem solving. In Engineering Design for Electrical Engineers,
design, at first reading, appears to be something more “...[a] creative process of identifying needs and then
devising a product to fill those needs...” a process that can be broken down into two parts: “... first you make
a project plan, then you implement the plan”. But then these too become problems to be solved:
Both parts of the creative design process require problem solving. Determining the information
that you need (in order) to set the design specifications is a problem. Likewise, it is an equally
substantial problem to design the product. Both can be addressed by the same problem-solving
techniques4.
1. “From Useful Abstractions to Useful Designs - Thoughts on the Foundations of the Engineering Method, PART II,
A Subject to Satisfy a GIR in the Engineering Method - Educational and Pedagogical Goals and Sample Curriculum”
Draft, 7 May, 2005, Engineering Council for Undergraduate Education, p. 3
2. Ibid, Part I, p.4
3. McCloskey, D.N., The Rhetoric of Economics, Univ. Wisc. Press, 2nd ed., 1998.
4. Wilcox, A.D. et al, Engineering Design for Electrical Engineers, Prentice-Hall Inc, 1990, p.3.
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L.L. Bucciarelli
Block-diagram rhetoric
A block diagram follows, one that (literally) frames the problem solving method as a sequence of steps, done
sequentially, as one haltingly travels down the diagram. The possibility of breaking out of the sequence is
allowed at the evaluation step where backtracking to the previous stage is allowed1.
Define the Problem:
Cause of problem?
What is need? Requirement?
What are constraints?
Generate & Select Possible Solutions
Analysis
Synthesis
Evaluate Solution:
Consequences?
Is it reasonable?
How well does it solve problem?
Select Best Solution
Evaluation
Decision
Implement Best Solution:
Coordinate
Control
Action
This movement through time is all contained in a box. The action is all in the present tense. The box can be
set down anywhere in time; the method applied at any point in history - yesterday, today, or tomorrow. This
holds for the questions, e.g., “...Is it reasonable?” as well as for the imperatives “Evaluate solution” (now, at
this step).
The passive voice prevails throughout. Who acts at each of the steps is not specified. It might be a lone individual responsible for the whole; or a different individual might be engaged at each step; or a team of two or
three or five hundred might be charged with carrying the ball from top to bottom. Alternatively, one can interpret the diagram as showing the workings of a problem solving machine, a device, an artifact which, once fed
the causes, requirements, constraints and set in motion, cranks away synthesizing, evaluating, deciding and
finally delivering a solution for action. In this light, the diagram shows a timeless, universally applicable,
computer algorithm.
While the block diagram suggests that a problem is not “given”, very little is said about how a problem comes
to be defined as such, e.g., what simplifications and assumptions are required; what reductive and abstract
methods and notions are deemed appropriate; what detail, what resolution, what confidence is demanded. The
first block does not say Construct the Problem which would be more akin to what the first step is truly about.
What is implied is that the problem is there at the start, out there in the world - its causes to be discovered,
needs to be identified, constraints to be met - all waiting to be found, reduced down, and fit to an existing disciplinary mold.
This is a myopic vision of the challenges engineers face. Generally they are not handed a problem, complex
or simple, but must engage with others, usually, in the construction of the beast. This requires knowledge and
know-how too but it is not of the same sort that figures in the abstraction and problem solving process. In
1. This makes designing an “iterative” process. John Robinson, in “Engineering Thinking and Rhetoric”, Jour. Eng.
Ed., July, 1998, distinguishes between simple and compound engineering problems. His characterization of the differences between the two resonates with the distinction made here between object-world analyses and the challenges of
design.
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L.L. Bucciarelli
Designing Engineers1 I argue that the real complexity of today’s engineering task derives from the fact that
each participant, with different competencies and responsibilities, sees the object of design differently. (One
object, different object worlds). This makes designing a social process not wholly amenable to instrumental
resolution. Complexity is of a kind different from that which might yield to a computational algorithm. But it
is the latter that is seen as what engineers do; the only tasks that are seen as engineering tasks, that require
engineering knowledge and expertise are those that can be formulated as problems solvable using instrumental, quantitative analysis.
I turn now to look more closely at the nature of object-world knowledge - not all of it, obviously, but texts
more or less arbitrarily selected as illustrative of what is taken as fundamental in abstraction, model construction and the problem solving process.
Three dimensions of an electronics, object-world text.
This text is about the workings of the diode - a device, an artifact, which is a key piece of the furniture of an
electronics engineer’s object-world. The selection is from a textbook intended for students in an introductory
level course at university.2 It describes how this fundamental device functions. It uses pictures and mathematics and refers to life in the big world to describe and explain the diode’s behavior. Just what knowledge is
presumed, what knowledge the text aims to “deliver” and what is left out, and how the text’s rhetoric frames
all of this, is of interest.
In analyzing this text - or any text drawn from an object-world - I find it useful to speak in terms of three
dimensions - Logic, Data and Narrative. The logic is generally explicit, appearing as mathematical expressions and relationships embedded within the narrative. Data, if included, makes reference to life in the big
world, either explicitly as quantitative data, nominal values and ranges of parameters and variables, or implicitly as simply pointing to an event in the world. The narrative can be modest or ample but is essential - it’s
what makes the text an engineering text.
Now the electronics text:
7.4.1 Circuit Symbol
A semiconductor diode is a two-terminal device containing a single p-n junction. The general
circuit symbol for a semiconductor diode is shown in Fig. 7.10, along with its relationship to the
p-n structure. The arrow portion of the symbol indicates the direction of the forward current.
The diode terminals are labeled “anode” and “cathode,” terms that are carry-overs from the era
of vacuum tube diodes. Forward bias results when the anode is positive with respect to the cathode, and the diode carries a forward current. Reverse voltage requires the cathode to be more
positive than the anode, and the reverse current is limited to the small saturation current.
p-n Junction
Ohmic contact
Ohmic contact
Anode
P
+
i
N
v
Anode
Cathode
Cathode
Figure 7.10
Circuit symbol for a semiconductor diode.
7.4.2 The Exponential Diode
1. Bucciarelli, Louis L., Designing Engineers, MIT Press, 1994.
2. Senturia, S. D. & Wedlock, B. D. Electronic Circuits and Applications, John Wiley & Sons, New York, 1975,p.
184
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A theoretical analysis of the p-n junction structure yields a single equation, shown below, which
correctly describes both forward-bias and reverse-bias operation.
i = Is ⋅ ( e
qv ⁄ kT
– 1)
(7.8)
In the above equation, Is is the reverse saturation current of the diode, q is the electronic charge
(q = 1.6 x 10-19 coulombs), k is Boltzmann’s constant (k = 1.38 x 10-23 joules/K), and T is the
absolute temperature (degrees Kelvin). A convenient mnemonic is that the quantity kT/q has the
dimension of a voltage, and that at room temperature, the magnitude of this voltage is
kT
------ = 26 mV (at 300 K)
q
(7.9)
qv ⁄ kT
For v much greater than 26 mV, the exponential factor e
must be much larger than unity;
hence the “1” in Eq. 7.8 may be neglected. Thus, for forward bias with v >> 26 mV, Eq. 7.8
becomes
Forward bias:
i = Is ⋅ e
qv ⁄ kT
» Is
(7.10)
Equation 7.10 is a simple exponential relationship between i and v that agrees with our physical
reasoning that the forward current is much larger in magnitude than the reverse current Is, and
that the forward current increases very rapidly with small increases in v. For example, at room
temperature, with kT/q = 26 mV, the forward-bias current increases by a factor of 10 for every
60 mV increase in v. In reverse bias, however, with v << 26 mV, the exponential term in Eq 7.8
becomes small compared with the “1” and the current reduces to
Reverse bias:
i = –Is
(7.11)
It is seen thus that Eq. 7.8 successfully combines a reverse saturation current -Is with a large forward-bias current.
Although the derivation of the exponential relation in Eq. 7.8 depends on some physical idealizations, the result is in good experimental agreement with actual junction diodes, at least over the
major portion of their operating region (see Section 7.4.3 below)....
First consider the data - the connections to the empirical world - described. It has, as one of its purposes, to
ground the model of how the diode functions in the real world. Reference is made to the unit of charge in the
expression “q = 1.6 x 10-19 coulombs” and Boltzman’s constant, “k = 1.38 x 10-23 joules/K”. Both are universal quantities prevailing throughout the natural sciences; these expressions situate the text within the bigger
world of physics.
The grouping, “kT/q = 26 mv (at 300K)”, presented as a single parameter and evaluated at room temperature
(300 degrees Kelvin), further sets the context, informing the reader where, in the quantitative world of voltages, we are operating. Fixing this grouping of three entities as a single parameter clears up any ambiguity
about what other parameters are free to vary. That kT/q is to be taken as a single parameter is reinforced by
calling it a “convenient mnemonic”.
The parameters that can vary - the voltage and current, v and i - are also described quantitatively in the sense
that ranges are given. (The “reverse saturation current” has been described earlier on in the chapter). The
quantity “60 millivolts” again situates the reader with respect to the fore mentioned “...portion of their operating range”. Reference, too, is made to “...good experimental agreement with actual junction diodes” and “...at
least over the major portion of their operating region (7.4.3)”, though no quantitative measure of how good is
given.
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L.L. Bucciarelli
Consider the logic embedded in this excerpt; it is quite explicit; I can extract it from the text, filtering it out of
the narrative, and the numbers as well, and set it off on its own. As such, its meaning, its consistency, its
“truth” as hard logic remains unaffected, i.e.,
i = Is ⋅ ( e
qv ⁄ kT
q
For v » ----kT
q
For v « ----kT
– 1)
we have e qv ⁄ kT » 1 and so
we have e qv ⁄ kT « 1 and so
i = Is ⋅ e
qv ⁄ kT
» Is
i = – Is
Stripped bare of narrative, this coherent set of statements says nothing about the physics of a diode or anything in the world of electronics. The reader might just as well be studying mathematics. Of course, knowledge of mathematics of an appropriate sort and level is essential to knowing how a diode functions, knowing
that it functions in accord with this hard logic. But knowing the mathematics does not suffice to know that and
how a diode functions.
One might argue that it is the mathematics, the logic, that makes the authors description of a diode “true”. But
there is something missing here; for to claim that the criterion for truth is that the text satisfies (is consistent
with, can be expressed in accord with) the rules of logic taken as a meta-language of mathematical representation says nothing about the meaning of the variables and their relationship that appear in the object world of
electronic circuitry1. The meaning comes from the narrative within which the mathematics is embedded. The
mathematics is like a skeleton, lacking character, persona, life. The narrative, to which we now turn, adds
flesh to the bones.
I take it that the ingredients of object-world narrative include images as well as words. The figure is a local
fabrication, consistent with the author’s explanation of diode laid out in prior chapters. The boxes with connections and fusing of two different materials is suggestive but certainly not a scale drawing, not an accurate
representation of the real device. What does it really look like? The bottom figure, on the other hand, is the
conventional circuit symbol for a diode. Evidently the authors felt the need to bridge the gap between the
actual material structure of the diode and the conventional symbol.
The simplicity of this figure is noteworthy. It’s sparseness of detail reflects the abstract nature of the model. It
has no depth in the sense that it is timeless; there is no suggestion of where it came from other than the chronology of the abstraction process - the authors’ bridging the gap between the physical world and the symbolic world. In the use of the present tense throughout the narrative, it proclaims the eternal nature of this
knowledge, of this text, this figure, this device. “A theoretical analysis yields...” not yielded, not will yield.
“Forward bias results when...”; “Reverse voltage requires...”; and this has always been, is, and will always be
the case2.
The action of the narrative has the diode at center stage “...the diode carries the forward bias current...”;
“...the reverse current is limited...”; the diode can saturate; it can limit; carry. We don’t really see how the
device acts - how it does all of this. The description of function is behaviorist, that is, in terms of input-output
behavior. The device is a black box. It has appendages - things that stick out like voltages and currents and it
changes these in accordance with the mathematical logic. The representation is an idealization, an abstract
model; a “theoretical analysis” is cited as the source of the “single equation... which correctly describes”
diode operation but no detailed data are given.
1. Putnam, H.,Words and Life, (ed. J. Conant), Harvard Univ. Press, 1994, p. 330 ff.
2. No grue or green ambiguity here. Goodman, N., Fact Fiction and Forecast, 4th ed., Harvard Univ. Press, 1983.
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The diode is the only active agent in the narrative. All else is reported in the passive voice. “...terminals are
labeled”; “...the symbol indicates”; “A theoretical analysis...yields a single equation.” Who did, does this
analysis, who labels, draws, tests, derives is irrelevant. The use of the passive voice makes the knowledge
universal, standing independent of any particular person’s or culture’s perspective. The reader need worry little about where the author is coming from (as long as the English is well written); they need only sit back and
observe the diode’s action unfold - as it would any place in the world or the universe.
What must students know? What are they to learn? The authors presume that readers have more than a knowledge of the mathematics of exponential relationships. (Plugging into equations is not what engineering education is about, though this might be the only option for the student who avoids, or can’t grasp, the narrative in
full). Students must already have some familiarity and facility in speaking the language of electronic circuits.
The authors explicitly address this need in their Introduction
Learning electronics is in many ways like learning a new language. The richly varied combinations of devices and circuits form a body of literature that becomes accessible to those conversant with the fundamentals of device behavior (vocabulary) and with the laws governing
network behavior (grammar). Although the analogy can be overstressed, we urge the student to
approach this subject as if it were a language, seeking to acquire not merely a modicum of
understanding, but a conversational skill in working with the new terms. (p. 13)
As part of this they must know how to manipulate quantities in a way consistent with the appropriate system
of units (coulombs, joules, degrees Kelvin); the author presumes the student could verify that kT/q computes
to 26 milli-volts when the temperature of the device is 300 degrees Kelvin with q and k as given. They must
already know something about the variety and use of conventional symbols in drawing circuit diagrams; they
must be capable of physical reasoning about current flows and voltage potentials. It’s implied that the student
already has witnessed a diode in operation when the authors state that the governing equation “...is a simple
exponential relationship between i and v that agrees with our physical reasoning...” And they are expected to
be familiar with “vacuum tube diodes” as a precedent, and so will readily accept the labeling of the p-n junction terminals as “anode” and “cathode”.
The reader need not be concerned with how this black box might be joined with others to yield a bigger black
box. That will come shortly in subsequent chapters. Nor need she know what the device costs. That will
become a factor to consider still later, perhaps in a design course or on the job. We might say that cost remains
an “externality”. If the student wonders how it is made, what resources it requires, what waste streams are
generated in manufacturing, that is expending time he can not afford. (The pace of “coverage” is rapid). So
too if she takes a serious interest in the historical development of the device - beyond knowing where the
labels anode and cathode come from. The student is boxed in, constrained to focus solely on the mathematics
and the science of the diode abstraction.
The specialized rhetoric of an academic journal article
The article is titled “Multidisciplinary Analysis and Optimization of Discrete Problems Using Response Surface Methods1.” The authors’ present a mathematical algorithm to optimize the design of multidisciplinary,
non-hierarchic systems. “Multidisciplinary” refers to the different participants, from different disciplines,
who must bring their proposals and findings into harmony if the design is to be successful, none the less an
optimum. But the authors, when they get down to the hard analysis at hand, don’t speak of participants, only
of disciplines. “Non-hierarchic” is not defined but is to be understood as implying that all (participants) disciplines’ contributions are considered on an equal footing.
The introduction is but three paragraphs long and frames the object-world analysis that follows. It provides
the motivation for the effort - why the world of engineering design and product development needs such an
algorithm - and situates this current work with respect to the work of others. It reads as a mix of the proper
language of mathematical modeling of design processes and of more ordinary English.
1.
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Korngold, J.C., & Gabriele, G.A., Jour. Mech. Design, vol. 119, Dec. 1997, pp. 427-433.
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L.L. Bucciarelli
Introduction
In the design of multidisciplinary non-hierarchic systems, the design of one subspace is dependent on the design of other subspaces. Traditional methods used to solve this type of problem
include performing costly system iterations at all design points, or making “educated guesses”
that rely on designers’ experience. These approaches become unreliable as systems become
more complex and surpass designers’ experience. In addition, they are time-consuming
approaches that, in today’s fast-paced design environment, leave little or no time for evaluating
alternative concepts at the earliest design stages.
Multidisciplinary Design Optimization (MDO) is a methodology for optimizing large systems
which have many contributing disciplines with conflicting objectives.These disciplines often
interact with one another during the design process, requiring and/or providing analysis results
to and from each other. Attempting to optimize with respect to any single discipline without
properly accounting for these interactions will result in a sub-optimal design.
In applying concurrent engineering without the aide of any quantifiable means to manage the
trade-off issues, the final design will often reflect the negotiating skill of the individual participants. MDO provides a means to quantify influences of one subsystem on another and incorporates these quantities in a methodology for optimizing the system....methods have been
successfully applied to complex engineering systems to manage trade-offs between disciplines,
resulting in improved system designs through better understanding of the interactions between
the disciplines.
Then follows a claim that these “...methods have been successfully applied to complex engineering systems
to manage trade-offs between disciplines, resulting in improved system designs through better understanding
of the interactions between the disciplines...” but it is not clear whether these were applied before or after the
fact, that is, from the start of the design or in retrospect.
The next section “Description of Current Work” starts the object-world narrative and makes reference to student experimentation using the algorithm in a gaming situation. (The data dimension). The hard core mathematics, the rigorous logic, appears in the this and the following section.
Description of Current Work
This paper describes a new algorithm developed to efficiently optimize multidisciplinary, coupled non-hierarchic systems with discrete variables. The algorithm decomposes the system into
contributing disciplines, and approximates the discipline models with linear or quadratic
response surfaces. First and second order Global Sensitivity Equations are formulated and
solved to approximate the global design space. The global approximation is optimized using discrete optimization methods....
Global Sensitivity Equations
An important part of any non-hierarchic algorithm is the methodology for approximating system
interactions. Using parameter sensitivities as a measure of these interactions has proven useful
in hierarchic decomposition studies. Global Sensitivity Equations (GSE), which quantify the
sensitivities of non-hierarchic systems, were described by Sobieski (1990).
The complete system problem, before decomposition, has an input vector, x, of design variables
and an output vector, y, of state variables. State variables represent analysis results... The local
state variables are contained in sub-vectors ya, yb, and yc. The state variables are actual values,
not approximations, and solved through a total system iteration. The subspace analysis can be
represented with implicit models as shown in Eq. (1).
A ( x, y a, y b, y c ) = 0
B ( x, y a, y b, y c ) = 0
(1)
C ( x, y a, y b, y c ) = 0
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The GSE provide a method for calculating the system sensitivity, dy/dx. The sensitivities mea-
sure the change in a state variable for a given change in design variables. The Taylor
Series approximation to the system sensitivities based on the values of the state variables and the subsystem sensitivities with respect to x and y provide the basis for
quantifying subsystem interactions.
The authors then sketch out, in mathematical matrix/vector notation, the system of equations whose solution
will give the first and second order approximations to the sensitivities; e.g.,
Equation (2) [not shown] provides a first order approximation to the sensitivities dy/dx. Second
order GSE approximations are found by differentiating the first order GSE relations with respect
to xk, with k = 1...n and evaluating all the terms using the chain rule. The GSE can be rewritten
as
dy = ∂y
A ⋅
d xj
∂ xj
(3)
My intent is not to describe the construction of the algorithm in detail, but rather only to show enough so that
the reader has some sense of the rhetoric of the main body of the article.
The first thing to note is the difference in voice of the Introduction and of this extract from the main body.
Both are written in English. Yet it is almost as if they were written in two different languages for two different
audiences. The Introduction reads like ordinary English; one’s everyday, common knowledge suffices to
grasp what is meant to be communicated there. We learn that traditional methods of design no longer suffice.
Educated guesses will no longer suffice. Designers’ experience will no longer suffice.The many contributing
disciplines engaged in a design task have conflicting objectives. This requires interaction and the shuffling of
analysis results back and forth. Optimization is not possible if you do not account for these interactions. Optimization with respect to any single discipline will be sub-optimal and the final design will depend upon the
negotiating skill of the individual participants. This, evidently, is to be avoided. You need to have quantifiable
means to manage trade-offs and optimize the design. MDO will do this.
Beyond the Introduction, the voice of the meat of the piece is that of the applied mathematician. The audience
is no longer the general reader, nor the undergraduate student, but rather the authors’ peers - other faculty and
graduate students of engineering, in particular, those who do research in the same domain. The piece differs in
other ways from the diode text: Whereas the latter is meant to prepare the engineering graduate for objectworld work, i.e., to design, build and test electronic circuits and subsystems, this text addresses a task that is
more managerial in nature, as evidenced in the Introduction. The authors are concerned with process; how do
you manage to (optimally) reconcile the different objectives of the different disciplines that contribute to the
design of a product or system as a whole? Their response is - take it out of the hands of the participants from
different disciplines and hand it over to the machine, a computer programmed with an MDO algorithm.
The focus is the development and evaluation of the algorithm; the construction and testing of a mathematical
method, a way of modeling, that will tell the user of the method how the “global” properties of the product
will (quantitatively) change when the different design variables associated with the different “local” disciplines change. The authors go further; they want to “optimize” the global design.
Just what this means in general is not clear, though in the “demonstration” of the method included toward the
end of the article, they optimize on the “profit” defined as the difference between the product of the Demand
and the Price and the product of the Production Volume and the Total Unit Cost. How costs are defined and
associated with each and everyone of the design variables of the different disciplines is not explained but this
is not surprising - most of the intended audience would find it a distraction - since the focus is on the mathematical method - what it is, how it was constructed, what accuracy one can expect, how numerically it might
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go wrong. In a related article1, the reconciling the different objective of different disciplines is made more
clear: Each discipline is allowed its own “objective function” to optimize. A “global objective function” is
then constructed as a weighted sum over the individual discipline objective functions. The weights are said to
be given “a priori” - which is a convenient way to dispense with the messy negotiation that is ordinarily
required in reconciling the proposals of the disciplines.
The logic is not as clear cut as the mathematical model of the behavior of a diode. If I were to cut out the
mathematical expressions and string them together in a box and attempt to make them stand as mathematics
alone, I would have to add many words of explanation to justify moving from one step to the next, none the
less include many caveats. Any extraction of the mathematics with the aim of having it stand on its own could
not be accomplished without extensive commentary, even going beyond what is there in the text. It’s not that
the mathematics as mathematics is advanced; it’s that it is messy. In their explanation of the algorithm, in
search of an optimum solution to any design problem that requires the reconciliation of the objectives of different disciplines - as most design tasks do - the authors are dealing with discrete as well as continuous variables; they construct a system of equations in which some variables are important, others not so, and the
method is supposed to sort that out. Then too the mathematics is a mix - a hodge-poge of the ordinary differential, of partial differentials, of statistical sampling, of regression analysis, of numerical schemes for finding
maxima and minima of functions of more than one variable. It’s a veritable montage of machinery crafted to
automatically reconcile the requirements of different disciplines in design. The authors’ construction has the
character of a Rube Goldberg invention with many moving parts subtly positioned and adjusted to output an
optimum design.
Turning to the data dimension: The connection to the big world is tenuous. The authors speak of “experiment” and of “demonstration” but the former is strictly numerical experimentation, accomplished within each
of the disciplines.
That is:
The [statistically] designed experiments sample the implicit models to generate data for the multiple linear regression methods that are used to create an explicit quadratic response surface for
each state variable. The response surface has the general form of Eq. (7).
T
T
y = y 0 + ∆ x ⋅ H local ⋅ ∆ x + ∆ x ⋅ g local
(7)
The “demonstration” is done by students, on multimedia workstations at the university.
The algorithm described above wa demonstrated on the Design and Manufacturing Learning
Environment (DMLE), a discrete multidisciplinary problem (Sanderson, 1993; Korngold et al.,
1994). [my emphasis]. The DMLE is used to teach principles of Concurrent Engineering to
engineering and management students by providing an exercise in product design.
In this respect, the demonstration is akin to a video game, like Sim City, a mathematical model with variables
changed and consequences observed all in the search for utopia. This is not to deny the educational benefit of
a computer simulation capable of introducing the student to the complexities of concurrent engineering but
certain deficiencies are evident if the purpose is to have the method used in the big world of design. Whereas
the idealization, the mathematical model of a diode, can be said (as the authors do say) to be an accurate and
useful picture of the real world behavior of that device, the same can not be said for the optimization algorithm we have here.
1. Tappeta, R.V. & Renaud, L.E., “Multiobjective Collaborative Optimization”, Jour. Mech. Design, vol. 119, Sept.
1997, pp. 403-411.
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First, there is no evidence given that this particular method has actually been used as intended in the real
world. Has any firm, small or large, actually automated the design process to the extent that is claimed and
relied upon its use ab initio, not after the fact? The articles’ references are all to academic resources e.g., textbooks, presentations at engineering society conferences, other journal articles and several doctoral dissertations, including the that of the lead author.
But more serious is the neglect of any direct engagement with the real complexities of multidisciplinary
design, the complexities pointed to in the Introduction but denigrated and ignored throughout the rest of the
article. It reads as if designing need no longer rely upon the negotiating skills of the individual participants in
the effort. The algorithm will reconcile all. This is perhaps too extreme: In reality, the method might very well
prove useful as a means for improving the negotiation among participants in design if they adopt it - not as an
omniscient optimization machine but as framework for arguing about input values, conjecturing costs, critiquing one anothers’ assumptions, and innovative design moves - but this is not the message conveyed in the
Introduction. And it’s the rhetoric that is at issue here and what it implies about knowledge, what is valued,
what is seen, ignored. In that sense, my observation is not extreme.1
The narrative as such is specialized, compact, laden with acronyms and references to authority and, as is the
norm, written in the passive voice. It’s not just that the action is done by who knows who, e.g., “Traditional
methods used [by whom?] to solve this type of problem...”. It’s that agency is attributed to bits of the method
itself, e.g., “The decomposition process divides the x vector...”; “The sensitivities measure...”; “The experimental design specifies...”2. While in the Introduction, real persons are active, (although incompetent), in this
section, the persons responsible for designing within the (conflicting) disciplines have receded into the background. The active agents are now the discipline models. Who constructs these is irrelevant, their fabrication
assumed unproblematic - products of a confined, straight-forward, object-world task. Just as in the diagram of
the problem solving method, the algorithm and its parts holds center stage.
The implication of this anthropomorphic assignment is that the method can stand on its own, uncontaminated
by human intervention. There is nothing to suggest that the algorithm might need extensive effort to make it
suitable for use in a setting other than educational, where canned modules of a Design and Manufacturing
Learning Environment, and an on-line tutorial, all written specifically for a single product (a slide projector),
provide the student with all the tools he or she needs to optimize discrete problems using response surface
methods. One can envision an intense and lively negotiation of engineers and managers attempting to adapt
the algorithm to their own purposes. This in itself might be taken as the design task, the subsequent running of
the algorithm so refashioned a product in its own right.
The use of acronyms - numerous in this article - has the same effect: True, an acronym is effective as an efficient short-hand for a cumbersome descriptive phrase but it does more than that, namely, it confers on the
object - in this case, computational method as object - a solidity unwarranted by the nature of the beast. It
implies sharp boundaries around method, suggesting the whole can be put in a box and distributed (or sold).
While it is true that a computational algorithm can become a product on the shelf, the methods described here
are not there yet. The acronym stands in for some principles and concepts of numerical analysis, not for an
item of shink-wrapped software.
What does the reader have to know in order to grasp something that may be relevant to their interests? The
article assumes a knowledgeable reader, one who has read Sobieski’s (1990) description of the GSE, who is
already familiar with Renaud and Gabriele (1992) development of the SGA algorithm, knows about Hessian
updating schemes (SR-1, BFGS, DFP) used to improve optimization performance, and is sufficiently familiar
1. The method might also prove useful in the final stages of design, helping to fine tune the values of design parameters. In one of the references, cost savings of 13% were claimed in the design of a “generic hypersonic aircraft configuration”. Sobieski, J.S., Tulinius, J., “MDO can help resolve the designer’s dilemma”, Aerospace America, Sept. 1991.
But note that this was a “paper study”. The rhetoric of this article points to more complete reliance on the MDO algorithm from start to finish.
2. It’s as if the authors had read and adopted the actor-network theory. Latour (1988), The Pasteurization of
France; Callon (1986), `Some Elements of a Sociology of Translation: Domestication of the Scallops and the Fishermen of Saint Brieuc Bay'
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with Renaud et al. (1994) use of successive simulated annealing (SSA) to optimize mixed continuous-discrete
NLP.1 Knowledge of numerical methods is essential; knowledge about the way designing is actually done is
not. The exercise is one in applied mathematics of a crude kind, passing under the banner of mechanical
design.
The mundane rhetoric of a company memo.
My next example is a cruder text written by an engineering employee of a firm devoted to the development,
production and sales of photovoltaic modules and systems. The author is engaged in a process of responding
to a request for proposals for a photovoltaic supply to power a remote site. The memo summarizes the objectworld efforts of a consultant hired to model the cooling and other energy requirements of a facility located in
Middle East. It was used in the negotiation and writing of the response to the RFP. As such, it presents only
the results of the object-world analysis but presumes that the reader knows what that entailed.
The Guard House project... comes to us from the [Gulf States] Petroleum Co. A two-man guard
has been placed at every well head in the [Able] gas field, and every pair of men is being provided a small hut with, eventually, electricity. Because most of the huts will be remote and too
far from the nearest a.c. grid, photovoltaic power has been suggested.
The huts as they are presently being constructed are approximately 23 ft. by 48 ft. of uninsulated
8-inch concrete block walls and a 6-inch concrete roof. This design would require two 16,000
BTU/hr air conditioners to keep a building at a reasonable and stable temperature. These air
conditioners would require 230 Vac at 10.9A, with a power factor of 0.89. For economic reasons, this would prohibit PV power. Our job was to determine how much building insulation
would be needed to bring down and optimize the cost of a PV system, including building modifications, PV system, and all appliances (e.g., air conditioner, lights, refrigerator, water pump,
etc.).
A computer analysis was performed on seven insulating schemes by [John Doe - a contractor].
The results gave the total heat gain per scheme, building modification costs, and air conditioner
consumption per worst case day. With these figures a total building energy consumption was
calculated and a PV system was sized to meet those requirements. Table I shows the breakdown
of energy consumption, Table II shows the breakdown of costs to the customer (budgetary estimates)
The data dimension dominates, is at the fore, in this text. The subject of the memo is a particular artifact, a
“hut” that already stands in the world. But the uninsulated, 23 ft. by 48 ft. structure, with walls of 8-inch concrete, needs upgrading with perhaps two 16,000 BTU/hr air conditioners - the latter we can assume also exist
in the big world, available “off the shelf”. Yet although this is hard data, I could not draw a very detailed picture of this hut, nor of the air conditioners for that matter. (How big are they?) Properties and features of these
worldly artifacts are quantified but only those properties that are essential to constructing and running a heat
transfer (object-world) analysis are specified.The author of the memo is not out to provide information sufficient to construct a detailed, visual representation of the house but only what is needed in order to construct a
mathematical model of the interplay of heat sources and flows into and out of the system.
The logic is hidden from view. In contrast to the journal article, the construction of the mathematical model
and its execution in this case is of little interest; it is presumed standard, conventional and so well-established
that it requires no comment. (The contractor who performed the object-world analysis was well known to the
firm). The engineer has faith in his ability to do the job. Like the journal article, the aim is to optimize a
design - to determine the size of a photovoltaic system and the extent of building modification that will minimize the cost of the system while keeping the building “...at a reasonable and stable temperature.” But no all-
1. We can ask who does read the published article. Does anyone outside the group of researchers who work in this
specific domain find it useful? In particular, does anyone outside the academy read the piece?
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powerful algorithm was used to reconcile the different features; an analysis of several different possible configurations was made and the results, the bottom line, of each displayed in a table.
True to form, the passive voice is used throughout the narrative. “A two-man guard has been placed...” The
compaction of predicate and object is characteristic of engineering texts; like the construction of acronyms, it
binds into one thing a more complicated arrangement. It has the effect of setting the two men off as an artifact; the men become one thing; like a two-headed hydra, they spend their days in the heat of the Middle East,
guarding a well-head. Who is providing the pair of men with the small hut and eventually electricity? Who is
doing the construction? And who is recruited to man the station? It is not necessary to identify the persons or
institutions responsible for all of this. Such knowledge is not required to carrying out the heat transfer analysis. Likewise, just who suggested that PV power be employed may matter to those concerned with the “politics” of the proposal review business, but for the purposes of “sizing” the photovoltaic system it matters little.
Even restating “A computer analysis was performed by John Doe” as “John Doe performed a computer analysis” is apparently not acceptable. The text as it stands puts the computer analysis up front suggesting that it
doesn’t matter who did the computer analysis; we could even leave off “by John Doe” altogether and it
wouldn’t matter1. Putting John Doe up front, on the other hand, makes him an active agent; we can’t erase
him from the picture now. It also implies that if Jane, instead of John, were to do an analysis, the results might
differ. The fact of the matter is that they would no doubt differ some, but presumably not in a critical way.
From a big world perspective, the kind of knowledge and reasoning that is required to construct the model,
carry out the heat transfer analysis and estimate the costs of a range of photovoltaic power systems is thoroughly instrumental and quantitative. One needs to know the electrical power requirements of the appliances
to be powered by the PV system. One needs to have access to data that defines the solar resource on site. One
needs to have learned how to construct heat transfer models, how to imagine a control volume through which
heat flows in and out over the course of a day, the values of parameters which characterize the way different
materials, such as concrete, transfer heat and one should, evidently know what qualifies as a “reasonable temperature.
One need not know what the hut looks like or who the persons are that are to be stationed (confined?) there to
grasp the intent and meaning of the text. If the reader begins to wonder about life in this “hut” with or without
air-conditioning he or she would be wasting his or her time (“...the huts as they are presently constructed.”
Does that mean that there are two men living there without air-conditioning?). This kind of knowledge - about
how people cope when living under such conditions - is irrelevant to figuring the cost/benefit of installing a
photovoltaic system atop the roof of this hut. (The heat generated by the inhabitants is a factor that might be
important to consider, however). In this respect, the reader need not know what a reasonable temperature is.
All knowledge that does not contribute to the construction of the model and running of the simulations is
irrelevant to the task “our job” - to determine the optimum size of the PV system. What’s outside the envelope
of the control volume is irrelevant, simply not there - other than those meteorological elements that the analysis requires.
On the other hand, a change in attitude toward the “two man guard” could render these fore mentioned questions relevant to the design task. If, for example, we allow the gurardsmen to actively participate in the operation of the system as an element in a control scheme - e.g., managing the electrical load on the system to
keep battery storage costs down, adjusting the thermostat to minimize energy requirements - then who these
men are, their competencies as well as their comfort needs, do become important features to take into
account. Taking this one step further, the guards might even be called upon to participate in the design of the
system.
The rhetoric of engineering within object worlds weighs in against this way of seeing. In the construction of
an algorithm to reconcile disciplines, as in the journal article, or in the design of a hut for a two man guard, as
is the case here, persons enter the picture only in what might be called a deformed state - as generators of discipline models, as an object subject to temperature control. There is not much to them. In my next selection,
1. Why didn’t they leave out his name out altogether? It’s a question of identifying sources, (ethically) accounting for
the work, as well as assigning responsibility.
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drawn from a chapter in a book on manufacturing ergonomics, person, as machine operator, is more than onedimensional, but still an artifact.
The rhetoric of the work- an ergonomic object-world text.
Those responsible for the design of manufacturing systems do reference people explicitly in their analyses.
How are they construed rhetorically? This next selection1 is meant as an overview of “Measurement in Manufacturing Ergonomics”; it describes in quite general terms some instrumental methods of the field but does
not delve into the details of this “applied science”. Like the excerpts on problem solving we started out with,
this selection reveals little about the logic, data and narrative of ergonomic, object-world work. It is introduction to the world, not a revelation of the science of that world. The chapter starts with a statement of purpose.
To understand the purpose of measurement in manufacturing ergonomics it is instructive to consider a traditional definition of the profession: the scientific analysis of human characteristics,
capabilities and limitations applied to the design of equipment, environments, jobs, and organizations. This definition clearly articulates that the profession is interested in both human capabilities and limitations. It also insists that ergonomics is an applied science...
The author has listed physiology, medicine, and psychology as proto-ergonomists “...although it is clear that
specialists in these professions may not necessarily consider themselves to be ergonomists.” Evidently these
are the sources of the science that is applied. As to the scope of the field:
The scope of ergonomics is virtually unlimited; it can be applied to any area of human experience....[It] can be applied to any area of design of the technological or organizational worlds and
to modification of the natural world.... Commonly, ergonomists limit their applications to analysis, design, test, and evaluation of the human interfaces with the technological, organizational,
and natural worlds.
One might wonder what area of human experience might be off limits. It is characteristic of the abstraction
process in engineering to claim universal applicability standing within an object world, but it can do so only
by severely constraining the totality of attributes of humans “seen” as relevant. The text continues with a listing of general attributes that are important to manufacturing ergonomics’ purposes. Quality and productivity
we can associate with the manufacturing process; safety, health and motivation with the machine operator,
though the text implies that they are all of one and the same kind.
MANUFACTURING ERGONOMICS PURPOSES
Manufacturing and manufacturing ergonomics have the purposes of quality, productivity, safety,
health, and motivation. The quality purpose [my emphasis] in manufacturing is to ensure that
the human operator can perform his or her job correctly so that the end product will be to the
customer’s liking. The ergonomics tools that can be applied to this purpose include selection,
job assignment and training (changing the operator), and error proofing (changing the components or manufacturing tools and equipment). The productivity objective may be achieved by
the application of lean manufacturing principles that reduce non value-added activity, such as
walking, and by applying work measurement principles to the design of tasks....The safety purpose of manufacturing ergonomics is the avoidance of acute incidents (e.g., slips, falls, or wrong
decisions) that may cause injuries to the human operators or damage to the hardware systems or
products.
1. Peacock, Brian, “Measurement in Manufacturing Ergonomics”, in S.G. Charlton and T.G. O'Brien, Handbook of
Human Factors Testing and Evaluation, Lawrence Erlbaum, New Jersey, 2002.
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Work-related musculoskeletal disorders [health?] have taken precedence as the principal purpose of manufacturing ergonomics in the past decade. Epidemiological studies have shown that
the interaction between inappropriate postures, movements, high forces, and high repetition
rates can give rise to musculoskeletal disorders of various severities. However, not everyone
doing the same job has the same outcome. This observation highlights both the raison d’être and
the Achilles heel of ergonomics - human variability....The intrinsic nature of many contemporary manufacturing processes- repeated short cycle work - may be productive, safe, and relatively error free, but it is not intrinsically motivating, and it may be associated with work-related
musculoskeletal disorders....
It is clear that we are again dealing with a severely deformed person, now as human operator of manufacturing machinery. Starting from the rear of the author’s list of purposes, motivation receives very little attention,
not just in this excerpt but throughout the chapter. And while health seems to receive more attention, it’s
health as musculoskeletal disorder, not as well being, that has taken precedence over the past decade. Safety
seems to require its own standing because it refers as much to the damage to the machinery of production or
the product itself as it does to the operator. This confounding of human with hardware is evident again in the
definition of quality, i.e., to improve quality you can change the operator or change the machinery. Finally,
walking becomes a “non value-added activity”, denying the god of productivity its due.
We continue the excerpt:
Consideration of these five purposes of ergonomics in manufacturing indicates that ergonomics
may be applied simply to the maximization or minimization of any one purpose. Ergonomics is
neutral. There are clear trade-offs - greater demands for productivity may result in lowered quality, health, and motivation. Conversely, greater concentration on quality and safety may result in
lower productivity... These trade-offs, per se, are not the province of ergonomists but rather of
their employers. The ergonomist has the responsibility to measure, analyze, articulate, and evaluate the trade-offs and explore ways of optimizing [my emphasis] the multiple outcomes or
maximizing a particular outcome, if that is the purpose of his or her activity.
Characteristic of engineering rhetoric of problem solving, ergonomics as applied science can be employed to
maximize or minimize whatever attribute one desires. All five - quality of the product, productivity of the
manufacturing process, well being of the machinery and the operator, and motivation too, apparently, can be
“traded off”, one against the other. The text clearly circumscribes, (bounds, limits) the responsibility of the
ergonomist - the task is to measure, analyze, articulate, and evaluate...explore ways of optimizing...” but the
responsibility for deciding which option to implement is not his/hers but their employers. Ergonomics is neutral.
Later in the chapter the author sets out work measurement principles which are to be applied in the design of
tasks.
...in proactive ergonomics there may be no actual operator or job, rather there may be a physical
mock-up of the task, or a laboratory abstraction, a similar job, a computer model, or just a drawing. Under these circumstances the operator is represented by population data, such as anthropocentric or biomechanics tables, an anthropomorphic model, or an experimental subject cohort
that is assumed to be representative.(p. 159).
The human operator becomes the same kind of object as the machinery of production. The human is not the
individual person but rather a member of a statistical ensemble. Variation among the members of this ensemble creates a problem; human variability is the Achilles heel of ergonomics. Like Taylorism, the person is
seen as a black box of sorts with statistical properties relevant to ergonomic analysis alone. Just as one can
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quantify objects, so too one can account for this variability through instrumental, quantitative means and construct models of humans behavior and design a manufacturing system in accord with their dictates.
In contrast to the prior texts, the demands on the reader are light; in large part, this may be attributed to the
introductory, overview intent of the author. Still, the reader should be familiar with the mathematics of statistical analysis, know where to go to find biomechanics tables perhaps, and know what a musculoskeletal disorder is like, though it’s not necessary to have had one. But the most important prerequisite to understanding is
a willingness to see the world of human operators and (their?) machines as a world of machinery alone. Having read Taylor would help in this regard.
Epistemic implications of engineering rhetoric
One might argue that engineering rhetoric is ideally suited to the purposes of the authors of engineering texts.
Engineering, after all, is about making ever more advanced technologies - “for the benefit of all mankind, as
we used to say - products and systems made mostly of material ingredients and designed in accord with
nature’s laws. One would expect then the language of engineering to be like the language of the natural sciences - and it is. Like science, so fundamental to object-world work, the passive voice prevails. Like science,
only those things that can be measured are significant and relevant. Like science, engineering knowledge is
timeless and universal.
But engineering does not deal with nature alone. The productions of engineers are developed and designed for
human use, for human benefit according to the (persuasive) rhetoric of the professional societies. Yet the
humans we read about in these texts are not whole. Just how they appear depends upon what might be called
their “object-world persona”. Their properties, considering them as objects, vary from one world to the next.
In the guard house, they are heat sources and subject to temperature control; in manufacturing ergonomics,
they are an anthropomorphic model, a statistical aggregate who can move, lift but only within certain constraints; in an algorithm for optimizing a multi-disciplinary design, they are but vaguely identified sources of
discipline models. In the block diagram showing how to solve a problem, people (or is it just one person?) are
banished completely.
Nor is there any cultural contingency. Engineering knowledge, again like science, appears to be culturally disjunct, general, available for globalization as it stands. A heat transfer analysis of a guard house in the Arabian
peninsular is just like the one you do of a single family residence in Boston - except for some changes in the
input parameters and prescribed temperature conditions. In this sense, absent historical and cultural contingency, engineering knowledge is “neutral”. The conventional symbol for a diode is known and displayed in
the same way around the globe. The algorithm for optimizing a design will run on any modern computer. In
this, engineering mimics science, and its claim to universal belief.
Instrumental, strategic thinking, and its handmaiden - quantification - reign. If we believe everything has its
price, then everything can be quantified - a person’s wants as well as materials, service costs, manufacturing
costs, etc. If this is the case we might claim to have a way to reach across object worlds with our analyses and
instrumentally reconcile the conflicting proposals of participants in design. But note, to move from objectworld results (quantity) to global costs measure generally requires ad hoc, extra instrumental, negotiation and
exchange. The “problem” at this global level is of a different sort than that of any object-world.
Turning to the engineers (as humans) designing - even they become a problem to be solved. In practice, while
generally engineers seek to find an optimum solution to a problem, rarely does the end-state of an engineering
task take the form of a unique, and “best”, solution, one justified in quantitative terms. Yet the texts claim this
as an ideal. How could it be otherwise if we accept the rhetoric? For if there were more than a single, appropriate solution to a task and, lacking an instrumental analysis with which to decide among these, then to proceed (with the design, the troubleshooting) would require negotiation among participants in the task and the
use of other than instrumental reasoning and judgement. The situation becomes a “not well-posed problem”
and would lie outside the scope of applicability of engineering knowledge, or so the rhetoric implies. The tendency in this situation is to force the problem to fit an instrumental analysis by ad hoc means such as constraining the behavior of the ingredients - human as well as natural or artificial - and/or ignoring those
ingredients that can’t be easily quantified, yet expecting still to say something significant.
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An alternative is to pass the buck to others. Our ergonomist defers to his/her employer when there are tradeoffs to be made between productivity and worker health. Our journal article authors rely upon the a priori for
weighting factors to include in their optimization algorithm. Wilcox hides the give and take among participants in design as a slender line indicating iteration - until a single solution is obtained. For engineers to
explicitly recognize the play of social exchange and negotiation in their work of design, none the less stress
its importance in their teaching, seems impossible given the accepted rhetoric. Communicative rationality1 is
not our thing. This is why design is perceived as difficult to teach and why research in design takes on such a
scientific, abstract, mathematical flavor and why other forms of knowledge that are not useful in solving an
engineering problem, so narrowly defined, get shunted off as someone else’s responsibility to teach, such as
the “ability to communicate” (take a writing course) or “engineering ethics” (take a philosophy course) or
even the ability to build the prototype (take a shop course).
There is evidence accumulating that this way of thinking, this way of seeing, will no longer suffice. Global
project work has brought to the fore the challenges of making clear and harmonizing the plans and proposals
of different participants in design; a block diagram of process now must acknowledge the coordinates and
interests of participants dispersed around the world. Ever more sophisticated and powerful information processing and computational tools and methods shifts the emphasis at work from tedious calculation and hand
analysis to model making, to world making. The scope of engineering concerns broadens and tests the rhetoric and ways of thinking. Increasing participation of the customer, the user, or client in the design of new systems also challenge traditional ways of modeling and ultimately of seeing and thinking. If and when these
challenges are met, and only then, might what qualifies as legitimate engineering knowledge be seen to
encompass more than instrumental reasoning. The rhetoric, in turn, will necessarily be different.
1. Habermas, J., The Theory of Communicative Action, vol. 1, 1984; vol. 2, 1987/
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Copenhagen October, 2005
L.L. Bucciarelli