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OCR for page 5
5
in'
DECISION MAKING IN ENGINEERING DESIGN
The role of decision making in an engineering design context can be defined in several ways.
As shown in Figure 2-1, the decision process is influenced by sets of conditions or contexts.
| Business Context-Controlled |
Input
\ Decision Tools and Processes Data Management Systems /
\ Culture /
\ Product Structure Product Business Cyc:
Technology Level
Requirements \ /
Constraints ~ Decisions
a/ Decision
\~ Process J
Knowledge Base '> ~` Qualifiers
Options / \
/ Customer Surveys \
/ Competition ~
/ Legal Considerations \
/ Government Regulation Unplanned Incident\
/ State of the Economy \
,
I Environment Context- Uncontrolled I
Figure 2-1 Decision process in the context of business and environment.
OUtpUt
The business context represents the long-term view of the engineering company and is largely
in the control of the company. The environmental context, such as the state of the economy, is not
controlled by the company and must be considered a variable. The input context, such as the
completeness of and variation in requirements and constraints, is established by the customers as
is the output context, such as state of readiness to implement decisions, risks, and qualifiers.
Closing with the customer is an iterative process reconciling the customer's needs with the
developer's design capabilities and requiring collaboration and experience with the product. In the
real world, decisions made by the experts can be delayed and overturned by higher-level
management based on poorly defined or unstated environmental issues.
In today's engineering environment routine decisions can involve geographically dispersed
teams working under challenging cost and timing constraints. Under these conditions, the quality
of most decisions can be improved through the application of computer-based tools. These tools
can be put in the following categories: knowledge-based engineering, workflow, and
collaboration.
5
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6
A PPR OF CHES TO IMPR O VE ENGINEERING DESI ON
Knowledge-based engineering tools provide computational representations of engineering
design rules, allowing engineers to execute modeling, analysis, and optimization far more quickly
while staying within design practice constraints. This frees the engineer from the routine portions
of analysis and allows more time to trade design options, thereby improving design decisions.
Work process management tools can help manage the execution and coordination of tasks via
the Internet by routing and tracking work throughout the design process. These tools manage both
schedule execution and process compliance (such as configuration control) globally.
Collaboration tools, including Internet-based conferencing and graphics sharing, help the
day-to-day working relationship between engineers at distributed locations. This ongoing real-
time collaboration results in timely and coordinated design decisions.
Figure 2-2 shows the character of decisions affected by two elements of the business context.
On one axis is the duration of the decision and on the other is the criticality of that duration for
the product. A decision in this context is a choice made by the design engineer for a particular
solution for the problem at hand. Decisions with long-term impact often are irreversible after
implementation; therefore the decision maker must seriously analyze the decision. A large
number of short-term incremental decisions can, however, be made relatively risk free. All
decisions can be plotted on the context chart and fall into one of the relevant subgroups.
Increasing investment of scarce time and other resources in the decision process is appropriate for
the decisions that are critical or irreversible.
| Long-term |
I Irreversiblel
Short-term
Incrementa
Cal
_.
3
o
CL
In
In
._
In
3
m
Few make-or-break
. . .
aeclslons
decisions ~
Many significant \
. .
c eclslons
\
Small
Schedule,
Performanc
Criticality
Figure 2-2 Decisions framed in relevant context.
Big
Safety
Correctly assessing the context for making a decision is important because it dictates the level
of effort and type of tools applied to the decision process. The most critical decisions often
employ multiple tools, preferably logically sound and internally consistent in all circumstances.
Framing addresses how to pose the decisions, and is described more fully in Chapter 3.
s
i
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IMPLICA TI ONS FOR ED UCA TI ON AND RESEAR CH
Table 2-1 Framing a Decision in the Relevant Context
7
Routine Decision
· Small impact
· Reversible
· Short term
· Data available
· Standard decision process
Make-or-Break Decision
· High impact
· Irreversible
· Long term
· Safety or product liability critical
· No well-defined decision process
Context
Decision
Processes and
Tools
· Use standard or automated
· .
decision process
· Supporting data available
· Use simple decision tools
· Small team or individual
· Low-level reviews
· Use a variety of decision processes
· Generate supporting data
· All functions involved
· Large team including management
Decisions that make or break the business (Table 2-1) are often laden with trade-offs, which
are usually complex in nature. Further, preferences for these attributes typically differ across
stakeholders, such as customers, operators, and manufacturers. Framing and resolving these
trade-offs are time pressured with much potentially relevant information to be considered. These
trade-offs are also subject to many uncertainties regarding customer buying preferences, user
abilities and preferences, technology maturity and availability, and competitive advantages of
possible functions and features. These trade-offs usually cut across disciplinary boundaries in
terms of balancing weight, power, speed, cost, and economy of use. In some cases these trade-
offs are resolved by fixing design requirements, which makes design more tractable but increases
the chances of noncompetitive solutions because of a restricted ability to trade off design options.
Most contemporary design methodologies avoid premature freezing of requirements.
The complexities just portrayed result in multi-disciplinary teams for most design efforts. The
notion of such teams once implied multiple disciplines, such as mechanical and electrical
engineers, working together. More recently, however, multi-disciplinary has come to mean
engineers, industrial designers, marketing and sales professionals, and finance experts working
together. This enables richer, more comprehensive trade-offs across form, Unctions, features, and
price. This challenges many design decision tools because of the bias and limitations of
individual disciplines. Richness in this context refers to the wealth of relevant information and the
many players in the decision making process. Because conceptual and mathematical
representations of different disciplines do not easily mesh, it is difficult to reach common
analytical solutions. Either each discipline tends to sub-optimize their piece of the problem or,
more likely, decisions are made more subjectively through negotiation rather than calculation.
Economic attributes ranging from financial metrics to consumer utility models cut across all
disciplines, especially when supported by the methods and models of probability and statistics.
These models and methods allow exploration of the intersections of market-driven preference
spaces and technology-driven physical spaces.
Economic attributes drive actual design decision making, regardless of the extent to which
the methods and tools include such attributes. Similarly, uncertainty and risks are pervasive and
must be addressed. These overarching issues must be considered regardless of the decision
making tools used. These cross-cutting approaches provide the context for design engineers to
synthesize and analyze ways of providing desired functions and features within economic
constraints, as well as quality, reliability, and maintainability considerations. Analyses of signal
it:
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8
APPROACHES TO IMPROVE ENGINEERING DESIGN
flow, stress characteristics, and control stability are in this way integrated into the overall design
context.
Traditional design engineering is still pursued, but it is less and less isolated by trade-offs and
optimization within a discipline-limited set of purely physical variables. This is nowhere more
evident than in the linkages of design variables to economic considerations. Representations of
interactions of product and process variables on costs have become central to product realization
in many domains. Multi-attribute, multi-stakeholder design contexts, laced with uncertainties and
rich in information, are the norm. The framing of critical design decisions across contributing
disciplines is central to success in such contexts.
3-
5
Representative terms from entire chapter:
engineering design
