Adonis Diaries

Posts Tagged ‘Multidisciplinary view of design

Reflecting again: On design errors in human-machine interfaces

Note:  I occasionally edit, translate, and re-publish articles that I deem them worth disseminating: Worthy articles are meant to be read.

Matthew Squair posted this May:  “Having recently bought a new car, I was driving home and noticed that the illuminated lighting controls were reflected in the right hand wing mirror. These sort of reflections are at best annoying, but in the worst case ,they could mask the lights of a car in the right hand lane and lead to a side-swipe during lane changing.

This is one of the classic system design errors that is well understood in domains such as the aerospace field.   Not so much in the car industry apparently.

But what really interests me is the fractured nature of engineering knowledge that this problem illustrates. I guess there is an implicit assumption we make that “we’re all getting smarter”.  But if that’s the case, why are the same errors committed over again?

Henry Petroski points to a study by Silby (1977) of bridge failures:  The study shows a 30 year-cycle between major bridge collapse and posits that, in any technology, we go through a cycle of learning, mastery, overconfidence, and subsequent failure due to over reach.

I’d point to the fragility of corporate memory within organizations and design teams:  I recognize that in the current  environment of rapid organizational change, it’s extremely hard to provide mentoring and oversight for young engineers, who unfortunately “don’t know what they don’t know“!

This remorseless cycle of destruction is exacerbated by codes and standards that record ‘what’ must be done from a compliance standpoint, but not the why”  Without the reason for compliance there is always the temptation…

I do agree with Petroski that failure breeds reflection, insight, and knowledge and that engineers, (especially young engineers), need in many ways to experience failure themselves or learn through the failures of others.

Evaluations of cockpit transparencies for reflections are required as part of the development of a new aircraft. These effects are particularly a problem for fighter aircraft with a large curved canopies and where the pilots’ displays sit comparatively close to the canopy.” (End of quote)

I have published over 30 articles on wordpress.com related to Human factors in design.

Human Factors professionals attempted to establish various error taxonomies, some within a specific context, during their study and analysis of errors that might be committed in the operation of nuclear power plants for example, and other taxonomy that are out of any specific context.

One alternative classification of human errors is based on human behavior and the level of comprehension; mainly, skill-based, or rule-based or knowledge-based behavioral patterns. This taxonomy identifies 13 types of errors and discriminates among the stages and strength of controlled routines in the mind that precipitate the occurrence of an error, whether during execution of a task, omitting steps, changing the order of steps, sequence of steps, timing errors, inadequate analysis or decision making.

With a strong knowledge of the behavior of a system, provided that the mental model is not deficient then, applying the rules consistently most of the errors will be concentrated on the level of skill achieved in performing a job.

Another taxonomy rely on the theory of information processing and it is a literal transcription of the experimental processes; mainly, observation of a system status, choice of hypothesis, testing of hypothesis, choice of goal, choice of procedure and execution of procedure.  Basically, this taxonomy may answer the problems in the rule-based and knowledge–based behavior.

It is useful to specify in the final steps of taxonomy whether an error is of omission or of commission.  I suggest that the errors of commission be also fine tuned to differentiate among errors of sequence, the kind of sequence, and timing of the execution.

There are alternative strategies for reducing human errors by either training, selection of the appropriate applicants, or redesigning a system to fit the capabilities of end users and/or taking care of his limitations by preventive designs, exclusion designs, and fail-safe designs.

You may start with this sample of two posts:

1. https://adonis49.wordpress.com/2008/10/14/whats-that-concept-of-human-factors-in-design-5/, and 2. https://adonis49.wordpress.com/2008/10/26/multidisciplinary-view-of-design/

Note 1: Petroski, H. Success through failure: The paradox of design, Princeton Press, 2008.

Note 2: Sibly, P.G., Walker, A.C., Structural Accidents and their Causes. In: Proc. Inst. Civil Engineers. 62 (May 1977), pp. 191–208 part 1. 1977.

 

Article “31 (December 18, 2005)

 “A seminar on a multidisciplinary view of design”  

The term “designing” is so commonly used that its all encompassing scope has lamentably shrunken in the mind of graduating engineers. This talk attempts to restore the true meaning of design as a multidisciplinary concept that draw its value from the cooperation and inputs of many practitioners in a team.

This is a scenario of a seminar targeting freshmen engineers, who will ultimately be involved in submitting design projects, is meant to orient engineers for a procedure that might provide their design projects the necessary substance for becoming marketable and effective in reducing the pitfalls in having to redesign. The ultimate purpose is to providing the correct designing behavior from the first year.

Answering the following questions might be the basis of acquiring a proper behavior in design projects, which should be carried over in their engineering careers.  Many of these questions are never formally asked in the engineering curriculum.

Q1. What is the primary job of an engineer?   What does design means?  How do you perceive designing to look like?

A1. The discussion should be reopened after setting the tone for the talk and warming up the audience to alternative requirements of good design.

Q2. To whom are you designing?  What category of people? Who are your target users? Engineer, consumers, support personnel, operators?

A2. Generate from audience potential design projects as explicit examples to develop on that idea.

Q3. What are your primary criteria in designing?  Error free application product? Who commit errors?  Can a machine do errors?

A3.  Need to explicitly emphasize that error in the design and its usage is the primary criterion and which encompass the other more familiar engineering and business criteria

Q4. How can we categorize errors?  Had you any exposure to error taxonomy? Who is at fault when an error is committed or an accident occurs?

A4. Provide a short summary of different error taxonomies; the whole administrative and managerial procedures and hierarchy of the enterprise need also to be investigated.

Q5. Can you foresee errors, near accidents, accidents in your design?

A5. Take a range oven for example, expose the foreseeable errors and accidents in the design, babies misuse and the display and control idiosyncrasy.

Q6. Can we practically account for errors without specific task taxonomy?

A6. Generate a discussion on tasks and be specific on a selected job.

Q7. Do you view yourself as responsible for designing interfaces to your design projects depending on the target users? Would you relinquish your responsibilities for being in the team assigned to designing an interface for your design project? What kinds of interfaces are needed for your design to be used efficiently?

A7. Discuss the various interfaces attached to any design and as prolongement to marketable designs.

Q8. How engineers solve problems?  Searching for the applicable formulas? Can you figure out the magnitude of the answer?  Have you memorized the allowable range for your answers from the given data and restriction imposed in the problem after solving so many exercises? Have you memorize the dimensions of your design problem?

A8.  Figure out the magnitude and the range of the answers before attempting to solve a question; solve algebraically your equations before inputting data; have a good grasp of all the relevant independent variables.

Q9. What are the factors or independent variables that may affect your design project? How can we account for the interactions among the factors?

A9. Offer an exposition to design of experiments

Q10. Have you been exposed to reading research papers? Can you understand, analyze and interpret the research paper data? Can you have an opinion as to the validity of an experiment? Would you accept the results of any peer reviewed article as facts that may be readily applied to your design projects?

A10.  Explain the need to be familiar with the procedures and ways of understanding research articles as a continuing education requirement.

Q11. Do you expect to be in charged of designing any new product or program or procedures in your career? Do you view most of your job career as a series of supporting responsibilities; like just applying already designed programs and procedures?

Q12. Are you ready to take elective courses in psychology, sociology, marketing, business targeted to learning how to design experiments and know more about the capabilities, limitations and behavioral trends of target users? Are you planning to go for graduate studies and do you know what elective courses might suit you better in your career?

A12.  Taking multidisciplinary courses enhances communication among design team members and more importantly encourages reading research papers in other disciplines related to improving a design project. Designing is a vast and complex concept that requires years of practice and patience to encompass several social science disciplines.

Q13. Can you guess what should have been my profession?

A13.  My discipline is Industrial engineering with a major in Human Factors oriented toward designing interfaces for products and systems. Consequently, my major required taking multidisciplinary courses in marketing, psychology and econometrics and mostly targeting various methodologies for designing experiments, collecting data and statistically analyzing gathered data in order to predict system’s behavior.

 


adonis49

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