Transcript Quality Gurus - Plymouth University

Problem Solving
Techniques
MST326 lecture 3
25 January 2007
MATS326-3 problem.ppt
Outline of lecture
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Brainstorming
Mind maps
Cause-and-Effect diagrams
Failures Mode and Effects Analysis
Fault Tree Analysis
Design of Experiments
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MATS326-3 problem.ppt
Brainstorming
• proposed by Alex Osborn
“for the sole purpose of
producing checklists of ideas”
• technique to identify causes
and develop solutions to problems
• “seeking the wisdom of ten people
rather than the knowledge of one
person” [Kaizen Institute]
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Brainstorming
• no criticism is permitted
o “only stupid question is one that is not asked” [Ho]
• wild ideas are encouraged
o often trigger good ideas from someone else
• each person contributes one idea
o further single ideas on second circuit
o repeat until no further ideas
• all contributions are recorded in view
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Brainstorming
• Osborn proposed 75 fundamental questions
• can be reduced to:
 seek other uses?
 modify?
 minify?
 rearrange?
 combine?
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 adapt?
 magnify?
 substitute?
 reverse?
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TRIZ
• Teorija Reshenija Izobretatel'skih Zadach
• loosely translates as
Theory of Inventive Problem Solving (TIPS)
• 40 Inventive Principles
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MATS326-3 problem.ppt
40 inventive principles of TRIZ
IP 01: Segmentation
IP 02: Taking out
IP 03: Local quality
IP 04: Asymmetry
IP 05: Merging
IP 06: Universality
IP 07: Nested doll
IP 08: Anti-weight
IP 09: Preliminary anti-action
IP 10: Preliminary action
IP 11: Prior cushioning
IP 12: Equipotentiality
IP 13: The other way round IP 14: Spheroidality or curvature IP 15: Dynamics
IP 16: Abundance
IP 17: Another dimension
IP 18: Mechanical vibration
IP 19: Periodic action
IP 20: Continuity of useful action IP 21: Rushing through
IP 22: Blessing in disguise
IP 23: Feedback
IP 24: Intermediary
IP 25: Self-service
IP 26: Copying
IP 27: Cheap short-lived objects
IP 28: Mechanics substitution IP 29: Pneumatics and hydraulics
IP 30: Flexible shells and thin films IP 31: Porous materials
IP 32: Colour change
IP 33: Homogeneity
IP 34: Discarding and recovering IP 35: Parameter change
IP 36: Phase transition
IP 37: Thermal expansion
IP 38: Strong oxidants
IP 39: Inert atmosphere
IP 40: Composite materials
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Mind maps
• attributed to Tony Buzan
o classic book “Use Your Head”
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Mind maps
Image from http://www.loanedgenius.com/scrabble_2_letter_words.gif
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MATS326-3 problem.ppt
Cause-and-Effect diagrams
• Cause-and-Effect diagram
o often referred to as a fishbone diagram
o or an Ishikawa diagram
• introduced by Kaoru Ishikawa
o simple graphical method to record and
classify a chain of causes and effects in
order to resolve a quality problem
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MATS326-3 problem.ppt
Cause-and-Effect diagrams
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Clarify the object effect
Pick causes
Determine the priority causes
Work out the counteractions
for priority causes
• implement appropriate solutions to
eliminate or reduce the causes of
problems
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Cause-and-Effect diagrams I
• Clarify the object effect
o a numerical measurement should be
established against which subsequent
improvement can be judged
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Cause-and-Effect diagrams II
Pick causes
• create a team of people to brainstorm
possible causes that may lead to the effect
• study the actual effect
in the problem environment
• on a horizontal line draw diagonal branches
for direct causes of the effect
• using arrows onto the branches create
sub-branches for appropriate secondary causes
• confirm all elements of the diagram
are correctly positioned
• quantify the causes wherever possible
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MATS326-3 problem.ppt
Cause-and-Effect diagrams III
• Determine the priority causes
o analyse any existing data for the problem
o if practical, create a Pareto diagram.
o otherwise, determine a ranking of the
relative importance of each cause.
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MATS326-3 problem.ppt
Cause-and-Effect diagrams IV
• Work out the counteractions
for priority causes
o put in place appropriate solutions
to eliminate or reduce
the causes of problems
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MATS326-3 problem.ppt
Cause-and-Effect diagram:
• Image from
http://www.ifm.eng.cam.ac.uk/dstools/gif/ishika.gif
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Failures Mode and Effects Analysis
• FMEA is
o a useful tool for reliability analysis
o systematic check of a product or process
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function
failure causes
failure modes
failure consequences
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Failures Mode and Effects Analysis
• Requires a thorough knowledge of
o functions of the components
o contribution of those components
to function of the system
• For every failure mode at a low level,
failure consequences are analysed at
o the local level
o the system level
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Failures Mode and Effects Analysis
• FMEA is usually qualitative
but may also be quantitative
• initiated during planning and definition
of a project to investigate qualitative
reliability demands of the market
• during design and development, for
quantitative reliability activities
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MATS326-3 problem.ppt
Table From Evans and Lindsay Chapter 13
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MATS326-3 problem.ppt
Failures Mode and Effects Analysis
• design-FMEA for design reviews
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definition and limiting of the system
choice of complexity level
check of component functions
check of system functions
identification of possible failure modes
identification of consequences of failures
possibility of failure detection and failure localisation
assessment of seriousness of failure
identification of failure causes
interdependence of failures
documentation
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MATS326-3 problem.ppt
Failures Mode and Effects Analysis
• quantitative design-FMEA a.k.a. FMECA
Failure Mode, Effects and Criticality Analysis
o consider every component
o quantify and rank different failure modes
• F = probability of failure
• A = seriousness (consequences of failure)
• U = probability of detection
o subjective judgements on a scale of 1-5 or 1-10
o Product (F*A*U) = Risk Priority Number (RPN)
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MATS326-3 problem.ppt
Failures Mode and Effects Analysis
• Process-FMEA for
o pre-production engineering
o design of process control
o process improvement
• FMEA is efficient where component failure
leads directly to system failure
• for more complex failures, FMEA may be
supplemented by Fault Tree Analysis (FTA)
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MATS326-3 problem.ppt
Some URLs for FMEA
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http://www.fmeainfocentre.com/
http://supplier.intel.com/ehs/fmea.PDF
http://www.cs.mdx.ac.uk/puma/wp18.pdf
http://www.sverdrup.com/safety/fmea.pdf
http://www.uscg.mil/hq/msc/fmea.doc
• http://www.competitiveedge.net/pdfs/fmea.pdf
• http://www.fmeca.com/ffmethod/methodol.htm
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http://www-personal.engin.umich.edu/~wmkeyser/ioe539/fmea.pdf
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http://www.engin.umich.edu/class/eng401/003/LCNotes/fmea.pdf
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MATS326-3 problem.ppt
Fault Tree Analysis
• Logical chart of occurrences
to illustrate cause and effects
• developed by DF Haasl, HA Watson,
BJ Fussell and WE Vesely
• initially at Bell Telephone Laboratories
then North American Space Industry
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Fault Tree Analysis
• Common symbols used 1
o main event
o basic event
o incompletely analysed event
o restriction
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Fault Tree Analysis
• Common symbols used 2
o or-gate
o and-gate
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1
&
o transfer to or from another place
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Figure From Evans and Lindsay Chapter 13
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Design of Experiments
• originally conceived by
Ronald Aylmer Fisher
at Rothampstead Experimental Station
during the 1920s
o analysing plant growing plots
under different conditions, and
needed to eliminate systematic errors.
Image from http://www.csse.monash.edu.au/~lloyd/tildeImages/People/Fisher.RA/
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MATS326-3 problem.ppt
Experimental design
• Randomisation
• Replication - repetition
so that variability can be estimated
• Blocking - experimental units in groups
(blocks) which are similar
• Orthogonality - statistically normal.
• Use of factorial experiments
instead of one-factor-at-a-time
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MATS326-3 problem.ppt
Design of Experiments
• full factorial experiment
o where a number of factors
may influence the output of a process,
it is possible to study all combinations
of levels of each factor
o if the number of factors considered increases,
then number of experiments required
increases more rapidly.
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MATS326-3 problem.ppt
Design of Experiments
• For two levels of n-variables,
the number of experiments required is 2n
o 4 experiments for two variables
(low-low, low-high, high-low and high-high)
o 16 experiments for four variables
o 64 experiments for six variables.
• If three levels (low - normal - high) or more
are to be studied, then a full factorial
experiment soon becomes impractical.
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Design of Experiments
• results plotted to indicate the influence of
each of the factors studied
• when one factor affects the response,
this is known as the main effect.
• when >1 factor affects the response,
this is termed an interaction.
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MATS326-3 problem.ppt
Design of Experiments
Genichi Taguchi developed orthogonal arrays
• fractional factorial matrix
• permits a balanced comparison
of levels of any factor
with a reduced number of experiments.
• each factor can be evaluated independently
of each of the other factors.
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MATS326-3 problem.ppt
Orthogonal arrays
L4: three two-level factors
L9: four three level factors
Arrays from http://www.york.ac.uk/depts/maths/tables/orthogonal.htm
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Common orthogonal arrays
Array
Levels
L4
L8
L9
L12
L16
L25
L27
3x2
7x2
4x3
11 x 2
15 x 2
6x5
13 x 3
Equivalent
Full Factorial
8
128
81
2 048
32 768
15 625
1 594 323
Table from Tony Bendell “Taguchi Methods”, 1989
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Taguchi
• Quality Loss Function
L(x) = k ( x - t )2
o L = the loss to society
of a unit of output at value x
o t = the ideal target value
o k = constant
• as non-conformance increases,
losses increase even more rapidly
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