Conference PaperPDF Available

Using Poka-Yoke Techniques for Early Defect Detection

Authors:
  • Serious Quality, LLC

Abstract

Poka-yoke is a quality assurance technique developed by Japanese manufacturing engineer Shigeo Shingo. The aim of poka-yoke is to eliminate defects in a product by preventing or correcting mistakes as early as possible. Poka-yoke has been used most frequently in manufacturing environments. Hewlett Packard currently develops its Common Desktop Environment software to run in twelve locales or languages. Traditional testing of this localized software is technically difficult and time-consuming. By introducing poka-yoke (mistake-proofing) into our software process, we have been able to prevent literally hundreds of software localization defects from reaching our customers. This paper describes the poka-yoke quality approach in general, as well as our particular use of the technique in our localization efforts. Poka-yoke is providing a simple, robust and painless way for us to detect defects early in our localization efforts.
Paper presented at the Sixth International Conference on Software Testing Analysis and Review
(STAR'97)
Using Poka-Yoke Techniques for Early Defect Detection
Harry Robinson
Abstract
Poka-yoke is a quality assurance technique developed by Japanese
manufacturing engineer Shigeo Shingo. The aim of poka-yoke is to eliminate
defects in a product by preventing or correcting mistakes as early as possible.
Poka-yoke has been used most frequently in manufacturing environments.
Hewlett Packard currently develops its Common Desktop Environment software
to run in twelve locales or languages. Traditional testing of this localized software
is technically difficult and time-consuming. By introducing poka-yoke (mistake-
proofing) into our software process, we have been able to prevent literally
hundreds of software localization defects from reaching our customers.
This paper describes the poka-yoke quality approach in general, as well as our
particular use of the technique in our localization efforts. Poka-yoke is providing a
simple, robust and painless way for us to detect defects early in our localization
efforts.
Poka-yoke
History
Poka-yoke (pronounced "POH-kah YOH-kay") [1] was invented by Shigeo
Shingo in the 1960s. The term "poka-yoke" comes from the Japanese words
"poka" (inadvertent mistake) and "yoke" (prevent) [2]. The essential idea of poka-
yoke is to design your process so that mistakes are impossible or at least easily
detected and corrected.
Shigeo Shingo was a leading proponent of statistical process control in Japanese
manufacturing in the 1950s, but became frustrated with the statistical approach
as he realized that it would never reduce product defects to zero. Statistical
sampling implies that some products to go untested, with the result that some
rate of defects would always reach the customer.
While visiting the Yamada Electric plant in 1961, Shingo was told of a problem
that the factory had with one of its products. Part of the product was a small
switch with two push-buttons supported by two springs. Occasionally, the worker
assembling the switch would forget to insert a spring under each push-button.
Sometimes the error would not be discovered until the unit reached a customer,
and the factory would have to dispatch an engineer to the customer site to
disassemble the switch, insert the missing spring, and re-assemble the switch.
This problem of the missing spring was both costly and embarrassing.
Management at the factory would warn the employees to pay more attention to
their work, but despite everyone's best intentions, the missing spring problem
would eventually re-appear.
Shingo suggested a solution that became the first poka-yoke device [3]:
In the old method, a worker began by taking two springs out of a large
parts box and then assembled a switch.
In the new approach, a small dish is placed in front of the parts box and
the worker's first task is to take two springs out of the box and place them
on the dish. Then the worker assembles the switch. If any spring remains
on the dish, then the worker knows that he or she has forgotten to insert it.
The new procedure completely eliminated the problem of the missing springs.
Shingo went on to develop this mistake-proofing concept for the next three
decades. One crucial distinction he made was between a mistake and a defect.
Mistakes are inevitable; people are human and cannot be expected to
concentrate all the time on the work in front of them or to understand completely
the instructions they are given. Defects result from allowing a mistake to reach
the customer, and defects are entirely avoidable. The goal of poka-yoke is to
engineer the process so that mistakes can be prevented or immediately detected
and corrected. Poka-yoke devices proliferated in Japanese plants over the next
three decades, causing one observer to note [4]:
It is not one device, but the application of hundreds and thousands of these very
simple "fail-safing" mechanisms that day after day has brought the quality miracle
to Japan. Each one is relatively simple -- something you easily could do on your
own. It is the totality, the hundreds of devices, that is almost frightening to behold.
Categories of poka-yoke devices
Poka-yoke devices fall into two major categories: prevention and detection.
A prevention device engineers the process so that it is impossible to make a
mistake at all. A classic example of a prevention device is the design of a 3.5
inch computer diskette. The diskette is carefully engineered to be slightly
asymmetrical so that it will not fit into the disk drive in any orientation other than
the correct one. Prevention devices remove the need to correct a mistake, since
the user cannot make the mistake in the first place.
A detection device signals the user when a mistake has been made, so that the
user can quickly correct the problem. The small dish used at the Yamada Electric
plant was a detection device; it alerted the worker when a spring had been
forgotten. Detection devices typically warn the user of a problem, but they do not
enforce the correction.
We are surrounded every day by both detection and prevention poka-yoke
devices, though we may not usually think of them as such. My microwave will not
work if the door is open (a prevention device). My car beeps if I leave the key in
the ignition (a detection device). At few years ago, some cars were designed not
to start until the passengers had buckled their seat belts (a prevention device);
but this mechanism was too intrusive and was replaced by a warning beep (a
detection device).
Characteristics of good poka-yoke devices
Good poka-yoke devices, regardless of their implementation, share many
common characteristics [5]:
they are simple and cheap. If they are too complicated or expensive, their
use will not be cost-effective.
they are part of the process, implementing what Shingo calls "100%"
inspection.
they are placed close to where the mistakes occur, providing quick
feedback to the workers so that the mistakes can be corrected.
Judged by these criteria, the "small dish" solution to the missing-spring problem
is an excellent poka-yoke device:
It was simple.
It was cheap, involving only the cost of a small dish.
It provided immediate feedback about the quality of the work; corrections
could be made on the spot.
Further reflections on the small dish solution
The small dish solution used at Yamada Electric is typical of many poka-yoke
devices:
It did not merely examine switches at the end of the operation; it changed
the procedure for assembling switches. The additional step of putting the
springs into the dish slowed down the individual operation, but the
increased reliability of the assembly eliminated the need for rework and
therefore sped up the overall process.
It was designed to stop a particular mistake -- a worker forgetting to insert
a spring. It did not stop all possible mistakes. It would not detect, for
instance, a situation where the worker, after removing the springs from the
dish, accidentally dropped one on the ground without noticing.
Being a detection device, the small-dish solution was not completely error-
proof. It could only warn of a problem, relying on the worker to correct the
situation. Unlike the 3.5 inch diskette, this solution did not make it
impossible to assemble a switch incorrectly. A worker wishing to ignore
the warning could do so.
The solution dealt with aspects of the assembly that were necessary,
though not sufficient, for correct operation of the product. This poka-yoke
ensured only that each push-button had a spring under it; it did not
attempt to detect whether the springs were the right height or made of the
proper materials.
Finally, the quality check done was independent of the actual, eventual
use of the switch. The poka-yoke device was oblivious to the overall goal
of a properly assembled switch. Instead, one could argue that the small-
dish solution was actually implementing a crude form of syntax checking
enforcing the one-to-one correspondence between push-buttons and
springs. I will return to this notion of syntax-checking later in this paper.
Poka-yoke and Software Quality
Being mainly a manufacturing technique, poka-yoke has only rarely been
mentioned in connection with software development, but the philosophy behind
poka-yoke has never been far from the heart of software quality. Gordon
Schulmeyer [6] and James Tierney [7] refer to poka-yoke explicitly, but many
software quality authors have championed detection and prevention methods in
software.
In 1990, Boris Beizer wrote in Software Testing Techniques [8]:
We are human and there will be bugs. To the extent that quality assurance fails
at its primary purpose -- bug prevention -- it must achieve a secondary goal of
bug detection.
In 1993, Steve Maguire echoed a similar sentiment in Writing Solid Code [9]:
All of the techniques and guidelines presented in this book are the result of
programmers asking themselves two questions ...
How could I have automatically detected this bug?
How could I have prevented this bug?
Prevention devices in software
From a poka-yoke perspective, the development of computer languages could be
viewed as a prevention device, since one objective of these languages is to
prevent us from creating code that can be error-prone. High level languages
prevent self-modifying code. Structured programming rescues us from spaghetti
code. Object-oriented programming keeps us from stepping on each other's data.
Detection devices in software
Software testing is a form of detection device, but traditional system testing
occurs too late in the process to allow quick, corrective feedback on mistakes.
Unit testing and "smoke testing" [7] come closer to the notion of poka-yoke, in
that they are located close to the source of the potential mistakes and the quick
feedback they provide can keep mistakes from moving further along in the
process.
The tools in software that most closely resemble poka-yoke devices are the
programs such as lint, printfck, cchk, clash [10] that examine the syntax of
programs and alert the programmer to a possible mistake in need of correction.
Static analysis utilities are simple and cheap to run; they aim to eliminate certain
classes of common mistakes; and they concentrate on the syntax of the program
rather than the program's function.
Some of Hewlett Packard's recent work in localizing software applications has
illuminated areas that yield more readily to a poka-yoke approach than to
traditional testing. The sections that follow describe our application of poka-yoke
principles to solve a problem that defied a traditional software testing approach.
Poka-yoke and Localization
Some background on message catalogs and localization
To create POSIX-compliant software that runs in multiple locales, developers
store locale-specific strings in files called message catalogs. [11] Rather than
hard-code a text string into the application, a developer stores the text string in
the message catalog and references it by its message set and message number.
A message set and message number uniquely identify any message string in the
catalog.
Localization is the process of creating a message catalog for a particular
language. Hewlett-Packard currently localizes its Common Desktop Environment
software for 11 locales: French, German, Italian, Korean, Spanish, Swedish, plus
2 Japanese locales and 3 Chinese locales.
Localization is typically done after the development of the software has stabilized,
and it is typically done by people external to the core development organization.
These people, called localizers, receive the application's message catalog from
the development organization. They then translate each message string into its
equivalent expression in the target language.
Localizing a software application is a difficult job. The localizers may be
unfamiliar with the application. They may be located halfway around the world
from the development organization. They may not even be familiar with
programming. Usually, therefore, the localizer performs translations based
almost exclusively on the contents of the message catalogs and the information
provided in the localization documentation.
Testing localized software
Testing localized software poses a unique set of challenges. The localizers know
what the translated messages say, but since they may never have seen the
application run, they cannot know if their translation is correct. The development
team knows what the translated messages are supposed to say, but since they
are not familiar with the target languages, they cannot know if that is what the
translated message actually says. Given the constraints of time and distance, it is
difficult for localizers and developers to work together, especially when
localization is being done in 11 languages.
The usual testing approaches do not offer much relief. Running the tests
manually can become tedious when there are 11 foreign locales to test. Testers
become fatigued running the same test in multiple locales.
The traditional way to test the message catalogs is as follows:
the test team receives the translated message catalog from the localizer
the test team installs the new message catalog and executes the test plan
in the target locales
obvious mistakes are referred back to the localizer and incorporated into a
later release of the catalog.
This approach has several drawbacks. It is difficult to execute a test
automatically in multiple locales. For one thing, image comparisons are not
portable across locales, since by the definition of localization something should
change on the screen when moving to a new locale. The alternative -- recording
golden images in a dozen locales -- would be a maintenance nightmare.
Yet some sort of testing of localized software is necessary because there are
many opportunities for a localizer to make mistakes when creating a message
catalog. A localizer could
specify an application menu incorrectly
inadvertently delete a message string
specify an invalid data format
specify an invalid conversion format
neglect to translate a message
translate a phrase incorrectly due to lack of context
All of these mistakes have different causes and different effects on the software
that is delivered to the customer. It is, however, possible to construct poka-yokes
to counteract each of these mistakes. As an example, we will go into some depth
about the poka-yoke we created to mistake-proof the localized application menus.
Of mice and menus
Many software users navigate through menus using only their mouse, clicking on
the selections they want. But users can invoke menu actions without a mouse, by
using menu mnemonics. Mnemonics are single characters (usually underlined) in
a menu label. If the mnemonic is typed at the keyboard while the menu is
displayed, the associated action is invoked, just as if the user had selected the
action with a mouse. For instance, in the English locale menu shown in Figure 1,
the selection that will close the application window has the label "Close" and the
mnemonic "C".
Figure 1: Text Editor File menu in English and French locales
Application menus are prime candidates for translation into various languages.
How else can users unfamiliar with English know what options are being offered?
And since we are translating each menu label, we must also translate the
mnemonic associated with each label. In the French locale menu shown in
Figure 1, for instance, the selection that closes the window has the label
"Fermer", and the associated mnemonic "F". In their respective message
catalogs, the English and French menus appear as follows:
Table 1: English and French locale menus in message catalogs
Using Poka-Yoke to Detect Menu Defects
We first decided to break the menu testing problem down into parts that we could
solve.
Our first advance on the problem was to understand that there were two separate
aspects to the message catalogs. There was the content aspect: the simple text
translations, such as changing "Close" to "Fermer". Since the test team was not
fluent in the 11 target languages, we had to leave this aspect to the language
experts.
The second aspect of the message catalogs was the structure, the syntax rules
that a properly constructed target catalog must obey. Unlike content, it would be
possible for the test team to verify the structural aspects of the catalogs.
As an example of what is meant by structure, consider the labels and mnemonics
of an application menu. A menu is made up of labels and associated mnemonics.
Each menu, regardless of its contents or its locale, must obey the following rules
listed in the Motif Style Guide [12]:
Each mnemonic must be contained in its associated label
Each mnemonic must be unique within the menu
Each mnemonic must be a single character
Each mnemonic must be in ASCII
These rules are invariant across locales, and can be used to verify that a menu is
constructed correctly in the target locale.
Design decisions
There were several possibilities for how to mistake-proof the menu mnemonics:
(Prevention device) We could write a program to generate mnemonics
automatically, given a list of the labels in each menu. This approach would
prevent mistakes, but the problem of choosing a good mnemonic is
difficult and the effort required to write the program would not be justified
by the benefit gained.
(Prevention device) We could write a program that would prevent the
localizer choosing mnemonics that did not meet the criteria. This approach
would also prevent mistakes, but the benefit gained would be minimal;
incorrect mnemonics are easy enough to detect and correct after they
occur.
(Detection device) We could provide a program to verify that the chosen
menu labels and mnemonics meet the criteria above. Our localizers could
run the programs on their translated message catalogs before sending the
catalogs to us. This approach would provide very quick feedback on
mistakes, and it is likely as a future step. For the moment however, since
we are still developing such scripts, it would be difficult to support them at
multiple remote sites.
(Detection device) We could write a program to verify the menu labels and
mnemonics, and run the program on message catalogs after they are
returned to us by the localizers. This approach is the path we are currently
taking. It is not as efficient as some of the above methods, and it can
require communication back and forth with the localizers, but the detected
errors are still easy to correct at this point.
The poka-yoke scripts
Several small poka-yoke scripts were used to validate the structural aspects of
the menus. The first step was to construct a small table for each menu, showing
the locations in the message catalogs of each mnemonic and label in the menu.
A typical layout is shown in Table 2.
A small poka-yoke script would read the table, retrieve the mnemonics and labels
from the message catalog, and compare the retrieved strings against the
established criteria noted above. For example, consider the script that checked
whether each mnemonic was contained in its label. The script would read the
mnemonic location (e.g., 11, 17) and fetch the string stored at that location in the
message catalog ("N"); the script would then get the label location (11, 18) and
fetch the string stored there ("New"); finally the script would determine if the
mnemonic "N" was contained in the label "New".
Table 2: Locations of mnemonics and labels for Text Editor File menu
All such rules are invariant across locales, so it was a simple matter to run the
script against a message catalog in a different locale. An error message is
printed for any mnemonic that fails to meet one of the criteria.
We eventually wrote a half-dozen such poka-yoke scripts to verify the rules of the
application menus
Results
The poka-yoke scripts were small (roughly 100 lines of Korn shell each).
They were easy to write; some were written in under an hour.
They were easy to run. Because the scripts concerned themselves only with the
message catalogs, we did not need to set up a system to execute the application.
We also did not need to worry about fonts, synchronizations, hard-to-reach
menus, or image comparisons.
We ran our poka-yoke scripts against 16 applications in the default English locale
plus 11 foreign locales. Each locale contained 100 menus, for a total of 1200
menus.
The menu utilities found 311 mistakes in menus and mnemonics. Few of the
problems we uncovered were earth-shattering, but in total they amounted to a
large annoyance in testing and running our localized applications. And though
311 seems like a large number, I encourage you to run similar checks on some
of your own localized applications. The results are likely to be eye-opening.
Lessons Learned
Implied rules
By creating and running these scripts, we discovered a rule for menu mnemonics
that was not explicitly stated in the Motif Style Guide. One developer used the
same mnemonic in the message catalog for items on two different menus. The
mnemonic at location "20,100" served as the mnemonic for "Rename" on one
menu and for "Reference" on another menu. This arrangement worked fine as
long as both labels kept the same mnemonic, "R". But when the German
localizer translated "Rename" to "Umbenennen" and changed the mnemonic
from "R" to "U", it broke the mnemonic-label relationship on the other menu. We
will eventually refine our poka-yoke scripts to include an inter-menu dependency
rule:
No mnemonic location can be reused within an application.
Handling exception cases
We found that our utilities needed to allow for exceptions. For example, in most
locales, the Text Editor Edit menu has a selection to check spelling. This option
does not appear on the menu in several of the Asian locales; therefore it is
permissible in those locales for that mnemonic and label to violate the usual
criteria for that menu. After discussing whether we should artificially enforce the
criteria in those locales (which would inconvenience the localizers) or change the
code in the utility (which would complicate a rather simple script), we decided to
pass the utility's output through a script that would explicitly filter out error
messages about that option in the affected locales.
Handling variant menus
Several menus had alternative forms. For instance, the fourth option in the Text
Editor File menu in the could have either "Save" or "Save (needed)" as its label,
depending on whether changes had been made to the file. We chose to treat
these alternatives as two distinct menus to guard against the possibility of a
localizer choosing a mnemonic for one variant that would not work for the other.
Conclusions
Poka-yoke scripts like the ones described here can eliminate entire classes of
errors. And once the scripts are in place they can run automatically without
human intervention, raising an alarm only when a problem is discovered. The
scripts we used provided quick feedback early in the process, detecting
localization mistakes before the application ever reached the formal testing
phase.
The poka-yoke approach proved very flexible, allowing us to validate aspects of
the menus early in the development, even without the associated applications.
However, it is useful to keep in mind that verifying the menus syntactically is not
the same as testing the menus in the application. The poka-yoke scripts did not
verify that the menus actually worked; that would require running the application.
The poka-yoke approach provided a simple and robust way for us to detect and
correct localization mistakes that would have been difficult to detect through
traditional system testing.
Recommendations for Creating Good Software Poka-Yokes
Think simple. It is better to have several simple poka-yokes, each with a single
purpose, than to have one large complicated script.
Think specific. Look at your process; identify a mistake that occurs frequently,
and design a poka-yoke to prevent or detect that particular mistake.
Think attributes. Rather than wait for the entire software application to become
available, look for aspects of the software that can be verified independently.
Think early. Try to detect and eliminate defects as early as possible so that they
do not pollute processes downstream.
Think responsive. Once a defect is detected, correct the mistake as soon as
possible.
Think re-use. Successful poka-yokes can be modified to serve new purposes.
Acknowledgments
I would like to thank Arne Thormodsen and Sankar Chakrabarti of HP
Workstation Technology Center for their helpful discussions of many of the
techniques mentioned in this paper.
I would also like to acknowledge John Grout's help in locating many resources
relating to poka-yoke techniques. Anyone interested in further information about
poka-yoke would do well to visit John Grout's Poka-Yoke Page.
References
[1] John Grout, Mistake-Proofing Production. Cox School of Business, Southern
Methodist University, page 2
[2] Shigeo Shingo, Zero Quality Control: Source Inspection and the Poka-yoke
System. Productivity Press, page 45
[3] Shigeo Shingo, The Sayings of Shigeo Shingo: Key Strategies for Plant
Improvement. Productivity Press, page 145
[4] NKS/Factory Magazine, Poka-yoke: Improving Product Quality by Preventing
Defects. Productivity Press, page vii
[5] Richard Chase and Douglas M. Stewart, Mistake-Proofing: Designing Errors
Out, Productivity Press, page 29
[6] G. Gordon Schulmeyer, Zero Defect Software. McGraw-Hill, Inc.
[7] James Tierney, Eradicating mistakes in your software through poka yoke.
MBC Video
[8] Boris Beizer, Software Testing Techniques, 2nd ed. Van Nostrand Reinhold,
page 3
[9] Steve Maguire, Writing Solid Code. Microsoft Press, page xxii
[10] Ian Darwin, Checking C Programs with Lint, O'Reilly & Associates, pages
55-63
[11] Thomas C. McFarland: X Windows on the World. Prentice-Hall, Inc.
[12] OSF Motif Style Guide, Open Software Foundation
Harry Robinson was a software engineer for Hewlett Packard's Workstation
Technology Center in Corvallis, Oregon from 1996 through 1998.
... Según Molina y Sánchez (2016), son dos factores los que generan problemas de calidad en las MIPYMES, el factor humano y la tecnología de sus procesos, factores fundamentales para una mayor competitividad. No obstante, es posible lograr mejores resultados de calidad aplicando las herramientas Lean (Robinson, 1997) [5,6]. En principio, la metodología Lean trabaja buscando un mejoramiento continuo de los procesos productivos (Tomar & Kumar Soni, 2007) [7]. ...
... 7. Levantar la botella y con las manos reforzar el pegado de la etiqueta. Considerando que la certificación ISO:9001 estandariza mundialmente los requisitos para establecer un Sistema de Calidad (Díaz et al., 2014) [19]. Mónica Armijos y Erika Ángulo (2018) proponen cumplir con ciertos principios que se han adaptado a la investigación como recomendaciones para el inicio de un sistema de gestión de calidad en la empresa Bohemian Brew Perú [19]. ...
... Considerando que la certificación ISO:9001 estandariza mundialmente los requisitos para establecer un Sistema de Calidad (Díaz et al., 2014) [19]. Mónica Armijos y Erika Ángulo (2018) proponen cumplir con ciertos principios que se han adaptado a la investigación como recomendaciones para el inicio de un sistema de gestión de calidad en la empresa Bohemian Brew Perú [19].  Enfoque de calidad: Este principio exige comprender las necesidades de los clientes y sus expectativas midiendo su satisfacción y enfocando sus objetivos a ello. ...
Article
Full-text available
SME (Small and Medium Enterprises) are becoming more representative in their countries; however, they do not achieve their objectives because they do not have good management, since they do not include a quality system, and they assume that this requires a high investment. Currently, the aim is to adopt Lean methodologies, generally used in large companies, so that this type of business can take advantage of its benefits. Therefore, a Poka Yoke application model is proposed to contribute to the solution of quality problems in a Peruvian brewery SME. Through the DMAIC matrix (Define, Measure, Analyze, Implement, and Control), trouble and solutions are identified, and quality indicators are reduced by more than 30%. The results show that the Lean methodology can be applied in all types of companies and that it can support entrepreneurs who have similar problems.
... detected vulnerability is addressed. The team either modifies the process to eliminate the vulnerability or adds a poka-yoke device (Robinson, 1997) that will prevent the breakdown from occurring. 15 If the process is modified, then its performance on criteria must be re-checked. ...
... Following Womack and Jones (2003), the Life Enabling model dictates that a process must satisfy three conditions before it can be made to flow. Contributors 21 Many people credit Shigeo Shingo (Robinson, 1997) as poka-yoke's originator. However, the use of such devices extends at least as far back as Frederick Taylor's famous efforts to optimize the performance of the Midvale Steel Works in the 1870s (Kanigel, 1997). ...
Book
Full-text available
Our economic system is destroying our society and our species ... We live in a society that accepts an economic system in which lies used to promote sales are legitimized by its courts as just an expression of the “puffery” capitalist enterprises use to get buy behaviors and not chargeable as fraud; products are designed to sell profitably, not benefit their recipients; packaging is designed to hide increases in price, not inform buyers; producers who pollute and destroy our ecosystem declare it is not their concern and we accept it; profits are generated from the sale of offerings that are harmful to humankind yet counted in our measures of economic growth and well-being; our right to redress for harm done to us is legally restricted so as not to discourage future commerce; and producers use the profits they extract from us to coop our political representatives and governance so they may continue their plunder undeterred. In response to all of this and more, we are told there is no better alternative economic system. Moreover, we are told that we can always choose not to buy. Thus, we have nothing to complain about because every transaction we undertake is, by our acceptance, fair and freely made. None of this is necessary or valid. All of it is predicated on the false assumptions of a sham economic system that serves the few. Its only legitimacy is as a method of control and exploitation of the many. Worse, the human environment it engenders denies the necessities of life that science has made clear are essential for human survival and humankind’s continuance as a specie. This book reveals why and how this is happening, why it is not necessary, and offers a valid alternative approach to commerce that elevates human sociality, fosters personal emergence, and nourishes the ecosystem that supports all life.
... The concept of Poka-Yoke includes changes made during design to minimize the possibility of error by adopting the idea that humans are not 100% reliable. The Poka-Yoke concept, developed by Shigeo Shingo in the 1960s, is to design a system so that mistakes or error opportunities are detected and corrected at the source [35]. Poka and Yoke are Japanese words that mean "to avoid" and "inadvertent mistake," respectively [36]. ...
Chapter
Full-text available
The causes of most incidents and accidents are attributed to humans, even though they are based on aircraft design and configuration. This chapter emphasizes the critical need for error-proof design in aviation to reduce human errors in aircraft maintenance and operation. Traditional models often assume human reliability as flawless, overlooking the potential for human error in complex systems. Findings reveal that many incidents and accidents stem from design-related issues rather than human shortcomings, suggesting that designing aircraft with error-proofing principles could significantly improve safety. This approach advocates for defining error-proofing levels, from basic awareness to advanced prevention, based on the severity of potential errors. Methods like Poka-Yoke and Murphy’s law applications enhance system resilience by minimizing opportunities for human error. The adoption of error-proofing standards as part of the aviation safety framework could lead to fewer incidents and operational disruptions, such as in-flight shutdowns and delays. The study calls for global aviation authorities to recognize the role of human reliability in system design and to incorporate comprehensive error-proofing standards. By shifting focus from blaming individuals to proactive design solutions, this approach aims to promote safer and more efficient flight operations across the industry.
... Over the years, diverse methods to ensure the quality of products have been developed [14]. Among the most used methods, we have Auditing, Total Quality Control (TCQ) [10], Poka-Yoke Techniques [17], the 100% control of each produced product [9], and statistical methods like the Statistical Process Control (SPC) [19]. Independently of the method used, the goal is the same, to avoid the production of faulty products, or to identify and remove them, before delivering those to the customer. ...
Preprint
Full-text available
Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.
... Over the years, diverse methods to ensure the quality of products have been developed [14]. Among the most used methods, we have Auditing, Total Quality Control (TCQ) [10], Poka-Yoke Techniques [17], the 100% control of each produced product [9], and statistical methods like the Statistical Process Control (SPC) [19]. Independently of the method used, the goal is the same, to avoid the production of faulty products, or to identify and remove them, before delivering those to the customer. ...
Preprint
Full-text available
Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.
... Over the years, diverse methods to ensure the quality of products have been developed [14]. Among the most used methods, we have Auditing, Total Quality Control (TCQ) [10], Poka-Yoke Techniques [17], the 100% control of each produced product [9], and statistical methods like the Statistical Process Control (SPC) [19]. Independently of the method used, the goal is the same, to avoid the production of faulty products, or to identify and remove them, before delivering those to the customer. ...
Preprint
Full-text available
Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.
... Over the years, diverse methods to ensure the quality of products have been developed [14]. Among the most used methods, we have Auditing, Total Quality Control (TCQ) [10], Poka-Yoke Techniques [17], the 100% control of each produced product [9], and statistical methods like the Statistical Process Control (SPC) [19]. Independently of the method used, the goal is the same, to avoid the production of faulty products, or to identify and remove them, before delivering those to the customer. ...
Preprint
Full-text available
Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.
Book
This two-volume set, IFIP AICT 663 and 664, constitutes the thoroughly refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2022, held in Gyeongju, South Korea in September 2022. The 139 full papers presented in these volumes were carefully reviewed and selected from a total of 153 submissions. The papers of APMS 2022 are organized into two parts. The topics of special interest in the first part included: AI & Data-driven Production Management; Smart Manufacturing & Industry 4.0; Simulation & Model-driven Production Management; Service Systems Design, Engineering & Management; Industrial Digital Transformation; Sustainable Production Management; and Digital Supply Networks. The second part included the following subjects: Development of Circular Business Solutions and Product-Service Systems through Digital Twins; “Farm-to-Fork” Production Management in Food Supply Chains; Urban Mobility and City Logistics; Digital Transformation Approaches in Production Management; Smart Supply Chain and Production in Society 5.0 Era; Service and Operations Management in the Context of Digitally-enabled Product-Service Systems; Sustainable and Digital Servitization; Manufacturing Models and Practices for Eco-Efficient, Circular and Regenerative Industrial Systems; Cognitive and Autonomous AI in Manufacturing and Supply Chains; Operators 4.0 and Human-Technology Integration in Smart Manufacturing and Logistics Environments; Cyber-Physical Systems for Smart Assembly and Logistics in Automotive Industry; and Trends, Challenges and Applications of Digital Lean Paradigm.
Conference Paper
Full-text available
Poka-Yoke devices have always been regarded by lean manufacturing companies as essential quality control and assurance tools to support efficient and effective manufacturing processes and procedures. Thanks to their ease of use and low cost, these devices help maintain high-quality standards and also encourage organisations to undertake Kaizen continuous improvement activities. With the advent of new digital and analytical technologies, these devices have undergone significant transformations. Based on a study of the scientific literature and the results of brainstorming sessions conducted with factory managers and lean experts, this paper analyzes how and to what extent digitalization changes the definitions, functions, approaches, and perspectives of traditional Poka-Yokes. Furthermore, it examines how the change in data collection, sharing, analysis, processing, and feedback (interpretation) approaches brought by the digitalization and smartification of Poka-Yoke devices affects the operational performance of modern Digital Lean Cyber-Physical Production Systems.
Zero Quality Control: Source Inspection and the Poka-yoke System
  • Shigeo Shingo
Shigeo Shingo, Zero Quality Control: Source Inspection and the Poka-yoke System. Productivity Press, page 45
Eradicating mistakes in your software through poka yoke. MBC Video
  • James Tierney
James Tierney, Eradicating mistakes in your software through poka yoke. MBC Video