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This paper provides a literature review on zero defect manufacturing based on the content analysis performed for 280 research articles published from 1987 to 2018 in a variety of academic journals and conference proceedings. The review summarises the state-of-the-art, highlights shortcomings and further directions in research. Accordingly, we investigated how zero defect manufacturing was implemented and evaluated the main research patterns in the sample by analysing key factors. Based on the extensive review of the zero defect manufacturing literature, we identified and highlighted four distinctive strategies based on overarching themes for zero defect manufacturing, i.e. detection, repair, prediction, and prevention. Evaluation of current research and descriptive analysis highlighted six major shortcomings of current research in zero defect manufacturing: (i) focus on a single strategy instead of a holistic approach for global optima; (ii) certain industries are under-researched; (iii) full potential of industry-academia collaboration is not achieved; (iv) not enough focus on the beginning of manufacturing lifecycle; (v) cost–benefit comparative analysis is not evident; (vi) standard and clear definition of terms are missing. Finally, we presented four further directions in which an advance of the topic would stimulate scholarly and practical needs: (i) shift from local to global solutions; (ii) investigate pros and cons; (iii) role of people and human activities in manufacturing; (iv) new business models for zero defect manufacturing.
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International Journal of Production Research
ISSN: 0020-7543 (Print) 1366-588X (Online) Journal homepage:
Zero defect manufacturing: state-of-the-art review,
shortcomings and future directions in research
Foivos Psarommatis, Gökan May, Paul-Arthur Dreyfus & Dimitris Kiritsis
To cite this article: Foivos Psarommatis, Gökan May, Paul-Arthur Dreyfus & Dimitris Kiritsis (2020)
Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research,
International Journal of Production Research, 58:1, 1-17, DOI: 10.1080/00207543.2019.1605228
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View supplementary material Published online: 19 Apr 2019.
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International Journal of Production Research, 2020
Vol. 58, No. 1, 1–17,
Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in
Foivos Psarommatis , Gökan May , Paul-Arthur Dreyfus and Dimitris Kiritsis
École Polytechnique Fédérale de Lausanne (EPFL), SCI STI DK, Lausanne, Switzerland
(Received 9 November 2018; accepted 1 April 2019)
This paper provides a literature review on zero defect manufacturing based on the content analysis performed for 280
research articles published from 1987 to 2018 in a variety of academic journals and conference proceedings. The review
summarises the state-of-the-art, highlights shortcomings and further directions in research. Accordingly, we investigated
how zero defect manufacturing was implemented and evaluated the main research patterns in the sample by analysing key
factors. Based on the extensive review of the zero defect manufacturing literature, we identified and highlighted four distinc-
tive strategies based on overarching themes for zero defect manufacturing, i.e. detection, repair, prediction, and prevention.
Evaluation of current research and descriptive analysis highlighted six major shortcomings of current research in zero defect
manufacturing: (i) focus on a single strategy instead of a holistic approach for global optima; (ii) certain industries are under-
researched; (iii) full potential of industry-academia collaboration is not achieved; (iv) not enough focus on the beginning
of manufacturing lifecycle; (v) cost–benefit comparative analysis is not evident; (vi) standard and clear definition of terms
are missing. Finally, we presented four further directions in which an advance of the topic would stimulate scholarly and
practical needs: (i) shift from local to global solutions; (ii) investigate pros and cons; (iii) role of people and human activities
in manufacturing; (iv) new business models for zero defect manufacturing.
Keywords: zero-defect manufacturing (ZDM); review; state-of-the-art; production; quality; strategies; detect; repair;
predict; prevent
1. Introduction
Recently, companies produce new products faster than ever for two main reasons: for achieving higher profits and due
to increasing demand from their customers. This phenomenon has imposed new rules for the manufacturing of products,
making strategies which had been successfully used in the past useless or not as efficient as needed (Halpin 1966). For
example, during the previous century, most of the industries like the automotive industry were essentially relying on mass
production within their production line. But today, with the rise of product customisation, they have shifted to manufacturing
methods based on lean (Marodin et al. 2018; Jabbour et al. 2013) and customers’ demand (Lu 2017). Therefore, it requires
more adaptability from firms to match their clients’ increasing expectations. It also becomes much more challenging to
apply systematic methodologies for monitoring and preventing defects occurrence within the manufacturing shop floors
due to the increasing complexity of both products and production systems (Jacob et al. 2018). Further to that, the time to
optimise the production process lines has been significantly reduced, because companies are not mass producing but making
smaller batches of customised products (Kletti and Schumacher 2014), and as a consequence, the rate of defected products
has increased. With these factors taken into consideration, newer and more sophisticated strategies and tools are needed
(Eger et al. 2018;Teti2015). More specifically, better techniques of quality management are required in order to cope with
the current needs (Utz and Lee 2017; Bufardi et al. 2017; Raabe, Myklebust, and Eleftheriadis 2017;Juetal.2013).
One of the most promising strategies today is called Zero Defect Manufacturing (Eger et al. 2018; Dreyfus and Kyritsis
2018; Myklebust 2013; Wang 2013). This strategy has the goal to decrease and mitigate failures within manufacturing
processes and ‘to do things right in the first time’ (Halpin 1966), in other words to eliminate defected parts during production.
But the idea of Zero Defect Manufacturing (ZDM) is not new, it was first mentioned during the cold war within the US army
regarding their defective weapon system (US Assistant Secretary of Defense 1965). ZDM is a disruptive concept that is able
to entirely reshape the manufacturing ideology.
The Zero Defect Manufacturing can be implemented in two different approaches. The product-oriented ZDM and the
process-oriented ZDM. The difference is that a product-oriented ZDM studies the defects on the actual parts and tries to
*Corresponding author. Email:
© 2019 Informa UK Limited, trading as Taylor & Francis Group
2F. Psarommatis et al.
Figure 1. Zero defect manufacturing concept.
find a solution while on the other hand the process-oriented ZDM studies the defects of the manufacturing equipment, and
based on that can evaluate whether the manufactured products are good or not. The latter one lays within the predictive
maintenance concept. In Figure 1, we illustrate these two approaches as one concept, i.e. the Zero Defect Manufacturing
concept which comprises two start points, one for each approach.
The reasons why the ZDM thinking is attractive for companies are manifold. First, it can considerably reduce the
costs of the company’s resources related to the treatment of defective products (Bai and Zhang 2018). The ZDM process
relies essentially on the fact that no useless element is present within a process. Useless element refers to anything that
does not bring any added value to the product, e.g. defective machines and tools, inefficient employees, etc. Significant
reduction of scrap production and therefore money expenditure can be realised with ZDM (Dong et al. 2017). Beyond
that, the overall production chain should be continuously improved. Any possibility of system enhancement must also
be meticulously and extensively assessed. In that way, product manufacturing is getting closer and closer to perfection
(Eleftheriadis and Myklebust 2016). This approach can also be motivated by increasing safety and customers’ satisfac-
tion, which might strengthen customer loyalty and soar the financial benefits of the company (Thangaiah, Sharma, and
Sundharam 2018).
This concept had been implemented only partially due to numerous technological limitations that were prohibiting
its implementation. Currently, with the evolution of Industry 4.0, ZDM concept is easier to be implemented due to the
availability of the required amount of data for techniques such as machine learning to work properly (Mourtzis, Vlachou,
and Milas 2016; Chien, Hsu, and Chen 2013; Kuo et al. 2013; Choi et al. 2012) but still a lot of effort is needed for better
integration and coordination of the capabilities of each technology. Furthermore, the equipment that is required for such data
recording used to be very expensive and companies were not investing on that (Ahuett-Garza and Kurfess 2018). However,
the landscape has changed now with computer power and data storage rising, while sensors price dropping significantly
together with the new technologies that make the concept of ZDM possible. ZDM will be the new standard for companies
towards more eco-friendly and more efficient production lines with zero defects. On that regard, in Figure 2we illustrate
how the ZDM concept can be implemented and also how the ZDM strategies are interconnected among them. The ZDM
consists of four strategies: detection, repair, prediction, and prevention (Psarommatis and Kiritsis 2018). Those strategies
are interconnected among them as follows. The following are applied in both product- or process-oriented approaches. If a
defect is detected then it can be repaired, also the data gathered by the defect detection module is populated to specifically
designed algorithms for predicting when a defect may occur and therefore be prevented. Here fits the phrase mentioned
earlier ‘to do things right in the first time’. Defects, at both product and process levels, are more likely to occur at several
International Journal of Production Research 3
Figure 2. Zero defect manufacturing implementation.
stages of the product life such as raw material transformation, manufacturing and assembly. Defects that were generated at
an early stage are also susceptible to be amplified and diffused along the following steps of the manufacturing process and
might cause manufacturing issues and critical malfunctions. Defects that are coming from manufacturing operations are not
always avoidable. One of the reasons is that they are intrinsic to the operation itself. A single defect in the manufacturing life
might also be the result of the accumulation of small defects that appeared in previous steps. If not treated or detected at an
early stage of the manufacturing process life, defects continue to spread in the following life stages, namely the use phase.
Use phase defects, are also caused by wrong or poor information regarding the use of the product from the manufacturer
to the user. However, it is possible to reduce these inconveniences by gathering data and information regarding materials,
manufacturing processes, tools and machines and also by using quality control strategies (Sinha and Anand 2018). That
way, a number of problems can be eliminated.
As explained above, there have been several researches concerning zero defect management in manufacturing since
decades, and we believe it is the time now to conduct a thorough analysis of the state-of-the-art in the field. Accordingly,
this paper provides a critical review of the literature on ‘Zero Defect Manufacturing’ based on the content analysis performed
for 280 research articles published from 1987 to 2018 in a variety of academic journals and conference proceedings, for
achieving the below objectives:
Present a comprehensive and systematic review of academic publications on the topic Zero Defect Manufacturing
Highlight the state-of-the-art in research as well as a summary of the key findings
Determine the shortcomings of existing research
Pinpoint further directions in which an advance of the topic would stimulate scholarly and practical needs
The structure of the paper is as follows. Section 2describes the methodology of the research, and Section 3analyses the
existing review papers and highlights the difference and contribution of this particular study. Section 4presents the results
of the review by providing the classification scheme for the analysed papers, and evaluates general research patterns in
the sample by analysing relevant aspects. Next, Section 5discusses the current state of research as well as limitations and
potential future advancements of the field, and ends the paper by highlighting the main findings and outcomes of the study.
4F. Psarommatis et al.
2. Research method
The review methodology of this study included two major phases of analysis. These phases followed the procedures aligned
with the systematic process of content analysis (Krippendorff 1980; Mayring 2000,2008; Shapiro and Markoff 1997)as
adapted and conducted by Seuring and Müller (2008) and May et al. (2017). The publications were searched on Scopus, Web
of Science, ScienceDirect, IEEExplorer, Compendex and Inspec online databases considering their coverage of all the high-
impact and well-known journals in the field including all journals listed on the SCImago Journal Rank among many others.
In the first step of searching and collecting the relevant articles, we focused on the academic papers in English published
within the last 32 years beginning from 1987 as shown in Figure 3. Since the topic of sustainability in manufacturing grew
significantly after the Brundtland Commission report, 1987 (Brundtland 1987) is seen as a reasonable starting year for
inclusion of papers and is a commonly accepted milestone agreed upon scholars (Seuring and Müller 2008).
In the phase of material collection, the following Search String was applied after being adapted to the specific search
queries on Scopus, Web of Science, ScienceDirect, IEEExplorer, Compendex and Inspec online databases: (TITLE ((defect*
OR ‘product quality’) AND (detect* OR predict* OR repair* OR prevent*)) AND TITLE ((manufactur* OR production
OR industr*))).
We applied the Boolean keyword search criteria1to find all the relevant articles with one string. The keywords in the
first parenthesis reflect the usage of both terms ‘defect’ and ‘product quality’ in the scientific literature. In the second
parenthesis, the four main categories of ZDM strategy, as provided in the introduction, are included. Last, in the third
parenthesis, we included the relevant terms which could refer to researches in the manufacturing industry. We performed
the above mentioned search in the titles of the research articles in the databases, and it resulted in a total number of 957
papers. After removing the duplicate articles gathered from different databases, 450 articles remained. Then, all remaining
papers were assessed by their title, abstract screening and content reading if they fit in the scope of our review based on
the following criteria: Is the article in English?; Does the paper deal with manufacturing?; Does it present a method and
not a review of methods?; Does the paper deal with the production stage of a product?; Does the paper provide a ZDM
This final evaluation resulted in 227 relevant papers. However, in order to include as many articles as possible in
the scope of our review and not to skip highly relevant papers in the field due to the limitation imposed by our search
criteria, we decided to add an additional step to extend our sample by second references (i.e. relevant articles cited in
our original final sample of 227 papers). This additional step yielded 53 extra papers increasing our final list to 280
research papers (see ‘References in the Literature Review’, at the end of this paper for the full list of articles). The fact
that, the additional step did not result in high numbers can be seen as a validation of our initial approach and reliabil-
ity of our search criteria. At the end, we analysed the 280 papers according to several criteria, which are explained in
Section 4.
Figure 3. Mapping the scientific literature search.
International Journal of Production Research 5
3. Pertinent literature
In this section, we analyse the pertinent literature that includes previously published reviews related to zero defect manu-
facturing and provide the difference and contribution of our study compared to those previously published review papers
on the subject. In the recent years, a number of review studies have been carried out related to zero defect manufacturing.
However, those publications simply focused on particular parts of the ZDM concept and strategies highlighted in our review.
To that end, we found 9 literature review papers, and analysed and mapped them in Table 1according to their scope and
relevant ZDM strategies addressed. These most relevant review papers are briefly explained in the following paragraphs.
Yaqiong, Mann, and Zhang (2011) investigates the fuzzy theory for quality management (QM). They classify the anal-
ysed papers according to the four dimensions of the QM (quality planning, quality control, quality assurance and quality
improvement). One of the main findings was that the quality improvement was one of the most researched topics. Cun-
ningham et al. (2018) analyses the Wire Arc Additive Manufacturing (WAAM) and how the quality can be improved by
improving the quality of the ancillary processes. It is mapped with the prevention strategy (indirectly), because their findings
can be interpreted as what should be avoided in order to prevent a defect. The same happens with another review on the
same topic WAAM (Wu et al. 2018), they analyse the materials properties and illustrate the common defects produced by
WAAM and also how the quality of the produced part can be improved. Köksal, Batmaz, and Testik (2011) investigated the
data mining methods for data analysis for implementing one of the quality improvement programmes, such as six sigma.
The main findings were that (i) data mining approaches are most commonly used for the prediction of the quality or the
Virtual detection according to ZDM concept presented in section 4.3, and (ii) data mining is used for the classification of
quality. Rostami, Dantan, and Homri (2015) is another review paper that analyses data mining techniques, and more partic-
ularly the support vector machine in order to address quality assessment problems. Perner et al. (2016) analyses the process
of automated carbon fibre reinforced plastic production. One of the conclusions is that one step towards the higher quality
product is by increasing the resolution of the defect detecting systems. Wu and Chen (2018) investigate the past and current
practices regarding quality issues and quality control in 3D printed parts. Kim, Lin, and Tseng (2018) presents a literature
review on quality control regarding the additive manufacturing process. They analyse different methods for detecting the
quality of the produced part. Last, Chauveau (2018) analyses and evaluates different methods for detecting defects in welded
4. Results and critical analysis
Here, we evaluate the main research patterns in the sample by analysing key factors such as number of articles by year, by
categories of ZDM, by industrial sector, by manufacturing processes, according to the level of automation, according to the
implementation of ZDM strategies, and by manufacturing stages.
4.1. Distribution across the time period
In this section, we analyse the distribution of the identified papers (N =280) over a time period, which illustrates how rel-
evant and saturated the topic of zero defect manufacturing is. The allocation of publications within the research period
between 1987 and 2018 (i.e. last 32 years) is shown in Figure 4, illustrating the distribution for each ZDM strategy.
Analysing the figure, it is evident that there is an increasing trend for the ‘Detection’ along with the ‘Prediction’ which
seems to follow a similar trend. Both increasing trends correspond to the evolution of both hardware and software for
Table 1. ZDM relevant papers mapping.
Purpose ZDM strategies
process specific
specific Detect Predict Prevent Repair
Yaqiong, Mann, and Zhang (2011)x x x
Köksal, Batmaz, and Testik (2011)xx
Rostami, Dantan, and Homri (2015)xx
Perner et al. (2016)xx
Wu and C hen ( 2018)xx
Kim, Lin, and Tseng (2018)xx
Cunningham et al. (2018)x x
Chauveau (2018)xx x
Wu et al. (2018)x x
6F. Psarommatis et al.
Figure 4. Papers yearly distribution vs ZDM strategies.
implementing those strategies. Furthermore, the ‘Repair’ and ‘Prevent’ strategies mostly appear during the last decade with
environmental regulations becoming stricter and production lines gaining higher efficiencies.
The zero defect manufacturing research has a tendency to grow within the last decade, and since early 2009 a significant
increase is observed with respect to the number of publications. Majority of the articles have been published in the last six
years since the subject gathered the year-by-year increasing interest of the academic scholars. Here, we should mention that
in Figure 4the total number of papers is 303 instead of 280 since some papers use ZDM strategies pairwise (see Section 3.2).
4.2. Authors and affiliations analysis
Here we investigate the distribution of authors and affiliation to help readers discover the most influential researchers and
institutions in the field of zero defect manufacturing, and Table 2presents the results of this analysis. The upper part of the
table contains the affiliations divided into three categories ‘Universities’, ‘Research Institute’ and ‘Company’. In total, we
found 289 different author affiliations which included 170 universities, 51 research institutes and 68 Industrial companies.
The universities are the dominant category and if combined with the research institutes they comprise the 76.47% of the total
sample which shows imbalanced and not strong collaboration between industry and academia. This point will be discussed
and analysed further in sections 5.2.2 and 5.2.3. Also, the pie chart in Table 2illustrates the distribution of the countries of
the affiliations showing 3 dominant categories: (1) affiliations from the United States with 54/289, (2) China with 37/289
and (3) countries with a frequency less than 8 times. At the lower side of Table 2, we present the most influential authors
in the domain of ZDM. In total 919 different authors have been found and Table 2presents only the authors with more
than 3 papers in our analysis. It is noticeable that the university with the highest frequency (university of Texas) is not the
affiliation of the authors with the highest frequency.
Another interesting observation is that the research works of the authors with the highest frequency deal with only one
out of four ZDM strategies, i.e. ‘detection’. These studies deal with both ‘physical’ and ‘virtual’ types of detection.
4.3. Distribution across the main categories of ZDM
The analysis of the corresponding papers regarding the ZDM concept led us to Figure 5, which illustrates the ‘Detection’
of defects, with a significant percentage of 80.20%, as the dominant ZDM strategy in which a lot of research studies have
been carried out and utilised in actual production environments. This result was expected because the detection of defects is
the starting point for implementing ZDM and also constitutes the foundation for the implementation of the other strategies.
‘Prevention’ is the second most common ZDM strategy with a significantly less percentage, 9.90%. Unexpectedly, along
with the ‘Repair’ strategy having 4.95% share of the total papers because repairing defected parts is a difficult and costly
process and therefore manufacturers prefer to discard defected parts rather than repairing them, the ‘Prediction’ strategy is
one of the most underutilised with the same percentage as the ‘repair’ strategy (4.95%), which might be due to the fact that
defining accurate prediction models is a very difficult and complex task and requires a vast amount of data in order to be
accurate. In most cases, prediction models are developed in applications in which the detection of defects are not possible
or not cost efficient and therefore these models substitute the detection module revealing a fact that is not aligned with the
ZDM concept illustrated in Figure 1. Only a few papers utilise the prediction modules as ZDM implies.
International Journal of Production Research 7
Table 2. Affiliation and authors analysis.
Affiliations Analysis (more than 3)
Universities Companies
University of Texas 7 ST Microelectronics 5
National Tsing Hua University 6 IBM 3
Zhejiang University 6 JSOL Corporation 2
Aix Marseille University 6 GF Machining Solutions 2
Pennsylvania State University 6 Taiwan Semiconductor Manufacturing Company 2
Rutgers University 5 TWI 2
University of Western Australia 4 KLA Instruments 2
Universite de Bourgogne 4 Luna Innovations Incorporated 2
Hong Kong Polytechnic University 4 Research Institutes
Hong Kong University of Science and Technology 4 Fraunhofer 7
University of Surrey 4 Centre national de la recherche scientifique (CNRS) 4
Carnegie Mellon University 4 Indian Institute of Technology Kharagpur 3
University of Wyoming 4 Georgia Institute of Technology 3
Karl Franzens University Graz 3 Huaiyin Institute of Technology 2
Southern University of Science and Technology 3 Karlsruhe Institute of Technology 2
Technical University of Ostrava 3 Fundación CARTIF 2
Politecnico di Milano 3
University of Naples Federico II 3
Nanyang Technological University 3
University of Oviedo 3
University of Valladolid 3
Loughborough University 3
University Kentucky 3
University of Massachusetts 3
Most influential Authors (more than 3)
Strojwas Andrzej 5 Carnegie Mellon University (1997)
Ouladsine Mustapha 5 Aix Marseille University (2015–2018)
Pinaton Jacques 5 ST Microelectronics (2015–2018)
Fang Tong 4 Rutgers University (1998–2003)
Jafari Mohsen A 4 Rutgers University (1998–2003)
Kohler Sophie 4 Universite de Bourgogne (3/4) (1998–2001),
University de Haute Alsace (1/4) (2015)
Ananou Bouchra 4 Aix Marseille University (2015–2017)
Melhem Mariam 4 Aix Marseille University (2015–2017)
Bakhadryron I 3 Rutgers University (1998)
Caron J 3 Universitk du Littoral (1997–1999)
Danforth Stephen 3 Rutgers University (1998–2003)
Douglas Craig 3 University of Wyoming (2009–2012)
Geveaux Pierre 3 Universite de Bourgogne (1998–2001)
Li Xiaolei 3 Carnegie Mellon University (1997)
Miteran Johel 3 Universite de Bourgogne (1998–2001)
Safari Ahmad 3 Rutgers University (1998–2003)
Teti Roberto 3 Fraunhofer (2015–2016)
According to the zero defect manufacturing concept highlighted in Figure 1, there are two types of detection. The first
one is the physical detection where measurements are taken from the physical part and then processed to decide if there is
a defect or not. The second type is the virtual detection which can be found in literature as ‘virtual metrology’ or with the
term ‘prediction’ referring to a set of algorithms that are fed with sensor and production data from the manufacturing of
the product and are analysed in order to find a defect without measuring the actual part. Those techniques are rather useful
in cases where the physical measurements are not possible or too expensive (Kang et al. 2011; Khan, Moyne, and Tilbury
2007; Chang et al. 2006). In the literature, mostly ‘virtual detection’ is referred as ‘quality prediction’. The term prediction
should not be confused with the prediction strategy imposed by the ZDM. The main difference is that the virtual detection
can happen utilising historical process data as well as the process data from the manufacturing of the component under
8F. Psarommatis et al.
Figure 5. ZDM strategies utilisation from 1987 to 2018.
Figure 6. Physical vs. Virtual detection over the years.
investigation, whereas the ‘prediction’ of ZDM predicts the quality of a part before it is produced considering only specific
models and historical process data.
Further to that, virtual detection is at a fairly early stage in comparison with the physical detection, but it is estimated
that in the future it will be as popular as the physical detection or even more, due to the lower operational costs (Kang and
Kang 2017; Kurz, De Luca, and Pilz 2015; Susto et al. 2015). This method is not widely used due to the lack of data and
accurate models. The distribution of the papers over the years according to physical vs. virtual detection is highlighted in
Figure 6.
Also, we noticed that only 6.27% of the analysed papers were aligned with the concept of ZDM and paired different
ZDM strategies. Accordingly, Figure 7highlights the found pairs as ‘Detection – Repair’ and ‘Prediction – Prevention’
Figure 7. Papers that paired ZDM strategies.
International Journal of Production Research 9
(see Figure 2) with 56.25% and 37.50% respectively, while for the pair ‘Detection – Prevention’ only one paper was
found (6.67%).
4.4. Distribution across the industrial sectors
Moving forward to a deeper analysis, the pie-chart in Figure 8depicts the main industries where ZDM is applied. The
dominant industry that ZDM is applied with a higher percentage is the Semiconductor industry with 20.33%, followed
by steel industry with only 4.67% and then automotive (4.33%), foundry (4.00%), food (3.33%), metal (3.00%), ceramic
(2.33%) and renewable energy Industries (2.33%). The rest of the industries had a percentage less than 2% and therefore
are included in the chart as the combined category of ‘others’ which comprise 25 different Industries where ZDM strategies
are applied. Among them, there are chemical, paper, plastic, textile, aerospace, glass, railway, wood, machining and pharma
industries with percentages between 2% and 1%. The rest of the industries are below 1% and therefore are not mentioned.
For all the categories of Figure 8, we extracted the utilisation of ZDM strategies and the results are summarised in the
diagram in Figure 9. The categories entitled ‘Others’ and ‘Not stated’, looks almost identical with the detection strategy
holding a very high percentage of about 85%. Other than that, all the other strategies appear with almost the same per-
centage. ‘Steel Industry’, ‘Foundry’, ‘Food Industry’, ‘Metal Industry’ and ‘Ceramic Industry’ are not utilising the repair
strategy because the defects might be non-repairable. Also, this explains the fact that for the ‘Metal Industry’, ‘Foundry’
and ‘Steel Industry’ the prediction strategy is more mature and utilised compared to the rest of the industries. Moreover,
in the ‘Semiconductor Industry’ the repair and prevention strategies are utilised with percentages of 8.20% and 14.75%
Figure 8. Industries that ZDM strategies are applied.
Figure 9. Four major categories ZDM strategies distribution.
10 F. Psarommatis et al.
,respectively. This shows that in the ‘Semiconductor Industry’ the ZDM concept is more mature compared to the other
The total number of papers that the queries found for repair strategy was only 15/280, which is quite low compared to
the other strategies. Moreover, the landscape for the ‘repair’ strategy is quite similar to the combined result. The dominant
industry that the ‘repair’ strategy is utilised is the Semiconductor with 36% and then is the automotive Industry with 14%.
Then all the other industries are at 7% (cutting tools, aerospace, nuclear and TFT-LCD) and also there is a 22% of non-
industry-oriented cases.
4.5. Distribution across the manufacturing processes
Besides the Industry-oriented analysis, also a Process-oriented analysis was performed. The pie chart illustrated in Figure 10
highlights the most common manufacturing processes to which ZDM strategies are applied. The most common process to
which ZDM is applied is the ‘Wafer manufacturing process chain’ (semiconductor industry), with 12.58%. This is not a
specific process but a group of processes during the production of semiconductors. The next one following is the ‘Additive
Manufacturing’ process with half of the previous category 8.28%, followed closely by ‘Casting’ with 7.95%, and then by
‘Welding’, ‘Assembly’ and ‘TFT LCD panel process chain’ processes with 5.30%, 4.30% and 3.31% respectively. In the
category ‘others’, we included all the other processes that had less than 3%. Some examples of the others category are
injection moulding, plastic extrusion, stamping and paper manufacturing (1.2–3%).
Three out of six manufacturing processes that are presented in Figure 10 (i.e. casting, additive manufacturing and
welding) are simpler processes compared to all the others (not only in the pie chart), in terms of the type of defects which
include cracks, pores, and material inside tension as the most common ones. Besides, these processes are characterised as
single material processes. Furthermore, casting and welding are relatively more mature than other processes and heavily
used nowadays. On the other hand, wafer manufacturing processes chain is on the top of the most common processes that
is utilising ZDM strategies for two reasons: (i) very high production and quality levels which impose waste reduction, and
(ii) the fact that it is a 2D process, which makes it easier to apply ZDM strategies.
4.6. Distribution according to the level of automation
Moving forward with the analysis, we identified the level of automation of the ZDM strategies that were used from the
analysed papers. The results from this analysis are presented in Figure 11. It is clear from the diagram that the dominant ZDM
Figure 10. Most commonly used manufacturing processes where ZDM strategies are applied.
Figure 11. Level of automation of ZDM strategies.
International Journal of Production Research 11
strategy is ‘Detection’ which is implemented in an automated manner, with 82.30%. Additionally, ‘Prevention’ strategy is
implemented semi automatically most of the times (42.00%) rather than automatically (26.66%). This is due to the fact that
the prevention strategy is a complex process and requires multiple inputs from different sources in order to be effective.
Furthermore, the ‘Prediction’ is mostly implemented automatically because of the nature of the process that requires high
computational power due to the amount of data required to predict future defects.
4.7. Distribution according to the implementation of the ZDM strategies
Next, we studied the level of the integration of the developed ZDM strategies into the manufacturing systems. Before
proceeding further with the corresponding results, we first provide the definition for different types of implementation of
the ZDM strategy as follows:
In-line: if the ZDM strategy is implemented along the production line;
Off-line: if the ZDM strategy is implemented within the industrial premises and in order to be applied, there should
be a discontinuity of the production process;
Laboratory: if the ZDM strategy is implemented outside the industrial premises.
The results are summarised in Figure 12. As it comes to the detection strategy which is the most frequent research topic
in this domain, 55.96% of the studies have been performed in the Laboratory set up whereas 39.50% were carried out in
industrial settings. However, in the case of the prediction strategy, there are more research studies carried out in laboratory
setups compared to industrial settings. This is due to the fact that the concept of defect prediction is newer than the detection.
4.8. Distribution across the manufacturing stages
The final analysis that was performed was to analyse in which manufacturing stage the ZDM strategy was implemented.
For that purpose, the following analogy was used from the Product Life Cycle theory. We divided the manufacturing stage
into three sections as the Beginning of Manufacturing Life (BOML), the Middle of Manufacturing Life (MOML) and the
End of Manufacturing Life (EOML). Below the definition of each can be found.
BOML (Beginning of Manufacturing Life): All the quality inspection or actions that take action prior to the start
of the manufacturing process (e.g. raw material quality check). +the checking and altering the design in order to
avoid defects
MOML (Middle of Manufacturing Life): All the quality inspection or actions that take place during the
manufacturing of the product.
EOML (End of Manufacturing Life): All the quality inspection or actions that take place after the manufacturing
of the product has finished.
Figure 13 illustrates the EOML as the dominant manufacturing stage during which the detection strategy is implemented.
This means that manufacturers use this strategy for inspecting the quality of the final product in order for rejecting those
products rather than using it for being able to avoid defects in the future. For the detection, 60.03% of the papers found are
utilising the detection at the EOML. Where the next higher percentage was for the MOML with 33.07%.
Prediction and repair have almost the same values and also evenly dispersed between the MOML and EOML. Also for
those two strategies, the BOML is undeveloped and not explored. Furthermore, for the prevention, the higher category was
the MOML, which is logical and aligned with the ZDM concept, because you can prevent defects during the processes and
not after they have finished.
Figure 12. Integration level of implemented ZDM strategies.
12 F. Psarommatis et al.
Figure 13. ZDM strategies vs. manufacturing stages.
The pie-chart shown in Figure 13 illustrates the total percentages for all the strategies compared to the various manu-
facturing stages defined previously. The dominant category is the EOML with 54%, which means that individual strategies
are applied after the completion of the manufacturing process in the context of inspection. The next category is the MOML
with 38% and then the BOML and ‘Not stated’ follows with 4% each. The percentage of BOML is very low and means that
manufactures are not taking into consideration, as much as they should, the health of the machine or the tooling and the raw
materials prior to the starting of the process.
5. Discussion and Concluding Remarks
5.1. Current state of research
Based on the extensive review of the ZDM literature, we identified and highlighted four distinctive strategies based on
overarching themes for zero defect management in manufacturing, i.e. detection, repair, prediction, and prevention. Con-
sidering how ZDM research and development could evolve within the next years, we expect prediction and prevention to
grow significantly, due to the fact that these strategies when implemented effectively can avoid production of a defected
part before the occurrence, rather than repairing it once a defect has already occurred.
Thorough analysis of the previous researches in the field indicates a large, and still unexploited potential in industrial
companies with respect to prediction and prevention of defects and thus the benefits are manifold. Detection is in a more
mature state than the rest of the ZDM strategies, but in certain industries (e.g. very high maturity in the semiconductor
industry), and repair stays in between with a low to moderate maturity.
Another fact to point out is that researchers in the field need closer collaboration with the industries, because as the trend
in the chart in Figure 14 from our analysis illustrates, more research works are carried out in the laboratory environments
year-by-year rather than the industrial plants itself. Figure 14 presents the number of papers per year in the form of 4 periods
moving average and not the actual numbers revealed by our analysis. A moving average is a technique to get an overall idea
of the trends in a data set. The technology readiness levels of developed technologies pertaining to certain strategies should
Figure 14. Trend over the years regarding research for ZDM.
International Journal of Production Research 13
be improved by ever closer collaboration between academia and industry thus leading to more and more implementation
and demonstration in operational environments.
5.2. Shortcomings of existing research
Within the last three decades, vast researches have been conducted with various approaches to improve the quality of
manufacturing processes. However, there are still many issues that occur from research reviews. Based on the evaluation of
current research and descriptive analysis we found six major shortcomings of current research in zero defect manufacturing
which at the same time should be understood as potential future directions as well.
5.2.1. Focus on single strategy instead of a holistic approach for global optima
As highlighted in Figure 4in the Results section, 95% of the analysed papers focus on a single strategy instead of a
holistic approach for global optima. However, for an effective zero defect management, ZDM strategies should be developed
pairwise as illustrated in Figure 2of Section 1concerning the ZDM implementation for identifying the triggering factors as
well as actions for prevention of defects and continuous improvement.
5.2.2. Certain industries are under-researched
ZDM strategy in the semiconductor industry is much more advanced than the other industries owing to the fact that the
‘Detection’ strategy is in a very mature point. This is for the reason that the manufactured final products are in 2D which
makes detection of defects much easier compared to the detection in complex 3D parts. Also since the volume of production
is high in the semiconductor industry leading to the possibility of collecting large defect data sample, certain algorithms
for prediction and prevention can be trained more effectively. Moreover, the repairing of such parts is more straightforward
compared to other products. However, as highlighted in Figure 8in the Results section, other industries such as steel, food,
metal, automotive, ceramic, chemical are under researched. For industries like steel, metal and ceramics, this might be due
to the fact that the final products of such industries are simple which makes it easier to detect defects without investing
heavily on research. Nevertheless, the automotive sector is a very large industry and there is a huge benefit in applying such
strategies and technologies more effectively. Thus, the automotive sector could significantly benefit from further researches
in the field. Besides the industrial sectors already mentioned above, we noticed that almost no industries in the medical sector
are investigated in the analysed research papers except a few studies in the pharma industry with a very low percentage, i.e.
1.29%. One of the reasons for this could be their prioritisation of focus on the R&D for the medical-pharma products rather
than the production process. Another reason is the very high standards required so that they discard the defected parts.
5.2.3. Full potential of industry-academia collaboration is not achieved
One of the major shortcomings we found out is that there is no evidence of strong and well-defined industry-academia
collaboration which could significantly boost practical implications of research works (i.e. almost 1/3 of the papers as illus-
trated in Figure 14). It is of vital importance to have an elaborate plan and strategy to better establish academia and industry
collaboration, and in particular manufacturing holds an enormous potential to benefit from such a symbiotic relationship.
On that regard, particular attention should be given to the re-definition of the relationships between industry and academia.
Companies should be more flexible and open to academia in sharing the relevant data and knowledge to foster the creativ-
ity of researchers to develop innovative methods and technologies. Academicians in the manufacturing domain should do
research closer to real industrial applications shifting from what & if mind-set towards how to develop and implement inno-
vative methods and technologies for achieving better and more realistic results. Such effective and synergic collaborations
could also lead to important societal and economic benefits.
5.2.4. Not enough focus on BOML phase
One of the major shortcomings we found out after analysing the previous studies was the lack of researches and imple-
mentations which consider the Beginning of Manufacturing Life (BOML) as an important focus area for improvements to
achieve zero defects in manufacturing as previously illustrated in Figure 13 in the previous section. Thus, we think that
more focus should be given to the BOML phase in order to minimise the cost due to defected parts during the production as
(i) checking the equipment prior to the initialisation of the manufacturing process and (ii) more thorough inspection of the
raw materials could significantly eliminate the possibilities of producing defected parts.
14 F. Psarommatis et al.
5.2.5. Cost–benefit comparative analysis is not evident
After the review of the papers, we also noticed that none of the researches included the financial and business aspects and
benefits of the described methods, making it difficult to evaluate whether a specific method or technology is worth investing
or not for the industries. ZDM is about reducing the defected parts, energy consumption, and scrap materials among other
several performance indicators, all of which at the end should be translated into cost functions to better understand their
real impact and benefits for business on the enterprise level. So far, this important dimension is under-researched and has
significant potential to be explored further.
5.2.6. Standard and clear definition of terms are missing
One of the problems we figured out during the review was the missing standard and clear definition of terms related to
zero-defect manufacturing, which could be solved by building a zero defect manufacturing ontology to define a common
vocabulary for researchers and practitioners who need to share information in the domain. To better highlight the issue,
we provide the following example. During the literature review, we observed that for all the ‘virtual detection’ cases, the
authors were using the term ‘prediction’, which is not aligned with the ZDM prediction term. The ZDM prediction aims to
estimate when a defect might occur at the near future while the ‘virtual detection’ – ‘prediction’ aims to find if there is a
defect to a product that has already been manufactured.
5.3. Further research directions
The shortcomings of the current state of the art have revealed six distinctive areas for improving the research topic.
Accordingly, we see four further directions in which an advance of the topic would stimulate scholarly and practical needs.
5.3.1. Shift from local to global solutions
An effective and effective zero defect manufacturing system could be achieved by adopting a holistic approach to not only
achieve zero defects but also maximise quality and performance via integration of the four strategies (detection, repair,
prediction, and prevention) all of which serve a different role acting synergistically with the others. There are currently
many collaborative projects aiming at achieving such ZDM platforms but the high impact scholarly papers on the subject
are yet to be published.
5.3.2. Investigate pros and cons
One of the most significant advantages of zero defect manufacturing is cost reduction due to waste elimination when building
products which could yield higher customer satisfaction. Nonetheless, having the goal of manufacturing with zero defects
may result in negative impact on other performances in trade-off such as time and resources required to achieve such an
objective. However, the studies investigating the pros and cons of zero defect manufacturing are quite a few and a more
thorough analysis on the subject should be considered for further researches in the field.
5.3.3. Role of people and human activities in manufacturing
The most important aspect of manufacturing is sometimes largely neglected: people. We tend to view the manufacturing
system as a collection of process chains comprised of several technologies. However, first and foremost manufacturing
quality is significantly impacted by people. Constructivist view is required to investigate the role of people on overall
manufacturing effectiveness and quality. This part by now is under-researched and is a potential topic to be explored further.
5.3.4. New business models for ZDM
According to what is highlighted in Section 4.2, financial analysis from the business point of view is missing for the devel-
oped ZDM strategies. Therefore, in the future researchers need to provide economic information regarding the performance
of their method concerning their particular use cases, hence building further insights into business aspects and benefits
concerning the implementation of the specific ZDM methods. Furthermore, it could be of benefit for each presented ZDM
method to calculate some boundaries within which the method could be effective and profitable for business. Doing so
would encourage companies to start implementing those methods and also it will be easier for researchers to improve their
methods by identifying the shortcomings.
International Journal of Production Research 15
5.4. Concluding remarks
This study has investigated zero defect manufacturing by means of a literature review. The review identified potential
epistemological, methodological, topical and theoretical advancement opportunities of the field. In the last years, the topic
of zero defect manufacturing strategy has gained momentum. Following debates in the scientific community and also the
wider public, achieving zero defects has moved from a mere abstract and idealistic concept towards a competitive weapon
in manufacturing. However, research endeavours on the subject are still in its infancy. We do hope that this structured
review provides foremost stimulating insights into the topic and researchers grasp out ideas for future research to saturate
this important yet complex topic in the next years. Concerning the implications for practice, the findings and insights of
this research work could provide general support for improving zero defect manufacturing principles and implementation
for certain industries. For academicians, the review highlights the shortcomings of research and provide suggestions for
further studies in the field of zero defect manufacturing. The descriptive analysis illustrates an evolving research field with
increasing consideration in academic journals especially in the last five years, and a more significant grow is expected for
the next 5-to-10 years.
Finally, there are some limitations to the study. First, academic databases are being consistently updated with new
articles, and hence the sample collected for this review work refers only to the period in which the study was conducted.
Second, although the quality of the research is imposed by different measures, the conceptual work, as every of its kind,
of identifying gaps and potential future research directions is shaped by the researchers’ experience and attitude. Last, we
also might have missed some potentially relevant scientific articles, although we followed a systematic search process as
presented in the method section of this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplemental data
Supplemental data for this article can be accessed here
Foivos Psarommatis
Gökan May
Dimitris Kiritsis
Ahuett-Garza, H., and T. Kurfess. 2018. “A Brief Discussion on the Trends of Habilitating Technologies for Industry 4.0 and Smart
Manufacturing.” Manufacturing Letters 15: 60–63. doi:10.1016/J.MFGLET.2018.02.011.
Bai, B., and J. Zhang. 2018. “Quality Cost Model Improvement Based on 6 σManagement.” International Journal of Manufacturing
Technology and Management 32 (4–5): 396–411.
Brundtland, G. H. 1987. Report of the World Commission on Environment and Development: Our Common Future, Available at:
Bufardi, A., O. Akten, M. Arif, P. Xirouchakis, and R. Perez. 2017. “Towards Zero-Defect Manufacturing with a Combined Online-
Offline Fuzzy-Nets Approach in Wire Electrical Discharge Machining.” WSEAS Transactions on Environment and Development
13: 401–409.
Chang, Y. J., Y. Kang, C. L. Hsu, C. T. Chang, and T. Y. Chan. 2006. “Virtual Metrology Technique for Semiconductor Manufacturing.”
In Neural Networks, 2006. IJCNN’06. International Joint Conference, 5289–5293. Vancouver: IEEE.
Chauveau, D. 2018. “Review of NDT and Process Monitoring Techniques Usable to Produce High-Quality Parts by Welding or Additive
Manufacturing.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics) 11277: 372–383.
Chien, C.-F., S.-C. Hsu, and Y.-J. Chen. 2013. “A System for Online Detection and Classification of Wafer bin map
Defect Patterns for Manufacturing Intelligence.” International Journal of Production Research 51 (8): 2324–2338.
16 F. Psarommatis et al.
Choi, G., S.-H. Kim, C. Ha, and S. J. Bae. 2012. “Multi-step ART1 Algorithm for Recognition of Defect Patterns on Semiconductor
Wafers.” International Journal of Production Research 50 (12): 3274–3287. doi:10.1080/00207543.2011.574502.
Cunningham, C. R., J. M. Flynn, A. Shokrani, V. Dhokia, and S. T. Newman. 2018. “Invited Review Article: Strategies and Processes for
High Quality Wire arc Additive Manufacturing.” Additive Manufacturing 22: 672–686. doi:10.1016/j.addma.2018.06.020.
Dong, W., S. Liu, Z. Fang, X. Yang, Q. Hu, and L. Tao. 2017, August. “Control and Optimization of Quality Cost Based on Discrete Grey
Forecasting Model.” In Grey Systems and Intelligent Services (GSIS), 2017 International Conference, 149–149. Stockholm: IEEE.
Dreyfus, P. A., and D. Kyritsis. 2018, August. “A Framework Based on Predictive Maintenance, Zero-Defect Manufacturing and Schedul-
ing Under Uncertainty Tools, to Optimize Production Capacities of High-End Quality Products.” In IFIP International Conference
on Advances in Production Management Systems, 296–303. Cham: Springer.
Eger, F., D. Coupek, D. Caputo, M. Colledani, M. Penalva, J. A. Ortiz, H. Freiberger, and G. Kollegger. 2018. “Zero Defect Manufacturing
Strategies for Reduction of Scrap and Inspection Effort in Multi-Stage Production Systems.” Procedia CIRP 67: 368–373.
Eger, F., C. Reiff, B. Brantl, M. Colledani, and A. Verl. 2018. “Correlation Analysis Methods in Multi-Stage Production Systems for
Reaching Zero-Defect Manufacturing.” Procedia CIRP 72: 635–640. doi:10.1016/j.procir.2018.03.163.
Eleftheriadis, M. S. R. J., and O. Myklebust. 2016. “A Guideline of Quality Steps Towards Zero Defect Manufacturing in
Industry.” Proceedings of the International Conference on Industrial Engineering and Operations Management, 332–340.
Halpin, J. F. 1966. Zero Defects: a new Dimension in Quality Assurance. New York, NY: McGraw-Hill.
Jabbour, C. J. C., A. B. L. de Sousa Jabbour, K. Govindan, A. A. Teixeira, and W. R. de Souza Freitas. 2013. “Environmental Man-
agement and Operational Performance in Automotive Companies in Brazil: the Role of Human Resource Management and Lean
Manufacturing.” Journal of Cleaner Production 47: 129–140.
Jacob, A., K. Windhuber, D. Ranke, and G. Lanza. 2018. “Planning, Evaluation and Optimization of Product Design and Manufacturing
Technology Chains for New Product and Production Technologies on the Example of Additive Manufacturing.” Procedia CIRP
70: 108–113. doi:10.1016/J.PROCIR.2018.02.049.
Ju, F., J. Li, G. Xiao, and J. Arinez. 2013. “Quality Flow Model in Automotive Paint Shops.” International Journal of Production Research
51 (21): 6470–6483. doi:10.1080/00207543.2013.824629.
Kang, S., and P. Kang. 2017. “An Intelligent Virtual Metrology System with Adaptive Update for Semiconductor Manufacturing.” Journal
of Process Control 52: 66–74.
Kang, P., D. Kim, H. J. Lee, S. Doh, and S. Cho. 2011. “Virtual Metrology for run-to-run Control in Semiconductor Manufacturing.”
Expert Systems with Applications 38 (3): 2508–2522.
Khan, A. A., J. R. Moyne, and D. M. Tilbury. 2007. “An Approach for Factory-Wide Control Utilizing Virtual Metrology.” IEEE
Transactions on Semiconductor Manufacturing 20 (4): 364–375.
Kim, H., Y. Lin, and T. L. B. Tseng. 2018. “A Review on Quality Control in Additive Manufacturing.” Rapid Prototyping Journal 24:
645–669. doi:10.1108/RPJ-03-2017-0048.
Kletti, J., and J. Schumacher. 2014. “Short Interval Technology (SIT).” In Die Perfekte Produktion, 9–33. Berlin, Heidelberg: Springer
Berlin Heidelberg.
Köksal, G., I. Batmaz, and M. C. Testik. 2011. “A Review of Data Mining Applications for Quality Improvement in Manufacturing
Industry.” Expert Systems with Applications 38: 13448–13467. doi:10.1016/j.eswa.2011.04.063.
Krippendorff, K. 1980. Content Analysis – An Introduction to its Methodology. Beverly Hills: SAGE Publications.
Kuo, C.-F., C.-T. M. Hsu, C.-H. Fang, S.-M. Chao, and Y.-D. Lin. 2013. “Automatic Defect Inspection System of
Colour Filters Using Taguchi-Based Neural Network.” International Journal of Production Research 51 (5): 1464–1476.
Kurz, D., C. De Luca, and J. Pilz. 2015. “A Sampling Decision System for Virtual Metrology in Semiconductor Manufacturing.” IEEE
Transactions on Automation Science and Engineering 12 (1): 75–83.
Lu, Y. 2017. “Industry 4.0: A Survey on Technologies, Applications and Open Research Issues.” Journal of Industrial Information
Integration 6: 1–10.
Marodin, G., A. G. Frank, G. L. Tortorella, and T. Netland. 2018. “Lean Product Development and Lean Manufacturing: Testing
Moderation Effects.” International Journal of Production Economics 203: 301–310. doi:10.1016/J.IJPE.2018.07.009.
May, G., B. Stahl, M. Taisch, and D. Kiritsis. 2017. “Energy Management in Manufacturing: From Literature Review to a Conceptual
Framework.” Journal of Cleaner Production 167: 1464–1489. doi:10.1016/j.jclepro.2016.10.191.
Mayring, P. 2000. “Qualitative Content Analysis.” Forum Qual. Sozialforsch 1: 1–10.
Mayring, P. 2008. Qualitative Inhaltanalyse – Grundlagen und Techniken. Weinheim: Beltz Verlag.
Mourtzis, D., E. Vlachou, and N. Milas. 2016. “Industrial Big Data as a Result of IoT Adoption in Manufacturing.” Procedia CIRP 55:
290–295. doi:10.1016/J.PROCIR. 2016.07.038.
Myklebust, O. 2013. “Zero Defect Manufacturing: A Product and Plant Oriented Lifecycle Approach.” Procedia CIRP 12: 246–251.
Perner, M., S. Algermissen, R. Keimer, and H. P. Monner. 2016. “Avoiding Defects in Manufacturing Processes: A Review for Automated
CFRP Production.” Robotics and Computer-Integrated Manufacturing 38: 82–92. doi:10.1016/j.rcim.2015.10.008.
Psarommatis, F., and D. Kiritsis. 2018. A Scheduling Tool for Achieving Zero Defect Manufacturing (ZDM): A Conceptual Framework,
271–278. Cham: Springer.
International Journal of Production Research 17
Raabe, H., O. Myklebust, and R. Eleftheriadis. 2017, September. “Vision Based Quality Control and Maintenance in High Volume
Production by Use of Zero Defect Strategies.” In International Workshop of Advanced Manufacturing and Automation, 405–412.
Singapore: Springer.
Rostami, H., J.-Y. Dantan, and L. Homri. 2015. “Review of Data Mining Applications for Quality Assessment in Manufacturing Industry:
Support Vector Machines.” International Journal of Metrology and Quality Engineering 6:401: 1–18. doi:10.1051/ijmqe/2015023.
Seuring, S., and M. Müller. 2008. “From a Literature Review to a Conceptual Framework for Sustainable Supply Chain Management.”
Journal of Cleaner Production 16: 1699–1710. doi:10.1016/j.jclepro.2008.04.020.
Shapiro, G., and G. Markoff. 1997. “Methods for Drawing Statistical Inferences From Text and Transcripts.” In Text Analysis for the
Social Sciences, edited by C. W. Roberts, 9–31. Mahwah: Lawrence Erlbaum Associates.
Sinha, A. K., and A. Anand. 2018. “Development of Sustainable Supplier Selection Index for new Product Development Using Multi
Criteria Decision Making.” Journal of Cleaner Production 197: 1587–1596.
Susto, G. A., S. Pampuri, A. Schirru, A. Beghi, and G. De Nicolao. 2015. “Multi-step Virtual Metrology for Semiconductor Manufacturing:
A Multilevel and Regularization Methods-Based Approach.” Computers & Operations Research 53: 328–337.
Teti, R. 2015. “Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining.” Procedia
CIRP 28: 3–15.
Thangaiah, I. S., V. Sharma, and V. N. Sundharam. 2018. “Analysing of Customer Feedback on Critical Quality Parameters to Improve
Productivity in Manufacturing–a Case Study.” International Journal of Productivity and Quality Management 23 (3): 349–368.
US Assistant Secretary of Defense. 1965. Guide To Zero Defects. Manpower Installations and Logistics. Qual. Reliab. Assur. Handbook,
Washington, DC.
Utz, W., and M. Lee. 2017. “Industrial Business Process Management Using Adonis Towards a Modular Business Process Modelling
Method for Zero-Defect-Manufacturing.” International Conference on Applied Science and Engineering ICIMSA 2017: 1–5.
Wang, K.-S. 2013. “Towards Zero-Defect Manufacturing (ZDM) – A Data Mining Approach.” Advances in Manufacturing 1 (1): 62–74.
Wu, H. C., and T. C. T. Chen. 2018. “Quality Control Issues in 3D-Printing Manufacturing: a Review.” Rapid Prototyping Journal 24:
607–614. doi:10.1108/RPJ-02-2017-0031.
Wu, B., Z. Pan, D. Ding, D. Cuiuri, H. Li, J. Xu, and J. Norrish. 2018. “A Review of the Wire arc Additive Manufacturing of Metals: Prop-
erties, Defects and Quality Improvement.” Journal of Manufacturing Processes 35: 127–139. doi:10.1016/j.jmapro.2018.08.001.
Yaqiong, L., L. K. Mann, and W. Zhang. 2011. “Fuzzy Theory Applied in Quality Management of Distributed Manufac-
turing System: A Literature Review and Classification.” Engineering Applications of Artificial Intelligence 24: 266–277.
... ZDM was coined during the later part of the 1980s, with the aim to reduce the defects in the output from various production processes (Lindström et al., 2020a). ZDM is a disruptive concept that can totally reshape the manufacturing ideology (Psarommatis et al., 2020a). According to Psarommatis et al. (2020a), the ZDM consists of four strategies (detection, repair, prediction, and prevention) and it has three ZDM strategy pairs: detect-repair, detect-prevent, and predict-prevent. ...
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... However, achieving these goals is not straightforward (Ferretti et al. 2013). Strategies to achieve ZDM goals include the Detect, Prevent, Predict and Repair of a defect (Psarommatis and Kiritsis 2018;Psarommatis et al. 2020). Amongst all, the most critical strategy is defect detection because the others rely on data collected in the detection phase (Psarommatis and Kiritsis 2019). ...
... Root cause analysis is the analysis of existing data records to extract relevant features and feature combinations for high or low product quality (Weichert et al. 2019). Virtual detection, also called virtual metrology or quality prediction in the literature, refers to a set of algorithms used to find a defect without measuring the actual part when the physical measurements are not possible or too expensive, as in semiconductor manufacturing (Psarommatis et al. 2020;S. Kang and Kang 2017). ...
The prediction of defects occurring during manufacturing processes is one of the strategies to be implemented by organizations to reach the goals of Zero-Defect Manufacturing (ZDM). In low-volume productions, characterized by a high level of complexity and customization, defects prediction may be challenging owing to the small amount of historical data typically available. This paper proposes a diagnostic tool that provides an in-line identification of critical steps of assembly processes. The method is based on a self-adaptive defect prediction model of the process, updated as new data are acquired. Assembly complexity of both the process and the design are used as predictors of the defect model. The methodology identifies critical assembly workstations where the respective average defectiveness deviates from the average defectiveness predicted by the model. Detecting critical workstations facilitates quality engineers in identifying the causes of non-conformities and undertaking appropriate corrective actions. The relevance of the method is emphasized by an application to a real case study related to the assembly of rotating ring wrapping machines used in end-of-line packaging.
... Zero Defect Manufacturing is an emerging approach that constitutes a viable solution for achieving sustainable manufacturing [12], [13]. It aims to improve the quality of all manufacturing aspects (product, process, service etc.) by reducing any form of waste using data driven technologies. ...
... In contrast with contemporary optimization methods ZDM is an holistic approach dealing with all the manufacturing aspects at the same time considering the effects between each other rather that focusing on isolated process optimization [14]. ZDM is comprised by four strategies, detect, predict which are the triggering factors and the repair and prevent which are the action strategies [13]. Those strategies are implemented in pairs one strategy from the triggering and one from the action strategies. ...
... Modern technological advancements provided capabilities that were not possible at the past. These technological advancements initiated the emergence of ZDM which constitutes a viable alternative solution for QMS [27] [13]. One major change in ZDM is about the flow of information. ...
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Over the years, the manufacturing industry has seen constant growth and change. On one level, it has been affected by the fourth industrial revolution (Industry 4.0). On another, it has had to enhance its ability to meet higher customer expectations, such as more customized products at a faster rate. These factors have led many manufacturing companies to produce new products quicker than ever for two main reasons: to achieve higher profits and to meet the increasing demand from their customers. This phenomenon has imposed new rules on product manufacture, such as shorter production time and smaller batch output, making strategies, which had been successfully used in the past, obsolete or less efficient. Furthermore, to embrace agility and deliver a rich portfolio of services to the industrial sector, industrial automation based on digital connectedness will challenge the traditional digital boundaries among industrial systems. The flow of data created and collected by various actors in the manufacturing value chain will have to break these barriers in order to allow for the full-scale digital collaboration among production entities, ensuring at the same time multi-layer optimization. Additionally, sustainable manufacturing with zero defects and zero waste is one of the top priorities of manufacturers these days. A key approach for achieving sustainable manufacturing is called Zero Defect manufacturing (ZDM), therefore the current article proposes a holistic framework which is identifying all the critical components of a manufacturing company that needs to be integrated together in order to achieve sustainable manufacturing utilizing ZDM. The core of the proposed framework are data and data driven technologies that can increase the sustainability of a manufacturing system. The proposed framework would represent exactly what Industry 4.0 paradigm imposes.
... Such choice is often motivated by a policy of brand protection: if a product is remanufactured by an IR, the brand appeal may be reduced, especially if the product does not respect the quality standards of the OEM products [35]. In fact, OEMs are moving towards the concept of ZDM to reduce the costs related to production and to be more eco-friendly [36]. Such a strategy also increases the brand appeal of the OEM by providing reliable products to the customer. ...
Remanufacturing represents one of the most promising strategies for promoting circular economy (CE) principles and achieving economic and environmental sustainability goals. The implementation of Industry 4.0 (I4.0) enabling technologies such as cloud computing and the Internet of Things in the classical remanufacturing process provides the opportunity to define a new approach called Cloud Remanufacturing, which realises a distributed system in which remanufacturing resources, located at different geographic sites, are shared and managed in a centralised environment. This paper aims to introduce the new concept of Cloud Remanufacturing by proposing a framework to explain its logic and characterise its main features. Furthermore, the mathematical relationships for evaluating the costs and the times for completing the remanufacturing process enhanced by I4.0 technologies will be developed, and, relying on them, the model will be tested for 2 scenarios involving different extended areas. In detail, the problem of activity scheduling in Cloud Remanufacturing will be addressed using two metaheuristic approaches. The results show the effectiveness and theoretical applicability of the model, even if a comparison with a real case study is not possible due to the lack of a real centrally managed remanufacturing distributed system.
... The potential inherent in the inter-stage relationships, which among other things are the cause of defect generation, but can also be used to compensate for defects, remains unexploited. Moreover, traditional approaches consider the aspects of product quality and process capability separately [11]. The interplay, however, is seen by Colledani et al. [2] as the most recent paradigm and the basis for implementing ZDM. ...
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Manufacturing systems for today's products are complex systems requiring a variety of different processes in order to be able to manufacture all necessary part features. This also applies to the production of rotating components, which have experienced increasing demand at the latest due to the growth in mobility. As in almost every manufacturing process, quality-reducing defects can occur due to deviations for example tool wear, which cannot always be avoided. Those, that have accumulated from previous process steps can cause the occurrence of superimposed defects. This leads to complex relationships between quality defects in the end product and the numerous parameters of the manufacturing processes. To remain competitive, production must be optimized in order to identify defects as early as possible, as well as their dependencies and variation patterns. The paper presents an approach to identify and model part variations within multi-stage production systems. Subsequently, based on a detected deviation, a downstream compensation strategy can be proposed at an early stage of the manufacturing process, which uses the capability of the overall system to fundamentally eliminate rejects.
... Zero-Defect-Manufacturing (ZDM) (Psarommatis et al., 2020) is one of the important targets of Industry 4.0 and DT is expected to empower this vision. When designing a DT model for a manufacturing system, all the relevant factors, such as production design, machining processes, and the inbound logistic purchasing all necessary materials, as well as the interactions among them need to be taken into consideration. ...
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Digital Twin is one of the fundamental enabling technologies for Industry 4.0 as it allows the convergence between a physical system and its digital representation. A proper modelling method is the prerequisite for successful digital twin implementation. The manufacturing process determines critically the quality of the manufactured products. The influential elements need to be systematically organized when modelling a manufacturing process. This paper proposes a semantic modelling method named RMPFQ (Resource, Material, Process, Function/Feature, Quality) aiming to interlink the main influential factors related to product quality during manufacturing processes. The proposed RMPFQ model is formalized with an application ontology following the IOF-Core middle-level and BFO top-level ontologies. Based on this ontology, a semantic-driven digital twin architecture is designed and mapped to the recently proposed Cognitive Digital Twin concept. A correlation matrix is designed to quantify the relationships among RMPFQ elements thus to facilitate the industrial applications. A case study based on the assembly process of a washing machine is conducted to demonstrate the implementation procedures of the proposed RMPFQ method.
Purpose The present paper proposes a framework for zero-defect manufacturing in Indian industries. Due to the current competitive market, there is a strong need to achieve zero defects from the customer's perspective. A survey questionnaire is analyzed based on the responses and a structured framework is drafted to implement zero defect manufacturing in the Indian industry. Design/methodology/approach To analyze zero-defect in Indian industries, a literature review and a survey questionnaire constituted a framework. This framework is independent of the type of process and product. Findings The findings of this study are based on a total of 925 responses received through survey questionnaires by different mediums. The framework has been tested in different manufacturing organizations to achieve zero-defect through the continuous improvement approach. Practical implications The study results aim to achieve zero-defect, help to improve customer satisfaction, reduce waste and rework in the manufacturing process. This framework is also used as a problem-solving approach to implement Six Sigma in the Indian industries. Originality/value Zero defect manufacturing is growing in India and globally. This framework helps to implement zero defect manufacturing in Indian industries. It is an essential tool to capture the voice of the customer.
Geometrical deviations of manufactured parts affect the quality of the resulting product. In an assembly, deviations accumulate so that the functional requirements may no longer be met. Therefore, the compliance of geometrical interfaces is of great importance in order to achieve a high-quality product. Modern 3D sensors capture dense point clouds of manufactured parts and thus enable creating a part’s geometrical digital twin. However, there are no suitable registration techniques that allow computing the assembly of the measured parts. With the knowledge of the assembly configuration, the geometrical accumulation can be determined, ultimately allowing the prediction of the quality of the final product. In the authors preceding work, the virtual assembly registration technique was presented methodologically. This paper focusses on the implementation of a dedicated use case. As real-world problem, the assembly of injection-molded covers and housings is examined. The data acquisition is fully integrated into an in-line measurement process. Therefore, a fringe projection system is used for capturing 3D data. Specific segmentation approaches are presented in order to detect the part and to segment assembly-relevant geometries in the measurement data. Due to possible artefacts in the point clouds, strategies for increasing the robustness are discussed. Finally, the developed quality prediction is embedded in the production context. In order to achieve time, cost, and quality benefits, a selective assembly strategy is applied that reduces the number of scrap parts by a factor of two and more.
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Due to the feasibility of economically producing large-scale metal components with relatively high deposition rates, significant progress has been made in the understanding of the Wire Arc Additive Manufacturing (WAAM) process, as well as the microstructure and mechanical properties of the fabricated components. As WAAM has evolved, a wide range of materials have become associated with the process and its applications. This article reviews the emerging research on WAAM techniques and the commonly used metallic feedstock materials, and also provides a comprehensive over view of the metallurgical and material properties of the deposited parts. Common defects produced in WAAM components using different alloys are described, including deformation, porosity, and cracking. Methods for improving the fabrication quality of the additively manufactured components are discussed, taking into account the requirements of the various alloys. This paper concludes that the wide application of WAAM still presents many challenges, and these may need to be addressed in specific ways for different materials in order to achieve an operational system in an acceptable time frame. The integration of materials and manufacturing process to produce defect-free and structurally-sound deposited parts remains a crucial effort into the future.
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The main objectives of applying NDT techniques are to ensure the quality of an assembly or a part according to a given specification including known acceptance criteria. It generally enables not only to detect an indication, but also to classify it (size, position, nature…). Many non-destructive testing (NDT) techniques are effective in testing welded components. Radiography, ultrasonic testing, penetrant testing and magnetic particle testing are widely used and standardised. Phased arrays, TOFD and multi-elements eddy current are more and more extensively applied. Tomography, acoustic emission, ultrasonic guided waves, laser ultrasonic and optical techniques continue to be a strong topic of interest. Each of these techniques is based on different physical principles to detect defects on the surface of the part or over its whole volume. However, the geometry, physical and material properties of the part being tested are key factors in the applicability and performance of a given NDT technique. To date, the development of reliable NDT methods for additive manufacturing (AM) parts is still a major challenge. The process may generate various defects such as cracks, voids, inclusions and porosities. NDT techniques need to be optimised or developed to address singular features of the AM processes: complex geometry, special internal structures, anisotropic material properties, typical defects. Knowledge of the potential occurring imperfections produced by the various AM process needs to be improved in order to be able to select the best suited NDT techniques.
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Many companies allocate significant resources to implementing lean on the manufacturing shop floor, but fewer resources to implementing lean in the product development processes. Despite a rich literature on lean manufacturing (LM) and a growing literature on lean product development (LPD), there is limited research on the relation between these two streams and their interaction effects on operational performance. This paper draws on configurational theory to investigate the moderating role of LPD on the effects of LM on quality and inventory performance. We used a survey instrument to collect responses from firms implementing lean across their enterprises. We tested our hypotheses using ordinary least squares regression models with moderation tests. We find that LPD practices positively moderate the effects of LM on quality performance. Although our research concerning the same moderating effect on inventory turnover is inconclusive, we find that the implementation of LPD has a direct and positive association with improvements in inventory turnover. Overall, our research suggests that companies can get more out of their lean implementations if they pursue a systematic implementation of both LPD and LM, rather than following the more common isolated approach.
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Wire Arc Additive Manufacturing (WAAM) is attracting significant attention in industry and academia due to its ability to capture the benefits of additive manufacturing for production of large components of medium geometric complexity. Uniquely, WAAM combines the use of wire and electric arc as a fusion source to build components in a layer-by-layer approach, both of which can offer significant cost savings compared to powder and alternative fusion sources, such as laser and electron beam, respectively. Meanwhile, a high deposition rate, key for producing such components, is provided, whilst also allowing significant material savings compared to conventional manufacturing processes. However, high quality production in a wide range of materials is limited by the elevated levels of heat input which causes a number of materials processing challenges in WAAM. The materials processing challenges are fully identified in this paper to includethe development of high residual stresses, undesirable microstructures, and solute segregation and phase transformations at solidification. The thermal profile during the build poses another challenge leading to heterogeneous and anisotropic material properties. This paper outlines how the materials processing challenges may be addressed in WAAM by implementation of quality improving ancillary processes. The primary WAAM process selections and ancillary processes are classified by the authors and a comprehensive review of their application conducted. Strategies by which the ancillary processes can enhance the quality of WAAM parts are presented. The efficacy and suitability of these strategies for versatile and cost effective WAAM production are discussed and a future vision of WAAM process developments provided.
Conference Paper
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Based on the amount of production steps and the related complexity, multi-stage production systems are very error-prone. In order to compensatefor this disadvantage and to achieve zero-defect manufacturing, a data-driven approach is needed. The increasing availability of sensor andmachine data provides a high informational content of the individual processes, which can be evaluated with appropriate methods. Literatureshows various methods of data analysis for examining the correlations of data sets. These methods and strategies are analyzed, hierarchicallystructured and extended by four developed algorithms. Finally, the data-driven analysis tool is presented and validated using two industrial usecases.
Conference Paper
This paper describes the development of the quantitative predictor of defects that works not only on the whole product but also on its modular units. The authors divide the assembly process of a product into small modules, and apply the Assembly Quality Methodology (AQM) to determine the assembly complexity of the modules. The authors also verify the correlation between complexity factor of the modules and their defect rates. The newly proposed floating threshold assembly time improves the correlation between complexity factor and defects per unit (DPU). The authors discuss the comparison of case studies and the interpretation of their results for the future improvement of the method.
Additive manufacturing (AM) allows for the production of custom parts with previously impractical internal features, but comes with the additional possibility of internal defects due to print error, residual stress buildup, or cyber-attack by a malicious actor. Conventional post process analysis techniques have difficulty detecting these defects, often requiring destructive tests that compromise the integrity (and thus the purpose) of the part. Here, we present a “certify-as-you-build” quality assurance system with the capability to monitor a part during the print process, capture the geometry using three-dimensional digital image correlation (3D-DIC), and compare the printed geometry with the computer model to detect print errors in situ. A test case using a fused filament fabrication (FFF) 3D printer was implemented, demonstrating in situ error detection of localized and global defects.
Nowadays exploiting the full potential of the humongous amount of data that manufactures can produce with their production means is a real challenge. Moreover, increasing production capabilities without large investments is a recurring objective for them. To reach this objective, many different strategies are in development i.e. zero-defect manufacturing, predictive maintenance and scheduling algorithms which deal with high uncertainty. In the end, they will all be implemented in industry. Their joined implementation in the industry is however missing a coherent framework that would allow to merge those different solutions. This paper proposes an approach that combines those three deeply interconnected technologies to bring a clean solution that significantly improves production capacity. This paper present the approach giving an idea of the possibilities and opportunities of the presented solution.
Contemporary manufacturing landscape has changed and has become highly volatile and demanding. Product Life Cycle has significantly decreased because of the consumers’ need for personalized products in shorter period of time. This need forced the manufacturers to produce smaller batches of highly diversified products instead of producing huge batches of the same product. These newly imposed production requirements made the manufacturers struggle to optimize their productions in such short period of time and therefore, these production requirements created the need for more efficient, productive and eco-friendly planning and scheduling. The solution to this problem can be given partially by a concept named “Zero Defect Manufacturing” (ZDM). The goal of ZDM is to eliminate defected parts and therefore achieving higher efficiency, eco-friendliness and lower production costs. There are four ZDM strategies that are interconnected with each other: detection, repair, prediction and prevention. The current research work focuses on the Beginning of Life of the product and considers the development of a dynamic Scheduling tool combined with an intelligent Decision Support System (DSS) that takes into consideration the ZDM strategies for eliminating defected parts during production. The elimination of defected parts would be done mainly by creating specific algorithms for predicting when a defect may occur and for taking the right decisions in order to prevent the defect occurrence. Finally, an industrial example is presented in order to illustrate the potential benefits of such an approach.