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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: https://www.tandfonline.com/loi/tprs20
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
To link to this article: https://doi.org/10.1080/00207543.2019.1605228
View supplementary material Published online: 19 Apr 2019.
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International Journal of Production Research, 2020
Vol. 58, No. 1, 1–17, https://doi.org/10.1080/00207543.2019.1605228
Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in
research
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: foivos.psarommatis@epfl.ch
© 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
(ZDM)
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
method?
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
components.
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
Generic
Manufacturing
process specific
Method
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
industries.
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.
Note
1. https://dev.elsevier.com/tips/ScopusSearchTips.htm
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplemental data
Supplemental data for this article can be accessed here https://doi.org/10.1080/00207543.2019.1605228
ORCID
Foivos Psarommatis http://orcid.org/0000-0002-2731-8727
Gökan May http://orcid.org/0000-0002-9634-999X
Dimitris Kiritsis http://orcid.org/0000-0003-3660-9187
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In this research, the normal distribution is assumed to be the product characteristic, and the DITM (Digital Integrated Circuit Test Model) model is used to evaluate the integrated circuits (IC) test yield and test quality. Testing technology lags far behind manufacturing technology due to the different rates of development of the two technologies. As a result, quality control will pose significant challenges in pursuing high-quality near-zero defect products (automotive and biomedical electronics and avionics, etc.). In order to ensure product quality, we propose an effective repeated testing method (three-repetition tests scheme, TRTS), which utilizes the move test guardband (TGB) to improve the test yield and test quality. Based on the data in the International Roadmap for Devices and Systems table in 2021, the DITM model is used to estimate the future trend of semiconductor chip test yield, and the retest method (TRTS) is applied improve the test results. The method of repeated testing can increase the test yield and increase the shipment of semiconductor products. By estimating the test cost and profit, the method of repeated testing can obtain chips with near-zero defects with more corporate profits through increased product shipments.
... Among these, an emerging one is the so-called Zero-Defect Manufacturing (ZDM), which aims to decrease and mitigate failures and defects in different manufacturing processes. The main challenges associated with ZDM are the integration of large amounts of data from many sources, the development of advanced technologies and methods, modeling of ZDM, and timely realtime computation (Lindström et al. 2020;Psarommatis et al. 2020;Eger et al. 2018). However, achieving these goals is not straightforward (Ferretti et al. 2013). ...
... 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). ...
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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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... 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. ...
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... 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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... 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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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.
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Chapter
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.
Chapter
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.