Daryl Powell
email: daryl.j.powell@ntnu.no

Rafael Lorenz
email: rafaellorenz@ethz.ch

The Effect of team size on the performance of continuous improvement teams: is seven really the magic number?

Keywords: Lean Production, Team size, Team performance, Continuous improvement

Continuous improvement teams play an essential role when implementing a corporate improvement programme, the success of which is significantly dependent on the organization of such teams. This paper specifically addresses the effect of team size on the performance of continuous improvement teams. We take insight into a Norwegian case study during the first two years of a lean transformation to explore if there are any indications of an optimum size for continuous improvement teams. The results suggest that there may not be a perfect size for a CI team, rather the performance outcome of different sized teams may depend on the maturity level of the lean programme. Whereas in the establishment phase teams of up to twelve employees seem to perform well, this number appears to reduce when the programme matures. This study may therefore assist practitioners in establishing the right team size dependent on the status of transformation.

Introduction

Since the term "Lean production" was popularized in The Machine That Changed the World [START_REF] Womack | The machine that changed the world: The story of lean production[END_REF], lean thinking has arguably become the most successful approach to business improvement of our generation. What is generally agreed to have begun in the automotive industry based on the management philosophy and working practices demonstrated by the Toyota Production System, lean thinking has now been adopted in most manufacturing environments, as well as construction, healthcare, government agencies, and higher education establishments to name just a few examples.

In order to adopt the lean management philosophy successfully, many companies have developed their own "company-specific production systems" or "corporate lean programmes" [2]. The successful deployment of such programmes is dependent upon a number of critical success factors, including management support and commitment [START_REF] Herron | The transfer of selected lean manufacturing techniques from Japanese automotive manufacturing into general manufacturing (UK) through change agents[END_REF], becoming a learning organization via constant reflection and continuous improvement [START_REF] Liker | The Toyota way: 14 management principles from the world's greatest manufacturer[END_REF], and the holistic adoption of lean as the new management "way" [START_REF] Bhasin | Lean viewed as a philosophy[END_REF]. The latter is considered to be largely reliant upon a resolute process of continuous improvement, where a team-based organizational design has been promoted as an effective means for a successful continuous improvement process [START_REF] Van Dun | Lean Teams[END_REF]. The collaboration of this team-based improvement process is operationalized through regular (e.g., weekly) meetings, directly on the shop floor. However, little is known regarding the ideal size of a continuous improvement team. This begs the question amongst researchers and lean program coordinators alike: How big should a continuous improvement team really be? To shed light on this topic, we pose the following research questions (RQs):

RQ1: How does the size of a continuous improvement team affect team performance?

RQ2: Is there an optimum team size for continuous improvement teams?

While the limited research on optimal team size is not conclusive, it does tend to suggest that a team size in the range of five to 12 team members is optimal. Nieva et al. go so far as to suggest that a team of seven is the best [START_REF] Nieva | Team dimensions: Their identity, their measurement, and their relationships[END_REF]. Many consultants also advocate that seven is the optimum size for a continuous improvement team, particularly in the field of agile (e.g. [8]), but is this really the case? Is seven really the magic number?

There are of course seven days of the week, seven colours of the rainbow, seven notes on a musical scale, seven seas and seven continents. Snow White ran off to live with seven dwarves, and there were seven brides for seven brothers. Whilst seven appears to be a very popular number in society, this paper explores if seven really is the optimum size for a continuous improvement team.

2

Literature Review

Lean production and the importance of teams

Lean production stems from the management philosophy and working practices of the Toyota Production System, which has been defined in terms of three fundamental constructs: Just-in-Time, Jidoka, and the Respect-for-Human System [START_REF] Suimori | Toyota production system and Kanban system Materialization of just-in-time and respect-for-human system[END_REF]. The respect-forhuman system is essential for the success of any lean programme, and requires management support and engagement as well as the subsequent empowerment of all employees.

Given that an organization's top-and middle management have pledged their full support and commitment to its corporate lean programme, the success of the lean deployment then becomes firmly rooted with the front line managers in the continuous improvement teams, at the lowest hierarchical level of the firm [START_REF] Spear | Decoding the DNA of the Toyota Production System[END_REF]. This is because continuous process improvement is difficult to achieve without the cooperation of front line workers, in particular the generation of their improvement ideas and improvement implementation efforts [START_REF] Van Dun | Human Dynamics of Effective Lean Team Cultures and Climates[END_REF]. As such, a continuous improvement team can be defined as (a) a group of two or more individuals, (b) who cooperate to deliver one or more core product(s) and/or service(s), (c) with the shared goal of eliminating waste and increasing customer value (adapted from [START_REF] Kozlowski | Work Groups and Teams in Organizations[END_REF]).

Effective team organization

Several authors present a detailed review of the performance and effectiveness of teams in work organizations, and consider several key factors for team effectiveness, including team cohesiveness, team composition (e.g. homogeneity / heterogeneity of the group), quality of team leadership, motivation, and clarity of group (team) goals. For example, Magjuka & Baldwin identified factors thought to contribute to the effectiveness with which employee involvement teams are designed and implemented [13].

They found that larger team size and greater access to information were positively associated with team effectiveness. This brings into play questions regarding the effect of team size on team performance. If Campion et al. [START_REF] Campion | Relations between work group characteristics and effectiveness: Implications for designing effective work groups[END_REF] also found team size to be positively related to effectiveness, why should we believe that the optimum team size is seven?

Team size

In sports, teams have a specific number of team players: football teams have 11 players, rugby teams have 15, and basketball teams have five. In the case of work teams, however, it is a little more complex. There is no hard-and-fast rule to determine the optimum size of a team. At Toyota, production employees are typically assigned to groups of 20-30, each with a group leader. These groups are then further subdivided into teams of 5-7, including a team leader [START_REF] Liker | The Toyota Way Fieldbook: A practical guide for implementing Toyota's 4Ps[END_REF]. Alternatively, in Agile Software Development, Rising & Janoff state that Scrum advocates the use of small teams, preferably 7 team members (+/-2) and certainly no more than 10 [START_REF] Rising | The Scrum software development process for small teams[END_REF]. Ironically, in the game of Rugby Union, a scrum is a formalised and heavily structured (i.e. non-creative) set piece that consists of eight forwards per team. Though several researchers have analysed the effect of team size on the performance of teams across different organizational settings, it remains unclear as to the significance team size has on the performance of the team. This is due to an array of inconsistent and inconclusive results as illustrated in Table 1. For example, Katzenbach & Smith suggested that work teams should contain a dozen or so members [START_REF] Katzenbach | The wisdom of teams: Creating the high-performance organization[END_REF], whereas Scharf suggested that seven was the best size [START_REF] Scharf | How to change seven rowdy people[END_REF]. A variety of other such recommendations are easily found in the extant literature, often with opposing views. Some research suggests that size has a curvilinear relationship with effectiveness such that too few or too many members reduces performance [START_REF] Nieva | Team dimensions: Their identity, their measurement, and their relationships[END_REF], whilst others suggest that increasing team size actually improves performance without limit [START_REF] Campion | Relations between work group characteristics and effectiveness: Implications for designing effective work groups[END_REF]. Haleblian & Finkelstein also found that firms' performance was better when [top-management] team size was greater [START_REF] Haleblian | Top management team size, CEO Dominance, and Firm Performance: The moderating roles of environmental turbulence and discretion[END_REF], whilst Behrens asserts that small teams tend to be more productive [START_REF] Behrens | Measuring the Productivity of Computer Systems Development Activities with Function Points[END_REF]. Useem even goes so far as to state that a small team size of precisely 4.6 team members is in fact the optimum [START_REF] Useem | How to build a great team[END_REF]. Other studies have simply found team size to be unrelated to performance [START_REF] Martz | Electronic meeting systems[END_REF][START_REF] Hackman | Effects of Size and Task Type on Group Performance and Member Reactions[END_REF].

Kozlowski & Bell conclude that overall, the question of the "optimal" group size is a complex one and future empirical research is needed to determine the impact of team size given specific team contingencies, such as the nature of the team's task [START_REF] Kozlowski | Work Groups and Teams in Organizations[END_REF]. This research therefore sets out to contribute to the field by examining the impact of team size on the performance of continuous improvement teams during the deployment of a corporate lean programme.

Research method

This empirical study examines the impact of team size on the performance of continuous improvement teams during the initial stages of the deployment of a corporate lean programme. The research method for collecting and analysing data is the case study method [START_REF] Yin | Case study research: Design and methods[END_REF][START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF] Data was collected at the case company by way of participant observation -allowing the researcher unlimited use of open-interviews and open-access for the analysis of company documentation. By taking such a role, the quality of the data collected as well as the results of the analysis was increased due to the inherent inside knowledge of the company. The case company in question is a Norwegian producer of hydro-acoustic sensor systems. Due to increasing competition from low-cost suppliers, in 2014 the case company began its lean journey through the deployment of a corporate lean programme, which itself is based on five fundamental lean principles, one of which is Continuous Improvement. In order to operationalize this principle, continuous improvement teams have been formed throughout the organization.

The continuous improvement teams meet weekly in front of team-specific Kaizen boards to discuss identified problems and improvement suggestions. The teams are encouraged to focus on small, simple improvements rather than larger project-type improvements, and as such are measured on the number of completed improvements, per employee per month (pepm). Larger project-type improvements are subsequently broken down into smaller individual tasks, making the data more comparable from team to team. The number of employees in each team varies across the teams. It is therefore interesting to examine the performance of teams in relation to team size. This study addresses six different continuous improvement teams of varying team size over a two year period (2015-2016).

Results

The following results are derived from six different continuous improvement teams and during the first two years of a lean deployment. The analysis is limited to six teams, as these teams are all organized under the same middle manager (the production manager), and all team members carry out manual assembly and test type work (Table 2): The results are interesting from several perspectives. Firstly, with the exception of team C, all teams reported more improvements in the second year. Secondly, all teams (with the exception of team D) encountered a reduction in team size during the investigation period. This was a consequence of a strategic downsizing process due to the increasingly difficult market conditions experienced by the case company. In spite of this, however, there was still a marked improvement in the number of implemented improvements in the second year. Thirdly, and perhaps essential for this investigation, is the result shown in Figure 1. It appears that there is in fact an optimum team size for effective continuous improvement process, though the result indicates that this may in fact lessen with an increase in lean maturity.

In line with the conclusions of [START_REF] Nieva | Team dimensions: Their identity, their measurement, and their relationships[END_REF], Figure 1 suggests that team size has a curvilinear relationship with team effectiveness, such that too few or too many members reduces performance. It shows an overall trend for greater team performance with a team size between (approx.) seven and 12. Interestingly, the "sweet spot" appears to become more prominent with a higher level of lean maturity (i.e. in year two of the investigation). In this case, it would appear that nine is the "magic" number. Furthermore, and regardless of the maturity level of the teams evaluated, there does appear to be an accelerated decline in team performance as team size increases beyond 12 members.

From the results of this research, we can make several propositions: P1. Team size seems to have a curvilinear relationship with team performance such that too few or too many members may reduce performance (as also suggested by [START_REF] Nieva | Team dimensions: Their identity, their measurement, and their relationships[END_REF]).

P2.

A team size greater than 12 may result in a significantly reduced level of team performance. (Contrary to the findings of [13] and [START_REF] Campion | Relations between work group characteristics and effectiveness: Implications for designing effective work groups[END_REF])

P3. For organizations in the early phases of a lean implementation, larger continuous improvement teams may provide the basis for greater team performance.

P4. As the lean programme develops and matures, a slight reduction in team size may allow for more effective team performance.

Conclusions

Though the results presented here cover only a sample of six continuous improvement teams during the first two years of a lean implementation, the results do seem to indicate that seven is not the magic number when it comes to peak performance of continuous improvement teams. Neither is 12. Nor in fact 4.6. Revisiting the RQs that guided this investigation, the results tend to suggest that there is not an optimum team size for continuous improvement teams. However, the results do allow us to present a set of propositions that may guide managers and practitioners in harnessing the greatest level of performance from continuous improvement teams during the initial stage of lean transformation.

We suggest that further work should evaluate if this is still true as the company continues to progress with the lean implementation, taking into consideration the maturity levels of the various teams, and including a greater sample of continuous improvement teams at the case company. Further work should also investigate the level of education and training of various continuous improvement teams contra their achieved performance. One would expect greater performance from teams with more skills from the continuous improvement toolbox, for example. Other factors that were identified in the literature review should also be evaluated in terms of their effect on team performance, in particular team composition (e.g. homogeneity versus heterogeneity), quality of leadership, level of motivation, and clarity of team goals.

Fig. 1 .

 1 Fig. 1. PEPM in relation of team size

Table 1 .

 1 Suggested team size by different studies

	Reference	Context	Suggested team size
	Useem (2006)	Business teams	4.6
	Liker & Meier (2005)	Lean teams	5-7
	Scharf (1989)	Work teams	7
	Rising & Janoff (2000)	Scrum teams	7 (+/-2)
	Katzenbach & Smith (1993)	Work teams	12
	Magjuka & Baldwin (1991)	Work groups	The larger the better
	Campion et al. (1993)	Work groups	The larger the better

Table 2 .

 2 Number of completed improvements (PEPM) per team, 2015 & 2016

		2015		2016
	Team Size	PEPM Size	PEPM
	A	7	1,3	6	2,5
	B	12	2,1	10	2,9
	C	17	1,4	15	1,4
	D	4	0,5	5	1,6
	E	13	1,5	12	2,0
	F	17	0,7	13	1,1

Acknowledgements

The authors would like to acknowledge the continued support of the Research Council of Norway, through the research programmes Lean Management and SFI Manufacturing.


Ugljesa Marjanovic

Slavko Rakic
email: slavkorakic@uns.ac.rs

Bojan Lalic
email: blalic@uns.ac.rs

Digital servitization: The next "big thing" in manufacturing industries

Keywords: Digital services, Servitization, Financial performance

Manufacturing firms increasingly produce and provide services along with their traditional physical products. This process, better known as servitization, is a mature theme in the literature, flourishing in recent years. Digital disruption is propelling manufacturers to move on towards digital transformation and deliver digital services. Prior research investigated the impact of servitization measured by the traditional services. However, the role of digital technologies in manufacturing is neglected. This paper intends to shed light on the impact of digital service portfolio antecedents on firm performance. Our analysis used the Serbian dataset of 240 manufacturing firms from the European Manufacturing Survey conducted in 2018. The empirical results show that, in manufacturing firms, digital services can significantly increase the turnover ratio. Results indicate that management in manufacturing companies should utilize digital services such as Web-based services for customized product design and Web-based offers for product utilization to maximize firm's turnover ratio and upgrade current service business model.

Introduction

A paradigm shift in business models is occurring in the manufacturing industry [START_REF] Cusumano | Services, industry evolution, and the competitive strategies of product firms[END_REF]. Durable goods manufacturers choose to innovate their offerings by providing services to accompany their existing products throughout the life cycle [START_REF] Visnjic Kastalli | Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance[END_REF]. However, with the advent of new technologies (i.e. information technologies), traditional services such as maintenance and repair [START_REF] Marjanovic | How to Increase Share of Product-Related Services in Revenue? Strategy Towards Servitization[END_REF] are not enough. Digital disruption is propelling manufacturers to move on towards digital transformation and deliver digital services. For example, latest report from McKinsey shows that manufacturing firms are introducing on average eight digital services [START_REF] Behrendt | Digital Manufacturing -escaping pilot purgatory[END_REF]. However, this number varies widely by country.

The literature has looked at the impact of servitization measured by the traditional services on performance [START_REF] Visnjic Kastalli | Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance[END_REF], [START_REF] Suarez | Services and the Business Models of Product Firms : An Empirical Analysis of the Software Industry[END_REF]- [START_REF] Eggert | Revenue and Profit Implications of Industrial Service Strategies[END_REF], however the role of digital technologies in ser-vice business transformation is under-investigated [START_REF] Ardolino | The role of digital technologies for the service transformation of industrial companies[END_REF]. This paper investigates and offers an understanding on how digital services affect the turnover in manufacturing firms.

This study relies on a unique dataset from the European Manufacturing Survey (EMS) with a sample of 240 industrial firms from Serbia. Our analysis shows that digital services have positive influence on firm's turnover ratio. Two out of five digital services (i.e. web-based services for customized product design and web-based offers for product utilization) positively affect firm performance.

2

Background and related work

Digital manufacturing services

In the literature, service strategy has a long presence through wide terminology such as servitization [START_REF] Opresnik | The value of big data in servitization[END_REF], servicizing [START_REF] Rothenberg | Sustainability Through Servicizing[END_REF], product-service systems [START_REF] Tukker | Eight types of product-service system: eight ways to sustainability? Experiences from SusProNet[END_REF], or even open service innovation [START_REF] Visnjic Kastalli | Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance[END_REF], et cetera. Today, the presence of digital technologies has brought the transformation of service business models [START_REF] Ardolino | The role of digital technologies for the service transformation of industrial companies[END_REF] and formed something that is called digital services. The provision of digital services, or 'Digital servitization', has become a sub-stream of service business model [START_REF] Vendrell-Herrero | Servitization, digitization and supply chain interdependency[END_REF]. Digital servitization is defined as "the provision of IT-enabled (i.e. digital) services relying on digital components embedded in physical products" [START_REF] Vendrell-Herrero | Servitization, digitization and supply chain interdependency[END_REF]. Digital services are different from traditional since the marginal cost of digital services is near zero and they are substitutes for traditional products [START_REF] Greenstein | Digitization and value creation[END_REF]. Digital technologies such as Internet of things, cloud computing and predictive analytics [START_REF] Ardolino | The role of digital technologies for the service transformation of industrial companies[END_REF] are considered a key elements of the fourth industrial revolution [START_REF] Suppatvech | The roles of internet of things technology in enabling servitized business models: A systematic literature review[END_REF]. For instance, Digital McKinsey [START_REF] Behrendt | Digital Manufacturing -escaping pilot purgatory[END_REF] report shows that two thirds of manufacturing firms worldwide say that digital solutions in manufacturing are one of their highest priorities. Technological development potentially offers a variety of benefits for firms able to utilize it and seamlessly embed them in business models and products to improve market competitiveness [START_REF] Suppatvech | The roles of internet of things technology in enabling servitized business models: A systematic literature review[END_REF] and provide more accurate information sharing inside and outside the boundaries of the firm [START_REF] Kindström | Service innovation in product-centric firms: A multidimensional business model perspective[END_REF]. While broadening the perspective from product-related services to digital services is fundamental to the servitization debate, its impact on a firm's performance remains in question. This is partly due to the fact that the possibility to charge for digital services that had previously been free represents a challenge to managers [START_REF] Kowalkowski | Any way goes: Identifying value constellations for service infusion in SMEs[END_REF].

Certainly, the concept of servitization could enhance the competitiveness of a manufacturing firms while simultaneously advancing economic conditions, resulting in a higher turnover from selling digital services [START_REF] Shimomura | A method for identifying customer orientations and requirements for product-service systems design[END_REF]. Studies that deal with the assessment of whether adding additional services, even digital, improves the financial performance of a firm are scarce and more empirical research is needed in this area [START_REF] Visnjic Kastalli | Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance[END_REF], [START_REF] Gebauer | Service-driven manufacturing Provision, evolution and financial impact of services in industrial firm[END_REF], [START_REF] Kowalkowski | Service growth in product firms: Past, present, and future[END_REF]. To shed more light on this important area of production research, we investigate how specific digital service portfolio choices influence firm's financial implications.

Research questions

Based on literature review, the following research questions were proposed in attempt to identify the different effects of digital services on manufacturing firm's turnover:

• RQ1: The deployment of digital services will positively impact firm's turnover ratio? • RQ2: Which digital services, if any, increase firm's turnover ratio?

The digital services presented in the model were identified based on exploratory interviews with practitioners and group discussions with experts in the field. All EMS consortium members were involved in this process which resulted in a universal transversal list of digital services so that all manufacturing sectors can apply it regardless of the product offered. Identified digital services are: (1) Web-based offers for product utilization, (2) Web-based services for customized product design, (3) Digital (remote) monitoring of operating status, (4) Mobile devices for diagnosis, repair or consultancy, and (5) Data-based services based on big data analysis. Consequently, these digital services were included in the EMS questionnaire. We use the turnover ratio as our dependent variable. The turnover ratio was defined as the annual turnover of the firm in the year 2017 divided by the annual turnover of the firm in the year 2015 [START_REF] Kowalkowski | Servitization and deservitization: Overview, concepts, and definitions[END_REF].

Figure 1 depicts the proposed research framework.

Data and methodology

Data for this empirical study derive from the European Manufacturing Survey, a survey on manufacturing strategies, application of innovative organizational and technological concepts in production, and questions of digital servitization in the European manufacturing industry [START_REF] Lalic | The Effect of Industry 4.0 Concepts and E-learning on Manufacturing Firm Performance: Evidence from Transitional Economy[END_REF]. The EMS is administered by the Fraunhofer Institute for Systems and Innovation Research [START_REF]European Manufacturing Survey[END_REF]. The objective of this regular, triennial questionnaire is to systematically monitor the innovation behavior of European manufacturing enterprises at the firm level. The concepts, constructs and questions are welltested and have been agreed upon in the EMS consortium. The six-page questionnaire includes questions on innovation activity, firm and industry characteristics, and general firm data. The survey is sent out repeatedly to senior managers of firms with 20 or more employees and designed to be representative of all regions, industrial sectors With regard to descriptive statistics, the sampled firms report, on average, a firm size of 124 employees (SD = 207). In total, 110 companies are small firms (fewer than 50 employees), 103 companies are medium-sized (between 50 and 249 employees), and 27 firms are large enterprises (more than 250 employees). Firms belonging to the food products (39 firms), fabricated metal products (36 firms), and rubber and plastic products (21 firms) sectors account for the most prominent observations in the sample. Tables 1 and2 depict the sample distribution regarding size and industry sector.

To analyze the relationships between digital services and financial implications we employed a multivariate data analyzes. In the regression model that tests both research questions, the overall model was significant, R 2 = .393, F = 3.782, p < .01. One predictor had a significant coefficient -Web-based services for customized product design (B = .532, p < .05), thus supporting the idea to include them in the service portfolio to increase the turnover ratio. The influence of Web-based offers for product utilization on turnover ratio was not significant at p<.05, but marginally significant at p<.1 (B = .481). Hence, manufacturing firms should also include them in the digital service offering. However, manufacturing firms focusing on the development of digital services such as Digital (remote) monitoring of operating status (B = -.434, p > .1), Mobile devices for diagnosis, repair or consultancy (B = -.973, p > .1), and Data-based services based on big data analysis (B = .083, p > .1) show no statistically significant effect on turnover ratio.

As a major finding, our research indicates that the deployment of specific digital service portfolio choices will exhibit distinct financial performance. For instance, our results indicate that manufacturing firms should provide web-based services for customized product design and web-based offers for product utilization as digital services to increase firm's turnover ratio. It is not clear from our data, however, whether the success of the two digital services is due to the additional managerial focus these firms received, or if, as we assume, cultural practices of customers in emerging market. These results are in line with previous studies [START_REF] Suppatvech | The roles of internet of things technology in enabling servitized business models: A systematic literature review[END_REF], [START_REF] Neely | Exploring the financial consequences of the servitization of manufacturing[END_REF], [START_REF] Oliva | Managing the transition from products to services[END_REF].

Conclusion

This study examines digital servitization strategies of manufacturing firms. Consequently, this paper provides theoretical and practical implications on how and in what way digital services impact the manufacturing firm turnover. The empirical results indicate that two digital services significantly or marginally influence a firm's turnover ratio. Today, many manufacturing firms are evolving their business strategy from the traditional focus on product offerings toward a new direction, so that business models based on digital services can bring higher performance [START_REF] Visnjic | Which Service? How Industry Conditions Shape Firms' Service Type Choices[END_REF], [START_REF] Bigdeli | Network positioning and risk perception in servitization: evidence from the UK road transport industry[END_REF]. Our results indicate actionable insights for managers of manufacturing firms to expand their understanding of how to increase turnover with provision of digital services. Specifically, managers in manufacturing firms should provide digital services such as webbased services for customized product design and web-based offers for product utilization, to maximize the firm's turnover. However, managers should be aware that not all digital service provisions will lead to a service business model. Our sample was collected from all manufacturing industries, and, perhaps due to the industry specificity, results could differ. Also, there are various aspects that should be taken into consideration for the assessment of digital service impact on a firm's turnover (e.g. type of customer served, seasonality, and promotion). Further research is necessary to assess the experience and challenges of firms with a focus on one industry (i.e. the manufacture of fabricated metal products) and to consider different challenges in measuring the impact of digital services provided by manufacturing firms. Development of these ideas could prove especially useful for firms facing the challenges of a particular industry, showing that specific digital services can improve their financial performance [START_REF] Visnjic Kastalli | Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance[END_REF].

Fig. 1 .

 1 Fig. 1. Research framework.

Table 1 .

 1 EMS database -distribution of firms by size.

	Firm size	n	%
	20 to 49 employees	110	45.8
	50 to 249 employees	103	42.9
	250 and more employees	27	11.3
		Total 240	100.0

Table 2 .

 2 Classification on manufacturing sectors according to share on total sample.

	NACE	Manufacturing industry	Share on total
	Rev. 2		sample (%)
	10	Manufacture of food products	16.3
	25	Manufacture of fabricated metal products	15
	22	Manufacture of rubber and plastic products	8.8
	27	Manufacture of electrical equipment	6.3
	28	Manufacture of machinery and equipment	6.3
	14	Manufacture of wearing apparel	5.8
	16	Manufacture of wood and of products of wood and cork	4.6
	23	Manufacture of other non-metallic mineral products	4.6
	29	Manufacture of motor vehicles, trailers and semi-trailers	4.2
		Others		28.1
	4	Results and Discussion

Table 3

 3

	presents linear regression model, for a dependent variable (turnover ratio),
	used to test research questions.

Table 3 .

 3 Results of linear regression.

	Digital services	Model parameters
	Web-based offers for product utilization	.481 +
	Web-based services for customized product design	.532 *
	Digital (remote) monitoring of operating status	-.434
	Mobile devices for diagnosis, repair or consultancy	.108
	Data-based services based on big data analysis	.083
		R 0.627
		R 2 0.393
		F 3.782
		Sig. 0.002
	Note: * p<0.05; + p<0.1;


Bongjun Ji

Farhad Ameri

Junhyuk Choi

Hyunbo Cho
email: hcho@postech.ac.kr

Hybrid approach using ontology-supported case-based reasoning and machine learning for defect rate prediction

Keywords: Data analytics, Yield, Defect rate, Machine learning, Ontology

Manufacturers always strive to eliminate defects using different quality assurance tools and methods but some defect is often unavoidable. To compensate for defective products, surplus batches should be produced. However, surplus production is costly and it results in waste. In this paper, we propose an approach to predict defect rate and to set an appropriate amount of surplus production to replace defective products. This will result in reduced overproduction and underproduction costs. In the proposed work, the production order is represented ontologically. A formal ontology enables building clusters of similar production orders. A defect prediction model is developed for each cluster using Mixture Density Networks when a new order is received, the most similar production order, and its related cluster is retrieved. The prediction model of the retrieved cluster is then applied to the new production order. Accordingly, the optimal production amount is calculated based on defect rate, the overproduction cost and the underproduction cost. The proposed approach was validated based on a use case from the cosmetic packaging industry.

Introduction

Overproduction is one of the major sources of waste in manufacturing. Overproduction is excessive production of parts and products beyond the actual need. In lean literature, overproduction is known as the first type of waste, because it induces another major waste, which is inventory. One of the most common causes of overproduction is the expectation of defects. Typically, underproduction cost is much larger than overproduction cost, so to compensate for the number of defective items, manufacturers tend to produce more than the needed amount of a product. For example, if the order is for 95 units, and the process has a 5% defect rate, then the manufacturer would produce at least 100 units to cover the expected defect rate. The surplus production might increase even more: 1) if the manufacturing process has not yet reached a stable state, 2) production proceeds in small quantity batch production 3) failure to produce the required amount causes stoppage of subsequent processes.

Kanban is a type of pull production system which is used to prevent overproduction in lean manufacturing [START_REF] Kumar | Literature review of JIT-KANBAN system[END_REF]. In a Kanban system, a work can be started only when production approval card, called Kanban, is available [START_REF] Dalllery | Extended kanban control system: combining kanban and base stock[END_REF]. However, the Kanban system assumes stable and repetitive production. Therefore, it is not suitable for fluctuating demand and product mix. Cosmetic packaging is an example industry with fluctuating demand and product mix. For this reason, overproduction occurs frequently in the cosmetic packaging industry. Customer needs change rapidly, and therefore, product life continues to shrink. Frequent introduction of new products hinders stabilization of the manufacturing process, which often calls for production of small batches. Another complicating factor is the number of steps involved cosmetic packaging. To improve the aesthetics of the products, several processes are required. Therefore, overproduction is often propagated to several downstream processes. Therefore, more intelligent and data-driven methods are required to predict the defect rate accurately and, consequently, avoid overproduction.

The objective of this paper is to propose a systematic approach for defect rate prediction using ontology-supported case-based reasoning and machine learning (ML) techniques in cosmetic packaging industry. This paper is organized as follows. Section 2 provides a brief review of the related works. Section 3 introduces the concept of an ontology-supported case-based reasoning approach for defect rate prediction in cosmetic manufacturing enterprise. Finally, Section 4 provides the concluding remarks and identifies the future work.

Related work

In this section, we provide a review of the proposed methods for predicting yield and number of defects in the manufacturing domain.

Yield prediction

Yield refers to the percentage of non-defective product; i.e., the complementary measurement of defect rate. Yield prediction is widely used in semiconductor manufacturing to improve yield by providing early alert of nonconforming wafers, and thereby decreasing monetary losses. Semiconductor wafer yield is affected many factors, so traditional statistical analysis models do not work well to predict it [START_REF] Li | Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system[END_REF]. Neural networks (NNs) have been used to predict yield. Tong et al. [START_REF] Tong | Using a neural network-based approach to predict the wafer yield in integrated circuit manufacturing[END_REF] proposed an NN-based approach, and also used fuzzy adaptive resonance theory to groups patterns into the appropriate number of clusters. Tong and Chao [START_REF] Tong | Novel yield model for integrated circuits with clustered defects[END_REF] used a general regression neural network (GRNN) because it can process both continuous and categorized output, and can be used if the linearity assumption is violated. Chen and Lin [START_REF] Chen | A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting[END_REF] proposed a fuzzy NN system, but it does not consider electrical parameters even though it is critical Wu and Zhang [START_REF] Wu | Fuzzy neural network based yield prediction model for semiconductor manufacturing system[END_REF] considered electrical parameters along with key attributed parameters and physical parameters; the authors conducted statistical correlation analysis to identify electrical parameters. Lee and Ha [START_REF] Lee | Recognizing yield patterns through hybrid applications of machine learning techniques[END_REF] integrated a back-propagation network with case-based reasoning. The approach consists of four phases: learning relations between case variables and yield, weighting of features, extracting similar cases, and calculating the weighted averages of extracted yield. The paper was the first attempt to hybridize machine learning with case-based reasoning for yield prediction. Pak et al. [START_REF] Pak | Yield prediction using support vectors based under-sampling in semiconductor process[END_REF] used a support-vector machine to predict yield; they also used an undersampling method to eliminate imbalance from the data.

Defect rate prediction in assembly process

Various approaches of prediction have been proposed based on design characteristics of products, and on ergonomics. A Design for Assembly (DFA) technique allows a manufacturer to examine design alternatives in early design stage, to reduce assembly cost [START_REF] Boothroyd | Product Design for Assembly[END_REF]. It is also used to evaluate the likelihood of mistake, and to identify potential failure [START_REF] Su | Measuring the assembly quality from the operator mistake view: a case study[END_REF]. The Hinckley model [START_REF] Hinckley | Global conformance quality model: A new strategic tool for minimizing defects caused by variation, error, and complexity[END_REF] is based on the idea that defect rate is positively correlated with assembly time and negatively correlated with the number of assembly operations. This model provides insight, but the real word is not that simple. Shibata [START_REF] Shibata | Global assembly quality methodology: a new method for evaluating assembly complexities in globally distributed manufacturing[END_REF] suggested a model that considers process and design factors, and Antani [START_REF] Antani | A study of the effects of manufacturing complexity on product quality in mixed-model automotive assembly[END_REF] considered human factors by developing a regression-based defect rate prediction model in automated and semi-automated assembly; this model was then validated in a manual automobile-assembly process [START_REF] Krugh | Prediction of defect propensity for the manual assembly of automotive electrical Connectors[END_REF].

Numerous prediction models that have been developed in section 2.1 are highly suitable for implementation in large scale manufacturing. The most of small and medium engineering enterprises (SMEs) cannot afford the cost of introducing equipment with real-time sensors or installing sensors on every existing equipment. Therefore, it is hard to be implemented in small and medium engineering enterprises (SMEs). On the other hand, the prediction approaches in section 2.2 is based on characteristics of product and process, do not require additional investment on equipment. Hence the approaches in section 2.2 are relatively easy to apply. However, previous approaches cannot consider the cost of error, only focused on accuracy. To overcome these issues, this paper tried to consider expected cost based on the probability of defect rate is used to consider the expected cost.

The Proposed Framework for Defect Rate Prediction

The proposed framework is composed of 3 main phases as shown in Figure 1. The first phase is the off-line phase of the framework when the clusters of existing work orders are created and their corresponding prediction model is developed. The second phase (the on-line phase) is related to predicting defect rate for new work orders. The third phase is a continuous phase where the actual and predicted defect rates are compared and the prediction models are further tuned and updated.

Ontology Development

One innovative aspect of the proposed frame work is to use an ontological approach for representing the data related to previous production work orders. Ontology-based approach can be used to determine the similarity between production orders. When data is annotated by ontological entities, one can easily and accurately retrieve the most similar production orders in from the repository of the existing orders and reused their related defect rate prediction model. Also, ontology helps the users understand, communicate, and manage information effectively by standardizing the terminology used for production order description. Some examples of the key notions in production orders include product, customer, production month, manufacturing process, production team, and production quantity. Figure 2 shows the major classes and the relationship between the classes for the Work Order Ontology (WOO).

Fig. 2. Classes and relationships in the ontology

The workorder ontology is publicly available at https://github.com/corori/Ontology1.

Phase 1. Prediction Model Deployment

In this phase, the production orders, that are represented ontologically, are clustered based on their similarities. Next, historical data collection is conducted to be used in development of the prediction model for each cluster of production order. Sensitivity analysis and data wrangling are used to verify the relationship between various properties of the production order class and defect rate. For example, for some types of production order, the defect rate may decrease as production volume increases (Figure 3). The prediction models are developed for clusters of similar production order because similar production order shows similar trend of defect rate. For example, the defect rate trend of tube which is made by extrusion is totally different with bottle set which is made by assembly process. In the clustering step, the concepts are treated as features and properties are treated as feature value. Also, we should consider that even though the manufacturer produces the product with same production order properties, the defect rate is not a single point, but has a distribution in some cases (Figure 4). For this type of problem, a Mixture Density Networks (MDN) is a suitable prediction algorithm. It can model general conditional probability densities and outputs the distribution [START_REF] Bishop | Mixture density Networks[END_REF]. Also, the distribution which is output of Mixture Density Network, can be used when the expected cost of defect rate is calculated. Defect rate (%) Production amount

Phase 2: Prediction Model Deployment

In this phase, the developed prediction model is deployed for use. When a new production order is received, the feature value of the production order is measured in WO description step. Then the most-similar production order is identified by calculating similarity between the new production order and all existing production orders. The similarity of the new production order to the stored production orders is determined by calculating the similarity between production order features. Three major methods can be used to determine the similarity between production orders: the edge-based method, the information-content-based method, and the feature-based method [START_REF] Ameri | A matchmaking methodology for supply chain deployment in distributed manufacturing environments[END_REF]. In the edge-based method, the path length between terms in an is-a taxonomy represents the similarity [START_REF] Rada | Development and application of a metric on semantic nets[END_REF]. In the information-content-based method, the similarity of two production orders depends on the degree of informativeness of the superclass that includes both production orders [START_REF] Resnik | Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language[END_REF]. The similarity is defined as

Sim (c 1 , c 2 ) = max 𝑐𝑐∈𝑆𝑆(𝑐𝑐 1 𝑐𝑐 2 ) [-log 𝑝𝑝 (𝑐𝑐)], (1)
where, c 1 , c 2 are production orders, and 𝑆𝑆(𝑐𝑐 1 𝑐𝑐 2 ) is the set of concepts that subsumes both production orders. The negative log likelihood is the information content of a production order c according to information theory [START_REF] Sheldon | A first course in probability. 9th edn[END_REF]. In the feature-based method, the similarity between production orders C 1 and C 2 is a function of their common and distinctive features [START_REF] Tversky | Features of similarity[END_REF]:

Sim (c 1 , c 2 ) = 𝑛𝑛 𝐶𝐶 1 ∩𝐶𝐶 2 𝑛𝑛 𝐶𝐶 1 ∩𝐶𝐶 2 +𝜇𝜇𝑛𝑛 𝐶𝐶 1 -𝐶𝐶 2 +𝜈𝜈𝑛𝑛 𝐶𝐶 2 -𝐶𝐶 1 , (2)
where 𝜇𝜇, 𝜈𝜈 ∈ ℝ are constants that are weighting factors, 𝑛𝑛 𝐶𝐶 1 ∩𝐶𝐶 2 represents the number of common features, and 𝑛𝑛 𝐶𝐶 1 -𝐶𝐶 2 and 𝑛𝑛 𝐶𝐶 2 -𝐶𝐶 1 are the numbers of distinctive features of C 1 and C 2 respectively. Each of these methods has its strengths and weaknesses. The first and second similarity measure methods need a taxonomy [START_REF] Ameri | A matchmaking methodology for supply chain deployment in distributed manufacturing environments[END_REF]. In the cosmetic industry, such a taxonomy is not available for production order, so the third method is preferred. The most-similar production order can be retrieved by using the third method. Then the prediction model of the cluster which include the most similar production order is applied. The last step in phase 2 is to set an optimal defect rate that considers the cost. The expected cost of under production and overproduction is calculated as

∑ 𝑝𝑝(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑐𝑐𝐷𝐷 𝑟𝑟𝑟𝑟𝐷𝐷𝐷𝐷 = 𝑖𝑖|𝑧𝑧 𝑖𝑖 , 𝑥𝑥) 𝑖𝑖 × 𝑢𝑢𝑛𝑛𝑖𝑖𝐷𝐷 cost 𝑢𝑢𝑛𝑛𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑐𝑐𝑢𝑢𝑖𝑖𝑢𝑢𝑛𝑛 (𝑜𝑜𝑟𝑟𝑜𝑜𝐷𝐷𝑟𝑟 -𝑧𝑧 𝑖𝑖 ), (3)
∑ 𝑝𝑝(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑐𝑐𝐷𝐷 𝑟𝑟𝑟𝑟𝐷𝐷𝐷𝐷 = 𝑖𝑖|𝑧𝑧 𝑖𝑖 , 𝑥𝑥) 𝑖𝑖 × 𝑢𝑢𝑛𝑛𝑖𝑖𝐷𝐷 cost 𝑢𝑢𝑜𝑜𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑐𝑐𝑢𝑢𝑖𝑖𝑢𝑢𝑛𝑛 (𝑜𝑜𝑟𝑟𝑜𝑜𝐷𝐷𝑟𝑟 -𝑧𝑧 𝑖𝑖 ), (4)
where 𝑥𝑥 is the set features, 𝑧𝑧 i is the production amount when defect rate i is applied. Underproduction cost includes a delivery-delay penalty, an additional transfer fee to meet the delivery deadline, the cost of additional production, and the cost of adjusting production planning. Overproduction cost includes additional production cost and inventory cost. Unit costs vary; examples of units are minimum lot size, production order, and date. The expected cost of each defect rate is the sum of the underproduction cost, overproduction cost and production cost. The optimal defect rate is the one that has the lowest expected cost. When this rate is determined, the production amount can be determined.

Phase3: Monitoring

Deployment is not the end of the phase. In many cases, the users of the model will the be manufacturing operators, and not the data analyst. For effective and efficient use of the model, it should be updated at appropriate times. Hence, a threshold is set such that if the difference between predicted defect rate and actual defect rate exceeds some threshold, the model should be re-trained.

Conclusion and Future Work

This paper proposes an approach to predict defect rate to minimize costs of underproduction and overproduction. Defects are unavoidable, so to compensate for expected defects, a manufacturer tends to produce more products than the quantity ordered. Overproduction wastes production resources and increases inventory cost. Underproduction causes delivery delay, and adds the cost of adjusting production planning; underproduction can even cause overproduction, because manufacturers must re-produce a minimum amount of production. We develop a method to predict defect rate by combining an ontology-supporting case-based reasoning approach with a machine-learning approach. Existing methods to predict defect rates have not considered the costs of underproduction and overproduction. The proposed approach has two main advantages: 1) it combines ontology-supporting case-based reasoning and machine learning to improve the accuracy of the prediction, and 2) it considers probability to minimize costs caused by both overproduction and underproduction. Although this approach is still conceptual at present, and must be developed and verified, it is an important step towards efficient production. Further study will include experiment, validation and verification of proposed approach.

Fig. 1 .

 1 Fig. 1. The framework for defect rate prediction

Fig. 3 .

 3 Fig. 3. Defect rate by production amount in coating process (bottle category)

Fig. 4 .

 4 Fig. 4. Defect rate of specific bottle product which has same values of properties


Patrick Mikalef
email: patrick.mikalef@sintef.no

Hans Yngvar Torvatn
email: hans.torvatn@sintef.no

Emrah Arica
email: emrah.arica@sintef.no

Task-Technology Fit in Manufacturing: Examining Human-Machine Symbiosis through a Configurational Approach

Keywords: Task-technology fit, human-machine symbiosis, fsQCA

With the last few years seeing an increased introduction of technological innovations in factories, one of the most pressing issues is how these technologies can be deployed to optimally support the activities of professionals that are actually utilizing them. Despite heavy investments in novel technologies, there are often negative consequences for the human factor, particularly when there is a lack of alignment between the task that it is used towards and the fit in terms of human training and the needs it is targeted to fulfil. In this research we build on the Task-technology Fit theory and a sample of 182 professionals working in Norway and explore the configurations of elements that drive positive impacts when introducing digital technologies to support factory work. We analyze data through a fuzzy set qualitative comparative analysis (fsQCA) method and demonstrate that there are several different combinations of conditions that can deliver positive impacts.

Introduction

While there have been substantial investments in digital technologies for manufacturing purposes over the last few years [START_REF] Bartodziej | The concept industry 4.0: an empirical analysis of technologies and applications in production logistics[END_REF], a critical issue is that in many cases the technologies used to support tasks of professionals are often not used as intended, or even not adopted at all [START_REF] Romero | The operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems[END_REF]. Some studies have highlighted that the intended value from investments in novel technologies is not actually realized. A sizeable number of studies suggest that the underlying cause of this is due to a miss-match between what is needed from the human side in relation to work activities and what is provided by the technology [START_REF] Shu | The impact of computer self-efficacy and technology dependence on computer-related technostress: A social cognitive theory perspective[END_REF]. In fact, some researchers have reported that professionals working in the manufacturing industry often have trouble adopting and routinizing newly introduced technologies [START_REF] Kaufman | Strategic human resource management research in the United States: A failing grade after 30 years?[END_REF]. This leads to negative impacts on their job performance when they have to utilize such systems [START_REF] Golightly | Manufacturing in the cloud: A human factors perspective[END_REF]. Nevertheless, this problem is not strictly identified in the domain of manufacturing and factory work, and has been a core concern in several other domains including the education, banking and financial sector, and even high-tech companies [START_REF] Pai | Applying the technology acceptance model to the introduction of healthcare information systems[END_REF][START_REF] Dahnil | Factors influencing SMEs adoption of social media marketing[END_REF]. It is surprising to see that the human factor has received only limited attention to date when taking into account the huge costs invested by companies to develop and adopt such technologies [START_REF] Romero | Towards a human-centred reference architecture for next generation balanced automation systems: human-automation symbiosis[END_REF].

A growing body of research underscores on the importance of looking into the symbiosis between humans and machine [START_REF] Romero | Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies[END_REF][START_REF] Jarrahi | Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making[END_REF]. The main idea is that human cyberphysical systems should serve to improve human abilities to dynamically interact with machines, and improve human physical-, sensing-and cognitive capabilities [START_REF] Romero | The operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems[END_REF]. The underlying logic of this perspective is that by optimizing human-machine cooperation organizations can realize improvements in performance and efficiency [START_REF] Jones | Modeling agents as joint cognitive systems in smart manufacturing systems[END_REF]. In doing so there have been a number of different approaches into examining how humans and machines can complement each other capabilities and create value, and what factors influence human intention to adopt and use technologies such as the Technology Adoption Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) [START_REF] Venkatesh | A theoretical extension of the technology acceptance model: Four longitudinal field studies[END_REF]. Nevertheless, these models don't account for which technologies best fit which task. The theory of task-technology fit (TTF) has been a particularly prominent perspective and well-suited in explaining how specific job-related tasks, aspects of the technology, as well as use practices coalesce to create fit, and subsequently positive impacts [START_REF] Goodhue | Task-technology fit and individual performance[END_REF]. The TTF theory has received substantial attention within other fields such as the Information Systems domain, yet when examining the adoption of technologies to support manufacturing and factory work studies are still scarce [START_REF] Tjahjono | Evaluation of a manufacturing task support system using the task technology fit model[END_REF]. There is a growing perspective within the broader field of technology adoption that there may be several different ways by which technological solutions can be used to support professionals work activities [START_REF] Kim | Motivations and use context in mobile tourism shopping: Applying contingency and task-technology fit theories[END_REF]. The underlying premise is that individuals in their work are faced with different tasks that they must complete.

The objective of this study is to adopt a TTF theoretical perspective, and examine which are those combinations of tasks, technology, and individual use practices that fit together and lead to positive impacts in the context of manufacturing and factory professionals work. We build on a recent large-scale empirical survey conducted with 182 professionals, and by applying the novel methodological approach fuzzy set qualitative comparative analysis (fsQCA) we reveal several configurations that lead to positive outcomes for job-related activities. In this way we can identify a number of different tasks, the aspects pertinent to technology that best fit task requirements, as well as individual use and adoption practices that facilitate optimal fit.

Background

To explore how technologies deployed within the manufacturing domain can contribute to positive outcomes of work performance, we develop this research on the tasktechnology fit theory [START_REF] Goodhue | Task-technology fit and individual performance[END_REF]. Based on TTF theory, digital technologies will be more likely to yield a positive impact when the functionalities they deliver can fit the tasks individuals must undertake. Since its inception, the theory has been extended in several ways, with the latest studies recognizing how individuals use these technologies as well as the design and training practices surrounding adoption and diffusion, have an important role on performance of technology use [START_REF] Aljukhadar | Management: Is more always better? Investigating the task-technology fit theory in an online user context[END_REF]. Hence, the task-technology fit theory has been applied at various levels of analysis, examining effects on individuals and groups [START_REF] Strong | Understanding Organization-Enterprise system fit: A path to theorizing the information technology artifact[END_REF], as well as in many different contexts, from specific technologies [START_REF] Furneaux | Task-technology fit theory: A survey and synopsis of the literature[END_REF] to effects on industries or particular professions [START_REF] Cady | e-Health: Task-technology fit of video telehealth for nurses in an outpatient clinic setting[END_REF]. When looking into the area of manufacturing and factory work, there has been very limited work following this approach. Despite this, recent work has recognized the importance of the human factor highlighting that there needs to be alignment between the jobs humans must perform, the digital technologies introduced to support them, and the training they receive to adopt such systems into their work [START_REF] Gill | Human machine symbiosis: The foundations of human-centred systems design[END_REF]. To determine how these aspects jointly contribute to drive positive impacts in job performance, we follow a configurational approach in data analysis [START_REF] Mikalef | Purchasing alignment under multiple contingencies: a configuration theory approach[END_REF].

Configurational approaches have been growing interest in the IS and technology management community over the past few years [START_REF] Mikalef | Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA[END_REF][START_REF] Van De Wetering | Driving organizational sustainability-oriented innovation capabilities: a complex adaptive systems perspective[END_REF]. Such approaches have the advantage that they enable the identification of multiple different paths, or solutions, that lead to an outcome of interest [START_REF] Fiss | Building better causal theories: A fuzzy set approach to typologies in organization research[END_REF]. In practical terms, this means that in the case of positive impacts of digital technology use in the manufacturing and factory work context, it would be possible to detect several successful cases of using technologies to perform specific tasks, along with the individual use characteristics that describe them. Nevertheless, in spite of the promise of such approaches, there is still very limited research in exploring how the different aspects pertinent to task, technology, and individual use coalesce to drive fit, and as a result positive impacts in the factory and manufacturing workplace [START_REF] Schneider | Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance[END_REF]. The bulk of research building on the task-technology fit theory has emphasized on the two main concepts (i.e. task and technology) [START_REF] Or | A systematic review of patient acceptance of consumer health information technology[END_REF], while a growing stream of research incorporates in the investigation the role of individuals and how technologies are deployed and routinized in work activities [START_REF] Kim | Adoption of personal information system: Innovation diffusion theory and tasktechnology fit[END_REF]. An increasing number of research is looking into the formal and informal mechanisms of adopting the use of technologies in the workplace, acknowledging the fact that just as important as the technology itself to support a task are the practices through which they are embedded in work [28].

Method

Data Collection

To examine the configurations of elements regarding tasks, technology, and individual use context that lead to positive impacts in the work environment, a survey instrument was developed. A professional data collection company was commissioned with conducting phone polls to individuals throughout Norway using a database of approximately 10.000 individuals in a variety of different industries, including those of factory and manufacturing workers amongst others. No specific areas of manufacturing were selected, and respondents covered tasks of several different functions. The scales of the questionnaire are described in section 3.2. The callers informed participants about the purpose of the study and asked respondents to answer a number of questions. The data gathering process lasts roughly four months (May 2017-August 2017), and the average time for answering the questions of the survey was 23 minutes. A total of 182 complete responses were received from the manufacturing industry. From this sample, most responses came from the age-groups 45-59 years (38%) and 30-44 years (37%). In terms of gender distribution, the largest proportion of the sample consisted of male employees (72%) while females accounted for 28% of the sample. When looking at the educational background of respondents, most of them had as a highest academic qualification a degree from high school (42.9%), while 35.67% had until 4 years in higher education (equivalent to bachelor's degree).

Measurements

Regarding attributes relevant to the task itself, we utilized measures that included questions on the types of tasks in which digital technologies were used, the difficulty ad time-criticality of the task, if the level of non-routineness. Specifically, we measured on a 5-point likert scale the frequency in which respondents used digital technology for core tasks, reporting and documentation tasks, and information/coordination [START_REF] Weiseth | The wheel of collaboration tools: a typology for analysis within a holistic framework[END_REF]. To determine if they held positions that required leadership skills, we asked respondents to indicate if they had no leadership responsibilities, personnel, managerial, or both. Finally, we asked respondents to indicate how often they were expected to work outside of paid work hours [START_REF] Cane | Analyzing the factors that affect information systems use: a task-technology fit meta-analysis[END_REF].

Concerning technology-related characteristics we followed a similar approach, looking at different aspects related to functionality and user-friendliness, while also incorporating specific types of devices in the questions that are commonly used by manufacturing professionals. We captured the extent to which respondents believed that digital technologies they used in the jobs were functional and reliable, userfriendly, and adaptable [START_REF] Lin | Informatics, Nursing: Mobile nursing information system utilization: the task-technology fit perspective[END_REF]. Furthermore, we assessed the extent to which respondents need to use different types of devices to perform their work such as personal computers, mobile devices (e.g. smart phones, tablets and portable equipment), and wearables (smart glasses, smartwatch), or augmented reality technologies [START_REF] Metcalf | Wearables and the internet of things for health: Wearable, interconnected devices promise more efficient and comprehensive health care[END_REF].

In terms of individual use context, we tried to capture elements that were relevant to how individuals adopt and utilize novel digital technologies, as well as what types of support mechanisms are set up to facilitate such usage. In congruence with past empirical studies we include aspects that can affect how easily and well individuals utilize digital technology [START_REF] Goodhue | Task-technology fit and individual performance[END_REF]. We examine the degree to which individuals have a support network from colleagues when using digital technologies, the extent to which they have been trained to use the latest digital technologies in their organizations (e.g. courses, e-learning, self-education through reading), as well as the level to which they have been involved in the joined planning of introducing new digital technologies [START_REF] Mikalef | Developing IT-enabled dynamic capabilities: a service science approach[END_REF]. Finally, when it comes to examining the impacts of digital technology use in the manufacturing sector, we operationalize this variable as the level to which the quality of work gets better, work is done fast, and the level to which the work performed relies on the use of digital technologies [START_REF] Chung | Management: Job performance through mobile enterprise systems: The role of organizational agility, location independence, and task characteristics[END_REF].

Analysis

To identify the configurations of task, technology, and use practice lead to lead to positive or negative work impact we employ a fuzzy-set Qualitative Comparative Analysis (fsQCA) approach. Applying such an approach is particularly relevant to the case of digital technology usage within the manufacturing context, since depending on the type of task, and characteristics of the individual, different digital technologies and use support mechanisms may be more or less relevant in producing positive impacts [START_REF] Mikalef | Big data analytics and firm performance: Findings from a mixed-method approach[END_REF]. A first step of performing a fsQCA analysis requires that we calibrate dependent and independent variables into fuzzy or crisp sets. To calibrate continuous variables such as the ones we have utilized in the survey into fuzzy sets we followed the method proposed by Ragin [START_REF] Ragin | Qualitative comparative analysis using fuzzy sets (fsQCA)[END_REF]. Following this procedure, the degree of set membership is based on three anchor values. These include a full set membership threshold value (fuzzy score = 0.95), a full non-membership value (fuzzy score = 0.05), and the crossover point (fuzzy score = 0.50) [START_REF] Woodside | Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory[END_REF]. Based on prior empirical research we computed percentiles for each construct so that the upper 25 percentiles serve as the threshold for full membership; the lower 25 percentiles for full non-membership; and the 50 percentiles represent the cross-over point. The results of the analysis are depicted in the table below and discussed in the final section.

Discussion

This study finds that there are different combinations of technologies, features, and mechanisms to routinize depending on the task at hand. Furthermore, the results indicate that tasks that revolve around core tasks, such as those in the workstation can be improved the right combination of technologies and attributes in their design, coupled with the necessary training practices. The black circles denote a presence of a condition while the crossed-out ones an absence of it. For instance, in column 2, the solution reads as follows. When the task of the worker includes activities of reporting and documentation and information coordination and is a routine activity, then high reliability coupled with the use of a personal computer and a collegial support regime are sufficient to drive positive work performance. Likewise, other solutions show different tasks and combinations of them, pinpointing to certain activities within the work environment and how they can optimally be enhanced by means of digital technologies. What is interesting to observe also is that apart from the physical technologies themselves, there are also functional characteristics and design principles that need to be considered. In addition, the strategies applied to deploy and routinize such technologies are identified in solutions. These solutions are by no means exhaustive and can be examined on an even more granular level, nevertheless, they do illustrate a novel approach for uncovering combinations of elements around the use of technology that lead to positive outcomes. Future research can extend on this method and identify how human-machine symbiosis can be optimized.

Table 1 .

 1 Descriptive statistics of sample respondents' profile

	Sample (N=182)	Percentage (%)

Table 2 .

 2 Configurations leading to high work performance

	Configuration			Positive Impacts
		1	2	3	4	5
	Task
	Core task				
	Reporting and documentation task				
	Information/Coordination task					
	Leadership					
	Non-Routineness				
	Technology
	Reliability					
	User-friendliness					
	Adaptability/Flexibility				
	Personal computer				
	Mobile devices					


Foivos Psarommatis
email: foivos.psarommatis@epfl.ch

Dimitris Kiritsis

Identification of the inspection specifications for achieving Zero Defect Manufacturing

Keywords: Zero Defect Manufacturing, Inspection Time, Inspection Cost, Inspection specification, Defect Detection

The contemporary manufacturing landscape is characterized by increased volatility shortened product life cycles and increased degree of product customization. Manufacturing companies are continuously facing the challenge of operating their manufacturing processes and systems in order to deliver the required production rates of high quality products, while minimizing the use of resources. They can achieve this with a concept named Zero Defect Manufacturing. This concept implies that inspection is performed to all the parts and therefore they can achieve zero defects. The problem is that inspection can be either time consuming process or a very expensive process. The goal of the current research work is a methodology for the identification of the acceptable inspection specification in order for the production system to stay efficient and at the same time produce non-defected products. In other words the outcome of this paper will be a set of maps that depicts which can be the acceptable inspection characteristics such as inspection time and inspection cost.

Introduction & State of the Art

The contemporary manufacturing landscape is characterized by increased volatility shortened product life cycles and increased degree of product customization. Further to that the required quality by the customers has been higher than ever and therefore, manufacturers in order to stay competitive should adapt to these changes and start adopting new strategies that will allow them to achieve higher product qualities keeping costs as low as possible and at the same time respecting the due dates. The minimization of cost is a major consideration for all manufacturing industries and this is directly related with the product's quality. In addition, the product's quality is directly related with the inspection policy that has been selected. One of the strategies that can take into account and provide high quality parts at low costs is called Zero Defect Manufacturing (ZDM), which has as a goal of eliminating with various ways defected parts in the production, but it is not an easy task [START_REF] Ferretti | Monitoring systems for zero defect manufacturing[END_REF]. As described in [START_REF] Psarommatis | Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research[END_REF][3] there are four main strategies for implementing ZDM the Detect, Prevent, Predict and Repair of a defect. The most critical is the detection strategy because all the other strategies are relying on the data collected during the detection step.

It is common that in most of the manufacturing systems the quality testing of the product is performed at the end the process chain [START_REF] Klippel | End-Of-Line Testing[END_REF]. This is happening for separating the defective parts from the acceptable in order to treat them with different way. ZDM imposes that part inspection should happen in multiple stages [START_REF] Eger | Zero defect manufacturing strategies for reduction of scrap and inspection effort in multi-stage production systems[END_REF] and not only at the end of the process chain.

The detection of a defect is a very important step of the manufacturing process [START_REF] Iglesias | Automated vision system for quality inspection of slate slabs[END_REF]. However, there is no clear approach of what would be the acceptable specification of the inspection points, relatively to the total processing time and total product cost. Therefore, this paper aims to develop a methodology for defining what should be the acceptable inspection characteristics in order for the manufacturers to stay competitive and be able to achieve Zero Defect Manufacturing by inspecting all of the parts.

The majority of the literature deals with the development of effective and accurate inspection and monitoring technics without concerning the implications that may have to the actual production [START_REF] Eger | Zero defect manufacturing strategies for reduction of scrap and inspection effort in multi-stage production systems[END_REF]. For that reason there is the need for integrated approaches to jointly optimize, at system level, maintenance, quality and production control strategies, in order to avoid local improvements that can bring minor, or even detrimental, effects at system level [START_REF] Colledani | Design and management of manufacturing systems for production quality[END_REF].

Genta et al. developed a method based on which he was able to predict the probability of defect occurrence relevant to each process step and that way they could plan an effective inspection [START_REF] Genta | Product complexity and design of inspection strategies for assembly manufacturing processes[END_REF]. Sarkar created an economic production quantity model where the process is deteriorated based on production of defective products. To avoid the higher cost of inspection, product inspection policy was performed instead of full inspection policy where the product inspection policy was not error free [START_REF] Sarkar | Product inspection policy for an imperfect production system with inspection errors and warranty cost[END_REF]. Mohammadi et al. developed a mathematical model for deteriorating process representing the expected total cost to obtain the optimal solution. The aim of this research was to determine the production period length and inspection policy such that the expected total cost is minimized [START_REF] Mohammadi | Optimizing integrated manufacturing and products inspection policy for deteriorating manufacturing system with imperfect inspection[END_REF]. In chapter 2, the mathematical formulation of the current work is presented. Then in chapter 3, the implementation of the proposed method to an industrial case is performed and finally in the conclusion section, the paper is summarized.

Proposed methodology

Currently, there are numerous of different inspecting technologies with different characteristics each, and the implementation of inspection points on every manufacturing stage is not possible due to cost and time constrains. Therefore, manufacturers need a tool that could indicate what could be the acceptable combinations between inspection time and inspection cost based on the total processing time and total product cost. This methodology should be applied to the critical points for selecting the most appropriate inspection equipment in order to be able to inspect all the products and that way guaranty 100% defect free order. The current methodology aims to assist manufacturers in the process of designing, re-designing, or adjusting manufacturing systems to new products in order to determine what inspection equipment they need. The method is quite simple but the outcome has a significant impact to the achievement of ZDM. Two measures were used for the initialization of the process, the Estimated Product Cost (EPC) and the Estimated Production Time (EPT), which includes all the costs and all the times for the manufacturing and inspecting the product, accordingly. Both measures consists the theoreticalnominal values of these product parameters and consider only the manufacturing of the product from start to finish without any inspection. Having those parameters, we define two ratios the Inspection Time Ratio (ITR) and the Inspection Cost Ratio (ICR) that are described by formulas (1) and ( 2). The ratio approach selected in order to provide a relative indicator that shows how much extra time or cost is needed for the inspection having as reference the nominal values.

𝐼𝑇𝑅 = 𝐼𝑛𝑠𝑝𝑒𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 𝐸𝑃𝑇 (1) 𝐼𝐶𝑅 = 𝐼𝑠𝑝𝑒𝑐𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 𝐸𝑃𝐶 ( 2
)
ITR and ICR take values between 0 and 1. ITR is an independent value whereas the ICR includes the calculated Inspection Time. The goal is to find all the sets of the two ratios that corresponds to acceptable inspection characteristics. Those sets will be used in order to create a map of the acceptable inspection characteristics in order to inspect all the products and that way achieve Zero Defect Manufacturing.

Performance Indicators used for the simulation

Three different PIs were used for the comparison of the different scenarios (SC): a) the tardiness of the order (OT), b) the quality of the solution (OQ), measuring how many defected parts were at the end and c) the actual product cost (PC). The PC should not be mixed with the EPC. PC is the actual product cost as it was calculated after the simulation. Equations 3, 4 and 5 present the formulas that the KPIs are calculated. Those values were normalized and weighted and summed to one value which corresponds to the quality of the solution [START_REF] Mourtzis | A multi-criteria evaluation of centralized and decentralized production networks in a highly customer-driven environment[END_REF], [START_REF] Mourtzis | A toolbox for the design, planning and operation of manufacturing networks in a mass customisation environment[END_REF].

𝑂𝑇 = max (𝐷𝑢𝑒𝐷𝑎𝑡𝑒 -𝑀𝑎𝑘𝑒𝑠𝑝𝑎𝑛, 0) (3)
𝑃𝐶 = ∑ 𝑅𝑎𝑤𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙𝑠𝐶 + ∑ 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝐶 (4)

𝑂𝑄 = 𝑇𝑜𝑡𝑎𝑙𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑠𝑀𝑎𝑑𝑒 -𝐷𝑒𝑓𝑒𝑐𝑡𝑒𝑑𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑠 𝑇𝑜𝑡𝑎𝑙𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑠𝑀𝑎𝑑𝑒

(5)

Experimental results

The use of the proposed method in an Industrial scenario will be described in this chapter. A dynamic scheduling tool was used for the simulation which is described in [START_REF] Psarommatis | A Scheduling Tool for Achieving Zero Defect Manufacturing (ZDM): A Conceptual Framework[END_REF].

In addition, the product used for the simulations is composed out of 13 processes and the corresponding bill of processes (BoP) is shown in the right side of Table 1. The simulation was including only a part of the factory, and more specifically 10 machines, configured as "open shop" [START_REF] Pinedo | Scheduling Theory, Algorithms, and Systems, Fifth edit[END_REF]. In addition, the simulation was performed for 3000 parts with a specific due date. In addition, the simulations were considering product inspection at the final manufacturing stage. The inspection parameters were defined according to the methodology presented in chapter 2. The left side of Table 1 depicts the scenarios that were defined for the simulations with the inspection times and costs used for each one. In addition, the product under investigation has EPC=341.2 € and EPT=108.2 min. Having these values and using the formulas ( 1) and ( 2), we were able to calculate the corresponding Inspection times and Costs. All the scenarios were compared among each other and with a benchmark scenario ("B") in which inspection was performed every 100 parts, but the same number of defects were introduced during the simulation. Four different set of weights were used at the performance indicators (PI), in order to demonstrate the different requirements that each manufacturer has. The results from the simulations are illustrated in Fig. 1. It can be seen that in the three out of four weight sets the quality of the benchmark scenario is lower than 0.66, only in the last weight scenario that Order Quality is not that important (0.2) benchmark is close to the best with 9.52% relative difference from the best of the set.

The next step is to use the results from the experiments, illustrated in Fig. 1 and create the maps for the acceptable inspection characteristics. For each weight set the 16 scenarios' values were compared with the benchmark and if they were above the benchmark then this value is acceptable and should be considered to the corresponding map. The benchmark scenario is the current situation on the shop floor and the current study aims to improve the performance of the system in terms of order quality but keeping production cost and makespan as low as possible. Then all the revealed points were put in a common graph, Fig. 2 and the area that is below the marked line depicts the acceptable inspection combinations in terms of inspection time and inspection equipment operational cost. The weight scenario with the least possibilities is the scenario in which quality is not that important. This case is not presented in Fig. 2 because only one scenario was better than the benchmark test, scenario 1, which imposes very quick and low cost inspection device.

Conclusions and future work

This paper provides a method identifying the acceptable inspection parameters in order to select the appropriate inspection equipment and efficiently inspect all the produced parts and achieve ZDM. In addition, the results were split into four categories, each category was considering different weights to the PIs. It was observed and expected, that the inspection scenarios that had low inspection time and low inspection cost, were common points to three out of four maps. It is obvious that the cheaper and the faster the inspection point the better it is, but when it comes to inspection this combination is not always the case. Therefore, manufactures need methods like the above mentioned in order to be able to decide if an inspection point would be efficient or not. Future work will focus on the study of multiple inspection point across the manufacturing of a product and simulating the entire shop floor. Further to that, the simulation of multiple product will be investigated as well as different shop floors set ups. In this industrial case, the shop floor was set up as an "open shop" configuration.

Fig. 1 .

 1 Fig. 1. Inspection Scenarios and Benchmark solutions quality

Fig. 2 .

 2 Fig. 2. Inspection Specification Map for different criteria weights

Table 1 .

 1 Experiment Inspection scenarios & product Bill of Processes (BoP)

	SC	ITR ICR	Inspection Time (min)	Inspection cost (€)	Inspection ma-(€/hour) chine cost	Product BoP
	1	0.1	0.1	10.82	34.12	189.2052
	2	0.3	0.3	32.46	102.36	189.2052
	3	0.5	0.5	54.1	170.6	189.2052
	4	0.7	0.7	75.74	238.84	189.2052
	5	0.1	0.3	10.82	102.36	567.6155
	6	0.3	0.5	32.46	170.6	315.342
	7	0.5	0.7	54.1	238.84	264.8872
	8	0.7	0.1	75.74	34.12	27.02931
	9	0.1	0.5	10.82	170.6	946.0259
	10	0.3	0.7	32.46	238.84	441.4787
	11	0.5	0.1	54.1	34.12	37.84104
	12	0.7	0.3	75.74	102.36	81.08793
	13	0.1	0.7	10.82	238.84	1324.436
	14	0.3	0.1	32.46	34.12	63.06839
	15	0.5	0.3	54.1	102.36	113.5231
	16	0.7	0.5	75.74	170.6	135.1466

Acknowledgment

The presented work presented supported by the project Z-Fact0r, an EU H2020 project under grant agreement No 723906. The paper reflects the authors' views and the Commission is not responsible for any use that may be made of the information it contains.