Industry 4.0 and Lean Production: an empirical study

9th IFAC Conference on Manufacturing Modelling, Management and 9th IFAC Conference on Manufacturing Modelling, Management and Control Control 9th IFAC Conference on Manufacturing Modelling, Management and Berlin, Germany, August 28-30, 2019 Available online at www.sciencedirect.com 9th IFAC Conference on Manufacturing Modelling, Management and Berlin, Germany, August 28-30, 2019 Control 9th IFAC Conference on Manufacturing Modelling, Management and Control Berlin, Germany, August 28-30, 2019 Control Berlin, Germany, August 28-30, 2019 Berlin, Germany, August 28-30, 2019

ScienceDirect

IFAC PapersOnLine 52-13 (2019) 42–47 Industry Industry 4.0 4.0 and and Lean Lean Production: Production: an an empirical empirical study study Industry 4.0 and Lean Production: an empirical study Industry 4.0 and Lean Production: an study Matteo Rossini*. Federica Portioli Guilherme Tortorella** Industry andCosta*. LeanAlberto Production: an empirical empirical study Matteo Rossini*. 4.0 Federica Costa*. Alberto Portioli Staudacher*. Staudacher*. Guilherme Tortorella**

Matteo Rossini*. Federica Costa*. Alberto Portioli Staudacher*. Guilherme Tortorella** Matteo Federica Costa*. Alberto Portioli Tortorella** *Politecnico di Milano, Italy [email protected]; Matteo Rossini*. Rossini*. Federica Costa*. Alberto[email protected]; Portioli Staudacher*. Staudacher*. Guilherme Guilherme Tortorella** *Politecnico di Milano, Milano, Milano, Italy (e-mails: (e-mails: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). *Politecnico di Milano, Milano, Italy (e-mails: [email protected]). *Politecnico di Milano, Italy (e-mails: [email protected]; [email protected]; **Universidade Federal de Catarina, Brazil [email protected]) [email protected]). *Politecnico di Milano, Milano, Milano, Italy (e-mails:Florianopolis, [email protected]; [email protected]; **Universidade Federal de Santa Santa Catarina, Florianopolis, Brazil (e-mail: (e-mail: [email protected]) [email protected]). **Universidade Federal de Santa Catarina, Florianopolis, Brazil (e-mail: [email protected]) [email protected]). **Universidade **Universidade Federal Federal de de Santa Santa Catarina, Catarina, Florianopolis, Florianopolis, Brazil Brazil (e-mail: (e-mail: [email protected]) [email protected]) Abstract: This study study aims aims at at investigating investigating the the impact impact of of the the association association between between the the adoption adoption of of Industry Industry Abstract: This 4.0 and Lean Production (LP) on the improvement level of operational performance. To achieve that, we we Abstract: This study aims(LP) at investigating the impact of the associationperformance. between the adoption of Industry 4.0 and Lean Production on the improvement level of operational To achieve that, Abstract: This study aims at investigating the impact of the association between the adoption of Industry performed a survey with 108 European manufacturers that have been implementing LP and Industry 4.0. 4.0 and Lean Production (LP) on the improvement level of have operational performance. Toand achieve that,4.0. we Abstract: This studywith aims108 at investigating the impact of the association between the LP adoption of Industry performed a survey European manufacturers that been implementing Industry 4.0 and Lean Production (LP) on the level of operational performance. To achieve that, we The collected data multivariate techniques. Further, findings evidence that higher performed a survey withanalyzed 108 manufacturers that been implementing Industry 4.0. 4.0 Lean Production (LP)European on through the improvement improvement level of have operational performance. Toand achieve we Theand collected data was was analyzed through multivariate techniques. Further, findings LP evidence thatthat, higher performed a survey with 108 European manufacturers that have been implementing LP and Industry 4.0. adoption levels of Industry 4.0 may be easier to achieve when LP practices are extensively implemented The collected data was through multivariate techniques. Further, findings evidence that higher performed a survey withanalyzed 108 manufacturers that haveLP been implementing LP and implemented Industry 4.0. adoption levels of Industry 4.0European may be easier to achieve when practices are extensively The collected was analyzed through multivariate techniques. Further, findings evidence that in company. opposition, when are robustly improvement adoption levelsdata ofIn 4.0 may beprocesses easier to achieve when LPdesigned practices are continuous extensively implemented The data was analyzed through multivariate Further, and findings evidence that higher higher in the thecollected company. InIndustry opposition, when processes are not nottechniques. robustly designed and continuous improvement adoption levels of Industry 4.0 may be easier to achieve when LP practices are extensively implemented practices are may adopting technologies either. in the company. opposition, whenbeprocesses are be not focused robustly designed and continuous improvement adoption levels ofInestablished, Industry 4.0 companies may easier achieve when LPon practices arenovel extensively implemented practices are not not established, companies mayto not not be focused on adopting novel technologies either. in the company. In opposition, when processes are not robustly designed and continuous improvement Copy-right © 2019 IFAC practices are not established, companies may not be focused on adopting novel technologies either. in the company. In opposition, when processes are not robustly designed and continuous improvement Copy-right © 2019 IFAC Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. © 2019, IFAC (International practices not established, companies may not be focused on adopting novel technologies either. Copy-rightare © 2019 IFAC practices are not established, companies may not be focused on adopting novel technologies either. Keywords: Industry 4.0; Human-automation integration; Production Control, Control Systems. Copy-right © 2019 IFAC Keywords: Industry 4.0; Human-automation integration; Production Control, Control Systems. Copy-right © 2019 IFAC Keywords: Industry 4.0; Human-automation integration; Production Control, Control Systems.  Keywords: Production Control, Control  Keywords: Industry Industry 4.0; 4.0; Human-automation Human-automation integration; integration; Productionimpacts Control, companies’ Control Systems. Systems. association operational performance. performance.  association impacts companies’ operational 1. INTRODUCTION 1. INTRODUCTION  Further, the gap of understanding is much increased if the the association impacts companies’ operational performance.  Further, the gap of understanding is much increased if 1. INTRODUCTION association impacts companies’ operational performance. effect of contextual factors is taken into account. Prior studies The wide dissemination of practices and principles inherent Further, the gap of understanding is much increased if the association impacts companies’ operational performance. 1. of contextual factors is taken into account. Prior studies The wide dissemination of practices and principles inherent effect 1. INTRODUCTION INTRODUCTION Further, the gap of understanding is much increased if the on LP (Shah and Ward, 2003; Netland, 2016) have to Lean Production (LP) systems by practitioners and effect of contextual factors is taken into account. Prior studies Further, the gap of understanding is much increased if the LP (Shah and Ward, 2003; Netland, 2016) have TheLean wide Production dissemination(LP) of practices inherent to systems and by principles practitioners and on effect of contextual factors is taken into account. Prior studies emphasized the importance of contingencies for properly The wide dissemination of practices and principles inherent academicians throughout different industries and contexts has on LP (Shah and Ward, 2003; Netland, 2016) have effect of contextual factors is taken into account. Prior studies the importance of contingencies for properly to Lean (LP) systems by principles practitioners and The wide Production dissemination of practices and inherent academicians throughout different industries and contexts has emphasized on LP and Ward, 2003; Netland, 2016) have implementing LP. Nevertheless, the impact of such to Lean (LP) systems by and consistently occurred during the last three decades (Krafcik, emphasized theLP. importance contingencies for properly on LP (Shah (Shah and Ward, of 2003; Netland, 2016) have Nevertheless, the impact of such academicians throughout different industries and contexts has implementing to Lean Production Production (LP) systems by practitioners practitioners and consistently occurred during the last three decades (Krafcik, emphasized the importance of contingencies for properly contingencies on the relationship between Industry 4.0 and academicians throughout different industries and contexts has 1988; Holweg, 2007; Nicholas, 2015). Such intensive implementingthe the impact emphasized importance of contingencies for of properly onLP. the Nevertheless, relationship between Industry 4.0 such and consistently occurred during the last three decades (Krafcik, academicians throughout different industries and contexts has contingencies 1988; Holweg, 2007; Nicholas, 2015). Such intensive implementing LP. Nevertheless, the impact of such LP is quite unknown, highlighting an additional lack in consistently occurred during the last three decades (Krafcik, dissemination occurs dueNicholas, to the thelast expected benefits that LP LP LP contingencies onLP. the Nevertheless, relationship between Industryof4.0 and implementing the impact is quite unknown, highlighting an additional lacksuch in 1988; Holweg, 2007; 2015). Such (Krafcik, intensive consistently occurred during three decades dissemination occurs due to the expected benefits that contingencies on the relationship between Industry 4.0 and literature. Therefore, this study aims at examining the impact 1988; Holweg, 2007; Nicholas, 2015). Such intensive implementation can entail, such as expected cost reduction, quality and LP is quite unknown, highlighting additional lack in contingencies on the this relationship between Industry and Therefore, study aims atan examining the4.0 impact dissemination dueNicholas, to theas benefits that and LP literature. 1988; Holweg,occurs 2007; 2015). Suchquality intensive implementation can entail, such cost reduction, LP is quite unknown, highlighting an additional lack in of the association between the adoption of Industry Industry 4.0 dissemination occurs due to the expected benefits that LP productivity enhancement, delivery and customer satisfaction literature. Therefore, this study aims at examining the impact LP is quite unknown, highlighting an additional lack in of the association between the adoption of 4.0 implementation can entail, such as cost reduction, quality and dissemination occurs due to the expected benefits that LP productivity enhancement, delivery and customer satisfaction literature. Therefore, this study aims at examining the impact technologies and LP LP practices implementation on the implementation can such as quality improvement, etc. Inentail, this sense, sense, diversity of organizations organizations of the association between adoption of Industry 4.0 literature. Therefore, this practices studythe aims at examining theon impact and implementation the productivity enhancement, delivery andreduction, customer satisfaction implementation canIn entail, such asaa cost cost reduction, quality and and technologies improvement, etc. this diversity of of the between adoption of improvement level of the manufacturers’ operational productivity enhancement, delivery and customer satisfaction has been investing investing lot ofsense, effort ato todiversity adapt and implement LP improvement technologies andlevel LP practices implementation on 4.0 the of the association association between the adoption of Industry Industry 4.0 of manufacturers’ operational improvement, etc. In thisof ofimplement organizations productivity enhancement, delivery and customer satisfaction has been aa lot effort adapt and LP technologies and LP practices implementation on the performance. To achieve that, we carried out a survey with improvement, etc. In this sense, a diversity of organizations in their processes and systems. With the advent of Industry improvement level of manufacturers’ operational technologies and LP practices implementation on the To achieve that, we carried out a survey with hastheir beenprocesses investing a lot ofsense, effortWith adapt and LP performance. improvement, etc. and In this atodiversity ofimplement organizations in systems. the advent of Industry improvement level of manufacturers’ operational 108 European manufacturers that have been implementing LP has been investing a lot of effort to adapt and implement LP 4.0, new management paradigms have been raised through performance. To achieve that, we carried out a survey with improvement level of manufacturers’ operational 108 European manufacturers that have been implementing LP in their and thebeen advent of Industry has been investing a lotsystems. of effortWith tohave adapt and raised implement LP performance. To achieve that, we carried out a survey with 4.0, newprocesses management paradigms through practices and already started to adopt Industry 4.0 in their processes and systems. With the advent of Industry novel technologies adoption (Lasi et al., al., 2014). As 108 European manufacturers that implementing LP performance. achieve that, wehave carried out aIndustry survey with andTo already started to been adopt 4.0 4.0, new management paradigms have raised through practices in their processes and systems. With thebeen advent of Industry novel technologies adoption (Lasi et 2014). As 108 European manufacturers that have been implementing LP technologies. The collected data was analyzed through 4.0, new management paradigms have been raised through 4.0 is characterized by modern information and practices and already started to adopt Industry 4.0 108 European manufacturers that have been implementing LP technologies. The collected data was analyzed through novel technologies adoption (Lasi et al., 2014). As Industry 4.0, new management paradigms have been raised through 4.0 is characterized by modern information and practices and already started to adopt Industry 4.0 multivariate techniques. This article comprises a preliminary novel technologies adoption (Lasi et al., 2014). As Industry communication technologies (ICT), products, machines and technologies. The collected data was analyzed through practices and already started to adopt Industry 4.0 multivariate techniques. This article comprises a preliminary 4.0 is characterized by modern information and novel technologies adoption (Lasi et al., 2014). As Industry communication technologies (ICT), products, machines technologies. The collected data was analyzed version of the thetechniques. full research developed by Rossini Rossini etpreliminary al.through (2019) 4.0 is modern information and processes can technologies become by interconnected, allowing the version multivariate This article a et technologies. Theresearch collected data comprises was analyzed through of full developed by al. (2019) communication (ICT), products, machines and 4.0 is characterized characterized byinterconnected, modern information processes can become allowing the multivariate techniques. This article comprises a preliminary and besides its theoretical implications, this study contributes communication technologies (ICT), products, machines and establishment of the concept ‘smart factory’ (Kagermann et version of the full research developed by Rossini et al. (2019) multivariate techniques. This article comprises a preliminary besides its theoretical implications, this study contributes processes canoftechnologies become allowing the communication (ICT), machines and establishment the conceptinterconnected, ‘smart products, factory’ (Kagermann et and version of the full research developed by Rossini et al. (2019) to practice as it provides managers evidence on how both processes can become interconnected, allowing the al., 2013). In this sense, the potential benefits of adopting and besides its theoretical implications, this study contributes version of the full research developed by Rossini et al. practice as it provides managers evidence on how(2019) both establishment concept ‘smart factory’ (Kagermann et to processes canof become allowing the al., 2013). In thisthe sense, theinterconnected, potential benefits of adopting and besides its theoretical implications, this study contributes approaches can coexist in order to achieve higher operational establishment of the concept ‘smart factory’ (Kagermann et technologies such as Internet of Things (IoT), 3D printers and to practice as it provides managers evidence on how both and besides its theoretical implications, this study contributes can coexist in order to achieve higher operational al., 2013). Insuch thisthe theof‘smart potential benefits of adopting establishment of concept factory’ (Kagermann et approaches technologies assense, Internet Things (IoT), 3D printers and to practice as it provides managers evidence on how both performance levels. al., 2013). In this sense, the potential benefits of adopting augmented reality models have been envisioned by many approaches can coexist in order to achieve higher operational to practice as it provides managers evidence on how both performance levels. technologies such Internet ofpotential Things 3D of printers and approaches can coexist in order to achieve higher operational al., 2013). In thisassense, the benefits adopting augmented reality models have been (IoT), envisioned by many technologies such as Internet of Things (IoT), 3D printers and authors (e.g. Rüßmann et al., 2015; Weyer et al., 2016), performance levels. approaches can coexist in order to achieve higher operational augmented reality models have been envisioned by many technologies as Internet of Things and performance levels. authors (e.g. such Rüßmann et al., 2015; (IoT), Weyer3D et printers al., 2016), 2. LITERATURE LITERATURE REVIEW REVIEW augmented models been envisioned by many generating great expectations and enthusiasm about the performance levels. 2. authors (e.g.reality Rüßmann et have al., 2015; Weyer et al., augmented reality models have beenenthusiasm envisioned by 2016), many generating great expectations and about the authors (e.g. Rüßmann et al., 2015; Weyer et al., 2016), theme. However, However, literature evidence regarding Industry 4.0’s 2. LITERATURE REVIEW generating expectations and regarding enthusiasm about the authors (e.g.great Rüßmann et evidence al., 2015; Weyer et al., 2016), theme. literature Industry 4.0’s 2. LITERATURE REVIEW generating great expectations and enthusiasm about the integration with other management approaches, such as LP, is 2.1 Industry 4.0 theme. However, literature evidence regarding Industry 4.0’s generating great expectations and enthusiasm about the integration with other management approaches, such as LP, is 2.1 Industry 4.0 2. LITERATURE REVIEW theme. However, literature evidence regarding Industry 4.0’s still scarce. Some previous studies (e.g. Jackson et al., 2011; integration with other management approaches, such as LP, is 2.1 Industry 4.0 theme. However, literature evidence regarding 4.0’s still scarce. Some previous studies (e.g. JacksonIndustry et al., 2011; integration other management as is Kolberg andwith Zühlke, 2015; Kolberg et al., al.,Jackson 2017)such attempted to 2.1 still scarce. Some previous studies approaches, (e.g. et al., 2011; Industry 4.04.0 represents the the integration integration of of automation automation integration with other management approaches, such as LP, LP, is Industry Kolberg and Zühlke, 2015; Kolberg et 2017) attempted to 2.1 Industry Industry 4.0represents 4.0 still scarce. Some previous studies (e.g. Jackson et al., 2011; examine how some LP practices could benefit from the Kolberg and Zühlke, 2015; Kolberg et al.,Jackson 2017) attempted to technologies, technologies, such as the Cyber Physical Systems (CPS), still scarce. Some previous studies (e.g. et al., examine how some LP practices could benefit from2011; the Industry 4.0 represents the integration of automation such as the Cyber Physical Systems (CPS), Kolberg 2015; Kolberg et al., attempted to incorporation ofsome certain set of technologies. technologies. Additionally, Industry represents the integration of automation examine and howZühlke, LP practices could benefit from the Internet of of4.0 Things (IoT) and cloud computing, in the Kolberg and Zühlke, 2015; Kolberg et al., 2017) 2017) attempted to Internet incorporation of aa certain set of Additionally, technologies, such as the Cyber Physical Systems (CPS), Industry 4.0 represents the integration of automation Things (IoT) and cloud computing, in the examine how LP practices benefit from the other researchers (e.g. Sanders et could al., 2016; 2016; 2017) have technologies, such as the Cyber Physical Systems (CPS), incorporation a (e.g. certain set of technologies. Additionally, manufacturing industry (Hermann et al., 2016). In this ICT examine how ofsome some LPSanders practices could benefit fromhave the manufacturing other researchers et al., 2017) Internet of Things (IoT) and cloud computing, in the technologies, such as the Cyber Physical Systems (CPS), industry (Hermann et al., 2016). In this ICT incorporation of certain set suggested positive relationship between bothAdditionally, approaches, Internet of Things (IoT) and cloud computing, in the other researchers Sanders et al., 2016; 2017) have driven driven industrial context, prominent technological incorporation of aa (e.g. certain set of of technologies. technologies. Additionally, suggested aa positive relationship between both approaches, manufacturing industry (Hermann et al., 2016). In this ICT Internet of Things (IoT) and cloud computing, in the industrial context, prominent technological other researchers (e.g. Sanders et al., 2016; 2017) have but without empirical validation of such synergy. Thus, the manufacturing industry (Hermann et al., 2016). In this ICT suggested a positive relationship between both approaches, frameworks for manufacturing processes at both intraand other researchers (e.g. Sanders et al., 2016; 2017) have but without empirical validation of such synergy. Thus, the frameworks driven industrial context, prominent technological manufacturing industry (Hermann et al., 2016). In this ICT for manufacturing processes at both intraand suggested aa empirical positive both approaches, body of knowledge knowledge onrelationship the association association levelsynergy. between Industry driven industrial context, prominent technological but without validation ofbetween such Thus, the inter-organizational inter-organizational levels have been been proposed (Ivanov et and al., suggested positiveon relationship between both approaches, body of the level between Industry frameworks for manufacturing processes at both intra-et driven industrial context, prominent technological levels have proposed (Ivanov al., but without empirical validation of such synergy. Thus, 4.0 and LP is incipient, and much investigation still needsthe to 2016; frameworks for manufacturing processes at both intraand bodyand of LP knowledge on validation the levelsynergy. between Industry Dolgui et al., 2019), entailing an array of solutions to but without such the 4.0 isempirical incipient, andassociation muchofinvestigation stillThus, needs to inter-organizational levels been proposed (Ivanov al., frameworks manufacturing processes at both intra-et and 2016; Dolguifor et al., 2019),have entailing an array of solutions to body of knowledge on association level Industry be addressed in order order to betterinvestigation comprehend how this inter-organizational levels have been proposed (Ivanov et al., 4.0 and is incipient, and much stillhow needsthis to the the growing needs of informatization in manufacturing body of LP knowledge on the the association level between between Industry be addressed in to better comprehend 2016;growing Dolgui etneeds al., levels 2019), entailing array solutions to inter-organizational have been an proposed (Ivanov et al., of informatization in of manufacturing 4.0 LP still needs to Dolgui al., entailing solutions be and addressed in orderand to much betterinvestigation comprehend 4.0 and LP is is incipient, incipient, and much investigation stillhow needsthis to 2016; the of informatization in of manufacturing 2016;growing Dolgui et etneeds al., 2019), 2019), entailing an an array array of solutions to to be addressed in order to better comprehend how this the growing needs of informatization in be addressed order to better comprehend how this 44 Hosting Copyright © 2019, 2019in IFAC needs of rights informatization in manufacturing manufacturing 2405-8963 © IFAC (International Federation of Automatic Control) by Elsevier Ltd. All reserved. Copyright © 2019 IFAC 44 the growing Peer review©under of International Federation of Automatic Copyright 2019 responsibility IFAC 44 Control. 10.1016/j.ifacol.2019.11.122 Copyright © 2019 IFAC 44 Copyright © 2019 IFAC 44

2019 IFAC MIM Berlin, Germany, August 28-30, 2019

Matteo Rossini et al. / IFAC PapersOnLine 52-13 (2019) 42–47

industries (Kagermann et al., 2013). Hence, there has been a growing demand for research regarding Industry 4.0 in order to provide insights into the issues, challenges, and solutions related to the design, implementation and management of Industry 4.0 (Ivanov et al., 2018; Xu et al., 2018). However, for many manufacturers the current ICT infrastructures may not be entirely ready to support the transformation into Industry 4.0, which aims at integrating horizontal and vertically, as well as end-to-end (Liao et al. 2017). Further, Industry 4.0 adoption may impact other key aspects of an organizational structure, such as human resources development (Dworschak and Zaiser, 2014) and customer relationship management (Schumacher et al., 2016). Thus, although the adoption of Industry 4.0 technologies may support the achievement of extremely novel performance standards, they might also require fundamental changes in organizations’ modus operandi which raises an additional challenge for its acceptance. Further, companies usually have problems in determining their current state with regards to Industry 4.0 development, undermining the clear identification of which actions should be addressed. Table 1 consolidates the main Industry 4.0 elements according to some authors. It is noteworthy that, out of the 16 identified technologies, ‘Big Data’, intended as the capability of collecting and transforming huge amount of data into information (Jackson et al. 2011), and ‘Augmented reality’ were the most frequently mentioned, with nine citations each. These technologies are widely deemed by the authors due to the potential innovation that they can entail on manufacturing processes (Lasi et al., 2014; Hermann et al., 2016). On the other hand, ‘Collaboration with suppliers/customers through real time data sharing’ appears to be less frequently mentioned in the investigated Industry 4.0 literature. Such fact may denote a lower emphasis that studies on Industry 4.0 are putting on customers/suppliers’ relationships. In other words, most works are carried out from an operational and internal perspective of the company, neglecting its benefits to the whole supply chain.

Additive manufacturing, rapid prototyping or 3D printing Big data Internet of Things

1 X

2 X

3 X

4

5

X X

X X

X

X

X

X

X X X

X X

X X

X

X

X X

X

X

X

X

X

X X

X X

X

X

X

X X

X

X

X X X X X X Authors: 1-Kolberg and Zühlke (2015); 2-Kolberg et (2017); 3-Jackson et al. (2011); 4-Xu et al. (2018); Kagermann et al. (2013)

X X al. 5-

2.2 Lean Production LP aims at streamlining the flow of value while continually seeking to reduce the resources required to produce a given set of products. It was conceived as an evolutional detachment from the precepts of traditional mass-production manufacturing (Marodin et al., 2016). Although the adoption of LP is not a new concept, few organizations fully understood the philosophy behind its practices and principles (Baker, 2002). Based on a human-based system where people are involved with continuous improvements, LP practices are deployed to allow employees solving problems at their own workplace (Liker, 2004). In this sense, each LP practice fits a different purpose and can be used to solve specific problems. However, there is not a precise understanding about the definition of LP practices, and researchers usually agree upon several overlapping practices (Marodin and Saurin, 2013). There is a general agreement that LP is positively associated with operational performance, in both developed (Demeter and Matyusz, 2011; Netland et al., 2015) and developing economies’ context (Panizzolo et al., 2012; Jasti and Kodali, 2016). However, Lewis (2000) claims that LP is highly context-dependent, which entails one of the major difficulties that hinder its implementation. In fact, the underlying causes of failures in implementation are frequently related to the internal and external context, which changes over time. Thus, the context associated with a specific region or country may strongly influence the way in which LP is implemented and its results (Marodin et al., 2017). In this sense, the comprehension of LP systems has significantly evolved during the last few decades. Moving from an exclusively shop floor practice-oriented approach to an integrated and contingency-based value system (Hines et al., 2004). Overall, the enhanced conceptualization of LP has allowed to better adapt and incorporate LP into several sectors that vary from discrete parts manufacturers (e.g. Billesbach, 1994; Saurin et al., 2011), healthcare (e.g. Brandao de Souza, 2009; Waring and Bishop, 2010), to public administration (e.g. Radnor, 2010; Radnor and Osborne, 2013).

Table 1. Consolidated Industry 4.0 elements Robotic stations on automated production line RFID-tag at working units Real-time scanning by smartphone or tablet application Machines with digital interfaces and sensors Augmented reality Cloud computing system Collaboration with suppliers/customers through real time data sharing Predictive maintenance through real time monitoring Artificial intelligent and machine learning algorithms Production process autonomous management Digital automation without sensors Sensors for product/operating conditions identification Integrated engineering systems

43

X X

2.3 Industry 4.0 and LP The relationship between Industry 4.0 and LP has been increasingly evidenced in operations management research. Over the past few years, researchers’ and practitioners’ have started to investigate how both approaches, when implemented together within companies, can raise operational and financial performance levels to a different pattern (e.g. Kolberg and Zühlke, 2015; Sanders et al., 2016; Mrugalska and Wyrwicka, 2017; Tortorella and Fettermann, 2018). In fact, the acknowledgement of the relevant integration of technologies into LP has been evidenced in

X

45

2019 IFAC MIM 44 Berlin, Germany, August 28-30, 2019

Matteo Rossini et al. / IFAC PapersOnLine 52-13 (2019) 42–47

early 1990s and denoted as Lean Automation (LA). More recently, much attention has been given to LA with the advent of Industry 4.0. However, Kolberg et al. (2017) comment that the existing LA approaches are usually proprietary solutions that have been tailored to individual and specific needs. In essence, while there may exist evidence that both approaches may be positively related; others might advocate that the usual high-tech and capital-intensive efforts of Industry 4.0 can conflict with the ground principles of simplicity, continuous and small improvements from LP. From a more conservative perspective, Rüttimann and Stöckli (2016), for instance, argued that Industry 4.0 initiatives are likely to fail unless they are put into the right context by considering fundamental manufacturing laws. In other words, this string of thought suggests that extensive applications of modern ICT that disregard LP implementation will lead to marginal gains that might frustrate managers in face of the high investment levels carried out. In turn, studies such as Wagner et al. (2017) and Sanders et al. (2017) provide a more optimistic view of such relationship. They claim that the concurrent implementation of Industry 4.0 and LP may allow companies to overcome traditional barriers in a lean transformation achieving major results. Despite the different indicatives, studies that investigate this relationship, in general, still lack empirical evidence to support their findings. In fact, Buer et al. (2018) have emphasized that the literature on Industry 4.0 and LP is unclear about the direction of such relationship. Additionally, it argues about the necessity of studying the impacts of this relationship on companies’ performance and the influence of external factors on the relationship between both approaches. Thus, although this relationship has motivated many studies and practical experimentations, much still need to be understood in order to comprehend the extent of it (Leyh et al., 2017).

higher than 0.05, indicating that there was no difference in means and variances between groups (Hair et al., 2014). The questionnaire was structured in four parts. The first part aimed to collect demographic information of the respondents and their companies. The second part of the questionnaire, comprised of 41 questions (Shah and Ward, 2007), assessed the implementation level of LP practices within the companies. Each practice is described in a statement that was evaluated according to a Likert scale that ranged from 1 (fully disagree) to 5 (fully agree) (Tortorella et al., 2018). The third part of the questionnaire measured the adoption degree Industry 4.0 technologies within the studied companies. For that, 16 questions were formulated according to different technologies (see Table 1). A 5-point Likert scale ranging from 1 (not used) to 5 (fully adopted) was applied to each technology. The final part of the questionnaire assessed the observed operational performance improvement during the last three years, according to five indicators: (i) productivity, (ii) delivery service level, (iii) inventory level, (iv) workplace safety (accidents) and (v) quality (scrap and rework). A 5point scale ranging from 1 (worsened significantly) to 5 (improved significantly) is used in the questionnaire. Further, we tested all responses related to the 41 LP practices, 16 Industry 4.0 technologies and the 5 operational performance indicators for reliability, determining their Cronbach’s alpha values. We used an alpha threshold of 0.6 or higher (Meyers et al., 2006). All responses displayed high reliability, with an overall alpha value of 0.940, 0.943 and 0.801, respectively. We carried out three clustering of observations based on: LP practices implementation level, adoption level of Industry 4.0 technologies, and operational performance improvement. Hence, we first applied Ward’s hierarchical method to identify the adequate number of clusters denoted by k. Then, we proceeded the k-means clustering method, to rearrange observations into k clusters (Rencher, 2002). For the implementation level of LP practices, two clusters were identified. Further, we performed an ANOVA (Analysis of Variance) to check for differences in means of clustering variables using data from each cluster, which indicated significant differences (p-values<0.05) in all 41 variables. Cluster 1 was comprised of 49 respondents whose average implementation level of LP practices was lower, which denoted this cluster as LLP (lower lean production). Cluster 2, consisting of 59 observations, presented a higher average implementation level, hence it was labeled HLP (higher lean production). When using the adoption level of Industry 4.0 technologies as clustering variables, the same procedure was performed, resulting two clusters with significant differences (p-value<0.01) in means. The first cluster, denoted as LTA (lower technology adoption), presented a lower average adoption level and comprised 76 observations. The remaining 32 observations were assigned to the second cluster, which had a higher average adoption level and was labeled as HTA (higher technology adoption). Finally, the same set of observations was clustered using improvement level of companies’ operational performance as variables. Two clusters with significant differences in means (p-values<0.01) were identified for the five performance indicators through an ANOVA. Cluster 1 corresponded to 49 respondents whose average improvement level of operational performance was

3. METHOD As our study is focused on European manufacturers, we limited our sample to these companies. The applied criteria for respondents’ selection followed the ones proposed by Tortorella and Fettermann (2018). Further, due to the relatively recent introduction of Industry 4.0 in manufacturing companies, we did not control the sample respondents in terms of industrial sectors, which entailed a cross-sector data set. Responses to the questionnaire were collected from companies that fulfilled the selection criteria through SurveyMonkey during the months of February, March and April 2018. The final resulting sample comprised 108 valid responses representing a response rate of 23.65%. The study sample presented a fairly-well balanced amount of companies: most respondents (62.0%) were from small- and medium-sized companies (≤500 employees) and belonged to a family-owned company (54.6%); 51.9% of companies directly deliver to the final consumers (Business-to-Customer or B2C) and the majority of them (35.2%) are from metalmechanics sector. Then, we evaluated the differences in means between the early (respondents to the first e-mail sent; n1= 43) and the late (respondents to the two follow-ups; n2= 65) respondents using the Levene's test for equality of variances and a T-test for the equality of means (Armstrong and Overton, 1977). Results showed significance levels 46

2019 IFAC MIM Berlin, Germany, August 28-30, 2019

Matteo Rossini et al. / IFAC PapersOnLine 52-13 (2019) 42–47

lower, being named as LPI (lower performance improvement); while cluster 2 was formed by 59 observations that had a higher average improvement level of operational performance. This second cluster was denoted by HPI (higher performance improvement). To proceed with the data analysis, we first checked data for normality using the Kolmogorov-Smirnof (KS) test and found that the sample data did not follow a normal distribution (p-value<0.05). Hence, suitable nonparametric techniques were identified to analyze these data. As dimensions obtained from the clustering analysis were deemed as categorical, the application of the chi-square test with contingency tables and adjusted residuals was selected as technique (Tabachnick and Fidell, 2013). It was tested whether the frequency of observations from clusters of LP implementation (LLP and HLP) was associated to the adoption level of Industry 4.0 technologies (LTA and HTA) according to the level of operational performance improvement (LPI and HPI). For that, chi-square tests were applied. We considered significant associations with adjusted residual values larger than |1.64|, |1.96| and |2.58|, corresponding a significance level of 0.10, 0.05 and 0.01, respectively.

45

*p-value<0.10; **p-value<0.05; ***p-value<0.01

Figure 1. Companies occurrences according to the level of operational performance improvement

4. RESULTS Thus, it is quite reasonable to observe that processes that underwent a lean implementation may entail some performance improvement disregarding the level of technology adoption. Second, initial reports on LP implementation in EU manufacturers date from mid 1990s (e.g. Antoni, 1996; Oliver et al., 1996), which is much earlier than the acknowledge of Industry 4.0. Therefore, one might expect that the maturity of EU manufacturers with respect to LP implementation is much higher than with Industry 4.0 technologies. The actual impact of Industry 4.0 technologies on operational performance has been much envisioned (e.g. Zuehlke, 2010; Kagermann et al., 2013); nevertheless, systemic evidence on operational performance improvement is still scarce due to its initial dissemination across industry.

Table 2 and figure 1 show the results for the contingency table for combinations between the observations frequencies for Industry 4.0 technologies and LP practices according to the improvement level of operational performance. For companies that observed a lower level of performance improvement in the last three years and claim to be poorly adopting Industry 4.0 technologies, adjusted residual values indicate that these companies are more likely to be barely implementing LP practices as well. However, the ones that appear to be highly adopting Industry 4.0 technologies are also quite likely to be highly implementing LP practices; although operational performance improvements have not yet been noticed. When a performance improvement is highly observed, the adoption level of technologies does not seem to be relevant, since both LTA and HTA observations are quite likely to be extensively implementing LP practices. Such results indicate that the effects of LP implementation still prevail over the benefits evidenced from the Industry 4.0 adoption. Our findings suggest that companies’ significant performance improvement can be more associated with LP implementation than with the solely technologies adoption. In other words, LP practices allow processes design based on a low-tech approach that excels for simplicity and effectiveness, underpinning robust and continuous achievements in the long run (Liker and Convis, 2011).

5. CONCLUSIONS This study aimed at examining the association between the implementation levels of LP practices and Industry 4.0 in European manufacturers. Contributions of this research are two-fold, impacting both academicians and practitioners. First, in theoretical terms, this research has provided arguments to empirically analyze the relationship between LP and Industry 4.0 according to operational performance improvement. More specifically, our findings indicate that EU manufacturers that aim to adopt higher levels of Industry 4.0 must concurrently implement LP practices as a way to support processes improvements. Further, the outcomes of this study pinpoint that the effects of LP on operational performance improvement still prevail over the impact of Industry 4.0, since all HPI companies seem to claim high levels of LP implementation, regardless of the technologies. This phenomenon is quite reasonable since companies’ understanding and implementation maturity with respect to LP are significantly larger than with regards to Industry 4.0. It is noteworthy that this result was observed in European manufacturers, context where Industry 4.0 was coined. From

Table 2. Contingency table and chi-square tests Performance

Industry 4.0

LPI

LTA HTA Total LTA HTA Total

HPI

Lean production LLP HLP 32*** 8*** 2*** 7*** 34 15 12* 24* 3* 20* 15 44 47

2019 IFAC MIM 46 Berlin, Germany, August 28-30, 2019

Matteo Rossini et al. / IFAC PapersOnLine 52-13 (2019) 42–47

practical perspective, this study also presents contributions. By comprehending that Industry 4.0 technologies are highly related with LP practices, managers from EU manufacturers can address the implementation of both approaches in a more assertive way. In other words, our research emphasizes that companies that aim at achieving higher levels of Industry 4.0 must have previously implemented a certain level of LP practices. This fact allows companies to fully benefit from the incorporation of technologies into well-designed and robust processes (either operational or strategic). A few limitations of this study are worth to notice. First, regarding the sample size, larger study samples could allow the investigation of the effects of contextual factors (e.g. industry sector, technology intensity, etc) on the relationship between LP and Industry 4.0. Moreover, the use of precise numerical thresholds as value for KPIs could improve the study of the relationship between LP, Industry 4.0 and companies’ performances. Additionally, our study simplifies both LP and Industry 4.0 into one dimension each. However, there may be LP practices that present different levels of synergy with Industry 4.0 technologies. In this sense, future studies with an increased sample could verify the correlations between specific sets of LP practices and Industry 4.0 technologies, enabling a more assertive recommendation. The investigation of these correlations could provide arguments to identify mixed bundles of LP practices and Industry 4.0 technologies that might present similar behaviours and synergistic effects. In addition, results showed a part of companies, which observe high performance but still have a low LP and a low Industry 4.0 levels. This suggests that there are other factors contributing to performance improvement that should be explored.

Dworschak, B., Zaiser, H. (2014). Competences for CyberPhysical Systems in Manufacturing – First Findings and Scenarios. Procedia CIRP, 25, 3–8. Hair, J., Black, W., Babin, B., Anderson, R. (2014). Multivariate Data Analysis, Pearson New International Edition (vol. Seventh edition), Harlow, Essex, Pearson. Hermann, M., Pentek, T., Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios. Proceedings of 2016 49th Hawaii International Conference on Systems Science, January 5–8, Maui, Hawaii. Hines, P., Holweg, M., Rich, N. (2004). Learning to evolve: a review of contemporary lean thinking. International Journal of Operations & Production Management, 24(10), 994-1011. Holweg, M. (2007). The genealogy of lean production. Journal of Operations Management, 25(2), 420-437. Ivanov, D., Dolgui, A., Sokolov, B., Werner (2018) The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics, International Journal of Production Research Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., Ivanova, M., (2016) A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0, International Journal of Production Research, 54:2. Jackson, M., Hedelind, M., Hellström, E., Granlund, A., Friedler, N. (2011). Lean automation: Requirements and solutions for efficient use of robot automation in the swedish manufacturing industry. International Journal of Engineering Research & Innovation, 3(2), 36-43. Jasti, N., Kodali, R. (2016). An empirical study for implementation of lean principles in Indian manufacturing industry. Benchmarking: An International Journal, 23(1), 183-207. Kagermann, H., Helbig, J., Hellinger, A., Wahlster, W. (2013). Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry. Final Report of the Industrie 4.0 Working Group. Forschungsunion. Kolberg, D., Knobloch, J., Zühlke, D. (2017). Towards a lean automation interface for workstations. International Journal of Production Research, 55(10), 2845-2856. Kolberg, D., Zühlke, D. (2015). Lean automation enabled by industry 4.0 technologies. IFAC-PapersOnLine, 48(3), 1870-1875. Krafcik, J. (1988). Triumph of the lean production system. MIT Sloan Management Review, 30(1), 41. Lasi, H., Fettke, P., Kemper, H., Feld, T., Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242. Lewis, M. (2000). Lean Production and Sustainable Competitive Advantage. International Journal of Operations & Production Management, 20(8), 959–978. Leyh, C., Martin, S., Schäffer, T. (2017). Industry 4.0 and Lean Production—A matching relationship? An analysis of selected Industry 4.0 models. In Computer Science and Information Systems (FedCSIS), 2017 Federated Conference on (pp. 989–993). IEEE. Liao, Y., Deschamps, F., Loures, E., Ramos, L. (2017). Past, present and future of Industry 4.0 – a systematic literature review and research agenda proposal.

REFERENCES Antoni, C. (1996). Lean production in Europe: a matter of technical adjustment or cultural change?. Applied Psychology, 45(2), 139-142. Armstrong, J., Overton, S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396-402. Billesbach, T. (1994). Applying lean production principles to a process facility. Production and Inventory Management Journal, 35(3), 40. Brandao de Souza, L. (2009). Trends and approaches in lean healthcare. Leadership in Health Services, 22(2), 121139. Buer, S., Strandhagen, J., Chan, F. (2018). The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. International Journal of Production Research, 56(8), 2924-2940. Demeter, K., Matyusz, Z. (2011). The Impact of Lean Practices on Inventory Turnover. International Journal of Production Economics, 133, 154–163. Dolgui, A., Ivanov, D., P. Sethi, S., Sokolov B. (2019) Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-theart and applications, International Journal of Production Research, 57:2, 411-432.

48

2019 IFAC MIM Berlin, Germany, August 28-30, 2019

Matteo Rossini et al. / IFAC PapersOnLine 52-13 (2019) 42–47

International Journal of Production Research, 55(12), 3609–3629. Liker, J. (2004). The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer. McGraw Hill Professional, New York. Liker, J., Convis, G. (2011). The Toyota Way to Lean Leadership: Achieving and Sustaining Excellence through Leadership Development. McGraw Hill Professional, New York. Mann, D. (2010). Creating a lean culture: tools to sustain lean conversions. Productivity Press, New York. Marodin, G., Frank, A., Tortorella, G., Fetterman, D. (2017). Lean production and operational performance in the Brazilian automotive supply chain. Total Quality Management & Business Excellence, 1-16. Marodin, G., Frank, A., Tortorella, G., Saurin, T. (2016). Contextual factors and lean production implementation in the Brazilian automotive supply chain. Supply Chain Management: An International Journal, 21(4), 417-432. Marodin, G., Saurin, T. (2013). Implementing lean production systems: research areas and opportunities for future studies. International Journal of Production Research, 51(22), 6663-6680. Meyers, L., Gamst, G., Guarino, A. (2006). Applied Multivariate Research. Sage Publications, Thousand Oaks. Mrugalska, B., Wyrwicka, M. (2017). Towards lean production in industry 4.0. Procedia Engineering, 182, 466-473. Netland, T. (2016). Critical success factors for implementing lean production: the effect of contingencies. International Journal of Production Research, 54(8), 2433-2448. Netland, T., Schloetzer, J., Ferdows, K. (2015). Implementing corporate lean programs: the effect of management control practices. Journal of Operations Management, 36, 90-102. Nicholas, J. (2015). Lean production for competitive advantage: a comprehensive guide to lean methodologies and management practices. Productivity Press, New York. Oliver, N., Delbridge, R., Lowe, J. (1996). Lean Production Practices: International Comparisons in the Auto Components Industry 1. British Journal of Management, 7, S29-S44. Panizzolo, R., Garengo, P., Sharma, M., Gore, A. (2012). Lean Manufacturing in Developing Countries: Evidence from Indian SMEs. Production Planning & Control, 23(10–11), 769–788. Pettersen, J. (2009). Defining lean production: some conceptual and practical issues. The TQM Journal, 21(2), 127-142. Radnor, Z. (2010). Transferring Lean into government. Journal of Manufacturing Technology Management, 21(3), 411-428. Radnor, Z., Osborne, S. P. (2013). Lean: a failed theory for public services?. Public Management Review, 15(2), 265-287. Rencher, A. (2002). Methods of Multivariate Analysis. Wiley-Interscience, New Jersey.

47

Rossini, M., Costa, F., Tortorella, G.L., Portioli-Staudacher, A., (2019), International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-019-03441-7 Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9. Rüttimann, B., Stöckli, M. (2016). Lean and Industry 4.0— twins, partners, or contenders? A due clarification regarding the supposed clash of two production systems. Journal of Service Science and Management, 9(6), 485. Sanders, A., Elangeswaran, C., Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: research activities in Industry 4.0 function as enablers for Lean Manufacturing. Journal of Industrial Engineering and Management, 9(3), 811–833. Sanders, A., Subramanian, K., Redlich, T., Wulfsberg, J. (2017). Industry 4.0 and Lean Management–Synergy or Contradiction?. In IFIP International Conference on Advances in Production Management Systems (pp. 341349). September, Springer, Cham. Saurin, T., Marodin, G., Ribeiro, J. (2011). A framework for assessing the use of lean production practices in manufacturing cells. International Journal of Production Research, 49(11), 3211-3230. Schumacher, A., Erol, S., Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP, 52, 161– 166. Shah, R., Ward, P. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of Operations Management, 21(2), 129-149. Shah, R., Ward, P. (2007). Defining and developing measures of lean production. Journal of Operations Management, 25(4), 785-805. Tabachnick, B., Fidell, L. (2013). Using Multivariate Statistics. Pearson, Upper Saddle River, NJ. Tortorella, G., Fettermann, D. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975-2987. Wagner, T., Herrmann, C., Thiede, S. (2017). Industry 4.0 impacts on lean production systems. Procedia CIRP, 63, 125-131. Waring, J., Bishop, S. (2010). Lean healthcare: rhetoric, ritual and resistance. Social Science & Medicine, 71(7), 13321340. Weyer, S., Meyer, T., Ohmer, M., Gorecky, D., Zühlke, D. (2016). Future modeling and simulation of CPS-based factories: an example from the automotive industry. IFAC-PapersOnLine, 49(31), 97-102. Xu, L., Xu, E., Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962. Zuehlke, D. (2010). SmartFactory – Towards a Factory-ofThings. Annual Reviews in Control, 34(1), 129–138.

49