Product-service system (PSS) complexity metrics

The International Journal of Advanced Manufacturing Technology https://doi.org/10.1007/s00170-018-1903-3

ORIGINAL ARTICLE

Product-service system (PSS) complexity metrics within mass customization and Industry 4.0 environment Dimitris Mourtzis 1 & Sophia Fotia 1 & Nikoletta Boli 1 & Pietro Pittaro 2 Received: 22 December 2017 / Accepted: 14 March 2018 # Springer-Verlag London Ltd., part of Springer Nature 2018

Abstract The design and evaluation of product-service systems (PSS) constitutes a challenging problem due to its multidimensionality. This challenge becomes bigger when the PSS customization is required within the new manufacturing paradigm of Industry 4.0. Nevertheless, limited literature work is observed regarding the customization of PSS and the PSS investigation within the Industry 4.0. Towards bridging these gaps, the present research work proposes a methodology for the quantification of PSS customization complexity, considering Industry 4.0 aspects. The proposed metrics are applied in a real industrial case study from a large laser machining industry, aiming to evaluate the different PSS alternatives in terms of complexity. It is demonstrated that the proposed approach can support the strategic level decision-making of a company, by quantifying the complexity and producing additional meaningful information towards the selection of the product and services that could be designed and offered to the customers. Keywords Complexity . Customization . Product-service systems (PSS) . Industry 4.0

1 Introduction The importance of servitization is widely recognized in industry moving towards the manufacturing systems of mass customization [1]. The topics of manufacturing servitization and product-service systems (PSS) have attracted considerable attention during the last decade [2]. PSS can be described as the result of product and service integration for the purpose of increasing value for customers [3]. PSS is an inherently dynamic, multidimensional concept as it is referred by [4] and [5], including various actors [6], presenting uncertainties in its design [7], rendering its evaluation a challenging task. Specific focus has been given to the PSS concept for the manufacturing under the current economic crisis [8, 9]. As it is mentioned: “The concept of PSS has its particular attraction

* Dimitris Mourtzis [email protected] 1

Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece

2

PRIMA INDUSTRIE S.p.A, Via Antonelli 32, 10097 Collegno, TO, Italy

under the current economic crisis. The shrinking global consumer market has led to the significant reduction of demands for industrial products such as molds and dies. On the other hand, the mold and die manufacturing industry must move a step ahead to become ready for economy recovery” [8]. The evaluation becomes even more challenging due to the interrelated structure of stakeholders who have a long-term relationship and communication with each other [3, 10]. The academic community has long been aware of the importance of evaluation in the PSS development stages [11], but as initially indicated by [10], later by [12], and more recently by [13], the evaluation of PSS is still an immature field with few concrete results and approaches. Considering the substantial differences between products and services, the concept of PSS evaluation differs from conventional evaluation problems due to the existing interdependencies, since product and service activities interact and influence each other [7]. Additionally, the relationships among the evaluation criteria of the PSS concept are much more complicated than those of pure products or services [5]. Those lead to the PSS solution evaluation process many times to be treated as a complex multi-criteria decision-making problem [7]. Considering the vastly growing number of the modern requirements that mass customization and the new manufacturing paradigm of Industry 4.0 imply, the competitiveness and sustainability of enterprises can only be maintained by a

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continuous monitoring of the performance of their PSS throughout their lifecycle. Measuring the performance of PSS is one of the most important tasks for a firm since it influences its competitiveness in the market, its cost-effectiveness, and finally, its business performance. Moreover, waste elimination, better process control, and efficient manpower utilization are important outcomes of the successful performance monitoring [14]. However, metrics that could be used for measuring the performance of the PSS are rare in the literature, and metrics for the measuring of PSS complexity are completely missing. The development of comprehensive and easily applied methodologies for the quantification of complexity, which will use the company’s information, is a major challenge. The quantification of complexity can significantly support the decision-making in the design phase, by supporting the evaluation of the alternative designs. The present work aims to contribute to this direction by proposing a methodology for measuring PSS customization complexity in the Industry 4.0 environment. Particularly, metrics for the PSS complexity are introduced aiming to easily quantify systems’ complexity based on the number of components of products and services, the variance of the components, and the received feedback from the development and use phase of the PSS. The proposed approach is applied in a real industrial case study from a large laser machining industry, aiming to evaluate the different PSS alternatives in terms of complexity. After this introductory section, the following section outlines the recent status of the literature about PSS, mass customization, and the Industry 4.0 paradigm. Subsequently, the detailed methodology that has been used for the definition of the PSS complexity metrics is presented. Finally, the proposed metrics are applied in a real industrial case, and the section of conclusions summarizes the outcome of the present research work and the intentions of the authors for future work.

2 State of the art Mass customization is the dominant last decades’ strategy for responding to the consumer’s continuously increasing demand for product variety, differentiating the companies in a highly competitive and segmented market [15, 16]. Customization was coined in the late 1980s as an innovative paradigm that offers the customers the capability of selecting among a variety of features and accessories, to shape a final customized assembly combination of a basic product [17], enabling them to satisfy their ever-increasing requirement for distinguishing and uniqueness. Following up, no surprisingly, manufacturers began to offer larger variations of their standard product [18] in order to ensure their sustainability. The design procedure was cost-effective because the basic product was re-designed, and consumers could select the

assembly combination they preferred the most. Under this manufacturing paradigm, new requirements have emerged, such as the need for more skilled workers [1]. Applications of mass customization have been extensively reported in the literature as success stories, and one of the most popular includes the automotive industry [19]. From the above derives the fact that there are commonalities between the production paradigms of mass customization and PSS, with a dominant common aspect the ubiquitous integration of the customer in the PSS development. Very recently, a comprehensive literature review about PSS customization was performed by Song and Sakao [20]. The authors, considering the gap of no existing comprehensive methodology for PSS customization, worked in that direction and proposed a design framework. Considering the customer demand for more flexible products, Kue et al. [21] propose a mass customization and personalization software development, aggregating the voices of customers. This development is divided into three parts: Quality function deployment, Modular design, and Cost evaluation. Kue et al. configuration tool, enables customers to develop, customize, or personalize their software. Similarly, Tu et al. [22] designed a customization development procedure for PSS, and also presented the decision-making criteria that could be considered together with many important elements for PSS customization development. More precisely, after generalizing the criteria of PSS and customization development, key elements of the customization development procedures in PSS were identified, leading to a customization development procedure for PSS. Waltemode et al. [23] presented a framework for the lifecycle-oriented quality assessment of PSS. Business sustainability is inseparable from the offered quality. Thus, Waltemode et al., recognizing that there is not a uniform approach for PSS assessment, they shifted their focus to that field, firstly, by providing an extensive literature review on quality assessment and subsequently by introducing a framework for lifecycle-oriented quality assessment of PSS. Additionally, Geum et al. [24] introduced a framework for road mapping product-service integration according to the role of involved technology. Mainly focusing on the design phase of PSS, Geum et al. proposed a product-service blueprint, in which the product use throughout the lifecycle, the service flow, and also the interdependencies between products and services were included. Finally, a modeling approach adopting a meta-ontology of PSS configuration, fact base, and rule base was proposed by Dong and Su [25]. Dong and Su included feature modeling of PSS variants in their study, using the structural knowledge of ontology, their main aim being the automatic configuration of a customizable PSS in order to satisfy the customer’s requirements. Regarding the evaluation of the design phase of PSS, Mourtzis et al. (2017) proposed an evaluation framework supported by KPI monitoring and context sensitivity tools [26].

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Additionally, as it is highlighted in the literature, the influence of stakeholders’ requirements on PSS design is still poorly investigated and guided by the current design methods [27]. Having reported recent research attempts in the field of PSS customization, the remaining of the sections is devoted to the transformation of today’s manufacturing systems into Industry 4.0 manufacturing systems. This shift is mainly based on the integration of cyber-physical systems (CPS) with production, logistics, and services [28]. Key enablers of this transformation are the higher availability and affordability of sensors, data acquisition systems, and computer networks [29]. The adoption of smart sensors and the integration with industrial communication protocols and standards will generate a vast amount of data. The smart objects, enabled by IoT, can be dynamically reconfigured to achieve high flexibility, whereas big data analytics can provide global feedback and coordination to achieve high efficiency [30]. Considering that the main aim of the present study is the proposition of a methodology for the quantification of PSS customization complexity from an Industry 4.0 point of view, it is important to shift our focus to a brief description of the Industry 4.0 pillars. Several approaches have been developed in the field of CPS from the design phase to manufacturing, assembly, and final product allocation. An approach for product configuration using augmented reality technology, integrating the customer easily and retrieving all the necessary data for designing or re-designing products, is presented by [31]. In the field of process planning, an approach for adaptive process planning considering the availability and capabilities of the machine tools is presented by [32]. Adaptive or integrating scheduling approaches and methods are also presented, taken into account the generated data from the different smart objects and systems. In [33], an integrated approach for production and maintenance planning is presented, considering the condition of the machines. One of the most important outcomes of the emergence of IoT is the generation of data in increasing volume, variety, and velocity, also referred to as Big Data. The analysis of this data constitutes the basis for the modern scope of mass customization, which implies the fulfilling of the needs of individualized customer markets [34]. The analysis of large sets of data can enhance the knowledge repositories and improve decision-making in different manufacturing stages, such as assembly operations [35]. Although the adoption of Industry 4.0 and IoT paradigms in manufacturing reveals positive consequences, the increased complexity as a collateral effect is a main challenge. Considering also mass customization aspects, the complexity of the manufacturing systems becomes even bigger. The literature review makes apparent that several approaches have been devoted to the field of Industry 4.0. However,

quantification approaches that investigate how the adoption of the Industry 4.0 paradigm will transform manufacturing, increasing its complexity, are limited. For the quantification of complexity in engineering design, several approaches have been developed, mainly based on statistics, probabilities, and heuristics [36]. These approaches have been widely used for measuring structural complexity of manufacturing systems [36–39]. The complexity of manufacturing systems is a resultant of the increased variety, the market volatility, and the distributed global manufacturing [38]. The globalization and the market uncertainty are directly proportional to the complexity of manufacturing systems. Thus, complexity modeling and management constitute significant objects of interest and research [23]. It is frequently mentioned in the literature that the PSS concept is very complex. Although there are a high number of research works focused on manufacturing systems’ complexity, there is very limited work on the investigation and quantification of PSS complexity. Initial work and concepts on the quantification of the PSS complexity are presented in the authors’ previous research works [40, 41]. As a step further to that, the authors propose a methodology for PSS complexity calculation considering also the Industry 4.0 paradigm, which is also applied in a real industrial case study. On the other hand, the adoption of the PSS business model in European SMEs is surprisingly limited, due to the fact that SMEs do not yet completely understand the adding-value from the adoption of such a PSS business model, as well as to the lack of comprehensive methodologies and software solutions, devoted to PSS collaborative design, evaluation, and risk assessment [42]. Towards that end, aiming to bridge the identified gaps, the present research work provides a methodology for the definition of appropriate metrics for the quantitative measurement of the PSS customization complexity within an Industry 4.0 Environment, applied in the real industrial practice.

3 Methodology for complexity metrics extraction The present work proposes a methodology for the quantification of PSS customization complexity, considering Industry 4.0 aspects. The methodology is based on the concept of El Maraghy et al. (Fig. 1), according to which three are the main factors that affect the complexity of a system: (i) the quantity of the information that a system has to manage; (ii) the diversity/variants of the exchanged information; and (iii) the content of the messages that transfer the information, which reflects also the effort to produce the desired results [36]. In the proposed methodology, these three factors

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are considered as the three main components of the constructed metrics of complexity. At this point, the basic terms which will be used in the mathematical formulation of the product-service system complexity are clarified. According to its definition, a productservice system consists of the product part, the service part, and the surrounding environment, which includes infrastructure and network [2]. Based on the three causes of complexity (quantity, variant, and content) [36], an explanation of these three aspects and their application to the product-service system is provided. Particularly, in the current industrial practice of modular product development, a product is designed including many components in order to support customization. Increasing the number of the components (complexity due to quantity) that the product consists of and increasing the number of possible variants that these components can have (complexity due to diversity/variant), naturally increase the complexity of the product. Apart from that, the information received from the surrounding environment from the emerging technologies of Industry 4.0 and can determine design changes in a product (complexity due to information content) and can attach additional complexity to the system. The same is valid for the service aspects considering that also a service includes a quantity of components and variance of them. The definition of metrics for measuring the complexity of a product-service development within the new era of mass customization and Industry 4.0 is based on the Vector Analysis in the Euclidian space. In particular, a PSS that consists of the tangible part which is the product and the intangible part which is the service [10] is represented in the 2D space as a vector. The vector’s horizontal axis represents the product complexity, and its vertical axis represents the service complexity. The axes’ ticks correspond to integer values. The unit vector which represents the product complexity axis is denot! ed by i , while the unit vector of service complexity is denot! ed by j . Before starting the analysis, all the used mathematical variables are listed and explained below: ! i unit vector which represents the product complexity axis ! j unit vector which represents the service complexity axis ! A vector that represents the complexity due to quantity of product and the service components Pc1,…,Pcn variables that represent each product component taking values 0 or 1 variables that represent each service Sc1.,…,Scm component taking values 0 or 1 ! B vector that represents complexity due to quantity of the product and the service variants

vpc1,…,vpcn

variables that represent the customization degree per product component vsc1,…,vscm variables that represent the customization degree per service component ! Q vector that represents the complexity due to both the quantity and the variants of the product and service components ! θ angle between vector A and the product complexity axis ! φ angle between vector Β and the service complexity axis a angle that represents the customization orientation (product-dominant or servicedominant) ! C vector that represents the information content and the effort of adopting the feedback received from external integrated sources for both the products and the services fpc1,… fpcn, fsc1,…, variables that represent feedback from fscm external integrated sources for both the product and the service components fvpc1,… fvpcn, variables that represent feedback from fvsc1,… fvscm external integrated sources for both product and service variants of the components ! R vector that represents the complexity of the product-service system ε angle that represents the complexity orientation (product-dominant or servicedominant) PSS_Compl The dimensionless complexity metric for product-service systems Based on the previous concept of Fig. 1, for considering into the present complexity analysis the factor of the information’s quantity in the description of a product-service, vector ! A (Fig. 2a) is defined as ! ! ! A ¼ Pc l þ Sc j

ð1Þ

where Pc = Pc1 + … + Pcn, and Sc = Sc1. +… + Scm. The variables Pc1,…,Pcn, and Sc1.,…, Scm represent each product and service component respectively, which is included in the investigated product-service and participates in the sum as a unit. For example, if a product is composed of 10 ! components, and the service of 2, then A ¼ ð10; 2Þ. Taking into consideration in the complexity, the second factor of Fig. 1, which is the diversity or variants of the infor! mation, vector B (Fig. 2b) is defined as ! !
! B ¼ vpc1 þ ⋯ þ vpcn l þ ðvsc1 þ ⋯ þ vscm Þ j

ð2Þ

Int J Adv Manuf Technol Fig. 1 The components of complexity [32]

The variables vpc1,…,vpcn and vsc1,…, vscm can take integer values equal to or greater than zero and represent the customization degree of each product or service component. A variable value equal to zero means no variants existed for the specific component. For example, if a product-service consisted of 2 product components and 1 service component, of which the first product component had a variant value of 3 and the second had a variant value of 2 (due to their customization capability), and the single service component had a ! variant value of 4, the corresponding vector would be B ! ! ! = (3 + 2) i + 4 j or B ¼ ð5; 4Þ. Providing a concrete explanation of this example, one of the product components could be the car hood, which could have three variants (simple, carbon, logo printed), and the service component could be the mobile app for cruise control which could offer four different user interfaces. ! Subsequently, vector Q is defined, which is illustrated in ! ! Fig. 3, as the summation of B and A . This vector represents the complexity due to both the quantity and the variants of the product and service components and is defined below:    !  ! ! ! ! Q ¼  Q cos a l þ  Q sin a j

ð3Þ

Finally, based on Fig. 1, the third factor affecting the complexity of the system is defined. This is the information content and the difficulty of the desired results’ production, considering the received information content. In the new manufacturing paradigm of Industry 4.0, a large amount of feedback is received from the integrated systems such as augmented reality (AR) and virtual reality (VR) tools, mobile apps, and social media, about the product-service components and the variants of the components. This feedback is translated into useful information, mainly for the design phase of the PSS, after being analyzed with several techniques of Big Data Analytics. Following the above, in the defined 2D space of product ! and service complexity, the vector C which defines the information content and the effort of adopting the received feedback is defined as presented below:  2 3 f þ f vpc1 þ ⋯ ! ! 4 pc1   5 l C ¼ þ f pcn þ f vpcn |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}




rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi     2  2 ! !! ! ¼  A  þ  B  þ 2 A  B cosðjφ−θjÞ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ ðPc1 þ ⋯ þ PcnÞ2 þ ðSc1 þ ⋯ þ ScmÞ2 ffi q
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 ¼ vpc1 þ ⋯vpcn þ ðvSC1 þ ⋯ þ vscm Þ

jφ; θj 2

ð5Þ

C2

! The magnitude of the vector C is   pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !  C  ¼ C12 þ C22

The angle a is calculated as a ¼ minðφ; θÞ þ

C1

  f sc1 þ f vsc1 þ ⋯ !
þ j þ f scm þ f vscm |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl}

where   ! Q   ! A   ! B

The two following possible cases are observed:  < 45∘ product−oriented customization a¼ > 45∘ product−oriented customization

ð4Þ

! As it is shown in the expression of vector C , feedback from external integrated sources can be received for both the product and the service components (fpc1,… fpcn, fsc1,…, fscm), and the product and service variants of the components (fvpc1,…

Int J Adv Manuf Technol Fig. 2 The 2D vectors that represent the two causes of complexity in the product-service systems. a complexity due to the quantity of the product-service ! components (vector A ). b Complexity due to the variants of the product-service components ! due to customization (vector B )

fvpcn, fvsc1,… fvscm). Feedback is considered as the number of reported comments/messages/requests and data flow from the key enabler technologies of Industry 4.0, for each component or variant of the component, and can take integer values greater than or equal to zero. In order to complete the present analysis and to define the complexity metric for PSS customization within the Industry ! 4.0 environment, vector R is calculated as the summation of ! ! ! ! vectors Q and C (or equivalently the sum of vectors A , B , ! and C ). Following the vector properties, the actual represen! tation of vector R is depicted in Fig. 4. ! Then, vector R is defined as    !  ! ! ! ! ð6Þ R ¼  R cos ε l þ  R sinε j ! where the magnitude of the vector R is defined as   rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2  2    ! ! ! !!  R  ¼  Q  þ  C  þ 2 Q  C cosðjω−αjÞ

The angle ε is given by the expression below: ε ¼ minðω; αÞ þ

jω−αj 2

In this case, the two following cases are observed:  < 45∘ product−dominant complexity ε¼ > 45∘ service−dominant complexity

ð7Þ

The angle ε is the metric for the determination of the complexity direction. When angle ε is calculated as lower than 45°, the PSS complexity is affected more by the product’s complexity, meaning that the product is more dominant in the calculation of complexity. Alternatively, when angle ε is greater than 45°, it means that the complexity of the service part is more dominant. This angle is an important measurement in order for a company to identify the orientation of the complexity and the directions in which more attention for supervising and control must be paid.

Service complexity

C

Service Complexity j

j

Q

Q

A 0,0

B

Q A B i

Fig. 3 Complexity due to quantity and variants

Product Complexity

0,0

φ a

A θ

ε a

ω

R

B

Product complexity

i

Fig. 4 Total complexity of the customized product and services within Industry 4.0 environment

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Another complementary metric for measuring the actual amount of PSS complexity due to customization within the Industry 4.0 environment is defined. This metric is formulated ! by normalizing the magnitude of vector R with the magnitude ! of vector A which is the standard existing complexity due to the number of product and service components. PSS complexity is given using the following (8):     ! ! ð8Þ PSS Compl ¼  R = A  It is noted that the presented methodology concerns a PSS which includes only one product and a set of services. The same procedure could be used for additional products and services. Based on the provided metric of PSS complexity, the following two different cases are observed: &

&

PSS_Compl = 1: This is the lowest  the PSS  value that ! ! complexity can take. In this case  R  ¼  A , meaning that the product and the service components are not customized, and also no feedback consideration exists. PSS_Compl > 1: This means that product and service are customized and/or feedback consideration takes place in our system. This leads to an inevitable increase on the complexity for the PSS.

4 Case study—complexity calculation of different PSS company’s alternatives The proposed metrics for complexity quantification will be implemented in real practice, in a set of different productservice configurations. Before that, trying to clarify some aspects of the current analysis, when the term product component is mentioned, the number of modules that a product consists of is meant. In mass customization, the product is required to be modular in order to be customizable [39]. The new technologies of Industry 4.0, among the many benefits they offer, support the companies to collect useful feedback from the entire lifecycle of products and services, from the design phase to the use phase, by integrating new technologies. This feedback could include comments/posts from the customer side concerning some product and/or service components or variants of them. Moreover, making a machine a thing on the internet (IoT) could provide meaningful feedback through implemented wireless sensor networks. The analysis of this data can provide useful insight for the company. This feedback, although greatly beneficial for the company’s competitiveness, is not a simple task, since re-design and appropriate adaptation of the production line could be required,

importantly increasing the complexity of the product-service development. Summarizing, it becomes clear that in order for a company to (i) increase the number of product and service components towards higher modularity, (ii) support different variants for each component, and (iii) collect and evaluate feedback for them, the system’s complexity increases significantly. Towards implementing the proposed methodology in a real industrial case, a leading manufacturer of laser and sheet metal machinery was selected. The main company’s activities are the manufacturing and marketing of laser systems for industrial applications, sheet metal processing machinery, and industrial electronics and laser technologies. As it is presented in Fig. 5, the company produces a flexible manufacturing system consisting of laser machines in different configurations aiming to fulfill their customers’ needs. The product configurations include a different number of components, and consequently, their production holds a different level of complexity. For each product configuration of Fig. 5, further details are provided in Fig. 6. As it can be observed in Fig. 6, the configurations A–F comprise three main components: (i) the Standard cabin, (ii) Palatino 2.0 cabin, and (iii) Basic cabin. These three cabin-type components can be offered in differed customization options, illustrated in Fig. 6 with color-coded line connection. Apart from the products that the laser machine industry produces, a number of services accompanying the products can be also offered, aiming to increase the added-value of the provided solutions and the competitiveness of the company [42]. Particularly, all the product configurations presented in Fig. 5 can be combined with the following services [43]: & & & & &

Machine health monitoring Performance optimization Product data report Machine data analysis Laser process quality control

Machine Health Monitoring is relative to the evaluation of machine health through vibration sensing. This service checks a machine for urgent and non-urgent maintenance needs. Performance optimization aims to optimize the process performance using machine natural frequency testing. This service periodically detects the natural frequency of a moving part of the machine and feeds this as a parameter to the motor controllers. The Product Data Report service targets to the visualization of production and energy consumption parameters. Machine data analysis service is based on the gathering of information from machines and sensors. This service analyzes data and identifies trends in one and multiple machines. It also provides reports and warnings comparing information to thresholds. Finally, laser process quality control is achieved through the use of an optical system in order to verify the

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Fig. 5 Laser machine’s different configurations

beam centering in relation to the nozzle hole and the wear state of the nozzle in terms of process quality. The industrial problem that the present methodology intends to solve is the strategic-level decision-making on which the above services should be designed in order to be offered together with the products.

Similarly, to the products, these services have a number of components and customization variants, as presented in Fig. 7. As also presented through this case, manufacturing companies aim to provide added-value solutions considering the aspects of the new manufacturing paradigm, the Industry 4.0.

Fig. 6 Basic product components and customization per product configuration

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Fig. 7 Basic service components and customization per configuration

Towards that, this company provides advanced services including remote health monitoring and machine data analytics for increased product quality and performance optimization. More specifically, following the Industry 4.0 paradigm, they have installed a wireless sensor network in their customers’ machines through a virtual privet network (VPN) connection, and through secure cloud services, they receive the data, analyze them, and after that, they provide a summary and recommendation insight to the customers. This has several benefits for the company, such as shorter response time, enhanced connectivity, and improved maintenance cycle. The new components which will be added to the machines in order to support the new services, as well as the collected feedback from the machines, which will imply design improvements and customizations for the customer, are considered within the proposed methodology, by substituting the corresponding information in Eqs. (3), (5), and (6). It is estimated that this feedback will lead to a number of modification improvements in the product-service components and variations, conservatively estimated from 1 to 10 changes. All the information regarding the different product and service configurations, components, their customization variants Table 1 Laser machinery data for the product components machinery

per component, and feedback that the laser machine industry produces and offers are collected in Tables 1 and 2, following the modeling presented in the previous section of the methodology. These tables summarize the above-collected results putting in the feedback part random integer values from 1 to 10. As previously mentioned, all the product configurations can be combined will all the services provided above. This means that 30 different PSSs can be produced, as Table 3 illustrates. For each one, substituting the data provided   in ! Tables 1 and 2 in Eqs. (6) and (7), the PSS complexity  R  and the angle ε, which gives a characterization of the complexity type, are calculated for each PSS. As Fig. 8 illustrates, the most complex PSS is the laser machine configuration F accompanied by the service of performance optimization, while the PSS of laser machine configuration E together with the product data report holds the lower complexity. Furthermore, as it is presented in Fig. 9, the complexity of all PSSs is product-dominant, meaning that the complexity is mainly high on the product side instead of the service side.

Configurations

Pc (product components)

vpc (product variants)

fpc (product feedback per component)

fvpc (product feedback per component variant)

A B C D E F

3 3 3 2 3 1

3 3 3 2 3 1

1 1 1 1 1 1

5 8 3 7 1 6

Int J Adv Manuf Technol Table 2 Laser machinery data for the service components machinery

Configurations

Sc (product components)

vsc (service variants)

fsc (service feedback per component)

fvsc (service feedback per component variant)

Machine health monitoring Performance optimization Product data report Machine data analysis Laser quality control

2

2

1

5

1

2

1

8

1

1

1

3

1

1

1

7

1

3

1

4

Thus, if the company needs to decrease the complexity of a PSS, the attention should be paid to the product part. However, there are limited available tools for estimating/ calculating the complexity of the system, independently of Table 3

financial aspects and focusing more on the nature of the developed product-service. The proposed approach contributes in this direction, supporting the company to quantify the complexity, creating another parameter towards the selection of

Complexity results for the different product-service configurations

Product

Services

PSS (product-service systems)

  !  R  (complexity)

ε (°) (orientation of complexity)

A

Machine health monitoring

PSS1

13.54

24

Performance optimization Product data report Machine data analysis

PSS2 PSS3 PSS4

29.59 8.95 24.13

20 16 16

Laser quality control

PSS5

11.99

22

Machine health monitoring Performance optimization

PSS6 PSS7

14.32 30.50

21 18

Product data report Machine data analysis Laser quality control

PSS8 PSS9 PSS10

9.88 25.06 12.88

13 14 20

Machine health monitoring Performance optimization Product data report

PSS11 PSS12 PSS13

13.05 28.99 8.32

27 24 19

Machine data analysis Laser quality control Machine health monitoring Performance optimization Product data report Machine data analysis Laser quality control Machine health monitoring Performance optimization

PSS14 PSS15 PSS16 PSS17 PSS18 PSS19 PSS20 PSS21 PSS22

23.50 11.41 17.23 41.83 12.66 34.12 16.96 12.53 28.29

20 26 28 23 18 18 26 37 34

Product data report Machine data analysis Laser quality control Machine health monitoring Performance optimization Product data report Machine data analysis Laser quality control

PSS23 PSS24 PSS25 PSS26 PSS27 PSS28 PSS29 PSS30

7.61 22.73 10.80 20.52 64.14 17.95 51.89 24.89

28 30 36 37 33 28 28 35

B

C

D

E

F

Int J Adv Manuf Technol Fig. 8 PSS complexity for the different combinations of product configurations (A-F) and services

the product and services that could be designed and offered to the customers. This means that the quantification of complexity complementary to the companies’ legacy tools (e.g., KPI and risk management tools) could guide strategic level decisions. The information a company gathers could estimate the complexity of different design alternatives, considering updated information regarding new industrial environment of Industry 4.0 and servitization.

5 Conclusions The present work investigates the complexity of a system that provides products and services in the new era of Industry 4.0 and the existing customization. Contributing to this direction, a quantitative metric for supporting the measurement of the PSS complexity within the environment of mass customization and Industry 4.0 has been developed, considering the main factors that affect the complexity of a system. Specifically, the defined Fig. 9 Degree of the different combinations of product configurations (A-F) and services

complexity metric is based on vector analysis and its variables include the number of product and service components, the variants of each of the components, and the feedback which is received from the integrated systems and should be considered for the improvement of a product-service design. The proposed methodology for the quantification of complexity is implemented in a real industrial case. The quantification of different PSSs that the company can produce is performed, proving that the calculated metrics can guide the decision-making on which PSSs are more complex to be designed and in which direction the complexity is increased. Particularly, the effectiveness of the proposed developed metrics is evaluated in the case of a leading manufacturer of laser and sheet metal machinery. Based on company’s intensions to offer product-service system (PSS), 30 alternatives of the offerings were investigated and evaluated through the proposed methodology, in order to evaluate which of them are more efficient to be developed, supporting the decision-making process. Following several interviews with managers and experts

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of the company, there are limited available tools for estimating/calculating the complexity of the system, independently of financial aspects and focusing more on the nature of the developed product-service system. The proposed approach contributes in this direction, supporting the company to quantify the complexity, creating another parameter towards the selection of the products and services that could be designed and offered to the customers. This means that the quantification of complexity, complementary to the companies’ legacy tools (e.g., KPI and risk management tools), could support strategic level decisions. Finally, the results of the proposed metrics, implemented in 30 product-services alternatives, prove according to company’s managers and experts that the most complex is the laser machine configuration F accompanied by the service of performance optimization, while the PSS of laser machine configuration E together with the product data report holds the lower complexity value. Future work will include the improvement of the methodology considering more aspects of the Industry 4.0 emerging technologies, as well as the development of a software application which easily calculates and visualizes the metrics results. Additionally, the PSS complexity metrics will be formulated in a more generic way, including relevant weights aiming to address the needs/nature of different industrial companies. Acknowledgements This work has been partially supported by the H2020 EC funded project “An Integrated Collaborative Platform for Managing the Product-Service Engineering Lifecycle – ICP4Life” (GA No. 636862).

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