Alessia Napoleone
email: alessia.napoleone@polimi.it

Ann-Louise Andersen

Alessandro Pozzetti

Marco Macchi

Reconfigurable Manufacturing: A Classification of Elements enabling Convertibility and Scalability

Keywords: Reconfigurable Manufacturing, Reconfigurability, Changeable Manufacturing, Scalability, Convertibility

Reconfigurable manufacturing, providing capacity and functionality on demand, is an ever more important factor of competitiveness in volatile, unpredictable, and rapidly changing markets. In this regard, scalability and convertibility are considered particularly relevant characteristics, as they directly reduce reconfiguration effort and enhance system responsiveness. However, in previous research, scalability and convertibility have predominately been addressed conceptually on a high level of abstraction with only limited consideration of how specific manufacturing elements can enhance and realize them. Therefore, the objective of this paper is first to identify and classify elements enabling scalability and convertibility in order to bring reconfigurability-related concepts closer to the world of practitioners. Moreover, as a result, the paper concretizes scalability and convertibility and provides a foundation for future empirical research on reconfigurability.

Introduction

Reconfigurability of manufacturing systems has been studied since the late 90's as a key to competitiveness in scenarios characterized by unpredictability of markets [START_REF] Koren | Reconfigurable manufacturing systems: Principles, design, and future trends[END_REF]. In addition, emerging intelligent manufacturing technologies promise to enable and enhance reconfigurability [START_REF] Koren | Reconfigurable manufacturing systems: Principles, design, and future trends[END_REF], [START_REF] Maganha | Understanding reconfigurability of manufacturing systems: An empirical analysis[END_REF]. However, despite these promising potentials, the wide implementation of manufacturing solutions realizing the reconfigurability characteristics still appears rather limited in industry [START_REF] Maganha | Understanding reconfigurability of manufacturing systems: An empirical analysis[END_REF], [START_REF] Andersen | Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation[END_REF]. It is widely recognized that the core characteristics of reconfigurability are: modularity, integrability, diagnosability, scalability, convertibility and customization. Among them, scalability and convertibility are considered essential characteristics [START_REF] Wiendahl | Changeable Manufacturing -Classification, Design and Operation[END_REF], [START_REF] Rösiö | Supporting the design of reconfigurable production systems[END_REF]. Scalability, i.e. the ability to make rapid and efficient incremental changes of capacity [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF], and convertibility, i.e. the ability to conduct quick changeover between existing products and adaptability to future products [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF], represent key reconfigurability characteristics, as they directly contribute to the goal of the reconfigurable systems, which is providing exactly the capacity and functionality needed when needed [START_REF] Koren | General RMS Characteristics. Comparison with Dedicated and Flexible Systems[END_REF]. Moreover, scalability and convertibility directly reduce reconfiguration effort and enhance system responsiveness [START_REF] Elmaraghy | Reconfigurable process plans for responsive manufacturing systems[END_REF], [START_REF] Ayman | Assessment of manufacturing systems reconfiguration smoothness[END_REF], as well as "bridge" between the remaining characteristics since they are enabled by modularity, integrability and diagnosability and, in turn, enable customization and thus reconfigurability itself [START_REF] Napoleone | A framework to manage reconfigurability in manufacturing[END_REF], [START_REF] Singh | Reconfigurable manufacturing systems: journey and the road ahead[END_REF]. Therefore, in this paper, scalability and convertibility are considered as primary characteristics in order to realize the advantages of reconfigurability in manufacturing firms. However, in previous research, these characteristics have predominately been addressed on rather abstract levels with only limited consideration of the manufacturing elements that enhance these characteristics, their inherent relations, and how they can actually be realized in manufacturing [START_REF] Hawer | Analyzing Interdependencies between Factory Change Enablers applying Fuzzy Cognitive Maps[END_REF]- [START_REF] Russo Spena | Requirements for the Design of flexible and changeable Manufacturing and Assembly Systems : a SME-survey[END_REF]. In this regard, reconfigurability is increasingly being promoted as a multi-dimensional and complex capability that can be designed and implemented specifically for different manufacturing contexts, by exploiting relationships between core characteristics and/or enablers [START_REF] Hawer | Analyzing Interdependencies between Factory Change Enablers applying Fuzzy Cognitive Maps[END_REF], [START_REF] Andersen | Understanding Changeability Enablers and Their Impact on Performance in Manufacturing Companies[END_REF] at multiple manufacturing levels [START_REF] Andersen | Reconfigurable Manufacturing on Multiple Levels: Literature Review and Research Directions[END_REF], [START_REF] Nyhuis | Adequate and economic factory transformability and the effects on logistical performance[END_REF]. Therefore, in order to support practitioners in realizing the potentials of reconfigurability and design appropriate manufacturing solutions that realize reconfigurability, it is important to consider not only structural aspects that enable scalability and convertibility but also their manufacturing levels of realization. Accordingly, the aim of this paper is to bring reconfigurability-related concepts closer to the world of practitioners and create the foundation for future field-based investigations on the topic, leading to the following research question: Which manufacturing elements enable scalability and convertibility as relevant characteristics of reconfigurability in manufacturing?

In addressing this research question, "elements" enabling scalability and convertibility are defined as concrete constituents of such characteristics. Moreover, focus is limited to the factory and lower manufacturing levels, where previous research on manufacturing "elements", "enablers" or "assessment criteria" (enabling specific characteristics of reconfigurability and/or enabling reconfigurability as a capability in itself [START_REF] Andersen | Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation[END_REF], [START_REF] Beauville | Identification of reconfigurability enablers and weighting of reconfigurability characteristics based on a case study[END_REF]- [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF]) has been considered. For this purpose, criteria have been defined for analyzing previous research and ensure coverage of reconfigurability as a multi-dimensional capability and in turn create a basis for future research based on case studies. The remainder of the paper is structured as follows: Section 2 presents criteria for identifying and classifying enabling elements, Section 3 provides the results and Section 4 presents conclusions and outlines future development of this research.

Classification Criteria for Elements Enabling Scalability and Convertibility

This section presents the criteria applied for classification of different elements identified in previous research. These have been introduced in order to cover relevant aspects related to scalability and convertibility from a multi-dimensional perspective. The criteria are described as follows:

 Manufacturing level of reference: from a hierarchical systems view, reconfigurability can be developed on different manufacturing levels [START_REF] Bi | Reconfigurable manufacturing systems: the state of the art[END_REF]. Moreover, reconfigurability on lower manufacturing levels positively influences reconfigurability on higher manufacturing levels [START_REF] Andersen | Reconfigurable Manufacturing on Multiple Levels: Literature Review and Research Directions[END_REF], [START_REF] Bruccoleri | Production planning in reconfigurable enterprises and reconfigurable production systems[END_REF], [START_REF] Goyal | Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS[END_REF]. Thus, elements influencing scalability and convertibility should be identified in regard to multiple levels. Specifically, this paper focuses on levels within the factory: (i) workstation (WL), (ii) system (SL), and (iii) factory (FL).  Effects on either short-term or long-term reconfigurations: as a multi-dimensional capability, reconfigurability has characteristics acting in the Configuration Period (CP) and others acting in the Reconfiguration Period (RP) [START_REF] Napoleone | A framework to manage reconfigurability in manufacturing[END_REF]. CP is the period for decisions on structural aspects (i.e. the design) of systems, whereas RP is the period for decisions on system changes (i.e. related to the operations). Scalability and convertibility are characteristics acting during the RP [START_REF] Napoleone | A framework to manage reconfigurability in manufacturing[END_REF], thus their enabling elements support reconfigurations with either short or long-term time horizon. Shortterm time horizon reconfigurations (ST) cover e.g. solving disruptions and quality problems, exploiting flexible production resources or changing from one existing variant to another (i.e. changes within workstations, leading to brief changeovers without any further investment in production resources) [START_REF] Bruccoleri | Distributed intelligent control of exceptions in reconfigurable manufacturing systems[END_REF]. Long-term time horizon reconfigurations (LT) are associated with changes of structural aspects at either workstation, system and factory levels, in order to change production capacity and functionality [START_REF] Koren | Choosing the system configuration for high-volume manufacturing[END_REF].  Classification in physical and managerial elements: depending on manufacturing levels, the nature of elements influencing scalability and convertibility may be different. As observed by many authors, reconfigurability at lower levels is generally associated with physical and "hard" elements, while reconfigurability on higher levels is generally associated with more managerial, logical, and "soft" elements [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF], [START_REF] Ayman | Assessment of manufacturing systems reconfiguration smoothness[END_REF], [START_REF] Andersen | Reconfigurable Manufacturing on Multiple Levels: Literature Review and Research Directions[END_REF], [START_REF] Bi | Reconfigurable manufacturing systems: the state of the art[END_REF]. Physical elements (PE) can be either technical systems of the factory (i.e. operational resources and processes) and spatial aspects (i.e. floor and ground areas) [START_REF] Nyhuis | Adequate and economic factory transformability and the effects on logistical performance[END_REF]. Managerial elements (ME) refer to logical elements, operational rules, organizational policies, and IT support for the management of technical systems [START_REF] Ayman | Assessment of manufacturing systems reconfiguration smoothness[END_REF], [START_REF] Nyhuis | Adequate and economic factory transformability and the effects on logistical performance[END_REF], e.g. rules established for re-planning production resources in order to accomplish market changes [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF].

In the following, nine elements have been identified and classified according to these criteria. In this regard, relevant literature was located from authors' previous systematic literature searches on reconfigurability, which collectively have used a broad spectrum of key words e.g. "reconfigurability", "reconfigurable manufacturing system", "changeable manufacturing", etc. in relevant databases e.g. Web of Science and Scopus, as well as through additional snow-ball search processes. Papers referring to: (i) "changeability", often used to denote to the capability of manufacturing systems to adapt to any changes independently on the manufacturing level of reference [START_REF] Wiendahl | Changeable Manufacturing -Classification, Design and Operation[END_REF], [START_REF] Terkaj | A review on manufacturing flexibility[END_REF]; (ii) "flexibility", often used at workstation and system levels [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF]; and (iii) "transformability" often used at factory level [START_REF] Andersen | Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation[END_REF], [START_REF] Wiendahl | Changeable Manufacturing -Classification, Design and Operation[END_REF] have been reviewed as well.

Research Results: Elements Enabling Scalability and Convertibility

This section presents the elements enabling scalability and convertibility identified in the literature review. Fig. 1 synthesizes the classification of these elements according to the aforementioned criteria through the color-coded labels visualized for each element. The figure shows that scalability and convertibility are enabled by a combination of nine manufacturing elements, which are comparatively less abstract than scalability and convertibility. These nine elements are enabled by a combination of other aspects that are, in turn, more concrete than the previous ones. For brevity, these have not been included in detail here. Even if not detailed, such aspects have been immersed and exemplified in the following description of the nine elements. As observable in the following descriptions of the nine elements, elements and aspects are positioned in Fig. 1 progressively enlarging, from left to right, the focus from WL, to SL and then to the entire FL. 1) Possibility to add new functionalities to production resources: this element has been located at WL and classified as PE. Indeed, operators and machines should be provided or designed (in case of machines) in a way that allows future conversions to new requirements. Specifically, manual workstations rely on operators' versatile skills [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF], [START_REF] Nyhuis | Adequate and economic factory transformability and the effects on logistical performance[END_REF]. Conversely, to facilitate future conversions of highly automated workstations with complex and partitioned architectures, machines' control interfaces should be designed for easy integration to newly introduced tools/machines [START_REF] Hawer | Analyzing Interdependencies between Factory Change Enablers applying Fuzzy Cognitive Maps[END_REF]. Universality of tools and fixtures adopted at the workstation would facilitate their employment for varying requirements and tasks [START_REF] Hawer | Analyzing Interdependencies between Factory Change Enablers applying Fuzzy Cognitive Maps[END_REF]. In general, the possibility to add new functionalities to production resources may allow ST scalability and convertibility (e.g. by recurring to operators' versatility) or even LT reconfigurations (e.g. exploiting machines interfaces allowing integration to newly introduced tools/machines).

2) Connectivity of production resources: this element has been classified as PE and defined at WL and SL. Indeed, as it allows information exchanges, from the perspective of supporting information systems, it should be defined and supported at every individual manufacturing level [START_REF]Enterprise-control system integration[END_REF]. Information may simplify the adaption of operators and/or machines to eventual new requirements in terms of implemented functionalities [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF], [START_REF] Otto | Automatic Parameter Estimation for Reusable Software Components of Modular and Reconfigurable Cyber-Physical Production Systems in the Domain of Discrete Manufacturing[END_REF]. Connectivity may certainly simplify ST reconfigurations (e.g. interactive screens and information boards connected to a central information system might guide operators in executing a certain variety of tasks [START_REF] Posada | Visual Computing as Key Enabling Technology for Industry 4.0 & Industrial Internet[END_REF]); conversely LT reconfigurations should be enabled by additional aspects allowing structural changes of the workstation (e.g. the integrability of machines interfaces).

3) Coordination rules and related IT support [START_REF] Napoleone | Core Characteristics of Reconfigurability and their Influencing Elements[END_REF]: these elements may be defined at both WL and SL. Rules are ME even if the possibility to benefit of the IT support is also related to the connectivity of production resources. Both rules and IT support are enablers of scalability and convertibility only if, during the CP, these are designed with a RP perspective: in this case, they would be oriented at speeding up the adaptation to change by improving production resources synchronization during reconfigurations, thus allowing more frequent reconfigurations [START_REF] Koren | The rapid responsiveness of RMS[END_REF], [START_REF] Cheng | Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey[END_REF].

4) Ability to identify and manage problems related to reliability and/or to production quality: this element may be defined at both WL and SL. It includes both PE, such as devices for detection and diagnosis of reliability/quality-related problems [START_REF] Beauville | Identification of reconfigurability enablers and weighting of reconfigurability characteristics based on a case study[END_REF], [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF], and ME such as problem-solving methodologies and related IT support [START_REF] Bruccoleri | Operation management in reconfigurable manufacturing systems: Reconfiguration for error handling[END_REF]. PE should be defined especially at WL and ME should be defined (and supported by adequate information systems) especially at SL. Moreover, the exploitation of modern digital technologies promise to improve such ability [START_REF] Maganha | Understanding reconfigurability of manufacturing systems: An empirical analysis[END_REF], [START_REF] Bortolini | Reconfigurable manufacturing systems: Literature review and research trend[END_REF]. It enables better scalability and convertibility at WL and SL by allowing the reduction of ramp-up periods after reconfiguration [START_REF] Koren | The rapid responsiveness of RMS[END_REF].

5) Rules and related IT support ensuring the correct reconfiguration of production/assembly activities and/or the correct execution of activities after reconfigurations: these elements may be defined at both WL and SL. These are ME, even if the possibility to benefit of the IT support also presupposes the presence of PE. Both rules and IT support are enablers of scalability and convertibility only if, during the CP, these are designed with a RP perspective. In this case, they would be oriented at avoiding problems related to human mistakes during reconfigurations and/or after reconfigurations, thus allowing more frequent reconfigurations by contributing to the reduction of reconfiguration ramp-up times [START_REF] Koren | The rapid responsiveness of RMS[END_REF]. At WL these might be mechanisms to detect the usage of right tool and right components for a certain task (poka yoke) [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF].

6) Incrementality of changes: at each level (i.e. WL, SL and FL) converting and/or adapting production capacity through as small as possible changes means higher convertibility and/or scalability [START_REF] Koren | Value creation through design for scalability of reconfigurable manufacturing systems[END_REF]. Intuitively, the incrementality of changes achieved at WL, strongly influences the incrementality of changes at SL and in turn at FL. As detailed below, many aspects of this element should be properly designed during the CP, these may enable, especially at higher manufacturing levels, LT scalability and convertibility by reducing the effort required for reconfigurations [START_REF] Koren | Value creation through design for scalability of reconfigurable manufacturing systems[END_REF]. At WL, an influencing aspect is the versatility of operators and integrability of machines' control interfaces, which favor implementing incremental changes of workstations without implying substantial changes of workstations themselves [START_REF] Hawer | Analyzing Interdependencies between Factory Change Enablers applying Fuzzy Cognitive Maps[END_REF]. At SL, the universality of workstations may allow accomplishing incremental changes, for example the possibility to share and or swap functional units among stations. [START_REF] Andersen | Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation[END_REF], [START_REF] Beauville | Identification of reconfigurability enablers and weighting of reconfigurability characteristics based on a case study[END_REF], [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF]. Moreover, features of the material handling system, such as the level of automation of its devices and the possible movements of these systems, impact on the incrementality of changes too [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF]. At FL, incrementality of changes can be seen as technical "upgradeability" of the factory [START_REF] Maganha | Understanding reconfigurability of manufacturing systems: An empirical analysis[END_REF]. It depends on the ability to integrate and disintegrate specific product and production technologies [START_REF] Nyhuis | Adequate and economic factory transformability and the effects on logistical performance[END_REF]. Finally, by analogy with SL, the level of automation of warehouse systems may affect factory reconfigurability. Thus, generally this element supports LT reconfigurations.

7) Possibility to vary sequences of operations and related IT support: this element may be defined at SL as a ME, however it also depends on PE, such as the presence of functional redundancies of workstations/ functional units [START_REF] Napoleone | Core Characteristics of Reconfigurability and their Influencing Elements[END_REF] and specific features of the material handling system [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF], [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF]. Examples related to this element are opportunities and decisions on machines relocation [START_REF] Abdi | RMS capacity utilisation: product family and supply chain[END_REF] and material handling redirections [START_REF] Abdi | RMS capacity utilisation: product family and supply chain[END_REF]. Even in this case, the possibility to benefit of the IT support is related to the connectivity of production resources at WL. It enables ST scalability and convertibility. Indeed, the number of feasible routes of all part types and the availability of this information for decisions on system operational changes positively impacts on changes in production capacity and functionality [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF]. In turn, this aspect depends on the versatility of operators' skills and/or the integrability of machines' control interfaces [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF].

8) Availability of space (this is mainly influencing scalability rather than convertibility): the utilization of space at SL and FL potentially allows increasing the number of resources in case of need [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF]. Thus, this element enables LT reconfigurations. 9) Organizational practices and related IT support to decide on changes on the operations to meet market requirements: such ME at FL enables LT reconfigurations. These are: labor reassignment [START_REF] Abdi | RMS capacity utilisation: product family and supply chain[END_REF] and training procedures (such as cross training and continuous improvement of operators skills; outsourcing and subcontracting possibilities [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF]; exploitation of different processing plans available for making a product [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF]; possibility to act on shifts and flexible labor time [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF]. For example, the possibility to change shifts and number of resources involved [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF], [START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF] is a ME that has to be defined at FL during the CP and, once defined, it allows implementing LT-oriented changes of production capacity and functionality at either WL and SL. It should depend on organizational aspects such as adopted company policy.

Conclusions and Further Research

The aim of the research presented in this paper was to bring reconfigurability-related concepts closer to the world of practitioners. Accordingly, nine manufacturing elements enabling scalability and convertibility have been identified from previous research and classified according to criteria defined with the aim of exploring the multi-dimensional aspects of reconfigurability, i.e. manufacturing level, time-span of reconfiguration effect, and as being either managerial or physical. These nine elements contribute with a concretization of abstract concepts of scalability and convertibility and bring these reconfigurability-concepts closer to practice. Future research should aim at further enhancing the theoretical classification through feedback and examples gathered directly in field. Moreover, cases should allow understanding how such elements can increase convertibility and scalability. Specifically, since empirically-founded or case-based research on reconfigurability is rather scarce, future research should aim at investigating e.g. elements of scalability and convertibility in practice in regard to their inherent relations, reinforcing effects, different types of applications, and resulting performance outcomes.

Fig. 1 .

 1 Fig. 1. Elements enabling scalability and convertibility and their classifications.


Zeinab Nezami1
email: z.nezami@eng.ui.ac.ir

Kamran Zamanifar
email: zamanifar@eng.ui.ac.ir

Damiano Arena
email: damiano.arena@epfl.ch

Dimitris Kirtisis
email: dimitris.kiritsis@epfl.ch

Ontology-based Resource Allocation for Internet of Things

Keywords: Internet of Things, Edge Computing, Context Awareness, Resource Allocation, Semantic Technologies, Ontology

The Internet of Things has revolutionized the lifestyle in all aspects. Considering the huge number of connected objects and the plethora of real-time services, edge computing approaches have emerged. Resource allocation is one of the most important challenges in the Internet of Things. Here edge computing allows the use of resources at the edge network, hence, filling the gap between cloud and end-devices. The network resource allocation should meet users' expectations and provide optimal use of resources. Today, most of the systems are moving toward a self-x concept, such as self-organizing. As a result, these systems must be aware of users' preferences and the current state of the IoT ecosystem in order to adapt themselves to the conditions. In this context, we benefit from the employment of semantic technologies as these enhance the current systems with information modeling and reasoning capabilities which effectively support the allocation of IoT network resources.

Introduction

Nowadays the Internet of Things (IoT) has dominated all aspects of our lives. As a result, objects are connected to the network and talk about each other. Around 18 billion connected objects are forecasted by 2022 related to the IoT [START_REF]IoT forecasts provided by Ericsson[END_REF]. There are a variety of applications (e.g., smart city, smart manufacturing, and video surveillance) provided in an IoT network. In this ecosystem, most of the service providers use the cloud data centers to process the huge volume data produced by objects and extract a value of it. However, this results in imposing a high load on the network and degrading the performance of the cloud. Meanwhile, because of the distance between the cloud and the source of data, the opportunity to act on data in real-time will be lost [START_REF] Nezami | Internet of Things/Internet of Everything: Structure and Ingredients[END_REF]. In order to solve these problems, a new distributed computing paradigm named fog computing [START_REF] Bonomi | Fog computing and its role in the internet of things[END_REF] has emerged which aims at filling the gap between cloud and end-devices. Fog computing enables network objects to cooperate and make their resources available, in order to reach a goal, i.e. providing services.

Regarding fog computing which aimed especially towards IoT, we consider an IoT network consists of three layers; cloud, fog, and end-devices. Fog layer includes distributed nodes such as routers, servers, and even mobile devices distributed between the end users and the cloud. In this context, we define the problem well-known as resource allocation as follows; given the IoT network and the IoT requests along with their requirements, find a mapping between requests and nodes at the network. The resulted nodes should execute tasks to reach the request's target and to satisfy the network performance and quality of services. Resource allocation is one of the most important challenges in the IoT context.

Fog computing is able to locally allocate edge devices (comprises end devices such as mobile phones, edge devices such as routers, and edge servers) to the IoT requests and prevent the transmission of huge amounts of raw data to the core network (including the core routers, regional serv-ers, and cloud centers). In order to do this, it is important to consider some key features regarding IoT objects:

o Limited available nodes' resources (e.g., electrical energy, memory, and processing power). o Network heterogeneity regards both nodes' capabilities and requests' requirements. o Dynamic behavior of IoT networks; connections among nodes are created dynamically. o A huge number of nodes deployed over an extensive area; network topology changes quickly. All these features conclude to a dynamic network, where all nodes need to interoperate in order to allocate available resources in a distributed way. Most of the decisions should be taken autonomously to avoid centralized solutions. Therefore, resource management should be continuously addressed to dynamically adapt the system to changes in terms of IoT requests' requirements and network topology. This is the reason we need "context awareness". Context awareness can exploit all available context information in order to make better decisions regarding a constrained pool of network resources. Therefore, it can improve the performance of resource management algorithms for IoT ecosystems.

The term "context awareness" refers to the ability of computing systems to acquire and reason about the context information and subsequently adapt the corresponding applications accordingly [START_REF] Makris | Context Aware Resource Management for Mobile and Fixed Networking Systems[END_REF]. In addition, context awareness is a foundation of all self-x properties including selfconfiguration, self-organization, self-optimization, self-healing, etc. [START_REF] Agoulmine | Challenges for Autonomic Network Management[END_REF][START_REF] Belschner | Optimization of Radio Access Network Operation Introducing Self-x Functions: Use Cases, Algorithms, Expected Efficiency Gains[END_REF]. As a result, the IoT network would be able to exploit resources in an efficient and self-organizing way. Semantic technologies use formal semantics to facilitate context-awareness, and reasoning on IoT. They enhance the raw data and link the data in any domain in real life. Ontologies provide a sophisticated semantic mechanism for resource modeling and allow representing both hardware and software, physical and virtual resources along with the relationships between them and in a variety of granularities [START_REF] Koorapati | Ontology Based Resource Management for IoT Deployed with SDDC[END_REF].

This paper focuses on the mentioned challenges in the IoT resource allocation and we propose to use context information to allocate IoT resources in an optimized way. To this end, this paper suggests to use ontologies to model our IoT network/requests. Then we leverage semantic rules/query engines to drive inferences and find a suitable mapping between IoT requests and resources. Some of the applications for our proposal include but not limited to:

o Autonomic and self-organizing IoT networks (e.g., automated manufacturing). o Power/workload management in the IoT ecosystem. o Monitoring the resources in the IoT network and detecting the likelihood of node failures and applying policies for remediation. o Context-aware strategies for the future regarding long-term reasoning (extracting trends and patterns). This paper is organized as follows. In section 2, we look at some related work using ontologies in the IoT domain. Section 3 describes the proposed approach, based on the unification of the IoT and cloud ontologies and leveraging it for optimized resource allocation. Finally, section 4 provides an overview of the benefits of the proposed approach and some indications for future research.

Related Works

In the context of this paper, we review cloud computing ontologies, IoT ontologies, and resource management in IoT as our related work literature survey. Zhang et al. [START_REF] Zhang | An ontology-based system for Cloud infrastructure services' discovery[END_REF] propose a cloud computing ontology called CoCoOn to discover suitable infrastructure services for the user's needs. The CoCoOn ontology defines a set of properties to describe infrastructure services. The authors implement a recommendation system based on the CoCoOn ontology in which SQL queries are used to interrogate the ontology and discover services. Rekik et al. [START_REF] Rekik | Cloud description ontology for service discovery and selection[END_REF] propose CloudO, a comprehensive cloud service description that plays a basic role for the discovery and composition of cloud services. The proposed ontology spans functional and nonfunctional aspects of cloud services at the three layers of cloud models, namely Infrastructure as a Service (IaaS), platform as a Service (PaaS) and software as a Service (SaaS). The proposed ontology helps user to discover and select appropriate cloud services through user's queries.

IoT-O [START_REF] Seydoux | IoT-O, a core-domain IoT ontology to represent connected devices networks[END_REF] ontology intends to cover two sets of requirements -Conceptual and Functional in an IoT ecosystem. The "conceptual" requirements are based on the description of devices, data, services and their lifecycle while, "functional" requirements are defined as the requirements that follow best practices define by the semantic community. The ontology provides concepts needed for representing a device and its functionality. It reuses some existing ontologies such as IoT-Lifecycle [START_REF]IoT-Lifecycle[END_REF] and SSN [START_REF] Compton | The SSN ontology of the W3C semantic sensor network incubator group[END_REF] to define the concepts related to the IoT domain such as duty cycles and sensing capabilities.

Moustafa et al. [START_REF] Moustafa | Model of a Context-Aware Middleware for Mobile Workers[END_REF] propose Continuum, a model of a context-aware middleware that can dynamically discover environment and self-adapt applications to the new contextual conditions. They address the issue of changing environment due to the mobility through a monitoring service that is capable of reasoning during the runtime. Koorapati et al. [START_REF] Koorapati | Ontology Based Resource Management for IoT Deployed with SDDC[END_REF] consider an ecosystem consisting of IoT, Software-Defined Data Center (SDDC) and cloud. The authors present a resource modeling framework based on semantic technologies insisting how semantic technologies are applied in addressing some of the key challenges in managing such an ecosystem. Delicato et al. [START_REF] Delicato | Resource Management for Internet of Things[END_REF] describe the challenges related to resource management in IoT considering different number of tiers; only cloud, only IoT, and three tier composed of cloud, IoT and edge nodes. The authors highlight the advantages of ontologies for resource modeling and provide a useful insight of OpenIoT [START_REF] Soldatos | Openiot: Open source internet-of-things in the cloud[END_REF]. OpenIoT is a middleware framework whose semantic-based resource management architecture enables managing the whole lifecycle of IoT applications and services infrastructure.

To the best of our knowledge, this paper is the first research paper which deals with using ontologies to discover, model, select, and allocate resources in an IoT ecosystem consisting of IoT, fog and cloud layers.

Proposed Approach

An IoT ecosystem with three layers is illustrated in the Error! Reference source not found. in which the bottom layer encompasses the things (the IoT devices/nodes/smart objects), the top layer includes the cloud nodes and an optional middle layer consists of fog nodes. IoT request (originate from any devices) are intended to be received by the closest (in terms of the distance between the IoT requester and fog nodes) fog nodes. Upon receiving a request, the receiver fog node locally generates an assignment between its received requests and the ecosystem, resource allocation. Regarding the assignment, the IoT tasks will be distributed and deployed on the intended resources and served users considering the quality of services. The lack of unification of heterogeneous cloud/fog service description makes resource discovery and selection very complex tasks for IoT users. To alleviate the complexity, it is necessary to have a unified service model integrating service descriptions obtained from heterogeneous sources. This paper proposes to combine two ontologies; IoT ontology (IoT-O) [START_REF] Seydoux | IoT-O, a core-domain IoT ontology to represent connected devices networks[END_REF] and cloud ontology (CloudO) [START_REF] Rekik | Cloud description ontology for service discovery and selection[END_REF] in order to model the IoT ecosystem using a unified ontology named IoT-Fog-Cloud. Fig. 2 and consist of: resource discovery, resource modeling, resource selection, and resource allocation. The figure depicts a context life-cycle [START_REF] Perera | Context aware computing for the internet of things: A survey[END_REF] and corresponding resource allocation tasks that are related to each cycle. There are other activities related to resource management such as resource monitoring, resource estimation, and resource remediation which can benefit from ontologies. However, these steps are not the focus of this research. At the first step, resource discovery, nodes acquire and share the concepts to attain essential information about the network. To do this, all nodes locally exchange their information with their neighbors to adapt to environmental changes. Also, upon receiving an IoT request, the receiver node can extract the requirements of the request. The second step, resource modeling, tends to represent the resources/requests in our IoT ecosystem. Using the IoT-Fog-Cloud ontology, we can define the entities, properties, and relationships that build up the resources/requests at the IoT ecosystem. The first two steps result the following models.

o IoT resources: cloud data centers, fog nodes, end-devices and the links between them along with their characteristics such as processing/storage capacity, load, energy, mobility status, sensing capabilities, link bandwidth/propagation delay (Network dynamic aspect). Domain/application (e.g., smart manufacturing, agriculture, health-care), peak time (Network static aspect). o IoT requests: requests along with their characteristics such as requester and his/her mobility status, demands (processing power, storage, network), type (real-time or batch), priority, security level, deadline. Third step, resource selection, considering modeled requests/resources and using reasoning techniques, decides about suitable resources to host the IoT requests regarding quality of services.

To do this, we use Semantic Web Rule Language (SWRL) [START_REF] Horrocks | SWRL: A semantic web rule language combining OWL and RuleML[END_REF], a language for expressing semantic rules as well as logic. The rules infer new/intended knowledge about network/requests from our existing OWL knowledge base. Each node can investigate and consult the structured ontology throughout SWRL to find the optimized hosts for its received requests. Finally, at the resource allocation, nodes disseminate the concepts resulted from resource allocation step to their neighbors (e.g., remained nodes' capacity regarding new deployed tasks).

A typical resource allocation procedure from each node's view in our proposal is shown in Fig. 3. In order to make the procedure clearer and show how the proposed solution addresses the aforementioned challenges in IoT resource management consider a resource allocation scenario; a mobile node sends an IoT request to a fog node. The fog node extracts the requirements and properties of the request. The fog node already has a global view of the network through the IoT-Fog-Cloud ontology. Consulting the ontology and using SWRL rules, the fog node specifies the best node to host the request. In this specification the matching between the request and the host is checked such as the required sensing capabilities and bandwidth. After that, the fog node forwards the request to the selected host. Then, network nodes update their information about the underlying network regarding this hosting. In this procedure network nodes constantly share their information and keep an up-to-date image on the network. As a result, by updating ontology, dynamic behavior of the network is well considered. In addition, by keeping the properties of the nodes and their resources and services, heterogeneity is addressed.

Conclusion

In this paper, the IoT resource allocation in a three layers IoT ecosystem composed of cloud, fog, and end-devices is introduced. Also, we showed how semantic technologies are applied in addressing some of the key challenges in the managing resources such an ecosystem. In addition, we have seen where of the IoT and cloud ontologies helps the various lifecycle of managing resources and allocating them smartly.

In the future, using this unified ontology, smart resource management applications can be developed which can predicate the future status of the network (such as failing a node given that they are hitting the threshold, going out of the coverage of a special server because of the mobility), and the requests (such as increasing required sensing frequency and changing the priority or security level), and results in a self-organized and self-healing system. On the other hand, the same unified ontology along with SWRL rules can be used to develop smart application deployment algorithms to propose optimized deployments in terms of energy saving, load balancing and secure deployment by just expressing of how the IoT ecosystem is supposed to be and then getting to know if the deployment place is good enough to meet the preference.

We plan to modify the unified ontology and the inference rules so those poor fog nodes can quickly infer required results and act on-time on the basis of the received data. We also consider using the cloud's capability to extract long-term patterns from the IoT ontology and feed the results to the fog nodes and study the behavior.

Fig. 1 .

 1 Fig. 1. IoT ecosystem comprising three layers: cloud, fog, and end-device layer [2]

Fig. 2 .

 2 Fig. 2. Context life cycle and activities involved in resource allocation for IoT ecosystem.

Fig. 3 .

 3 Fig. 3. Resource allocation work flow for each fog node in IoT ecosystem

Acknowledgements

We thank Reza Bostani, Electrical Engineering researcher, Babol Noshirvani University of Technology, for assistance and comments that greatly improved the manuscript.


Nina Pereira Kvadsheim

Deodat Mwesiumo
email: deodat.e.mwesiumo@himolde.no

Jan Emblemsvåg
email: jan.emblemsvaag@nov.com

Examining Circular Economy Business Models for Engineer-To-Order Products

Keywords: Circular Economy, Circular Economy Business Models, Engineer-To-Order Products

Realization of the circular economy (CE) and its full potential requires firms to rethink their supply chains and business models. Although several frameworks in the literature propose circular economy business models (CEBMs) to redefine how companies create and capture value while adhering to CE principles, there has yet to be an examination of CEBMs in engineer to order (ETO) context. To address this gap, this study maximizes the depth of the phenomenon under investigation by leveraging a case study of a Norwegian company that deals with a large number of small ETO projects. Our findings increase knowledge of how ETO products can adapt to CEBMs.

Introduction

Victor Hugo's infamous quote, "you can resist an invading army; you cannot resist an idea whose time has come", is what possibly best encapsulates the concept of circular economy (CE) being the idea, that needs to replace the current production and consumption systemtake-make-dispose linear model [START_REF] Tonelli | Strategic Management and CE[END_REF]. Most would agree that CE has become mainstream. It would be hard-pressed to find a firm that has not at least started a recycling initiative or engaged in some kind of community development project, even if the efforts were sparse. Auspiciously, many firms have gone far beyond, overhauling products and processes and looking outwards at system-wide innovation, and CE initiatives that promise to radically reshape their businesses now and in the future [START_REF] Clinton | Model Behavior: 20 Business Model Innovations for Sustainability[END_REF]. As more firms begin to recognize the potential of this novel approach, the CE quickly moves from theory to practice and the demand for businesses to embrace it strategically and operationally, and for the governments to establish a consistent set of rules for CE [START_REF] Guglielmo | Circular Business Models for the Built Environment[END_REF] is growing accordingly. More importantly, CE calls for novel business models that will simultaneously ensure the creation of environmental quality, economic prosperity, and social equity to benefit current and future generations [START_REF] Kirchherr | Conceptualizing the circular economy: An analysis of 114 definitions[END_REF]. This is understandable since a business model is the essence of a firm, representing the architecture through which firms create value, deliver value to their customers and capture value for themselves. Thus, if the role of the firm is vital for a successful transition to CE, then the implementation of new business models is warranted. The extant literature addresses related topics, including a framework that integrates circular economy business models (CEBMs) and circular supply chain management [START_REF] Geissdoerfer | Business models and supply chains for the circular economy[END_REF], the development and application of a circular business experimentation framework [START_REF] Bocken | Experimenting with a CBM: Lessons from eight cases[END_REF], and a visualization tool to map CEBMs [START_REF] Nußholz | A CBM mapping tool creating value from prolonged product lifetime and closed material loops[END_REF]. Although the literature provides useful insights, further research is still required on how firms can implement CE practices for value creation and capture [START_REF] Ünal | Managerial practices for designing CEBMs: The case of an Italian SME in the office supply industry[END_REF]. This is crucial because CE implementation strategies are mainly associated with individual products [START_REF] Ünal | A taxonomy of CE implementation strategies for manufacturing fi rms : Analysis of 391 cradle-to-cradle products[END_REF]. As such, in this paper we use product as a unit of analysis when examining CE implementation strategies. Since CEBM implementation largely depends on individual products, with the assumption that varied products might require varied manufacturing plants, settings and strategies, and business models, the present study focuses on engineer-to-order (ETO) products. Against this backdrop, this study seeks to empirically identify the characteristics of specific ETO products and examine their appropriate CEBMs. To the best of our knowledge, no study has specifically investigated the CEBMs for ETO products and considering the uniqueness and relative complexity of ETO products, insights from this paper should be of interest to scholars, practitioners, and policy makers.

The paper proceeds as follows. We commence by reviewing literature related to CE-BMs and production of ETO products. We then provide a framework that shows the general applicability of CEBMs for ETO products, which is followed by a methodology section and presentation of the case study. Finally, we discuss the findings and provide an outline for further work.

Circular Economy Business Models (CEBMs)

Before discussing CEBMs, it is important to understand the business model concept and its implications. A business model is defined as "the design or architecture of the value creation, delivery, and capture mechanisms [a firm] employs. The essence of a business model is in defining the manner by which the firm delivers value to customers, entices customers to pay for value, and converts those payments to profit" [START_REF] Teece | Business models and dynamic capabilities[END_REF]. In operational terms, a business model includes a value proposition (product and service; customer needs; geography), a revenue model (pricing logic; channels; customer interaction), and a cost model (core assets and capabilities; core activities; partner network).

To be successful, these components must be coherent and internally aligned [START_REF] Schön | Business Model Modularity -A Way to Gain Strategic Flexibility?[END_REF].

A CEBM can be defined as "the rationale of how an organization creates, delivers, and captures value with and within closed material loops" [START_REF] Mentink | Circular Business Model Innovation:A process framework and a tool for business model innovation in a circular economy[END_REF]. Studies on CE have addressed the design of CEBM in light of CE principles, and approaches to reconfigure and realign business model components according to CE practices [e.g., [START_REF] Ünal | Managerial practices for designing CEBMs: The case of an Italian SME in the office supply industry[END_REF][START_REF] Mentink | Circular Business Model Innovation:A process framework and a tool for business model innovation in a circular economy[END_REF]. Such models embrace cyclical material flows, renewable energy sources and cascading-type energy flows [START_REF] Korhonen | Circular Economy: The Concept and its Limitations[END_REF]. Put succinctly, to be regarded as 'circular', a model does not necessarily need to close material loops by itself within its own internal system boundaries; it could also be part of a larger system that together close a material loop echoing the Industrial Ecology thinking.

Studies by Stahel [START_REF] Stahel | Circular economy[END_REF], and Bocken et al. [START_REF] Bocken | Product design and business model strategies for a circular economy[END_REF], describe three key strategies for CE, from the resource perspective. 1) slowing loops, which extend a product's life to slow down resource usage (e.g. through repair); 2) closing loops between post-use and production through recycling, resulting in a circular flow of resources; and 3) narrowing loops, which aim to use fewer resources per product (e.g. through efficient manufacturing processes). Clearly, each of these strategies requires a different configuration of the value proposition, the revenue model, and the cost model, i.e., the business model. An intriguing question, then, is how to implement these for ETO products? Before addressing this question, the next section sets the scene by presenting ETO production.

Production of ETO Products

ETO refers to a strategy whereby design, engineering and production is based on specifications provided by a specific customer order. As such, the customer order's decoupling point for ETO is at the design stage such that each order is tied to the design phase of a product [START_REF] Gosling | Engineer-to-order supply chain management: A literature review and research agenda[END_REF]. Given the product-process characteristics of this type of environment, the products are highly customized, produced in low volumes (often one-of-a-kind), and customer specific; also, the processes are labor intensive, usually non-repetitive, and often demand highly skilled labor [START_REF] Powell | A new set of principles for pursuing lean ideal in ETO[END_REF]. The entire execution of the ETO product is often organized as a project particularly for capital goods. Since companies are generally unaware if and when a customer will place an order, accurately forecasting demand, ordering materials and producing products in advance are very challenging [START_REF] Bertrand | Production control in engineer-to-order firms[END_REF]. Drawing on Willner and colleagues' [START_REF] Willner | Exploring the archetypes of ETO: an empirical analysis[END_REF] ETO matrix, products can be produced using four ETO archetypes (complex, basic, repeatable, and non-competitive), depending on the number of units sold per customer order, the existence of repetitive customer orders and the engineering hours needed to produce the product. Given these four ETO archetypes, CE strategies for ETO products will plausibly vary according to the type of ETO production.

The subsequent section examines the appropriate CEBMs in ETO context.

Examining appropriate CEBMs for ETO products

CEBMs require the appropriate reconfiguration of value creation, delivery and capture, activities according to the nature/type of the product. We provide a framework, which shows the general applicability of CEBMs in ETO context (see Table 1). The framework proposes four major CEBMs for all ETO products (circular supplies, product life extension, product value extension and resource value). We further divide these major CEBMs into sub-CEBMs and describe them along four business model dimensions (value proposition, value delivery, value creation and value capture). This is derived from major frameworks such as business model canvas [START_REF] Osterwalder | Business Model Generation: A Handbook for Visionaries, Game Changers and Challengers[END_REF] and widely accepted conceptual analyses [START_REF] Schön | Business Model Modularity -A Way to Gain Strategic Flexibility?[END_REF][START_REF] Zott | The Business Model : Recent Developments and Future Research[END_REF]. This alignment is essential in ensuring that ETO companies can understand and apply these CEBMs.

'Circular supplies' are business models based on industrial symbiosis in which the residual outputs from one process can be used as feedstock for another [START_REF] Accenture | Circular Advantage: Innovative Business Models and Technologies to Create Value in a World without Limits to Growth[END_REF]. Through it, ETO companies may replace linear resource approaches and phase out the use of scarce resources while reducing waste and removing inefficiencies. Whereas, 'product life extension' business models aim to extend the lifecycle of products through engineering solutions including repair, maintenance and/or upgrade [START_REF] Accenture | Circular Advantage: Innovative Business Models and Technologies to Create Value in a World without Limits to Growth[END_REF][START_REF] Kiørboe | Moving towards a circular economysuccessful Nordic business models[END_REF]. With this model, ETO companies can help ensure that products stay economically useful for as long as possible and that product upgrades are done in a more targeted way (e.g., fatigue/ obsolete components are replaced instead of the entire product). Moreover, 'product value extension' business models offer product access and retain ownership to internalize benefits of circular resource productivity [START_REF] Bocken | Product design and business model strategies for a circular economy[END_REF]. This model would be attractive to a few ETO companies with high cost of operation for their products and that have skill advantage relative to their customers in maintaining the products, hence, giving them an advantage in selling services and recapturing residual value at end-of-life. 'Resource value' business models for these ETO products, is built on mostly 'recycling and waste management' [START_REF] Planing | Business Model Innovation in a Circular Economy Reasons for Non-Acceptance of Circular Business Models[END_REF], and generally, they can take different forms, thus, upcycling (which creates higher quality materials and improved functionality) and downcycling (e.g., the conversion of used materials into materials of lower value). This model is a good fit for most ETO companies especially where waste material from products can be reclaimed through take-back management and reprocessed cost effectively.

Nevertheless, for a CE to function these different types of CEBMs will need to interact and work together. For example, in the case of an advanced equipment to maritime industry, the 'product value extension' business model implies the equipment manufacturer either takes back used components of the products or retains ownership of the products, and is incentivized to repair/refurbish/upgrade the equipment over time to maintain maximum efficiency. However, the full circular benefit is only realized if the business models would allow for product and process design change, so that the products/components are repairable, upgradable and disassemblable to reduce the use of virgin materials [START_REF] Guglielmo | Circular Business Models for the Built Environment[END_REF]. Based on a CE, these ETO firms have the opportunity to expand their services they offer or collaborate with others to maximize value. For instance, a firm which bases their CEBM on refurbishing and remanufacturing their components may need to partner with a 'tracking facility' provider so they can monitor the whereabouts of their products, and then work with a logistics company or some of their B2B customers to ensure the end of life components are returned (take-back management).

Next, we present key methodological choices for the present study.

Research methodology

Our study explores an emerging phenomenon -CEBM, hence an embedded single-case design was deemed appropriate since single case design is suitable for revelatory purposes [START_REF] Yin | Case study research and applications: Design and methods[END_REF]. We selected a company (hereafter company X) that manufactures and supplies propulsion systems to shipyards and ship owners. With more than 1000 engineering hours per unit and 120 bespoke units sold annually, the propulsion system is a perfect example of an ETO product. We used three data collection approaches: face-to-face semi-structured interviews; direct, non-participant observation and document review. The next section describes company X and its ETO product characteristics, which are then matched with CEBMs identified in the developed framework.

6

The case company, its ETO products and suitable CEBMs Company X's business concept is to design, manufacture, market and service complete systems for the propulsion, positioning and maneuvering of larger vessels. Products are highly customized, manufactured on-site, and are manifested through a project organized execution model covering engineering design, procurement and production, which are done in-house to ensure quality in integration of systems. Most of the equipment is designed to meet the demanding requirements of North Sea offshore standards and is used on different types of vessels (e.g. offshore, fishing, cruise, coast guard). In general, company X creates value by understanding customer requirements, translating them into specifications at the product and component levels, and integrating components and subsystems into products. Table 2 presents company X's ETO products characteristics and their descriptions.

Table 2. Company X's ETO product characteristics and their descriptions

ETO product characteristics

Description

Low volume

On average, 120 units are produced and sold annually. This varies significantly year by year, depending on the condition of the market, etc.

High degree of customization

The products have high-engineering complexity (i.e. more than 1000 hours per unit) and are engineered per precise customer specifications.

Project-based environment

The ETO operations are typically project-oriented, thus, the customer is exposed to the lead-time that encompasses the entire project duration.

Product design

Each product is engineered/designed according to specific requirements, which vary from one customer to another. Customer involvement/ Customer specific Customer orders drive production. Each project is highly customer-specific and requires over 1000 engineering hours. The main drivers are different propulsion system requirements, regulations imposed by class, authorities and customer preferences (shipyards and ship owners). Labor intensive/ high-skilled labor Given the complexity of the products, the manufacturing process is labor intensive and often demands highly skilled labor.

Type of modularization

There is a slight product modularization. Its design elements are split up and assigned to modules based on a formal architecture or plan.

7

Appropriate CEBMs for company X

From Table 1, we propose two major CEBMs that are appropriate for company X's propulsion systems: 'product life extension' (which builds on 'repair and maintenance', 'reuse and redistribute', and 'refurbishment and remanufacturing'), and 'resource value' (that builds on 'recycling and waste management'). 'Repair and maintenance' business models are part of product sales and stand-alone services for company X; they are more about offering superior product experiences and engaging customers in co-creation (e.g. following maintenance schedules), than they are about selling products [START_REF] Clinton | Model Behavior: 20 Business Model Innovations for Sustainability[END_REF]. These models are addressing almost all the business model dimensions, indicating how issues, such as customer-centric services, fast learning and problem-solving capabilities, and up-to-date product expertise, are essential and of high value to company X. Since company X provides these services itself, there may a reduction in the costs for virgin materials.

Given the nature of the propulsion systems, 'reuse and redistribute' business model is the most relevant; there are only a few components and/or parts that are/can be reused. Instead of having used components flow (back) to company X directly or via an intermediary for repair and replacement, the company's aftersales service engineers typically decide whether components can be reused (whilst in the field with customers), depending on their level of wear or fatigue. The same applies to the 'refurbishment and remanufacturing' business models, which require that company X establishes the necessary reverse logistics to have access to used components [START_REF] Ludeke-Freund | A Review and Typology of Circular Economy Business Model Patterns[END_REF]. It should be noted that, in this type of industry, buying back used components by contacting customers directly is challenging; it is extremely time-consuming to trace customers all over the world, and it is even harder to identify them after ship ownership changes. More so, buy-back method would involve high logistics costs. Since the products are located under the waterline, there is also the practical difficulties involved of obtaining the components except when the vessel is up for the 5-year class and maintenance dockings.

Considering 'recycling and waste management' business model, company X pays much attention to the downcycling of very few components such as electrical and electronic equipment. This is because recycling is quite challenging as most components have material grades and material mix adapted to the specific requirements, which over time changes due to technological improvements and most components degrade in seawater rendering them slowly less and less attractive for reuse or upcycling.

Put succinctly, these identified CEBMs would therefore allow company X greater control of their resource streams, innovation through the supply chain and enhanced collaboration within the supply chain amongst all actors.

Conclusion and further work

Most ETO manufacturers stand to benefit more from the multifarious collaborations that can be built based on these CEBMs and from component take-back to complex industrial symbiosis networks. CEBMs for ETO products mostly address one of the three key CE strategies: the slowing resource loops. There is, nonetheless, a need to convince manufacturers to adapt narrowing and closing loops strategies to retain product and material value, respectively, if they are to successfully transition to CE; to do so, companies must rethink their supply chains and how they create and deliver value. We have noticed that a recycling business model for ETO products is greatly underutilized partly due to the complexity of the product. Hence, further work may elucidate distinctly how ETO manufacturers should design a collection and recycling infrastructure for such a recycling business model, and how they may capture value from it.


Natalia Iakymenko
email: natalia.iakymenko@ntnu.no

Marco Semini
email: marco.semini@ntnu.no

Jan Ola Strandhagen
email: ola.strandhagen@ntnu.no

Aspects for better understanding of engineering changes in shipbuilding projects: in-depth case study

Keywords: engineering change, shipbuilding, engineer-to-order, case study

Engineering changes are driving forces of product development and improvement in any company. In shipbuilding projects, engineering changes are introduced throughout the project duration and cannot be planned or introduced to the next production run since products are only made once and never repeated. A lot of research exists on engineering changes and their management in general, but the focus of this study is to increase the awareness and understanding of the engineering changes specifically in shipbuilding projects. In this study, an in-depth investigation of engineering changes and aspects having influence on engineering change implementation performance in Norwegian shipbuilding company was performed. The findings indicate that engineering changes and their implementation performance are related to such aspects as market segment, supply chain network, design maturity, overlapping project stages, and timing of EC occurrence in the project. These aspects, their influence on the number of ECs, and EC implementation and performance, are described and analyzed in the paper, and future research plans are presented.

Introduction

There is a fair share of standardized shipbuilding in the world, but European, and particularly Norwegian shipbuilding industry, is still producing highly customized products in short series, or even volumes of one. Their products are designed and built as separate projects. Typical strategy for this production environment is engineer-toorder [START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF]. Engineering changes (ECs) are common and unavoidable in this production environment [START_REF] Hamraz | A holistic categorization framework for literature on engineering change management[END_REF]. As opposed to make-to-stock environment, where ECs are batched and implemented before the start of production, ETO companies have to be able to implement changes at any stage of the project duration [START_REF] Semini | Strategies for customized shipbuilding with different customer order decoupling points[END_REF]. In this study ECs are defined as modifications to structure, behavior and function of a technical artefact that has already been released during the design process [START_REF] Hamraz | A holistic categorization framework for literature on engineering change management[END_REF][START_REF] Jarratt | Engineering change. Design process improvement[END_REF]. Even though ECs are implemented in order to improve the product or adapt it to the new requirements and needs, they often result in project cost overruns and delays [START_REF] Jarratt | Engineering change. Design process improvement[END_REF]. Extensive amount of the literature suggests strategies, practices and tools for improved management of ECs. However, different contextual conditions of production environments and indus-tries are not always explicitly staged in the studies. Hence, the aim of this paper is to investigate what aspects have moderating effects on EC implementation process, cost and time of ECs, as well as number of occurring ECs, in shipbuilding companies. The paper begins by presenting a brief theoretical background in section 2. Section 3 outlines a research methodology used in this study. In section 4, case company is introduced, followed by the case study findings in section 5. Section 6 concludes the paper and describes further research agenda.

Theoretical background and research motivation

There is a vast amount of research dedicated to ECs and their management. [START_REF] Wänström | Creating a model to facilitate the allocation of materials planning resources in engineering change situations[END_REF], Ho (1994) [START_REF] Ho | Evaluating the impact of frequent engineering changes on MRP system performance[END_REF], and Lin and Zhou (2011) [START_REF] Lin | The impacts of product design changes on supply chain risk: a case study[END_REF]. In addition to that, most studies on ECs still do not differentiate between the different production environments, even though back in 2009 Eckert et al. [START_REF] Eckert | Engineering change: drivers, sources, and approaches in industry[END_REF] suggested that future ECM practices and tools should be developed considering such factors as production volume, the degree of customer involvement in customization, the degree of internal and external uncertainty, and the inherent product complexity. This study explores if these research gaps are worth studying; are there moderating aspects of the specific production environment that differentiate it from other environments when it comes to ECs and their management? How do these aspects affect EC implementation performance? This study goes outside the design domain to explore production, logistics and supply chain aspects of EC implementation.

Research methodology

In Fig. 1, the basic research framework is outlined, which shows the relationships between the studied constructs. When EC occurs, it is analyzed and implemented using engineering change management (ECM) processes, procedures, and tools. ECs come at a cost of course; money and resources are spent on their implementation. This study hypothesizes that both number of occurring ECs, how they are managed, and cost and time of their implementation will be influenced by the moderating aspects specific for the shipbuilding environment. No specific hypotheses as to exact relationships between these aspects and EC implementation were formulated prior to conducting the study. Therefore, an inductive research approach was chosen [START_REF] Thomas | A general inductive approach for analyzing qualitative evaluation data[END_REF]. In-depth case study in Norwegian shipbuilding company was conducted. The case study method was chosen because of its suitability for theoretical development, where constructs are not defined or only partially defined [START_REF] Voss | Case research in operations management[END_REF][START_REF] Eisenhardt | Building theories from case study research[END_REF]. In addition to that, case study method allows for a more comprehensive understanding of the nature and complexity of a phenomenon studied [START_REF] Voss | Case research in operations management[END_REF][START_REF] Eisenhardt | Building theories from case study research[END_REF]. This paper presents part of the findings from the performed case study.

Fig. 1. Research framework

The data was collected over three years' time span and several visits to the case company, ranging from 1 to 5 days in duration. Several meetings and semi-structured interviews were conducted with deputy CEO, deputy managing director, business analysts, project managers, design and engineering managers, and production planners. The interviews consisted of two stages. At a first stage, ECM processes in the case company were mapped and ECM practices and tools used by the company were identified. At the second stage, interviewer went through the whole ECM process with the interview participants and asked what aspects were contributing to the way ECs are managed at each stage of the process, the number of occurring ECs, and finally their cost and time of implementation. Since no data on exact EC implementation time and cost is available in the company, qualitative measurement was used,interview participants were asked to describe the degree of a given aspect's effect on EC implementation performance. In addition, documents related to ECs were collected, including descriptions of EC management procedures, change evaluation sheets, company presentations, publicly available information, and reports on the previous research projects conducted with the case company by the research group. For data analysis, recommendations of Eisenhardt [START_REF] Eisenhardt | Building theories from case study research[END_REF], and Miles, Huberman and Saldana [START_REF] Miles | Qualitative data analysis[END_REF] were followed. The NVivo software was used to store, transcribe, code and analyze the data. First, interviews were transcribed, and a narrative description of the case history was created, ECM processes were described, ECM practices and tools were identified. After that, a two-stage coding was performed. At the first stage, descriptive codes were assigned to data chunks to detect reoccurring patters in all interviews and the documents available in an inductive way. From this, at the second stage of coding, similar codes were clustered together to create a smaller number of categories (pattern codes). The results of the coding were tabulated to compare findings across different interviews. The researchers iterated repeatedly between the data and emergent theory until a strong match between theory and data was reached. Several main aspects were identified, but due to the limited length of the paper, only five (most mentioned by the interview participants) aspects are presented.

4

Case study

The study was conducted in a Norwegian shipbuilding company (further called "N"). Typical shipbuilding project consists of following stages: design (concept, contract and basic), engineering, production, procurement, commissioning, and after-sale [START_REF] Semini | Strategies for customized shipbuilding with different customer order decoupling points[END_REF]. N performs several roles in shipbuilding industry, both as a ship designer, a shipyard, and a main equipment supplier. N is a part of global shipbuilding supply chain. This supply chain generally consists of four main actors: ship designer, shipyard, main equipment suppliers and shipowner (customer). In addition to that, classification organizations are involved; they establish and check technical standards for the construction and operations of ships. A simplified N's supply chain is presented in Figure 2.

Fig. 2. Shipbuilding supply chain

First, N Design and ship owner develop and negotiate on vessel details. N designs vessels and delivers design to the own yard or sells to the external yard, depending on the customer preferences. Contractual relationships are established between the shipowner and the yard, meaning there are no contractual relationships between the designer and the shipowner. Detailed engineering is performed by the engineering team at the yard. Even though partner yards buy designs from N and carry contractual responsibility for delivering the vessel to the shipowner, both the designer and the yard share responsibility since the customer will receive a vessel with the designing company's brand name on it. Most often ship designer coordinates the engineering and procurement of the main equipment, however sometimes this equipment is procured by the yard or customer. In this case, designer does not have a direct contact with suppliers. In cases when the vessel is built in N yard, the hull is produced at external yards to cut shipbuilding costs. After the hull is towed to N yard, it is outfitted with main equipment, pipes, cabling, and HVAC in the dry dock. After that, the vessel is moved outside the dock, where final outfitting is done on the water. Procurement (especially procurement of main equipment) and production usually starts before the engineering is finished, which, according to the company, shortens project duration by up to 12 months. Project delivery time is around 2 years.

Aspects affecting EC implementation in shipbuilding projects

In this chapter, we provide five aspects that affect ECs and their implementation in the case company with some real-life examples.

(1) Market segment. For a long time, main case company's specialty was building offshore support vessels (OSVs), but they have recently moved into a new market segmentcruise vessels. Here, professional culture, new standards, rules and regulations, and experience of the company were mentioned as three closely related aspects affecting ECs and their implementation in the company. Professional culture. Customers in OSV market were local, they had long relationships with N based on mutual trust. All issues related to ECs were resolved by making phone calls. New customers, however, are much more formal and stricter in their approach. Each issue is thoroughly checked against specifications and standards. Consequently, it takes much longer time to agree on who carries monetary responsibility for the change implementation. This is further complicated by the fact that N must work with new specifications, rules and regulations for cruise vessels. Often, something that was not an issue for OSVs appears an issue for the cruise vessel. This increases the number of ECs, which N is not able to predict in advance and must take full responsibility for. Ability to detect such potential issues in OSV market was based on the experience of the company, which still needs to be developed for the cruise vessels.

(2) Supply chain network. N operates in a global supply chain, where either design, engineering or production are often performed by external actors. Here, following aspects are affecting ECs: coordination of ECs across multiple companies, and competence and experience of external actors.

Coordination of ECs across multiple companies. In shipbuilding projects design, engineering or production are often performed by separate companies. ECs in this situation result in higher number of iterations between the companies and longer EC implementation time. For example, if an EC propagates from the shipyard to one of the suppliers, receiving input from this supplier can be a bottleneck. The time for supplier to give the necessary information might be even longer if the design and engineering do not have a direct contact with the supplier. For example, in one of the projects several changes affecting the whole design of the vessel (i.e. stability, power supply, etc.) occurred. To update design drawings, N needed updated technical specifications from several suppliers (i.e. engine supplier). However, N was not able to contact them directly since it was external shipyard, not N, who had contracts with these suppliers. Hence, N had to resolve all issues by contacting the shipyard first, who in turn contacted the suppliers. This considerably increased EC processing time and led to project delays.

Competence and experience of external actors. N yard is able to cope with some minor design and engineering changes without necessarily involving the design and engineering departments. The yard is able to fix the issues and predict possible propagations of such change. External yards are often do not have such experience and the production often must be stopped to resolve occurred problems. For example, when building one of the offshore support vessels in a Chinese shipyard, a lot of problems with cargo system occurred, which could have been solved by the production stuff if the vessel would have been built locally. However, in case of a Chinese shipyard, production had to be stopped, drawings updated by N and translated into Chinese, which took additional several weeks of work.

(3) Design maturity affects both the number of ECs, and EC assessment and implementation. Projects with low levels of design maturity (e.g. maturity of the whole product or separate systems and parts) usually generates more changes in the downstream stages of the project. In addition to that, implementation of new technology makes it difficult to predict possible EC propagations to other systems of the vessel. For example, when building platform supply vessel, new exhaust system was introduced. The issue of back pressure on bending pipes was not immediately understood, which led to rework (rearranging pipes) at the production stage. Such propagations are very hard to detect, and they will typically be noticed at a later and more costly project stages; as noted by the interview respondents, cost of implementing EC in shipbuilding projects increases tenfold with each subsequent stage of the project. (4) Overlapping design, engineering, procurement and production stages implies that instead of performing these activities sequentially, each activity starts before the previous is finished. By doing this, products are produced in less time. N follows this approach. However, this approach also implies that there should be design freezes at the design and engineering stages, after which changes are not allowed. However, the shipowner might want to introduce changes at any stage of the project. In this situation ECs affect a bigger number of activities and increases needed coordination efforts. For example, more ECs need to be handled by involving production stage because engineering was not finished before the start of production and incomplete drawings were shared between the project actors.

(5) Timing of EC occurrence. As it was already mentioned, the cost of an EC implementation in shipbuilding projects increases tenfold with each subsequent stage of the project. Early ECs, before procurement and production has started, are resolved by engineering and only include administrative costs of changing the drawings. However, when moving into procurement and production, ECs lead to rework, demolition and scrap of already produced components. This finding is in line with the Rule of Ten, stating that the cost of ECs increases exponentially with each stage of the project, and hence late ECs should be avoided [START_REF] Fricke | Coping with changes: causes, findings, and strategies[END_REF]. In addition to that, there are specific cost drivers appearing at different stages of shipbuilding process. The shipbuilding process consists of following stages: part production, block building, block outfitting, block (hull) assembly, dock outfitting and quay outfitting. For example, when moving from block building to block outfitting, following cost drivers will appear, such as reduced accessibility (workers need to operate in narrow spaces), higher risk of damage of already installed parts (some parts need to be covered and protected in case of rework). When moving from block outfitting to assembly, in addition to accessibility and risk of damage, following aspects need to be considered: crowdedness or stacking of trades (several production disciplines working in the same area, which leads to productivity decrease), vertical transportation and long distances to the place of work, work position (not possible to turn sections around). At the quay outfitting, all the spaces are closed, there is no roof in case of bad weather conditions (especially expensive for paint work), fewer available cranes. There additional production specifics of different types of vessels. For example, cruise vessels have 2-3 times more decks, which drastically increases travelling time for the workers. All the above-mentioned aspects are not easily quantifiable, but they are important to understand and consider when implementing ECs in shipbuilding projects.

Conclusions and further research

This study had provided insights into some aspects affecting ECs and their implementation in shipbuilding projects. First, this study has showed that there are specific aspects of the shipbuilding industry that influence ECs, their management and implementation performance. Specifically, the study showed that entering into a new market segment, low competence and experience of actors in shipbuilding supply chain network, and a low maturity of the design increases the number of ECs at different stages of shipbuilding projects. Supply chain actors' experience and design maturity increase the chance of unpredicted EC propagations. Furthermore, different professional culture of the customers, global distributed supply chain network, competence of supply chain actors, design maturity, overlapping of project stages, and timing of EC occurrence are the aspects that contribute to EC implementation cost and time. These aspects should be taken into consideration by the researchers when working on ECM practices and tools. Future research should perform in-depth analysis of ECs in several shipbuilding (or ETO) projects to determine all the aspects contributing to EC implementation performance. A new approach for EC assessment that goes outside engineering design domain should and takes into consideration such aspects.

It is expected that this paper can support practitioners in making sense of ECs in complex shipbuilding projects. However, the results should be handled with care since, like any other single case study, this paper has its limitations in terms of external validity and generalizability.