Collaboration in a Data-Rich World

The notion of Industry 4.0 is having a catalyzing effect for the integration of diverse new technologies towards a new generation of more efficient, agile, and sustainable industrial systems. From our analysis, collaboration issues are at the heart of most challenges of this movement. Therefore, an analysis of collaboration needs to be made at all dimensions of Industry 4.0 vision, complemented with a mapping of these needs to the existing results from the collaborative networks area. In addition to such mapping, some new research challenges for the collaborative networks community, as induced by Industry 4.0, are also identified.

Industry 4.0 is the called 4th technological revolution, where digital and physical marketplaces and manufacturing technologies converge to enable smart manufacturing and factories of the future. This paper presents an overview of a representative set of marketplace platforms available to support supply chain processes underpinning Industry 4.0. We develop a gap analysis of existing marketplaces assessing their ability to support Industry 4.0 requirements. Finally, we position our survey and gap analysis in the context of the European Union’s Horizon 2020 programme, in particular on the Digital Automation call topic addressing the theme of collaborative manufacturing and logistics.

Industrie 4.0 is changing the industrial landscape in an unanticipated way. The vision for manufacturing industries is to transform to an agile company, in order to react on occurring events in real-time and make data based decisions. The realization requires also new capabilities for the information management. To achieve this goal agile companies require taking measured data, analyzing it, deriving knowledge out of this and support with the knowledge their employees. This is crucial for a successful Industrie 4.0 implementation, but many manufacturing companies struggling with these requirements. This paper identifies the required capabilities for the information management to achieve a successful Industrie 4.0 implementation.

Collaboration has increasingly been considered a key topic within the small and medium-sized enterprises, allowing dealing with the intense competitiveness of today’s globalised markets. The European H2020 Cloud Collaborative Manufacturing Networks Project proposes mechanisms to encourage collaboration among enterprises, through the computation of collaborative plans. Particularly, this paper focuses on the proposal of a holistic algorithm to deal with the automated and collaborative calculation of the Materials Requirement Plan. The proposed algorithm is validated in a collaborative network belonging to the automotive industry.

New urbanization approaches aligned with public- private partnership (PPP) which arose in the early 1990s, have become acceptable and even better solutions to outstanding urban municipal constructions. However, PPPs are still problematic regarding value for money (VFM) process which is the main driving force to deliver public services. The current VFM structure requires an integrated platform to manage multi-performance and collaborative relationship in project life-cycles. Building information modelling (BIM), a popular approach to the procurement in AEC sectors, provides the potential to ensure VFM while also working in tandem with the semantic approach to holistically measure the life cycle performance. This paper suggests that BIM applied to the PPP life cycle could support decision-making regarding VFM and thus meet service targets.

The current dominant computing mode in the AEC (Architecture, Engineering and Construction) domain is standalone based, causing fragmentation and fundamental interoperability problems. This makes the collaboration required to deal with the interconnected and complex tasks associated with a sustainable and resilient built environment extremely difficult.This article aims to discuss how the latest computing technologies can be leveraged for the AEC domain and Building Information Modelling (BIM) in particular. These technologies include Cloud Computing, the Internet of Things and Big Data Analytics.The data rich BIM domain will be analysed to identify relevant characteristics, opportunities and the likely challenges. A clear case will be established detailing why BIM needs these technologies and how they can be brought together to bring about a paradigm shift in the industry.Having identified the potential application of new technologies, a future platform will be proposed. It will carry out large scale, real-time processing of data from all stakeholders. The platform will facilitate the collaborative interpretation, manipulation and analysis of data for the whole lifecycle of building projects. It will be flexible, intelligent and able to autonomously execute analysis and choose the relevant tools. This will form a base for a step-change for computing tools in the AEC domain.

The growing amount of data to be handled by collaborative networks raises the need of introducing innovative solutions to fulfil the lack of affordable tools, especially for Small and Medium-Sized Enterprises, to manage and exchange data. The European H2020 Project Cloud Collaborative Manufacturing Networks develops and offers a structured data model, called Standardised Tables, as an organised framework to jointly work with existing databases to manage big data collected from different industries belonging to the CNs. The information of the Standardised Tables will be mainly used with optimisation and collaboration purposes. The paper describes an application of the Standardised Tables in one of the pilots of the aforementioned project, the automotive industry pilot, for solving the collaborative problem of a Materials Requirement Plan.

A complete network traceability to identify suppliers and customers’ activities and share information along the entire network is not an easy objective to achieve. It requires the involvement of all the network stages: manufacturing, purchasing and distribution processes. This research aims to study traceability for collaborative network within the fashion industry. We conducted an in-depth case study using an interview protocol specifically designed for this research investigating drivers as well as practices for network traceability.

Declining oil prices have made it necessary for oil and gas companies to scrutinize their operations and associated costs. The increase in data richness from the digitization of supply chain processes could help these companies manage risks, and increase collaboration and profitability. This paper explores the challenges and opportunities for oil and gas industry in this context.

The successful introduction of flexible, reconfigurable and self-adaptive manufacturing processes relies upon evolving traditional automation ISA-95 automation solutions to adopt innovative automation pyramids. These new approaches target the integration of data-intensive cloud and fog-based edge computing and communication digital manufacturing processes from the shop-floor to the factory to the cloud. The integration and operation of the required ICT, automation and robotic technologies and platforms is particularly challenging for manufacturing SMEs, which account for more than 80% of manufacturing companies in Europe. This paper presents an insight into the business and operational processes, which motivate the development of a digital cognitive automation framework for collaborative robotics and modular manufacturing systems particularly tailored to SME operations and needs; i.e. the AUTOWARE Operative System. To meet the requirements of both large and small firms this paper elaborates on the smart integration of well-established SME friendly digital frameworks such as the ROS supported robotic Reconcell framework, the FIWARE-enabled BEinCPPS Cyber Physical Production framework and the OpenFog compliant TAPPS hardware framework.

The advances in Information Technology (IT) which allowed the transformation of products into cyber-physical systems (CPS), brought new challenges and opportunities for development in manufacturing. Connected product networks (CPN) which bare more advanced features opposing to regular products, require special resource management and more flexible processes during the whole lifecycle of a product, from conceptualization to development and use. Cloud computing and data analytics enhance the opportunities for reconfiguration and optimization of machines and products, leading to reduced costs for the factories. Situational awareness driven by ontologies, together with Big Data analytics exhibit significant potential for competitive manufacturing that is able to follow the fast-evolving market, nevertheless, with respect to the user and the environment. The current paper suggests an approach for the exploitation of manufacturing context, using sensors and data management techniques in industrial business cases, to achieve reuse of information, resulting to reduced manufacturing waste.

This paper describes an innovative approach to adopt the next-generation manufacturing paradigm based on flexible production units and eco-systems that can be quickly reprogrammed to provide fast time-to-market responses to global consumer demand, address mass-customisation needs and bring life to innovative products. The approach utilises the capabilities offered by digitalisation to facilitate (i) in-depth (self-) monitoring of machines and processes, (ii) decision support and decentralised (self-) adjustment of production, (iii) effective collaboration of the different IoT-connected machines with tools, services and actors (iv) seamless communication of information and decisions from and to the plant floor and (v) efficient interaction with value chain partners. The paper presents the conceptual architecture under development to support those functionalities for two specific domains in manufacturing.

When developing product-services (P-S) it is important to take a collaborative and user-centric perspective to ensure that the P-S fits to customer needs. There are different approaches for the user involvement from intensive co-creation to the collection of customer feedback about the P-S design or P-S behavior. The feedback again can be collected using different methods and tools. The paper discusses different methods for collecting customer feedback for product-service innovation and design. The context of the study is the Manutelligence project in which a P-S collaboration platform is developed, also to support interaction with customer. In the paper four Manutelligence use cases from different industrial fields are analysed to identify the different customer types, lifecycle stages of the feedback, feedback forms and how the platform can support the feedback collection and use. The benefits of the feedback collection in different cases are also described.

Nowadays, cyber physical systems support the improvement of efficiency in intralogistics by controlling and manipulating the production and logistic environment autonomously. Due to the complexity of the individual production processes, designing suitable cyber-physical systems based on their existing production environment is a challenge for companies. This paper presents a new methodology on how to design cyber-physical systems conceptually to suit an individual production environment. Compared to existing design approaches, this methodology matches immediately the required functions to existing information and communication technology’s components insisting on the neutral assimilation of requirements. Therefore, the requirement specification asks for needed functions in relating to offered functions of information and communication technology (ICT) components. The paper focusses the use case of implementing a cutting-edge mobile network technology into an existing tracking and tracing process.

This paper proposes a novel method for the structuring of the knowledge of a service process in order to be processed by lightweight declarative computing infrastructures. Through the identification of self-similarities in the process, the flow of the structured information and the sequence of activities performed in the process are easily implemented by means of cyber-physical systems technologies, in order to timely meet the customer/stakeholder’s requirements. The study was performed in a telecommunication service providing organization. Service teams create a collaborative network. With the use of the CPS proposed in this work they can communicate problems and disseminate solutions. This methodology uses the information of a set of performance indicators of the service organization to achieve a better control of the effectiveness and the bottlenecks in the supply network. The methodology is borrowed from the mechatronics field and it is prone to a natural extension and reuse for the similar information structures in manufacturing processes.

Exploiting the collaboration between human and robots is a fundamental target for industrial Cyber-Physical Systems. Several studies have already addressed the evaluation of collaborative robotic cells, especially in automotive industry. Feasible tasks assignment to workers and robots were proposed in a few use-cases. However, previous studies start from an existing configuration of the collaborative assembly cell. Due to the moderate diffusion of collaborative robotic applications in the industry, it would be better to define a method orienting the design of a new instances of collaborative cells, by taking into account the different classifications of collaboration deriving by the new ISO 15066 standard. The classification depends on the kind of information that must be made available within the cell, and the possible methods of acquisition and communication of such information. This knowledge base will be represented in the form of ontology, as an extension of the CORA (Core Ontologies for Robotics and Automation) ontology, by IEEE Robotics and Automation Society. By adopting this ontology, it will be possible to support the design of new collaborative cell. An industrial case-study will prove the efficacy of the proposed method.

The paper aims to analyze multi-agent management structures in Small-Mid Enterprises (SME), such to investigate how collaborations among agents could be improved. A SME manager has generally to face with three issues: data management, organization of the production phases, interactions with mid-level management (denoted by “agents”). Data management is necessary in order to have a clear representation of the many types of data about products, orders and resources of the company. The organization of production phases consists on the management of the production process phases from design up to recycling. Based on a clear representation of the product life cycle, the manager will be able to organize the interactions between these agents (each one dedicated to handle a process phase) so as to facilitate the collaboration, and to make effective each mid-level management as far as it is concerned with controlling the operations to be carried out in each process phase. A model of the mid-layer multi-agent management negotiation, according to the “game theory” viewpoint, is proposed, and its main characters are analyzed, in view of its application.

This paper will examine the importance of big data tools and digital technology for organizational design. Drawing on principles of cybernetics, particularly Ashby’s law of requisite variety and Beer’s Viable System Model (VSM), it will examine the potential implications of big data and digital developments for whether organizations need to be more centralized, decentralized or adopt networked arrangements with different level of stability and flexibility. The premise of the paper is that, for systems (such as organizations or collaborative networks) to remain viable, their internal complexity needs to reflect that of the environments in which they are based. Examples are provided from the case of the network agreement framework in Italy, which are analyzed using VSM as a theoretical framework.

Firms need comprehensive knowledge and understanding of the opportunities available in the market by creating supply market intelligence (SMI). SMI can facilitate in finding the best partners and combination of capabilities within collaborative networks (CN). However, despite of its evident managerial relevance, SMI is still little researched topic. Simultaneously, big data analytics (BDA) has developed rapidly, becoming vital for businesses across industries. The objective of this paper is recognizing the importance of SMI and opportunities of BDA through qualitative research. The data derives from two focus group discussions of 75 purchasing professionals and six qualitative interviews of BDA experts. This research contributes to our understanding of the opportunities of BDA in creating systematic SMI to reinforce strategic collaboration, and to the understanding of knowledge as a strategic resource for forming strategic CN. Collaborative big data intelligence creates value through, for instance, creating transparency in business processes and discovering market changes.

The article describes the missing link between the information type and quality required by the process of decision making and the knowledge provided using the recent developments of ‘big data’ technologies, with emphasis on management and control in systems of systems and collaborative networks. Using known theories of decision making, the article exposes a gap in present technology arising from the disparity between the large amount of patterns that can be identified in available data using data analytics, and the lack of technology that is able to provide a narrative that is necessary for timely and effective decision making. The conclusion is that a second level of situated logic is necessary for the efficient use of data analytics, so as to effectively support the dynamic configuration and reconfiguration of systems of systems for resilience, efficiency and other desired systemic properties.

Open-source projects in GitHub exhibit rich temporal dynamics, and diverse contributors’ social interactions further intensify this process. In this paper, we analyze temporal patterns associated with Open Source Software (OSS) projects and how the contributor’s notoriety grows and fades over time in a core-periphery structure. In order to explore the temporal dynamics of GitHub communities we formulate a time series clustering model using both Social Network Analysis (SNA) and technical metrics. By applying an adaptive time frame incremental approach to clustering, we locate contributors in different temporal networks. We demonstrate our approach on five long-lived OSS projects involving more than 700 contributors and found that there are three main temporal shapes of attention when contributors shift from periphery to core. Our analyses provide insights into common temporal patterns of the growing OSS communities on GitHub and broaden the understanding of the dynamics and motivation of open source contributors.

Today, Big Data Analytics (BDA) are definitely the key basis of competitiveness for enterprises in their Supply Chains. The outburst of data captured, accumulated and analyzed is impacting the value-added-chain at all levels from manufacturers to customers. In this paper, we develop a structured methodology to provide a deep analysis of Big Data Analytics methods across the Supply-Chain Operations Reference (SCOR) model processes. An exhaustive literature review is illustrated to afford a comprehensive Mind-Map cartography with a BDA-SCOR matching matrix. The proposed approach points to a number of research concerns that need to be addressed by research community. Outcomes of this study may highlight relevant guidelines for upcoming works of both academics and industrials. It highlights the need for collaborative Big Data to manage SCM more intelligently. Our objective is to provide an effective analysis to understand how Big Data Analytics become even more valuable for better Supply Chain Management.

The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services.

Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature “Named Entities”. By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process.

This paper demonstrates an application case for the concept of Data Service design and composition techniques established by the Big-Data Data Service (BD-DS) modelling method realized using the ADOxx meta-modelling platform. In the domain of energy-efficiency assessments of buildings and their operations, the collection of energy related data does not pose a problem anymore as the necessary infrastructure is available in a non-intrusive way, at low installation and operation costs. An issue identified relates to the realization of value-adding services based on these continuous data streams in such distributed, heterogeneous environments. In the context of the ORBEET project, a dynamic, close-to-real-time data access/composition design and exploration framework has been realized. This framework builds upon the concept of Energy Data as a Service combining different sources for a dynamic and enhanced operational rating deployed in 4 different pilot sites in Europe.

Businesses, tourism attractions, public transportation hubs and other points of interest are not isolated but part of a collaborative system. Making such collaborative network surface is not always an easy task. The existence of data-rich environments can assist in the reconstruction of collaborative networks. They shed light into how their members operate and reveal a potential for value creation via collaborative approaches. Social media data are an example of a means to accomplish this task. In this paper, we reconstruct a network of tourist locations using fine-grained data from Flickr, an online community for photo sharing. We have used a publicly available set of Flickr data provided by Yahoo! Labs. To analyse the complex structure of tourism systems, we have reconstructed a network of visited locations in Europe, resulting in around 180,000 vertices and over 32 million edges. An analysis of the resulting network properties reveals its complex structure.

The purpose of the research is to develop an intelligent system able to support the design and management of a Customer Experience (CX) strategy based on the emotions tracked in real time at the different touchpoints in a store. The system aim is to make the shopping experience responsive to the customers’ emotional state and behaviour and to suggest successful product/service design guidelines and customer experience (CX) management strategies whose implementation may affect current and future purchases. In this particular, the present paper focuses on the description of the integrate approach developed to design the overall CX and on the emotional recognition tools to elaborate the rich-data captured by a network of optical and audio sensors distributed within the shop.

Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.

The availability of sensors, smart meters, and so called ‘intelligent devices’ (IoT) enables owners and tenants to better understand and flexibly adjust the status of buildings and their systems according to their needs. However, it also requires a more intense and detailed knowledge about how to exploit, analyse and manage ‘big data’ compiled from these devices. Building operators, facility managers and energy suppliers are expected to collaborate and to share this data aiming to deliver more holistic, comprehensive services to clients (i.e. owners and tenants of buildings). This paper discusses how so called ESCO-business models (energy service companies) and CC-business models (continuous commissioning) can be integrated through sharing of big data and collaboration of major stakeholders involved in building operation, energy supply and engineering consultancy. It explains how building owners will benefit from the availability of such comprehensive, collaborative services.

This paper discusses the potential impacts of MyData in healthcare business, more precisely occupational health insurance companies, and how the coming of MyData will transform the business models and the whole logic of value creation and capture of health insurance businesses. These companies have traditionally acted alone and relied on organization-centric business models. Through an empirical study, we demonstrate how these organizations are now heading towards acting as active members of collaborative health service ecosystems.

Managing process variability enable the process model adaptability according changes in the application environment. In the healthcare area, flexibility is essential to provide a quality treatment because, even patients with the same diagnostic, may follow different paths and suffer different proceedings. Besides, there are many aspects to be considered for the selection of a path, as patient’s symptoms, and clinical guidelines, among others. In this context, this research presents a framework for the management of the process variants through semantic reasoning. The enrichment of the business process with semantics enables the automation of the configuration, thus promoting more flexible and adaptive solutions. The proposed framework helps selecting the appropriate process variant according the patient’s symptoms by reasoning on ontologies based known expertise. In our specific use case, we will use the expertise of the Brazilian guideline for acute ischemic stroke.

The modern evolution of healthcare systems towards even more complex networks has highlighted the emerging need of a standardized and interoperable model for the management of health data. Several studies in the past years have underlined how the adoption of Semantic Web technologies can provide a valuable solution for both of the aforementioned needs. Semantic modelling of health-data can indeed provide a sound and sharable conceptualization of a patient’s health condition and can leverage automatic generation of new knowledge related to the clinical contexts. In fact, thanks to reasoning processes these technologies can be used as part of decision-support systems in a variety of domains. In this paper two examples of ontologies are presented. Both models rely on a worldwide-known classification and are the basis for two decision-support tools related to the Ambient Assisted Living and Work Reintegration domains, which enable cooperation among different clinical and non-clinical stakeholders.

Industry 4.0 is a key strategic trend of the economy. Virtual factories are key building blocks for Industry 4.0 where product design processes, manufacturing processes and general collaborative business processes across factories and enterprises are integrated. In the context of EU H2020 FIRST (vF Interoperation suppoRting buSiness innovaTion) project, end users of vFs are not experts in business process modelling to guarantee correct collaborative business processes for realizing execution. To enable automatic execution of business processes, verification is an important step at the business process design stage to avoid errors at runtime. Research in business process model verification has yielded a plethora of approaches in form of languages and tools that are based on Petri nets family and temporal logic. However, no report specifically targets and presents a comparative assessment of these approaches based on criteria as one we propose. In this paper we present an assessment of the most common verification approaches based on their expressibility, flexibility, suitability and complexity. We also look at how big data impacts the business process verification approach in a data-rich world.

The concept of service ecosystems emerged recently in service research as an important notion that underlines the complexity of structures in service interactions and the need for comprehensive approaches for the study of service systems. In this paper we focus on the role of the user as the ‘keystone entity’ of service ecosystems – especially for the creation of value. The research objective is to understand better the requirements and the implications of a user-centric perspective on service ecosystems and provide some basic modelling abstractions for the analysis of the structure and the objectives of the service ecosystem. Τhe paper develops the concept of the user-centric service ecosystem at the beginning and then provides a conceptual model of its structure and a goal model for the intentions of the actors. The paper can contribute to the better understanding of service ecosystems, the explanation of the role of the user and the fulfilment of the initial phases of requirements analysis for service ecosystems.

A service-oriented collaborative network (SOCN) supports collaboration among a network of organizations through their shared business services. SOCN in comparison with traditional collaborative networks, promotes and simplifies reusability and interconnection of shared software services, in a distributed manner. Our work contributes to comprehensive support of software service oriented collaboration among networked organizations enabling semi-automated service discovery, selection, and composition in collaborative environments. With the help of an organizational monitoring tool, we improve the accuracy of claimed characteristics based on non-functional criteria of services. The reference framework and implementation architecture are defined in this paper to support implementing SOCN.

Organizations that are part of collaborative service networks need to handle increasing amounts of data in their information systems to construct complex customer-oriented solutions from dynamically selected service elements. This brings numerous challenges in today’s highly competitive markets, where companies need to provide customers with services that have a high level of quality, in a time and cost effective manner. Having prior knowledge of the performance associated with specific choreographies of services allows companies to provide customers with services tailored to their specific requests, while maintaining a high quality of the services. This paper presents an approach for service selection and ranking considering customers’ requirements, knowledge of the historic behavior of services, business process constraints and the characteristics of the execution environment. Following the specifications of the proposed framework, a prototype has been implemented targeting the automotive sector. Tests of this prototype illustrate the usability and validity of the proposed approach.

The upward integration endeavor is making informatics systems (I-systems) increasingly complex. The modeling techniques, methodologies, development strategies, deployment and execution environment, maintenance and evolution, and governance, to mention just a few aspects are making the resulted (un)integrated informatics technology system a vendor lock-in landscape. The relation between informatics science and engineering and the organization’s business or control processes automation, or services provisioning and adaptation, has demonstrated to be difficult to converge to a common understanding of clear computational responsibility borders. Existing approaches and standards fail to be complete with respect to establishing a landscape of informatics technology under vendor agnostic model (lock-in free). In this context, this paper extends previous research by proposing an organization´s level modularity framework aiming at formally identifying an agnostic, and open informatics system of systems (ISoS). A definition of its components is provided, and a validation case study is discussed.

Resources sharing between members are a key issue in Collaborative Networked Organizations (CNO). In the software services sector most of companies develops their services by their own and stores them at their local silos without sharing them with other partners. However, the development of services-based applications can be very complex and costly, which is a difficult issue to overcome as most of IT companies are SMEs. In this line this paper proposes a digital catalogue environment to leverage services sharing and larger reuse between CNO members. It is strongly grounded on standard business processes models that would be adopted by all the involved members. The catalogue acts as a collaborative environment that logically embraces all the public services made available by the CNO members, enabling software services developers to compose new services-based applications more agilely. A prototype has been implemented and its results are presented and discussed.

Generic representation of web services is targeted, in order to generate machine readable specification of business processes that run at each partner organization within the virtual organization (VO). A holistic and formal service specification model is defined that can then be used unambiguously for discovery of business services, i.e. for finding a suitable service available at the VO to perform a given task. Especially, the proposed model, called C3Q, augments the description of service by its behavioral specification. A light extension of the WSDL documents is represented to specify all aspects of C3Q. Finally, a GUI is implemented to assist users with the behavioral description of the VO services.

In this paper, we draw attention to the importance of incorporating aspects of localization into design of e-Services in order to address the differences among e-Services consumers such as linguistic differences, and cultural diversity. In the past, many companies have realized that the idea of promoting an e-Service through a single version of a portal/website is not suitable for all of the potential users or customers. This has led companies to consider new and creative design principles for e-Services, especially those who are in direct interaction with the consumer and act as service provider in a Business-to-Customer (B2C) setting. In this regard, this paper initially reviews the different aspects of service design that highlight the need to include cultural usability aspects in the service design process and successively determines the different cultural dimensions that have a substantial influence in determining the level of e-service localization.

Increasing market dynamics rapidly change the business landscape. Collaboration amongst organizations is a common way to cope with these dynamics. Achieving a state of Business/IT-alignment (BITA) within Collaborative Networked Organizations (CNOs) appears to be a valuable endeavor. Therefore, this paper investigates CNO characteristics, as a basis, to incrementally design BITA artifacts that facilitate CNO-dynamism. Via a structured literature review and an expert session, we synthesized a list of 6 main and 22 sub-characteristics for CNOs. This list provides more detailed characteristics than we found in the literature. We also discuss the importance of the characteristic “Dynamic and self-regulating network” and the need for new BITA models that can cope with the dynamics.

Public policies documents convey strategic directions and framework of actions of government in a particular sector. For most societal challenges, there is a need for government entities at the same and different levels to coordinate their policies and collaborate on the implementations of policies. However, this coordination and collaboration efforts are seriously hampered by the lack of a central repository for public policy documents from which policy makers and researchers can access related policies on a particular topic, industry or group of stakeholders. To address this challenge, this paper describes the development of Vocabulary to underpin the implementation of a shared public policy repository in Europe. The Core Public Policy Vocabulary (CPPV) is developed as a semantic interoperability resource for government organizations for consistent description and documentation of public policies to enable efficient discovery, cross-referencing and analysis of policy documents. We describe our approach, conceptual model, elements of the vocabulary, its implementation and concrete scenarios for the use of the vocabulary.

Information is a crucial asset to successful internationalization, as it allows to reduce risks and uncertainty and to facilitate international expansion. Information about foreign markets and activities can be obtained by companies from internal or external sources, and in formal or informal ways. Industrial business associations (IBAs) are one of the external information sources. Due to their resource constraints, small and medium enterprises (SMEs) tend to highly depend on the services and information of IBAs to expand their business overseas. However, to improve their role in supporting SME internationalization, one important development is for IBAs to evolve towards more collaborative networks, by sharing more organized and valued information with their associates, but also by fostering information flows and more collaborations between them. This paper contributes to this view by synthetizing current data and information sources to support SME internationalization, based on a literature review, on interviews with Portuguese IBAs, and on expert opinion. An innovative aspect is to present a first instantiation of a socio-semantic model, opening paths for further developments in this area, and contributing to the community of Collaborative Networks.

Collaborations are based on mutual trust to strengthen confidence in the sharing of various resources such as information. Particularly in logistics, collaborations benefit emerged rich-data environments to successfully manage demand fluctuation and visibility of in-store logistics; as well as the sharing of physical assets. Shared information is gathered from various sources and manipulated by specific partner to match or maximize individual payoff. Such information may become vulnerable to information sharing behavior of the partner to henceforth affect trust. This paper investigates the influence of the information sharing behavior on trust. It focuses on a dimension of information accuracy to answer a research question: how do information sharing behaviors of partner affect trust in logistics collaboration? A framework of information behavior is established, and subsequently a trust model specified. Afterwards, simulation experiments are conducted to observe resulting impacts. Results unveil that both, the positively and negatively manipulated information influence trust in similar magnitudes. It is further argued that partner’s deceitful behavior underlying information sharing can be reduced although it might be difficult to eliminate.

The objective of this article is to move a step forward towards the general knowledge about how to manage supply chain risks by developing a theoretical risk index using exposure analysis. Two questions “why logistics systems are exposed to risks? to which type of risk, a supply chain organization is exposed to?” have to be raised and investigated. The answers to these questions will be used as a basis for the risk index methodology, which allows organizations to assess their exposure level and also to identify which type of risk they need to deal with it. The results of previous step will be computed into a supply chain risk index. To achieve our objective, the article is organized as follows: Sect. 2 provides a brief literature review to position the proposed model against other existing theoretical models. Section 3 provides the methodology used to develop our approach. Section 4 presents the conclusion, limitations and future research.

Online interactions between unknown parties are increasingly common. This is particularly exemplary in Virtual Organizations (VO). A VO is understood as a permanent or temporary alliance of enterprises that share their resources and skills to achieve specific business goals (collaboration opportunity). In this sense, the lack of trust between partners is seen as an hindrance to the VO’s operation. Thus, reputation systems can be applied to trust establishment and management among VO’s partners. Therefore, this paper presents a taxonomy to classify and compare trust and reputation systems applied to VO’s context. The taxonomy is based on literature studies regarding characteristics observed through a systematic review process. In order to evaluate the proposed taxonomy, it was applied to trust and reputation systems obtained through a systematic review process. Finally, according the presented results, it is possible to note a gap concerning reputation systems applied to the context of VO and Cloud Computing integration.

Open innovation is a strategy with increasing adoption by value-seeking companies using or sharing technology with the outside world. But this strategy is also accompanied by risk. However, risk management seems to have been overlooked by researchers on open innovation networks. This exploratory work clarifies to what extent the issue of risk has been considered in open innovation research. Presented results are based on interviews and analysis of existing literature on open innovation.

The present paper clarifies the methodological framework to implement the integration of Cyber Physical Systems (CPS) in the perspective of environmental sustainability. Firstly, authors identify main methodological, software and hardware pre-requisites enabling the use of CPS systems in accordance with the literature and environmental standards. Then, the paper focuses on the main barriers in the case the information is distributed along a flexible supply chain and identifies a methodology to coordinate environmental information from ubiquitous sources by the use of modularization. This methodology is applied in the field of energy management for the steel sector for different production lines. In particular, it is highlighted as the consumption tracking may be linked to the real-time management of suppliers and processing cycles in order to pursue strategies for environmental impact minimization. Results emphasized as the development of the CPS systems that are designed for sustainability can produce relevant results for the effective restrain of industrial environmental impacts.

The hybrid Economic Input Output - Life Cycle Assessment (EIO-LCA) coupled with qualitative methods for consensus creation and participative policy building is a proposed methodology for dealing with complex and data-rich contexts that have to be connected in order to construct a robust regional bioeconomic profile that informs policy makers and other stakeholders about socio-economic and ecological impacts – and suggest at the same time strategies for its validation and implementation. An important result of this work is that the process of data collection and systematisation also provided ample opportunity to be in touch with a wide and diverse range of actors that characterize the different supply chains, ranging from the primary (farming, aquatic resources, forest sector) to manufacturing and services-related companies. These contacts encouraged the establishment of a network exchanging knowledge on crucial aspects for the further development of bioeconomy.

The topic of sustainable growth is becoming central in the debate over the rural development policies. Rural communities can completely fulfil the new challenges in the area of sustainability only with the implementation of innovative forms of collaborations among their business networks. In this work, we consider a particular form of business collaboration arising within rural communities, namely Agritourism Rural Network (ARN). In an ARN, a farm, providing agritourism activities, represents a touchpoint between a network of business and organizations in a rural area and tourists interested in enjoying the local territory. With the aim to deeper the extend of the agritourism phenomenon in a rural region and the potential of the related ARNs in being means of sustainable development, we report main results of an empirical survey carried out in 2016 on a sample of 105 agritourism farms all belonging to the same region (Calabria, Italy). Results confirm our intuitions about the importance at farm level of setting agritourism activities and their impact for the ARN related to the farm and for the sustainable development of a local community as a whole.

The vast diversity of current sensing devices and mechanisms enable the creation of data-rich environments which gives the opportunity to create smart systems. The smartness of systems must transcend individual interests and fulfill collective aspirations and environmental needs. Therefore, new products, processes, enterprises, communities and any kind of systems must be envisioned as Sensing, Smart and Sustainable (S3). This paper discusses the trends and challenges for S3 applied to everything. Furthermore, it suggests that the knowledge produced in the field of collaborative networks might be used to leverage the S3 capabilities.

Due to growing concerns with sustainability issues and the emergence of the Circular Economy (CE) paradigm, combined with recent technological changes and consequent increase in competitiveness, there is a pressing need to redefine the Product Lifecycle Management (PLM) approach. PLM needs to incorporate aspects that would enable the shift to this paradigm, such as enhanced collection and evaluation of information coming from production processes, distribution, retail, consumers, and collaboration in an extended enterprise context, by implementing enabling technologies such as the Internet of Things (IoT) and Big Data. This paper proposes a vision, based on the state of the art, for a CE enabled PLM, having the Portuguese footwear industry scenario as a reference.

Shifting towards Circular Economy will have significant implications on how value offerings are created, delivered and their residual value recovered as well as how shared value is created and captured by various actors in a collaborative (business) network where information, materials, water, energy and cash will flow in different cycles and based on various dynamic interactions within a closed-loop value system. In this context, Green Virtual Enterprise Breeding Environments (GVBEs) will act as long-term strategic networks of green enterprises and their related support institutions established with the aim of providing the necessary conditions, including common (circular) operating principles and infrastructure, for increasing their members’ preparedness towards rapid configuration of Forward- and Reverse-Green Virtual Enterprises (GVEs) in order to provide a dynamic model for operations management of closed- loop supply networks. This paper focuses on how GVEs and their breeding environments can contribute to enable McKinsey’s RESOLVE framework of six core-principles, and corresponding actions, for circularity: REstore/REgenerate, Share, Optimise, Loop, Virtualise and Exchange.

Industrial symbiosis can well represent a new kind of collaborative network which demands resolute attention to the flow management of materials, by-products, and waste through local and regional economies. Industrial symbiosis engages traditionally separate companies in a collaborative approach to competitive advantage involving physical exchange of different kind of products which are usually disregard by traditional market transitions. The identification of innovative models to support decision-making process in this kind of networks is urgent since it is necessary to close the loop assuring profitability at a single node as well as at system level. This paper proposes the application of the bi-level optimization model as a way to handle this two-stage problem organized into two cooperating layers. As a preliminary result, the model is applied to a simplified case where the nodes can sell their output to the market or share them in the industrial symbiosis to increase the global efficiency.

Business Ecosystems supported by the increasing use and expansion of communication networks represent nowadays a powerful form of collaboration, enabling organizations better responding to more challenging business opportunities. In this context, performance indicators are needed for measuring collaboration benefits of a business ecosystem as a whole and of its individual members, the organizations. But such indicators may also induce a self-adjustment of the organizations’ behavior, as there is a natural tendency of individuals and organizations to adapt to the way they are evaluated. As such, an adequate selection of performance indicators can help the sustainability of the ecosystem. The level of reaction to indicators is nevertheless not the same for all members of the ecosystem, i.e. there are different classes of responsiveness. Using system dynamics and multi-agent systems, the focus of this paper is the study of the evolution of the agents of a collaborative business ecosystem, depending on the performance indicators used to assess their performance and considering their class of responsiveness.

The setup of an efficient collaborative situation between organizations can be considered according to four main complementary dimensions: the context (geographical, social, economical environment), the partners (the actors, their capabilities, their resources and their relationships), the objectives (the collaborative goals of the network, the risks to be avoided, etc.) and finally the behaviour (the collaborative processes to be implemented by the partners to achieve the objectives considering the context). Some research works try to infer the behaviour based on the three other issues assumed to be known, by using different approaches such as model-driven engineering (MDE), optimization, heuristics, planning, etc. MDE helps studying the opportunity of inferring the objectives part from the context and partners dimensions in order to feed the behaviour issue. We use a non-equilibrium thermodynamics analogy where partners and context attributes and methods are mapped to thermodynamic state variables of an organisation seen as an open system in the frame of dissipative structure thermodynamics. We apply it specifically to enterprises and discuss briefly the analogy of behaviour as an irreversible trajectory aiming to maintain the enterprise activity alive.

The aim of this paper is concerned with a conceptual framework to get about the smart factory concept in the mould/tools industry, by retrieving knowledge from historical data and data analysis. Specifically, it will be addressed the requirements to design and develop the next generation of open decision making informatics systems as tools for high variability manufacturing systems as in the tooling industry. The proposed open computational platform as open informatics systems (I-systems) developed under the specifications of the conceptual framework mention above will combine new balancing and simulation algorithms that allow the optimization of the production systems suggesting new and credible collaborative production sequences in a forecasting manner. Coupling smart monitoring with real-time decision support I-systems, the proposed approach will be able to cope with the aim of reducing lead-times and costs on the shop floor. The proposed open system of systems approach aims at contributing to adaptive higher integration levels, contribute for a collaborative factory and, at the same time, reduce the vendor lock-in dependencies risks.

During a crisis, being able to understand quickly the situation on-site is crucial for the responders to take relevant decisions together. Social media, in particular Twitter, have proved to be a means for rapidly getting information from the field. However, the deluge of data is heterogeneous in many ways (location, trust, content, vocabulary, etc.), and getting a model of the crisis situation still requires laborious human actions. In addition, depending on which kind of information is mined from them, tweets have to be handle one-by-one (e.g. find victims), or as a whole - amount of tweets - (e.g. occurence of an event). This paper proposes a framework for automatically extracting, interpreting and aggregating streams of tweets to characterize crisis situations. It is based on a specific metamodel that determines the different concepts required to model a crisis situation.

Virtual organizations and enterprises are prominent examples for collaborative networks (CN). Trends like Web 2.0 and Social Media refine the technological base of CN, namely through enterprise social networks (ESN). To strengthen knowledge management and through it the innovation capabilities of CN, further research on supporting knowledge exchange and social learning within ESN is needed. Approaches like serious gaming or digital social learning already tackle this problem. However, there is still a significant lack in integrated approaches, concepts or tools for ESN and digital social learning. In order to address this issue, within the work presented in this paper a design research approach has been chosen to consider the lack of methodological clearness of the iteration process. Thus, the paper will firstly introduce a framework for the iteration process of design research and secondly show first insights on how to integrate ESN and digital social learning.

As the number and adoption of social networking sites (SNSs) supporting business representation in the form of business pages continues to escalate, more scalable and robust mechanisms for integrating data from different networks in order to serve the special purposes need to be envisaged. An important concern of such SNS data integration is the platform dependencies that different networks impose in collecting, organizing, and presenting the business information hosted on their servers. In this esteem, this paper deals with overcoming the challenge of different business categorization schemes being varyingly used by the existing SNSs. In doing so, we present a content-oriented approach for determining the business category on the basis of a semantic analysis of the textual information available in the business profile. The approach has been operationalized in the CoDiT (Company Discovery Tool) which is a web-based tool to facilitate the integrated business page search over multiple SNSs.

Future industrial and societal projects must be designed for sustainable operations, evolutionary capabilities, and business operations. To exploit the potential of digitalization, we must design practical cases and roles, workspaces and collaboration spaces, creating evolutionary action- and work-sensitive contexts. Intelligent workspaces and operation models will complement, enhance, and replace natural language specifications, coded systems and information flows. Design of visual knowledge models, enabled by Active Knowledge Architectures, will revolutionize industrial computing and collaboration. The MADONE partners have performed industrial and public pilot projects verifying that sector adaptable platforms give actors agile approaches and evolving collaboration capabilities and services. Evolutionary workplaces and collaboration spaces resolve present challenges and open new opportunities, supporting flexibility, adaptability, reusability, traceability, predictability and sustainability. Holistic design of projects and operations is the industry case demonstrator described.

Companies in the Culture and Creative Industry are characterized by highly networked value chains. However, this value network lacks a profound support in terms of information technology and structure resulting in time-consuming and error-prone manual labor. To overcome these challenges, we conceptualize an application framework for creative industries. In particular a software architecture design and a data design will be proposed with the help of a design science research approach. The goal of the resulting application framework is to support the digital transformation of companies in the creative industry towards collaborative networks.

Over recent years, in a wide range of countries, grassroots initiatives have emerged aimed at overcoming the limits of the mainstream agro-business system. These initiatives aim at improving farmers’ access to local and regional markets and consumers’ access to fresh local produce. Among these initiatives, Food Hubs have emerged as a promising way to improve local food supply systems. They represent collaborative networks of producers and consumers that aggregate, distribute, and market local food products. ICTs enable these collaborative networks by allowing information exchange among their actors and by providing collaborative tools that allow quick co-ordination between members of the network. The paper aims to analyse how the adoption of ICTs have fostered the development of new, initiatives oriented at establishing local food networks and to reconnect producers and consumers. The study will present results from the analysis of two food-hub initiatives based in South West England, which are adopting informative systems to support their activities and to implement novel business models: Stroudco Food Hub and Dean Forest Food Hub.

Agri-food supply chains are subjected to many sources of uncertainty. If these uncertainties are not managed properly, they can have a negative impact on the agri-food supply chain (AFSC) performance, its customers, and the environment. In this sense, collaboration is proposed as a possible solution to reduce it. For that, a conceptual framework (CF) for managing uncertainty in a collaborative context is proposed. In this context, this paper seeks to answer the following research questions: What are the existing uncertainty sources in the AFSCs? Can collaboration be used to reduce the uncertainty of AFSCs? Which elements can integrate a CF for managing uncertainty in a collaborative AFSC? The CF proposal is applied to the weather source of uncertainty in order to show its applicability.

Food information is a crucial tool for facilitating consumers in decision-making activities related to their consumption process. Recent advances in “Internet of food” technologies (such as food sensors, cloud computing, food data analysis, and mobile app technologies) makes possible to conceive new consumer information platforms. The rationale is to empower consumers by letting them get more relevant food information than they usually obtain through on-product labeling, mass media or other traditional channels. In this paper, we envisage a new generation of food information provision services, called intelligent food services (IFSs), which would be responsive to consumer’s expectations and information needs. We outline IFS structure and main features as well as constitutive elements of user-IFS interaction context. Particularly, we focus on food-in-context awareness capability and we discuss its influence on consumer and IFS behaviors.

Risk and uncertainty are ubiquitous and varied in agri-food supply chains. As environment volatility increases, only having capabilities to manage agri-food supply chain risks may not be enough, resilience factors also need to be integrated into daily operations. This paper undertakes a systematic literature review on risks sources and resilience factors in agri-food supply chains. A five-stage systematic review methodology has been followed. The findings suggest that agri-food supply chains are highly vulnerable to various risks due to its unique characteristics of products. Main risk sources include antibiotics resistance, weather related risks and natural disasters, policy and institutional risks, and unethical issues. Five key resilience factors identified from the literature are traceability, knowledge management, collaboration, culture, and agility. The paper makes a contribution to the extant literature in the field of agri-food supply chain risk management and agri-food supply chain resilience.

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.

Increasingly challenging global and environmental requirements have resulted in agricultural systems coming under increasing pressure to enhance their resilience capabilities. This in special to respond to the abrupt changes in resource quality, quantity and availability, especially during unexpected environmental circumstances, such as uncertain weather, pests and diseases, volatile market conditions and commodity prices. Therefore, integrated solutions are necessary to support the knowledge-management, collaborative ICT solution, risk management and regulation management across agriculture stakeholders. Therefore, and based on the on-going work under the H2020 RUC-APS project research network, this book chapter is oriented to contribute to agriculture value chain decision-making field to cover the current need on gathering a common understanding and appreciation of new trends in agriculture value chain, in special the multi-disciplinary challenges. For this, a short a literature review is conducted to summarise the main findings on real application and current research trends. This within the objective to propose an integrated framework based on better use of communication ways, standardised structures, development of training and awareness, regulation based initiatives and vertical Integration.