Stephane Pagano
email: pagano@em-lyon.com

Gilles Neubert
email: gneubert@em-lyon.com

Building Resilient SCs: Mapping and Measuring Key Value Drivers through a Multi-perspective and Multi-stakeholder Value Creation Framework based on Intangible Assets

Keywords: Value Creation, Collaborative ecosystems, Resilient SC, Intangible Assets, Stakeholders

Until recently, the evolution of strategy and of business models mainly focused on shareholder Value Creation with global offshoring and outsourcing. The competitive cost models of globalized flows, translated in worldwide Supply Chains, with intensive energy consumption of fossil fuels and optimized processes with outsourcing to specialized agents, highly dependent on globalized infrastructures resources and markets. Our approach is that to build resilient companies and networks, all the stakeholders of the Supply Chain must be able to shift and align their perspectives, understand, map, value and document the key drivers that constitute the core competences needed, the appropriate intangible assets. Our proposition is a methodology to analyze, map and measure the key drivers of Value Creation through of multiperspective and multi-stakeholder framework, based on intangible assets.

Introduction

In a rapidly changing environment, companies have to face many challenges while the competitive and technological environment keeps evolving at a fast pace: Globalization of markets through trade agreements and communication technologies, consumers' maturity and power of influence, strong trend towards a service-focused and dematerialized economy. Beyond a direct confrontation on their markets, with a particular focus on costs, firms have seen their competitiveness being challenged over new dimensions [START_REF] Kim | Value Innovation[END_REF]. Consumers and non-profit organizations' pressure, legislation from states trying to limit externalities and reduce climate change impacts, real time information technologies being exploited by versatile consumers and investors. In a time where cost based competition remains harder and harder to maintain, the integration of stakes such as those addressed by Corporate Social Responsibility sets new standards for innovating strategies and competitiveness [START_REF] Lash | Competitive Advantage on a Warming Planet[END_REF]. These concepts acknowledge that failing to do so inevitably leads to higher pressure from stakeholders, rising costs of environmental issues, and development of legal measures from states enforcing companies to share more of the value they create. All of these rising global risks on value creation, if not destroying value by rising sources of pressure on competitiveness.

The paper is organized as follow: after the introduction, the second part reviews essential concepts underlying our approach, the third part presents our framework and gives some details on our research and propositions. The fourth part presents some findings, while the conclusion draws expected contributions and future operational outputs.

Context

Today's value chains consist of globally spread stakeholders, globally spread production processes, global distribution and retail channels to reach clients. It is very fragile in essence, either because of geopolitical bottlenecks, environmental and social issues or because of natural disasters [START_REF] Ambulkar | Firm's resilience to supply chain disruptions: Scale development and empirical examination[END_REF]. As part of the procurement process, the purchasing function is a fundamental hub for the firms, upstream of the Supply Chain, orchestrating and managing fundamental inputs in term of value creation, adapting to business models evolutions by configuring the SC to create and capture value.

Value and Value Creation

Value is a multi-perspective concept that extends beyond the limits of the firm. Globalization and technological innovation have reorganized the way value is created and delivered through dynamic networks or chains of interconnected firms or supply chain. This raises the question on how a particular relationship helps a firm creating value in terms of offerings, and what factor(s) are essential to successfully establish a particular relationship [START_REF] Srivastava | Value creation through relationship closeness[END_REF]. In a globalized Supply Chain, the strategic models of Value Creation based on arms-length confrontation with suppliers has led many companies on a short term value creation path, which meant value destruction for many suppliers and their economic ecosystem, impacting their entire value chain and their stakeholders. The value proposition is a strategic concept that allows firms to better analyze and describe their competencies and capacities, on the strategic as well as on the operational levels [START_REF] Martinez | Aligning value propositions in supply chains[END_REF]. The deployment of the concept of Value Proposition had a significant impact on operational models of firms [START_REF] O'dell | Knowledge Transfer: Discover Your Value Proposition[END_REF][START_REF] Bititci | Creating and managing value in collaborative networks[END_REF].

To take into consideration these impacts, we position the Supply Management and the Supply Chain at the heart of the strategic models of firms.

Corporate Social Responsibility

There's an increasing pressure to consider the environmental and social aspects in purchasing policies: expectations are rising from stakeholders such as customers, the general public, NGOs, and governments who now hold companies responsible, not only for their own actions, but also all the partners within the supply chain [START_REF] Jiang | The effects of interorganizational governance on supplier's compliance with SCC: An empirical examination of compliant and non-compliant suppliers[END_REF][START_REF] Kovács | Corporate environmental responsibility in the supply chain[END_REF][START_REF] Goebel | The influence of ethical culture on su plier selection in the context of sustainable sourcing[END_REF][START_REF] Igarashi | What is required for greener supplier selection? A literature review and conceptual model development[END_REF].

Corporate Social Responsibility is a concept that is becoming mainstream because it raises the question of establishing sound and balanced relationships between companies, a wider type of stakeholders, and their environment, at all different levels: global, regional and local, but also strategic, tactical and operational. CSR approach advocates the idea that companies need to change their business models and value propositions, actually integrating the idea of creating value not only for themselves, but also for a larger number of stakeholders. The Strategic Intention of the firm can practically be translated into strategic, tactical and operational actions.

The translation of this focus in the Supply Chain and the purchasing process reflects in a growing number of international recommendations and is now common in academics [START_REF] Linton | Sustainable supply chains: An introduction[END_REF]. The focus on sustainability reporting initiatives and on CSR shows the necessity for companies to adopt a multi-perspective approach on the supplier selection and management: purchasing process and supply chain play a strategic role, which must go beyond optimization of processes, and take into account the temporal dimensions of the ROI (short, mid and long term) and the management of intangibles.

Relationships and Risks Mitigation

Usual competitive strategies based on competitive advantage, such as Porter's models [START_REF] Porter | Competitive advantage: creating and sustaining superior performance: with a new introduction[END_REF] or Resource Based View [START_REF] Barney | Firm Resources and Sustained Competitive Advantage[END_REF], can't last. To address this issue, firms have investigated other domains: process optimization to save resources, flexibility, changes in relationship with other partners to secure exclusive access to suppliers because upsurge in globalized externalization and sourcing of trade, and the use of the Internet, has enlarged purchasers' accesses. Changes in organizational forms involve more decision-makers. These developments impact the complexity and importance of purchasing decisions [START_REF] De Boer | A review of methods supporting supplier selection[END_REF][START_REF] Weele | Purchasing & supply chain management: analysis, strategy, planning and practice[END_REF][START_REF] Oshri | The Handbook of Global Outsourcing and Offshoring[END_REF]. Supplier management is a key issue in supply chain performances and this reflects in a continuing growth of publications [START_REF] Ho | Multi-criteria decision making approaches for supplier evaluation and selection: A literature review[END_REF][START_REF] Cao | Supply chain collaboration: Impact on collaborative advantage and firm performance[END_REF][START_REF] Wu | A literature review of decision-making models and approaches for partner selection in agile supply chains[END_REF][START_REF] Chai | Application of decision-making techniques in supplier selection: A systematic review of literature[END_REF]. Since suppliers can represent up to 90% of firms turnover in industrial companies, the potential impact of inappropriate portfolio of suppliers can seriously impact the value of a company. The question of risks, systemic and global, predictable or not, strategic or external must be part of the strategy of firms [START_REF] Kaplan | Managing Risks: A New Framework[END_REF].

At first, firms developed collaborative strategies, internal then between business units and departments, based on information sharing, and co-development of products and services. Instead of becoming experts, firms managed to collaborate with partners that could provide a competitive advantage [START_REF] Maccormack | Learning the Fine Art of Global Collaboration[END_REF]. Collaboration became a model for buyer-supplier relationship. Still, this remained in a narrow scope, without considering other stakeholders and that it's at a strategic level that is designed the Value Proposition for which the tactical and operational actions are translated. Firms are interconnected with their societal, natural and economic environment [START_REF] Porter | The Competitive Advantage of Corporate Philanthropy[END_REF] thus depending on these in term of competitiveness: trained employees, infrastructures, communications, political stability. The state of the environment impacts the firms activity, the operational costs, the availability of the resources, and the value of the firms viewed by the stakeholders. Global environment is not just about financial crises and competitions: human operations and natural catastrophes can generate "future simultaneous shocks to systems (that) could trigger the 'perfect global storm', with potentially insurmountable consequences" [START_REF] Howell | World Economic Forum. Risk Response Network, Global risks 2013[END_REF]. These risks are putting a high pressure on companies that need to adapt rapidly to innovative or disruptive situations.

Companies, their suppliers and networks of partners need to be aligned to fulfill the strategy to deliver the value and adapt rapidly whatever challenge arises, and in this situation, a power relation may turn irrelevant if not destructive. To be resilient, companies and their networks have to build business ecosystems in a systemic thinking with relationships that are beyond transactional, with constant feedback, collaborative models, creating and extracting value in cascade and in interaction.

Research Framework and Method

The purchasing function is the architect of the globally spread value chain: many functions are now outsourced. In this regard, the value of the firm largely depends on the value purchased from the suppliers and appropriately delivered to internal customers, hence giving purchasing departments a fundamental role to build collaborative, cooperative and successful relationships both internally and externally.

Focusing on Intangibles

As we see it, creating value extends beyond the limits of the firm and must question the different dimensions and boundaries of the firm, their strategies and business models, what value they place at the heart of their value proposition and how they intend to create, capture and share value (figure 1). This requires considering internal organization of the firm, its networks of connections and partners, strategic models, its tangible and intangible assets and the notions of capital and investment. It is possible to comprise in the same model different values such as economic, financial, marketing but also intangible assets such as, management, services, strategic position or technical performance [START_REF] Parung | A metric for collaborative networks[END_REF].

Though there is an abundant literature in economy and management on intangibles assets [START_REF] Sveiby | Methods for Measuring Intangible Assets[END_REF], we think that there is still a great work to do to operationalize intangible assets management with value creation for firms, according to a multi-perspective view, namely time frames, and according to a multi-stakeholder view, that is, in term of value created or destroyed by the firm for the different stakeholders in the firm's ecosystem. Our objective is to identify the value drivers in the organizational processes, going beyond the drivers already mastered by the buyers and more broadly the supply-chain: cost, quality, lead-time, financial performances and risk mitigation. The stake here is to demonstrate the contribution of intangible assets in the processes, seen here as drivers of successful relationships, by identifying their strategic importance in the interactions taking place in the processes. Finally, our applied research proposition is to build tools for the management of value creation in the buying and supply-chain processes that integrate these new perspectives. Measure, management and value creation in the firm are spread over time and must take into account the stakeholders. To ensure the longevity of the firm, the following dimensions and perspective must be included in the processes:

-The time horizon of the firms' strategies; -Value creation/destruction for the firm and its ecosystem; -Actual place of intangible assets as drivers of competitiveness and value creation.

To achieve this, we are constructing on an approach by value "enablers" or "drivers" for future value creation [START_REF] Marr | Intellectual capital -defining key performance indicators for organizational knowledge assets[END_REF], mapping intangible assets, processes and stakeholders through events that will eventually generate economic value and performance for the firm.

Ground for the Research

The global research ground for our projects is based on several companies operating in different industries. For this specific project, three companies offered the ground for initial explorations. Later on, among the three initial, two offered a long-standing participation for the conduct of the full research project for a period of two years.

-The first company, company A, is a food-products corporation operating on the global markets of waters, specialized nutrition and dairy products. -The second company, company B, is an affiliate of the first, operating in France, specialized in dairy food and infant nutrition. -The third company, company C, is an international family-owned group, operating in 13 countries, specialized in aerolic and thermal systems for the building industry.

Empirical Method

Our position is that "the organizational world is socially constructed" and that "the people constructing their organizational realities are knowledgeable agents (...) and can explain their thoughts, intentions, and actions" [START_REF] Gioia | Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology[END_REF]. We build an iterative process where in each step, fieldwork confronts hypothesis and theory constructs elaborated on the previous ground observation. It borrows empirical investigation methodologies found in cases study methods specifically qualitative, in an interactive way so that the components of the research, i.e. purpose, context, questions, methods and validity of the research are always considered simultaneously at each step.

-The first step of the research is an exploration where we confront our initial question, "value creation & collaborative relationships" to the stakeholders of the research ground. We state a hypothesis and test it. -The second step, based on the findings of the precedent, is to reformulate the research question accordingly if need be, identify the underlying concepts, do literature reviews on these concepts, and go back on the ground to observe organizations and processes, through the lenses of the concepts, engaging in a near ethnographic approach. -The third step is to build and share a conceptual framework where all the elements observed are connected to theories, build a methodology to gather and analyze qualitative data, and test the methodology with the stakeholders on the ground. -The fourth step, where we are now, is to deploy the methodology in the field, refine it, collect data and code it in a dynamic way to avoid holes. This step is the core of the empirical investigation where different qualitative methods are interactively used: observation, interviews, questionnaires and documents analysis. -The fifth and last step is two-fold: in terms of scientific research, we will have to decide which form will take the conclusion. In terms of applied research, we will negotiate on outputs that help companies that provided the ground to operationalize the scientific work.

Main Outcomes

As this research project is still ongoing, we only present partial findings from selected phases tested in the field. This communication wishes to present the research method, the principles of the practical output, and main outcomes of the empirical work.

Initial Outcomes

To initiate the research, we extended on a previous literature review on collaboration [START_REF] Parung | A metric for collaborative networks[END_REF] and completed it with a literature review on value and on value creation. Then, we elaborated documents and presentations to share with the participating companies. Afterwards, we organized informal meetings with two groups of buyers from the three companies to share these views and gather some qualitative data regarding the proposed definitions and approaches.

The groups consisted in thirteen buyers from the company C, and two head of purchasing departments from companies A and B. These two meetings were conducted by asking open-ended questions to the audience and animating the discussions to gain insights. The initial open-ended questions were about the criteria used by the buyers and their managers to evaluate the success or the failure of a relationship with their suppliers.

While we were trying to find a pattern linking levels of collaboration and levels of (perceived) success, buyers seized the opportunity to focus on the fact that the "life" of the buyer-supplier relationship is as complex as any human based interaction between organizations and services, because, according to the buyers, it greatly relies on the "qualities" of the organization, carried and developed by persons of both sides of the relation (buyers, vendors, and other stakeholders involved).

We gathered structured data to complete the fuzzy findings of the meetings, by distributing formal surveys of the same questions, based on a pre-existing model [START_REF] Parung | A metric for collaborative networks[END_REF] but providing this time precise criteria associated to likert-like scales. Buyers were asked to evaluate collaborative relationships criteria, for a relationship they estimated as successful, and for a relationship they estimated as failing. The results of criteria evaluation showed that certain collaboration criteria are consistently highly rated with a relationship considered as successful. On the opposite side, few criteria are consistently rated when it comes to failed relationships so buyers tend to have a more fuzzy perception, as far as criteria are concerned, of what characterizes a failed relationship.

The comments in the surveys were very consistent with the feedback from the meetings. In the cases of successful relationships, some buyers insisted in their comments on the facts that the personal behavior and skills of their corresponding contacts were the main reasons why they were satisfied with the relationship. In the cases of relationships considered as failing, it's the quality of the interpersonal relationships and organizational issues that were described.

Though in the same organization, each individual deals with a series of expectations and means to achieve them, according to different perception, interpretation and perspective. Participants proposed discussions based on their experience while we were biased trying to use a pre-conceived model: our assumptions assumed collaborative relationships meant successful relationships, which meant value creation.

Empirical Outcomes on the Tools Constructs

To help companies in creating value through a multi-perspective and multistakeholder model, we are building a practical methodology to analyze and manage value creation in the processes, to objectively link the activation of intangible assets to value creation and achieve performance. At the heart of the methodology, we want to analyze the drivers represented by intangible assets (human capital, relational capital, structural capital) to connect them with the value proposition made to the clients. To do so, we need to clarify what happens in what we call a fuzzy box (figure 2) where players activate processes and stakeholders to achieve objectives and create value. Key steps here are to identify helping events occurring during the life of the project to evaluate the role and contribution of intangibles assets, and breaking events to capitalize on experience and construct feedback.

We have elaborated an interactive tool that allows the mapping, contextualization and linking of the stakeholders, their interactions and the value created or destroyed for each one of them (figure3). Helping and breaking points of a given projects' life are positioned on timelines. Events' interactions (a) are visually mapped. It is possible to map the value destruction caused by breaking events (b) and value created by helping events (c). Assets created during one project can be identified as a significant source of a helping event in a future project (d), or directly create value (e). A concept that is at the core of our research work is that assets mobilized in one project are probably the result of a previous investment, just like assets generated in the current project can be drivers of future value.

To validate the method and the tools with the participating companies to the research project, we have organized a workshop with a team from each of the firms. The three teams add-up to a total of seven participants with varying profiles. To reinforce triangulation of this experience, we have asked the participating teams to bring to the workshop the story of a past project, that ended successfully or that failed.

All the team members found easy to list the stakeholders, to map the helping and breaking events, to connect the values generated, even though these values were outside of direct economic results, and to point value destructions for the different stakeholders. A very significant result is that the method offered the possibility to identify and map the ripple effects of the outcomes of a given project, allowing the affectation of more value to the global outcome of a project due to previously unaccounted dissemination to other projects. Comments were here that the method offered unsuspected outputs: some assets generated are reusable and thus multiplying their effects and value while remaining unaccounted or miss-affected. Value generated by one team could be capitalized by third parties without recognition of the original creators. Some assets generated by one project could be integrated in an innovative Business Models not foreseen before.

Conclusion

Two participating companies offered to follow one project each, during the full length of the projects. These two projects found their origin in the apparition of an important threat to their operations that needed urgent responses involving many internal and external stakeholders of their value and supply chain. When the incident or the pressure arises, the organizations temporarily reacts to respond to the issues in a resilient fashion: they appoint a project manager with appropriate credentials to mobilize all the departments of the company in a transversal team and mobilize the web of their partners, supply chain and value chain, to initiate multi-level tactical actions.

Their resiliency relies on an adaptive line: -1 Companies follow their normal operations; -2 They get hit by a stressful event that impacts their operations and puts them at risk in term of viability; -3 They create a temporary structure, mobilizing the full web of internal and external stakeholders; -4 They elaborate a multi tactical strategy to have appropriate responses, deploy them to adapt or rebuild the necessary flows; -5 Once the response has been delivered and the crisis resolved, the temporary project structure is dissolved; -6 They return to their "normal" operations. This ability for firms to adapt, reconfigure and respond, greatly relies on the capacity of the different stakeholders involved to collaborate and mutually adjust to each-others, diffuse information and realign. All this relies almost exclusively on the very own capacities of the actors involved (human, relational and structural capital), thus illustrating the central importance of intangible assets.

Very formalized processes can alter the adaptive capacities of companies and networks, building the case for a transversal, project based organization of processes and a qualitative approach of relationships and information sharing, all these heavily relying on the intangible assets of companies. Our objective is to identify the value drivers in the organizational processes, going beyond the drivers already mastered by the buyers and more broadly the supply-chain: cost, quality, lead-time, financial performances and risk mitigation.

While purchasing managers often apply methods focused on savings, a focus that limits their scope of action, participants to our research acknowledged positively our approach and identified intangible assets that could be drivers of the overall "value" created, because "any factor that enhances operational performance" and allows strategic alignment of the actors potentially generates "value". This is consistent with the literature review [START_REF] Parung | A metric for collaborative networks[END_REF]. This research builds meaningful and applied methods to track, document and manage intangibles assets in response to internal or external pressure on value creation. It provides tangible and practical ways to objectivate very qualitative concepts all actors recon as essential to maintain and develop successful operations but have very few if no tools to leverage on in an environment governed by quantitative methods.

Expected outputs are: a tool to analyze the strategy of the firm and how it is translated along the supply chain, an analysis grid to identify and take into account all the stakeholders, a tool for mapping, contextualizing and linking the stakeholders, their interactions and the value created or destroyed for each one of them. These deliverables are being developed and refined through fieldwork.

Among scientific contributions, we expect this research will contribute to Resource-Based View theory, to Knowledge Management, to Sustainable Supply Chain and to Supply Chain performance.

Fig. 1 .

 1 Fig. 1. Placing intangible assets at the heart of renewed value propositions.

Fig. 2 .

 2 Fig. 2. The fuzzy box.

Fig. 3 .

 3 Fig. 3. Visual canvas, mapping events, value and stakeholders' interactions.

Acknowledgements

This project is part of the PEAK (Purchasing European Alliance for Knowledge). The authors would like to thank THESAME, the companies that co-finance this research project, and more generally the financial supporters of the PEAK program: member companies of the program, the F2I (Fund for Innovation and Industry) funding structure of the UIMM (Union of Metal Industries and Crafting), the UDIMERA (Rhône-Alpes' Union Of Metallurgical and Electrical Industries), the Research Division of the Regional Council of Rhône-Alpes, and the General Council of Haute-Savoie which provides support to the Arve Industries competitiveness cluster.


Arij Lahmar
email: arij.lahmar@hotmail.fr

François Galasso
email: francois.galasso@mines-albi.fr

Habib Chabchoub

Jacques Lamothe
email: jacques.lamothe@mines-albi.fr

Towards an Integrated Model of Supply Chain Risks: An Alignment between Supply Chain Characteristics and Risk Dimensions

Keywords: Supply Chain Risk Management, Supply Chain Risk dimensions, risk management methodologies, SCRIM model

Within any Supply Chain Risk Management (SCRM) approach, the concept "Risk" occupies a central interest. Numerous frameworks which differ by the provided definitions and relationships between supply chain risk dimensions and metrics are available. This article provides an outline of the most common SCRM methodologies, in order to suggest an "integrated conceptual model". The objective of such an integrated model is not to describe yet another conceptual model of Risk, but rather to offer a concrete structure incorporating the characteristics of the supply chain in the risk management process. The proposed alignment allows a better understanding of the dynamic of risk management strategies. Firstly, the model was analyzed through its positioning and its contributions compared to existing tools and models in the literature. This comparison highlights the critical points overlooked in the past. Secondly, the model was applied on case studies of major supply chain crisis.

Introduction

As risks at different levels of the supply chain, crises and organizational weaknesses and the complexity of interactions are increasing [START_REF] Zsidisin | A Grounded Definition of Supply Risk[END_REF]. Risk Management has become, in recent years, a fundamental and a better control factor of the supply chain as well as a necessity to ensure the sustainability and the survival of organizations and businesses ( [START_REF] Kouvelis | Supply chain capacity and outsourcing decisions: The dynamic interplay of demand and supply uncertainty[END_REF], [START_REF] Zsidisin | A Grounded Definition of Supply Risk[END_REF], [START_REF] Singhal | Supply Chain Risk Management: review, classification and future research directions[END_REF], [START_REF] Boote | Scholars before researchers on the centrality of the literature review in research preparation[END_REF]). This term "Supply Chain Risk" is used in a variety of contexts and domains. References to notions like "risk identification", "risk evaluation", "risk treatment", "risk management", "risk discovery" and so forth have been found. Extensive research over the past 30 years by academics, practitioners and others, has greatly attempted to improve the understanding of Supply Chain Risk (SCR) profiles and Supply Chain Risk Management (SCRM) approaches and actions.

Numerous conceptual and analytical frameworks and mitigation techniques, tools and standards are now available to help managers and supply chain organizations to manage risk and to assure robustness and resilience of their networks. [START_REF] Christopher | Building the Resilient Supply Chain[END_REF] state that managers seek to create an effective and efficient supply chain to ensure a competitive advantage. For this reason, they need to find a balance between costs, efficiency, effectiveness, resource use and therefore, risk management has become a reality for businesses to succeed. Thus, the SCRM is a support to the SCM in order to maintain the creation of value through the supply chain ( [START_REF] Marques | Collaboration and risk management support in uncertain supply chain context[END_REF] and [START_REF] Simchi-Levi | From super storms to factory fires[END_REF]). This highlighted the link between risk management and supply chains in order to ensure the sustainability and survival of organizations and businesses, in a dynamic and unstable environment.

Therefore, more proactive and predictive risk management approach and strategy are needed ( [START_REF] Dani | Supply chain risk management: present and future scope[END_REF]). This explains why supply chain risk management and resiliencerobustness approaches have become such an attractive and powerful scientific and empirical discipline ( [START_REF] Yang | Supply disruptions, asymmetric information, and a backup production option[END_REF]).

There is a common consensus amongst researchers in this field about the needs to develop a better understanding of risk and how it affects supply chain continuity. Every type of risk introduces different mechanisms of disruption, exposure level, impacts severity and poses different challenges for supply chain adaptability and recovery ( [START_REF] Zegordi | Developing a supply chain disruption analysis model: Application of colored Petri-nets[END_REF]). This creates the need for broader studies on supply chain risk decomposition and conceptualization within the context of dynamic supply chain networks ( [START_REF] Gaonkar | A conceptual and analytical framework for the management of risk in supply chains[END_REF]).

Informed by the above critical aspects of the field and stressing the need for a better understanding of the concept of SCR, this article proposes a conceptual integrated model (SCRIM model) that helps in understanding, evaluating, measuring and managing these disruptions. In order to achieve this objective, the organization of the paper is as follows. After the introduction, section 2 presents an overview of the most common conceptualization and decomposition of SCR and identifies SCRM implied methodologies. Then, an integrated conceptual model "SCRIM model" associated with SCRM domain and enriched with appropriate supply chain metrics is suggested in section 3. The SCRIM model does not attempt to describe yet another model of Supply Chain Risk, but rather to offer a concrete structure incorporating the characteristics of the supply chain in the risk management process. In section 4 experiments and results of model application are reviewed. Finally, section 5 details the conclusions, limitations, and future directions regarding our conceptual model.

Supply Chain Risk Methodologies

SCRM has received during the last decade a considerable interest from researchers, practitioners and organizations. This led to the development of a plethora of different models and methods under the label of supply chain risk management and mitigation. Drawing from the literature review, this section presents an analysis of the most common SCRM frameworks. Only methodologies and tools that define decompose and conceptualize risks or their constructs are selected. These latter has been investigated from a variety of aspects, summarized in the Table 1:

Table 1. SCRM Methodologies.

SCRM Methodologies

Generic models The supply chain is analyzed from two possible states: normal or disturbed functioning of the chain. They are based on the estimates of risk targets and decisions to make. The objective of these models is the optimization of the supply chain and is not the risk management.

Limitations: The logistics processes can have the same probability of risk, but with different risk situations.

Risk analysis and assessment models

The aims are: Evaluation of risks and disturbances and their effects, Evaluation of some configurations (locations, capacities, etc.) and strategies for supply chain networks, integrating one (or more) risk,. Comparison between different logistics strategies or risk management, enabling the reduction of the level of risk.

Several common themes emerge from reviewing these methodologies. First, different kinds of methods, processes, models and approaches are identified in order, either to avoid future risks, or to mitigate the impact of identified risks. The extent to which the various approaches differ or complement each other is often unclear. The problem partly relies in the absence of common conceptual framework of supply chain risks. Many researchers viewed risk as a product of the probability of occurrence and severity of impact ( [START_REF] Klibi | The Design of Robust Value-Creating Supply Chain Networks: A Critical Review[END_REF], [START_REF] Wu | A model for inbound supply risk analysis[END_REF]). According to this point of view, they establish that risk could be measured through the following formula: Supply Chain Risk = Probability * Impact.

(

This method of risk measurement has a well-established place in the supply chain risk management domain. 67 % scientific articles follow this formula [START_REF] Yang | Supply disruptions, asymmetric information, and a backup production option[END_REF]. However, Williams [START_REF] Williams | Supply chain security: an overview and research agenda[END_REF] and Levi [START_REF] Simchi-Levi | From super storms to factory fires[END_REF] demonstrated that "calculating risk as a probability-impact matrix to quantify and prioritize risks is misleading" [START_REF] Williams | Supply chain security: an overview and research agenda[END_REF]. [START_REF] Chapman | Risk engineering: basic controlled interval and memory models[END_REF] affirm that risk analysis need not to use probabilities because these latter may be irrelevant. Paulson et al [START_REF] Dani | Supply chain risk management: present and future scope[END_REF] have suggested that this simple calculation of supply chain risk need to be reconsidered. Furthermore, they suggested also that companies need to use more appropriate measures for supply chain risks and to develop programs to manage the critical risks [START_REF] Christopher | Building the Resilient Supply Chain[END_REF].

A second commonality among these methodologies is that they propose a guide for managing supply chain risks, including the following procedures: identifying sources of risk, evaluating and estimating the severity of consequences and damages, and providing the approaches to mitigating and managing these risks.

However, few methodologies or studies explore the key elements, dimensions or constructs for managing supply chain risks.

The ability to identify which dimension of a Supply Chain Disruption often significantly impacts the supply chain is a critical factor in managing this disruption ( [START_REF] Ouabouch | Risk Management In The Supply Chain: Characterization And Empirical Analysis[END_REF], [START_REF] Parker | Timing and extent of supplier integration in new product development: a contingency approach[END_REF]). [START_REF] Karningsih | Development of SCRIS: A Knowledge Based System Tool for Assisting Organizations in Managing Supply Chain Risks[END_REF] highlights the lack of a common tool to identify SCR and their interrelations within supply chain networks. They affirm that understanding dynamic development of risks and their causal factors are essentials for effective SCRM strategies, helping managers making the right decisions. According to their work, each SCR is not an isolated event. Moreover, these prior frameworks focus on formalized and sophisticated tools for SCRM [START_REF] Zsidisin | A Grounded Definition of Supply Risk[END_REF]. Such frameworks are difficult to implement without mathematical expertise or specialized tools, focus on quantifying networks vulnerabilities, provide little insight into underlying risk mechanisms and do not facilitate including supply chain factors in risk ratings. [START_REF] Fidan | Ontology for relating risk and vulnerability to cost overrun in international projects[END_REF] stated: "supply chain risk has been explored from one perspective, neglecting the sequences of various dimensions and constructs. Even methods that have taken into account the sourceevent relationship have failed to reflect the possible interactions among separated risk scenarios. Authors discussed the importance of studying the combination of diverse risks in the form of possible cause effects scenarios and made encouraging efforts". [START_REF] Fidan | A risk and vulnerability ontology for construction projects[END_REF] highlighted the importance of a framework developed in the field of vulnerability studies and risk modeling. But he stressed the need for a common research structure that combines these two themes. According to [START_REF] Oehmen | System-oriented supply chain risk management[END_REF], there are two main shortcomings related to the SCRM research, which are the missing of an integrated model that address the interactions between SCR factors and how this model can be integrated in the process of SCRM. Authors such as [START_REF] Datta | A Complex system, agent based model for studying and improving the resilience of production and distribution networks[END_REF], [START_REF] Hallikas | Risk management processes in supplier networks[END_REF], [START_REF] Sodhi | Researchers' perspectives on supply chain risk management[END_REF] and [START_REF] Karningsih | Development of SCRIS: A Knowledge Based System Tool for Assisting Organizations in Managing Supply Chain Risks[END_REF] highlight the importance for gaining a more complete picture of SCR ( [START_REF] Paulsson | Estimation of disruption risk exposure in supply chains[END_REF]) drawing the key variables, relationships, interactions and dynamic development of the SCR ( [START_REF] Chopra | Managing risk to avoid supply-chain breakdown[END_REF]), down to revealing its impacts on the structure of the supply chain ( [START_REF] Faisal | Supply chain risk management in SMEs: analysing the barriers[END_REF], |25], [START_REF] Ouabouch | Risk Management In The Supply Chain: Characterization And Empirical Analysis[END_REF]).

The study of these different methodologies highlights the need for specific model to address the main shortcomings identified, such as:

1. The need to capture the causal factors and the dynamic development of the Supply Chain Risk. 2. The impacts of mentioned risks on SC networks. 3. The need for a holistic and generic methodology for managing risks in the supply chain. In order to address the issues identified, the SCRIM model is developed in section 3.

Supply Chain Risk Integrated Model

The analysis of different methodologies is helpful in presenting several research explorations and orientations that have been used to provide a basis for our SCRIM model and depicted in Fig. 1. This model is mainly focused on the relationship between SC characteristics ( [START_REF] Ritchie | Supply chain risk management and performance: A guiding framework for future development[END_REF], [START_REF] Blackhurst | An empirically derived agenda of critical research issues for managing supply-chain disruptions[END_REF]) and risk dimensions and constructs ( [START_REF] Ellis | Buyer perceptions of supply disruption risk: a behavioral view and empirical assessment[END_REF], [START_REF] Wagner | An empirical investigation into supply chain vulnerability[END_REF], [START_REF] Craighead | The severity of supply chain disruptions: design characteristics and mitigation capabilities[END_REF]). In this section, the approach followed (see Fig. 1) in order to develop the SCRIM model is described. This approach improves the classical process of SCR model, with the appropriate SC metrics and Risks dimensions. Firstly, we started by investigating how risk is described, analyzed and modeled in the previous frameworks, in order to identify the main causal factors and to shed light over the development path of SCR. This analysis is incomplete without highlighting the SC networks vulnerability ( [START_REF] Karningsih | Development of SCRIS: A Knowledge Based System Tool for Assisting Organizations in Managing Supply Chain Risks[END_REF], [START_REF] Datta | A Complex system, agent based model for studying and improving the resilience of production and distribution networks[END_REF]). So, a step is added regarding the modeling of the vulnerability factors. This step considered as a preparedness step that supply chain managers can apply in order to accelerate the risk analysis phase. These two previous steps "vulnerability and risk analysis" are combined and integrated into an alignment phase. The objective here is to present or measure the "Key Risk Indicators" (KRI).

Another salient feature is the incorporation of "Integration step" into the traditional process of SCRM. During this phase, the characteristics of supply chain are integrated into the results of the previous step (response design and conception) and a suitable strategy is selected. As mentioned earlier, a variety of tools, approaches and strategies exist to mitigate or to prevent SCR ( [START_REF] Tang | Perspectives in supply chain risk management[END_REF], [START_REF] Ouabouch | Risk Management In The Supply Chain: Characterization And Empirical Analysis[END_REF]). The choice inside this amount of frameworks is not easy and could present an important issue for managers ( [START_REF] Karningsih | Development of SCRIS: A Knowledge Based System Tool for Assisting Organizations in Managing Supply Chain Risks[END_REF]).

In some cases, a wrong decision can aggravate the level of risk instead of mitigate it. [START_REF] Craighead | The severity of supply chain disruptions: design characteristics and mitigation capabilities[END_REF], [START_REF] Song | Risk control research framework of supply chain of embeddedness structure based on perspective of risk conduction[END_REF] and [START_REF] Vermeulen | Analyzing risk and performance using the multi-factor concept[END_REF] highlight through their framework the impacts of SC design characteristics on the severity of SCR. However not only the structural characteristics of SC networks could affect risk management approach and strategy selection. [START_REF] Badea | Assessing risk factors in collaborative supply chain with the analytic hierarchy process (AHP)[END_REF], [START_REF] Chen | Supply chain operational risk mitigation: a collaborative approach[END_REF] and [START_REF] Li | Joint supply chain risk management: An agency and collaboration perspective[END_REF] have proved through their studies, that relationship dimensions between SC actors could influence the decision process and even the risk level and SCRM efficiency ( [START_REF] Lavastre | Effect of firm characteristics, supplier relationships and techniques used on Supply Chain Risk Management (SCRM): an empirical investigation on French industrial firms[END_REF]). According to [START_REF] Christopher | Building the Resilient Supply Chain[END_REF], the success of any SCRM strategy relies on the "SCRM culture" shared between SC entities. This could be achieved only through sharing knowledge and information about SCR. These two sharing mechanisms are concerned with three main SCM principles, which are collaboration ( [START_REF] Grudinschi | Relationship risk perception and determinants of the collaboration fluency of buyer-supplier relationships in public service procurement[END_REF], [START_REF] Lavastre | Effect of firm characteristics, supplier relationships and techniques used on Supply Chain Risk Management (SCRM): an empirical investigation on French industrial firms[END_REF]), trust ( [START_REF] Hou | The effects of a trust mechanism on a dynamic supply chain network[END_REF], [START_REF] Wang | Justice served: Mitigating damaged trust stemming from supply chain disruptions[END_REF]) and visibility ( [START_REF] Goh | RiskVis: Supply chain visualization with risk management and real-time monitoring[END_REF], [START_REF] Yu | A multi-objective approach to supply chain visibility and risk[END_REF]) within SC networks ( [START_REF] Scholten | The role of collaboration in supply chain resilience[END_REF]).

As a result, when selecting one or more methods or actions for a given set of risks, one should also take into the account the capabilities. Any choice of SCRM method should not be made before verifying if the SC structure is compatible with the implementation requirements of the selected tool. The SCRIM model is proposed as an enriched and integrated SCRM approach. Risk identification is the first and the crucial step in the risk management process. However, the nature and the complexity of the SC network make risk identification becoming a challenging task. Therefore, there is a need for a tool to assist organizations in identifying risk in their SC network. Given that, we suggest an interface for additional analysis of the SCR based on the alignment of two known steps: risk and vulnerability analysis. The interaction between these two phases allows to estimate and to calculate the Key Risk Indicators (KRI). New metrics and dimensions have been established to capture the complexities of SCR and to overview the classical description of risk as probability multiplied by impacts. Basing on the value of KRI, a panel of strategies and decisions could be opposed to the identified risks. In order to assess the decision process, an integration step was incorporated in the Risk Management process. This step helps to identify and prioritize the actions needed based on SCRM implementation capabilities.

The enriched and integrated SCRM approach can be decomposed into the following steps:

Step i. Concepts related to SCR: include the dimensions, metrics and the main attributes that are relevant to SCR analysis and could be used when defining and assessing risks:

Event: is defined as a negative change or outcome that causes deviation or disruption and triggers risks. It is characterized by the probability of occurrence. Risk cause: is a description of how risk can be generated and propagated. It could be viewed as an associative entity between risk event and vulnerability. Risk: is defined as one or more unforeseen events, with a probability of occurrence varies between o and 1, that have a financial, human, legal, managerial consequences (positive or negative), on logistics networks, ranging from, a probability of gain, to a failure of logistics organizations. Key Risk Indicators (RKI): is a set of measures or indicators (NR: Negative Result, RPN: Risk Priority Number, TTR: Time To Recovery, EI: Exposure Index, TRI: Total Risk Impact, TTD: Time To Detection, DIU: Losses Impacts, DI: Detection Impacts) that could be used to evaluate the SCR and thus to define the appropriate risk mitigation strategies. ii. Concepts related to Supply Chain : It can be characterized by two elements: Elements used or exploited, leading to one or more risks, and elements which enable or contribute to risk treatment:

The vulnerability: is a characteristic of an entity or a system within the supply chain, which measure the sensitivity level to external or internal disruptive events. It can be assessed in terms of three attributes: Exposure (the extent to which an asset is exposed to risk), Sensitivity (degree to which the asset is affected) and adaptive capabilities (The ability of an asset to react or to adapt to unexpected event).

SCRM Capabilities: to manage risks in terms of objectives, requirements and constraints. SC asset: could be viewed as the risk object. It can be a process, or a function, or an enterprise or a network within the supply chain. iii. Concepts related to mitigation and treatment process: describe the strategies, measures, actions and plans which have to be defined, studied and implemented in order to manage the identified SCRs.

Case Studies

This section describes one of the known SCR cases studies, used to test the usability of the SCRIM model in a real case study. The considered case study is the battery recall of Nokia India, one of the leader's mobile phone manufacturers (see Fig. 3). Because overheating problems affecting battery during charging, Nokia announced the recall of batteries for its handsets from India markets. A total of 46 million batteries were recalled [START_REF] Desai | Identifying backfire of communication on perceived hazard level during product recall[END_REF][START_REF] Charles | Nokia India: Battery Recall Logistics[END_REF]. We followed the process described in Fig. 3 in order to identify the severity of situation that Nokia had to deal with it. From the first investigation, Nokia have detected the risk after two years of the first occurrence sign. Unfortunately, no prevention measures were applied. As result, Nokia has recalled 46 million handsets [START_REF] Charles | Nokia India: Battery Recall Logistics[END_REF]. The first estimation of possible impacts was around 180 million dollars. Measuring the severity of this incident on the company's performance, the classical formula (probability multiplied by impacts) was applied. But, the given results do not represent the critical situation that Nokia managers have to face. With the low value of the involved risk, the severity was high. So, another calculation logic was needed to: firstly represent the real severity of risk and secondly, to help managers to make the mitigation decisions. As result, we adopt the logic of "Key Risk Indicators" to overcome this shortcoming. Basing on the value of Key Risk Indicators, managers ought to mitigate the supply risk for two main reasons: risk is critical, and the affected asset is crucial for Nokia supply chain (following vulnerability analysis). But, what Nokia managers haven't taken into account when they did apply their mitigation strategy is their implementation capabilities. This has led to decelerate the response time, from 15 days to 4 months ( [START_REF] Charles | Nokia India: Battery Recall Logistics[END_REF]). Moreover in this case, Nokia was finally constrained to recall 46 million batteries leading to in depth modifications of schedules.

Based on the collected data from this case study and application of SCRIM model, the summary of the key results can be found in Table 2:

Conclusion

SCRM is a critical process for the business continuity within supply chain. Nevertheless, most of the existing frameworks deal with this research as a two separated area. Another concern is the classical view of supply chain risk. There is a shortage of calculation and interpretation of risk. Most of the developed frameworks were typically structured around the probability of risk and the possible impacts of its occurrence. However, no indication for using this formula in order to assess SCR is given. To overcome these gaps, we have proposed the SCRIM model as an enriched and integrated risk management approach within supply chain networks. In order to achieve our objective, several steps, each associated with an intermediate objective, were adopted. This model is used in order to align supply chain risk dimension with supply chain characteristics required for a better understanding and managing of SCR.

Considering the risk dimensions, a set of measures or indicators (NR: Negative Result, RPN: Risk Priority Number, TTR: Time To Recovery, EI: Exposure Index, TRI: Total Risk Impact, TTD: Time To Detection, DIU: Losses Impacts, DI: Detection Impacts) is built and create a Key Risk Indicators (KRI) which encompass the usual measure: Probability * Impact. The SC side is fully part of the SCRIM as each asset has an impact on the Risk Profile. Particularly, the supply chain characteristics have a strong influence on the selection process for the mitigation strategies. Such a choice relies on the intrinsic risk management capabilities of the SC as a whole. Thus, the level of collaboration, information sharing and trust within such a supply chain has been pointed out as an improvement issue for the SCRM capabilities.

The usability of the SCRIM model is investigated referring to Nokia [START_REF] Desai | Identifying backfire of communication on perceived hazard level during product recall[END_REF] as an application case study. The application of the proposed model on the Nokia case showed that the model could be used in order to identify and to evaluate the supply chain risks and for giving an overall picture of the risk exposure situation. In this case, the supplier risk was underestimated by Nokia and led to the use of a mitigation strategy and a reactive strategy of avoidance instead of a proactive one. An improvement of the SCRM capabilities may have reduced the final impact. In that sense, a better sharing of information and knowledge about the SC could have led to a new evaluation of the vulnerability in which the exposure was graded as low.

Unfortunately, this study is still very limited. First of all, it is only focused on the few factors of both risk and supply chain on purpose of simplicity. Other metrics may contribute to further development of the SCRIM model. Secondly, the main difficulty encountered in this study was the limited amount of data available in the literature regarding the case Nokia. This statement could be identified as a common point between other case studies we identified in the literature.

Thus, future studies and more empirical investigations may allow the SCRIM model to be deeply improved.

Fig. 1 .

 1 Fig. 1. Approach for SCRIM model

Fig. 2 .

 2 Fig. 2. SCRIM Class ModelThe concepts presented, in the SCRIM class model, are considered as the most common key factors reoccurring amongst different SCRM frameworks and could be classified into three main subcategories:i. Concepts related to SCR: include the dimensions, metrics and the main attributes that are relevant to SCR analysis and could be used when defining and assessing risks:Event: is defined as a negative change or outcome that causes deviation or disruption and triggers risks. It is characterized by the probability of occurrence. Risk cause: is a description of how risk can be generated and propagated. It could be viewed as an associative entity between risk event and vulnerability. Risk: is defined as one or more unforeseen events, with a probability of occurrence varies between o and 1, that have a financial, human, legal, managerial consequences (positive or negative), on logistics networks, ranging from, a probability of gain, to a failure of logistics organizations. Key Risk Indicators (RKI): is a set of measures or indicators (NR: Negative Result, RPN: Risk Priority Number, TTR: Time To Recovery, EI: Exposure Index, TRI: Total Risk Impact, TTD: Time To Detection, DIU: Losses Impacts, DI: Detection Impacts) that could be used to evaluate the SCR and thus to define the appropriate risk mitigation strategies. ii. Concepts related to Supply Chain : It can be characterized by two elements: Elements used or exploited, leading to one or more risks, and elements which enable or contribute to risk treatment:The vulnerability: is a characteristic of an entity or a system within the supply chain, which measure the sensitivity level to external or internal disruptive events. It can be assessed in terms of three attributes: Exposure (the extent to which an asset is exposed to risk), Sensitivity (degree to which the asset is affected) and adaptive capabilities (The ability of an asset to react or to adapt to unexpected event).

Fig. 3 .

 3 Fig. 3. Nokia supply chain

Fig. 4 .

 4 Fig. 4. Class diagram of supply risk within Nokia network

Implementation and treatment Step 5: Review and control

  Control the KRI after implementing the actions and monitor: 4.1. If Risk is reduced, Then continue the treatment process until risk disappears. 4.2. If Risk is eliminated, Then go back to step 2 4.3. If New risk appears, Then repeat the process

	1: Determine Key Risk Indicators (KRI)
	1. Conduct risk analysis by identifying the critical factors and dimensions of
	SCR and their relationships.
	2. Identify the vulnerabilities of SC that could lead to a disruption or risk within
	supply chain networks
	2.1. Identify the critical component or asset within the supply chain
	networks
	2.2. Identify the possible weakness causes for selected assets or
	components
	3. Developing risk measurement criteria and define KRI
	Step 2: Develop response design and conception:
	1. Develop risk management strategies and actions to mitigate identified risks
	basing on the KRI measured in previous step
	Step 3: Integration step
	1. Identify the SCRM capabilities to applied the chosen strategies
	If
	SCRM capabilities < capabilities needed for RM strategy, then return in
	step 2
	Else
	Move to step 3.2
	2. Selection and prioritization of mitigation strategies and actions.
	Step 4:

Table 2 .

 2 Summary of the key results presented in Nokia case study.

				Case : Nokia India : Recall battery (2007)
	Description			Nokia issued a 'product advisory' for these BL-5C
				batteries for getting overheated and bursting during
				charging. 46 million batteries were recalled to prevent
				any damage to customer's life and to protect the Nokia
				reputation. This problem was caused by a defective
				battery produced by the main Nokia's supplier.
	The	Risk analysis		Event	Quality default in supplier's product
	application			Risk	Supply risk (low probability, high
	of SCRIM				impact,	unpredictable,	cause
	model				transportation problem, low, at
					operational level)
		Vulnerability		Sensitivity	Critical component
		analysis		factors	Sourcing strategy
				Protection	Quality standard protocol
				system
				Exposure	Low
		Key	Risk	EI : Exposure	EI = NR*TTR
		Indicators		Index	= 3 *20.8 =62.4 million USD
				DI : Detection	DI = DIU * TTD
				Impact	= 3.5*20.2 = 70.7 million USD
				TRI: Total Risk	TRI = EI + DI
				Impact	=62.4+70.7 = 133.1 million USD
				RPN:	Risk	1
				Priority Number
		Possible strategies	Mitigation strategy
				Avoidance strategy
		SCRM Capabilities	Customer protection
		Selected strategy	Mitigation strategy
		Actions		Recall of 46 million batteries
		Results		800 million USD loses

Table 2

 2 illustrates the application results of SCRIM methodology to the Nokia's case. With limited data, the analysis is reduced to few risk dimensions and supply chain metrics. The result was supported by a class diagram, with the objective of giving a simple, complete and holistic picture of supply risk within the Nokia supply chain. The class diagram representation is depicted in Fig.4.


Sébastien Truptil
email: truptil@mines-albi.fr

Anne-Marie Barthe-Delanoë

Tiexin Wang
email: tiexin.wang@mines-albi.fr

Frédérick Bénaben
email: benaben@mines-albi.fr

Towards a Collaborative Networks Governance Framework

Keywords: governance, collaborative network, Big Data, Internet of Events

Nowadays Collaborative Networks (CN) have to evolve in an unstable world and therefore appear, change and disappear quickly. Although IT solutions like SOA can support these evolutions, there is a lack of governance at the business layer. This lack relies on the poor (i) aggregation of data to information, (ii) comparison of CN vision of the world and real world, (iii) sharing of partner's objectives. Moreover, organizations have to manage exponential growth in the amounts of data with the development of Internet of Events. Therefore, organization should not manage information with an empirical approach. This paper aims to define a Collaborative Networks Governance Framework (CNGF) in order to support CN. CNGF manages (i) snapshots, which are timestamp pictures of the world, (ii) indicator function, which transforms sets of data into information, (iii) agility function, which describes partners' objectives and (iv) analysis function in order to evaluate the performance of CN.

Introduction

Since the 70's, Collaborative Networks of Organizations (CNO) have evolved from single workshops collaborative situations to inter-organizational collaborations [START_REF] Camarinha-Matos | Classes of Collaborative Networks[END_REF]. This evolution could be easily explained by the development of new technology. Indeed, a set of heterogeneous organizations, geographically distributed, could instantaneously exchange information in order to achieve common or compatible goals thanks to computer networks. Nevertheless, organizations are still heterogeneous by nature (mission, equipment, culture, vocabulary, etc.). To be efficient, they have to collaborate, or at least coordinate their actions, in order to build a coherent way to reach the shared goal of the CNO.

Although the new technologies have pushed the boundaries of CNO, in terms of variety of forms, geographical distribution, variety of data to manage, the CNO life cycle is always composed of five main stages [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice[END_REF]: 1. Creation: can be divided into two parts, namely (i) initiation and recruiting, dealing with the strategic planning and (ii) foundation, dealing with the constitution and start up 2. Operation: the ''normal" stage of the CNO existence.

3. Evolution: when small changes in membership, roles, or daily operating principles happen. 4. Dissolution: when the CNO ceases to exist. 5. Metamorphosis: when major change in objectives, principles, and membership take place, leading to a new form of organization.

The need for information management all along the CNO lifecycle is obvious as underlined in [START_REF] Camarinha-Matos | Collaborative networked organizations-Concepts and practice in manufacturing enterprises[END_REF]. Even if it is exposed with a manufacturing point of view in [START_REF] Camarinha-Matos | Collaborative networked organizations-Concepts and practice in manufacturing enterprises[END_REF], the Challenge 3 could be defined in any domain: "Challenge 3: ''Instantaneously" transform information gathered from a vast array of diverse sources into useful knowledge for making effective decisions".

This challenge is crucial to the ensure the success of CNO especially due to the fast development of new technology that produces amounts of data. Indeed, amounts and variety of data arose from the development of domain ontologies and the development of Internet of Event (IoE). According to [START_REF] Van Der Aalst | How People Really (Like To) Work[END_REF][5], IoE could be divided in four sources: 1. Internet of Things: all physical objects connected to the network. This includes all things that have a unique id and a presence in an Internet-like structure. Things may have an Internet connection or tagged using Radio-Frequency Identification (RFID), Near Field Communication (NFC), etc. 2. Internet of Location refers to all data that have a spatial dimension. With the uptake of mobile devices (e.g. smartphones) more and more events have geospatial attributes. 3. Internet of People: all data related to social interaction. This includes e-mails, Facebook, Twitter, forums, LinkedIn, etc. 4. Internet of Content: all information created by humans to increase knowledge on particular subjects. This includes traditional web pages, articles, encyclopedia like Wikipedia, YouTube, etc.

According to [START_REF] Hashem | The rise of "big data" on cloud computing: review and open research issues[END_REF], the information management all along the CNO lifecycle could refer to a big data challenges. Indeed, "Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale" [START_REF] Hashem | The rise of "big data" on cloud computing: review and open research issues[END_REF] and Big Data is characterized by 4V: volume, variety, velocity and value. Value refers to the process of discovering huge hidden values from large datasets with various types and rapid generation of more valuable information that is similar to the challenge 3.

Talia [START_REF] Talia | Clouds for scalable big data analytics[END_REF] pointed out that obtaining useful information from large amounts of data requires scalable analysis algorithms to produce timely results. As researchers continue to probe the issues of big data in cloud computing, new problems in big data processing arise from the transitional data analysis techniques. The speed of stream data arriving from different data sources must be processed and compared with historical information within a certain period of time. Such data sources may contain different formats, which makes the integration of multiple sources for analysis a complex task [START_REF] Assuncao | Big Data Computing and Clouds: Challenges, Solutions, and Future Directions[END_REF].

Moreover, due to the heterogeneity of organizations and the CNO lifecycle, the information management has to be defined without requesting a change for the organization. Therefore a middleware is needed [START_REF] Wiederhold | Mediators in the architecture of future information systems[END_REF] and, as explain in [START_REF] Papazoglou | Service-Oriented Computing: Concepts, Characteristics and Directions[END_REF], target architecture for this middleware is Service Oriented Architecture (SOA). SOA is composed of several components and the SOA governance is in charge to manage the lifecycle of the middleware.

Even if there is lots of work on the SOA governance [START_REF] Niemann | Towards a generic governance model for service oriented architectures[END_REF][12] [START_REF] Adam | How to better align BPM & SOA-Ideas on improving the transition between process design and deployment[END_REF], the alignment between organization (business aspect) and SOA (technical aspect) starts from processes and there is a lack concerning the information management, especially how to generate valuable information from incoming data, and process adequacy with the objectives of cross-organization.

A framework is proposed in this paper in order to fill the previous lack. This framework is a novel approach to reach the goal to manage data and information in order to help CNO to make effective decisions all along their lifecycle. This approach aims to define a business governance as an extension of the SOA governance in a Service Oriented Architecture. The remainder of this paper is divided into two sections. Section 2 identifies the objectives of the framework in order to list the required functionalities and before concluding, Section 3 presents the architecture of the framework.

Objectives of the Framework

In order to reach the Challenge 3 and thus to help CNO to make effective decisions, the aim of the framework is to build snapshots of the reality. The idea of snapshots is to have pictures of the world state that is composed of all information related to a specific CNO context. There are at least three kind of snapshots:

-Situational snapshot is a picture of the world state and it is independent from CNO activities. -Expected snapshot is a picture of what the CNO expect to obtain after executing activities. -Forecast snapshot: what the situational snapshot or expected snapshot looks like in future.

By comparing information embedded into each snapshot, the CNO could make effective decision. Fig. 1 represents an overview of the comparison principles over the time.

To illustrate the benefits of the comparison, it is possible to take a common example as in criminal stories. A murderer exists; his picture is the situational snapshot. Policemen don't know exactly how the murderer looks like but based on gathered information, they build a facial composite, which is the expected snapshot. If the facial composite is closed to the picture, the policemen have a chance to capture the murderer. Another example is when you are looking for someone for a long time; it is possible to generate a new picture, the forecast snapshot, thanks to accelerated ageing models based on a picture or a facial composite.

Another example in CNO context could be the following: several organizations want to produce together a product and so they define a production process. Based on this process, the supplier has to finish a part of the product two weeks after the beginning of the process but it has a technical malfunction. Therefore thanks to a comparison of forecast snapshots, it is possible to find the impact at the end of the process and thanks to comparison between situational and expected snapshots, it is possible to detect the problem as soon as possible.

Nevertheless, each snapshot depends on time. Therefore, snapshots have to be either changed directly by user or indirectly by the use of IoE, results of CNO activities.

Therefore, the framework has to propose the following functionalities: -Compare snapshots.

-Propose forecast snapshot (based on simulation).

-Transform data into information (in order to update snapshot). These functionalities have to be built in compliance with the problematic of heterogeneity of data and the evolving aspects of CNO. Therefore it is necessary to generate and manage snapshots by transforming and combining amounts of data without manual effort.

CNO Governance Principles

Snapshot and Models

This section describes the architecture of the proposed framework in order to fit with the functionalities identified in the previous section. The main purpose of the framework is to manage snapshots in order to update and compare them. Therefore, each snapshot needs to be understandable to the same (or at least similar) structure. For this reason, a model will represent each snapshot because a model "represents reality for the given purpose; the model is an abstraction of reality in the sense that it cannot represent all aspects of reality. This allows us to deal with the world in a simplified manner, avoiding the complexity, danger and irreversibility of reality" according Rothenberg [START_REF] Rothenberg | The Nature of Modeling[END_REF]. However to be comparable, a model has to be written according to a specific language, shared by several users, called metamodel [START_REF]Object Management Group, Meta Object Facility (MOF) Specification[END_REF].

Accordingly, each snapshot is a model and each model is conform to a metamodel. Nevertheless it is obvious that defining one metamodel able to cover all CNO contexts is a pipe dream. But for one given CNO context, it is possible to have one metamodel describing all information. For this purpose, the framework is based on the core-metamodel presented in [START_REF] Lauras | A metaontology for knowledge acquisition and exploitation of collaborative social systems[END_REF], which is an extendable metamodel from generic concepts of collaboration to a definition of specific context (such as road management in winter period).

Finally, the objectives of the CNO governance are (i) to update models based on information provided by users, IoE or CNO activities, (ii) to compare models, (iii) to simulate in order to build forecast models and (iv) to design collaborative behaviour in order to merge, when it is needed, the expected and situational models.

The last point is in conformity with the lifecycle of CNO, especially the creation, evolution and metamorphosis stages. The chosen model to represent the collaborative behaviour is the process model in BMPN. Indeed, process model aims to capture the different ways in which a case can be handled. Process model is composed of ordering activities and could be described with temporal properties, creation and use of data and resources [START_REF] Van Der Aalst | Business process management: A comprehensive survey[END_REF]. Hence, the simulation could be based on process model in order to build forecast models. Moreover, on-going research works [START_REF] Mu | Collaborative Mediation Information System Design Based on Model-Driven Business Process Management Approach[END_REF] and [START_REF] Montarnal | A PaaS to Support Collaborations through Service Composition[END_REF] propose a methodology to build collaborative processes based on collaborative aims.

Framework of CNO Governance

Fig. 2 represents the proposed framework. It is divided into three main layers in order to support all the previous identified functionalities and thus reach the aim of ''Instantaneously" transform information gathered from a vast array of diverse sources into useful knowledge for making effective decisions".

Agility layer:

Numerous authors largely discussed the notion of agility. The Collins dictionary defines agility as the power of moving quickly and easily. For Badot [START_REF] Badot | Théorie de l'entreprise agile[END_REF], agility is a reconfiguration of the system to satisfy a need of adaptation. For other authors, such as Kidd [START_REF] Kidd | Agile manufacturing: Forging new frontiers[END_REF], Lindberg [START_REF] Lindberg | Strategic Manufacturing Management: A Proactive Approach[END_REF] and Sharifi [START_REF] Sharifi | A methodology for achieving agility in manufacturing organisations: An introduction[END_REF] agility is a need of flexibility, responsiveness or adaptability. In logistics, flexibility is seen as "the ability to meet short-term changes" [START_REF] Sheffi | Demand Variability and Supply Chain Flexibility, Entwicklungspfade und Meilensteine moderner Logistik[END_REF] and is differentiated to the over time adaptation in response to a change [START_REF] Mc Cullen | The F1 Supply Chain: Adapting the Car to the Circuit-The Supply Chain to the Market[END_REF]. Therefore, the agility layer of the framework is in charge to ensure (i) the assessment of the need for CNO to evolve or to metamorphose, (ii) the help to adapt the CNO.

Hence the agility layer is divided into three components:

-Model comparison: this component evaluates at any time the divergence between the expected model and the situational model. If the value of the divergence is higher than a threshold value, an alert is sent to the detection component.

-Detection: this component is in charge of analysing the divergence and thus defining the new objectives of the CNO. This analysis is possible because the situational model and the expected model are defined based on the same metamodel. The result of this analysis is sent to the collaborative process design component.

-Collaborative process design is the component in charge of adapting or defining new collaborative process. This deduction is based on the result of the detection component. The result of this component is composed of two models, which are (i) the collaborative process and (ii) a mapping between CNO activities and objectives. This second model will be reuse in the Updating layer (see below) in order to update the expected model.

This layer was defined in previous research works. The mechanisms of the model comparison and detection components were described in [START_REF] Barthe-Delanoë | Event-driven agility of interoperability during the Run-time of collaborative processes[END_REF], and [START_REF] Mu | Collaborative Mediation Information System Design Based on Model-Driven Business Process Management Approach[END_REF][19] present a solution for the collaborative process design component.

Simulation layer:

This layer aims to produce a forecast model of the situational model and of the expected model, in order to apply the agility layer to the two forecast models. Hence it is possible to anticipate, instead of react, an evolution.

The two forecast models are obtained thanks to manipulation of existing information, e.g. existing model, or by the result of a simulation. The idea of this layer is inspired from the results of the European funded CRISMA project [START_REF] Dihé | CRISMA ICMS Architecture Document V2[END_REF].

Updating layer:

This layer has a simple but difficult objective to achieve: build both situational and expected model from data. The difficulties arise in the variety and the amount of data sources. Three main kinds of data sources could be identified (i) IoE, (ii) data from users or their ontologies and (iii) the achievement of CNO activities, e.g. the progress of the collaborative behaviour. This variety of data sources implies issues concerning (i) Trust management, especially regarding IoE (especially Internet of person), (ii) comparison of data due to the heterogeneity of semantics and levels of description, e.g. granularity issue.

In order to achieve this simple objective, the Updating layer is divided into seven components:

-Trust Management: this component aims to filter no-trustable data. For instance, if a sensor always sends the same expected value during one second, thus we have a Dirac delta function. This value may be wrong and the governance does not have to take it into account. Trust management is more complex than just this example as explains in [START_REF] Afsarmanesh | On management of 2nd generation virtual organizations breeding environments[END_REF].

-Information function: this component is the key point of the governance. It aims at transforming data to information. This component is compliant with Big Data challenges [START_REF] Hashem | The rise of "big data" on cloud computing: Review and open research issues[END_REF] to automatically define the transformation from data to knowledge. However, it is possible to manually define information function that transforms data into information. For example, if sensors could send the heart rate and another could send the body temperature of a human being, it is possible to deduce if the human being is ill or not. Indeed, if his/her body temperature is above 39°C (fever) and his/her heart rate is above 120, so he/she is ill.

-Model Adaptation: this component is used to update the situational model. Based on information generated by Information function component, the situational model is updated thanks to principles defined in [START_REF] Barthe-Delanoë | Event-driven agility of interoperability during the Run-time of collaborative processes[END_REF].

-Compare to process deduction: this component is used to update the expected model. As described in [START_REF] Barthe-Delanoë | Event-driven agility of interoperability during the Run-time of collaborative processes[END_REF], this update is possible thanks to information generated by Information function component and the model defining mapping between CNO activities and objectives.

-Process state: this component is used to also update the expected model based on the progress of collaborative process. This is allowed thanks to the model defining mapping between CNO activities and objectives.

-Information added by user: this component allows user to directly add, remove or modify information from the situational or expected models.

-Semantics reconciliation is needed because we cannot assume that each user and/or each information function will generate information based on a coherent glossary or taxonomy. A semantics reconciliation component is used with the aim to unify semantic as well as the level of description, e.g. the granularity level, of information. This component is based on work described in [START_REF] Wang | Semantic Approach to Automatically Defined Model Transformation[END_REF].

Conclusion and Next Challenges

This paper presents a framework for CNO governance that aims to support CNO all along their lifecycle. Especially, the objective of the governance is to ''Instantaneously" transform information gathered from a vast array of diverse sources into useful knowledge for making effective decisions. In order to achieve this objective, the idea is based on snapshots of the reality that allow CNO to make a comparison between the situational state and the expected state from the CNO point of view. This comparison is possible if data provided by various heterogeneity sources could be filtered (using trust principles), aggregated, and transformed into information.

In order to achieve the objectives of the governance, a three-layers architecture is proposed. The agility layer aims at detecting divergence between CNO behaviour and reality and then deducing a new version of the CNO behaviour. The simulation layer aims to make forecast in order to anticipate evolution. The updating layer is the key point of the governance. It aims to manage a huge number of data from various sources in order to produce information usable by other layers.

This framework is based on the result of three PhD works, two on the agility layer and one on the updating layer. Each PhD provides theoretical solution and a demonstrator. Our future work consists in merging the demonstrator and developing the simulation layer.

Fig. 1 .

 1 Fig. 1. Overview of the use of snapshots in order to help CNO to make effective decisions.

Fig. 2 .

 2 Fig. 2. Overview of the CNO governance framework.