Evmorfia Biliri
email: evmorfia@suite5.eu

Minas Pertselakis

Marios Phinikettos
email: marios@suite5.eu

Marios Zacharias
email: mzacharias@singularlogic.eu

Fenareti Lampathaki
email: fenareti@suite5.eu

Dimitrios Alexandrou
email: dalexandrou@ubitech.eu

Designing a Trusted Data Brokerage Framework in the Aviation Domain

Keywords: data brokerage, collaboration platform, aviation

In recent years, there is growing interest in the ways the European aviation industry can leverage the multi-source data fusion towards augmented domain intelligence. However, privacy, legal and organisational policies together with technical limitations, hinder data sharing and, thus, its benefits. The current paper presents the ICARUS data policy and assets brokerage framework, which aims to (a) formalise the data attributes and qualities that affect how aviation data assets can be shared and handled subsequently to their acquisition, including licenses, IPR, characterisation of sensitivity and privacy risks, and (b) enable the creation of machine-processable data contracts for the aviation industry. This involves expressing contractual terms pertaining to data trading agreements into a machine-processable language and supporting the diverse interactions among stakeholders in aviation data sharing scenarios through a trusted and robust system based on the Ethereum platform.

Introduction

The aviation industry encompasses all the activities that are directly dependent on transporting people and goods by air. This covers airport and airlines operations, aircraft construction and maintenance, air traffic control and regulation, passenger and freight services, among others. Aviation stakeholders produce and consume, nowadays, vast amounts of data and the industry is already investing on them, aiming to utilise their full potential in order to improve passenger experience and flight efficiency, expand sales and reduce costs.

In this direction, the emergence of big-data technologies has pushed the aviation domain many steps forward: the collection and storage of massive data sets has become easier, the parallel processing provides the necessary computation infrastructure, and data science has developed the prerequisite tools that can provide insights and predictions directly from high volumes of data. Recent academic works on machine learning techniques for aviation applications provide strong proof regarding the ways in which data analytics can contribute to significant domain issues, indicatively including air travel demand modelling and operational safety and quality [START_REF] Maheshwari | A Comparative Study of Machine Learning Techniques for Aviation Applications[END_REF], [START_REF] Ariyawansa | Review on state of art data mining and machine learning techniques for intelligent Airport systems[END_REF], [START_REF] Gavrilovski | Challenges and Opportunities in Flight Data Mining: A Review of the State of the Art[END_REF]. However, they also hint to the lack of cross-section data availability in real-world cases, i.e. at scale, which hinders the development of more powerful multi-source data analytics solutions. One of the main reasons for this is that the highly competitive business players of the aviation sector remain sceptical when data diffusion and sharing comes into the discussion. Beyond technical limitations that need to be overcome, establishing trust and fairness in the scope of data sharing is also an important, yet highly challenging first step towards a sustainable collaborative network spanning across the broader aviation ecosystem.

Several EU projects and works in literature have tackled in the past the challenges of collaborative networks and platforms for enterprises [START_REF] Camarinha-Matos | Ecolead And Cno Base Concepts[END_REF], [START_REF] Facca | COIN: Platform and services for SaaS in enterprise interoperability and enterprise collaboration[END_REF] even for data sharing [START_REF] Biliri | Big data analytics in public safety and personal security: Challenges and potential[END_REF]. Most of these solutions however prefer to follow a more generic approach, ignoring the special characteristics and peculiarities of serving a particular domain. The few exceptions to this rule, like [START_REF] Camarinha-Matos | Collaborative Business Scenarios in a Service-Enhanced Products Ecosystem[END_REF], which explores business scenarios in the solar energy domain, do not involve the data analytics target of collaboration, nor the data sharing and asset exchange using a clear and secure framework for intellectual property rights (IPR).

The ICARUS project aims to fill this gap by bringing together all aviation related stakeholders and accelerate their collaboration on data exploration and analysis through an innovative big data enabled sharing and collaboration platform, removing current barriers in data aggregation, sharing and IPR protection. These features, combined with a targeted aviation oriented data model and metadata model, constitute the novelty of the proposed framework.

Background

Data Sharing Motivation and Initiatives in Aviation

Data sharing is not a new concept for the aviation industry. GAIN, the Global Aviation Information Network, was proposed by the Federal Aviation Administration (FAA) in 1996. Since then, numerous multi-airline and multi-national data sharing programs and initiatives which involve centralising airline flight data storage have been established: Flight Data eXchange (FDX) is an aggregated de-identified database of FDA/ FOQA type events that allows to identify commercial flight safety issues for a wide variety of safety topics. The ASIAS program, developed by FAA and the aviation industry aims to promote an open exchange of safety information. Over 90% of IATA member carriers have agreed to participate in GADM, the IATA Global Aviation Data Management programme and platform. STEADESTM, the IATA's aviation safety incident data management and analysis program that constitutes one of the GADM data sources, has over 200 members. SKYbrary is an electronic repository of safety knowledge related to flight operations, air traffic management (ATM) and aviation safety. Partners of the European Airport Collaborative Decision Making (A-CDM) share timely information through adapted procedures and tools enabling real-time collaborative decision-making.

The information being shared is broad and includes, among others, aeronautical data, flight trajectories, aerodrome operations, historical and current meteorological data, surveillance data (e.g. from radars). The primary goal of such initiatives is to ensure safety in the air travel, which in turn requires the optimisation of a wide range of operations, e.g. the way Airline Operations Centres plan flight routings. Nevertheless, the launch of such Aviation Data Exchange programmes has opened the door to data sharing with trusted third parties [8].

Advantages of data sharing in the aviation industry are numerous and multifacetted and bolster the development of further larger-scale collaborations. IATA, Eurocontrol and other core stakeholders of the aviation industry but also stakeholders of the broader spectrum, all report on the expected advantages of collaborations built on shared data and insights [START_REF]Performance Success Stories -Data Sharing Helps Airlines Reduce Delays[END_REF]. Ad-hoc collaborations, such as EasyJet's partnership with Gatwick Airport [START_REF]EasyJet and Gatwick Launch Mobile Host to Simplify Airport Experience[END_REF] and Aer Lingus' partnership with the Dublin airport [START_REF]Data Sharing Transformation Driving Kerb to Gate Improvements at Dublin Airport[END_REF], are emerging, while Airbus has launched the Skywise platform that aspires to become the reference platform for core aviation stakeholders. However, an inclusive solution for the aviation industry has not been established yet.

Data IPR and Marketplaces

Even when incentives for data sharing are strong and the expected benefits are well comprehended, privacy, legal and organisational policies and even infrastructure limitations may hinder data sharing and, thus, the benefits that stem from it. An important milestone towards addressing such limitations is the definition of a clear and robust IPR framework. [START_REF] Grabus | Toward a Metadata Framework for Sharing Sensitive and Closed Data: An Analysis of Data Sharing Agreement Attributes[END_REF] analysed data sharing agreements from industry, academia and government and identified six high-level aspects of data licenses that affect data sharing: (i) attributes regarding the project and the agreement itself, e.g. description of data, (ii) privacy & protection of sensitive information, (iii) access policies, (iv) legal and financial responsibility, data ownership and rights, (v) compliance, (vi) permissible interactions during data handling. Each of the aforementioned aspects encapsulates numerous more specific attributes and properties of the sharing agreement and the underlying data assets. Depending on the context where they are used, specific practices and derived terms are found, e.g. the work presented in [START_REF] Cao | MARSA: A marketplace for realtime human sensing data[END_REF] regarding only the pricing aspects. The definition of a concrete data IPR handling process to cover broader and more generic data sharing needs, like the ones manifesting in the emerging data marketplaces, is a challenging task, especially when many-to-many data marketplaces are examined, which is the case in ICARUS. As explained in [START_REF] Koutroumpis | The (unfulfilled) potential of data marketplaces[END_REF], these marketplaces often emphasise on data discoverability and other facilitation activities, including online payment. In their most common form, the platforms do not have ownership of the data, but only act as intermediaries that facilitate transactions, therefore it is extremely important for the interacting parties to be presented with an intuitive yet trustful way of engaging into such transactions which are gradually starting to pertain to machine processable data contracts. In this context, there is an emerging need to develop contract engines that provide querying and validation mechanisms for access to and usage rights of data assets, as well as the status of the agreements being performed. However, the majority of existing marketplaces are far from this level of automation and lack an adequately expressive, but not prohibitively -for implementation-complex, information model [START_REF] Vu | Demods: A description model for data-as-a-service[END_REF]. Establishing rigorous provenance through verifiable information for the data being shared/ sold is under this prism of paramount importance to increase trust between interacting stakeholders. Towards this goal, distributed ledger technologies (DLTs) are now being leveraged in the design of the decentralised multilateral platforms (e.g. [START_REF]IOTA[END_REF], [17]). The advantages of DLTs in this context are numerous and widely accepted and include transparency and data democratisation [START_REF] Özyilmaz | IDMoB: IoT Data Marketplace on Blockchain[END_REF].

ICARUS Data Policy and Assets Brokerage Framework

The landscape review presented in Section 2 reveals that although both research-and industry-oriented approaches are emerging for facilitated data sharing in various domains and stakeholders in the aviation industry have begun to grasp the underlying potential of collaborative data analysis, no concrete solution has been proposed and implemented for the aviation industry. One of the key identified reasons is that data trading is not a primary activity for most ATM stakeholders, hence their incentivisation to participate in this activity is largely affected not only by the envisioned benefits, but also by the effort that will need to be devoted into understanding, learning how to use and ultimately engaging a data sharing and collaboration platform. Furthermore, strong KYC (Know-Your-Customer) requirements in the aviation industry, make it hard for stakeholders to trust a broader data marketplace initiative. Finally, apart from the technical difficulties, data privacy and sensitivity aspects especially in the GDPR context are troublesome for many aviation stakeholders. In this perspective, the proposed ICARUS Data Policy and Assets Brokerage Framework has a dual role: 1. To formalise all data attributes and qualities that affect, or are in any way relevant to, the ways in which data assets can be shared / traded and handled subsequently to their acquisition. This involves licenses, IPR, characterisation of sensitivity and privacy risk levels, but also more generic metadata regarding data content and structure, as well as properties derived from the underlying data model. It should be noted that at least in its initial version, the ICARUS platform will not handle any type of personal data, hence GDPR compliance is not discussed in the current work. 2. To enable the creation of structured, machine-processable data contracts for the aviation industry. This entails the expression of contractual terms pertaining to data trading agreements into an appropriate machine-processable language. The framework foresees the interactions of stakeholders in aviation data sharing scenarios and defines the system's functionalities in this context.

Data Sharing and IPR Model

ICARUS adopts a DLT-based solution for data brokerage to ensure the robustness of the framework and increase transparency and trust. The first step towards delivering the ICARUS data sharing system was to select a set of clearly defined properties that can be used to compose a virtual representation of a data sharing contract, i.e. to identify the data license and IPR attributes and policies. For this process, the insights gained through the literature review were combined with findings from the traditional data sharing agreement documents used by the ICARUS industry partners. The ICARUS data sharing framework is built on top of three core entities, namely the Data Asset, the Policy and the Contract and two supporting entities, namely Attributes and Terms, with the latter being specified as one of Prohibition, Permission and Obligation, as shown in Fig. 1.

Fig. 1. High-level view of ICARUS data sharing model

For each of the presented entities, concrete instantiations are defined, like the ones presented in Table 1 for policies. Balancing expressivity with applicability was necessary, therefore the adopted model is simplified when compared to the complete set of considerations and options of data trading in aviation. Yet the performed assumptions were deemed reasonable for the initial piloting applications of the system and in any case do not harm the future extension and refinement of the framework.

Data License and Agreement Manager

The ICARUS data brokerage framework includes the conceptual work on the IPR and license terms, the design of the envisioned and allowed data sharing workflows and, finally, the instantiation of the above in a prototype entitled data license and agreement manager that implements the designed functionalities. This component as part of the complete ICARUS platform, is responsible for handling all processes related to the data licenses and IPR attributes, as well as the drafting, signing, and enforcing the smart data contracts that correspond to data sharing agreements between platform users. Its smart contract functionalities are developed on Ethereum, a popular decentralised platform for smart contracts, using the Truffle Framework. The component has three interconnected roles: 1. It allows the users to define, review and update the data license and IPR attributes discussed in the previous section. 2. It allows users to draft, review, negotiate on, and sign a smart data contract that concretely defines the terms under which a dataset will be shared. 3. It handles all processes required to prepare a smart contract for each (paid) asset transaction and, finally, upload it to the blockchain. Depending on the exact terms and attributes included in a contract, the corresponding key-value pairs are stored either as-is or hashed in the blockchain. The component enables the activation (i.e. status change) of a smart contract when both parties -data owner (seller), data consumer (buyer) -approve it and the payment is completed and can also report on the validity of a given smart contract. Assuming the first step (step 0) of defining the data license, IPR and access policies foreseen by the framework has been completed for a given data asset by its owner (provider), the data brokerage process has the following three high-level phases: Phase I -Data Assets Exploration: The workflow is initiated by an ICARUS user performing a query to search for data. The search functionality is facilitated by the fact that all data assets conform to the same data model and there is rich additional information defined by the designed metadata and sharing model, which ensures that the results presented to the user adhere to the limitations imposed by the defined terms and policies. Phase II -Smart Contract Drafting: The step includes the definition of and negotiation upon the terms of the data sharing contract to be signed (Fig. 2). Different statuses are foreseen for the smart contract in order to denote whether it is in draft, negotiating, accepted or rejected state. Phase III -Smart Contract Validation: If the contract reaches the "Accepted" status and the payment obligations are satisfied, the ICARUS platform will allow the consumer to obtain the dataset to use either externally to the platform or to explore and perform analytics on it leveraging the ICARUS functionalities. To avoid issues caused by potentially malicious data providers, a security mechanism to bypass the data provider's validation process is foreseen to address cases where the consumer has proof of a completed payment, but the provider does not honour the agreement.

The proposed framework has been presented to 15 different aviation stakeholders (representing airlines, airports, ground handlers, and generally aviation data providers) during the external validation activities performed in the ICARUS project so far, and has gathered unanimously positive and enthusiastic feedback.

Conclusions and Next Steps

The ICARUS policy and brokerage framework sets the foundations of the ICARUS platform that will link data providers and data consumers at all levels of the data value chain in the aviation industry, through a combination of state-of-the-art approaches in academia and industry regarding data brokerage and IPR.

It needs to be noted that the enforceability aspects of smart contracts constitute one of the major challenges, both from a technical and legal perspective. In this context, the future work along the proposed framework will focus on two parallel streams: (a) to investigate how the complexities of multi-sharing, i.e. sharing multiple datasets among multiple stakeholders, can be addressed based on license compatibility analysis, and (b) to conclude the framework's evaluation and validation process by a broader group of aviation stakeholders, which is currently ongoing in the context of the ICARUS project, and leverage the respective feedback to further improve the presented approach.

Fig. 2 .

 2 Fig. 2. Contract Drafting Page

Table 1 .

 1 Indicative terms definition for the ICARUS data brokerage framework

	Policy Category	Indicative Terms
	Data Asset		description; encrypted & unencrypted columns; included data model
			entities from the ICARUS aviation data model; provider; creator;
			contributor; version; created date; modified date; published date
	Contract		temporal validity; spatial validity & coverage; validation date; liability;
			involved provider; involved consumer; termination clause
	Responsibility		ownership; addressed to; liability & indemnification
	Rights and Usage license & copyright notice; derivation; attribution; reproduction;
			distribution; target purpose; target industry; re-context allowed
	Quality		accuracy; completeness; consistency; credibility; accessibility & online
			availability
	Privacy	and	privacy & sensitivity compliance (levels, disclaimers, guarantees);
	Protection		liability; applicable law

Acknowledgments. This work has been created in the context of the ICARUS project, that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 780792.


Ben Luetkehoff
email: ben.luetkehoff@fir.rwth-aachen.de

Volker Stich

Moritz Schroeter

Felix Steinlein

Development of a Methodology for the Analysis and Evaluation of Alternative Actions in Disruption Management in Production

Keywords: Decision making, decision support, disruption management, reaction strategy

Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past yearsleading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future.

Introduction

Offering customer specific products defines small and medium enterprises (SMEs), especially mechanical and plant engineering companies. This, combined with increasing product varieties and deceasing product lifecycles, leads to highly complex production processes, which in turn lead to an increasing amount of potential disturbances [START_REF] Elmaraghy | Product variety management[END_REF][START_REF] Ferreira | Product lifecycle management in knowledge intensive collaborative environments: An application to automotive industry[END_REF]. These company-internal or -external disturbances are to blame for disruptions and their effect on, for example, delivery dates. Efficient Production Planning and Control systems are the most important means to react to this changing environment [START_REF] Böckenkamp | A Versatile and Scalable Production Planning and Control System for Small Batch Series[END_REF]. However, while solutions -even automated ones -exist in the field of detailed planning, the production controller is usually left unsupported in many areas when it comes to disruption management [START_REF]Cyber-Physical Production Management: Advances in Production Management Systems. Sustainable Production and Service Supply Chains[END_REF]. This makes the job of a production controller increasingly difficult. They need to comprehend the effect of the disturbances on production as well as the outcomes of the possible alternatives with regard to the production system and the logistical target system of the company. Therefore, the goal of this paper is to introduce a methodology that supports the production controller by providing possible alternative actions in disruption management for production. The methodology is part of the research project "iProd" with the goal of developing a collaborative platform [START_REF] Luetkehoff | Self-learning Production Control Using Algorithms of Artificial Intelligence[END_REF][START_REF] Luetkehoff | Development of a Collaborative Platform for Closed Loop Production Control[END_REF].

This paper starts by discussing the motivation of the subject in a more detailed manner. Afterwards, the state of the art discusses the current solutions found in practice and in research. As a basis for the methodology to be developed, requirements are collected and discussed. Chapter 5 will introduce the methodology itself, covering the aspects of the data model, setting up rules as well as the analysis and derivation of alternative actions. The findings will be concluded and a short outlook on upcoming research will be given.

Motivation

Production Planning and Control plans the current production program at regular intervals in advance according to type and quantity over several planning periods. Its goal is to implement the program with given or planned capacities as economically as possible while taking unavoidable disruptions such as personnel losses, delivery delays or rejects into account [START_REF] Wiendahl | Betriebsorganisation für Ingenieure[END_REF].

Disruption management therefore is part of the short-term production management and closes the gap between regular production control and closed-loop production control [START_REF] Luetkehoff | Development of a Collaborative Platform for Closed Loop Production Control[END_REF]. The goal is a short, medium and long-term reduction of disruptions. Generally, preventive disruption management takes place to avoid disruptions before they arise, whereas reactive disruption management starts only after the occurrence of a malfunction in order to reduce its effect [START_REF] Schwartz | Störungsmanagement in der Produktion-Simulationsstudien für ein hybrides Fließfertigungssystem[END_REF]. For this, appropriate reaction strategies (i.e. prefabricated decisions that intervene through the system and define measures to eliminate the disruption) are required. These are control processes that eliminate operational interruptions or occur as preventive measures to prevent malfunctions [START_REF] Opfermann | Kostenoptimale Zuverlässigkeit produktiver Systeme[END_REF]. Dealing with disruptions in an organized way has several advantages, since the time it takes to react to a disturbance can be shortened noticeably. This leads -aside from the time-benefit -to a higher benefit of the adaption itself [START_REF] Gu | Manufacturing System Design for Resilience[END_REF].

Within the research project "iProd", a collaborative platform is developed, that allows the analysis of production data using Artificial Intelligence (AI). The presented method is part of the project and deals with the disruption management as part of the platform. Due to the connectivity to the shop floor and IT-systems within the company, the platform notices the disruption, analyzes it and feeds it back to the system. Thus, the presented methodology is the backbone of the collaborative platform which in turn allows a closed-loop production control [START_REF] Luetkehoff | Development of a Collaborative Platform for Closed Loop Production Control[END_REF].

State of the Art

The state of the art shows an analysis of the research already available in the areas of disruption management, reaction strategies and simulation. Moreover, the industry also provides solutions in the field of disruption management, which this chapter will also discuss.

Literature in the Field of Disruption Management, Reaction Strategies and in the Field of Simulation

The literature review showed research focusing on the classification of disruptions in production processes as well as preventive and reactive disruption management, e.g. for customer specific production [START_REF] Schwartz | Störungsmanagement in der Produktion-Simulationsstudien für ein hybrides Fließfertigungssystem[END_REF][START_REF] Wünscher | Störungsmanagement im Entwicklungs-und Herstellungsprozess komplexer, kundenindividueller Produkte[END_REF]. Other researchers focus on the evaluation of countermeasures. The focus here lies on mathematical models [START_REF]Assessment of Counter-Measures for Disturbance Management in Manufacturing Environments[END_REF] or very specific areas in production, such as the assembly [START_REF] Burggräf | Cost-benefit analysis for disruption prevention in low-volume assembly[END_REF]. Moreover, there are different approaches in the field of simulation (discrete event as well as agent based) in the context of production [START_REF] Melouk | Stochastic resource allocation using a predictor-based heuristic for optimization via simulation[END_REF][START_REF] Krenczyk | Simulation Aided Production Planning and Scheduling Using Game Theory Approach[END_REF][START_REF] Burggraef | Simulating the benefit of disruption prevention in assembly[END_REF][START_REF] Greenwood | Simulation Optimization Decision Support System for Ship Panel Shop Operations[END_REF][START_REF] Galaske | Disruption Management for Resilient Processes in Cyber-physical Production Systems[END_REF]. Comparing the relevant literature references shows that researchbased approaches to simulation-based decision support are often limited to certain use cases or cannot be transferred to other industries.

State of the Art in the Industry

In practice, approaches for decision support can already be found in the area of ERP (Enterprise Resource Planning), APS (Advanced Planning and Scheduling) as well as MES (Manufacturing Execution System) systems. ME systems integrate information relevant to production (personnel, material resources, production equipment including tools and fixtures) and link them to the planning framework conditions from the higher-level systems [START_REF] Kletti | MES -Manufacturing Execution System: Moderne Informationstechnologie Zur Prozessfähigkeit Der Wertschöpfung[END_REF]. This way, MES generate more realistic plans than ERP systems and can react promptly to changes (e.g. due to disruptions in the production area) and calculate and initiate plan changes. ERP and MES cannot replace the production planner during planning and should rather support him interactively. However, the support of the production planner is prevented by the lack of transparency of the algorithms and the IT system structures, since the comprehensibility of the calculation by the production planner is a prerequisite for checking and supplementing the calculated proposal [START_REF] Gachet | A Decentralized Approach to Distributed Decision Support Systems[END_REF].

APS systems also obtain their data from ongoing value-added processes from transaction-based ERP and ME systems but deliver more exact planning results. APS systems generate plans based on advanced mathematical models. On the operational level, the detailed planning takes place throughout the entire supply chain. Here, most of the parameters and dependencies are already known; it is simply a matter of getting the optimum planning results out of the input. Thus, Companies can plan their requirements, quantities, due-dates, capacities to the point of distribution and logistics across the entire supply chain. Unfortunately, the planning horizon of APS systems ranges from days to weeks and is therefore not fast enough for short-term disruption management. The exact scope of APS systems remain to be defined.

In summary, it can be stated that there is no holistic recording and evaluation model of alternative actions for short-term disruption management. In the literature, there are various papers on aspects such as disruption management, reaction strategies, simulation and quantitative evaluation alternative actions. The research deficit is therefore a decision support system for short-term production control in the field of disruption management.

Requirements

The proposed concept must meet specific requirements to fulfill the need of the methodology. First, the data basis must be up to date and at the same time have access to historical values and planning data. Since the response time in short term production management is highly important, the evaluation of alternative actions must happen in or close to real-time. The goal of this methodology is that it either serves as an extension of existing IT systems or the production controllers themselves can work with the solution (e.g. for mobile use). Therefore, the interface must be open and allow displaying the results in a self-explanatory and user-centric manner [START_REF] Nyhuis | Die Bedeutung von Industrie 4.0 als Enabler für logistische Modelle[END_REF].

Aside from the above-mentioned requirements, the following questions arise and need to be addressed when implementing the model: 1) Which decision cases occur in production? Which data is therefore required as input for a decision tool? 2) What effects do the potential measures have and how can these effects be quantified? 3) Which information must be prepared for the decision maker and how?

Methodology

The methodology consists of several steps that allow an evaluation of the reaction strategy and the selection of the most promising one. The first step consists of setting up the relevant data model (i.e. which data can be found where). The second step is to collect the existing and potential disruptions as well as counter measures manually (e.g. by interviewing the production controller) and automatically (e.g. by analyzing the historical data). Based on this, rules for the identification of future disruptions and the derivation of alternative actions. The basic framework for the methodology is illustrated in figure 2.

Identification of Relevant Data

In many companies, the worker or production controller notices disruptions, however, the goal is that the system can detect them automatically. The latter is done via an automatic transfer of order data from the ERP and ME system as well as from production or machine data acquisition (PDA/MDA). Manual input from the worker is required if the data cannot be transferred from the IT systems automatically. This input may contain additional parameters for the in-house production such as shift plans (e.g. from a human resource system), amount and qualification of workers, disruptions or maintenance at resources, blocked parts as well as the availability of resources from Production Data Acquisition (PDA).

In this first step, it is necessary to define the scope of disruption management within the company in order to identify the relevant data objects. The typology of the company in focus will heavily determine the needed data. For example, while for one company the batch-number and size may be relevant, others simply do not produce batches. This data then needs to be allocated (i.e. which databank stores this data, e.g. ERP system) and its meaning defined. This means that, even within the same company, the understanding of the used vocabulary may differ.

Collecting Disruptions, Alternative Actions and Rules

The goal of the digital transformation in production is to support the worker and to turn their implicit knowledge into usable data for IT systems. Thus, when setting up this methodology for disruption management, the worker needs to be interviewed and the known disruptions as well as their countermeasures need to be collected. If the company already has a reliable database, this can also be used in order to identify disruptions and alternative actions from the past. This step usually results in unveiling missing data which then needs to be added to the data model.

In the presented project, a matrix has been developed, that groups the disruptions in reference objects. Possible causes, their effect and characteristic define the reference object in more detail. The alternative actions are grouped in reaction strategies (i.e. delay, delegation, replanning, relaxation, negotiation and cancellation). Based on the matrix, the reaction strategies can be allocated to the disruptions and thus alternative actions be derived.

Analyze the Data and Knowledge Base

When a disruption occurs, the first step is to identify the disruption. Depending on the grade of automation within the company, the reaction for disruptions with low impact can be performed automatically, without requiring an intervention of an employee. At the same time, there are disruptions that can solely be solved by manual interventions from an employee. The type of disruption that is the focus of this paper is the type that can be encountered semi-automatically. Here, part of the input can come from the system and the other part still requires some kind of manual input. [START_REF] Meissner | Adaptives Abweichungsmanagement in der Fertigungssteuerung bei Kleinserien[END_REF] After the disruption has been identified, it needs to be analyzed in more detail. This is necessary in order to see what the reaction strategy and alternative actions looked like in the past. The AI-tool (Jupyter Notebook) is used to extract the necessary data and analyze them separately regarding the disruption. Afterwards, the analyzed data is fed back via the interface 1 . The result of this analysis depends on the defined scope. An example is a clustering of disruptions according to their conditions (e.g. product variant or past alternative actions) in order to anticipate further disruptions, and therefore enables a short-, medium-and long-term disruption management.

Fig. 2. Methodology to approach disruptions 6 Conclusion and Outlook

Companies try to encounter the turbulent market, customer specific production and thus higher susceptibility to disruptions in production by collecting data and supporting the worker with IT systems. While many aspects of production have already been facilitated by this digital transformation, the disturbance management remains unsupported and mostly based on implicit knowledge and intuition of the worker. In order to improve the decision making process in disruption management, this paper discussed a possible methodology by discussing the data model, rules (disruptions and alternative actions) as well as their analysis briefly. The findings can be used as an extension of existing IT systems or for standalone support systems. However, within the presented project, the methodology still involves many manual steps and is not yet ready for a direct implementation. Future research therefore needs to focus the area of data models for disruption management as well as their ontology. Moreover, the methodology needs validation in practice and standardization of the analysis using AI.

For companies it is crucial to collect the implicit knowledge of the worker in order to allow the described methodology in practice.

Acknowledgement

The European Regional Development Fund (ERDF) funded the presented research (grant number EFRE/ERDF-0800924). The authors would like to thank the European Regional Development Fund for the kind support and making this research possible.

Fig. 1 .

 1 Fig. 1. Closed-loop production control [according to 11]

In the presented project, a platform is developed that uses REST API interfaces in order to feed data back to the IT systems or to a web-based dashboard. For more information please see www.projekt-iprod.de and[START_REF] Luetkehoff | Development of a Collaborative Platform for Closed Loop Production Control[END_REF][START_REF] Luetkehoff | Self-learning Production Control Using Algorithms of Artificial Intelligence[END_REF].


Khurshid Aliev
email: khurshid.aliev@polito.it

Dario Antonelli
email: dario.antonelli@polito.it

Ahmed Awouda
email: ahmed.awouda@polito.it

Paolo Chiabert
email: paolo.chiabert@polito.it

Key Performance Indicators Integrating Collaborative and Mobile Robots in the Factory Networks

Keywords: KPI, ISO 22400, cobots, mobile robots, factory network protocols

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Collaborative robots (Cobots) and mobile robots are being introduced massively in the smart factory. Indeed, they have been considered as an advanced manufacturing solution and are an enabling technology in Industry 4.0 (I4.0) [START_REF] Lorenz | Man and machine in industry 4.0: How will technology transform the industrial workforce through 2025[END_REF]. The main reason for their success is the combination of robot strength and endurance with the dexterity and the flexibility of human operator Going in detail, there are several assets of Cobots with respect to traditional robots: easy to program, safe interaction with human, possibility to share the workspace with other humans and robots, no need for fixed workplace surrounded by fences. This last feature means that cobots are easily movable, while mobile robots, by design, are able to move in the plant without following fixed trajectories. In the framework for workcell architecture design proposed by [START_REF] Djuric | A Framework for Collaborative Robot (CoBot) Integration in Advanced Manufacturing Systems[END_REF], this feature addresses specifically the operative perspective.

During the operation, it is practice to monitor the Key Performance Indicators (KPIs) of the process. Unfortunately, as the robot is no more constrained to stay in a specific cell, the measure of production performances is made both difficult to execute and to assign to a specific process. The choice of suitable indicators and their monitoring on a mobile robot or on a robot that does not belong to a specific workcell poses new and unprecedented problems [START_REF] Weiss | Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics[END_REF] that are the target of this paper.

Transfer of robot data from the field level to the factory internet allows to leverage the exploitation of collaborative networks. In this network collaboration is the process of various agents working together on a voluntary basis by respecting a set of behavior rules [START_REF] Talukdar | Collaboration rules for autonomous software agents[END_REF]. Evaluation of Cobots' performance in such a collaborative network (CN) plays significant role in the domain of Industry 4.0 and can be supportive tool for a successful business.

Industrial machines and sensors from different vendors have different standardized protocols which can be challenging for data exchange and to align them in the CN. For that reason, this paper proposes a framework of connecting different robots and sensors with different protocols to increase the overall performance of the CN and to monitor performance of the robots [START_REF] Piedade | Using Key Alignment Indicators for Performance Evaluation in Collaborative Networks[END_REF].

In section 2, the related literature is presented. In section 3, the implementation of the data transfer in order to measure cobot performance is discussed. In section 4, the KPIs suitable for cobot operations are defined and calculated for the case study of section 5. Eventually, in section 6 the study's outcomes are discussed and the future work is introduced.

Related Works

Low cost and precise way of measurement of the machine performance in industry grasped the attention of managers and researchers for many years. The integration of performance monitoring with maintenance systems can assist the manufacturer in achieving the goal of maintaining the performance of machines and support the control strategies [START_REF] Weiss | Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics[END_REF].

Therefore, there are various proposed frameworks and systems for performance monitoring. Authors of [START_REF] Ferrer | Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems[END_REF] implemented and visualized KPIs according to ISO 22400 standards within a discrete manufacturing web-based interface to monitor and control an assembly line at runtime. In [START_REF] Domínguez | A Taxonomy for Key Performance Indicators Management[END_REF] review has been made by focusing on aspects of KPIs management, unification of taxonomy gathers relevant aspect highlighted by the literature and which captures the unique characteristics of KPIs in a more fully way. Observed characteristics can help researchers to decide about the most suitable solution for their requirements. General definition of the KPI scheme for monitoring on-line production process is described with 8-step iterative closed-loop model by authors of [START_REF] Rakar | Assessment of production performance by means of KPI[END_REF]. Real time monitoring energy consumption using five selected KPIs of the work cell that composed: robot, cabinet and conveyor successfully implemented in [START_REF] Zhang | Key Performance Indicators for energy efficient asset management in a factory automation testbed[END_REF]. All above mentioned reviews mainly focused on models and simulations of the models. Moreover, proposed models and frameworks are not low cost which can be suitable for education and Industrial IoT (IIoT) applications. In next sections, we describe the opensource and reduced cost implementation of KPIs for collaborative and mobile robots and sensors within the factory networks. Modbus is an application layer protocol that defines the rules for organizing and interpreting data independent of the underlying communication layers and the transmission medium used [START_REF] Modicon | Modicon modbus protocol reference guide[END_REF]. Modbus TCP/IP is an alternative of the Modbus protocol where the message frame is encapsulated in a TCP/IP wrapper. TCP/IP refers to the Transmission Control Protocol and Internet Protocol, which provides the transmission medium for Modbus TCP messaging. The primary function of TCP is to ensure that all packets of data are received correctly, while IP makes sure that messages are correctly addressed and routed.

Modbus TCP combines a physical network (Ethernet), with a networking standard (TCP/IP), and a standard method of representing data which results in Modbus TCP being fully compatible with the already installed Ethernet infrastructure of cables, connectors, network interface cards, hubs, and switches. Simply stated, Modbus TCP shares the same physical and data link layers of traditional IEEE 802.3 Ethernet and uses the same TCP/IP suite of protocols.

Another protocol used in this work and resident to the IOT domain is MQTT and REST. Message Queuing Telemetry Transport (MQTT) is a protocol designed to connect the physical world devices and networks, with applications and middleware to develop Web applications in IT area. It is designed to minimize network bandwidth and device resource requirements whilst also attempting to ensure reliability and some degree of assurance of delivery, also MQTT protocol is a good choice for wireless networks that experience varying levels of latency [START_REF] Modicon | Modicon modbus protocol reference guide[END_REF].

MQTT uses the Publish/Subscribe Model which consists of three main components: publishers, subscribers, and a broker. Publishers are the lightweight sensors and devices that connect to the broker to send their data and go back to sleep whenever possible. Subscribers are applications or devices that are interested in a certain topic, or sensory data, so they connect to brokers to be informed whenever new data is received. The brokers classify sensory data in topics and send them to subscribers interested in those topics only. A device can behave as a publisher and a subscriber at the same time by publishing to specific topics and subscribing to others, the term MQTT client is used to distinguish publishers/subscribers from brokers.

Representational State Transfer (REST) or RESTful web services describes a set of architectural principles by which data can be transmitted over a standardized interface such as Hypertext Transfer Protocol (HTTP). REST does not contain an additional messaging layer and focuses on design rules for creating stateless services. A client can access the resource using the unique URI (Uniform Resource Identifier) and a representation of the resource is returned. With each new resource representation, the client is said to transfer state. While accessing RESTful resources with HTTP protocol, the URL of the resource serves as the resource identifier and GET, PUT, DELETE, POST and HEAD are the standard HTTP operations to be performed on that resource.

In next section we describe KPIs and most suitable KPIs for collaborative and mobile robots according to the standards.

Selection of KPIs for Cobots and Mobile Robots

Key performance indicators (KPIs) gives possibility to measure the performance and progress of the manufacturing machines and systems. Acquired data from different machines and things in Industry can serve to monitor and visualize energy consumption, planning and scheduling, maintenance, product quality, inventory, machines capability and etc. Standardized KPIs are defined in ISO 22400 and can be used in different fields of industry. On the other hand, implementation of those KPIs in real industrial applications are challenging. The title of the ISO 22400 is "Automation systems and integration-Key performance indicators (KPIs) for manufacturing operations management" that is collection of 34 KPIs. ISO 22400 standards composed of two parts: ISO 22400-1 describes basic overview and terminologies of the KPIs framework in the manufacturing [START_REF]Automation systems and integration -Key performance indicators (KPIs) for manufacturing operations management -Part 1: Overview, concepts and terminology[END_REF]. ISO 22400-2 is dedicated to definitions and descriptions of 34 possible KPIs which can be used in industry [START_REF]Automation systems and integration -Key performance indicators (KPIs) for manufacturing operations management -Part 2: Definitions and descriptions[END_REF]. In [START_REF]Top 5 Cobot Key Performance Indicators: HOW TO MEASURE AND IMPROVE THE PERFORMANCE OF COLLABORATIVE ROBOTS[END_REF] five top KPIs that can improve the performance of cobots and robots are defined and possible measurement techniques are described. Moreover, importance of standard metrics such as Overall Equipment Effectiveness (OEE) that is standard for measuring manufacturing productivity is explained. Taking account above mentioned standards, following five KPIs has been selected to implement for cobots and mobile robots in our case study: Cycle time -Summation of the steps time of the robot while executing sequence of tasks

𝐶𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒 = ∑ 𝑇𝑖𝑚𝑒 𝑡𝑜 𝑒𝑥𝑒𝑐𝑢𝑡𝑒 𝑎 𝑠𝑖𝑛𝑔𝑙𝑒 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 (1)
Cycles completed -increment a variable every time a cycle is completed

𝐶𝑦𝑐𝑙𝑒 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 = ∑ 𝐶 𝑖 𝑛 𝑖 = 𝐶 1 + 𝐶 2 + 𝐶 3 + ⋯ + 𝐶 𝑛 (2)
Wait timepercentage of time that the robot is waiting or time that not executing productive tasks. Wait time is defined as sum of all individual wait times:

𝑊𝑎𝑖𝑡 𝑡𝑖𝑚𝑒 = ∑ 𝑅𝑜𝑏𝑜𝑡 𝑠𝑡𝑎𝑡𝑖𝑐 𝑡𝑖𝑚𝑒 = 𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑢𝑠𝑒 𝑡𝑖𝑚𝑒 -∑ 𝐶 𝑖 (3)
Planned Use Time is defined from the Cycle Time per unit, multiplying by the number of products that are going to be processed. Utilization-measures how long a robot is being used compared to how long it could be used. Utilization for robots can be defined as following formula:

𝑈𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 = 𝑇𝑜𝑡𝑎𝑙 𝑈𝑠𝑒 𝑡𝑖𝑚𝑒 𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 (4)
Efficiency -percentage of time that the robot performs productive work while running a program.

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = ∑ 𝐶 𝑖 (𝑇𝑜𝑡𝑎𝑙 𝐶𝑦𝑐𝑙𝑒 𝑇𝑖𝑚𝑒𝑠) 𝑇𝑜𝑡𝑎𝑙 𝑈𝑠𝑒 𝑡𝑖𝑚𝑒 (5)
5 Implementation and Application to a Case Study

Collaborative Network Integrating Data from Cobots and Mobile Robots

The proposed CN to collect data from industrial machines and to transfer them to the cloud is presented in Fig. 1. The framework is composed by three layers. In the bottom layer all industrial machines are located (here the mobile robot MIR100 and the cobotUR3) together with external sensors like temperature, proximity. Data acquisition from MIR100 and UR3 has been executed using the Modbus TCP/IP protocol where the robots are a Modbus Server and the RPi is a Modbus Client the client sends requests to read specific registers available on the robot's internal memory, the robot responds by providing the value of the requested register. Both UR3 and MIR100 registers can hold discrete variables such as On/Off status. Moreover, UR3 has access to analog values such as joint velocities, angles and also robot temperature and input current. At the same time, it was possible to retrieve battery level, positions, orientation data and control the mobile robot through MODBUS TCP/IP.

The middle layer is responsible for gateway and networking the machines and things using open source hardware and software. Raspberry Pi is used as operating system that runs open source software such as Node-red (used to create and visualize live data on browsers), Mosquitto (massage broker that implements MQTT protocol) and NGROK (reverse proxy software that creates a secure tunnel on a local machine along with a public URL). NGROK is used to allow the Node-Red Dashboard to be accessible via a public URL on the internet. The ESP8266 is Wi-Fi module with a full TCP/IP Stack and microcontroller capability. The module has been operated as an MQTT client that acquires sensor data from the sensors connected to it and then transmits the data to the Raspberry Pi via MQTT protocol.

On the top layer of the framework, all data is transmitted to the Thingspeak platform [13] to store data and perform analytics. Moreover, Node-red dashboard is used to visualize KPIs on the browsers. The implementation and resulting graphs of this component are presented in further sections.

Calculation KPIs from Robot Data and their Visualization on the Dashboard

In case study, KPIs of the mobile robot has been evaluated and implemented on the dashboard. KPIs of the mobile robot was implemented for the transportation tasks of the metallic pieces to the UR3 manipulator [14]. Basically, MIR100 has 10 missions to transport metallic piece and the aim of the experiment was to estimate KPIs of the MIR100 in this demonstrator. The sequence of the mission on the dashboard of the MIR100 is shown in Fig. 2.

The experiment was conducted in the following way: to demonstrate KPIs of the MIR100 on the dashboard and on the cloud, we considered that MIR100 was working for an hour. Total time is 60 minutes but the robot was On for 30.78 minutes which results in a total use time of 30.78 minutes. The robot spent 6.3 minutes to transport all workpieces and completes its task with a total cycle time of 6.3 min. Taking account values coming from MIR100 and using above mentioned KPIs formulas, Utilization and Efficiency of the MIR100 have been estimated as 51% and 20,5% respectively. Cycle time, cycle completed and wait time, utilization and efficiency have been calculated triggering registers of the MIR100 and programmed on the node-red using user defined functions (formulas has been converted into functions) and transferred to the dashboard.

Conclusions and Future Works

The paper demonstrates the practical integration and implementation of KPIs inside a CN by gathering data from field level and using them to measure performance at factory level. Developed system is composed by open access tools and protocols that are a suitable demonstrator of Industry 4.0 for academics and manufacturers. Future work will be the application of the measuring system in an actual industrial contest to assess the robustness of the system in factory conditions.

Fig. 1 .

 1 Fig. 1. Architecture of the CN for sharing information among factory machines.

Fig. 2 .

 2 Fig. 2. Sequence of the missions of the mobile robot on the control dashboard.On the top level of the proposed framework, KPIs are integrated and visualized. KPI formulas are implemented on the Node-red software with user defined functions. Nodered requests necessary data (state, start time, uptime, downtime etc.) through MODBUS TCP/IP to calculate KPIs. Received raw data are converted in human readable data and robot KPIs are calculated. Results are sent to the Node-red dashboard (Fig.3). Dashboard reports general information (battery level, state of the robot, distance and length of the mission), cycle time (number of CT completed, previous and average cycle time, initial mission time), Supervisory Commands (pause, play, cancel, clear mission queue) and selected KPIs (utilization, efficiency and wait time).

Fig. 3 .

 3 Fig. 3. KPIs of the mobile robot on the Node-red dashboard.

3 High Level and Low Level Protocols in Factory Networks to Connect Machines into Cloud.

  Developing Industrial IoT networks which can connect and exchange data between sensors and machines to the backend systems are very challenging. Protocols provide device-to-device or device-to-server communications. There is a big variety of protocols used in today's industrial scene and the choice among these different protocols is application dependent and device specific. Industrial communication protocols are classified as Ethernet and non-Ethernet protocols: Non-Ethernet-Fieldbus protocols (Modbus RTU, Profibus DP), and Industrial Ethernet protocols (such as

Modbus TCP and Profinet). Industrial communication protocols are low level or device management protocols. High level open source IoT domain data exchange protocols are Message Queuing Telemetry Transport (MQTT), CoAP (Constrained Application Protocol), Advanced Message Queuing Protocol (AMQP), Extensible Messaging and Presence Protocol (XMPP). In this section the implemented ones are discussed.


Fábio Müller Guerrini
email: guerrini@sc.usp.br

Juliana Suemi Yamanari

A Systematic Review of Collaborative Networks: Implications for Sensing, Smart and Sustainable Enterprises

Keywords: Collaborative networks, S ^ 3 Enterprises, Competitiveness

The formation of collaborative networks advocates the efficient sharing of information and knowledge, agile and intelligent decision making for the improvement of operational, financial and organizational performance. Such configuration is motivated by the technological advances, increasing demand for customized products and dynamism characteristic of the digital era. Sensing, Intelligent, and Sustainable Enterprises (S ^ 3) address a new vision to be disseminated among organizations seeking competitive advantage. Based on the literature on Collaborative Networks and S ^ 3 Companies, the article proposes to systematically review the state-of-the-art of collaborative networks, identifying challenges and future research opportunities present at the intersection of these two approaches. As a contribution of the research, is presented a conceptual framework based on the main results of the review..

1 Introduction [START_REF] Weichhart | Challenges and current developments for sensing, smart and sustainable enterprise systems[END_REF] proposed the Sensing, Smart and Sustainable Enterprise, which characterizes a company's sensitive ability to understand its own environment and act in concern with the many collected information, making decisions faster, more efficient and more in order to become competitive and sustainable. Sensing enterprise indicates the ability to anticipate future decisions by capturing multidimensional information, enabling the company to reach and know the different scenarios [START_REF] Ferro-Beca | A framework for enterprise context analysis based on semantic principles[END_REF]. Smart enterprise refers to the ability of companies to adapt rapidly to competitive market changes and challenges in an agile way to create and exploit knowledge in response to the opportunities [START_REF] Filos | Smart organizations in the digital age[END_REF]. Sustainable enterprise is associated with environmental, social, economic and ethical concerns [START_REF] Mauricio-Moreno | Design S3-RF (Sustainable x Smart x Sensing -Reference Framework) for the Future Manufacturing Enterprise[END_REF]. [START_REF] Weichhart | Challenges and current developments for sensing, smart and sustainable enterprise systems[END_REF] signaled challenges and developments for the S ^ 3 approach to become a reality. S ^ 3 Enterprises cover a number of domains of knowledge in, among them, business collaboration. The review seeks to answer the main question:

How has the discipline of collaborative networks addressed in recent years the advances of companies in relation to the new requirements of the competitive market?

This review article intends to contribute to knowledge by identifying mechanisms in the context of collaborative networks that inspire companies that seek to become S^3, contributing by identifying the main domains of application, challenges and research approaches.

Methodological Procedures

According Fischl, Scherrer-Rathje and Friedli (2014), the literature review aims to map, summarize and evaluate the relevant knowledge of a particular subject. The approach used in this research is based on three main steps [START_REF] Tranfield | Towards a Methodology for Developing Evidenceinformed Management Knowledge by Means of Systematic Review[END_REF]: planning, conduction and dissemination. In the planning stage, it was identified that S ^ 3 Enterprise need studies on the characterization of the collaboration domain. It was decided to systematically review the concept of "collaborative networks" in search of evidence on the motivators of collaboration. Already in the stage of conducting the study, three databases were selected: Scopus, Web of Science and SciELO. In these databases, the keyword entered was "collabo * network *" with asterisks to contemplate the term and its plural and between quotation marks to search the two words together.The first filter applied in the systematic search was the keyword "collabo * network *" (for Scopus and Web of Science) and "collaborative networks" or "collaborative network" or "collaboration network" (for SciELO) in abstracts, keywords and document titles. The second filter refers to the time limitation, from 2012 to 2017. Because it is an incipient approach (S ^ 3 Enterprises), it is considered that such a limitation does not negatively affect the quality of the research, since the last years are (1988), which is characterized by intense advances in information and communication technologies (ICTs), internet and social networks [START_REF] Fornasiero | Implementation of customization strategies in collaborative networks through an innovative Reference Framework[END_REF]. The third filter was the exclusive selection of papers in journals indexed and related to engineering. The 138 selected articles were read in full and the contributions acquired will be exposed in the sequence, in the stage of disclosure of the evidences found. After reading in full the 138 articles, fifteen articles were disregarded in the analysis because they did not present relevant contributions. Therefore, in total, 123 articles contributed to the research. Facing the necessity of synthesis, the paper presents the main results citing directly 75 papers.

In the next item the main motivators of collaboration are discussed, basing the construction of evidence on S ^ 3 Enterprises and, consequently, the elaboration of a conceptual framework.

Results

The results are based on the identification of the main motivators of the collaboration, search approach and application field.

Motivators of Collaboration

The main factors that motivate organizations to collaborate are sharing information and knowledge, resources and skills, risks and benefits; growing demand for product variety and customization, dynamicity and market competitiveness, exploration and capture of new business opportunities , innovation, improving business performance, rapid advancement of Information and Communication Technologies (ICTs), costs reduction, economic, social and environmental sustainability.

Sharing information and knowledge: The ability to share information and knowledge motivates companies to create collaborative networks [START_REF] Jayaram | A holistic view of knowledge integration in collaborative supply chains[END_REF]. The tools to support sharing are based on information and communication technology (ICT), RFID systems [START_REF] Quetti | Factors affecting RFId adoption in a vertical supply chain: the case of the silk industry in Italy[END_REF], standards-based systems that allows computer-human interaction through a web service [START_REF] Lin | Design of a Global Decision Support System for a manufacturing SME: Towards participating in Collaborative Manufacturing[END_REF]. Knowledge management allows the aggregation of different factors used and people in order to improve decision making, adapt to new technologies and share knowledge through collaborative networks [START_REF] Kumar | A knowledge based reliability engineering approach to manage product safety and recalls[END_REF]. Collaboration with universities, and with suppliers, reflects on the efficiency of innovation through technical communication and knowledge transfer [START_REF] Huang | The relationships between regulatory and customer pressure, green organizational responses, and green innovation performance[END_REF]. Technological frontier countries seek incentives to collaborate with other countries investing in research in order to create networks of excellence [START_REF] Di Cagno | The impact of participation in European joint research projects on knowledge creation and economic growth[END_REF]. In this context, the European Union funds collaborative projects between organizations with the intention of disseminating and eventually creating new knowledge [START_REF] Protogerou | Twenty-five years of science-industry collaboration: The emergence and evolution of policy-driven research networks across Europe[END_REF]. Clusters are more likely to receive research and development subsidies, benefiting from the ease of access to the knowledge disseminated in this concentration of companies seeking collaboration [START_REF] Broekel | Another cluster premium: Innovation subsidies and R&D collaboration networks[END_REF]. Scientists promote the exchange of knowledge in exchange for recognition in the scientific environment [START_REF] Welch | Network and perceptual determinants of satisfaction among science and engineering faculty in US research universities[END_REF]. The process about knowledge requires adequate strategies, tools and methods, as well as definitions of the consequences and needs of collaborative partnerships [START_REF] Swarnkar | A framework for collaboration moderator services to support knowledge based collaboration[END_REF].

Sharing of resources and skills:

The manufacturing industry faces challenges and the principle of business collaboration helps to share resources and competences [START_REF] Liotta | Optimization and Simulation of Collaborative Networks for Sustainable Production and Transportation[END_REF], reflecting the capture of business opportunities [START_REF] Hsieh | Context-aware workflow management for virtual enterprises based on coordination of agents[END_REF], profitability of the company [START_REF] Swarnkar | A framework for collaboration moderator services to support knowledge based collaboration[END_REF], access to information and knowledge and the company's reputation [START_REF] Michaelides | Collaboration networks and collaboration tools: a match for SMEs[END_REF]. An entrepreneur faces uncertainties such as resource capacity that can be viable through collaboration [START_REF] Leung | Networks as sponges: International collaboration for developing nanomedicine in China[END_REF]. Resource sharing fosters the search for collaboration [START_REF] De Prato | Global technological collaboration network: network analysis of international co-inventions[END_REF].

Risk and benefit sharing: The investment in capacity expansion is the uncertainty that motivates a decision maker to collaborate with other companies, since the technologies suffer a rapid obsolescence [START_REF] Renna | Capacity investment decision by Monte Carlo approach in a cooperation network[END_REF]. R&D projects are motivated to form partnerships to share risks and benefits [START_REF] Bojanowski | The structure and dynamics of the global network of inter-firm R&D partnerships 1989-2002[END_REF], specifically the initial phase of product development. Online collaboration platform can encourage risk management more efficiently and effectively, especially in cases with diminished technical, financial and human resources [START_REF] Effio | A Look at State-Level Risk Assessment in the United States: Making Decisions in the Absence of Federal Risk Values[END_REF].

Growing demand for product variety and customization: The customization of products enables aggregation of value, quality of service and increase in sales volume [START_REF] Cheikhrouhou | Trust categories and their impacts on information exchange processes in vertical collaborative networked organisations[END_REF]. The growing demand for customized products generates the stakeholders need for knowledge [START_REF] Lyons | The application of a knowledge-based reference framework to support the provision of requisite variety and customisation across collaborative networks[END_REF]. Collaboration between companies is a mechanism to manage the growing demand for services associated with production processes [START_REF] Zhang | Promoting efficient communications for high-speed railway using smart collaborative networking[END_REF]. The e-work encompasses computer-supported collaborative operations in distributed organizations can serve as a tool to assist traditional enterprises [START_REF] Zhong | Co-Insights framework for collaborative decision support and tacit knowledge transfer[END_REF].

Dynamicity and market competitiveness: The complexity and dynamism of the market are a highly competitive business environment [START_REF] Cao | Investigating the role of business processes and knowledge management systems on performance: A multi-case study approach[END_REF]. Intense changes such as rapid technological development, internationalization of the market and trade, as well as socioeconomic and political issues have affected society and business [START_REF] Maccarthy | Sustainable collaborative supply networks in the international clothing industry: a comparative analysis of two retailers[END_REF]. Collaborative networks improve the sociability and usability of ICTs [START_REF] Durugbo | Collaborative networks: A systematic review and multi-level framework[END_REF], and are considered effective for geographic and social issues, as it enables a trust relationship between partners, establishing common policies, infrastructure and interoperability [START_REF] Noran | Collaborative networks in the tertiary education industry sector: A case study[END_REF]. Market demands interfere with the harmonization and interoperability of collaborative networks [START_REF] Jardim-Goncalves | Reference framework for enhanced interoperable collaborative networks in industrial organisations[END_REF]. The dynamic environment reflects in the growing number of publications, conferences, responsibilities and impact society as a whole [START_REF] Sochat | A collaboration network and behaviorally-based visualization tool of activation reports from the neuroscience literature[END_REF]. It is necessary to renew, expand, adapt and modify products and processes [START_REF] Minguela-Rata | Cooperation with suppliers, firm size and product innovation[END_REF]. Consumers demand quality products with agility and cost-effectiveness [START_REF] Moghaddam | The collaborative factory of the future[END_REF]. Collaboration networks allow the management of complex and dynamic systems [START_REF] Lelah | Collaborative network with SMEs providing a backbone for urban PSS: a model and initial sustainability analysis[END_REF]. During the management of these systems, the loss of knowledge during product development should be avoided [START_REF] Shankar | A collaborative framework to minimise knowledge loss in new product development[END_REF]. Social capital as a key resource for collaborative practices [START_REF] Macke | Social capital in collaborative networks competitiveness: the case of the brazilian wine industry cluster[END_REF].

Exploration and capture of new business opportunities: Collaboration allows capturing new business opportunities. The collaborative networks provide conditions in new and unexpected business opportunity [START_REF] Taticchi | A management framework for organisational networks: A case study[END_REF]. In the academic context, a collaborative can facilitate access to resources [START_REF] Sabatier | Is there a first mover advantage in science? Pioneering behavior and scientific production in nanotechnology[END_REF]. Collaboration allows the increase of visibility and the recognition of own competences [START_REF] Welch | Network and perceptual determinants of satisfaction among science and engineering faculty in US research universities[END_REF]. The search for collaborative partners is an important channel for winning new clients [START_REF] Jansson | Using CNOs in international marketing and outbound logistics[END_REF].

Innovation: Collaborative networks have six times the capacity to develop innovation compared to organizations that operate individually [START_REF] Dias | The open nature of innovation in the hospital sector: The role of external collaboration networks[END_REF]. Companies that collaborate technologically are more likely to generate product innovations and specifically radical innovations, and the larger the company, the more likely it is to generate innovations [START_REF] Minguela-Rata | Cooperation with suppliers, firm size and product innovation[END_REF]. Public research funding facilitates the production of knowledge and is a key element to develop technology innovations [START_REF] Beaudry | Impact of public and private research funding on scientific production: The case of nanotechnology[END_REF]. Knowledge management, trust building and communication among partners in a collaborative network are essential factors for innovation [START_REF] Guan | The impact of multilevel networks on innovation[END_REF]. The failure to harness the knowledge embedded in the collaborative network is detrimental to the innovation [START_REF] Shankar | A collaborative framework to minimise knowledge loss in new product development[END_REF].

Improving business performance: Several organizations seek to stablish collaboration networks to gain agility, complement activities, competitiveness, and improve resources, gain innovation power, flexibility, efficiency and effectiveness of operational activities [START_REF] Saetta | The innovative model of the Virtual Development Office for networked enterprises: the GPT network case study[END_REF]. The collaborative scenario encompasses criteria, mechanisms, and decisions that improve business performance [START_REF] Liotta | Optimization and Simulation of Collaborative Networks for Sustainable Production and Transportation[END_REF]. Collaboration is proven an effective strategy to improve service levels, process stability and the utilization of productive resources [START_REF] Moghaddam | Design and administration of collaborative networked headquarters[END_REF]. Collaboration among researchers aims to improve the quality of research, increase scientific productivity [START_REF] Kim | Bibliometric Analysis of Collaboration Network and the Role of Research Station in Antarctic Science[END_REF] and longevity [START_REF] Chakraborty | An author is known by the context she keeps: significance of network motifs in scientific collaborations[END_REF]. In R&D projects, collaboration fosters the exchange of knowledge, sharing of resources and costs among employees, improving expected results, and industry performance [START_REF] Guan | Does country-level R&D efficiency benefit from the collaboration network structure?[END_REF]. In humanitarian projects, intergovernmental collaborative networks improve the quality of services provided to clients, as well as optimize the use of resources and reduce expenses [START_REF] Tchouakeu | Humanitarian inter-organisational collaboration network: Investigating the impact of network structure and information and communication technology on organisation performance[END_REF]. Collaborative networks are motivated by the urgency to rescue lives, thus advocating for efficiency and interoperability in services [START_REF] Noran | Collaborative disaster management: An interdisciplinary approach[END_REF]. In the academic and industrial contexts, the motivation is associated with the search for improvement of business performance and, consequently, competitiveness [START_REF] Zhang | Modeling and analyzing of an enterprise collaboration network supported by service-oriented manufacturing[END_REF].

Rapid advancement of ICTs: The organizations in collaborative networks aim to meet the needs of the market [START_REF] Andrés | Relevant problems in collaborative processes of non-hierarchical manufacturing networks[END_REF], such as virtual organizations and regional clusters [START_REF] Renna | Decision model to support the SMEs' decision to participate or leave a collaborative network[END_REF]. ICTs support collaboration and the breakdown of geographical barriers of the global market [START_REF] Cardoso | Pro-Active Service Ecosystem Framework[END_REF]. ICTs accumulate knowledge from different domains to leverage the collaborative relationship [START_REF] Durugbo | Managing information for collaborative networks[END_REF]. ICTs are relevant to help manage the highly unpredictable nature of demand and the short lead times associated with the products [START_REF] Lyons | The application of a knowledge-based reference framework to support the provision of requisite variety and customisation across collaborative networks[END_REF]. Network technologies enable the harmonization of internal and external communications [START_REF] Durugbo | Managing information for collaborative networks[END_REF]. The demand for constant technological improvement and advances capable faces new challenges such as interoperability [START_REF] Moghaddam | The collaborative factory of the future[END_REF].

Costs reduction:

The importance of the efficient structuring of the collaborative network so that the costs of adopting this strategy do not exceed the desired benefits [START_REF] Kandjani | Reducing the structural complexity and transaction cost of collaborative networks using extended axiomatic design theory and virtual brokerage[END_REF]. Uncertainties on the global stage drive collaboration to reduce costs, improve trust, and meet consumer demand [START_REF] Andres | A Cloud Platform to support Collaboration in Supply Networks[END_REF]. The reduction of transportation costs through the formation of collaborative networks among suppliers [START_REF] Paltriccia | Supplying networks in the healthcare sector A new outsourcing model for materials management[END_REF].

Economic, social and environmental sustainability: Global warming as a factor that motivates companies to improve their environmental and social reputation [START_REF] Jaegler | Carbon friendly supply chains: a simulation study of different scenarios[END_REF]. Managers have been attacking the question of environmental sustainability for the development of products, intensifying the collaborative partnerships [START_REF] Dangelico | Developing Sustainable New Products in the Textile and Upholstered Furniture Industries: Role of External Integrative Capabilities[END_REF]. There is a trend of international collaborative partnerships for mitigating climate change [START_REF] Karimi | Evolution of carbon capture and storage research: Trends of international collaborations and knowledge maps[END_REF]. Collaborative efforts contribute to the reduction / minimization of environmental impacts. Network sustainability requires strategic planning as well as a common understanding of how partners collaborate, and interact with society and the physical environment [START_REF] Maccarthy | Sustainable collaborative supply networks in the international clothing industry: a comparative analysis of two retailers[END_REF].

Search Approaches

The first approach is conceptual. Durugbo and Riedel (2013) proposed a conceptual model to assess the readiness of organizations in collaborative networks to service the product / service system, with multiple case study in four different sectors. [START_REF] Grilo | Construction collaborative networks: the case study of a building information modelling-based office building project[END_REF] proposed a model for business interoperability in the context of collaborative networks. [START_REF] Kumar | A knowledge based reliability engineering approach to manage product safety and recalls[END_REF] proposed a framework for knowledge management, which could serve as an early warning system to avoid excessive product recalls. This framework was approached in a practical study in the food sector. The second approach is bibliographic review. Di Cagno, Fabrizi and Meliciani (2014) conducted a study on R & D projects and concluded that participation in projects funded by the European Union is an important channel for knowledge transfer. [START_REF] Karimi | Evolution of carbon capture and storage research: Trends of international collaborations and knowledge maps[END_REF] developed a bibliometric review, considering the years 1980 to 2013, on the evolutionary trend of international carbon capture and storage collaborations, analyzing the collaborative network of the countries that publish on this subject. [START_REF] Kim | Co-Authorship Network Analysis in Industrial Ecology Research Community[END_REF] considered the years from 1997 to 2012 to carry out a bibliometric review of industrial ecology.

The third approach was the proposition of models, frameworks, prototypes, systems, architectures, etc., in a conceptual or mathematical way, without practical applications. [START_REF] Li | Building a boundary-spanning service for coopetition[END_REF] proposed a framework to support the sharing of data among all the participants of a collaborative network, proposing the cloud as the main support tool. This framework is an intelligent intermediary system that helps companies to search, question and recommend potential collaborators.

Surveys and case studies were addressed in fewer investigations. [START_REF] Macke | Social capital in collaborative networks competitiveness: the case of the brazilian wine industry cluster[END_REF] conducted a survey to identify the relationship between the elements of interorganizational social capital and the competitiveness of the collaborative network. Dias and Escoval (2012) also used the survey to identify the main pillars of innovation in hospitals and concluded that external collaboration is the main driver of innovation, while technology behaves as a mechanism that facilitates innovation in hospitals. The case study was used in the research of Saetta, Tiacci and Cagnazzo (2013), where they presented the first organization that adopted the model of Virtual Development Office. [START_REF] Boehm | A jack of all trades': The role of PIs in the establishment and management of collaborative networks in scientific knowledge commercialisation[END_REF], in turn, conducted a qualitative case study to analyze collaborative networks between universities and industries.

In summary, approximately 55% of the selected studies proposed models, architectures, prototype frameworks or systems, considering both the studies that applied in a real case and those that did not apply in a real case.

Fields Application

Fields application are diverse, however, the intention to systematize their occurrence is to elucidate possible applications within the scope of S^3 Enterprise. Approximately 34% of studies were performed without specifying a sector. These studies usually present problems and solutions without necessarily associating them to a given field. Chen et al. ( 2013) proposed a collaborative network security management system that can identify and treat new distributed attacks more quickly and effectively. [START_REF] Huang | The relationships between regulatory and customer pressure, green organizational responses, and green innovation performance[END_REF], in turn, studied how much regulatory pressure and customer pressure affect the organizations' environmental decisions.

The studies about collaborative networks in the academic context have been quite frequent. Boehm and Hogan (2014) investigated the role of the lead researcher in collaborative research projects and empirically validated the entrepreneurial role in building collaborative networks. Chakraborty et al. (2015) proposed two classification models to predict at the beginning of a researcher's career, how long the group to which it belongs will remain active and how productive it will be. Other studies focused on R & D, technology, virtual organization and other sectors. [START_REF] Arriagada | Knowledge management and maturation model in construction companies[END_REF] proposed a model to develop management strategies and maturation of knowledge applied to civil construction. [START_REF] Baek | A socio-technical approach to design for community resilience: A framework for analysis and design goal forming[END_REF] proposed a framework for the design of collaborative services in agricultural communities.

Seventeen publications have specified the field of study, however, were unique in addressing such a sector. Studies were carried out on start-ups in industry: auto parts, state environmental agencies, silk, malting, wine, bio-pharmaceutical, furniture, consultancies, and humanitarian organization, among others. This diversity of fields in the framework of collaborative networks motivates the conduction of studies on S^3 Enterprise. Regardless of the industry or focus of the research, companies and universities are generally looking for collaborative relationships to meet the new challenges imposed by current business models.

Results Systematization

The systematic review of a widespread and consolidated discipline (collaborative networks) sought to detect how much the researchers have directed, albeit indirectly, their respective research to the new vision mentioned above. It was possible to highlight the main motivators for the formation of collaborative networks: sharing of information and knowledge; resources and skills; risks and benefits; increasing demand for variety of products and customization; dynamism and competitiveness of the market; exploration and capture of new business opportunities; innovation; improving business performance; ICTs; cost reduction and economic, environmental and social sustainability. The main challenges identified by the researchers were: trust among partners; the interoperability of the information systems of the partners involved; the stage of selecting the collaborative partners; the alignment and clear definition of strategies and objectives; ensuring the security of data and information shared over the internet or other informational system.

Figure 1 presents a conceptual framework contemplating the challenges and opportunities identified in the systematic review with the objective of organizing and facilitating the understanding of the objectives intended in the work, as well as contribute with literature and future practical work on the theme. The study of collaborative networks was adopted through the intersections with the S ^ 3 approach.

Conclusions

The systematic review on collaborative networks was carried out because it is a consolidated subject in the literature and that meets the research gaps identified in the work of [START_REF] Weichhart | Challenges and current developments for sensing, smart and sustainable enterprise systems[END_REF]. The approach of a mature discipline aims to guide the development of the new paradigm discussed in the present study, S ^ 3 Enterprise.

The research sought to identify the main motivators, research approaches, most frequent application domains and the list of countries that published the most, for the construction of a conceptual framework with the main challenges and opportunities of collaborative networks and S ^ 3 Enterprise. Motivators represented the reasons why companies seek collaboration, and consequently the benefits they seek are implicitly associated. It is worth mentioning that they are implied in the proposed framework, since they were essential for understanding the challenges and opportunities of the areas under study. The most common research approaches, application domains, and the list of countries that most published on the subject were complements that sought to reinforce the idea that collaborative networks are comprehensive and effective strategies for addressing aspects of the digital age.

However, although collaborative networks have advantages, they also have internal limitations such as the availability of resources (physical, human, organizational and financial) and the ability to coordinate them in an integrated way; external constraints expressed by institutional factors governed by rules and conduits in the network operating environment; and associated challenges such as developing trust between partners, interoperability of information systems, selection of collaborative partners, alignment and clear definition of strategies and objectives among all stakeholders, ensuring data security and information shared over the internet or other informational system.

The systematic review allowed us to identify some opportunities in the intersection of collaborative networks and S ^ 3 Enterprise, such as: the need to develop new business models; the fostering of a digital ecosystem; the development and / or adaptation of emerging technologies; the optimization of manufacturing processes; the exploration of the aspects brought by the internet of everything; the use of cloud computing; the development of interoperable systems; the elaboration of ontologies, architectures, models, frameworks that support the development of the disciplines in question; and sustainable development in the economic, social and environmental aspects of business.

  Lyons et al. (2013) developed a knowledge-based framework to demonstrate how different collaborative networks can support product variety and customization.

Figure 1 :

 1 Figure 1: Conceptual Framework of Collaborative Networks and Enterprises S ^ 3