Hendrik Jähn
email: hendrik.jaehn@wirtschaft.tu-chemnitz.de

Including the Evaluation of the Compliance to Delivery Dates into a Performance Analysis Concept

Keywords: Virtual Enterprise, Performance Analysis, Delivery Date, Collaborative Network Model

The delivery date is one of the most important performance parameters of a business contract. In order to guarantee the customers' satisfaction and sustainable success it is essential to keep the fixed delivery date. Therefore a performance measurement system needs to be applied. In the following a specific approach for the performance analysis for Virtual Enterprises is introduced. Hereby both the comprehensive approach for performance analysis and the procedure for the evaluation of the compliance to delivery dates are considered. This concept allows an adaptive applicability which is necessary to support the short-term management of order-specific configured Virtual Enterprises within smart networked environments.

Motivation

The agreement of an exact delivery date represents an essential part in a contract between supplier and customer. Adherence of the delivery date should have highest priority for the supplier as deviations from the agreed delivery date often entail farreaching consequences for the buyer. This may also lead to negative consequences for the supplier, e.g. in form of contractual penalties or loss of customers. In connection to Virtual Enterprises / collaborative systems, adherence of the delivery date gets even higher importance by a particularly close and time-referenced cooperation.

Deviations of the delivery date represent a serious problem field as buffer time can hardly be planned. In that context the research question arises how to analyze and evaluate the adherence of the delivery date in networked production structures. As a relevant research methodology it represents one part of a comprehensive approach for the analysis of enterprise-related performances. That approach allows a consideration of different performance parameters, e.g. product quality, response time, price and soft-facts. The analysis is realized related to value-added processes. That means conduction is done separately for each production process or transaction. This form of modeling represents an essential precondition for the value-added-related performance analysis. As major research objectives are to be mentioned a high degree of flexibility and adaptability in combination with clear rules for the evaluation.

Regulatory Framework

Towards a Performance Analysis Approach

For the realization of the performance analysis in Virtual Enterprises a comprehensive model has been developed [START_REF] Jähn | Value-added process-related performance analysis of enterprises acting in cooperative production structures[END_REF]. That approach both includes value-added process neutral and value-added process-related process steps. The structure of the model and the interdependencies of the steps are displayed in Figure 1. The performance analysis approach includes the measurement, evaluation and analysis of relevant services performed by an enterprise operating in a collaborative network based on selected performance parameters. Herein, special attention is paid to aspects relating to one specific value-added process. This operational perspective allows acquiring cognitions about services performed by an enterprise after finishing a value-added process. Hereby, consequences, e.g. concerning the allocation of profit shares can be deduced in case of an unsatisfactory performance of an enterprise [START_REF] Jähn | A comprehensive Approach for the Management of Virtual Enterprises including Performance Analysis, Provision of Incentives and Allocation of Income[END_REF].

The primary task of the performance analysis approach is to determine the degree of services performed by an enterprise. For this purpose, primarily quantitative methods are applied. The result is considered by the implementation of incentive and sanction mechanisms. Herein, methods for the calculation of profit shares play a specific role. Within this context, it is necessary to analyze the services performed by the enterprises based on selected and relevant performance parameters [START_REF] Jähn | Fundamentals for the Development of a Performance Analysis Approach in Collaborative Networks[END_REF].

The determination of performance parameters is realized by the involvement of an adapted Balanced Scorecard. Performance parameters considered within the performance analysis are the price, date of delivery, response time, product quality, reliance and cooperation climate [START_REF] Jähn | Value-added process-related performance analysis of enterprises acting in cooperative production structures[END_REF]. The last two parameters form soft factors, whose perceptions primarily have to be quantified through appropriate methods. Each of those performance parameters is characterized by a specific key figure. For the evaluation of the services performed, specific evaluation functions, similar to utility functions, are applied. In order to regard their different relevancies, the evaluations can be weighted individually. By multiplying weighting and level of utility, aggregated utility values are calculated, similar to the value benefit analysis. The sum of the aggregated utilities of all performance parameters represents the actual performance. This can be compared to the target performance. Hereof, an enterprisespecific degree of target fulfillment is calculated. This allows a deviation of consequences e.g. incentives and sanctions. Consecutively, performance analysis is demonstrated in detail by considering the performance parameter "date of delivery".

Literature Research

As already mentioned, focus is put on the integration of performance parameter "date of delivery". Within a a comprehensive approach for the enterprise-related performance analysis, the aim is to analyze the degree of service provision. This means that a deviation of the realized from the agreed date of delivery needs to be recorded correctly. For this purpose, monitoring and workflow management instruments are applied. Subsequently, the evaluation analysis is realized by an adapted form of the value benefit analysis in combination with selected mathematical methods. First of all, an appropriate key figure including evaluation function has to be determined for the performance parameter "date of delivery". Within the context of a collaborative network, the date of delivery does not present the final date of delivery of the finished product to the customer, but the respective completion date at the analyzed enterprise. In consideration of possible effects of upstream enterprises within the collaborative network, an extended and differentiated problem to consider arises. Approaches for the evaluation of performances within networked organization structures are available and have been published in a quite unmanageable number.

In this context, one forerunner is Neely, who deals with questions concerning the performance measurement in supply chains and networks [START_REF]Business Performance Measurement[END_REF]. Herein, analyses can be arranged from several perspectives [START_REF] Chendall | Multiple Perspectives of Performance Measures[END_REF]. In general, however, it has been observed that primarily medium-and long-term approaches are suggested. Background for this is the financial focus of those approaches with regard to external effects of the company. During the development of those approaches, it is often reverted to the Balanced Scorecard, followed by an adoption of the same in a modified form considering supply chains or networks [START_REF] Brewer | Using the balanced scorecard to measure supply chain performance[END_REF], [START_REF] Gunasekaran | A framework for supply chain performance measurement[END_REF], [START_REF] Bhagwat | An application of the integrated AHP-PGP model for performance measurement of supply chain management[END_REF]. A more specific focus on performance indicators for collaborative networks based on collaboration benefits can be found in [START_REF] Camarinha-Matos | Performance indicators for collaborative networks based on collaboration benefits[END_REF]. Most networks exist long term whereas virtual enterprises represent the cooperation of several enterprises for completing a value-added process. This more relevant perspective is focused by Westphal et al. by investigating methodologies of measuring the collaborative performance in virtual enterprises [START_REF] Westphal | Measuring Collaboration Performance in Virtual Organizations[END_REF]. However, this publication primarily considers soft-facts and therefore is less relevant here. Another publication in that field is [START_REF] Rao | Delivery Performance Measurement in an integrated Supply Chain Management: Case Study in Batteries Manufacturing Firm[END_REF] which introduces a case study for delivery performance measurement. The determination of the key figure "adherence to delivery date" respectively "deviation to delivery date" of an enterprise occurs through an evaluation in consideration of the cause or the initiator of the deviation. In general, it has to be distinguished whether the delivery of an enterprise did occur early, on time or delayed. The deviation of the planned / agreed date of delivery l i a from the realized date of delivery l i r results from the difference of both values and is described as (local) deviation to delivery date or adherence to delivery date ∆l i l , which leads to equation [START_REF] Jähn | Value-added process-related performance analysis of enterprises acting in cooperative production structures[END_REF]:

∆l i l = l i r -l i a .
(1)

∆l i l is an enterprise-related figure.
Depending on the development of the delivery situation, the consequence is, that in case of ∆l i l >0 the delivery is considered as delayed, for ∆l i l =0, the delivery arrived on time and for ∆l i l <0, the delivery occurred early. That interpretation is based on the method of cumulative quantities. That means a delivery on the 20 th day while having an agreed delivery date on the 15 th day can be interpreted as a delay of 5 days (20-15=5). By the application of this method, it is possible to calculate an absolute value for the performance analysis. The delivery dates or appointments have to be offset against each other. The decision about the metric should be made depending on the designated accurateness. According to the chosen metric (week, day, hour, minute), the statements concerning the deviation are rough (week) or comparatively accurate (minute) by trend.

To stress the significance of the performance figure "deviation to delivery date" of an enterprise and to allow an activity-based evaluation likewise in the context of the performance analysis, it is necessary to rectify the locally caused delivery adherence of an enterprise ∆l i l by possible delays by enterprises, which accomplish a previous process step. These enterprises are denoted as "upstream enterprises" (i-1) in the collaborative network. It is therefore possible that an enterprise will, only because of one late delivery of one or more upstream enterprises, also deliver delayed. Due to this fact, the "upstream delay" ∆l i v has to be considered as a further influencing value for the performance analysis of that parameter. In the following, the possibilities for the determination of adherences to delivery dates of upstream enterprises are discussed.

If a value-added chain is considered, in general there is only one upstream company (linear process) existent, so that the relevant deviation to delivery date ∆l i v complies with the deviation to delivery dates of the upstream company ∆l i-1 l . In this case, it is valid:

v i l i l l 1 - ∆ = ∆ . ( 2
)
Here value ∆l i-1 l describes the schedule variance of the upstream enterprise. In case several upstream companies have to be considered (networked process), the specific local delivery delays ∆l i-1 l have to be taken into account for all upstream enterprises (i-1). The delay ∆l i v , which finally has to be considered herein, is calculated out of the maximum of delays of all enterprises, which are directly preceded within the value-added process. This highest delivery delay of a direct forerunner max(∆l i-1 l ) represents the delay, which has to be considered for the currently observed enterprise ∆l i v . The following equation is valid:

) max( 1 l i v i l l - ∆ = ∆ . ( 3
)
After determination of the locally relevant delivery date variances of an enterprise ∆l i l as well as the delivery date variance of direct upstream enterprises ∆l i v , which has to be included for a value correction, the deviation to delivery dates ∆l i , which is the one important input factor for the performance analysis, can be calculated as follows:

v i l i i l l ∆ - ∆ = ∆ . ( 4
)
The value deviation to delivery dates equals the performance figure for the performance parameter delivery date and forms one input variable for the performance analysis. At this stage, one can ask for possible reasons for a schedule variance ∆l i caused by an enterprise. The starting point for arguing is the assumption that within the context of the tender preparation, each enterprise determines an orderspecific processing time within the production control, which can be expressed by a corresponding completion date of production. The processing time, which forms the basis of the delivery date fixed in an offer, is t i PTa . A schedule variance ∆l i , which was caused by the enterprise itself, is expected in cases where the eventually realized processing time of the tender t i PTr deviates from the planned processing time t i PTa during the value added process. By this procedure, ∆l i can be calculated alternatively as follows:

PTa i PTr i i t t l - = ∆ . ( 5
)
Due to the fact that the calculation of the deviation to delivery date of an enterprise is only possible if all delivery dates within the network are known (both offer dates and realized dates), the collection of all performance figures has to be accomplished after completion of the value-added process.

Measurement of the Key Figure "Adherence to Delivery Date"

By the application of the calculation formulas for the performance figure "(corrected) deviation to delivery date" ∆l i , the result is an accurate time specification. For further processing, in principle two ways are conceivable. On the one hand, it is possible to work with the accurate values of the company-specific schedule variance, which however causes a considerable calculating effort. A further possibility is a provision for the deviation to delivery dates in form of tendential schedule variances. This approach is introduced in the following.

The evaluation function for adherence to delivery dates f i (∆l i ) used for calculating the evaluation credits x i l in this approach only considers the influencing factor of the corrected (actually caused) schedule variance ∆l i , whereas local delay ∆l i l and upstream delay ∆l i v are input values.

To allow an evaluation / analysis, all potential combinations of those three influencing factors have to be identified. This is to evaluate the practical relevancy. Figure 2 introduces all combinations of the three values ∆l i v , ∆l i l and ∆l i which are possible. Consequently, not the accurate deviation serves as the main distinction criterion herein, but only the kind of deviation by trend, whereas "+" signifies a late delivery, "-" an early delivery and "0" represents a delivery in time. Starting from these theoretic possibilities, only practically relevant situations are contemplated in the following analysis. This concerns situations which are highlighted in grey.

(∆l i ) (∆l i l ) (∆l i v ) Description 0 0 0 all

Evaluation of the key figure "Adherence to Delivery Date"

As it has already been shown, an adjusted value can be determined for the considered value-added process and the analyzed enterprise for the adherence of the delivery date ∆l i . This value has to be transferred to a score evaluation afterwards. Therefore and formally stated, a relation between the adherence to the delivery date ∆l i and the score evaluation x i l can be formulated in the form of a mathematic function. This function is called evaluation function. It has to be seen as a utility function in the context of the value benefit analysis.

The method of Lagrange interpolation is intended to determine exact (utility-) functions out of selected relevant combinations of ∆l i and x i l given. This procedure allows the generation of a polynomial, which leads through an arbitrary number of points. First of all, selected distinctive points of the function, which has to be determined, need to be defined. Here it has to be considered that the values of the abscissa (x-values) are distributed all about the same considered interval, whereas the probability is increased that a function is generated, which is consistent with the desired process. Normally, the selection of four credits is adequate, whereas two credits shall reflect the relevant exceptional conditions. In a concrete way, full marks are given for exact date adherence taken as an example, while for an exceeding of the maximum justifiable deviation, 0 credits are assigned. The value of 10 is usually given as full marks. Division within the parameters of 0 to 10 makes a significant evaluation possible. At this point, all relevant combinations have been assigned to adequate groups. This leads to a specific evaluation function (6):

( ) l i i i x l f = ∆ . (6)
Adherence to the date of delivery can be interpreted as one essential characteristic of making delivery of an enterprise, as missing this target will have effects on all downstream processes, so that an influence on the due-date of the final product can be expected.

One possibility for a simplified consideration is the formation of different groups. According to the present modeling, there is an evaluation function, which may be applied for all kinds of combinations of the input parameters from Figure 2. Seen from a practical perspective, not all kinds may be treated and evaluated the same way. So, it is important for the evaluation whether delays were caused by an enterprise or if existing delays were reduced. Therefore, it is recommended to introduce grouping, which can be evaluated in a similar way but regarding the specific situation. The division into different groups allows for a more detailed observation with a high flexibility regarding potential adjustments. However, a specific evaluation function has to be modeled then for each existing group. This can easily be achieved by means of the Lagrange interpolation.

Analysis of the Key Figure "Adherence to Delivery Date"

After determining the score evaluation of the performed service, it can be incorporated into the performance analysis. For this purpose, the score evaluations of the remaining performance parameters have to be known. To be able to consider the different meaning of the single performance parameters regarding the whole evaluation, performance parameter related weightings are included.

An actual value of making delivery is calculated from the weighted sum of the single score evaluations, which is then compared to a target value of making delivery. This comparison then allows for a statement if an enterprise has delivered the desired performance in a certain value-added process. If this is not the case, there is the possibility that negative consequences occur for the company, for instance in form of sanction payments or reduced share in profits. However, this subject matter shall not be enlarged upon at this point, as this concerns tasks relating to the whole performance analysis model.

Conclusions

An approach for the evaluation and analysis of the performance parameter "delivery date" and its key figure "adherence to delivery dates" has been introduced. It was developed for enterprises operating in Virtual Enterprises. Major advantages are the consideration of different classifications ("groups") for possible scenarios and the development of specific evaluation functions. They allow modeling a calculation scheme dependent on the degree of meeting the agreed delivery date.

The approach represents a quantitative model. Similar models have also been developed for further performance parameters. Therefore, a universal concept for a performance analysis, which relates to the value-added process, is available. This approach allows a complete and comprehensive analysis of the service performed by an enterprise operating in a collaborative network. The introduced approach has consistently been modeled and therefore, it allows for an application related to practical requirements. One limitation is the static character of the approach.

Efforts regarding a realization of the approach from an information-technical point of view as well as its integration into the comprehensive model of the performance analysis are being made currently and represent the challenge for future works.

Fig. 1 .

 1 Fig. 1. Performance Analysis Approach.

3

  The Performance Parameter "Date of Delivery" 3.1 Identification of the Key Figure "Adherence to Delivery Date"

Fig. 2 .

 2 Fig. 2. Possible combinations of the input variables.


Adam Pawlak
email: adam.pawlak@polsl.pl

Håvard D Jørgensen
email: havard.jorgensen@commitment.no

Holistic Design of Collaborative Networks of Design Engineering Organizations

Keywords: collaborative engineering network, holistic design, collaborative network modeling, active knowledge model, design house, design task pattern

The paper presents a visual knowledge-based approach for designing collaborative networks of engineering design organizations. The novelty of this holistic approach lies in integrated modelling of business, engineering, and learning processes. Distributed design tasks are easier coordinated due to explicit representation of management procedures as visual knowledge models of design flows and workflows. Furthermore, these visual models enable definition of design task patterns that can support design innovation management and learning across distributed teams. Proliferation of this approach in engineering communities is conditioned by availability of appropriate open platforms enabling distributed collaborative design and continuous learning processes in a frame of a collaborative engineering network.

Introduction

Network-based electronic system design becomes a common engineering paradigm, as a result of ubiquitous access to the broadband Internet and a progress in design engineering technologies [START_REF] Spiller | EDA and the Network[END_REF]. The engineer's workplace gets more and more virtualized [START_REF]Future Workspace -Perspectives on Mobile and Collaborative Working[END_REF] with a global access to design resources. This virtualization process transforms both the individual working environment [START_REF]Future Workspace -Perspectives on Mobile and Collaborative Working[END_REF], a collaborative work in a distributed team [START_REF]The Future Workspace -Perspectives on Mobile and Collaborative Working[END_REF], as well as it enables creation of virtual design organizations [START_REF] Filos | Towards the smart organization: An emerging organizational paradigm and the contribution of the European RTD programs[END_REF][7] [START_REF]VO Concept Source Information[END_REF] [START_REF] Witczyński | Virtual organisations in electronics sector -case study[END_REF], and additionally networks of collaborating virtual organizations [START_REF] Camarinha-Matos | The emerging discipline of collaborative networks[END_REF], as well as establishment of virtual engineering communities [START_REF] Dorn | Flexible Social Workflows -Collaborations as Human Architecture[END_REF] [START_REF] Jeners | From Groupware to Social Media -A Comparison of Conceptual Models[END_REF]. Network access has a predominant influence on enterprises that deliver design engineering, as a service. These enterprises, called design houses achieve a new dimension of flexibility related to the operation on the global market of design resources, partners with complementary competencies, continuous contact with clients, etc. Due to the access to the virtual infrastructures designers' teams from distributed design houses may undertake new innovative complex and heterogonous designs. New design approaches become feasible, like participatory design [START_REF] Kensing | Participatory Design: Issues and Concerns[END_REF]. Traditional Computer Aided Design (CAD) and Electronic Design Automation (EDA) design disciplines have been especially influenced by the new network-based engineering possibilities [START_REF] Spiller | EDA and the Network[END_REF].

Current systems, like cyber-physical systems [START_REF] Rajkumar | Cyber-Physical Systems: The Next Computing Revolution[END_REF] are complex and heterogeneous. Their design requires multidisciplinary and usually multi-organizational efforts [START_REF]Future Workspace -Perspectives on Mobile and Collaborative Working[END_REF] that integrate diverse design teams from different design houses (enterprises) into collaborative networks (CN) that encompass complementary engineering competencies. However, modern design methodologies like, platform-based design focus on system engineering design itself and rather neglect cross-organizational multidisciplinary issues in design flows. Further design challenges include: transformation and integration of models realized by multidisciplinary teams [START_REF] Jensen | A Model-Based Design Methodology for Cyber-Physical Systems[END_REF], shared understanding in distributed teams, engineering knowledge representation [START_REF] Radeke | GEN -Global Engineering Networking[END_REF], as well as use in a design process of enterprise knowledge [START_REF] Pawlak | Distributed Collaborative Design -A Case Study for Mixed-signal IP Core[END_REF]. Due to a dynamic character of a design teams' membership, a need for agile learning of team new members becomes evident. Thus a requirement for a holistic and agile design approach that encompasses modeling of a design process of heterogeneous systems realized in networks of collaborating engineering organizations. This approach should comprise enterprise knowledge resources, their structure, business models, as well as feedback from users of designed products.

In the paper we point to the organizational context of complex distributed design processes. They are an essential part of design house knowledge. At the same time, we would like to underline that modeling and management of knowledge of a design house can influence design decisions and can contribute to agile design of more innovative products. In the presented work, visual knowledge models (AKM) have been used to represent multi-organizational, design, product, as well as infrastructural aspects of heterogeneous system design.

Design Engineering in Collaborative Networks

Design processes of cyber-physical systems (CPS) are usually conducted in multidisciplinary, often distributed, design teams [START_REF] Rajkumar | Cyber-Physical Systems: The Next Computing Revolution[END_REF]. Designers realising these design processes necessitate heterogeneous tools from different engineering domains. Mastering interdependencies between digital (cyber) and heterogonous (physical) domains, like: analog, mechanical, or optical design parts that are designed by teams with complementary design competencies constitutes a real challenge. In order to address it companies are motivated to collaborative engineering, especially in networks where trust among partners already exists. Inter-organizational collaboration requires an additional care concerning security.

Collaborative networks [START_REF] Camarinha-Matos | The emerging discipline of collaborative networks[END_REF] or collaborative engineering networks [21][27][32] offer to engineers much broader spectrum of collaboration functionality enabling integration of design teams. The last ones are the most developed form of engineering networks, as they offer advanced engineering collaboration services for designers and tight cross-organizational integration of their tools. Through the evolution of the virtual organization concept [START_REF] Filos | Virtuality and the Future of Organisations[END_REF] from the extended enterprises [START_REF]CE-NET: A Roadmap towards the Collaborative Enterprise -CE Vision[END_REF], virtual enterprises, and smart organization [START_REF] Filos | Towards the smart organization: An emerging organizational paradigm and the contribution of the European RTD programs[END_REF] concepts until collaborative networks [START_REF] Camarinha-Matos | The emerging discipline of collaborative networks[END_REF], the available support for collaborative engineering was changing [START_REF] Witczyński | Virtual organisations in electronics sector -case study[END_REF]. We have distinguished four categories of engineering networks [START_REF] Witczyński | Virtual organisations in electronics sector -case study[END_REF]: digital engineering libraries, engineering brokers, engineering networks, and the most advanced collaborative engineering networks. Issues that are central to design engineering in collaborative engineering networks are shortly addressed below.

New techniques for management of distributed tools that support designers in creation of virtual design environments are required. These techniques should enable straightforward integration of engineering design tools and services, like: concept specification graphical tools, compilers, design verification and simulation tools, behavioural, architectural and logic synthesis tools, physical design tools, test generators, product data management systems, and/or databases of predesigned intellectual property components. All these tools and services can be distributed over different Internet sites and platforms. Issues of concern are: portable tool descriptions; management and control of access rights; configuration, registration, search, and invocation of remote tools.

Business aspects of engineering processes, like use of more cost effective technologies compromising some product parameters, like: power consumption, size or weight need to be addressed during the design process. Usually, business process management (BPM) is addressed by economists and managerial staff, who are not involved in design engineering. Integration of their BPM tools with engineering design workflow (WfM) technology is a challenging endeavour [START_REF] Kokoszka | Are Workflow Management Systems useful for Collaborative Engineering ?[END_REF]. A common modelling paradigm is needed in order to address this challenge. Issues requiring special attention are related to distributed workflows definition, verification, and coupling. Further issues are related to collaboration among dispersed teams' members including synchronous and asynchronous communication, shared understanding, firewalls crossing, and assurance of robust Internet connection. Finally, we point to the need for open platforms that enable engineering in CNs.

Modeling of CNs of Design Engineering Organizations

The paper refers to the experiment in modelling of the engineering Collaborative Network (CN) which has been conducted by industrial partners of the MAPPER project (mapper.eu.org). It encompassed two design houses in complementary engineering domains, namely digital and analogue electronics. The industrial design spanned over two countries, Germany and Poland and involved dispersed branches of one company, and an additional academic enabling party responsible for maintenance of the used MAPPER virtual collaborative infrastructure [START_REF] Jorgensen | MAPPER Collaboration Platform for Knowledge Intensive Engineering Processes[END_REF]. MAPPER was extensively using the visual knowledge modelling approach based on Active Knowledge Models (AKMS).

The paper reports on the use of this approach to CN modelling that included integration of BPM and engineering WfM. The concept of Active Knowledge Models and its applications in various domains have been covered in the literature [START_REF]Active Knowledge Modeling[END_REF][12] [START_REF] Lillehagen | The foundations of AKM technology. Enhanced Interoperable Systems[END_REF][17] [START_REF] Lillehagen | Active Knowledge Modeling of Enterprises[END_REF]. Enterprise architecture modelling, IT governance and BPM are the most common applications. The additional motivation of this paper is to demonstrate use of the AKM technology in the domain of electronic system design. AKM representation in principle is more general than workflow management. It can thus be applied to representation and modelling of various enterprise processes and products. Engineering processes, including EDA (Electronic Design Automation) ones, can be modelled using AKM. AKM-based design strategy enables capturing in a model, knowledge on: enterprise architecture, design processes, designers' competencies, used tools, IT infrastructure, products, and patterns of good practices. This knowledge is gathered in visual knowledge models, operational models [START_REF] Jensen | A Model-Based Design Methodology for Cyber-Physical Systems[END_REF], and patterns [START_REF] Pawlak | Distributed Collaborative Design of a Mixed-Signal IP Component[END_REF].

Fig. 1 -A visual design of a collaborative network of two design companies

Active knowledge models (AKMs) [START_REF] Lillehagen | Active Knowledge Modeling of Enterprises[END_REF] are visual representations of unfolding and dynamic business knowledge. The models, when supported by an appropriate platform, can be used to actively customize the underlying IT infrastructure. AKM models are executed through process enactment and rule engines provided by that infrastructure. In the context of engineering design processes, AKMs can be applied as an active and systematically unfolding knowledge model of the design process and its environment. This knowledge model can integrate: relevant design processes, required human resources with engineering expertise profiles, and appropriate design tools. Such a model, if systematically updated, can simplify transfer of design knowledge concerning a particular design phase to new employees. It supports design knowledge accumulation and transfer through learning patterns around the distributed teams. Furthermore, AKMs can model families of electronic products at various levels of abstraction well managing various configurations of product functionality.

Towards Holistic Design of CNs of Design Houses

Models of collaborative engineering networks are generally defined as explicit representations of some portions of design houses reality, i.e. their resources in terms of available tools, human experts' profiles, design and verification processes, as well as organizational structures, and collaborative links. knowledge models are actively used during the operation of the system. Models need an appropriate IT infrastructure in order to be developed, managed, stored, and executed. The MAPPER infrastructure [START_REF] Jorgensen | MAPPER Collaboration Platform for Knowledge Intensive Engineering Processes[END_REF] has been used in below shortly reported experiments. The infrastructure applies active model configuration in the following ways:

• Model-configured workplaces constitute simpler means of interacting with information and knowledge, adapted to the designers' preferences, competences, roles, and responsibilities;

• Portal navigation structures reflect model structures;

• Portal and web services from other tools can be invoked using general plug-in mechanisms, mapping data from the models to the parameters of the service; • Task execution is used for plugging together solutions for a particular process or task pattern, invoking parameterized infrastructure services for each step; • Intellectual property rights can be protected using a model-configured access control mechanism, as defined in [START_REF] Jørgensen | Interactive Process Models[END_REF].

Tool Invocation Workflows in TRMS

The Tool Registration and Management Services (TRMS) [START_REF] Kostienko | Advanced Infrastructure for Collaborative Engineering in Electronic Design Automation, The Vision for the Future Generation in Research and Applications[END_REF][24] system comprising three main components: Global Tool Lookup Service (GTLS), Tool Servers (TSs), and Client Applications has been used for tool invocation workflows in the conducted design engineering experiments [START_REF] Pawlak | Distributed Collaborative Design of a Mixed-Signal IP Component[END_REF].

Tool Server 1 GTLS Tool Server 2

Client interface

Task 1 (compilation) Task 2 (simulation) Fig. 3 -Tool invocation workflows in TRMS. Fig. 3 illustrates a simplified design scenario based on TRMS. A designer executes a workflow that constitutes a sequence of design tasks. The TRMS Client interface enables a designer to define in a graphical way the workflow of tasks. The simplified workflow consists of two design tasks, namely design compilation and simulation. The first Tool Server is responsible for execution of compilation, whereas the second one executes simulation. Both tool servers have been identified by the GTLS service. A task, being a part of the workflow available at the GUI level, is a representation of a service that is executed at the specified Tool Server.

EDA tools operating in the "batch mode" can also be supported by TRMS which enables their encapsulation and flexible execution. Design data transfer to the tool servers can be facilitated, either through the TRMS Client or through the consecutive use of tools, like CVS.

Model-Configured Task Execution

The mapping of the high level knowledge models of design flows onto the concrete tasks and workflows that are available through TRMS requires that the atomic tasks of the knowledge models are connected to the TRMS specific workflows or tasks. In consequence, the AKM task execution engine will be able to invoke a particular lower level tool for each process task. The parameterised URL interface of the TRMS applet is used for handling interactive tasks, while automatic workflows may be invoked using the TRMS web service interface. Both these integration mechanisms are supported by the AKM task execution engine.

From the AKM models data and parameters are extracted that are needed as input to TRMS. Definition of mapping between model elements as well as, service input and output parameters, assures that any kind of modelled content may be used by lower level services. Different types of models concerning: documents, organisations and people, design processes and tasks, or product structures can be configured to be used by services. The integration of AKM and TRMS is thus flexibly modelconfigured, and may be dynamically adapted to meet particular designers' requirements. This assures designers' freedom that in consequence supports creative design.

Tool Server Service

TaskInvokeManagament

GTLS Service

Integration of Task Execution and TRMS Workflows

The web services-based architecture of TRMS enables straightforward integration with other web service-based systems. A set of services offered by TRMS includes: search for a predefined task or workflow (group of tasks), as well as task control and execution. Two TRMS Clients independently invoking services from the GTLS server and from the Tool Server are illustrated in Fig. 4. The TRMS Client enables the complete control over the TRMS environment. It supports management of users and tool properties. A designer is supported in building his own visual representation of the environment based on the available web services.

Design of Virtual Electronic Components in the CN

A distributed collaborative design process aiming at a virtual (Intellectual Property) IP component required for hardware implementation of standard serial communication protocols is shortly described in this section. This design process integrated two electronic design companies from Germany (Recklinghausen) and Poland (Fig. 1) (two branches in Gliwice and Bielsko-Biała). This design process was realised in the collaborative engineering network that was enabled by the MAPPER project infrastructure [START_REF] Jorgensen | MAPPER Collaboration Platform for Knowledge Intensive Engineering Processes[END_REF]. The network integrated engineers' workspaces with design tools and remote specialized tools that processed automatically design data upon invocation through the TRMS environment (Fig. 2 and Fig. 3).

The IP component design process begins with an informal specification that is agreed upon by both partners. Once a precise specification is defined, both companies precise their design flows and workflows as visual models (AKMs), and agree upon appropriate analogue-digital interfaces. The AKM-based design flows and workflows cover: specification, synthesis, verification, and product preparatory phases for both digital and analogue design flows. Consecutively, designers responsible for particular design phases and tools to be used are defined. The common design workflow defines all design steps at both companies that are needed for designing and production of the designed IP component for standard serial communication. This common design workflow is a result of numerous negotiations between designers and also managers of both companies that are performed using collaboration services of the MAPPER infrastructure (e.g. CURE environment).

The active knowledge model developed for the IP component design integrates a wide spectrum of information and knowledge related to the joint product, namely: the companies' structures, human resources including their competencies, the available IT infrastructure, the project organisation with assigned responsibilities, the detailed structure of the joint product, as well as the project planning with management and design workflows. Fig. 2 presents the top-level view of this model for one design organization (design house). The detailed model comprises a very large number of elements, i.e. objects and their relationships that are not easy to comprehend all at once. It is therefore necessary to focus only on the portion of the model at the time. The AKM model browser enables control of visibility of model elements by creating different views with selected model components only.

Further details of the discussed distributed collaborative design process are available in: [START_REF] Pawlak | Business Process and Workflow Management for Design of Electronic Systems -Balancing Flexibility and Control[END_REF][23] [START_REF] Pawlak | Distributed Collaborative Design -A Case Study for Mixed-signal IP Core[END_REF], as well as from mapper.eu.org (D15).

Conclusions

Modeling of collaborative networks that includes: modeling of partition of design tasks onto different design houses, designers' and resources' allocation enables verification of correctness of design decisions, and in general it supports distributed design processes. The use of active knowledge models has been demonstrated in modeling of organizational, process, product and system aspects of collaborating design houses.

The experiments conducted during the MAPPER project and later the MADONE network [19] have confirmed that Active Knowledge Models constitute an innovative approach to holistic design of CNs due to their support for: -Multidimensional modeling management of design house knowledge, -Integration in a model of organizational, process, product and system aspects, -Virtual collaboration of designers and their continuous learning processes, -Creation of meta-models, -Modeling and generation of configurable model-driven workspaces that support collaboration in multidisciplinary design teams, -Representation of design task patters, and -Automation of selected design tasks with appropriate infrastructures.

Fig. 2

 2 Fig.2 consists of the top view of a design house model comprising such components as: design flows, design documents, design house organization and infrastructure.

Fig. 2 -

 2 Fig. 2 -The design house representation as AKM.

Fig. 4 -

 4 Fig.4-Model-configured task execution in the MAPPER platform[START_REF] Jorgensen | MAPPER Collaboration Platform for Knowledge Intensive Engineering Processes[END_REF].

  Practical realization of the holistic design methodology depends on the availability of the underlying IT infrastructure for all design houses that are involved in the collaborative network. Community efforts are thus required towards development of open platforms enabling collaborative engineering [19].

Acknowledgments. The authors acknowledge MAPPER project partners and the SUT TRMS team: Paweł Fraś, Tomasz Kostienko, Piotr Penkala, and Marek Szlęzak.


Andrew Crossley
email: andrewdcrossley@servq.com

Gareth Coles

Rhian Edwards

Sue Hurrell

Nina Ruddle

The Development and Adoption of a New Joint Bidding System for Public Contracts in Wales

Keywords: Joint Bidding, Procurement, Public Sector, project management

The paper describes the adoption of new SME friendly collaborative procurement processes in Wales and reports on some early successes. In 2012 the Welsh Government commissioned a team to research how SMEs could collaborate to access larger public sector contracts. This led to the publication of the new Joint Bidding Guide in 2013. The Guide leads both the selling and buying side through the procurement process ensuring joint bids are assessed in a fair and transparent manner. In 2014 the team was commissioned by the Welsh Government to advise leading procurement professionals in Wales on the adoption of the new Guide and map progress on up to 12 demonstration projects. The demonstration projects total more than €200 million in value.

Introduction

A major challenge for both micro businesses and Small to Medium sized Enterprises (SMEs) is how to grow efficiently. One possibility is through winning and delivering larger public sector contracts as consortia, often termed Virtual Organizations (VOs), to access higher value contracts than they have historically been awarded individually. There are significant reasons for SMEs and micro-organizations wanting to create VOs [START_REF] Camarinha-Matos | On reference models for collaborative networked organizations[END_REF]. In the context of public sector work, the main advantages are that: a) in most of the developed world, public sector contracts offer a wide range of opportunities for SMEs to grow; b) there is a high degree of transparency on the tender and selection process; c) public sector contracts tend to be financially more secure and less volatile than private sector contracts demanding strong internal and external governance for the VO which can minimize the risk of potential conflict [START_REF] Rabelo | A Governance Reference Model for Virtual Enterprises[END_REF]; d) in many countries public bodies offer strong contract management and align payments to delivery of results either by effort (time) or outcome (success); e) well run VO contracts provide good references for future opportunities; and, f) public bodies want the best value they can from the supplier base and recognise the high levels of service and innovation that SMEs can deliver. The paper describes some of the challenges and findings of a three year programme for the design and implementation of a new toolkit for buyers and sellers to the public sector called the Joint Bidding Guide [3]. It demonstrates a practical approach to bidding and awarding larger contracts to VOs working as consortia in Wales.

The Economy of Wales

Wales is a country within the United Kingdom of Great Britain and Northern Ireland, an EU member state. Its population in 2012 was 3,074,067 and Wales has a total area of 20,782 km 2 [4]. Its population density of 148 people per square kilometre is therefore relatively low compared to the more congested areas of the United Kingdom, most notably the South East around London and the major industrialised cities in the Midlands and North. The majority of the people live in a series of coastal conurbations along the M4 corridor in the South East, together with its hinterlands often known as the Welsh Valleys, and the A55 corridor in the North. Rapid growth of the two corridor communities in the 19th and 20th centuries centred on the abstracting, processing and export of coal, steel and the production of slate mainly for roofing. From the early 1980s some sectors of the Welsh economy have been reinvigorated with advanced technologies in electronics, motor manufacturing, creative and green energy. More recently financial, ICT and support services centred on Cardiff and Swansea have seen rapid growth. The Welsh Government and its agencies have played a major part in encouraging inward investment and created seven Enterprise Zones in Anglesey, Cardiff, Deeside, Ebbw Vale, Haven Waterway, Snowdonia and St. Athan-Cardiff Airport. They are focused on power generation, business services, advanced manufacturing, low carbon development and aerospace.

Employment density is a function of the location and skills availability of the people. The largest companies working in Wales are mainly based along the two economic corridors. Local supply networks tend to be based close to these centres of operation. This means that large areas of Wales remain rural and semi-rural with historic 'market towns' whose economies are traditionally centred on agriculture and tourism. The rural economy makes the most of the countryside and its areas of outstanding natural beauty such as the two national parks: the Brecon Beacons and Snowdonia. Recent advances in high speed broadband and new transport investment will enable more rural and remote economies to participate in the nation's future success.

The Welsh Government plays a major role in the country's economy. Its 2015 budget is €21.4 billion which funds health, education and many public services. Funds are also distributed and spent via local government, the National Health Service and a variety of agencies. The annual spend on external suppliers who contribute to the delivery of public services is €6 billion (28%). Thus getting more of this delivered by VOs helps grow regional and national employment within SMEs. SMEs share innovation and best practices creating a Welsh Virtual Breeding Environment (VBE) [START_REF] Paszkiewicz | Modelling Virtual Organization Architecture with the Virtual Organization Breeding Methodology[END_REF] capable of more rapid VO configuration and positioning for future opportunities.

Small Businesses in Wales

99% of indigenous businesses in Wales are classed as SMEs (up to 250 employees) of which 95% are micro businesses (less than 10 employees) [6]. The Welsh Government provides advice and support for small businesses as they are seen as potential catalysts for innovation, growth, new jobs and apprenticeships. A publically funded organization called Business Wales helps train owners and managers of small businesses in a variety of subjects including bidding for public sector contracts. However, the awarding of public contracts is traditionally dependent upon a bidder's revenue, track record and capacity. Hence, working together as extended enterprises, configured as consortia of VEs, can increase the volume and value of opportunities for members to win work from the public sector.

Development of Relevant Policy Guidelines

In 2012 the Welsh Government realised that it needed to create a more supportive tendering environment in order for more businesses to be eligible to bid and win larger contracts as collectives of VOs termed consortia. One of the recommendations of the Welsh Government's 2012 Task and Finish Group on micro-businesses was that the Government should encourage micro-businesses to consider consortium approaches to public sector procurement. In 2008 the EU issued a code of practice on improving access for SMEs to procurement contracts [START_REF]Facilitating Access by SMEs to Public Procurement Contracts SEC[END_REF]. It explicitly refers to the benefits of joint bidding, keeping selection criteria proportionate, and allowing sufficient time for drawing up tenders. These goals are reinforced in the superseding Directive 2014/24 on Public Procurement [8]. The UK Government is an early adopter of the Directive through its Public Contracts Regulations 2015 [9].

Sustainable Development and Sustainable Procurement are vitally important and recognised as the best practice approach by the Welsh Government, ensuring that 'maximum value is achieved for the Welsh pound by delivering the maximum social, economic and environmental benefits'. In its 2012 Procurement Policy [10] the Welsh Government stated that in carrying out procurement activity the public sector will: a) Define 'value for money' as ' the optimum combination of whole-life costs in terms of not only generating efficiency savings and good quality outcomes for the organization but also benefit to society and the economy, whilst minimising damage to the environment.' (Principle 3) b) Ensure that the 'delivery of added value through Community Benefits policy must be an integral consideration in procurement.' (Principle 4) c) Continue to embrace all the principles of Opening Doors -the Charter for Small and Medium sized Enterprises [11]. 'Public bodies should adopt risk based, proportionate approaches to procurement to ensure that contract opportunities are open to all and smaller, local suppliers are not precluded from winning contracts individually, as consortia, or through roles within the supply chain.' (Principle 5) Until 2013 there were limited practical guidelines and advice available for small organizations, not-for-profits and charities on how to track opportunities, team up with VO partners then prepare joint bids for higher value public sector tenders. Procurement teams had also experienced challenges in prequalifying and assessing tenders from joint bidding teams. The challenge was how to design, test and validate a new approach to help smaller organizations create effective consortia. They could then access more of the Welsh public sector's €6 billion annual external spend.

Joint Bidding Guide Research

Three sponsors from the Welsh Government's team at Value Wales, the Wales Cooperative Centre and the Wales Centre for Voluntary Action (WCVA) came together in 2012 to commission research and analysis in order to produce a guide to joint bidding. The 2012 Wales Procurement Policy Statement [10] refers to consortium bidding as a 'means of increasing access to procurement opportunities'. This was a priority issue for the sponsors because: a) They had identified that procurement processes can often work against organizations attempting to set up VO consortia to bid for contracts. b) In some instances, public money that had previously been awarded as grants was being spent via competitive tendering. In mid-2012 the Sponsors identified that there was no detailed guidance available that addressed the challenges set out above. Therefore they prepared a joint specification and project budget. This reduced their collective procurement costs and demonstrated practical collaboration between the public and third sectors. A second requirement was to modify the Welsh Public Sector's SQuID (Supplier Qualification Information Database) question set [12] and guidance to make it more suitable for consortia bids.

In September 2012 the Sponsors selected a specialist Cardiff based body with expertise in collaboration, virtual working and alliances to carry out the work. The Sponsors created a Project Board to liaise with the the ServQ Alliance. Over five months the programme consisted of: desk research; creating a research database; interviews with successful and unsuccessful consortia; review of best practices, including standards on collaborative business relationships; report drafting; sign off, eventual translation and publication in Welsh and English, in compliance with the Welsh language standards and regulations. The updates to the SQuID system were then cross-referenced into the new guide. This created a series of new templates for consortium design and working than both the buying side and the bidding VOs could adopt building a climate of trust and understanding between the respective bodies.

Design of the Joint Bidding Guide

Joint Bidding Guide addresses the technical, organizational, and financial hurdles faced in procurement by both the buying and supplying sides. It is an example of a Wales-based innovation that has potential for widespread application both within Wales and elsewhere, especially within the European Union. It comprises of 18 short chapters with supplementary checklists and assessment tools, a technical glossary, further reading and a set of case studies for reference. Some examples of the innovations within the Guide are: an evaluation checklist of procurement policy; alignment to BS11000 [START_REF]2010 Framework specification for collaborative business relationships[END_REF] the most current internationally available standard for collaborative working; a new Procurement Assessment Model (PAM) for Joint Bidding with worked examples; methodologies for measuring 'hybrid' financial reports for consortia; capacity modelling; advice on the relevant timescales needed to encourage more consortia bids; design systems for creating viable consortia; acceptable legal structures for contract, including compliance with competition law; checklists and assessment models to enable potential consortia to score the tender opportunity against 'market bidding resources, delivery competence, commercial standing and risk'; configuration and leadership behaviours; trust measurement and due diligence; governance, compliance, intellectual property and asset management; a new approach to integrated risk assessment and planning for consortia with stage gates (see Fig. 1); advice on estimating and pricing for consortia including target pricing and how to share risk and profits; bid planning templates; how to review joint bids, analyse tenders and objectively interview consortia. The entire document was ultimately published on the Welsh Government website on a dedicated Joint Bidding Guide home page [3].

A critical issues raised by both the procurement teams and the VO's potential lead members was how to professionally plan and manage risks. Fig. 1 shows the process.

Demonstration Projects

The Joint Bidding Guide was formally launched by Jane Hutt AM, Minister for Finance and Government Business for the Welsh Government in October 2013. At the launch the Minister announced that a set of Demonstration Projects would be established to test the Guide's processes. Two such cases are listed below.

Having a high quality guide was the first part of the implementation challenge. It was also critical to promote its use to procurers and consortia bidders. Awareness raising started in 2013 and the Demonstration Programme implemented between mid-2014 and 2015. The key lessons learned from the programme to date are as follows: a) More consistent planning. Procurers need to configure contracts suitable for VO consortia at the outset. They use the Procurement Assessment Model (PAM) for rapid appraisal of a contract's suitability for consortia working. b) Some modifications to terms of contract are needed. These are traditionally written based on a lead contractor and its sub-contractors. c) Document review. Examining tender and specification documentation to ensure it is appropriate for VOs using the updated SQuID is critical. d) Allow more time for potential VO consortia to be configured. Not all potential consortia exist at the time of advertising Prior Information Notices (PINs). Hence signposting the potential of VOs at events is important. e) Provide bid support facilities. For new VOs bid support is needed to help them through the necessary planning and consortium bid writing processes. f) Best practices, innovation and sustainability. Additional documentation has been created including a new web site jointly created with the Institute for Collaborative Working [14]. A handbook was written with processes designed using swim lanes following the path of both procurer and VO.

g) Promotion outside of Wales. The Minister highlighted that the best practices were transferable outside of Wales at the Guide's launch. For example, the guide has been referenced at UK national conferences and major events.

Annex

Demonstration Project Case Study 1 in Carmarthenshire

In 2014 Carmarthenshire County Council used the Guide to plan the procurement of a new €3 million framework. Consortia of public and private/third sector organizations needed to work together to design and run an integrated support service called Families First. The contract was awarded in early 2015. Alan Aitken, the Procurement Director was an early advocate of the Joint Bidding Guide. His views are as follows:

"The Welsh Government's Joint Bidding Guide offers practical and straight forward advice to both parties. In Carmarthenshire we have embraced the approach. We were the first Welsh Local Authority to pilot the Guide. From the earliest stages of the project, we could identify clear benefits and a very positive market response to the new Guide. This positive response was confirmed when two consortiums were formed using the Joint Bidding Guide, with the assistance of the Wales Co-operative Centre, in order to specifically bid for the work. It was rewarding to see both consortium bids win business and be awarded contracts as part of the exercise."

Demonstration Project Case Study 2 in Caerphilly

In 2014 Caerphilly County Borough Council started to procure suppliers for its External Works construction framework agreement. This is a five year agreement with an estimated spend value of €30 million starting in 2015. The works had been tendered in two lots, one lot for works packages below €1 million and one lot for works packages above €1 million. Twelve contractors were accepted on each lot starting in 2015. The Council used the Guide to prepare its tender and a new VO consortium used it to plan its bid. The VO was then awarded a place on the larger value lot. The proposed consortium worked with the Wales Co-operative Centre and Caerphilly County Borough Council's Supplier Development team to ensure a good quality compliant bid was submitted. In line with the latest EU procurement regulations, the successful consortium will be legally constituted as part of the conditions of tender/contract. This is similar to the Italian model [START_REF] Ricciardi | Strategic Context, Organizational Features and Network Performances: A Survey on Collaborative Networked Organizations of Italian SMEs In[END_REF]. A pathfinder project is being used. Caerphilly's procurement team won a Welsh National Procurement Award in 2015 in part for their innovative work supporting consortia.

Conclusions

paper described the challenges for micro and small to medium sized businesses in accessing larger contracts from the public sector in Wales. From 2012 to 2015 a series of reports, field research and case studies helped shape a new set of processes to help both the buying side (procurers) and the selling side (bidders). In 2012 the Welsh Government and two other sponsoring organizations commissioned a new integrated approach to planning and managing bids from VOs working as consortia. The processes were published as a new document called the Joint Bidding Guide. After its launch a practical follow up project was commissioned to support the public sector and the VOs of micro business/SMEs through a series of demonstration projects. The projects are a mix of national and local procurements with values ranging from €3 million to more than €100 million. They range from large volumes of lower value products such as motor vehicle tyres and hire services, through to large value contracts in construction.

An informed and enthusiastic Steering Group, with expert researchers and practitioners has managed the successful roll out of the Guide. The Guide is strengthened with the 2014 EU and 2015 UK Government procurement regulations. It is tailored for the needs of both the public sector and SMEs to access larger contracts. The Guide is a dynamic document that can change with lessons learnt from the demonstration projects. Updates can be incorporated to maintain it as a practical, well researched toolkit that can help SME's win more business working as VOs in Wales.

  -An Overview of Risk Assessment and Planning for Joint Bidding/Working Assess any gaps and fill them -Sponsor and Consortium together. An Overview of Risk Assessment and Planning for Joint Bidding/Working The Development and Adoption of a New Joint Bidding System in Wales 619

		Public Sector Sponsor	Procurement Team	Consortium		Consortium Lead Member	Consortium Member
	0 Governance	Design a Joint Bidding Policy Create a Joint Working Risk Assessment
		and Management
		Template
			Create a Joint Bid		Gate 0	Create a Joint Bid
			Risk Assessment			Risk Assessment
						Create a Joint
						Working Risk
		Establish your Body's Risk		Establish Risk	Gate 1	Management Template
		Management		Management
		Procedures for		Procedures for
		Joint Bidding		the Consortium
		Assess internal issues that could cause risks with joint bidding				Assess internal issues that could cause risk to the Consortium	Assess internal issues that could cause risk to the Consortium
	1 Strategic	Prepare an Impact Assessment for				Prepare an Impact Assessment for	Prepare an Impact Assessment for
		joint working and				joint working and	joint working and
		continuity				continuity	continuity
		Assess how joint working will affect your sustainability and social responsibility policies	Ensure relevant Bid Policies and Procedures are updated for joint bids			policies Assess how joint working will affect your sustainability and social responsibility	policies Assess how joint working will affect your sustainability and social responsibility
		Outline joint risk ownership and	Communicate policies and	Outline joint risk ownership and		Gate 2
		reasoning -	acceptance	reasoning -
		prepare a Project	criteria on risk for	prepare a Project
		Risk Register	joint bids in ITT	Risk Register
				Gate 3
	2 Engagement	Assess Consortium's approach to Risk Management as part of bid process Have an open book on any exit and legacy based risks as part of contract Agree the Consortium's Risk Management Process negotiation. Create MASTER PROJECT RISK PLAN. Gate 4
			Build the combined Shared Risk Plans into the Consortium's Project Plan
		Fig. 1

Acknowledgements. The Joint Bidding Guide is publically available. Its design and development was part funded by the European Union and the principal Sponsors.


Afshin Jamshidi
email: afshin.jamshidi.1@ulaval.ca

Samira Abbasgholizadeh Rahimi

Daoud Ait-Kadi

Angel Ruiz

A New Decision Support Tool for Dynamic Risks Analysis in Collaborative Networks

Keywords: Risks analysis, Collaborative networks, Fuzzy cognitive maps, virtual enterprises, Expert knowledge

Collaborative networks are complex systems and consist of many factors with dependencies among them. Although the number of collaborative networks such as advanced supply chains or virtual organizations/laboratories/e-science is growing and their significance is increasing in the world, many of them are unsuccessful. In addition, very little attention has been paid to the risk analysis of collaborative networks by considering the dependencies among risk factors. So, the precise risks analysis associated with collaborative networks projects is crucial to attain a satisfactory performance. To address this, we are proposing an advanced decision support tool called "Fuzzy Cognitive Maps" (FCM) which can deal with risks of such complicated systems by considering the interrelationships between factors. FCM states the behaviour of complex systems accurately and illustrate any complex environment based on the experts' perceptions and by graphical representations. It is able to consider uncertainties, imprecise information, the interactions between risk factors, Information scarcity, and several decision maker's opinions. FCM is not only able to evaluate risks more precisely in collaborative networks, but also it could be applied in different decision makings problems related to collaborative networks such as partner selection and forecasting behaviors, policy analysis, modeling collaboration preparedness assessment, etc. Hence, the proposed tool would help practitioners to manage collaborative network risks and decision making problems effectively and proactively.

Introduction

Collaborative networks (CNs) such as virtual organizations, dynamic supply chains, professional virtual communities, collaborative virtual laboratories, etc. are complex systems associated with uncertainties in dynamic business environments. This uncertainty and complexity could lead to critical risks which could influence on the enterprises' performance. According to Munyon & Perryman [START_REF] Munyon | The Dynamics of Effective Organization Alliances[END_REF], failure rate of alliances are estimated between 60% and 70%. Risk evaluation of CNs is a complex and critical task since several tangible and intangible risk factors should be considered in this process. In addition, there are always some dependencies among risks that can influence each other mutually and these dependencies make the evaluation process more complex and challenging. Therefore, an effective method for evaluating the risks is fundamental and essential. In recent decade, many problems related to CNs such as partner selection [START_REF] Hexin | A Partner Selection Method Based on Risk Evaluation in Virtual Enterprises[END_REF] [3] [START_REF] Jarimo | Optimal Partner Selection in Virtual Organisations With Capacity Risk and Network Interdependencies[END_REF], modeling collaboration preparedness assessment [START_REF] Rosas | Modeling collaboration preparedness assessment[END_REF], etc. have been investigated. However, very little attention has been paid to the risk analysis of collaborative networks by considering the dependencies among risk factors [START_REF] Li | Decision support for risk analysis on dynamic alliance[END_REF] [START_REF] Zhou | Risk Evaluation of Dynamic Alliance Based on Fuzzy Analytic Network Process and Fuzzy TOPSIS[END_REF].

Li & Liao [START_REF] Li | Decision support for risk analysis on dynamic alliance[END_REF] identified all possible risks which could influence on the operation of alliance and measured their priority numbers using three criteria; probability of risk, severity of risk and risk detection number. Das and Teng [START_REF] Das | A Risk Perception Model of Alliance Structuring[END_REF] developed a risk perception model. The model consists of the following components: the antecedents of risk perception, relational risk and performance risk, risk perception and structural preference, and the resolution of preferences. Ip et al. [START_REF] Ip | Genetic Algorithm Solution for a Risk-Based Partner Selection Problem in a Virtual Enterprise[END_REF] described and modeled a risk-based partner selection method by taking into account risk of failure, due date and the precedence of sub-project. In addition, a rule-based genetic algorithm with embedded project scheduling was proposed to solve the problem. Huang et al. [START_REF] Huang | A Fuzzy Synthetic Evaluation Embedded Tabu Search for Risk Programming of Virtual Enterprises[END_REF] developed a risk management model for virtual enterprises (VE) and presented a tabu search algorithm by considering uncertainties in experts' opinions. Huang et al. [START_REF] Huang | A Distributed Decision Making Model for Risk Management of Virtual Enterprise[END_REF] proposed a two level Distributed Decision Making (DDM) model for the risk management of dynamic alliance. A Particle Swarm Optimization (PSO) algorithm is used to solve the resulting optimization problem. Their proposed model improves the description of the relationship between the owner and the partners.

However, research about the risk assessment of CNs by considering the interrelationships among risks factors and forecasting the impact of each risk on the other risks don't exist in the literature of CNs and further research in this field is required. Considering the interdependencies among risks could lead to more accurate risk assessment to enterprises. In addition, during the risk assessment process, there are lots of uncertainties and imprecise information associated with experts opinions that should be taken into account. Recently, Zhou and Lu [START_REF] Zhou | Risk Evaluation of Dynamic Alliance Based on Fuzzy Analytic Network Process and Fuzzy TOPSIS[END_REF] presented a methodology for choosing a coalition partner using Fuzzy Analytic Network Process (FANP) and by considering the interaction and feedback relationships between risk factors. Although ANP is able to consider interdependencies among factors, it has some disadvantages. Sometimes it is not easy even for experts to compare the importance of a factor to another [START_REF] Yu | A soft computing method for multi-criteria decision making with dependence and feedback[END_REF]. In addition, different structures could lead to the different rankings and it is usually difficult for experts to provide the true relationship structure by taking into account several factors. Moreover, ANP is time-consuming due to the large number of pair-wise comparisons needed for comparing the risk factors.

Therefore, this paper deals with risk assessment of CNs as the most important phase of risk management, and proposes an advanced decision support tool called "FCM" to overcome the shortcomings of current risk evaluation tools applied in CNs. FCM is a useful tool that states and evaluate the dynamic behaviour of complex systems by considering the interrelationships among factors [START_REF] Kosko | Fuzzy cognitive maps[END_REF]. It considers the uncertainties and imprecise information by using linguistic variables. Hence, expert perception is considered in the model more precisely. Moreover, FCM can even be used when the information is scarce. This tool recently has been applied successfully in evaluating risks in complex and critical environments such as Enterprise Resource Planning (ERP) maintenance [START_REF] Lopez | Dynamic risks modelling in ERP maintenance projects with FCM[END_REF] [15] and IT projects [START_REF] Salmeron | Fuzzy Cognitive Maps-Based IT Projects Risks Scenarios[END_REF], and therefore we think it has a good potential to be applied in complex CNs for evaluating risks and forecasting the impact of each risk. The reminder of this paper is organized as follows. Section 2 introduces the FCM fundamentals. Section 3 explains the proposed tool with an example related to risk evolution in dynamic alliance and conclusions are drawn in Section 4.

FCM Fundamentals

Fuzzy Cognitive Map (FCM) was originally introduced by Kosko in 1986 [START_REF] Kosko | Fuzzy cognitive maps[END_REF] as a tool for modeling complex systems, able to consider dependencies among different criteria and based on experience and knowledge of experts. In the last decade, FCM has been widely employed in the variety of applications such as, decision analysis, engineering science, political decision making, failure detection, process control, and medical diagnosis [START_REF] Zhi | An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation[END_REF]. More information about the different applications of FCM are available in [START_REF] Glykas | Fuzzy Cognitive Maps, Advances in Theory, Methodologies, Tools and Applications[END_REF] and [START_REF] Papageorgiou | Fuzzy Cognitive Maps for Applied Sciences and Engineering (From Fundamentals to Extensions and Learning Algorithms[END_REF] books.

FCMs are a combination of fuzzy logic and neural networks and are able to represent both quantitative and qualitative data. An FCM consists of some nodes/concepts which indicate the main features of the system and some edges/arcs between nodes showing the relationships between them [START_REF] Zhi | An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation[END_REF]. Fig. 1 shows a simple FCM diagram with five nodes and nine weighted edges, where each node takes values in the range ∈ [0, 1], and each edge between two nodes, and , has a weight, in the interval [-1,1] which denotes the influence of each node on the others. The sign of arcs' weights (+ /-) states the increase/decrease between concepts in the same or opposite directions. In the other words, if an increase in concept results in an increase in concept (same directions), the sign of will be positive, and vice versa. The types of concepts and dependencies among them, directions of dependencies, and initial weights of concepts and arcs are significantly based on the experience and knowledge of experts [START_REF] Elpiniki | Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization[END_REF].

After assigning initial values to the concepts and weights, the FCM converges to a steady state through the interaction of equation ( 1). At each step, the value of the concept is influenced by the values of concepts connected to it and it is updated [START_REF] Kosko | Fuzzy Engineering[END_REF].

= ( ( ) + ∑ ( ) ) , (1)
where, is the initial weight of the arc between concepts and ; is the value of concept at step + 1; stands for the iteration counter; is a threshold or barrier function, which is used to restrict the concept value into [0,1] range. Different types of threshold function could be applied in Eq. 1 depending on the concepts interval. The most common types are; tangent hyperbolic ( ( ) = ℎ( )), bivalent function (f(x) = 0 or 1), sigmoid function ( ( ) = 1/(1 + " #$% )), and trivalent function (f(x) = -1, 0 or 1). At each iteration, Eq. 1 produces a new value for the concepts and when FCM arrives at one of the following three states, the iterations end and it results in a steady state [START_REF] Papageorgiou | Fuzzy Cognitive Maps for Applied Sciences and Engineering (From Fundamentals to Extensions and Learning Algorithms[END_REF];

1) The value of concepts have stabilized at a fixed value (fixed equilibrium point), 2) A limited state cycle is reached, 3) Chaotic behavior has appeared. As mentioned by Papageorgiou et al. [START_REF] Elpiniki | Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization[END_REF], one of main drawbacks of FCMs is the potential convergence to undesired steady states. In the recent decade, several authors have tried to resolve this drawback by developing learning algorithms such as Differential Hebbian Learning [START_REF] Papageorgiou | Active Hebbian learning algorithm to train fuzzy cognitive maps[END_REF] [24], particle swarm optimization (PSO) [START_REF] Elpiniki | Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization[END_REF], Simulated Annealing (SA) [START_REF] Somayeh Alizadeh | Learning FCM by chaotic simulated annealing[END_REF], and etc. Learning algorithms can increase the efficiency and robustness of FCMs by updating the initial weight matrix ( & ' () ). According to Papageorgiou, the training methods are categorized in three groups; population-based, Hebbian-based, and hybrid, which is a combination of Hebbian-based and evolutionbased algorithms [START_REF] Papageorgiou | Learning algorithms for fuzzy cognitive maps: a review study[END_REF]. Since hybrid based algorithms ensure near-optimum solutions in the weights search space, in this paper we have applied hybrid based algorithm for training FCM and finding the optimal weight set of the FCM.

The Proposed Method

In order to illustrate the proposed tool, we adopted the risks identified in Li & Liau [START_REF] Li | Decision support for risk analysis on dynamic alliance[END_REF] study regarding dynamic alliance. Dynamic alliance or VE is a temporary network of specialised individuals and independent institutes who work together and share skills and costs in order to better respond to fast changing market opportunities [START_REF] Li | Decision support for risk analysis on dynamic alliance[END_REF]. The identified risks are shown in Table 1 and the related FCM graph is depicted in Fig. 3. The definitions of these risks are available in study [START_REF] Li | Decision support for risk analysis on dynamic alliance[END_REF].

The FCM graph is depicted based on experts' opinions in order to show the dependencies and feedbacks among factors. To make the initial weight matrix ( ), each expert individually determines the dependencies between concepts, using fuzzy linguistic terms such as Very High (VH), Low (L), etc. Then, the linguistic variables are aggregated and defuzzified to numerical values [START_REF] Elpiniki | Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization[END_REF]. The initial weight matrix, is shown in Table 2.

In order to deffuzify a triangular fuzzy number (l, m, u) the following Equation is usually applied:

= ) * * + (2)

FCM Building Process

Two types of FCM model could be developed for evaluating risks. The first type is scenario-based which is used in this paper and the second type is based on initial concept values obtained from multi criteria decision making tools such as AHP/ANP or eigenvalue approach. Scenario-based FCM is a new method recently presented by different authors and it is becoming popular in complex and fast-changing domains such as business environment, therefore it is critical to predict the impact of potential risks that could be happened in the future. In order to evaluate the impact of risks in a scenariobased FCM model, several what-if analysis scenarios should be developed using different initial concept values [START_REF] Rosas | Modeling collaboration preparedness assessment[END_REF]. In each scenario, a risk or a set of risks are activated and using Eq. 1 and using learning algorithms the initial vector ( 5) is updated in order to show the impact of activated risks on the other risks. Note that when a risk is activated, its value in the initial vector ( 5) is considered 1. This number is 0 for the rest of the risk factors which are not activated.

In second type of FCM modeling, the initial concept values ( 5) is updated by using initial weight matrix ( ) and Eq. 1 until it converges to the steady state condition. The updated concept values * shows the importance of each risk. Since this type of FCM is unable to assess the impact of each risk on the other risks, we propose to apply the first type in evaluating the risk of CNs. To illustrate the risk evaluation process, in this paper we only assess the impact of "Market risks" on other risks. In this scenario, at the initial time only risks related to market risks including "Demand fluctuation risk (C1)", "Competition risk (C2)", and "Spillover effect risk (C3)" are activated. 5 = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]; Using matrix, Initial concept vector 5, Equation (1) and learning algorithm, the training process starts. In this paper we applied NHL-DE algorithm for training FCM which is a combination of nonlinear Hebbian learning (NHL) and differential evolution (DE) algorithms. According to Papageorgiou [START_REF] Papageorgiou | Fuzzy Cognitive Maps for Applied Sciences and Engineering[END_REF], the hybrid training approaches such as NHL-DE emerge less limitations as they combine two training algorithms and inherit the benefits and shortcomings of both of them. The training process in NHL-DE has two steps. The first step starts with NHL algorithm and in the second step, the result of first step is used to seed the DE algorithm. We imported the data into Matlab code and we used MATLAB version R2012a software to obtain the updated concept matrix ( * ). In this paper, the values of learning rate parameter [START_REF] Zhou | Risk Evaluation of Dynamic Alliance Based on Fuzzy Analytic Network Process and Fuzzy TOPSIS[END_REF], mutation constant (8), crossover constant (CR), and weight decay learning parameter (9) have been selected 0.04, 0.5, 0.5, 0.98 respectively. The population size is considered 50. It should be noted we performed 1000 iterations for the algorithm per experiment and 100 independent experiments were performed. * = [0.7, 0.47, 0.85, 0.7, 0.98, 0.4, 0, 0.94, 0, 0.97, 0.2, 0.49, 0.78, 0.21, 0.93, 0.7, 0.91, 0.1, 0.78, 0.99, 0, 0.37];

The steady state vector * shows that activating C1, C2, and C3 risks have a strong influence over the remainder risks in particular risks C5, C8, C10, C15, C17, and C20. The same procedure should be done for all other risks by activating their sub-risks each time. The results reveals that which risks are critical. In addition, the proposed tool is able to predict the impact of each risk on the other risks more accurately because it take into account the multiple connections between risks. Therefore, decision makers will be able to manage the risks of CNs properly and accurately. It should be noted that the process for developing a FCM is strongly dependent on the experts' opinions. Then, special attention should be paid to matters such as the selection of experts' team and the feedback with them.

Conclusion

This paper proposes an advanced decision support tool called "Fuzzy Cognitive Maps" (FCM) which can deal with risks of collaborative networks by taking into account the interrelationships among factors. This tool can be adapted to a wide range of multi criteria decision making problems such as predicting behaviors in CNs, partner selection, policy analysis, modeling collaboration preparedness assessment, etc.

The main features of FCM in contrast with those of other existing methods are; 1) the relationships among variety of factors and also importance of factors could be considered , 2) uncertainties and imprecise information are taken into account on the decision-making process, 3) several experts can state their opinions, 4) it has capabilities to handle both qualitative and quantitative factors, 5) several alternatives can be considered in decision making about best partner and 6) by using the casual graphs in FCM, it is easier for decision makers and experts to understand the factors and their dependencies. Moreover, by relying on FCM models, the decision makers have a strong support, and therefore are able to decide more precisely and accurately when evaluating risks or choosing the partner. As a future research topic, application of other hybrid algorithms for training FCM could be considered. Currently, we are working on developing a comprehensive framework for partner selection problem in dynamic alliance by using an integrated FCMbased method.

Fig. 1 .

 1 Fig. 1. A simple FCM graph.

Table 1 .

 1 Risk factors in dynamic alliance[START_REF] Li | Decision support for risk analysis on dynamic alliance[END_REF].

	Risk	Sub-risks	Index
		Demand fluctuation risk	C1
	Market risk	Competition risk	C2
		Spillover effect risk	C3
	Financial risk	Interest rate risk	C4
		Exchange rate risk	C5
	Natural risk	Natural risk	C6
		Trust risk	C7
		Moral risk	C8
	Relational risk	Motivation risk	C9
		Communication risk	C10
		Organization risk	C11
		Information sharing risk	C12
	Operational risk	Information integration risk	C13
		Information conveyance risk	C14
	Political risk	Social risk	C15
		Policy risk	C16
		Quality risk	C17
		Cost risk	C18
	Competency risk	Time risk	C19
		Technologic risk	C20
		Investment recovery risk	C21
	Investment risk	Investment implementation risk	C22

Table 2 .

 2 Initial weight matrix.

	, -./ C1	C2	C3 C4 C5 . . C18 C19 C20 C21 C22
	C1	0	0.2 0.5	0	1	. .	0	0	1	0	0.3
	C2	0.2	0	0.6 0.5	0	. . 0.26	0	0	0.1	0.3
	C3	1	0	0	0	0	. .	0	0	0.8	0	0.3
	C4	0.8	0.9	0	0	0.2 . . 0.12	0	0	0.1	0
	C5	0.7	0	0.8 0.4	0	. .	0	0	0.4	0	0
	C6	0.8	1	0	0	0.2 . .	0	0.1	0	0	0
	C7	0.8	0	0	0.6 0.6 . . 0.78	0	0	1	0.38

Acknowledgements. This research was partially financed by grants [OPG 0293307 and OPG 0118062] from the Canadian Natural Sciences and Engineering Research Council (NSERC). This support is gratefully acknowledged.