Helia Sohrabi
email: helia.sohrabi@cirrelt.ulaval.ca

Benoit Montreuil
email: benoit.montreuil@cirrelt.ulaval.ca

From Private Supply Networks and Shared Supply Webs to Physical Internet enabled Open Supply Webs

Introduction

The way supply networks are designed has evolved through the years in order to address changes in the business environment, such as increasing customer service requirements, shortening of product life cycles and never ending pressures for inventory reduction. It is thus not surprising that hundreds of supply chain and supply network researches have been reported since the introduction of the concepts, focused on designing supply chains and networks so as to concurrently minimize logistics costs and maximize customer service in a turbulent and competitive environment.

Yet there are still evidences of supply network malfunctions in terms of stock excess, lost orders and unused facility space as the whole supply network is designed and planned at a strategic level long before operation time while the current business environment is fast changing. This urges the redesign of supply networks and their adaptation to real time business.

The Physical Internet (PI or π) recently introduced by Montreuil [START_REF] Montreuil | Towards a physical internet: meeting the global logistics sustainability grand challenge[END_REF] offers a vision that can potentially enable a fundamental rethinking of supply network design. As one of its thirteen key characteristics, the Physical Internet empowers a global Open Supply Web (OSW) enabling producers, distributors, retailers and users to realize, move and store their products for fast, efficient and reliable response to quickly evolving demand. In such an Open Supply Web, products embedded in standardized modular containers can be dynamically deployed through an open geographically dispersed web of product realization centers, distribution centers, warehouses, hubs and transit centers. In an open supply web, the design of a supply network shifts from being in a strategic long-term decision territory to an operational and tactical decision making territory. It can be finely attuned to market conditions, efficient through better facility utilization and more reliable service to customers, and robust in responding to distributed stochastic demand.

This paper presents an exploratory research study aiming to assess the potential gains associated with moving away from the current ways of supply network design, expressed as Private Supply Networks and Shared Supply Webs, to the envisioned Physical Internet enabled Open Supply Web way. Section two proceeds with defining existing supply network designs and introducing further the concept of Open Supply Web. Section three presents the exploratory investigation, including numerical case generation and design optimization. Section four analyzes the experimental results. Section five concludes and synthesizes the value contribution of the paper, discusses its limitations, and provides future research avenues.

On the evolution of supply network design

Here we divide supply network designs introduced in the literature into two major groups; Private Supply Networks (PSN) and Shared Supply Webs (SSW). PSN are single-company focused. The design decisions are made in order to provide the company's products, from sourcing the required materials and components to producing and delivering final products to its clients in its targeted markets. When designing its supply network, the company is bound by a compromise between on one side providing better service and potentially gaining market revenues and on the other side requiring higher investments and incurring higher costs. This impacts its choices in terms of number, location and capacity of facilities in the network, and the service level that they are capable of offering and delivering to their clients in terms of speed and reliability. Illustrations of private supply network designs can be found in [START_REF] Miranda | Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand[END_REF]- [START_REF] Martel | The design of production-distribution networks: a mathematical programming approach[END_REF]. The compromise expressed above is a strong incentive for companies to engage in partnerships with each other, so as to share their production and logistic facilities as well as transportation means. Partnering companies aim to reduce both investments and operating costs associated with achieving their target service level. The synergistic coupling of their respective supply networks forms a shared network of supply networks, here called a shared supply web. Illustrations of shared supply web designs are discussed in [START_REF] Pan | The reduction of green-house gas emissions from freight transport by pooling supply chains[END_REF][START_REF] Nagurney | A system-optimization perspective for supply chain network integration: the horizontal merger case[END_REF].

In the private supply network of a company, resources such as facilities are available only to that company. In shared supply webs, resources are available for exploitation in each partnering company's supply network. In a Physical Internet enabled open supply web, companies can exploit any openly available resource and deploy their products dynamically within this open web.

Open supply webs are characterized by (1) their nodes being openly accessible to any actor, be they producers, distributors, logistics providers, retailers or users; (2) the service capacity of their nodes available for contract on demand, on a per-use basis, be it for processing, storage or moving activities; and (3) by dynamic and interlaced virtual private networks created by actors to realize and deploy the products, services and solutions in anticipation of and response to real time business characteristics [START_REF] Montreuil | Towards a physical internet: meeting the global logistics sustainability grand challenge[END_REF]. Table 1 contrasts the characteristics of existing private supply networks and shared supply webs with those of the proposed Open Supply Webs.

Except in special contexts, strategically designed private supply networks are hard to efficiently adapt to the real time demand and business environment. Shared supply webs allow a higher adaptability, yet are still constrained to the set of partnering companies and their strategic configuration of their shared supply web. When exploiting an open supply web, companies can readily adapt their supply network to the real time characteristics of demand and business environment. They can dynamically realize and deploy their products through a globally extended open web of available facilities. Open supply webs are currently a vision. Some parts of this vision are dependent on the implementation of the Physical Internet. Some parts can be readily achievable by actors changing their worldview, their mental model of the best way to design supply networks, logistics and facilities.

Exploratory investigation

An exploratory investigation of the potential of open supply webs relative to private supply networks and shared supply webs is introduced in this section. The studied context involves a set of companies, each having to design its supply network so as to best serve its targeted markets. The supply networks here embed factories to make products, distribution centers to serve clients, factory-DC flow links and DC-client flow links. Transportation is limited to truck travel. Here we have simplified the supply network design model to a location-allocation model and assumed that business characteristics such as demand and cost functions are deterministic. In forthcoming research, we plan to extend our investigation to uncertain environments requiring to model adaptability and robustness in a rigorous way, such as proposed in [START_REF] Klibi | The design of effective and robust supply chain networks, research document CIRRELT[END_REF]. In the following subsections, the numerical case generation method and the optimization models are described.

Case generation: business, territory, demand and potential sites

The investigation has been bounded to a maximum of fifteen businesses, each producing a single product in a single factory and serving its targeted markets across the USA and Canada. This geographical area has been divided into 13 regions, four for Canada (northern territories are not considered due to their low population) and nine for the USA (based on US census regions).

Generating a case first involves generating for each company the locations of its customers, its overall demand and the demand from each customer, and the location of its factory. Second, generating a case requires generating the set of potential locations for opening distribution centers. The set of potential customer locations includes the 283 cities in Canada and the USA having a population larger than 100,000 inhabitants. Each company sells a distinct product whose price is randomly chosen between 200 and 400 $. Annual sales of a company are randomly generated between 10 7 and 10 9 $. The number of served regions by a company is randomly generated. In each served region, a company has demand in a randomized number of cities, including those with higher population. In each served city, a randomized percentage of citizens are clients of a company. The annual demand in each city for the product of a company is computed from the company's overall demand by applying the ratio of the city's served population over the total number of clients served by the company. Each company's factory is randomly located in one of its served cities. Potential sites for distribution centers include 273 locations at the intersections of intercity highways. Distances between every potential site and every client city as well as every factory are calculated using their road distance from Google Map.

Design and assess: PSNs, SSWs and OSW

In this section, the location-allocation models used to respectively optimize the design of each company-specific private supply network, the design of the shared supply webs and the design of the open supply web are described. The models use well known network modeling and integer programming techniques to support the goal of this research to compare performance of different companies designed in three different design modes and investigate the improvement achieved by exploiting an open supply web.

Here are the decision variables and parameters used across the three models.

: Binary variable for the implementation of a distribution center in site s

A scp : Binary variable for the assignment of site s for servicing city c with company p product F s : Non-negative continuous variable for the product flow through the distribution site s (one distribution center in PSN and SSW, a potential combination of open distribution centers in OSW)

F ps : Non-negative continuous variable for the flow of company's p product through the distribution site s F scp : Non-negative continuous variable for the product flow from distribution site s to client city c for company p product : Throughput capacity of the distribution site s : Client demand in city c for the product of company p f ps : Present value of unitary factory-to-site transport cost for site s served from the factory of company p

I s c s d cp
From Private Supply Networks and Shared Supply Webs to Physical Internet 239 f scp : Present value of unitary transport cost of products of company p flowing from site s to client city c : Present value of distribution center implementation at site s : A large number : Intended number of distribution centers to exploit t scp : Present value of site-to-city transport cost for serving from site s the entire demand for company's p product by clients in city c First is the private supply network design model for a company with product p.

Minimize ∑ ൫݅ ௦ ܫ ௦ + ݂ ௦ ܨ ௦ ൯ + ∑ ݐ ௦ ܣ ௦ ௦, ௦ (1)
Subject to

∑ ܣ ௦ ௦ = 1 ∀ܿ (2)
∑ ݀ ܣ ௦ = ܨ ௦ ≤ ܯ * ܫ ௦ ݏ∀ (3)
∑ ܫ ௦ = ݊ ௦ (4)
In this mixed integer programming model, the objective function ( 1) is minimizing the total cost of opening new distribution centers, transporting products to client cities from their assigned distribution center, and transporting products from the factory to the distribution centers. Through constraint (2), each client city has to be served by a single distribution center. Constraint (3) insures that the flow through a distribution center equals the sum of the demands of all client cities assigned to it, and that no flow can go through it if it is not implemented. This flow is unconstrained, so that the distribution centers are capacitated as needed. Through constraint (4), the total number of implemented distribution centers is constrained to a specified number for experimental reasons. Second is the shared supply web design model given previously existing private supply networks.

Minimize

∑ ݂ ௦ ܨ ௦ + ∑ ݐ ௦ ܣ ௦ ௦,, ௦, (5)
Subject to

∑ ܣ ௦ ௦ = 1 ∀ܿ,  (6)
∑ ݀ ܣ ௦ = ܨ ௦ ,ݏ∀  (7)
∑ ܨ ௦ = ܨ ௦ ≤ ܿ ௦ ݏ∀ (8)
The objective function (5) of the SSW is similar to that of PSN, except that it is summed over all companies' products and that there are no installation costs, reflecting the fact that in the experiment the partnering companies share the distribution centers that they installed when designing their private supply networks. Assignment constraints [START_REF] Martel | The design of production-distribution networks: a mathematical programming approach[END_REF] are company specified, as contrasted to constraints (2) of the PSN. Constraints [START_REF] Pan | The reduction of green-house gas emissions from freight transport by pooling supply chains[END_REF] compute the flow of company p product incoming each open distribution site s. Constraints (8) sum up the product flow of each company p in each open site s and insure that the open site capacity is not exceeded. In the experiment, i s M n the capacity of each site is set equal to the total demand of the cities assigned to it in the PSN, plus an additional percentage experimented with three values 20%, 50% and 70%. The site capacity is expressed in terms of product flow, assuming unit load equivalence among the products.

Third is the Supply Network Exploiting an Open Supply Web design model.

Minimize ∑ ݂ ௦ ܨ ௦ + ∑ ݂ ௦ ܨ ௦ ௦,, ௦, (9)
Subject to constraints [START_REF] Nagurney | A system-optimization perspective for supply chain network integration: the horizontal merger case[END_REF] and

∑ ܨ ௦ = ܨ ௦ ,ݏ∀  (10) ∑ ܨ ௦ = ݀ ௦ ∀ܿ,  (11)
The objective function of OSW minimizes both distribution center feeding costs and client city serving costs, directly computing the flows instead of relying on assignment variables as in PSN and SSW since clients of each company p may be fed from multiple open distribution sites. Constraints (10) compute the flow of products form the factory of company p to each open distribution site s. Constraints ( 8) are used to insure site capacity respect. Constraints (11) ensure that the demand of each client for the product of each company p is satisfied form the combination of open distribution sites.

In the OSW model, it is assumed that there is distribution center capacity openly available in each city. The capacity available in a city is considered to be a random number normally distributed with an average set as the total demand of all companies at that city plus an additional percentage (with four values 0, 10, 20 and 50%). The capacity available at intersecting highway locations has been purposefully omitted. Companies exploit available spaces in the currently open distribution centers. To determine the open available capacity in the open supply web, first for each city the total demand over all companies is calculated. Then the closest distance between each site and all cities is calculated. Finally for each site a weighted summation of the closest cities' demand is calculated while the larger weights are assigned to the closer cities. In the experiment, to fix the site capacity, this value has been enlarged by 0, 10, 20 and 50 % and the available capacity is set equal to a normal random number with average equal to any of these four values boosted 10, 20 and 50 %, resulting in twelve capacity scenarios.

Analysis of Experimental Results

The private supply network model described above has been solved for each of the fifteen generated companies for four variants each, respectively with one, three, five and ten distribution centers to be implemented. Fig. 1(a) presents as an illustration the optimized private supply network of a company having three distribution centers.

The shared supply web model has been solved for three groups of companies, respectively with five, ten and fifteen partners. Again each of these has been optimized for four variants, respectively assuming that each partner company contributes one, three, five and ten distribution centers. The open supply web model has been solved assuming that all fifteen companies are to concurrently exploit it, under twelve distinct randomized capacity scenarios for open distribution centers. Fig. 1(c) displays the resulting massively meshed web, taking a collective perspective displaying the interplay of all flows from all companies, for a given capacity scenario. From an individual perspective, Fig. 1(d Table 2 provides a summary of the experimental results, based on model resolutions using the ILOG OPL 5.0 solver. As the three models have distinct objective functions, in line with the experimentation, instead of displaying them, for each set of designs Table 2 provides key comparable surrogate indicators extracted from analyzing the solutions: the scenario-based number of distribution centers available for exploitation as well as the number of distribution centers exploited in the optimal solution, the number of served cities and average delivery time offered to the customers.

The delivery time results assume that each 500 km distance shipment by truck corresponds to a one-day delivery time, considering loading-unloading times and truck speed limits. By increasing the number of companies partnering in a shared supply web, the service coverage attained by private supply networks can be achieved by exploiting a significantly lower number of DCs. For example, when companies each contribute five DCs, the average service coverage is 16 hours exploiting their private supply network while it is 13 hours, 11 and 10 hours respectively when they collaborate in a shared supply web of five, ten and fifteen partners.

In the open supply web, all open distribution sites were available for usage. Out of these 273, 191 were indeed exploited, which is more than available in the biggest sharing strategic partnership in SSW scenarios. Without explicit strategic partnerships with other companies or significant investment, the achieved mean delivery time is 6 hours, which was achieved in the shared supply web only when at least 10 companies entered in a sharing strategic partnership, each contributing 10 distribution centers, engaging at least 100 facilities in which the partners had invested in. is 6 hours, achieved when 10 or15 companies each contributed ten DCs. In the two SSW scenarios where the 6-hour mean delivery time was achieved, the partners did not even exploit all their engaged facilities, 77 out of 100 in one scenario and 114 out of 150 in the other, thus leaving significant unused wasted space. In order to explore the performance potential at stake, a case including 15 businesses serving clients in US and Canada were generated and optimized according to the three studied design alternatives. The results were highly conclusive.

Even though the added synergy potential made it clear that performance improvements were to be incurred when switching from the more restrictive to the more open alternatives, the amplitude of that gain has been highly significant and revealing. Open supply webs allowed companies to achieve the best service coverage by exploiting a large number of open DCs distributed throughout the territory while avoiding large investments, long-term leasing or long-term partnership commitments.

This exploratory investigation has clear limitations, with a case simplified to single-factory single-product companies, a low number of companies and potential sites, no dynamism consideration, known deterministic demand, no inter-company competition, only truck-based transportation, a geographical area limited to Canada and the USA, and performance measures limited to flow costs, investments, delivery time and number of sites available and exploited. Yet the obtained results confirmed the objectives of research, motivating further research toward investigating the potential of open supply webs under more comprehensive and realistic scenarios.

Fig. 1 (

 1 b) presents the optimized shared supply web for five partnering companies, each contributing three DCs.

  ) displays the optimized flows associated with one company in the open supply web. Even in this simpler view, it is clear that the individual companies exploit widely more distribution centers than in the other settings, adapting their supply network much more finely to the distributed client demand.

Fig 1 .

 1 Fig 1. Map of a single company 's private supply network having three DCs (a), shared supply web of five partnering companies having three DCs each (b), Open Supply Web from collective perspective (c) and Open Supply Web from individual company perspective (d)

Table 1 .

 1 Contrasting characteristics of existing private supply networks and shared supply webs with the proposed open supply webs.

	Supply Network design	Decision making level	Facility utilization	Geographical extension	Customer service level
	PSN	Strategic	Poor as facilities are utilized by a single company through long-term contracts	Constrained to the single company's	Compromised by location and capacity of single
				network	company's facilities
	SSW	Strategic	Improved relative to PSN but still constrained to partnering companies and long-term commitments	Constrained to the shared web of partnering companies	Compromised by location and capacity of partnering companies' facilities
	OSW	Tactical, Operational	Improved by opening available space to other companies within short-term contracts	Globally extended	Fast and reliable by exploiting globally dispersed open facilities

  Table2. Performance of each supply network design in terms of number of required DCs, mean and maximum delivery time served in the network This paper has contrasted the concepts of private supply networks, shared supply webs and open supply webs. It then investigated the potential of switching from the formers to an open supply web. The companies exploiting an open web have access to a wide number of open distributed facilities that they can use through short-term contracts without requirement to engage in large investments, long-term leasing or strategic partnerships. Therefore their supply networks can be redesigned in accordance to changes in the business environment of the operation time in an easy, cheap and quick way compare to the conventional supply network designs.

	5 Conclusion and Research Perspectives
	Supply network design	Available DCs	Exploited DCs	Mean Delivery time [hr]	Number of served clients
		15	15	90
	Private supply network (Total over 15 companies)	45 75	45 75	25 16	110
		150	150	9
	SSW-5 companies-1 DC each	5	5	57
	SSW-5 companies-3 DC each SSW-5 companies-5 DC each	15 25	12 22	19 13	573
	SSW-5 companies-10 DC each	50	41	7
	SSW-10 companies-1 DC each	10	9	54
	SSW-10 companies-3 DC each SSW-10 companies-5 DC each	30 50	24 40	17 11	1087
	SSW-10 companies-10 DC each	100	77	6
	SSW-15 companies-1 DC each	15	12	51
	SSW-15 companies-3 DC each SSW-15 companies-5 DC each	45 75	37 63	16 10	1645
	SSW-15 companies-10 DC each	150	114	6
	Open supply web	273	191	6	1645


Raimar Scherer
email: raimar.scherer@tu-dresden.de

Wael Sharmak
email: wael.sharmak@tu-dresden.de

Process Risk Management Using Configurable Process Models

Keywords: Uncertainty, Risk, Change, Configuration, Business Process Modeling, Reference Process Model

Almost no construction project performs totally as planned as dynamic changes are frequently needed. These changes can be ascribed to the high uncertainty, which is evaluated as potential causes of risks and risks is in turn are evaluated as potential causes of plan changes. Several concepts from the Business Process Modeling domain are adopted to build an explicit processoriented knowledge documentation of the construction processes. A standardized process description is suggested that offers flexibility in its content to be usable in different contexts. Configurable fragments in the course of the process models express the uncertain parts of the process. A general structure of a Configurable Reference Process Model (CRPM) is developed as an ontology model to document the construction processes in a Process-oriented Knowledge base. By using this knowledge base a considerable planning effort will be saved. Moreover, the quality of the process schedules, since they will be derived from the reference process models, will be enhanced and the expenditure of time will be reduced.

Introduction

The construction industry is a project-based business sector. Each project can be considered as a temporary alliance among diverse partners that will execute, in a private or in a cooperative way, several processes. This results in achieving the planned project objectives.

In this industry, the experience showed that mostly no construction project goes totally as planned because of the needed dynamic changes to different management plans. These changes can be ascribed to the relatively high uncertainty in the planning phase of a construction project. Several factors play a role to increase the total project uncertainty, such as the lack of experience with a certain type of construction processes or the changeable environment that surrounds and affects a construction process. In view of that, the risk of changing the already planned course of work as a need to adjust to current situation is really high. Such adjustments could be carried out, as a proactive or reactive risk treatment, even several times within the execution phase of the targeted process, if different risks in different points in time will arise.

The process knowledge collected from different resources is valuable for the effective process management, as it can reduce the uncertainty and, consequently, the error rate in the process planning will be lower. Hence, this knowledge should be documented in an explicit way to enable all the interested people to share and use their experiences properly. In a view of that, the standardization of construction processes that will include the best process practice can be a possible solution to reduce the accompanied uncertainty. Nevertheless, it is mostly impossible to apply the exact construction process as explicit work route in different projects. Accordingly, a degree of individuality is required within each process use. For that reason, a standardized process description should offer a certain degree of flexibility in its content to be usable in different project contexts.

In other business sectors, like insurance, banking and government, different Business Process Modeling techniques were successfully used to standardize and optimize business process knowledge. Moreover, they were used to communicate process contents between different disciplines or else to automate, partially or completely, process execution. Instead of that, for the modeling of construction processes, the traditional critical path method is still widely used. However, this method has no ICT support in its background.

The paper introduces a novel, process-centered view on process standardization that focuses on the risk management using some configuration aspects from the Business Process Modeling domain. Accordingly, a structure of a configurable reference process model (CRPM) that includes configurable fragments is suggested. These configurable fragments are based on the configuration templates mentioned in chapter 3. For a better management of the CRPMs, a knowledge base is introduced in chapter 5 that reflects the structure of the CRPM and enables the maintenance of the process-oriented knowledge.

Dependencies Between Uncertainty, Risk, and Change Terms

Generally, the uncertainty in construction is a result of the lack of needed reliable information, which can be attributed to different environmental, organizational, or technical factors. The role and importance of each factor differs from project to project according to the specific project characteristics and to its surrounding environment. This uncertain nature may lead to a big variance between the results of deterministic methods used for project management planning and real practice. Usually, the deviation of the project management baselines can be reduced when the degree of uncertainty in the project can be decreased. This can be done as soon as a comprehensive knowledge about the project is available. As the practice in construction is project-based, better knowledge can be acquired from lessons learned and experiences gained from the past projects. This knowledge will help to make better planning decisions as well as to reduce mistakes on past projects to be repeated. When the project progresses, the accuracy of the made decisions becomes more clear and the ambiguity disappears gradually. However, it is generally impossible to acquire a comprehensive knowledge about the project in advance. Therefore, a systematic risk analysis should be undertaken to evaluate the project's risks and opportunities and, consequently, to reduce the negative effect of uncertainty on the project objectives.

Nevertheless, changes in construction projects are likely to occur at any stage and can affect different project management plans. In the literature on change management [START_REF] Lee | Web-enabled System Dynamics Model for Error and Change Management on Concurrent Design and Construction Projects[END_REF], [START_REF] Sun | A change management toolkit for construction projects[END_REF], [START_REF] Hao | Change Management in Construction Projects[END_REF] it is emphasized that changes are inevitable in construction projects even if comprehensive studies have been made. The uncertainty in the project should be evaluated as potential causes of risks in the project [START_REF]PMBOK Guide: A Guide to the Project Management Body of Knowledge[END_REF]. In turn, in this work it is assumed that risks should be evaluated as potential causes of changes in the project management plan (Fig. 1). Based on this, a risk as an uncertain event may invoke one or more changes in the one or more project plans.

Configuration Templates

A main hypothesis of this work is that all possible ways of changes in the structure of process models, as a treatment to probable or even actual risks, can be standardized by generalized templates. For this purpose, several templates are suggested as types of configurable fragments within process models [START_REF] Sharmak | Risk Treatment Templates for Configurable Reference Modeling in the Construction Industry[END_REF]. Those are assumed to cover all kinds of process model related structure changes and consequently they will offer the needed flexibility in the process model. These templates are based on the concepts suggested by [START_REF] Mendling | Generating Correct EPCs from Configured C-EPCs[END_REF] as an extension to the event-driven process chains (EPC) elements. In view of that, a configuration template can be defined as a basic type of configurable fragments in the process model that describes one possible way of model structure change as a response to a certain event. As a result, a configured and specified template can be used to represent the structural change in a process model caused by the treatment of a specific risk event. Nevertheless, more than one configuration template may be used to represent a complex effect of one risk when it will cause changes in more than one location in the process model. In this sense, dealing with complex issues does not require complex templates as the basic suggested templates can be used together to cover a complex change case.

Building upon the classifications of risks and changes in the literature of construction management two major aspects were considered important from the process-oriented perspective. These aspects are (1) the timing of response to the risk (proactive or reactive response) and ( 2) the effect of the risk on the progress of the process, i.e. will it cause progress interruption or not. In view of that, the templates are classified into three main groups. These groups are:

-General Templates; these templates can be implemented in either proactive or reactive treatment cases. The general templates group contains the Insertion, Substitution, Cancelation, and Parallelism templates. -Interruptive Templates; used to describe a disturbance risk that affects and stops a process during its execution. The interruptive templates group contains the Stop and Action templates. -Reactive Templates; used only as response to events which have affected tasks or products considered to be completed. The reactive templates group contains the Repetition and Demolition templates.

Using Configurable Fragments in Process Models

A configurable fragment is an implemented instance of one of the configuration templates in a process model. In this work, a configurable process model is defined as a process model that contains one or more configurable fragments. Fig. 2 shows a configurable process model that contains three configurable fragments, namely (I) insertion of T(r) after T(i), (II) T(y) parallelism to T(k) as well as (III) T(m) and T(n) substitution by T(s). The configuration requirement R(1) links the configurable fragments (I) and (II) together. So according to the Risk 1 status (ON, OFF) these two configurable fragments will be configured. The link of configurable fragments (I) and (II) through the requirement R(1) builds a way to reflect the case when a risk may cause changes in different positions in the process model. The configurable fragment (III) in Fig. 2 is configured according to the (ON, OFF) status of Risk 2. Accordingly four Configured Cases (CCs) can be derived from the configurable process model in Fig. 2.

Knowledge-Base for the Specification of Configurable Reference Process Models

The configuration templates offer an approach to represent the flexibility, needed in construction processes, in the shape of configurable fragments within the process models. The configurable fragments represent risk treatments that may be adopted or skipped according to the risk status. Generally, a process model that contains, amongst others, all the exceptional cases, in the shape of configurable fragments, of the process is a candidate to be a CRPM for this particular process. Nevertheless, if the process may have several interrelated exceptional cases the corresponding model can be complex and, consequently, difficult to illustrate graphically, use or to update. Therefore, a knowledge-base (KB) is suggested to manage these reference models.

The KB structure is designed as an ontology model that consists of several processoriented concepts related to each other via proper properties [START_REF] Gomez-Perez | Ontological Engineering: With examples from the areas of Knowledge Management, e-Commerce and the Semantic Web[END_REF]. The configuration templates represent a fundamental part of the KB structure, see Table 1. In view of that, every risk treatment fragment within the CRPM should be associated to a suitable configuration template that describes how to integrate it later within the process model. Using such a KB within a construction company will enable the description of its process knowledge in a project neutral context. As a result, this could support decision-making and increase the intelligence of the construction processes. Consequently, the expenditure of time and money in the process design will be reduced. Also, A clearly defined semantics for the process models can be achieved by instantiating the elements of a CRPM from the concepts of an ontological KB. Accordingly, the ambiguity and the shortage in formality in process models can be avoided. Moreover, storing the construction CRPMs in an ontological KB will enable making advanced queries when retrieving information. These queries may even infer facts that were not explicitly created by the modeler of the CRPM. As a KB is passive data source, it is here considered as a commodity to develop a process management software tool. Therefore, it is needed to improve the access to the knowledge included in the CRPMs by developing methods, which can support the choice, the configuration, and the composition of the relevant CRPMs.

Software Architecture for CRPM-based Process Management

The architecture of the suggested software tool consists of three tiers (Data, Application, and Graphical User Interface (GUI)). The data tier includes the Process-DB and the CRPM-KB. Within this tier, different types of information, CRPMs and versions of instantiated process models (IPMs), can be stored and retrieved. The retrieved information will be then passed through to the application tier for processing, and then, eventually, to the user. The data tier keeps the comprised information neutral and independent from the application tier, which improves the scalability and performance of the intended software tool. The Application tier moves the process data between the two surrounding layers. Moreover, it performs the needed procedures, e.g., configuration tasks, and makes the required procedures on the retrieved process data. Amongst others, the Application tier has to do the following:

-Query the CRPM-KB to get the relevant CRPMs.

-Configure the selected CRPM according to the preferences of the user.

-Instantiate the configured CRPM and to store it after that to the Process-DB.

-Establish a new CRPM.

-Compare different versions of the same IPM.

The GUI tier displays the processed and original information related to the process and, accordingly, allows the users to interact with the software application.

An example of the software implementation is shown in Fig. 3. During the implementation time of an IPM an exceptional event, i.e. risk, may arise and, accordingly, changes will be needed in the already planned IPM. The KB can be queried in order to find out if there is any relevant solution for the new condition. In the case that a solution is chosen from the KB, it needs to be configured, instantiated and integrated in the already planned IPM. Otherwise, an external solution will be sought after. An external solution can be used for the specific IPM and can be as well integrated in the CRPM to enhance the process knowledge included in the KB.

Conclusion

The presented approach can be used in the planning phase as well as in the execution phase of the project. In the planning phase this can help to speed up the planning and to ensure getting more reliable process schedule plans. In the execution phase it can help to accelerate the adaptability of the changed schedule plans by offering ready integrable solutions for recurring problems.

The exceptional events included in each CRPM can be used, in the planning phase of the project as a part of the checklist for risk management. Accordingly, risks can be identified based on the project relevant CRPMs and, subsequently, risk assessment can be carried out. Hence, the risks with probability/impact values above the agreed thresholds should be considered in the planning phase of the project in a proactive way. This means that the configurable fragments, which represents the risk treatment, should be adopted (included) as a part of the configured CRPM. Anyway, to realize these CRPMs in the construction practice it is still required from the construction companies to structure their knowledge in a process-oriented way and to classify the process model parts to essential and configurable fragments. Subsequently, the configurable fragments will be connected to their potential trigger-risks using the configuration templates. For this purpose, only qualified personnel should carry out the modeling of the needed CRPMs. Qualification means here that the modelers should possess enough knowledge concerning the construction process technical issues and the process modeling issues.

Fig. 1 .

 1 Fig. 1. The possible causality between uncertainty, risk and later change.

Fig. 2 .

 2 Fig.2. A configurable process model and the four possible configured process models derived from it.

Fig. 3 .

 3 Fig.3. The iterative adaptation cycle of an IPM

Table 1 .

 1 The properties relate the configuration fragments to the Task concept through the Configuration Template concept in the ontology model.


Sri Krishna Kumar

J Harding

Risk Assessment in the Formation of Virtual Enterprises

Keywords: Virtual Enterprise (VE), risk analysis, partner selection, performance risk, network risk

A Virtual Enterprise (VE) is considered as a temporary consortium of member enterprises formed to pool their core competencies and exploit the market opportunities. Although a VE has many phases, such as business opportunity identification, formation and partner selection, operation and dissolution. The partner selection phase is considered to be of the utmost importance and care should be taken to assess all the risk factors. This paper examines the partner selection problem by considering three types of risks, individual performance risk, collaborative performance risk and network risk. Based on the information provided by the potentially collaborating enterprises, a mathematical model has been developed for calculation of all three types of risks.

Introduction

With the introduction of a global economy and drift towards customized products, enterprises are seeking new paradigms, such as lean production, agile manufacturing, and virtual enterprises (VEs), to grasp market opportunities in a competitive global environment. A VE can be considered as a temporary coalition of globally distributed independent enterprises which share resources, skills, and costs, through the support of Information and Communication Technologies (ICT). A VE is formed when a market opportunity is realized and is dissolved when the opportunity or goal is achieved. In a VE, members of the alliance keep their own independent business processes and contribute their 'core competencies' in different complementary areas.

A VE operates in different phases as [START_REF] Wildeman | Alliances and networks: the next generation[END_REF] opportunity identification, (2) VE formation and partner selection (3) operation and (4) dissolution. It is arguable, that of all the phases of a VE's lifecycle, partner selection is the most difficult task as it not only requires integration of core competencies but must also address different management styles and corporate cultures within the potential partners. A review of research literature reveals cases of the failure of VEs due to improper partner selection. According to [START_REF] Wildeman | Alliances and networks: the next generation[END_REF] 60-70% of VEs are disbanded prematurely and identified trust, cultural differences, and different levels in the use of information as reasons for VE failure.

Although member enterprises of VEs, are capable of overcoming some of their individual limitations due to the advantages coming from being small (i.e they tend to be reactive, proactive and so forth), nonetheless collaborating within a VE brings a higher degree of risk than is experienced by individual enterprises. Due to such peculiarities, risk in VE formation requires a dedicated study.

In literature risk has been defined as ''the variance of probability distribution of outcomes'' [START_REF] March | Management perspective on risk and risk taking[END_REF]. The achievement of objectives realized by a VE depends on individual partners' capabilities and their cooperative relationships. This produces the multi-dimensional risk associated with a VE and can negatively affect the desired outcomes of the VE. In an early study of risk factors, [START_REF] Baird | What is risk anyway?[END_REF] defines risk as emerging from eight different perspectives. [START_REF] Das | A risk perception model of alliance structuring[END_REF] studied the collaborative or network risk and divided the risk factors into performance and relational risks, where consideration of another dimension of risk, i.e. of accessing and managing risks rather than identifying them, [START_REF] Harland | Risk in supply network[END_REF] proposed a supply network risk tool to identify, assess and manage risk to support the single partner decision making process concerning network evaluation. According [START_REF] Link | risikomanagement von Innovationskooperationen[END_REF], when a network is chosen to run a business the consequent risk is higher than the risk related to the same business run by a single company. It is obvious from the findings of researchers that, although, return of investment, opportunities and risk sharing abilities are higher in VEs, they still operate in higher risky environments than single enterprises and therefore care must be taken in the formation (including partner selection) of VEs as it plays an important role in their success.

In the formation of a VE, the whole project is divided into subprojects and for each subproject a single enterprise is selected. In this study, the overall risk in the VE has been divided into three categories (as shown in figure 1) individual performance risk, collaborative performance risk and network risk. To clarify the proposed VE risk decomposition the overall risk can be considered as a combination of performance and network risk. Performance risk can be further subdivided into individual and collaborative performance. Individual risk is associated with and quality constraints. Collaborative risk is the measure of member enterprises' capability to undertake the project. Network risk is related to lack of trust, inaccurate information sharing and asymmetry in reciprocal position that hinders the effective collaboration [START_REF] Ojala | Investment decision-making in supplier networks: management of risk[END_REF].

This research aims to support risk management during the formation of VEs, firstly through the quantitative estimation of the overall risk and then through the evaluation of the overall risk associated cost. This approach will help decision makers to find the best possible consortium.

Quantitative Measure of the risk in the formation of a VE

The partner selection problem can be described as follows: A project N is divided into subprojects which are indexed n , {1, 2,...., } n N ∈ = N . i m is the set of enterprises bidding for the subproject i, i ∈ N . Every enterprise, bidding for the subproject provides information about its ability, capacity, technology, competency and other related information. Based on the information provided, individual performance risk is calculated, which is related to the enterprise's ability to finish the subproject, in terms of time, quantity, quality and other aspects. In most cases, with improper and incomplete information, individual risk can be given by interval values as: [ , ]

k k i i
Lr Ur , where L and U corresponds to the lower and upper limit of the individual performance risk associated with k th enterprise and i th subproject. Depending upon the optimism present in the partners this risk can vary between its lower and upper limit and can be taken as:

(1 )

k k k i i i Lr Ur α α = + - r , with [0,1] α ∈
defining the degree of optimism. Hence, a more optimistic network will have a lower risk value when compared to a pessimistic network.

The decision variable {0,1}

k i

x ∈ , determines the subproject and enterprise relation with respect to the constraint that a subproject cannot be awarded to more than one enterprise. If i c is the cost of subproject i, then after considering the individual performance risk the estimated cost of the subproject will be: [ ] (1 )

k k i i i i k E c x c = + ∑ r

Collaborative performance risk

Collaborative performance risk analyses the risk level for each enterprise due to the presence of the other member enterprises in the VE. Based on the collaborative performance risk factor, an individual enterprise can decide whether to join the consortium. Collaborative performance risk ( ) can be calculated as:

' ' ' ' ' ' (1 ) [1 (1 ) ] k k k k k i i i i i i i k k x x ≠ ≠ = - - - ∏∏ R r r
The first part of the formula calculates the probability that the k th enterprise will be successful for i th project (if assigned) and second part calculates the probability that at least one of the other subprojects will fail. The collaborative performance risk will increase the expected cost of the operation for each enterprise as the higher the risk in the environment, the higher will be the risk of return of investment. So now the cost of operation, considering the collaborative risk factor can be given as:

'[ ] [1 ( ) ] k k k i i i i i k E c x c = + + ∑ r R

Network Risk

The first two types of risk, discussed above were related to individual risk factors and their consequences on the other partners. However, in a VE, where partners need to be seamlessly interoperable and cooperative, lack of communication, social, technological or cultural factors may hinder the desired output. This type of risk has been categorized as network risk in a VE, and this can be defined as: The total network risk can be given as:

' ' ' ', ' ', ' ( / 2)
k kk k k i i i k k k i i i nr x x γ ≠ ≠ = ∑ ∑
k i k i nr = ∑ ∑

NR

Mathematically, partner selection problem with risk analysis can be given as:

Min ( ) { } i i i i E c c + ∑ ∑ NR (1)
Subject to

k i i k x ∑ ∑ = 1 (2)
Equation ( 1) defines the objective function which minimizes the performance risk (first part) and network risk (second part). Constraint (2) determines that only one enterprise will be selected for each project.

Numerical Experiment

For numerical analysis, this paper considers a project which can be divided into four sub projects. The cost of each subproject is taken between 15,000 -25,000. Three enterprises are bidding for the each subproject and their lower and upper limit for the individual performance risk are generated randomly between 0.1 and 0.2. The degree of optimism has been taken as 0.5 i.e. the mean of lower and upper value of the risk. Network risk between the enterprises is generated randomly between 0 to 1. The experiment result obtained has been shown in table 1. The first column in the table depicts the optimal network according to the all three risk factors taken separately and corresponding rows depicts the monetary value of the risk factor.

From Table 1 it is clear that the optimal network is not the optimal for any of the risk factors. In determining member enterprises for VE, this risk analysis will help in determining optimal network which will have lowest possible risk.

Conclusion

This paper considers the risk associated with the formation or partner selection of the virtual enterprise (VE). [START_REF] Lo Nigro | How risk consideration can affect inter-organization relationship decisions[END_REF] had studied the performance and relational risk in the lieu of direct and indirect risk for the Network. However, individual risk and collaborative performance risk plays an important role along with network risk. As, the risk factors discussed in this paper not only help in forming optimal consortium for VE, but also provide useful information regarding risk and its associated cost for individual enterprises, which will help them in decision making regarding joining the consortium.

Fig. 1 :

 1 Fig. 1: VE formation risk

  k=k'. In order to avoid double calculation we have divided the factor by 2.The value of' kk γ will determine the affinity of collaboration between two enterprises (k and k') in the VE. If the value is closer to 0, higher will be the affinity of collaboration due to low risk factor.

Table 1 :

 1 Cost associated with risk factors

	Optimal	IR cost	CR cost	NR cost	TR
	Network				cost
	Individual	85500	38074	172500	296124
	risk(IR)
	Collabora	87350	33721	135000	256070
	tive risk
	(CR)
	Network	88800	34095	120000	280395
	risk(NR)
	Total risk	86100	34795	125000	245895
	(TR)

IR: Individual risk, CR: Collaborative risk, NR: Network risk, TR: Total risk

Communicating Author: J.A.Harding@lboro.ac.uk


Rilwan Basanya
email: rilwan@iist.unu.edu

Adegboyega Ojo

Tomasz Janowski

Franco Turini
email: turini@di.unipi.it

Mining Collaboration Opportunities to Support Joined-Up Government

Keywords: Joined-Up Government, Collaborative Networks, Goal Hierarchy, Data Mining, Text Mining, Process Mining

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

Introduction

Governments face enormous pressures in coping with their regular responsibilities of delivering the required services to their stakeholders -citizens, businesses and governments, and developing effective policy interventions to societal issues in various national and international contexts characterized by financial and economic pressures, increasing terrorism-related threats to national security, and increasing incidents of health pandemics and natural disasters. A common element in various responses by governments to these challenges is the adoption of a holistic, whole-ofgovernment approach. This approach aims at creating a single virtual organizational view [START_REF] Camarinha-Matos | On Reference Models for Collaborative Networked Organizations[END_REF] from various government and non-government entities, based on collaboration across different levels and functions of government as well as collaboration with non-state actors from the private and voluntary sectors [START_REF] Stanton | Improving Collaboration by Federal Agencies: An Essential Priority for the Next Administration[END_REF].

Developing collaboration structures and relationships requires coordination and integration of services, processes and information-related assets like databases, applications, devices, hardware and networks, offered by collaborating parties. Specifically, establishing any collaboration requires information on the shared goals, prospective participants, their interests and resources [START_REF] Camarinha-Matos | On Reference Models for Collaborative Networked Organizations[END_REF] [START_REF] Reillly | Collaboration in Action -An Uncertain Process[END_REF]. However, such information is rarely available as structured data (e.g. in databases) or from a single source. The information is generally unstructured and found in disparate sources like agency portals and online publications related to the entities of interest. This makes the discovery, analysis and integration of information on prospective collaboration parties difficult. Even when such information is available, integrating this information to achieve the objectives of a joined-up government remains difficult due to various organization issues and in the absence of explicit domain ontologies.

Text mining, a data mining technique for unstructured or textual data, which also draws from various disciplines including information extraction, semantic web and computational linguistics, offers significant promise for automatic extraction of important terms, concepts and patterns. Presently, there are a number of tools for text mining which enable annotation of text with user information. When considering text mining in a specific context, like government-wide collaboration, specific domain structures (e.g. goals, services and processes) would be of interest. In this regard, process mining [START_REF] De Medeiros | Semantic Process Mining Tools: Core Building Blocks. 16th European Conference on Information Systems[END_REF] offers concrete technique for mining process-related structures. However, workflow logs used in process mining are structured.

In this work, we are concerned with textual descriptions of such processes and the services they implement. We aim at investigating how text mining, and particularly semantic text mining (text mining supported by relevant ontologies) could be used in discovering various collaboration structures from their descriptions in various textual sources, provided by government agencies, private sector organizations and other entities within the joined-up government context. Our final goal is to enable semiautomatic discovery of shared and integrated processes to realize collaborations and joined-up government. We adopt ontology-based tagging of terms in collaboration descriptions, using service-oriented process ontology obtained from our analysis and augmentation of existing e-government service models. The tokenized terms are subsequently combined, guided by their meanings expressed in the ontology, into collaboration structures. This paper contributes to the final goal through the synthetic analysis and augmentation of existing e-government service description models.

The rest of this paper is structured as follows. Section 2 presents the background in semantic text mining, information extraction, collaboration networks and joined-up governments. Section 3 explains the problem to be addressed. Section 4 discusses the approach adopted to solve this problem, followed by solution steps in Section 5. Finally, Sections 6 and 7 present discussion and conclusions respectively.

Background

A number of foundational concepts are essential in explaining our motivation and research approach. These concepts, drawn from the domains of Text Mining, Collaborative Frameworks and Joint-Up Government are briefly introduced below.

Text Mining: Data mining [START_REF] Han | Data Mining: Concepts and Techniques[END_REF] refers to the extraction of knowledge from large amounts of data. A key step in knowledge discovery, it is an iterative process aimed at identifying valid, novel, potentially useful, and ultimately understandable patterns in data. Text mining intersects with Natural Language Processing (NLP), machine learning, data mining, and information retrieval. Knowledge discovery in text, however, involves intelligent text data analysis aimed at uncovering previously unknown patterns in existing unstructured textual resources [START_REF] Novak | Towards Semantic Data Mining with g-SEGS[END_REF]. By appropriately integrating techniques from each of these disciplines, useful new methods of discovering knowledge from large textual sources can be developed. For our purpose, we explore such techniques in extracting useful patterns in describing e-government services provided by prospective collaborators. Information Extraction (IE) is a key component in text mining, as it enables the application of data mining to unstructured text corpora to discover predictive rules useful for improving the performance of text mining tasks [START_REF] Nahm | Text Mining with Information Extraction[END_REF]. Handling the representation of background knowledge in IE is a very important challenge [START_REF] Stavrianou | Overview and Semantic Issues of Text Mining[END_REF] and ontologies are used to supply such knowledge and annotations, as they effectively enable the representation of domain schemas containing technical terms, domain concepts and the relationships between them.

Collaborative Frameworks: Collaboration involves exchanging information, altering activities, sharing resources, and enhancing the capacity of another for mutual benefit and a common purpose. Each participating organization aims to help others become the best that they can be at what they do [START_REF] Himmelman | Collaboration for a Change: Definitions, Decision-Making Models, Roles, and Collaboration Process Guide[END_REF]. Studies in network theory and its effect on inter-agency collaboration [START_REF] Bardach | Can Network Theory Illuminate Interagency Collaboration[END_REF] have also shown enhanced cooperation among government agencies. For our purpose, we adopt the definition of collaboration as the act or process of "shared creation" or discovery [11] involving the creation of new value by doing something new or differently.

Joined-Up Government: Joined-Up government denotes public service agencies working across portfolio boundaries to achieve shared goals and an integrated government response to pressing issues [START_REF] Christensen | The Whole-of-Government Approach to Public Sector Reform[END_REF]. The rationale for joined-up government is two-fold [START_REF] Baker | Quick Impact Projects: Towards a Whole of Government Approach[END_REF]: 1) avoiding duplication of efforts through improved information flow resulting in increased situational awareness and enhanced capacity for strategic planning and intelligent decision-making; and 2) harmonization of efforts among all stakeholders recognizing the co-dependent nature of their shared and individual goals, and the required compromise and understanding among collaborators in jointly achieving the goals towards attaining a more agile government enterprise. Achieving such goals must be based upon collaboration across different levels and functions of government, and across public and private sectors. However, the complexities of configuring all possible collaborations among agencies are enormous and require a systematic approach dealing with the various scenarios. We seek an environment where collaboration structures for all agencies -processes, capabilities and competencies -can be extracted and modeled along with the collaboration objective to be achieved, leading to the configuration of a collaborative initiative.

Problem

Our major goal is to investigate how data mining can be used to support the discovery of information to build collaborative government services towards joined-up government. Since government services are usually described in textual forms on government portals and in other sources, we employ semantic text mining to identify different parts of service descriptions and on this basis discover opportunities for developing shared and integrated services. Discovering such services will optimize the use of financial, human, data, organizational and technical resources. It will also enable improvements in the delivery of government services.

There are two inherent challenges to this problem. The first is addressing the contextual semantic issues posed by textual sources containing service descriptions. For example, a step such as "Issue license" extracted from two different service descriptions e.g. driving license or construction license, could be very different in terms of implementation complexity. The second is mining processes representing shared and integrated services from process ontology instances, as opposed to using structured event logs which is the standard practice in traditional process mining.

Methodology

Our approach is to first model Joined-up Government as a Collaborative Networked Organization (CNO) whose members are agencies involved in shared or integrated service arrangements. We then develop the required ontologies to guide the extraction of information from textual descriptions of services and to represent the extracted information as process models. Two ontologies are required for this purpose: 1) a domain ontology to define and relate e-governance concepts and provide a hierarchy of service delivery goals, and 2) a process ontology to represent extracted process models. Since our process models are service-oriented, the process ontology is developed by extending existing e-government service-related ontologies by additional elements discovered through our review of service descriptions available on six national government portals -South Africa, United States, United Kingdom, Canada, Korea, and Germany. Fig. 1 below shows three major steps of our approach to the problem, with steps 1 and 2 executed concurrently.

Step 1 Extraction of Process-Related Information

Step Using these ontologies, models and recognizers will be developed to identify and tag different elements (e.g. goals, subjects or eligibility criteria) of a service from its descriptions. The extracted information will be stored in the process ontology as process instances. With such instances captured in the process ontology, the final task will be to: 1) discover common sub-processes across process models as a basis for building shared services and 2) suggest possible super-processes as a basis for seamless or integrated services using the service delivery goal hierarchy.

Solution

This section describes in some detail the three steps in Figure 1.

Step 1 -Extraction of Process-related Information: This step involves preprocessing process-related descriptions to conform to some standardized and structured process syntax. We gather service descriptions from various government websites and carry out rigorous analysis to extend existing public service description models. This task involves selection of sentences describing key service components and processes. It also involves decomposing the sentences into tokens for the recognizer to identify named entities and co-references. Next, the tokens will be combined into structures based on their meanings in the ontology. Figures 2 and3 show the examples of two service descriptions on the South African Government's (www.gov.za) and Alberta Government's (www.alberta.ca) portals.

The resulting service-oriented process ontology consists of the following elements: 1) Service -a clear reference to the nature of the service, 2) Type -the type of service based on the authorization, certification, control, and production types [START_REF] Ojo | Domain Models and Enterprise Application Framework for Developing Electronic Public Services[END_REF], 3) Source -the government offering the service, 4) Subject -the actors involved in different roles in delivering the service including service requestor, provider and their collaborators, 5) Eligibility -the rules determining which requestor can receive the services, 6) Evidence -the confirmation of receipt of request to provide a service, 7) Location -the place where interactions in the service delivery process take place, 8) Duration -how long will it take to deliver the service, 9) Legal -the legal provision e.g. government decrees empowering the provider to provide the service and specifying which requestors can receive the service, 10) Payment -fees to be paid for receiving the service, 11) Outcome -the result of the service, 12) Review -what can be done if the requestor is not satisfied with the outcome, 13) Suspension -if a positive outcome can be suspended and for how long, 14) Validity -when a positive outcomes will expire, 15) Contact -who can be reached for enquiries on the service, and 16) Appeal -if and how to challenge the negative outcome of a service request.

Following the development of the process ontology, the next task is to develop recognizers for each element and tag extracted text with the appropriate element. In summary, this step involves: 1) preprocessing, 2) development of service descriptionbased recognizers, and 3) extracting service elements from their textual descriptions. Step 2 -Creating Process-Ontology Instances: This step involves developing a formal process ontology based on the service description elements and the domain ontology. The extracted text is created as individuals or instances in the developed process ontology, where each instance represents a complete service description. This is followed by creating relationships between the instances based on the service goal hierarchy defined in the domain ontology. The goal hierarchy specifies how a higher level or conjugate goal is obtained by fulfilling simpler goals in addition to goal prioritization. For instance, the service goal related to Obtaining Licenses can be fulfilled by goals Successfully Filing an Application and Obtaining Decision on the Application. Therefore, a process that implements the submission of applications and another process implementing decision making on applications could be combined into a single process with the goal of license service delivery.

Step 3 -Discovering Shared and Integrated Services: Our target is to extract two main types of process information structures. The first is processes common to several agencies. For example, most agencies require authorization services to evaluate or decide on applications based on some criteria. The second is integrated processes that could be constructed from the processes of various entities towards fulfilling a higherlevel goal. For instance, responding to a health pandemic may require that screening services (checking travelers) at all ports of entry are linked with ambulatory services (for transfer) and with emergency services (treatment) at hospitals.

Discussion

So far, our research has focused on the Step 1 -Extraction of Process-Related Information. To this end, we attempted to extract process-related information from the online sources provided by the national governments of United States, United Kingdom, Canada, Germany, Korea and South Africa. However, we were confronted with a number of challenges that further highlight that meaningful extraction, parsing and construction of processes and other higher-level structures from these descriptions require semantic support. Here we describe three specific challenges. The first challenge is non-standard form of service descriptions. We observed that there is no standard form, nor a controlled language, for describing available government services even within the same government. In general, the descriptions present across various government portals use free narrative forms. We also discovered that service descriptions and presentations vary according to the government's level of egovernment maturity. For example, some portals like the US federal government's www.usa.gov tailored and personalized their service descriptions to individuals based on their interactions with the portal. The second challenge concerns the existence of huge variations in the details of service descriptions. Some descriptions, for instance, may not contain process-related information. The third challenge is inconsistent naming of services, with the same or related services referenced differently across different government portals. For example, Child Support Grant, Child Care and Development Fund, Child Care Subsidy, and Child Care Benefit all refers to essentially the same services. We are currently working to address these challenges through the techniques employed in resolving semantic conflicts in general.

Conclusions

The goal of this paper is to present the research approach and initial implementation of this approach in automating the identification of shared or integrated services to underpin Joined-up Government -one of the most important forms of Collaborative Network Organizations in the public sector domain targeting complex, cross-sector challenges. Unlike existing work on service development and composition that relies on structured repositories and service descriptions, we seek to identify opportunities for shared and integrated services from non-structured textual descriptions commonly found on government portals and in government publications using semantic text mining. Despite various semantic challenges encountered in the course of this work, we consider the discovery task feasible. In our view, once the implementation of step 1 in our research methodology (extraction of process-related information) is complete, creating ontology instances from extracted information (step 2) and discovering shared and integrated processes (step 3) through ontology-based reasoning will be more tractable. This is the direction of our future work.

Fig. 1 :

 1 Fig. 1: Three-Step Research Methodology

Fig. 2 :Fig. 3 :

 23 Fig. 2: Sample Service Description from www.gov.za


Mahdieh Shadi
email: m.shadi@uva.nl

Hamideh Afsarmanesh
email: h.afsarmanesh@uva.nl

Addressing Behavior in Collaborative Networks

Keywords: Collaborative Networks, Individual Collaborative Behavior, Collective Behavior, Causal Reasoning

Many factors such as weak partner commitment, lack of proper partners' alignment on interest and values, individualism, lack of flexibility, and loss of autonomy may cause partnership's failures in collaborative networks. Most of these serious causes for conflicts in collaborative networks are rooted in partner's behavior, therefore analyzing and modeling the behavioral aspects of collaborative networks are important to enforce their success. This paper first addresses two kinds of behavior related to collaborative networks including Individual Collaborative Behavior and Network Collective Behavior, and then introduces an approach to measure the comparative individual collaborative behavior of partners in networks.

Introduction

With advances in communication and information technology, today organizations can interact and communicate with each other and exchange sensitive information without the traditional limitations of time and location. Collaborative networks, such as global supply chains, support industries to manufacture and deliver products to markets with the required speed and efficiency. A CN (collaborative Network) is an alliance constituting a variety of entities (e.g. organizations and people) that are largely autonomous, geographically distributed, and heterogeneous in terms of their operating environment, culture, social capital, and goals, and that cooperate and collaborate to better achieve common or compatible goals, and whose interactions are supported by the computer network [START_REF] Camarinha-Matos | Collaborative networks: Value creation in a knowledge society[END_REF], [START_REF] Afsarmanesh | Modeling and Management of Information Supporting Functional Dimension of Collaborative Networks[END_REF].

In general, each organization (or person) involved in a collaborative partnership works and interacts with other members to achieve common or compatible goals. During this interaction partners expose various behaviors, according to the situations in which they are involved. This kind of behavior is called Individual Collaborative Behavior, regarding to how one behaves within a CN. If it were possible to analyze, model and predict the organizations' behaviors, many conflicts and difficulties in CNs could have been resolved and ultimate productivity of individuals at the CNs would have been drastically improved [START_REF] Rosas | Assessing organizations collaboration readiness, a behavioral approach[END_REF]. Another kind of behavior related to CNs is the Network Collective Behavior which shows the behavior of a group of partners, as a whole. Effective Network Collective Behavior leads to improved CN productivity and higher quality, thus more customer satisfaction, as well as more profit and more power for the CNs.

Inspired by the modeling frameworks introduced earlier in the literature related to collaborations and network aspects [START_REF] Camarinha-Matos | A comprehensive modeling framework for collaborative networked organizations[END_REF] and considering the complexity of CNs, the ARCON (A Reference model for Collaborative Networks) modeling framework is developed. This framework divides the CNs complexity into a number of perspectives. Its environment characteristics perspective includes two subspaces that cover the internal elements characteristics of CNs, labeled as "Endogenous Elements (Endo-E)", as well as the external interactions characteristics, labeled as "Exogenous Interaction (Exo-I)", that address the logical surrounding of the CNs. The Endo-E subspace consists of four perspectives, as follows: Structural dimension, Componential dimension, Functional dimension and Behavioral dimension [START_REF] Camarinha-Matos | On reference models for collaborative networked organizations[END_REF], [START_REF] Afsarmanesh | The ARCON modeling framework[END_REF]. Among these four, the behavioral dimension is the least studied in the research on CNs. However, there are some base applicable research related to human behavior and agent behavior. In [START_REF] Han | Soft control on Network Collective Behavior of a group of autonomous agents by a shill agent[END_REF], the authors show the feasibility of soft control on collaborative behavior of a group by a case study. In [START_REF] Wu | Modeling and simulation of group behavior in e-government implementation[END_REF], a new attempt for modeling and simulation of group behavior in e-government organizations is provided and also [START_REF] Hu | Integrated Description and qualitative simulation method for group behavior[END_REF] presents a qualitative simulation method for analyzing employee group behavior by integrating QSIM (Qualitative SIMulation) with basic causal reasoning. Moreover, some issues such as the small world phenomenon, clustering and power relationships from the area of Social Networks Analysis (SNA) may contribute to this topic. For example, in [START_REF] Abreu | A contribution to the development of a theory for collaborative networks[END_REF], some indicators related to promote the fairness and transparency are proposed. Some Other indicators, such as reliability, responsiveness, flexibility, etc. are defined in the SCORE model [START_REF] Beamon | Measuring supply chain performance[END_REF] known throughout industrial branches. Standardizing processes within a supply chain because of making the process comparable and compatible is the goal of this model and these indicators are used to measure the performance of organization in supply chains.

This paper is organized as follows: in section 2, the individual collaborative behavior and a new approach to measure the comparative individual collaborative behavior are addressed. In section 3 the Network Collective Behavior is discussed, and finally in section 4 some conclusive remarks are drawn.

Individual Collaborative Behavior

The principles, policies, and governance rules that either drive or constrain the behavior of the CN and its member over time, are addressed in behavioral dimension of the ARCON reference model [START_REF] Camarinha-Matos | On reference models for collaborative networked organizations[END_REF]. This may include elements such as honesty and integrity, trust, openness, well performance, professionalism, mutual respect, commitment to network, code of ethics and IPR policy. Considering these governance rules and some other related factors, it is possible to introduce an approach for measurement of comparative individual behavior. Below, we first discuss the importance of the individual collaborative behavior (in section 2.1) and then we explain our approach to model it (in section 2.2).

The Role of Individual Collaborative Behavior in CNs

Due to the impressive number of both human and organizational behavioral factors found in the analyzed reports, an assumption can be made that probably most partnership failures and successes are strongly related to, if not caused by, the behavioral factors of the involved partners [START_REF] Rosas | Assessing organizations collaboration readiness, a behavioral approach[END_REF]. Good communication, decision by consensus, creativity, fairness, flexibility, best use of interests, knowledge sharing, joy in working together, visible leadership, readiness, open and honest participation, willingness to commit resources and capabilities, development of social skills, transparency in provision of information are behavioral factors leading partnership's success.

Moreover, partners' behavioral factors have a vital influence on their collaboration readiness and establishing trust relationship with other partners, while individual collaborative behavior can be motivated through creation of incentives and rewarding models and mechanisms as a part of CN's governance model.

Analyzing the behavior of a CN's members is therefore necessary. Obviously, the purpose of modeling and analyzing the individual behavior may target specific objectives. These objectives may include identification or prediction of conflicts, selecting the best-fit members to create a new VO, better role and right assignment in CNs, effective membership structure management, and general measuring of individual collaborative behavior in networks.

Measuring the Comparative Individual Behavior

Modeling and analyzing behavior of CN partners involve different sets of characteristics, and depends on the perspective for which the behavior is considered. Therefore, it is better to consider some perspectives for each of above mentioned objectives.

As such, behavior for a CN partner cannot be represented by an absolute value; rather it can be measured for instance regarding a specific purpose against the behavior of other CN partners. Mechanisms can be defined to assess the individual behavior of a CN partner in comparison to others. For this purpose, every perspective can be characterized by a set of traits / actions. Also, specific metrics (variables that can be measured) and constraints shall be specified for each trait/action. For example, when a VO planner wants to select a VBE member as a partner for a VO, different aspects of the past individual behavior of that member can be compared with others, according to different perspectives as exemplified by three perspectives presented in table1.

Furthermore, in order to assess the individual behavior, inter-relation among different traits/actions should be studied and well understood. In this work, causal relationships among different traits/actions are used to represent how they influence each other. To present this approach, consider the first perspective in Table1, conforming to past VO regulations. A causal diagram for this example is shown in Figure 1. In this causal diagram, the plus sign (+) indicates that the increase or decrease of the first factor causes the increase or decrease of the second factor, while the minus sign (-) indicates that the increase or decrease of the first factor causes the decrease or increase of the second factor [START_REF] Pearl | Causality Models Reasoning, and Inference[END_REF]. subtraction or division depending on the metrics that are related to the traits/actions. The selection of the correct arithmetic operator depends on the metrics that scales it and the balance of dimensions [START_REF] Krikwood | System Dynamics Method[END_REF], [START_REF] Ge | System dynamics modeling for supply-chain management[END_REF]. For example, for measuring the rate of proactivity (PA), it is needed to add the number of recommended new members (RM), number of articles in press (AR) and number of awards won (AW) as equation 1 shows:

) 1 ( . AW AR RM PA + + =
The derivative of equation ( 1) represents the rate of change of each of the factor with respect to time and the relations among the changes, as illustrated in equation 2. The integration of equation 2 provides the accumulation of PA, which represents the total rate of pro-activity for a period of time 1

t to 2 t (equation 3). ) 2 ( . AW dt d AR dt d RM dt d PA dt d + + = ) 3 ( . 2 1 2 1 2 1 2 1 AW dt d AR dt d RM dt d PA dt d t t t t t t t t ∫ ∫ ∫ ∫ + =
The rates of change as well as the accumulations equation for all factors in Figure 1 can be considered in the same way (according to their relationships). After that, there is a formula for measuring the rate of Conforming to past VO Regulations (CR). Also, it can be done for the other two mentioned perspectives in Table1 (VA and ST). Consequently, to calculate the final comparative behavior score for suitable partner selection objective, we should compute an average of weighted scores of all perspectives, as equation 4 illustrates:

) 4 ( ). ( 3 2 1 ST W VA W CR W average Score × + × + × = The weights ) , , ( 3 2 1

W W W

shall be defined by experts in the field, depending on the specific type of VO to be established. These weights should belong to the [0,1] interval and the sum of all these weights should be equal to 1. Finally, since we calculate the score for each member in the VBE, when a VO planner decides to select a most suitable member, she/he can compare how they rate against each other.

Network Collective Behavior

Analyzing and modeling the Network Collective Behavior which refers to how a group behaves as a whole and the factors which influence it, is more complex than individual behavior. In fact, modeling collective behavior involves contributions from multiple disciplines.

Mechanisms, such as incentives and rewards, assessing of alignment of values and approach applied to the governance of a CN are factors affecting Network Collective Behavior of the CN. These factors are discussed respectively in the following paragraphs.

Incentives and rewards mechanisms may result in changes in the behavior of a CN. Networking sustainability through fair and transparent distribution of benefits, and addressing contributors' expectations are objectives of theses mechanisms. The basis of the incentive system is on the motivation that a CN convinces its participants to be pro-active in relation to business opportunities.

The individual and Network Collective Behavior in a CN are influenced by the value system of network members. Therefore, it is important to identify the value system of the networks and their individual members to have an improved collaborative process. A CN consists of independent and autonomous entities which each of them has its own value system. Many conflicts among partners might come up because of misalignment of values. In the absence of identical value systems, the members' perceptions of the results of the collaboration processes may be different and consequently, they may show some non-collaborative behaviors, such as unwillingness to share knowledge and other inter-organizational disagreements [START_REF] Camarinha-Matos | Towards A Conceptual Model Of Value Systems In Collaborative Networks[END_REF].

CN Governance includes some structures, principles and rules for resources allocation and rights assignment. Moreover, it supervises the entities and activities within a CN. Governance and the behavioral aspects influence each other, mutually. The constraining rules of CN governance may lead changes in collective and individual behavior of network members, and also behavioral aspects such as character of a CN member influence the CN's governance.

For modeling the Network Collective Behavior, it is needed to identify the objectives, perspectives, traits/actions and metrics such as presented for modeling the individual collaborative behavior in the previous section. In this case, objectives may include improving: customer satisfaction, benefits to CN, CN achieving more power in the market/society. These objectives can be considered according to the two perspectives: financial and social perspectives.

Conclusion

Quantitative causal modeling is a powerful aid to understand and enable practical decisions about what might be the best action to take in a certain circumstance. Therefore, in our approach we measure the comparative individual collaborative behavior by using quantitative causal modeling. This approach could lead to identify or predict conflicts, select the best-fit VO members, assign the roles and rights in CNs more effectively, and measure the individual collaborative behavior in networks. Analyzing and improving the individual collaborative behavior of CN members will influence the Collective Behavior of the CN. According to our approach, partners' behavior could be compared with each other, and partners will be informed that enhancing their individual behavior will result in more success in their collaboration.

As such, improving the individual behavior through factors such as incentives and rewards as well as alignment of values and fairness in governance rules are proposed to influence the network's collective behavior.

Fig. 1 :

 1 Fig. 1: Quantitative analysis of relations among trait/actions using causal diagram.Causal effects in Figure1represent the relations among different traits/actions in form of mathematical equations. By reasoning on the base of these causal relationships, some mathematical formulas can be derived to calculate values for comparative individual behavior of CN members. The plus sign (+) in the causal diagram represents either addition or multiplication, and the minus sign (-) represents

Table 1 :

 1 Perspectives, traits and metrics for partner selection objective

	Perspectives	Traits/Actions	Metrics
		Punctuality	e.g. average hours/days of delay in
				product delivery
		Pro-activity	e.g. number of articles published in the
				press, number of new recommended
				members, and number of awards won
		Flexibility	e.g. average period of time (in hours or
				days) to develop or change a new
				process and percentage of time
				without process faults
		Responsiveness	e.g. average response time to emails
				(in hours or days)
		Responsibility	e.g. number of successful collaboration
				involved, number of responsibilities or
				roles accepted in past experiences
		Innovation	e.g. average number of industrial
	Conforming to past VO Regulations (CR)	Keeping commitment	Problem/co nflict resolution Willingness	patents per year in last five years e.g. number of problems that escalated to VO management e.g. percentage of accepted adaptations
			to adapt	to new required plans
			Willingness	e.g. difference between delay in input
			to	and delay in output
			compensate
			providing	e.g. number of suggestions realized for
			impulses	improvements
			Problem	e.g. provided reaction time (in hours or
			avoidance	days) when critical status is reported
				e.g. number of contacts with other VO
				members
		Trustworthiness	e.g. number of tasks successfully
				completed
	Value Adding to past VOs (VA)	Sharing assets Reputation	e.g. number of different assets which a member shares in a VO e.g. number of Years in business, number of customers, size of market
		Being Fair	e.g. number of disagreement on
				benefits distribution, either against the
				original contract, or against the
	Special collaborative business Traits (ST)	Reuse of existing results Tolerance to stress	consortion majority e.g. number of use and provision of references to other member's articles/results e.g. number of times pulling out of
				commitment
		Being courageous	e.g. number of administration roles
				accepted, number of risks taken
		Information sharing	e.g. frequency of information provision
				to other collaborators