Hodjat Katarzyna Adamiak
email: adamiakkasia@yahoo.com

Soleimani Malekan

Hamideh Afsarmanesh
email: h.afsarmanesh@uva.nl

Hodjat Soleimani Malekan
email: h.soleimanimalekan@uva.nl

Semi-automated BP Integration in Virtual Organizations

Keywords:

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

Introduction

Due to globalization and increased dynamism of industrial environment, organizations increasingly form partnerships, such as virtual organizations (VOs), in order to improve their performance and increase their competitive advantage. In VOs, organizations share knowledge, skills and resources to seize market opportunities, create innovative products, and provide value-added services [START_REF] Afsarmanesh | A framework for management of virtual organization breeding environments[END_REF]. As such they become a great enabler to increase enterprise agility, consequently ensuring better ability to survive in dynamically changing market conditions. In this research we adopt the following definition of the VO: "an association of legally independent organizations (VO partners) that come together and share resources and skills to achieve common goals, such as acquiring and responding to a market/society opportunity [START_REF] Afsarmanesh | A framework for management of virtual organization breeding environments[END_REF]". We further focus on addressing the BPs that are practiced by individual VO partners, and how they can be effectively shared, integrated, and used for the benefit the VO as a whole.

This paper introduces our semi-automated approach through one real example case of cooperation from the construction industry, which also serves as the proof of concept to validate our approach. In construction industry, partnership formation is traditionally a popular practice; however, often performed in an unstructured way. In a typical project, different organizations specialized in various disciplines are required to jointly fulfil multiple construction tasks. In our real example scenario, organizations co-work during the life time of every project, while they do not share and/or lend their business processes (BPs) to each other. In other words, each involved organization provides a set of own services needed for each construction project, and executes its services during different stages of the project. But currently organizations do not share/lend their processes, which prohibit them from working more efficiently together and/or delivering composed value-added services in the VO, as it typically happens among different departments within one large organization.

A collaborative operation in a niche market in construction industry is exemplified here, involved in construction of eco-friendly buildings. This type of projects is currently popular, with increasing number of interested investors. However, construction of eco-friendly buildings requires the use of novel technology, materials, and expertise from various disciplines. Through cooperation among a set of organizations however, a VO can successfully deliver such niche projects. This provides more opportunities and increases the reputation of the VO, consequently bringing more profit to the involved smaller organizations and increasing their chance of survivability in market turbulences. Continued collaboration among organizations and sharing all their abilities in the VOs create the base for partners to provide more efficient as well as a wider range of services and scope of operation. For instance, to enable proper management of their joint operations in VOs, participating organizations need to share their BPs. In the past, organizations who worked only in one project were reluctant to share and/or merge their BPs. They considered this against their autonomy and confidentiality concerns. However today, once negotiating a VO collaboration contract, organizations are more willing to do so; especially once realizing advantages resulted from their BP integration in VOs. BP integration helps organizations to standardize their practiced BPs and use each other's experiences and best practices. Furthermore, it helps with getting to know about the expertise of other organizations, which also underpins the potential of their additional collaboration in future. Another advantage that is the target of this paper is that the BP integration facilitates formation of an optimized and consistent repository of BPs at the VO level. Such processes can then support formalizing the VO collaboration plans among different organizations, so that together they can act as one larger organization. As such our approach guarantees the lack of redundancy in the BO pool, which can be resulted from simply storing all BPs of all organizations in one VO repository.

The main aims of our BP integration in VOs are twofold: (i) creating a shared BP repository while removing redundancies, thus reducing the size and enhancing common understanding about existing BPs by all VO organizations, and (ii) facilitating creation of composed value-added services. While the methodology presented in this research primarily addresses the former aim above, its last two stages are also applicable to the latter aim of supporting BP integration.

To build a reliable research background for the area of BP integration and to verify the existing approaches against requirements for our research, an extensive Systematic Literature Review (SLR) [START_REF] Kitchenham | A systematic review of systematic review process research in software engineering[END_REF] is performed, identifying research gaps in the area. Accordingly, it is shown that step-wise definition of a BP integration methodology is missing in approaches introduced in related research, such as in [START_REF] Gottschalk | Configurable Process Models[END_REF][START_REF] Rosa | Merging business process models[END_REF]. The results of this extensive SLR, is the subject of a forthcoming paper. We therefore aim at introducing a semi-automated methodology needed to constitute the optimized set of processes in the VO context. Our general research question (RQ) is: How to ensure successful co-creation of clean and valid integrated-processes among member organizations in a VO? Three partial research questions follow this main RQ, as follows: RQ.1 Which steps are needed to be taken by VO member organizations for creating a common set of integrated-processes? RQ.2 How to ensure the created integrated-processes are clean (i.e. syntactically correct) ? RQ.3 How to ensure the methodology is valid and applicable to the VO context?

The paper is structured as follows. Section 2 introduces the methodology applied for achieving an optimized pool of BPS and for BP integration. Section 3 addresses the suitability of our introduced methodology in an example case from construction industry. Finally, the paper is concluded, addressing a few areas of future research.

Methodology for BP Integration

This section addresses the RQ.1 and RQ.2 We design a semi-automated methodology, guiding the process of integrating BPs within a VO. The methodology consists of a set of practical steps that can be performed using the suggested tool chain, in order to successfully introduce a collection of clean and valid integrated-processes for collaborating organizations. Depending on some specific situations, when the BPs of an organization are already formalized, the steps 1 and 2 can be omitted (see Fig. 1). Also some other steps might need to be applied multiple times, e.g. the steps 4 and 5 that for instance need to be executed multiple times when a specific merge candidate group includes more than two BPs. Also, if more than one merge candidate groups are found in the VO, the part of the methodology from steps 4 to 6 must be executed multiple times. Please note that the steps in our methodology, while mostly automated, apply different methods varying among: manual, semi-automated and automated methods. All mentioned steps are captured in Fig. 1 that illustrates the BP integration methodology flowchart. Each step, from 1 to 6 in Fig. 1, is then described in more details in sub-sections 2.1 to 2.6 respectively. We furthermore introduce justifications for design of our methodology based on its compliance to [START_REF] Hevner | Design science research in information systems[END_REF] -Design Science guidelines. Section 3 provides the validation of our proposed methodology, based on its application to a real construction case study.

Formalize Representations of BPs

Each VO member organization performs a number of business processes (BPs) within its individual operations. Those processes are not always formalised, and sometimes described only textually or in another ad hoc manner. For the purpose of processes' comparison, analysis, and integration, a collection of relevant BPs must be formalized in a consistent and common manner along all participating organizations. The formalization includes: process identification, process discovery and process model design [START_REF] Dumas | Fundamentals of business process management[END_REF]. In real-life, organizations formalize the design of their BPs in various ways, e.g. with use of workflow charts, UML Activity Diagrams, BPMN process models, EPC process models, etc. Different business process modelling languages (BPMLs) were evaluated in [START_REF] Malekan | Overview of Business Process Modeling Languages Supporting Enterprise Collaboration[END_REF], who defined the Business Process Model and Notation (BPMN) as the most suitable BPML for use in CNOs. Therefore, BPMN is decided as the language used in our methodology. Varieties of tools are available for designing the BPMN process models. We indicate the web-based Signavio software, as one of the top tools and most used to support the BP design [START_REF] Chrobak | Overview of business process modelling software[END_REF]. Please note that we use Signavio as the base environment to better represent the executed processes. In summary, our formalization of BP representations includes following sub-steps:

• BP identification -presenting a list of relevant BPs performed by all VO member organizations and providing the appropriate naming of the identified BPs, • BP discovery -collecting the data regarding each BP and investigating it to properly describe the overview of actions performed in the specific BP, • BP design -illustrating the described BP by designing its graphical model representation in BPMN.

Check Quality of Inputs

The input BP models must be executable. This means, the XML logs of the entire designed BPs must be correct and readable by the software. Existence of any modelling errors disables the further comparison, analysis and proper integration of the BPs. Therefore, all the possible inconsistencies, including dead-lock or live-lock incidents, must be identified and removed at this early stage. Also we use Signavio, as it incorporates the functionality that provides the check of BP model's correctness.

Find the Qualifiers for Merge

Multiple similar BPs may exist among various organizations. In case of forming a VO, we need to collect all its member organizations' BPs in a shared repository, which may contain redundancy occurrence among the BPs in the repository. What is more, the repository may contain a number of BPs with repeated process instances, which are difficult to analyse. Therefore, as a first step we identify the similar BPs within a VO, to enable their merge in the further steps thus, aiming to reduce the size of the BP repository to the extent possible.

A number of process similarity evaluation algorithms are introduced by the researchers and a few are implemented in open source software. One such algorithm, in [START_REF] Dijkman | Similarity of business process models: Metrics and evaluation[END_REF] provides the graph similarity calculation based on labelling, structural and behavioural analysis of the input processes. This algorithm is implemented in ProM 5.2 tool that is used in our methodology as the means for performing the BPs similarity check.

However, by only evaluating the similarity among all gathered BPs, the merge qualifiers' identification is not possible. In other words, here we are not searching for the similar pairs of processes, but the potential groups of BPs that are similar with each other and can be merged. To provide the more accurate semi-automatic merge qualifiers identification we identified two sub-steps. Those steps require pre-processing of BP collection, by word similarity checking on the names of processes.

A. BP outliers potential: Exclusion candidates identification

First sub-step aims at retrieving a set of BPs, which stand highly individual in the BP repository and can be excluded from the further analysis for similarity with other BPs. The main goal here is to potentially narrow the number of BPs included in the analysis.

B. BP cluster potential: Merge candidates identification

Second sub-step aims at providing a set of potential merge candidate groups that will be next analysed to consequently form a final set of BPs to be merged.

Both sub-steps are supported by semi-automatic identification of the BP clusters and BP outliers, based on the semantic distances related to their names. A function that displays the process names' map (illustrating the string semantic distances) is available in the WordStat software [START_REF] Pollach | Software Review: WordStat 5.0[END_REF], trial version, which we have used. Based on the obtained word map, we can manually draw the circles around observable identified clusters, at the same time indicating the BP outliers.

Find Commonalities and Differences

The particular BP models located within the identified groups of BPs (the final merge candidates) at this stage must then be compared with each other to enable the merge.

…

In [START_REF] Dijkman | A classification of differences between similar Business Processes[END_REF] technique to compare BP models and indicate their differences is suggested, which is implemented in ProM 5.2 software. However, such detailed analysis can only be performed only on pairs of two BPs. Therefore, we approach both the differences identification and merging activities in hierarchical steps. In the provided hierarchy, we compare pairs of the most similar BPs with each other, define their differences, and at the same time their commonalities (containing the same fragments), and merge the processes as described in the next step. Next, one pair of merged processes is next compared with another pair of merged processes (or a single BP), their commonalities and differences are examined, and they are merged at a higher hierarchical step. This iteration lasts until we reach the top level of the hierarchy and all the BPs in a merge candidate group are integrated forming one final integrated-process. The abstraction of this approach is illustrated on Fig. 2.

Merge

Based on the information retrieved in the previous step, we can merge the processes by identifying the common fragments and resolving the differences. Some BP merging algorithms are available in research and implemented in an open source software, e.g. "EPC merge" function introduced in ProM 5.2 in [START_REF] Gottschalk | Configurable Process Models[END_REF]. We evaluate the identified automated merging algorithm, by comparing the automatically obtained result with the desired manually created integrated-processes. Based on evaluation of this algorithm, after applying it on our construction case study (see section 3.5), we decided to provide this task manually. This was mainly due to the maturity level of the involved companies. The manual merging is performed mostly according to a method of [START_REF] Rosa | Merging business process models[END_REF] and is provided with the use of Signavio as the base environment. The main merging tasks include:

-Deciding on final labelling of equivalent nodes (e.g. deciding on using a more descriptive label) -Adding a node (preceded by "XOR" split) that is not present on one of the merged models

…

-Rearranging the structure of fragments, which represent different sequence; e.g. if two nodes are provided in two BPs in an opposite sequence, we rearrange them into a parallel structure preceded by "AND" split -Adding gateways where necessary, e.g. if two BPs contain different nodes placed on the same position, both nodes should be included in a merged process, preceded by an added "XOR" gateway -Merging gateways, e.g. if two BPs contain different gateways ("AND" and "XOR") placed on the same position, these gateways should be merged into a configurable gateway ("OR")

The processes build upon each other, by adding the next BPs within the specific merge candidate group. After merging a pair of BPs, we return to step 2.4 to define the commonalities and differences of the next pair of BPs and merge this pair into one BP. This iteration lasts until all BPs in a specific merge candidate group are merged. Consequently, we obtain an integrated-process that includes all the nodes, structures and behaviours included in its entire set of initial BPs.

Check Quality of Outputs

This step together with step 2.2. on Check quality of inputs, address concerns of RQ.2. Cleanness of the output processes is examined in the same way, as for the input processes, i.e. by Signavio automatic process quality check. The indicated errors and/or structure warnings must be reviewed, after which the process can be improved. After obtaining a clean integrated-process, we proceed to integrating the next merge candidate group's BPs, by returning to step 2.4. This iteration lasts until we obtain a complete set of integrated-processes.

Methodology Justification

The purpose of the presented solution is to contribute to development of the field by introducing a semi-automated BP integration methodology for organizations that cooperate in a VO. It is important to ensure that the presented solution is relevant and useful in practice. Therefore, the methodology was created in compliance with seven guideline criteria for proper design science research, as introduced in [START_REF] Hevner | Design science research in information systems[END_REF] and briefly explained below. 1) Design as an Artifact -our methodology of BP integration is a viable academic artifact aiming to provide practical support in the operation phase of formed VO. 2) Problem relevance -the solution supports an important and relevant business problem: sharing processes among partners in VOs, of special interest to SMEs. 3) Design evaluation -our methodology is evaluated based on a real case study of ten individual organizations in a VO, to examine its usability and usefulness. 4) Research contributions -the proposed methodology is innovative, effectively supports VO formation, and contributes to partners' BP sharing and integration. 5) Research rigor -the rigorous research methods is applied to both: identifying the gaps and building research foundations, through the SLR, and evaluating every step of the proposed solution through the real case study in section 3.

6)

Design as a search process -addressing the identified gaps, each methodology step is designed applying the state of the art in research, when it addresses the needs of the step, e.g. algorithms needed for the BP graph similarity evaluation. 7) Communication of research -we aimed at designing a methodology that is understood and usable by both technical and managerial staff at the organizations for their effective BP integration in VOs. Additionally, we aimed to provide the base for software system engineers and designers that intend to develop software to support the automated BP integration in VOs, thus at providing a set of guidelines for building the new software architecture.

Evaluation of BP Integration Methodology

In this section RQ.3 is addressed by applying and thus evaluating the methodology introduced in section 2 on a real large example case from building industry. This exercise proved fruitful, and the lessons learned and encountered limitations during this practical exercise, provides the base for future research in this area, as addressed in Section 4. This industry case concerns ten SMEs operating in the Polish market. These organizations have different complementary expertise, including: medium-size Construction (CC), Plumbing (P), Heating/Ventilation and Air-Conditioning (H), and Landscaping (LC), as well as some very small (one-person) SMEs including the Electrician (E) , Carpenter (C), Surveyor (S), Excavator (EX), Tiler (T), and Roofer (R). Fig. 3 provides a context model for their typical interactions in a construction project, where the ten companies are classified as contractors. A commonly used name for "customer" in building industry is investor. The investor is usually not a specialist in a construction domain, so the help of control inspectors is often needed during the project. Control inspector is a source of practical information for the architect and the investor, and is a representative of the investor on the construction site, controlling the quality of provided services.

At present, contractors operate individually at the construction site. While the same group of companies usually work together in similar projects, there is currently no longterm agreement for collaboration among the involved organizations. Therefore, the situation does not support process sharing and delivering value-added services by them. However, the construction industry remains increasingly competitive, and difficult to survive in, especially for SMEs that are analysed in our case study. They are continuously surpassed by large construction companies that offer fast services, which are cheaper but with lower quality. Therefore, the emergence of niche high quality construction projects enables SMEs collaboration through VOs, to increase their profitability, survival rate, and seizing more market opportunities. In the remaining of this section we briefly address in subsections 1 to 6, the application of the steps introduced in section 2 for our BP integration methodology, to the real case study of ten collaborating contractor companies, as represented in Fig. 3.

Formalize Representations of BPs

For the ten contractor members in this VO, a total number of 82 BPs are identified. Using textual descriptions of these BPs provided by companies, their BPMN models are developed. One example such process, for "supplying materials" performed by the Electrician company [E], is presented in Fig. 4 and preceded by its textual description.

Electrician -supply materials [process E2]: Supplying starts when a need for materials appears. Then, Electrician calculates the amount of needed materials, in parallel with searching other suppliers' offers. As an outcome of these tasks, a materials request is created and sent to another specific supplier. In the end, both the requested materials and an invoice are delivered and the process ends.

Check Quality of Inputs

All designed BPs are quality checked by Signavio software for potential syntactic Fig. 5. Quality check by Signavio -process with error and warnings next to XOR problems. As an example, imagine that by mistake for E2 we generate the BP in Fig. 5. Running Signavio on it indicates some problems found for Electrician's "supplying materials" process (E2), as indicated in Fig. 5, with red "!" next to the XOR operation (in the center). The cleaned process is then produced to correct the syntactical errors, as shown in Fig. 4.

Find the Qualifiers for Merge

Analysing the formalized 82 BP models can spot several BPs that are more or less repeated by a number of organizations within this VO. Identification of the so called merge qualifier processes is the necessary first step in BP integration. Supported in semi-automated manner, this step identifies similarities among such processes.

Candidate BPs for both classes introduced in Section 2.3 -potential BP outliers and potential BP clusters -are identified based on two similarity checks: first on name similarity analysed with WordStat software, and second on graph similarity analysed with ProM 5.2 software. Note that only cross-organizational BP similarity is checked and not intra-organizational similarities. Then using the Link Analysis function a word map is created visualizing all similarities. Using the map, we first manually cluster potential candidate BPs for merge, as presented in Fig. 6. Each of the eight groups indicates a potential candidate for clustering and the rest are 27 potential outliers among the 82 BPs. The applied logic is explained further below.

Exclusion/outlier candidate identification -Running the two automated similarity checks identify that 27 out of 82 processes have little similarity (up to 6.7%) to any other BP. Therefore these processes are 93.3% different than others. Hence, produced results indicate BP outliers, and are excluded from further steps of this methodology. Merge/cluster candidate identification -Eight groups are manually created on the results from running of the two automated similarity checks addressed above. In order to validate these clusters for merge purposes, we run automated graph similarity calculations once within each group, and once outside of these groups, with the use of ProM 5.2 tool. This task however requires a double conversion of the BPMN models (through Petri Net into EPC), since the automated comparison tool is only available for the EPC models, and to the best of our knowledge, there are no open source software that offers a single conversion between the BPMN and EPC languages. In practice, the first conversion (into Petri Net) was provided in ProM 6.4, and the second conversion (into EPC) was done in ProM 5.2. While this is the shortest possible way, we are certainly aware of the fact that the double conversion may cause some data loss, and thus may influence the results. For the similarity evaluation we used a threshold of 35%, even higher than the acceptable estimation stated in [START_REF] Sirageldin | Graph-based simulated annealing and support vector machine in Malware detection[END_REF] that uses 33% similarity threshold. Table 1 shows positive (above threshold) and negative (below threshold) similarity results, inside and outside each merge candidate group. Based on the right part of Table 1, we assess the usability of the proposed method. We can notice that inside all groups, the rates of their BPs being similar to any of the BPs outside of merge candidate groups (indicated in the 2nd column from right) is significantly lower than the rates of BPs that are not similar (indicated in the 1st column from right). In fact, the positive similarity with BPs outside the group is measured mostly under 25%. Therefore, the method proves its applicability, by showing lack of similarity with other BPs, at a satisfactory level. By analysing the left part of Table 1, we can evaluate the internal group BPs and their candidacy for integration within the specific group. Furthermore, checking similarities inside each group, the similarity among BPs is larger than 50% and sometimes 100% (indicated in the 4th column from right). The main aim here is to identify and suggest to users groups of BPs as candidates for merging and those that are outliers in this process.

To assess the internal structure of each group, we must look closer into individual BP graph similarity check results. Groups 2, 4, and 6 show completely similarity internally, therefore they represent very good candidate for BP clustering. So we then analyse only each remaining group manually by domain experts, examining details of similarity checks among all their BPs, and deciding if they can or cannot be merged.

Find Commonalities and Differences

With automated software, we identify differences in the merge hierarchy for each of the eight groups, and resolve difference with manual help from experts. To give an example, consider merging of two BPs in group 5: C2 for "materials ordering", and T2 for "procure materials", which are identified as being highly similar (identified with 79.6% similarity, in previous step), only 4 simple differences are identified by the software. The BP models of these two processes are presented in Fig. 7.

Merge

At this stage common fragments are identified and differences are resolved manually to reach the merged BP, as described in Section 2.5. The manual merge is however provided step-wise, according to the merging hierarchy developed in Section 3.4. For the introduced example case (merging C2 and T2) we can notice that the process C2 actually contains the structure of process T2. The activities of "Searching suppliers offers" and "Calculating material needs" that are organized in a sequence in T2 can be therefore added to that sequence using additional AND split and join for them. However, for the needs of process C2, this sequence can be executed in parallel, as illustrated on its initial model. Concluding this merging instance, the process "C2 + T2" generates exactly the same as process C2, as in Fig. 8.

Note that, as mentioned in section 4.5 we aimed at including all the nodes, structures and behaviours of the initial BPs. However, some additional decisions are always made by expert decision makers, e.g. regarding removal of a node and not including it in the model.

Check Quality of Outputs

Since our integrated-processes are also created in Signavio software, we provide the quality (i.e. the syntactical correctness) check the same way as introduced for the section 3.2. In general, the integrated-processes are complex, so there is a higher risk of generating certain gateway inconsistencies, e.g. causing dead-lock errors in the resulted integrated BP. Therefore, it is essential to check if any resulted process includes such errors or ambiguities and if so, to disambiguate them. Semi-automated disambiguation of BP models is the subject of one of our ongoing research area, and will be reported in forthcoming publications.

Conclusions

The introduced BP integration methodology indicated the main tasks to be performed, and tools to be used by organizations, in order to integrate BPS in an established VO. We have identified that similarity checking based on BP names causes many difficulties for the software, and using shared BP names dictionary would increase the quality of this stage of the methodology. Another aspect indicated in research limitations is that the methodology needs a proper automatic merging tool that ensures a good quality of outcome processes that can be also disambiguated and executable. The step of merging the BPs is currently the most time-consuming task, highly influencing the methodology application time. Therefore, creation of an efficient and accurate merging tool is the next step of improving the BP integration methodology.

Fig. 1 .

 1 Fig. 1. BP integration methodology flowchart

Fig. 2 .

 2 Fig. 2. Identifying differences in the merge hierarchy

Fig. 3 .

 3 Fig. 3. Construction case study context model

Fig. 4 .

 4 Fig. 4. E2 process formalization ("SP-M" -supplier of materials)

Fig. 6 .

 6 Fig. 6. Process names word map and clustering

Fig. 7 .

 7 Fig. 7. BP models C2 and T2 before merging

Fig. 8 .

 8 Fig. 8. Merged model C2 + T2

Table 1 .

 1 Merge candidate groups' results overview

		Percentage of results that are:	Percentage of results that are:
		(inside the group)	(outside the group)
	Merge	Similar	Not similar	Similar	Not similar
	candidate	(result > 0,35)	(result ≤ 0,35)	(result > 0,35)	(result ≤ 0,35)
	group:
	1	57,14	42,86	26,43	73,57
	2	100	0	12,50	87,50
	3	77,14	22,86	13,75	86,25
	4	100	0	23,56	76,44
	5	61,43	38,57	8,00	92,00
	6	100	0	22,47	77,53
	7	55,00	45,00	10,76	89,24
	8	86,67	13,33	14,81	85,19

Signavio http://www.signavio.com/bpmacademic-initiative

[START_REF] Kitchenham | A systematic review of systematic review process research in software engineering[END_REF] WordStat http://provalisresearch.com/prod ucts/content-analysis-software/

http://www.promtools.org/doku.p hp?id=prom52


Ahm Shamsuzzoha
email: ahm.shamsuzzoha@uwasa.fi

Petri Helo
email: petri.helo@uwasa.fi

Maqsood Sandhu
email: maqsoods@uaeu.ac.ae

Green Virtual Business Network for Managing and Reusing Waste between Partner Organizations

Keywords: Business network, waste reuse, industrial ecology, sustainable environment

Today, industry is under intense pressure to stay competitive in market segment with limited resources. To withstand such challenges manufacturing organizations need to be collaborative. This collaboration not only benefits many aspects in their daily businesses but contributes other aspects like waste management. In this research, especial focus is given on managing industrial wastes through recycling and reuse between partner organizations as resources within a business network. This concept turns out mutual benefits between partners and managing Eco-friendly business environment based on substituting a new resource by an undervalued resource such as waste. This paper also presents a modelling approach for industrial wastes management within business network, which intends to support the identification of wastes substitution opportunities.

Introduction

The green virtual business network is an environment where companies cooperate with each other for achieving mutual benefits. Such network can be built both physically collocated and virtually distributed regionally or globally. The objective of either form of network is to share resources and expertise within the networked partners under mutual trust. The trust plays the main role to be successful of such networks.

There are various forms and formats of business network with different goals. From literature, it is noticed that most of the business network is mainly focused on developing collaborative products and/or services to gain competitive power over competitors [START_REF] Romero | Green Virtual Enterprise Breeding Environments: A Sustainable Industrial Development Model for a Circular Economy[END_REF][START_REF] Camarinha-Matos | Collaborative Networked Organizations -Concepts and Practice in Manufacturing Enterprises[END_REF]. However, little or no research is done so far with the issue of managing waste collaborate within the green virtual business network. With the view to fulfill such objective this is concentrated on managing industrial waste through forming and executing collaborative business network. The objective of this research can be categorized as follows:

(1) To propose a methodology to support formation and execution of a business network for mutual benefits [START_REF] Camarinha-Matos | Collaborative Networked Organizations -Concepts and Practice in Manufacturing Enterprises[END_REF] To investigate the possibility to managing wastes within collaborative partners (3) To promote environmental sustainability through green virtual business network

The paper is structured as follows: Section 3 illustrates relevant literature in the proposed field, while Section 3 highlights the research methodology. Section 4 concentrates to demonstrate the life cycle of business collaboration. Brief explanation of industrial waste is outlined in Section 5, whereas; Section 6 states the techniques of collaborative waste management in industry. A framework of networked waste management is illustrated in Section 7, while, mapping of waste management between companies are defined along with mathematical model to calculate the waste-resource analysis within a business network in stated in Section 8. This research is concluded with future research directions in Section 9.

Literature Review

Today's emerging concept of circular economy motivates global companies to be sustainable in production process and innovation [START_REF] Romero | Green Virtual Enterprise Breeding Environments: A Sustainable Industrial Development Model for a Circular Economy[END_REF]. To achieve sustainability within manufacturing companies need to focus various perspectives. Waste reduction or make the waste reusable as resource is one of an important way. This waste reuse as resource can be done within individual companies or group of companies forming a virtual business network. This kind of network can be termed as green virtual business network [START_REF] Chertow | The Eco-industrial Park Model Reconsidered[END_REF].

Formation of green virtual business network to improve customer services is a growing trend between companies [START_REF] Chertow | The Eco-industrial Park Model Reconsidered[END_REF][START_REF] Romero | Green Virtual Enterprise Breeding Environments Reference Framework[END_REF]. This trend is helping today's business domain in various ways to be competitive in the market segment [START_REF] Romero | Green Virtual Enterprise Breeding Environments: A Sustainable Industrial Development Model for a Circular Economy[END_REF][START_REF] Camarinha-Matos | Collaborative Networked Organizations -Concepts and Practice in Manufacturing Enterprises[END_REF].

Managing waste as a result from industrial operations is nowadays is a huge environmental concern. Manufacturing companies are forced by rules to obey global environmental regulations [START_REF] Henriques | the Relationship Between Environmental Commitment and Managerial Perceptions of Stakeholder Importance[END_REF][START_REF] Zsidisin | Environmental Purchasing: a Framework for Theory Development[END_REF]. It is therefore become an obligation for companies to invest resources to manage or treat their waste properly. However, often it becomes difficult for companies to manage waste individually but it asks for cooperation with other companies, which motivates the company to form a business network. This network not only gives added business benefits but also support to manage each other's waste cooperatively.

The application of business network to manage waste is not widely analyzed and found in the literature [START_REF] Hein | Modelling Industrial Symbiosis using Design Structure Matrices[END_REF][START_REF] Gossain | Reinventing Value: The New Business Ecosystem[END_REF][START_REF] Romero | Green Virtual Enterprise Breeding Environments Reference Framework[END_REF][START_REF] Cherubini | Life Cycle Assessment (LCA) of Waste Management Strategies: Landfilling, Sorting Plant and Incineration[END_REF][START_REF]Sustainable Solid Waste Management: An Integrated Approach for Asian Countries[END_REF]. Most of the networks are confined with to achieve business benefits through developing products or processes through collaboration and not management of wastes. This research gaps is identified from literature survey and this research presented a guidelines and aspects to manage industrial waste collaboratively.

Research Methodology

The methodology of this research is highlighted with various stages as displayed in Figure 2. From Figure 1 it is noticed that the first step in the methodology is to identify the problem specification, which is followed by developing the green virtual business network based on the specified problem. When the green virtual business network is formed, in the next available stage of the methodology is to collect necessary data from the case companies within the virtual business network. Initially, five case companies were selected based on their products, whose by-products or wastes can be used between each other as potential resources after necessary treatments. Necessary wastes data were collected through physical visits to the selected case companies' premises, which were mostly historical records from the companies. All the collected data will be analyzed based on its suitability to solve the identified problem. At the third stage, a model will be developed which is basically a problem solving framework defining each stages how to solve the identified problem. This model will then be tested and validated through implementing it to solve real life case examples. If the model is validated successfully, a generic theory will be proposed to be used to solve specific problem.

Virtual Business Network for Mutual Benefits

The concept of virtual business network comes from the theme where manufacturing companies are formed through the implementation of information and communication technology (ICT). This ICT framework supports the selection of partners, formation of the partnership network, communication between partner organizations and execution of the collaborative network [START_REF] Romero | VO Breeding Environments & Virtual Organizations Integral Business Process Management Framework[END_REF][START_REF] Camarinha-Matos | A Comprehensive Modeling Framework for Collaborative Networked Organizations[END_REF].

A leading partner known as 'Initiator Company', after rigorous market survey, starts this business network after identifying business opportunities. After identifying the business opportunities, the initiating partner company looks for the possible partner organizations within similar business domain. When the partners are selected based on predefined criterions, an agreement is signed between the partner companies, where each of the partner agreed on the specific guidelines to continue this network until the business objectives are fulfilled. The next step after signing the agreement is to start designing and developing the product or services based on the pre-identified business opportunities. The product or service design stages are iterated within the partner organizations before finalize it. At this phase, necessary planning and scheduling are done depending on the resources availability within the networked partners.

Ended up

A communication framework is developed after signing the contractual agreement to continue the network in order to get real-time information from each partner organization. This framework is critical for executing the network efficiently. It ensures on time status of the collaborative product and/or services. This virtual network is dissolved after the product is manufactured and sales in the market successfully. At the final phase, all the liabilities and profits are shared among the partners depending on the agreed contract as signed at the beginning of the network. Complete lifecycle for the virtual business network can be seen in Figure 2.

Similar steps can be followed to form and execute the green virtual business network. In case of green virtual business network, only the business objectives will be difference, which are mainly concentrating to exchange and reuse of wastes as possible resources among partner organizations for mutual benefits.

Industrial Waste: Managing Environment for Sustainability

Waste is defined as any material, byproduct or energy that is generally considers as a valueless. Wherever, if there are human activities involved, there will be a waste. Waste can be divided into many categories such as industrial waste, electronic waste, municipal solid waste, health care waste, etc. Industrial waste can be defined as the output of any manufacturing process that is considered to retain no value. Industrial waste can be in three formats: energy, water and material.

The proper definition of waste management is the act of managing the waste from its beginning until its disposal and in each step of the production process asks how to control, modify and eliminate the process in order to reduce waste generations. The waste can also be considered as resource or food for other sector. The waste management is based on the notion of capturing all the missed values in all of the waste flows and can be treated in smart and innovative ways. In addition to, waste management is defined as minimizing the risks of waste to all society, not shifting to other sectors [START_REF] Goldman | Hazardous Waste Management: Reducing The Risk[END_REF][START_REF] Prahinski | Emprical Research Opportunities in Reverse Supply Chain[END_REF]. There are many technologies used in waste management. Table 1 shows the different waste management techniques and their descriptions.

Table 1. Available waste management techniques

Waste management techniques

Description

Storage facility

Storage facility can be defined as the holding of waste materials that prior to use, transport, recycling treatment, or disposal (Schumacher, 1988).

Recycle

Recycling of waste results from proper valuable treatment of waste, which is not harmful for human or environment.

Reuse

Reusing and recovery of the waste are part of recycling and make the waste a valuable resource.

Recover

Recover defines to use of waste by repair it to make it usable.

Exchange

Exchanging waste is mainly a reusing function; where different facilities are involved by matching one industry's output to other industry's input.

Minimization /reduction

In minimization/reduction, efforts are made to minimize the production of waste through various approaches such as process/raw material substitution, efficient prevention and modification of industrial wastes, etc.

Treatment

Changing the chemical and physical of waste in order to reduce its volume and render its hazard.

Disposal

Is the last technology in management of the waste if the upper ones is not applicable in this technology the waste is stored for a long periods in a landfills to isolate it from the surrounding environment.

Collaborative Waste Management: Potential for Mutual Resources

It is become important for manufacturing companies to abide by environmental rules and regulations. To respect such rules and regulations manufacturing managers need to adopt various strategies to limit the impact of their operations and products on the natural environment. Manufacturing companies produces huge amount of wastes that affects environment if they are not treated or recycled properly. These wastes need to be managed effectively and efficiently for the benefits of companies themselves, diverse stakeholder groups, including end-customers, industrial customers, suppliers and financial institutions [START_REF] Henriques | the Relationship Between Environmental Commitment and Managerial Perceptions of Stakeholder Importance[END_REF].

Manufacturing companies are evolved with various environmental aspects such green purchasing [START_REF] Zsidisin | Environmental Purchasing: a Framework for Theory Development[END_REF], reverse logistics [START_REF] Prahinski | Emprical Research Opportunities in Reverse Supply Chain[END_REF], product stewardship [START_REF] Snir | Liability as a Catalyst for Product Stewardship[END_REF], etc. All such activities involve varying degree of interaction with the companies in manufacturing domain from suppliers to end customers. The limited knowledge and understanding of individual companies for environmental awareness has hampered the development of widely accepted model or framework to manage costly environment. Although, several business networking or collaboration can be found in the literature, however, few researches found on collaborative management of environment.

The level of wastes as manufacturing companies are producing in their daily production lines often become cumbersome and unmanageable by individual company's efforts. In such challenging situation, combined effort to manage such wastes can offer companies economic benefits as well. In collaborative way, wastes can be treated internally by the knowledge and expertise from partner companies or can be treated in a common place for mutual efforts. The way of waste treatment may be varied from company to company but the objective will be same to get benefit from it. There are some ways already exists, where partner companies can manage their wastes in collaboration. Some of the ways to waste management is highlighted in Table 2.

From Table 2 it is noticed that the waste can be managed through several organizational forms and processes. In case of organizational forms, such as industrial symbiosis, eco-industrial parks, etc., wastes are managed through collaboration and such synergistic possibilities are offered by geographic proximity. Whereas, in case of processes, such as recycling, reuse, etc., wastes are treated depending on the wastes natures. For instance, in waste recycling the process can be up-cycling or downcycling and for reuse case, it can be direct reuse or use as second-hand, which is dependent on the waste nature.

Collaborative waste management

Brief description

Industrial symbiosis [START_REF] Chertow | Industrial Symbiosis: Literature and Taxonomy[END_REF][START_REF] Chertow | Uncovering" Industrial Symbiosis[END_REF] It is associated with the concept to utilize each other's by-products and otherwise share resources within the business collaboration. Industrial eco-park [START_REF] Lowe | Creating By-Product Resource Exchanges: Strategies for Eco-Industrial Parks[END_REF] With this concept business partners cooperate with each other and with the local community to reduce waste and pollution by efficiently sharing resources.

Waste-to-feed exchange

In this concept, one company's by products are used as resources to other company through mutual agreement through forming the business network. Business ecosystem [START_REF] Gossain | Reinventing Value: The New Business Ecosystem[END_REF] In this business domain, collaborative partners shares both resources as well as wastes between each other to stay environment friendly.

Systematic approach

In this approach, collaborative partners take the form of waste cascading, shared logistics for building green retrofits and new strategies in existing industrial developments. Waste-to-energy At this concept, collaborative partners attract complementary businesses to use the waste to produce energy between each other that could benefit from eco-industrial strategies. Eco-industrial park [START_REF] Lowe | Creating By-Product Resource Exchanges: Strategies for Eco-Industrial Parks[END_REF][START_REF] Chertow | The Eco-industrial Park Model Reconsidered[END_REF] In this concept, partner companies are collocated in a certain geographic location to participate in the industrial waste management system with the objective to reduce environmental impact.

A Framework for Collaborative Waste Management: Green Virtual Business Network View

Although, several researches are done on virtual business networks successes and limitations but limited researches are done on green virtual networks directly involved with managing industrial wastes [START_REF] Romero | Green Virtual Enterprise Breeding Environments Reference Framework[END_REF][START_REF] Cherubini | Life Cycle Assessment (LCA) of Waste Management Strategies: Landfilling, Sorting Plant and Incineration[END_REF]. Several IT-based tools are available to manage waste in general but due to cost and complexity they may not be suitable for many companies, especially small and medium size enterprises (SMEs) [START_REF]Sustainable Solid Waste Management: An Integrated Approach for Asian Countries[END_REF]19]. Most of the individual company treats its by-products or wastes separately, which may not be very cost effective and efficient. In such circumstances, companies (SMEs) can collaborate by forming a green virtual business network with the objective to monitor and manage their wastes and look for the possibilities to exchange or recycle\reuse of wastes as resources among each other.

Green virtual business network can play an important role in managing and reusing/recycling industrial waste among the network chain. This kind of network can be defined specifically to focus on inter-organizational goal setting, shared environmental planning and to work together to reduce environmental damage through wastes treatment processes like industrial ecology, industrial symbiosis, reuse/recycling/discard, etc. These practices with the objective to reduce environmental impact can be directed either upstream toward partner organizations or downstream toward customers. The upstream practices are closely linked with process-based performance monitoring, management, while downstream practices are related with product-based performance monitoring, and management.

To maintain industrial ecology, it is necessary to build collaborative effort within companies. Such collaborative effort supports to enhance waste management through recycling, reuse and discard. In order to fulfill such objectives, companies need to follow a framework that guidelines companies step-by-step to manage their harmful wastes. The framework can be based on both physical network within companies and/or green virtual network. This kind of network helps to utilize each other's byproducts, otherwise, share as resources into economic products among the partner organizations. A framework for green virtual business network to manage waste is displayed in Figure 3. From Figure 3 it is noticed that there is an iterative relationship between the business network and the waste management system. The basic functionally of the business network is to manage distributed business processes, which are supported by necessary administration and process monitoring. The green virtual business network is tightly coupled with partners, stakeholders, logistics chain, market segment, communication network and customer as highlighted in Figure 3.

In addition to managing the business processes, the green virtual business network is interacting with the collaborative waste management system. This waste management system has three formats such as recycle/reuse, non-recyclables and discard. In the recycle/reuse phase, collected wastes from the partner companies are treated and the by-products are used internally. In the non-recyclable phase, the collected wastes are used as possible resources within the network partner organizations. In the discard phase, recyclable and non-recyclable wastes are used as landfills/incineration as depicted in Figure 3. The framework as outlined in this research can be successfully implemented to manage collaborative industrial wastes and keep the costly environment clean for human being.

Networked Waste Management: Illustrative Business Cases (a) Mapping of waste management among the green virtual business network

As explained earlier, the networked waste management is an idea, where one company's waste within a business network can be a useful resource for other company. This concept contributes to the input-output matching approaches by including different types of resource substitutions. It can be demonstrated as the modelling approach, which is used for identifying potential waste, which is considered as output from an industry, and use as input resource to other industry. This type of waste management technique can be successfully implemented in a green virtual business network, where the collaboration starts after identifying business opportunity to exchange waste between partners. Figure 4 demonstrates an idea of waste management through mapping among collaborative partner organizations within a business network. As seen from Figure 4, the wastes from one or more partner organizations can be successfully used as resources for the other company or companies depending on their matching and suitability. From Figure 4, it can also be noticed that all the companies within the business network may not exchange wastes among each other due to unsuitable, or individual company might be specialized to treat its wastes for internal use. For instance, from Figure 4 it is seen partner factory 3's waste may be or may not be used or suitable as an input resource for partner factory one. Most of the cases, some wastes might be needed some kinds of processing before using them as resources by the other partners companies. This collaboration practices support network partners to minimize and reuse their waste effectively and efficiently and promote to maintain environmental sustainability.

Often such wastes might need to be processed within the network partners to them useful resources. Such processes can be incineration, waste symbiosis, departed, sorting, recycling, etc. In case of the reprocessing needs, individual partner organization or group of similar kind of waste producing partners can collaborate with each other with their skills and expertise to treat such wastes useful. There should be balanced between supply wastes and resources demand. In case of overstocked wastes can be discarded by the partners in an environmental friendly ways.

(b) Mathematical model of collaborative waste management

The exchange of waste and resource is of course depends of the demand-supply relationships between the companies. Each of the waste to be used as resource has a quantity. The amount of supply of waste cannot exceed the available demand as resource. In case of mismatch between supply and demand, priority will be given on the first come first serve basis. Even waste exchange policy can be formulated based on the evaluation on which waste demand can give better use to the waste. This waste supply and resource demand can be presented as of a mathematical model as follows (adapted from [START_REF] Hein | Modelling Industrial Symbiosis using Design Structure Matrices[END_REF]:

WSij ≥ ∑ = n j 1 wsij
Where, wsij is a waste supply i that meets a resource demand j. In any situation, the sum of wsij cannot exceed the total available waste supply WSij. In addition to that, the sum of waste supplies wsij for a resource demand RDj has to be equal or less than the resource demand. This relationship can be stated as:

RDj ≥ ∑ = m i 1 ci wsij
Where, ci is a conversion factor for treating up and downstream substitutes. For example, if the resource demand of an organization is equal to waste supply from another organization and it is used properly as resource then the cj factor will be equal to 1. This conversion factor allows for calculating the summation of new resources that are substituted. Three criterions can be used for evaluating the waste-resource conversion within green virtual business network:

• Maximize the quantity of substituted new resources wsij • Maximize net profit by substituting earn revenue ERij minus operating cost OCij for a match WSij • Minimize capital investment CIij for a technology to transfer wsij The cost of capital investment can be CIij due to cost incurred to transportation cost to transfer waste from one factory to another one, waste conversion or recycling cost to make it useable resource for other factory if needed, cost for CO2 emission process if there is any, etc. In order to calculate cost-revenue balance sheet, it is useful to formulate multi-objective optimization problem with the objective functions as of:

∑ = m i 1 ∑ = n j 1 ci wsij = maximum ∑ = m i 1 ∑ = n j 1 (ERij -OCij) = maximum ∑ = m i 1 ∑ = n j 1

CIij = minimum

Subject to the constraints:

∑ = n j 1 wsij -WSi ≤ 0 and wsij ≥ 0
The waste data from the case organizations are to be analyzed following the abovementioned mathematical model and multi-objective optimization in order to make the waste management process economically feasible for the organizations within a business network. The analysis will be done in a manner as highlighted in Table 3. Based on the predefined objective functions total amount of waste from one organization as the replacement of resource for other organization, capital investment and net profit can be calculated and summed up as presented in Table 4 as sample. It is noted that only profitable waste-resource exchange (feasible solutions) will be allowed and non-profitable scenarios will be discarded from consideration. In this research, in total 10 companies within a business network will be analyzed. Relevant data related to waste for five case companies are already collected. After collecting remaining five companies' data, necessary cross mapping of waste exchange will be done. Eventually, supply-demand balance between waste-resource will be done in association with economic benefits from this approach too.

Conclusions and Future Research Avenues

The objective of this research is to manage waste, especially industrial wastes properly and to contribute to maintaining environmental sustainability. In this study, it is proposed that waste management in a more collaborative way can be beneficial for partners' organizations within a virtual business network. Of course, such network can be within an industrial district or park or can be formed globally, where contractual agreement is done through mutual trust. This networking philosophy is existing in different format such as virtual factory, virtual organization, industrial ecosystem, etc. General trend to forming such collaborative networks is to gain business benefits after identifying market opportunities.

The collaborative network as proposed in this research is also gaining mutual benefit for not only dominating the market segment by the collaborative partners, but also maintaining environmental sustainability through managing wastes. This waste management is done through environmental collaboration with partner organizations contributed to a relatively broad range of competitive benefits. Collaboration with partner organizations on environmental issue is linked to improvement in three traditional dimensions of organizations performance such as quality, delivery and flexibility in addition to environmental performance.

Management of waste in a collaborative way allows greater control over contextual and operational factors of the partner organizations in the network. It allows partners to interact between each other's in order to joint environmental planning and shared environmental know-how or knowledge. As such, any monitoring or control elements usually included in green production process within the business collaborators with the view to minimize wastes as much as possible.

In this research study, a framework is presented highlighting the possibility to manage collaborative waste efficiently. Five case companies within a business network's waste data is under study. In the coming months, more partner organizations waste data will be collected to analyze for possible cross mapping of waste-resource exchange within the partner organizations. This mapping will also be economically analyzed based on multi-objective optimization model as explained in this research. At the end of the research, the supply-demand analysis will be done based on the available waste (supply) with the required resource (demand). In future, in addition to exchange of waste between partners, the collaboration-performance relationship will be studied with the view for better performing green virtual business network.

Fig. 1 .

 1 Fig. 1. Stages within research methodology

Fig. 2 .

 2 Fig. 2. Life cycle of virtual business network

Fig. 3 .

 3 Fig. 3. Collaborative waste management framework

Fig. 4 .

 4 Fig. 4. Mapping of waste reuse within a green virtual business network

Table 2 .

 2 Various ways to collaborative waste management

Table 3 .

 3 Exchange of waste-resource with quantity within a collaborative business network

	Waste supplier	Waste receiver as	Resource name	Quantity
		a resource
	Organization 1	Organization 3	TBA	TBA
	Organization 2	Organization 1	TBA	TBA
	Organization 3	Organization 2	TBA	TBA
	Organization …n	Organization …m	TBA	TBA

Table 4 .

 4 Various objective functions and values from collaborative waste management

	Objective functions	Values from multi-objective
		optimization model
	New resources substituted from available	TBA
	waste
	Capital investment (CIij)	TBA
	Net profit margin (ERij -OCij)	TBA