A Global Multi-agent Decision Support System (GMDSS) for a manufacturing SME: Towards participating in Collaborative spunlace nonwovens Manufacturing REGUIEG Sedik*, TAGHEZOUT Noria*, ABBAD Haffid* and ASCAR Bouabdellah** * Laboratoire d’Informatique d’ORAN (LIO) Université d’ORAN, Algérie ** Entreprise INOTIS, ORAN, ALGERIE
[email protected] Abstract. This paper discusses the conceptual design and the development of a Global Multi-agent Decision Support System (GMDSS) for a manufacturing Small or Medium Enterprise (SME), which actively participates in Collaborative Manufacturing. A collaborative WEB 2.0 platform is proposed to implement the collaborative manufacturing environment. This is to offer maximum interoperability between all the distributed participants of a Collaborative Manufacturing Network (CMN) and their management information systems. Furthermore, this paper develops a simple framework to help understand the collaboration that is afforded by Web 2.0 applications. The proposed framework is used to examine how production agents can create, share and exchange experiences on diagnosis and resources failures with each other to have new ideas or useful information for the decision-making. The agent-based approach provides a collaborative WEB 2.0 interface to facilitate the remote interaction with human users involved in the risk management process. The global multi-agent DSS is applied to the spunlace nonwovens production industry. Key-words: Global Multi-agent Decision Support System (GMDSS), Collaborative Manufacturing (CM), WEB 2.0, Spunlace nonwovens production, Small or Medium Enterprise (SME).
1. Introduction Collaborative Manufacturing (CM) is a concept that involves the establishment of Collaborative Manufacturing Networks (CMNs) in order to fully exploit the core competencies of every manufacturer within a network. The strategy is aiming to achieve best possible fulfillment of customer demands and improvement of their overall net profit, agility, and competitiveness towards the global market (Kuik et al., 2010). However, CM heavily relies on improved data, information, and knowledge transparency typically a commonly recognized decision-making approach to achieve balanced profits, costs, and risks among the participants (D’Amours et al., 1999) (Zhang et al., 2004). This reliance suggests that an integrated manufacturing decision-support infrastructure is essential for a CMN to successfully deliver the positive outcomes. The developed system will enhance the existing capabilities on supporting the management and production activities which are traditionally restricted to in-house operations and department-oriented operations.
Thus, participants within the network must invest invaluable resources in performing substantial updates just to maintain the operation of their existing systems. In order to conform to these integration architectures, the system must be commonly endorsed by all business partners to ensure smooth transaction of collaborative management activities (Lin et al., 2009). In our work, the development of a Global Multi-agent DSS enables optimized decision-making via facilitating interactions amongst the stand-alone manufacturing systems, and the adoption of a generic collaborative decision-making model. The study we describe here deals with the typical spunlace nonwovens production. INOTIS enterprise is a candidate for our study. Established in 2003, its major mission is to develop, manufacture and sell world-class spunlace fabric for critical environments where contamination control and comfort of use are of of vital importance. This will be achieved through cost-effective production, consistency and reliability, a high level of service and quality and in partnership with customers (INOTIS, 2012). In fact, the market of nonwovens bonded by water jet (also called spunlace or hydroentanglement nonwovens) showed in the last years a considerable growth rate and will also in future grow strongly. These products are mainly used in the medical and hygienic field but also the technical applications are getting more and more important. The advantage of spunlace nonwovens compared to the classical needle punched products is that the same specific characteristics can be often reached with a lower quantity of fibers. This saves resources and allows very high production speeds. In this study, we propose a distributed approach where the components of a Small and Medium Enterprise are modeled as intelligent agents that collaborate to create models that can evolve over the time and adapt to the changing conditions of the environment. Thus, making possible to detect risky situations for the SMEs and providing suggestions and recommendations that can help to avoid possible undesirable situations. The core of the multi-agent system are the production and coordinator agents, that incorporate new techniques to analyze the data from enterprises, extract the relevant information, and detect possible failures or inefficiencies in the operation processes. In the following section, we will briefly describe the major reasons that motivate our study and focus in particular on the decisions problems in INOTIS enterprise production management. The article is organized as it is explained next. In Section 2, we describe the application area (INOTIS enterprise). In Section 3, some related works on manufacturing SMEs are presented. Next, we present our approach: We firstly give a detailed description of the agents, then the principles of WEB 2.0 technologies. The most important steps for building the domain ontology for spunlace nonwoven productions are also summarized in Section 4. This section is followed by a discussion of the obtained results (Section 5). Finally, conclusions and future research perspectives are outlined.
2. Case-study: the spunlace nonwovens production industry As defined in (INDA, 2002), spunlacing is the method of bonding a web by interlocking and entangling the fibers high velocity streams of water (synonymous with Hydroentangling). The web of fabric may have other bonding methods in addition to spunlacing. Spunlacing, not to be confused with spunlaid, is generally produced from
a web made up of staple fibers from a dry formed, carded system, but small quantities of spunlace bonding are done on production lines that use a wet laid laid forming process. More precisely, spunlacing is a process (White 1990; INDA 2002) of entangling a web of loose fibers on a porous belt or moving perforated or patterned screen to form a sheet structure by subjecting the fibers to multiple rows of fine high-pressure h pressure jets of water (Figure 1).
Figure 1 a) Spunlace process scheme, b) Spunlace process in shop floor. The process employs jets of water to entangle fibers and thereby provide web and eventually fabric integrity. Softness, drape, conformability, conformability, and relatively high strength are the major characteristics that make spunlaced products unique among all nonwovens. In addition, all success in the competitive, but highly profit-yielding, profit yielding, market of technical textiles and nonwovens is based on experience exper and know-how how in traditional textile products manufacturing. Some cotton spinners already are working successfulsuccessfu ly with spunlacing lines. As traditional textile manufacturers, they know how to hanha dle fibers, so there basically is no big difference between between spinning and nonwovens, at least at the start of the production process (Jurg, (Jurg 2008). As a brief description, we can say that the production chain in INOTIS is equipped with multiple synchronized machines with the latest technology capable of producing produci several product ranges with different types of fibers. The spun lace process applied to INOTIS is very complex, each machine has its own settings, changing one of the parameters automatically leads to changes in the appearance and quality of the fif nishedd product. Due to its hydraulic entanglement of cellulose fibers and polyester fibers, INOTIS created a product category, which is defined according to the needs of the market. The fibers entanglement is made by the process of sending hydroentanhydroenta gling jets of water through perforated strips fed by High pressure pumps reaching a maximum pressure of 250bar.
In this paper, we describe the development of a Global Multi-agent Multi agent Decision SupSu port System (GMDSS) for the spunlace nonwovens production industry. Herein, a CMN is formed when a SME establishes highly transparent collaborative relationrelatio ships with its business partners, who include customers, suppliers, and contractors as illustrated in figure 2.
Figure 2. Collaborative Manufacturing Network topology (scheme heme adapted from (Lin, 2012).
3. Related Works A traditional Small or Medium Enterprise (SME) management system must allow capabilities on supporting the management and production activities as restricted to in-house house operations and department-oriented oriented operations. Thus, any enterprise interinte ested by the collaborative manufacturing integration must have a piloting system that controls and manages the whole production management. The necessity of adopting some ITC solutions for decision making is recognized by all managers regardless of the industry they belong to, the size of the company run by them or the origin country. The adoption and the selection of a decision support solution by companies requires consideration of some aspects, mainly, mainly, internal ones. From the company perspective is necessary to consider some aspects regarding the ITC infrastructure, human and financial resources; obviously, in the case of resources, what need to be analyzed is, on one hand, the potential budgetary resources allocated for the adoption, implemenimpleme tation of DSS and on the other hand, the existence of human resources starting with the managerial level to the operational level that can provide support for the efficient exploitation of the adopted solution. Thus, to cope with the highly dynamic global market, a typical SME should investigate efforts on expanding the manufacturing capabilities and capacities. Since production systems can easily be envisaged as complex systems comprised of individual agents with their own behaviors and complex interactions, Agent Agent-Based Modeling (ABM) is a suitable methodology for studying such systems (Taghezout and Zaraté, 2007).
Through agent-based models, it is possible implementing an environment with its features, forecasting and exploring its future scenarios, experimenting possible alternative decisions, setting different values for the decision variables and analyzing the effects of these changes (Park and Tran, 2012). There are a number of decision support and intelligent systems available to the market. In this study, we focus on the most common systems applicable to the operations and management of manufacturing systems. Bing-hai et al. addressed in (Bing-hai et al. 2008) the problem of dynamic scheduling in a Flexible Manufacturing System (FMS) by developing an agent-based Decision Support System. The proposed DSS included several components such as User Interface Agent, Criteria Selection Agent, Performance Evaluation agent scheduling decision selection agent and FMS database. In their work, an agent was responsible of the criteria and rules selection. This agent was considered as the heart of the agentbased DSS. Deshpande and Cagan (2004) and Gao, Shang, and Kokossis (2009) illustrate decision support tools for dealing with several problems arising respectively in manufacturing and chemical industry environments (Deshpande and Cagan 2004; Gao et al. 2009). A similar work has been done in (Taghezout and Zarate, 2009); authors have proposed to integrate agents in a Decision Support System to demonstrate that the proposed approach could implement a new type of multi-agent-based coordination engine to solve some production management problems such as resource allocation, resource failures, etc.
4. Collaborative decision-support model: The proposed approach The use of social networking for decision support is still a rather new subject, although some research has been done on determining the adequacy of social software towards decision support. In fact, Web technologies have been widely employed in developing manufacturing systems to associate various product development activities, such as marketing, design, process planning, production scheduling, customer service, etc., which are distributed at different locations into an integrated environment. More precisely, the concept of Web 2.0 was originated from the 2004 International Symposium hosted by O’Reilly and MediaLive (O’Reilly, 2007). However, there was no clear definition of Web2.0 and only an initial discussion of its principles was available. Furthermore, Web 2.0 did not have a strict boundary; but it consists of the following attributes such as ‘‘interaction, participation and sharing’’ as core values. In contrast to Web 1.0, Web 2.0 is characterized by services rather than software, and its platforms include any devices that can be connected to the Web rather than just personal computers. Web 2.0 can be thought of as the technical infrastructure that enables the social phenomenon of collective media and facilitates consumer-generated content. The latter are distinguished by the difference in focus: social media can be thought of as focusing on content, and consumer generation on the creators of that content. Simply, Web 2.0 enables the creation and distribution of the content that is social media (Mussser, 2006).
The scope of Web 2.0 is very broad since it includes social networks, search, multimedia, portal, online entertainment, and electronic business. We were inspired by two main research works. Firstly, in (Musser, 2006) a brief overview of Web 2.0, social media, and creative consumers was provided, and the challenges and opportunities that these phenomena present to managers generally and to international marketers and their strategies in particular were explored. To help managers understand this new dispensation, authors in (Musser, 2006) proposed five axioms as follows: (1) social media are always a function of the technology, culture, and government of a particular country or context; (2) local events rarely remain local; (3) global events are likely to be (re)interpreted locally; (4) creative consumers’ actions and creations are also dependent on technology, culture, and government; and (5) technology is historically dependent. At the heart of these axioms is the managerial recommendation to continually stay up to date on technology, customers, and social media. Secondly, Meredith et al. presented in (Meredith et al., 2010) a functional model of social media and its application to Business Intelligence (BI). Their study explored the application and the role of Web 2.0 concepts within BI applications. It classified the functions that are provided in social media platforms to foster user collaboration and contribution. More precisely, they developed an argument that the social nature of organizations implies that support of collaboration and interaction between end-users of a BI system would be useful addition to the standard BI system functionality. Our main basic research consideration is that the developed framework in (O’Reilly, 2007) provides developers of BI systems with a structured and comprehensive basis for design decisions they make about the use of Web-based social media within BI applications. A number of articles discuss architectural issues, frameworks, usability, and other technology topics that are generally applicable to Multi-agent DSS. See for example works presented in (Leitao, 2008); (Lopez-ortega, 2009); (Mahesh et an., 2006); (Marik, 2007). From the technology perspective, a novel and open architecture for a global multiagent DSS has been designed. By integrating agent technology with Web 2.0 technologies to make use of the advantages from both, this approach leads to more intelligence, flexibility and collaboration in securities trading simulation Different Webservices and Web 2.0 services can cooperate together to provide comprehensive trading facilities (Taghezout et al., 2012). Our earlier work provided the base functionality needed to integrate agents into a DSS for the purpose of automating more tasks for the decision maker, enabling more indirect management, and requiring less direct manipulation of the DSS. Thus, we proposed an integrated approach for the development of a collaborative platform that gives the possibility to production agents to share exchange and discuss failures diagnosis and resolution of machines breakdowns with each other (Taghezout, 2007). An overview of the collaborative platform is given in Figure 3.
4.1. GMDSS architectural design As shown in Figure 4, the system architecture design consists of 4 modules: (i) the knowledge domain ( database, knowledge base and domain ontology ), (ii) the WEB 2.0 collaborative environment interface, (iii) The collection of decision support that enables the functionalities ities of the GMDSS (named agents), and (iv) the WEB interinte faces that enable information exchange among the enterprises recognized as participartic pants. 4.1.1 Knowledge domain: domain: For our study, this module focuses on the designing of a relational database, a knowledge knowledge base that is capable of storing structured decisiondecision making data, knowledge and some agent’s behaviors. One of the critical challenges in our study is to assist a CMN in the conversion of tacit knowledge to explicit knowkno ledge so that it can be formally stored in the knowledge base. Domain ontology has been developed for this manner. 4.1.2 Collaborative WEB 2.0 environment interface: interface: This interface allows decidec sion-makers makers to interact with the GMDSS with minimum requirements and highest flexibility. This WEB EB 2.0 based environment can best support the dynamic features and maintain the correct displaying format of the user-interface. user interface. This module has an important role to guide decision-makers decision makers (participants) through a decision-making decision workflow. 4.1.3 Agents: Here, e, agents mentioned above reside at distinct physical computing servers so that each acts independently and the system workload balanced. FurtherFurthe more, every agent can communicate with other agents to smooth out the decision process. When an agent conducts a decision-support support activity, it consults the knowkno ledge base and the database located in various sectors of the information center. The proposed Web-based based DSS mainly includes four components: Ontology Agent (OA), (O Resource agent (RA), Production Agent (Prod_A), (P A), and Coordinator Agent (CA). In
particular, the agents are used to collect information and generate alternatives that would allow the user to focus on significant solutions. 4.1.4 WEB interfaces: interfaces: This module is responsible for managing all the modules and interfaces of other agents control access to the system. It also allows the introduction of agents CA, Prod A and RA in particular, and the sniffer is an agent that allows the display of results and messages exchanged between agents. 4.2. Building Domain main Ontology for Production Management in spunlace nonno wovens industry According to the most popular definition, ontology is an explicit specification of a conceptualization (Gruber, (Gruber 1993) in which ‘conceptualization’ is an abstracted view of domain world that we wish to represent. Therefore, ontology has become popular as a paradigm for knowledge representation and engineering by domain experts (Qiu, (Qiu 2006) by providing a methodology for the shared understanding of a domain of interinte est. Related research on using ontology has attracted a great deal of interest and is extensive. The sharing and understanding of the knowledge in a given domain is a central role of the ontology.
Here, the Domain Ontology is developed by acquiring knowledge from documents, domain experts and the collaboration of INOTIS managers. Applying such domain ontology to decision support improves effectiveness of decision-making decision making process. Currently, we have implemented mplemented the spunlace nonwovens concepts for production management in Protégé 4.2. Figure 5 depicts an overview of the main classes of the domain ontology.
5.. Issues of implementation As illustrated in Figure 6, Production Agent (Prod_A) (P that detected cted the resource failure will connect to the Collaborative WEB 2.0 environment; it will retrieve the resource list and will activate other production agents asking them for a solution. After a complete search, one of the groups will send the response without without executing the GMDSS program. Each agent production will make its identification if already registered before connect our application, authentication is done by giving email and password that hat already exists in the database. We first check the filling of of fields email and password and the existence in the database to access the interface of the social network. Otherwise, it must complete the registration form given in other steps. FurFu thermore, it will express its preferences preference as it is mentioned in figure 6.
In Figure 7,, the results demonstrate that the TRUTZCHLER requires huge cost of repair because it needs the German Company intervention (functioning program or pieces) whereas other resources can be repaired locally by the maintenance service operators and the production manager. In other hand, the pumps showed a high failure rate compared to other resources which may cause degradation of the finished product and the material itself if the repair is not done immediately; this is possible for many ma reasons: • First, if a pump does not supply the injector with the necessary pressure, the fibers wetting cannot be performed properly and this affects the physical characterischaracteri tics and appearance of the finished product. • Second, the importance of quality quality pistons at the pumps is paramount, their damage created vibrations and leakage of water; It’s why INOTIS contact a subconsubco tractor for the metallization of these pumps every 06 months.
Figure 7. Some Statistical results in terms of repairing cost per per resources and periods
6. Conclusion Manufacturing enterprises to remain competitive, must respond closely to customer demand by improving their flexibility and agility while maintaining their productivity and quality. In particular, The Small or Medium Manufacturing Enterprises (SMME) ( must be proactive with decision-making, decision making, so that the organization is capable of conco fronting forecast distortions, unexpected events, and demanding customers in an efe fective manner. In this paper, we describe the development of an agent-based based DeciDec sion Support System (DSS) in the spunlace nonwovens production industry. industry The global goal that agentss in the agent-based agent DSS should strive to achieve is to support the decision making process while analyzing a and employing information from the INOTIS environment. A conceptual design of the GMDSS is provided in this paper as a key contribution. The developed GMDSS is justified by a simulated case-study case on Algerian manufacturer (the most important enterprise enterprise in North Africa). In our study, we have found that the GMDSS is capable of integrating the existing distributed ini formation systems and its CMN. This enables the managers of local manufacturer to efficiently conduct collaborative decision-making decision activities vities in relation to other particparti ipants of the CMN.
The key points of our conclusions could be summarized in the following: • Social networking enabled Multi-agent DSS could have the capability to support different groupings of users (e.g. agents), either reflecting preexisting social networks in the organization, or the formation of new groupings based on interests in various topics covered by the collaboration tool. For example, users (operators or managers) from a variety of services in shoop floor may have interest in the impact of a marketing campaign for a new product. In this sense, the kinds of coordination of interest, rather than just a coordination of activity in an organization. • Web-based technologies have enabled companies to reach out their customers and influence decision-making in new and different ways. This study presents a comprehensive framework for selecting a suitable solution based on decision analysis process. The proposed procedure allows production agents to share and exchange points of view or diagnosis built upon their experiences and knowledge.
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