International journal of computer integrated manufacturing , tập 24, số 4, 2011

International Journal of Computer Integrated Manufacturing Vol 24, No 4, April 2011, 285–301 An integrated decision support system for global manufacturing co-ordination in the automotive industry S Liua*, R.I.M Youngb and L Dingc a School of Management, University of Plymouth, Plymouth, Devon, PL4 8AA, UK; bWolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, UK; cDepartment of Mechanical Engineering, University of Bath, Bath, BA2 7AY, UK (Received April 2010; final version received January 2010) Global manufacturing increasingly faces decision challenges of how to better manage the dependencies between different activities that take place either locally or across different locations Co-ordination decision making not only requires the right information to be provided in the right place at the right time, but also requires the right level of support from models for decision analysis and decision evaluation Furthermore, the alignment of co-ordination decisions with a global firm’s global environment and its operations performance has been identified as crucial to the firm’s success, but remains a challenge to decision makers This paper proposes an integrated decision support system (IDSS) that can facilitate manufacturing managers to make more efficient and effective global co-ordination decisions A combination of qualitative and quantitative analysis and assessment functions has been provided through the system’s four key components (a global context modeller, a multi-criteria scoring modeller, a configurator and a co-ordinator) The evaluation of the decision system has been undertaken through a case study within the automotive industry, which demonstrates the applicability of the system to provide decision support for realistic global manufacturing co-ordination problems Keywords: global manufacturing context; dependency and co-ordination; integrated decision support; multi-criteria decision making Introduction Over the last three decades, along with the phenomenon of globalisation, manufacturing management has been experiencing a paradigm shift from the local through the international to the global level (Meixell and Gargeya 2005) This paradigm shift has triggered many industries to innovate the ways they deliver their products through globally networked production systems A direct consequence to the automotive industry is a fundamental change to their organisational structure Specifically, it caused the recent emergence of a new structure and configuration of manufacturing networks (Trappey et al 2007) Traditionally, manufacturing networks were organised in tiers (Veloso and Kumar 2002, Mondragon and Lynos 2008) For example, original equipment manufacturers (OEMs) would design and assemble the cars First tiers in the manufacturing network would manufacture and supply components directly to the automaker (e.g the fuel pump) Second tiers would produce some of the simpler individual parts that would be included in a component manufactured by a first tier (e.g the housing of the fuel pump), and third and fourth tiers would mostly supply raw materials This relatively *Corresponding author Email: shaofeng.liu@plymouth.ac.uk ISSN 0951-192X print/ISSN 1362-3052 online Ó 2011 Taylor & Francis DOI: 10.1080/0951192X.2011.554869 http://www.informaworld.com simple configuration required less co-ordination effort across the manufacturing network, because the majority of the interactions and communications only happened between the two consecutive tiers However, this simple configuration no longer fits the actual structure of the industry in today’s globalisation environment (Doran et al 2007) The new direct suppliers are becoming large global firms, which are either specialised in complex systems or integrators of a series of subsystems Studies within the International Motor Vehicle Program and other outside analysts suggest that the new configuration involves a division (based on roles and responsibilities) along the following four lines (Veloso and Kumar 2002): Systems integrator: a company capable of designing and integrating systems, subassemblies and components into modules that are shipped or placed directly by the suppliers in the automakers’ assembly plants Global standardiser – systems manufacturer: a company that sets the standard on a global basis for a system and components These firms are 286 S Liu et al capable of design, development and manufacturing of complex systems Systems manufacturers may supply motor vehicle manufacturers directly or indirectly through Systems integrators Component specialist: a company that designs and manufactures a specific component or subsystem for a given car or platform These firms will increasingly work as suppliers to systems integrators and global standardisers Raw material supplier: a company that supplies raw materials to the OEMs or their suppliers Some of the raw material suppliers are also moving into component specialists to add value to their products With the new configuration of global manufacturing networks, global firms are forced to take a substantial responsibility in the design and engineering of the systems, and more importantly in co-ordinating the networks for their manufacturing, assembly and services (Nunes et al 2005) Figure illustrates the increasing complexity of interacting relationships that Figure can be identified in the new flattened structure of global manufacturing networks Therefore, in the new flat structure, the co-ordination requirements have been raised to a higher level (EIMaraghy and Mahmoudi 2009) It has been acknowledged that the ultimate success of operations in global manufacturing enterprises depends on the companies’ capability of coordination, synchronisation and integration of business activities (Weston and Cui 2008) Global manufacturing co-ordination has been proved to be challenging because of the overarching issues confronting global manufacturing, namely its dynamics, complexity, uncertainty and high risk (Pontrandolfo and Okogbaa 1999, Rudberg and West 2008) The dynamics of global manufacturing exists in many respects These include the unbundling of different stages of the production process across the globe, the growing capacity for firms to outsource internationally, greater product differentiation and the growth of the phenomenon of ‘global value chain’, whereby different businesses add value by different Interactions requiring co-ordination for the new flattened structure International Journal of Computer Integrated Manufacturing processes or activities at each stage of production (Nagurney and Matsypura 2005, Needle 2005, Slack et al 2010) Accordingly, the traditional production model where firms were responsible for all stages of the production process of a particular product has changed Many manufacturers now choose to specialise on particular steps in the production process, such as design, research and development, or sales and marketing, either within individual geographic locations or through participation in the global value chain, or through utilising outsourcing possibilities (Dreyer et al 2009) The complexity of global manufacturing can be understood from two dimensions First, there is a complex network of inter-relationships between different activities Second, these activities take place in a set of contexts including the strategic (e.g management and leadership style, business ethics), organisational (e.g structure, ownership and size) and environmental (e.g economy, the state, culture difference) contexts (Needle 2005) There are complex interactions between the activities and the context where they take place (Kazmer and Roser 2008) Furthermore, it is also believed that the relationship between the global manufacturing activities and the contexts is not static but dynamic (Liu and Young 2004, Meixell and Gargeya 2005) Uncertainty of global manufacturing has been well acknowledged from the supply chain perspective, i.e uncertainty from both the demand and the supply side (Kazmer and Roser 2008) For example, Verdouw et al (2010) explored how to master demand and supply uncertainty with combined product and process configuration Exchange-rate uncertainty and its impact on price setting are discussed in Kazaz et al (2005) In Acar et al (2010) the relative impact of three sources of uncertainties (supply, demand and leadtime) on cost and service performance is studied using mathematical models Furthermore, factors such as regional, national and international economic (e.g inflation and recession) and political instability, as well as the regulatory environment can raise extra challenges to the global manufacturing co-ordination Depending on the modes of entry, there are various degrees of risk in relation to global manufacturing Among the six common modes of entry, exporting, licensing and franchising are considered as relatively low risk, while wholly owned subsidiary (also known as FDI), international joint venture and off-shore outsourcing are considered as high risk (Lowe et al 2009) There are many causes for the high risks, which are usually summarised as the ‘4Cs’ – capability, compatibility, commitment and control Capability risk is the main cause for delays of end product and service delivery due to the inability of suppliers to 287 produce on time and to the required quality (Canbolat et al 2007) Compatibility risks arise in working together and often not emerge until the implementation phase Such risks can arise as a result of differences in culture, management style, personality and administrative and accounting procedures (Rudberg and West 2008) Many alliances fail through a lack of staying power because partners are not willing to continue the commitment in resources and effort Control risk is normally high for weaker partner(s) in a joint venture or strategic alliance When one partner is dominant, then the weaker partner(s) may risk having its (or their) core competencies reduced or eliminated (Nagurney and Matsypura 2005) In today’s highly competitive, fast-paced global business environment, there is no room for error in making global co-ordination decisions Companies’ success (or survival) depends on the manufacturing managers’ capability in making consistent, rational and optimal decisions In order to succeed in such an unforgiving environment, manufacturing managers need efficient and effective support that can provide an appropriate level of decision analysis and assessment through using a wide range of models, along with data and information sources available to them This paper is concerned with integrated decision support for global manufacturing co-ordination across multiple functions and multiple (international) locations A global context modeller is defined to address the dynamics, complexity, uncertainty and risks of the business environment Global manufacturing performance measurements are captured through a multicriteria scoring modeller The purpose is to integrate the global context modeller and the multi-criteria scoring modeller within an integrated decision support system, which has the ability to align the manufacturing management decisions with the firm’s global business environment and its performance objectives A case study has been undertaken to evaluate the decision system in the automotive industry The main contribution of this paper (to the body of knowledge in general and to global manufacturing co-ordination systems specifically) is that it advances the state of the art in model-driven decision support systems, by addressing the most commonly stated shortcomings of the traditional methodologies including lack of model integration and lack of model usability/ accessibility The paper is organised as follows: Section reviews work in relation to decision making and support in global manufacturing Then an integrated decision support system is proposed to support the decision making in Section Section discusses the issues related to integration and the system implementation The evaluation of the decision support system is 288 S Liu et al discussed in Section before conclusions are drawn in Section Literature review Decisions in global manufacturing can be classified into two types of structures: centralised and decentralised (Canbolat et al 2007) Within a decentralised decision structure, local decision makers can make decisions based on their own goals and preferences, without constraints from their suppliers, consumers or partners In fact, in this case the co-ordination effect along the manufacturing network at the global level is minimal One severe consequence of decentralised decision making is that it can lead to a loss of control for the upper-level managers in the OEMs, systems integrators and global standardisers As a result, the OEMs will not be able to deliver the products and services to customers to meet the specified performance criteria Therefore, many argue that co-ordination decisions need to be centralised so that decisions across different functions and locations in the whole manufacturing network are well co-ordinated (Acar et al 2010) Research has shown that centrally co-ordinated decisions are more advantageous Within the centralised decision structure, decision makers at different organisational levels aim to resolve conflicting interests and work towards one common goal, i.e to meet the global manufacturing network overall performance objective Upper-level managers at OEMs, systems integrators and global standardisers can interfere with lower level decisions when needed (usually only in ‘exceptional’ circumstances) (Kouvelis and Gutierrez 1997) There are, however, implementing and control difficulties associated with central co-ordination which needs more investigation For example, the decision dependencies within the whole decision network can become really complex Therefore, decision management such as the decision propagation path and decision change has to be well addressed This paper attempts to address the issues concerning the centralised decision structure and explores how this type of decision can be supported through advanced ICT technologies and systems Decision support system (DSS) is a well-established research and development area, originating from computer science and organisation management represented by the work undertaken by Simon et al at the Carnegie Institute of Technology and by Gerrity et al at MIT, during the late 1950s and early 1960s (Keen and Morton 1978) A DSS is defined as an interactive computer-based system that is designed to support solutions to decision problems (Bhatt and Zaveri 2002, Shim et al 2002) DSS research and its applications evolved significantly over time DSS’s power in handling large amount of information with speed and accuracy together with its capability of computing for complex analysis has made it an ideal aid for decision makers In global manufacturing, diverse DSS have been developed to support various types of decisions, including systems that could support facility location (Canbolat et al 2007), supply network planning and control (Leu et al 2008, Dreyer et al 2009), multi-site capacity planning and control, demand management, outsourcing decisions (Loebbecke and Huyskens 2009), simulation and optimisation (Tyagi et al 2004) A closer look into the literature on DSS for global manufacturing reveals that most DSS can be classified as data based Data-based DSS argue for the utilisation of ICT as enablers for immediate access to information/knowledge and thus reduce response time and increase flexibility (Guerra-Zubiaga and Young 2006, Young et al 2007, Dreyer et al 2009) For example, a DSS utilising distributed artificial intelligence techniques (mobile agents in particular) is developed for the transfer of product design and manufacturing information throughout the global manufacturing network (Nassehi et al 2006, Newman et al 2008) With the support from the intelligent DSS, distributed decision makers can make the right decisions on the manufacturing resources and process plans to achieve interoperability between disparate manufacturing venues Provision of the right information and knowledge is important to decision makers However, modelbased DSS has gone one step further in supporting decision making Along with the access to data and information resources at various internal and external repositories, model-based DSS can also provide the capability of decision analysis and evaluation based on a wide range of qualitative and quantitative models (Narasihan and Mahapatra 2004, Phillips-Wren et al 2009) Therefore, model-based DSS are advantageous over data-based DSS in terms of informing decision makers about the consequences of each decision alternative There have been vast amount of interests and development recently in model-based DSS for global manufacturing Leu et al (2008) presented a DSS for global supply network configuration based on linear programming optimisation models In Canbolat et al (2007), an integrated modelling approach brought together a decision tree and multi-attribute utility theory for global manufacturing facility location decisions A DSS using mathematical programming models for global network optimisation is discussed by Tyagi et al (2004) Despite its wide application, existing model-based DSS have been heavily criticised Some most commonly pronounced shortcomings include lack of model International Journal of Computer Integrated Manufacturing reusability (for single purpose, throwaway efforts), lack of integration of models to the real world (isolation from the environment that they represent) and lack of model utility/accessibility (not available to non-modelling specialists and therefore with limited usage and value) (Delen and Pratt 2006) To address the issues related to model integration and model utility/accessibility, first, this paper has developed the concepts of a global context modeller and a multicriteria scoring modeller to adequately reflect the complexity, uncertainty and risks of a real-world global manufacturing environment Second, the paper implements the models within an integrated DSS (IDSS) based on a standard integration platform, where non-modelling specialists can conveniently access the models through the platform’s professional, user-friendly interface The IDSS is designed and developed to support the decision making in global manufacturing co-ordination (i.e management of the dependencies) across multiple business functions (manufacturing, transportation and distribution) and multiple geographical locations (different countries, continents and free trade zones) Key components of the integrated decision support system Figure shows the architecture of the integrated decision support system (IDSS) The architecture comprises three basic components inherited from Figure Architecture of the IDSS 289 traditional DSS and four new key components defined in this paper especially for global manufacturing coordination DBMS (database management subsystem), MBMS (model base management subsystem) and UI (user interaction management subsystem) are considered as the three basic components for a traditional DSS (Hopple 1988) The IDSS takes the concept of these three components and instantiates them in the scenario of global manufacturing The main functions of the three basic components remain the same as in traditional DSS, i.e to manage data, models and interaction with users, which have been well discussed in the literature (Carlsson and Turban 2002) This section focuses on the four key components (proposed in this paper), i.e a global context modeller (GCM), a multi-criteria scoring modeller, a configurator and a co-ordinator 3.1 Global context modeller The purpose of defining the global context modeller (GCM) is to provide the decision makers with an appreciation for the complexity, uncertainty and risks of the global business environment at which coordination decisions are situated The global manufacturing context can be identified from different perspectives, for example, from strategic, organisational and environmental perspectives To manage the characteristics of a global manufacturing context, the GCM captures the information of the identified factors 290 S Liu et al and classifies them in three main categories Figure is a class diagram of the global manufacturing context represented with SysML (Weilkiens 2008) For the environmental context class, five sub-classes have been further defined: economy, social and cultural differences, technology, state and politics, and labour market For the organisational context, four subclasses are defined: structure, ownership, size and goals Two sub-classes for the strategic context are management and leadership style, and business ethics Attributes have been specified for all classes to capture further details of the factors For example, important attributes of the state and politics class include membership of a free trade agreement (such as NATO, EU, or NAFTA), investment incentives (regarding taxes, energy, etc.), demand (sales market) and infrastructure The stability of the state and politics can be considered as either stable, disturbance likely (e.g occasional violence) or not stable (e.g regular war zone) The whole point of capturing global manufacturing context information through the classes and attributes is to allow the decision makers to use the right information to gauge the likely level of uncertainty and risks of the business, to appreciate the complexity and dynamics of environment and make informed decisions To assess specific characteristics of global manufacturing, managers need to find all the necessary information by searching through a series of classes modelled in the GCM Table gives examples of Figure Class structure of the global manufacturing context information captured in relevant classes and attributes that can be used to assess the characteristics of uncertainty and risk (the definitions of the characteristics have been discussed in Section 1) As Table shows, to assess uncertainty, information from the following classes can be used: state and politics (stability and infrastructure attributes), economy (exchange rate attribute), supply networks (both supply side and demand side) and technology (affecting leadtime) To estimate different aspects of the risks, information from the following classes and attributes can be used For capability assessment, users can use technology, size, infrastructure and labour market Similarly, for compatibility assessment, classes of technology, social and cultural differences, management and leadership style, and business ethics can be used Ownership can be used to assess control factor, and the goals class can help assess commitment aspect The impact of uncertainty to decision making is that decisions will be made on inaccurate information if uncertainty is not anticipated, for example if the fluctuation of demand is not considered, then manufacturers may either have insufficient capacity to deal with extra demand or have excess capacity and waste resources when the demand is actually lower Owing to lack of information about risks in the ‘4Cs’ (not able to fulfil the capability, compatibility, commitment and control as defined in Section 1), decision makers could make wrong decisions For example, when decision makers are not informed of manufacturing networked International Journal of Computer Integrated Manufacturing Table Relevant information in GCM that can be used to assess uncertainty and risks Representative indicators of the characteristics Uncertainty Risk Lead time uncertainty Supply uncertainty Demand uncertainty Exchange rate uncertainty Mode of entry Capability Compatibility Commitment Control Classes and attributes that captured relevant information in GCM State and politics, technology, infrastructure Structure/supply networks/supply side Structure/supply networks/demand side Economy Structure/mode of entry Technology, size, infrastructure, labour market Social and cultural differences, management and leadership style, business ethics, technology Goals Ownership resources and their capabilities, it is impossible for them to formulate potential alternatives and make rational choices Based on the information captured and organised in the global manufacturing context, GCM can then provide a qualitative assessment of the factors for each facility involved in the global manufacturing network, quantify the attributes through weighting according to the manufacturing manager’s domain knowledge and the decision maker’s preferences, calculate the aggregated value of the factors, and estimate the potential uncertainty and risk level for the partnerships 3.2 Multi-criteria scoring modeller Decision criteria for global manufacturing depend on the metrics adopted for the measurement of manufacturing network performance The definition of manufacturing network performance has been broad because a company’s mission, strategy and objectives can vary considerably based on the value of the products offered to the customers (Meixell and Gargeya 2005) Although real world manufacturing networks emphasise a variety of performance measures in practice, many argue that commonality does exist and fundamental measures can be identified For example, the five performance objectives proposed in Slack et al (2010) are widely accepted They are cost, quality, speed, dependability and flexibility Earlier, the Supply Chain Council (2005) identified five 291 performance metrics as cost, assets, reliability, flexibility and responsiveness Under globalisation, some researchers also recognise access to new technologies and broadened supply base as benefits (Needle 2005) The sharp economic downturn in recent years has led to an increased emphasis on cost reduction This paper takes the view that no single performance metric can sufficiently represent the complexity of global manufacturing and, therefore, treats the global manufacturing co-ordination as a multi-criteria decision problem Subsequently a multi-criteria scoring modeller (MCSM) is proposed to address the decision problem For decision makers, a multi-criteria decision problem that requires a trade-off among the several criteria is difficult to solve (Nagurney and Matsypura 2005) In this section, an MCSM is defined to assist in analysing the global manufacturing co-ordination problem and help identify the preferred decision alternative The MCSM has the following five functions: Function 1: Develop a list of the criteria to be considered For the global manufacturing coordination decision problem, five criteria have been considered based on the recommendations from Slack et al (2010) and the Supply Chain Council (2005): cost, quality, reliability, flexibility and responsiveness (speed) Function 2: Assign a weight to each criterion that describes the criterion’s relative importance In the IDSS, wi represents the weight for criterion i Function 3: Assign a rating for each criterion that shows how well each decision alternative satisfies the criterion In the IDSS, rij is used to represent the rating for criterion i and decision alternative j Function 4: Calculate the score for each decision alternative In the IDSS, Sj represents the score for alternative j The equation used to compute Sj for each alternative is Sj ¼ w1r1j þ w2r2j þ w3r3j þ w4r4j þ w5r5j Function 5: Order the decision alternatives from the highest score to the lowest score to provide the MCSM’s ranking of the decision alternatives To realise Function 2, i.e assign a weight to each criterion to indicate the criterion’s relative importance perceived by decision makers in a specific decision making process, a five-point scale is specified in which means very important and unimportant By repeating this question for each of the five criteria, the MCSM can capture the weightings assigned by 292 S Liu et al decision makers and record them in the database for later calculation of Wi To realise Function 3, i.e rate each decision alternative in terms of how well it satisfies each criterion, a nine-point scale system specified by Saaty (2005) is employed The scoring process must be completed for each combination of decision alternatives and decision criterion Assuming the number of decision alternatives is N, and because five decision criteria must be considered, then a total of N ratings must be provided and captured in the MCSM When N is big such as over a hundred, without support from computer systems it is impossible for human decision makers to comprehend the appropriateness of all the decision alternatives against decision criteria, in which case the benefit of having the MCSM is considerable The results of Function (weighting the decision criteria) and (rating decision alternative against each decision criterion) will enable Function to calculate the overall satisfaction of decision alternatives based on the aggregated weight of all decision criteria It should be noted that quantitative measures have been used for the cost criterion, in which aggregated cost has been considered (Newnes et al 2008) The mathematical model for the aggregated cost calculation (so far information about four types of cost elements is collected and captured in the IDSS) is formulated as: X X X X Aggregated cost ¼ Cp þ Ci þ Ce þ Ct where Cp is the production cost incurred for a particular component, Ci the inventory cost incurred for a particular storage location or warehouse, Ce the currency exchange cost incurred for a particular transaction, and Ct the transportation cost incurred for a particular movement of products To sum up, the MCSM utilises a combination of quantitative (for cost criterion) and qualitative (for other criteria) assessment to provide analysis of the decision alternatives 3.3 Configurator The configurator provides the IDSS with the capability of organising the facilities into a manufacturing network The key for the configurator to generate a manufacturing network is to understand the organisational structures such as the flat structure shown in the Figure Each facility’s function and characterisation (as OEM, system integrator, global standardiser, system manufacturer, component specialist or raw material supplier) should be identified and the information needs to be stored in the system database in advance, and ready for the configurator to query 3.4 Co-ordinator The co-ordinator is designed to manage decision hierarchies and dependencies among the OEM, systems integrators, global standardisers, systems manufacturers, component specialists and raw material suppliers in a manufacturing network if a flat structure is configured by the configurator Alternatives of coordination strategy and mechanism are also provided Integration and system implementation 4.1 Relationships between the four key components While the four components have their distinguishing roles and functions, the specification of the relationships between the components holds the key for integration Integration was and remains to be one of the most often used words, yet poorly defined notions (Ding et al 2009, Liu et al 2010) However, it is widely accepted that integration is a property of component (in the form of models, services, tools, methods, systems or subsystems) interrelations Therefore, it is believed that the key notion is the relationships and the nature of these relationships In the context of IDSS, integration means sharing of consistent and current information, sharing of model analysis functions (through remote service calls), and sharing a common decision making process through co-ordinated activities (triggered at the right time for the right decision makers in the right order) This section discusses the modelling of the relationships with SysML (systems modelling language) SysML is a visual modelling language and an evolution of UML (unified modelling language) SysML aims to support the audience in systems engineering, particularly to allow them to address the integration of systems (Neaga and Harding 2005, Weilkiens 2008) The main reasons to choose SysML for modelling IDSS is that, with SysML, the complex relationships between the four key components, i.e the GCM, the MCSM, the Configurator and the Co-ordinator can be better represented, communicated and understood Furthermore, SysML tools provide the mechanism for the models to be transformed into programming languages such as Java, which could save the system developers considerable time and effort in code generation Key relationships between the four key components in the IDSS have been defined and represented using SysML component models, as shown in Figure In SysML, component diagrams define how components/subsystems are collected into a high level system and interfaced (through ports) with the connections between them As shown in Figure 4, between the four key components, communication of the messages are International Journal of Computer Integrated Manufacturing Figure 293 Relationships between the four key components in IDSS directed through 12 dedicated pairs of ports The nature of the relationships is attached as labels on each connection For example, the key information provided to other modules by the global context modeller is global manufacturing context (discussed in Section 3.1) Information provided by the configurator includes all configuration types for the manufacturing network The multi-criteria scoring modeller provides decision evaluation results against decision performance metrics Finally, the co-ordinator provides the information about decision dependency and propagation path The ports will be mapped to the computer network within the IDSS By understanding the relationships between the four key components and how the information and functions can be efficiently and effectively communicated through dedicated interacting points (i.e the port-pairs), it ensures that the right information and functions are available at the right time in the right place for the right decision makers 4.2 The integration platform The IDSS is implemented by adopting a professional integration platform, namely the SAP ERP NetWeaver, which is provided by SAP (one of the world’s leading companies in professional software) SAP ERP NetWeaver supports enterprise management using web services technology Since the 1970s there have been major technology waves in software solutions: from the mainframe computing to client and server architecture, and now to service-oriented networks (Ng and Ip 2000) The services provided by the SAP ERP NetWeaver platform utilise the portal’s capabilities, making use of the SAP Business Information Warehouse and the Strategic Enterprise Management functions such as balanced scorecard and management cockpit (Malik 2005) SAP ERP Netweaver is an open platform The four key components discussed in Section are first developed as independent modules using Java programming (facilitated by an automatic 360 Y Wu probability of occurrences of the future economic situation, other conditions in the three tests are the same (See Table 2) The test data are shown in Table Test I represents the situation where it is most likely that demand will perform well, Test II is for the situation where it is most likely that the economic condition will be fair (See Section 6.3) and Test III represents the situation where the economy will be poor Table Test I Test II Test III p1 ¼ Pr{s1} p1 ¼ Pr{s1} p1 ¼ Pr{s1} 0.8 0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.8 Test I II III Table We perform four tests for a weekly plan when l ¼ 0, 0.1, 0.5 and 0.9 for each test Table gives computational results regarding the related cost In a weekly logistics plan, when the value of l increases from to 0.9, variability decreases by 60.55, 15.78 and 6.44% in Tests I, II and III, respectively The total cost increases by 12.23, 7.81 and 19.53% in Tests I, II and III, respectively Three tests Possibility Table 6.4.1 Tests for the robust model with solution robustness 6.4.2 Tests for the robust model with model robustness Table shows the related costs when o ¼ 0, 5, 10 and 15 in the three tests When o ¼ 0, there is no delivery in the whole planning horizon because there is no penalty for not satisfying the demand Costs incurred in the robust model with solution robustness under different l in three tests l Expected variability First-stage cost Second-stage cost Expected cost Expected variability cost Total cost 0.1 0.5 0.9 0.1 0.5 0.9 0.1 0.5 0.9 4917 4917 4917 2155 2473 2473 2083 2083 5436 5436 5436 5086 21800 21800 21800 21800 22725 22725 21850 21850 21550 21550 21550 21750 8180 8180 8180 9906 2235 2235 3185 3185 3160 3160 3160 3210 29980 29980 29980 31706 24960 24960 25035 25035 24710 24710 24710 24960 492 2459 1940 247 1042 1875 544 2718 4577 29980 30472 32439 33646 24960 25207 26077 26910 24710 25254 27428 29537 Costs incurred in the robust model with model robustness under different o Test o Expected infeasibility First-stage cost Second-stage cost Expected cost Expected infeasibility cost Total cost I 10 15 20 25 10 15 20 25 10 15 20 3910 540 520 500 10 3490 230 220 95 10 3350 520 180 120 17400 21800 21800 21800 21800 21800 17400 21850 21850 22725 22725 22725 17400 20000 21550 21550 21550 130 300 540 7940 8180 40 120 660 1995 2235 160 640 1360 3160 17400 21930 22100 22340 29740 29980 17400 21890 21970 23385 24720 24960 17400 20160 22190 22910 24710 2700 5200 7500 200 0 1150 2200 1425 200 0 2600 1800 1800 17400 24630 27300 29840 29940 29980 17400 23040 24170 24810 24920 24960 17400 22760 23990 24710 24710 II III 361 International Journal of Computer Integrated Manufacturing Table Test I Trade-off between solution robustness and model robustness under different l and o l o Expected variability Expected infeasibility Expected cost Expected variability cost Expected infeasibility cost Total cost 0.1 10 15 20 25 10 15 20 25 10 15 20 25 10 15 20 25 30 10 15 20 25 30 35 10 15 20 25 30 35 40 45 10 15 20 10 15 20 25 30 35 10 15 20 25 80 250 255 5061 4917 0 126 101 413 4917 0 36 101 2155 40 120 85 2121 2281 2473 0 18 85 85 115 1022 2083 0 11 85 85 85 115 686 686 2083 160 640 2045 5436 160 61 597 597 2045 2045 5436 160 0 0 3910 550 525 452 10 3910 570 539 471 375 3910 570 524 471 389 3490 230 220 83 20 10 3490 240 226 83 83 81 36 3490 240 224 83 83 83 81 42 42 3350 520 180 113 3350 520 185 172 172 113 113 3350 520 182 182 182 182 1700 21880 22050 23060 29740 29980 17400 21800 21940 22806 24350 29980 17400 21800 22163 22806 24350 31707 17400 21890 21970 23575 24520 24720 24960 17400 2185 21942 23575 23575 23608 24400 25035 17400 21850 21960 23575 23575 23575 23608 24400 24400 25035 17400 20160 22190 23015 24710 17400 20160 22382 22296 22286 23015 23015 24710 17400 20160 22443 22443 22443 22443 25 26 506 492 0 63 50 207 2459 0 33 91 1940 12 212 228 247 0 43 43 57 511 1042 0 10 77 77 77 103 617 617 1875 16 64 205 534 80 31 298 298 1023 1023 2718 144 0 0 550 5250 6780 200 0 2850 5392 7062 7507 0 2850 5240 7062 7776 0 1150 2200 1243 400 250 0 1200 2257 1243 1657 2020 1090 0 1200 2243 1243 1657 2072 2424 1482 1693 0 2600 1800 11695 0 2600 1852 2581 3441 2826 3391 0 2600 1824 2736 3648 4560 17400 24638 27325 29866 30446 30472 17400 24650 27395 29918 32063 32439 17400 24650 27463 29958 32126 33646 17400 23044 24182 24827 25132 25198 25207 17400 23050 24208 24861 25275 25685 26001 26077 17400 23050 24213 24895 25309 25723 26135 26499 26711 26910 17400 22776 24054 24915 25254 17400 22840 24264 25175 26035 26863 27428 27428 17400 22904 24267 25179 26091 27003 0.5 0.9 II 0.1 0.5 0.9 III 0.1 0.5 0.9 (continued) 362 Table Test Y Wu (Continued) l o Expected variability Expected infeasibility Expected cost Expected variability cost Expected infeasibility cost Total cost 30 35 40 45 1555 1535 5086 182 118 107 22443 23243 23658 24860 1400 1382 4577 5472 4134 4284 27915 28777 29325 29537 6.4.3 Tests for the robust model with trade-off between solution robustness and model robustness Table shows the summary of costs incurred in the robust optimisation model with trade-off between solution robustness and model robustness in terms of different values of l and o From Table 8, we arrive at the following conclusion: there is always a trade-off between variability and infeasibility The role of weight o and l in the robust optimisation model objective function is to measure the trade-off between model robustness (‘almost’ feasible for any realisation of all scenarios) and solution robustness (‘close’ to optimal for any realisation of all scenarios) Robust optimisation allows for infeasibility in control constraints by means of penalties When o ¼ 0, there is no penalty for infeasibility of random constraints in the objective function The infeasibility that represents under-fulfilment attains a higher value Clearly, decision makers not adopt this kind of production plans However, a large weight of o shows that the infeasibility penalty dominates the total objective function value and results in a higher variability and a higher total cost This is an inappropriate approach for decision makers who are open to assume risk and prefer to pay less Therefore, there is always a trade-off between risk and cost For the decision makers, it is necessary to test the proposed robust optimisation with various o and l on global logistics problems When l is a constant: Figures and denote computational results for Test I in terms of expected variability, expected infeasibility and total cost, when l ¼ 0.1, 0.5 and 0.9, respectively Figure shows the trend of the variability when o increases As the weight o increases, the variability increases In particular, when weight o is more than 15, variability increases dramatically After weight o reaches 25, variability does not change This means that for larger values of o, the solution obtained approaches ‘almost’ feasible for any realisation of scenario s On the other hand, as weight o increases, the total under-fulfilment denoted by the infeasibility drops dramatically (see Figure 3) When weight o is greater than or equal to 25, infeasibility is equal to zero This means there is no Figure Variability when l keeps constant Figure Infeasibility when l keeps constant Figure Total cost l keeps constant International Journal of Computer Integrated Manufacturing under-fulfilment, i.e all constraints can be satisfied for any scenario Figure shows the trend of the total cost When o is a constant: Figures 5–7 denote computational results for Test I (See Table 2) in terms of expected variability, expected infeasibility and total cost, when o increases for l ¼ 0.1, 0.5 and 0.9, respectively Figure shows the trend of the variability when l increases for o ¼ 5, 15, 25, 35 and 45 If l increases from 0.1 to 0.9, for o ¼ 5, variability decreases from 40 to 0; for o ¼ 25, variability decreases from 2281 to 85 and for o ¼ 35, variability decreases from 2437 to 2083 Figure shows the trend of the infeasibility when l increases for o ¼ 5, 15, 25 and 35 The greater the value of o, the less impact value of l has on variability Figure Variability when o keeps constant Figure Infeasibility when o keeps constant Figure Total cost when o keeps constant 363 Figure shows the trend of the total cost when l increases for o ¼ 5, 15, 25, 35 and 45 If l increases from 0.1 to 0.9, the total cost for o ¼ 5, o ¼ 15, o ¼ 25, o ¼ 35 and o ¼ 45 increases by 0.026, 0.274, 2.083, 5.163 and 6.834%, respectively Compared with the changes in variability and infeasibility, the total cost increases by only a small amount when l increases This means that the robust model proposed in this study is not expensive for a low risk dual-response logistics system Summary Because of globalisation of the economy and the market, there has been a sharp increase in logistics activities between different countries International transportation problems that occur in global supply chain networks are studied in this article This study develops three types of robust optimisation models: the robust optimisation model with solution robustness, the robust optimisation model with model robustness and the robust optimisation model with trade-off between solution robustness and model robustness These models can be applied to different decisionmaking processes for international transportation problems that encounter uncertainty and risk When the objective function under different scenarios is asymmetric and decision makers are risk averse, robust optimisation is appropriate Decision makers can choose international logistics strategies based on their risk appetites By analysing parameters of the three types of models, decision makers can obtain their favourite route plans with truck composition as well as warehousing plans This article provides a modelling framework for international logistics and inventory problems under uncertainty There are several paths we can take for future research These are: The models developed in this article need input data The quality of the data clearly affects the solutions offered by the models Particularly, development of forecasting models for stochastic demand in international logistics is an important area for further investigation Robust optimisation models not provide means of specifying a scenario Development of means of determining scenarios for different types of international logistics and inventory problems is a potential area for further research This article only examines logistics problems from country A to country B Simultaneously, transporting goods from country B back to country A is a potential research area to examine, which should substantially improve 364 Y Wu the fleet’s efficiency, while reducing logistics costs Future research could consider integrating logistics and inventory processes with manufacturing processes Reference Ambrosini, C and Routhier, J., 2004 Objectives, methods and results of surveys carried out in the field of urban freight transport: an international comparison Transport reviews, 24 (1), 57–77 Bergan, A.M and Bushman, R., 1998 Crossing the border: the NAFTA perspective In: The first Latin American ITS regional Conference, March, Buenos Aires, Argentina Bochner, B., Stockton, B., Burke, D., and Harrison, R., 2001 A prototype southern border facility to expedite NAFTA trucks entering the United States In: The 80th Annual Meeting Proceeding of the Transportation Research Board, Paper Number: 01-0406, January, Washington, DC Bowersox, D.J., Closs, and Cooper, M.B., 2002 Supply Chain Logistics Management London: McGraw Hill Cohen, M.A and Lee, H.L., 1989 Resource deployment analysis of global manufacturing and distribution networks Journal of Manufacturing Operations Management, 2, 81–104 Cohen, M.A and Mallik, S., 1997 Global supply chains research and allocations Production and Operations Management, 6, 193–210 Coyle, J.J., Bardi, E.J., and Langley, J.J., 2003 The management of business logistics 7th ed Canada: Thomas Learning Darlington, J., Pantelides, C.C., Rustem, B., and Tanyi, B.A., 1999 An algorithm for constrained nonlinear optimization under uncertainty Automatica, 35, 217–228 Fraering, M and Prasad, S., 1999 International sourcing and logistics: an integrated model Logistics Information Management, 12 (6), 451–459 Goldsborough, W., 1992 Global logistics management: gaining a competitive edge through integrated systems Business Intelligence Program, SRI International, Report No 818 Gutie´rrez, G.J., Kouvelis, P., and Kurawarwala, A.A., 1996 A robustness approach to uncapacitated network design problems European Journal of Operational Research, 94, 362–376 Ho, G.T.S., Lee, C.K.M., and Ip, A.W.H., 2006 A hybrid intelligent system to enhance logistics workflow: an OLAP-based GA approach International Journal of Computer, 19 (1), 69–78 List, G.F., Wood, B., Nozick, L.K., Turnquist, M.A., Jones, D.A., Kjeldgaard, E.A., and Lawton, C.R., 2003 Robust optimization for fleet planning under uncertainty Transportation Research Part E: Logistics, 39, 209–227 Leung, S., Wu, Y., and Lai, K.K., 2002 A robust optimisation model for a cross-border logistics problem with fleet composition in an uncertain environment Mathematics and Computer Modelling, 36, 1221–1234 Muller, G., 1999 Intermodal freight transportation 4th ed Washington, DC: Eno Transportation Foundation, Inc Mulvey, J.M., Vanderbei, R.J., and Zenios, S.A., 1995 Robust optimization of large-scale systems Operations Research, 43, 264–281 Mulvey, J.M and Ruszczynski, A., 1995 A new scenario decomposition method for large-scale stochastic optimization Operations Research, 43, 477–490 Reddy, R and Reddy, S.R., 2001 Supply chains to vertical integration New York, US: McGraw Hill Sabuncuoglu, I and Goren, S., 2009 Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research International Journal of Computer Integrated manufacturing, (22), 138–157 Va´ncza, O´., Egri, P., and Da´vid, Karnok., 2010 Planning in concert: a logistics platform for production networks International Journal of Computer Integrated manufacturing, 23 (4), 297–307 Vassiadou-Zeniou, C and Zenios, S.A., 1996 Robust optimization models for managing callable bond portfolios European Journal of Operational Research, 91, 264– 273 Wu, Y., 2006 Robust optimisation applied to uncertain production loading problems with importing quota limits under global supply chain environments International Journal of Production Research, 44, 849–882 Yin, X.F and Khoo, L.P., 2007 Multiple population search strategy for routing selection and sequence optimization of a supply chain International Journal of Computer Integrated manufacturing, 20 (1), 39–56 Yu, C.S and Li, H.L., 2000 A robust optimization model for stochastic logistic problems International Journal of Production Economics, 64, 385–397 International Journal of Computer Integrated Manufacturing Vol 24, No 4, April 2011, 365–374 Coordinating collaborative assembly involving heterogeneous computer-aided design agents by an automated coordinator Kaushik Mishraa, Chiranjiv Mohantya, M.K Tiwarib* and L Benyoucefc a Department of Mechanical Engineering, NIT, Rourkela, India; bDepartment of Industrial Engineering, IIT, Kharagpur, India; c INRIA-COSTEAM Project, ISGMP Bat A, Ile du Saulcy, 57000 Metz, France (Received December 2009; final version received January 2011) This article presents a new automated modelling system to aid in collaborative designing with heterogeneous computer-aided design (CAD) agents It is a multi-user based modelling process but is less time consuming The article presents an effective data transmission and transformation technique to ease the mode of information sharing The complete assembling system has been considered as a discrete event system with an automated coordinator The automated coordinator has all the logical inputs to make the decisions Our idea focuses on application of automata theory to the coordination of heterogeneous CAD systems The proposed methodology has been applied to an illustrated example taken from literature for a better understanding of the model Keywords: automated coordinator; collaborative modelling; heterogeneous CAD modelling; CAD networks; information sharing; discrete event system Introduction Economic globalisation and an alacritous ascent in data sharing technologies have raised the demands for collaborative product development and designing With every passing day, industries with dispersed manufacturing units and product development shops are experiencing an augmentation that is customer demands Evidently, there is a complete necessity of collaborative designing (Rosenman and Gero 1998) and automation (Gruver and Boudreaux 1993) in the manufacturing and the product development sector In this new product development scenario, there is an evident necessity of a real-time environment (Joseph and Goswami 1989) for collaborative assembling tasks in systems supporting heterogeneous computer-aided design (CAD) environment and automated coordinators, which reduces concurrency conflicts (Wong et al 2000) and human–computer interfacing and increases production efficiency, thus abating the time consumption factor An assembly is the association of the designed segments of any body, where the segments are designed at different locations and are brought together to be assembled in a particular sequence Let us take the case of a manufacturing plant in USA This enterprise has to manufacture a product that consists of several parts and requires individual designers for the designing of each part Now, it is going to be a gruelling factor for the firm to bring all the designers under one roof for the designing of its single product *Corresponding author Email: mkt09@hotmail.com ISSN 0951-192X print/ISSN 1362-3052 online Ó 2011 Taylor & Francis DOI: 10.1080/0951192X.2011.557091 http://www.informaworld.com In these kinds of scenarios, there is an absolute necessity to promote real-time collaboration and automation Our proposed system focuses basically on solving the tasks of such situations, which require the collaborative action of various agents Compared with the existing CAD systems, the emerging system emphasises the feature of collaboration in product development and designing with overseas designing agents in heterogeneous CAD environments In this kind of a CAD system, the designers are aided with a completely dynamic working platform, where collaborative assembling and planning task are fully automated This system radically strikes to the problems faced by the manufacturing sectors that involve several designers across the globe, who are proficient in various dissimilar CAD environments From the simplest to the products of varying complexities, can be planned, and collaborative exercise and knowledge sharing can be executed using the emerging system Concurrency conflicts are reduced to a great extent with the automation of the assembling system by the aid of automated supervisory controllers An integration-based system has been implemented for the designing of a platform that has automated coordinators and support agents in heterogeneous CAD environments Automation in real-time assembling task is the pre-eminent objective of this article In a nutshell, the methodology focuses on the application of supervisory control theory (SCT) to multi-user 366 K Mishra et al heterogeneous CAD environment to aid in automation of the assembling process using a reference CAD system The automated coordinator, so framed using the principles of automata theory, controls the decision making involving logical transactions and directs the stable product designing The major points that have been touched in this article are as follows: Designing of a stable assembly product Minimisation of computational complexities and concurrency conflicts Involvement of designers proficient in heterogeneous CAD environments Automation of the coordination procedure In the rest of the article, we will present an overview of related works in section and then, we will give an introduction of our proposed mechanism in section 3, where the mechanism of data collection and assembling is explained consecutively We present a case study in section 4, where we take a particular task to display our proposed approach followed by discussions in section and concluding remarks in section Related works A lot of research has been carried out in the field of collaborative engineering, such as CAD modelling and designing This has led to the development of several prototypes, most of which focus on having a human coordinator In these kinds of environments, the major focus is on the communication system between the designers In the study by Min et al 2007, a tokenbased locking mechanism has been proposed in which a human coordinator gives the editing permission to any event proposed But in these kinds of systems, the reduction in concurrency conflicts is insignificant Mostly, collaborative CAD systems provide a common white board; our proposed system is mostly in line with these systems (Pang and Wittenbrink 1997, Agrawal et al 2002) Instead of a white board, we have provided for a dynamic environment All the transactions during the operational period will be in this dynamic system only This dynamic environment is controlled by an automated coordinator For the working of the automated coordinator, we have proposed a computational process, which is an extension of the SCT (Wonham 2004) For the purpose of delivering and manipulation of interactive 3-D objects, streaming based communication methods have been developed in the studies by Li et al 2003, Qiu et al 2004, Wu and Sarma 2004, Li et al 2005 Li et al (2004) have made a feature-based approach to develop a distributed and collaborative environment For the purpose of filtration of the varied information of the modelled part, a distributed feature manipulation mechanism is proposed by him But, these work mostly in homogeneous environments We have proposed somewhat different mechanism for our data transfer system In the proposed system, the server becomes a rest house for the information transferred from any kind of agent systems A centralised network of agent systems (Wang et al 2009) has been proposed that works as per the clientserver networking system presented in the study by Papadimitriou et al 2003 In the proposed system, we make use of an automated coordinator to supervise the logical decision making in the assembling procedure We have closely worked as per the automatons proposed in the study by Thorsely and Teneketzis 2005 Proposed collaborative mechanism The proposed method involves a server–human interface through which designers placed at different locations communicate their products and preferred assembling sequence to the automated coordinator The automated coordinator, then works on the input data, acknowledges various constraints in the submitted parts through pattern recognition techniques and directs assembling operations (through commands characteristic to a particular CAD system) to obtain the stable product as desired by the manufacturing unit As the approach includes input parts designed in heterogeneous CAD environments, a translation mechanism is involved to transfer the designed products from the agents to the reference platform installed in the central coordinating system (CCS) The translator operates in a fashion tantamount to the translator mechanism involved in the data transfer between the neutral modelling command (NMC) and system modelling operation (SMO) (Gruver and Boudreaux 1993) The NMCs are basic codes generated in machine level language by the translator These codes are again regenerated into parts by the SMOs After the data transferring event terminates, the server transfers the collected data to the CCS which then engages the automated coordinator to assemble the designed CAD products 3.1 Data entry At the outset, every designer is communicated about the products that are supposed to be designed by them Now, all the designers in their respective systems design the parts, which are mostly offline activities The operational freedom of the user is maximised in this case, as every agent system is ensured with a CAD environment that assures the proficiency of the designer International Journal of Computer Integrated Manufacturing Every agent system will have a NMC library installed in it to convert the designed part into neutral commands When the designer submits the designed part, it is transferred to the server encoded in the NMC format The CCS is connected to the server This collects the data from the server and loads in the reference CAD system installed in it The reference CAD system is going to be the CAD system preferred by the manufacturer The server is a common platform from where the agents’ systems submit the products designed in their respective systems in an encoded format The CCS is installed with a SMO library This library is dependent on the reference system installed in the CCS The CCS first collects the encoded datum packets from the servers Each packet is the encoded format of a product It then refers these packets to SMO library, which then decodes each packet into the product The topology of the entire network is similar with the client–server network architecture as in Figure This server network is preferred for its efficiency in data transferring, especially when a lot of agent systems are associated with operational system (Paillasa 1997) The server has got the task of collecting all the designed parts from all the agents in the form of encoded packets It stores the package in its database (Ramakrishnan 1997) till it has collected all the parts from the agents After the completion of this task, it extradites each encoded packet one by one to the CCS, which after decoding of one packet calls for the next package 3.2 Assembling and computation After the conversion of the encoded packets into parts, the CCS stores these in the same sequence as they were Figure Network topology of the system 367 received from the server Feature recognition softwares are then utilised by the CCS to identify the parts and then pile them as per the sequence stored as input from the manufacturer from the very beginning, as shown in the Figure It then activates the automated supervisor The CCS gives the assembly sequence as input to the automated supervisor, which then assembles the parts The assembling procedure is closely in line with the automata theory Illustrative example: optical lens modelling The core idea of our proposed CAD model is concurrency free assembling and designing In order to facilitate the presentation of our approach, we consider the case of designing and assembling the parts of an optical lens Figure 2, earlier referred by Zha et al (2006), is now being considered again for analysis The assembly model consists of five parts as shown in Figure There are various sequences in which the parts can be assembled From the optimisation techniques involved in the study by Zha et al 2006, we get the best assembly sequence of the parts The parts are given indices, as shown in the Table 4.1 Translator operations Each designer’s designs are supposed to design a single part For the purpose of increment of ease in our presentation, we name the designers and agent systems as per the indices of the parts they design So, we can say each designer Di designs the part Pi in an agent system Ai So, the information about the designer and the agent’s system can be stored in a three-tuple set, called address set So, the address set Ei for a part Pi is 368 Figure Table 1 K Mishra et al Process plan diagram Indices for the parts of an optical lens Doublet2 Spacer Doublet1 Lock ring Sub-assembly {Ai,Pi,Di} The address set solves the purpose of maintaining the record of the parts and their designers in a very much systematic format Every part designed in the process will have an address set A system Ai will be having a NMC library installed in it as an add-on to the CAD environment configured in it As soon as the designer Di has finished the designing of the assigned part Pi, a parametric design operation is applied on the agent CAD system After the application of this operation, the part designed is captured by the SMO-NMC translator The SMO-NMC translator compares the parts designed in the local NMC library and develops a template that corresponds to the NMC The property of the NMC library has been discussed earlier to be very much system dependent, but the NMC library of the homogenous systems should be consubstantial Figure represents the translation activity between the SMOs of the agent systems and the NMC database stored in the server A NMC object is created by the NMC template, and the part is given as input to encode it in a neutral language understood by the NMC, including all parameters and necessary geometrical constraints The translator then calls for string serialisation technique for the purpose of creation of NMC packets which is NMC understood The server receives these NMC packets and stores them in database stack After this, it sends each packet from the stack in the order they were received from the agents The CCS after receiving the NMC packets reads each packet and stores them in the order they were received from the server The parts collected not have the indices to get identified by the automated supervisor to get assembled For this purpose, pattern and feature recognition software are used to identify each part and assign the required indices 4.2 Assembling operations Here, we basically observe the modelling of the control specifications of the assembling task through the use of an automated coordinator The automated supervisor describes all possible transactions and liaisons by imposing a set of operational constraints on the sequence of transaction This is done in accordance with the liaison graph and the datum flow chain (Lee and Shin 1990) The CCS then assembles the parts as per the described transactions International Journal of Computer Integrated Manufacturing Figure Optical lens assembly Figure Data sharing operation In the proposed system, the control specifications are modelled in lines with the automata theory If we describe an automaton, it is a five-tuple given as À Á A ¼ Q; S; d; s0 ; F Q ¼ {set of all states} S ¼ {set of all events} d ¼ transition function s0 ¼ initial state F ¼ {set containing all the stable states} From the above, we conclude that s Q and F is a subset of Q The automaton is graphically represented as a state transition diagram, where the nodes indicate the states in Q and the arcs indicate the transition function d So, the transitions defined induce the transition set T ¼ {(q, e, q0 ) |q0 ¼ d (q, e), e S} Each arc in the graph describes a corresponding event in S In the assembling application, the system is composed of automata associated with multiple agents An operational cycle that occurs during the execution of the assembling process of one part encompasses all the activities of the CCS The complete operational cycle is one transaction composed of the following activities Associating: The automated supervisor associates or attaches the part with reference to the coordinate system of the CAD system installed in the CCS The event Aij is used to describe such an activity The transaction Ti Figure 369 Relationship between subsets of event set associates the part Pi during the liaison ej Since this event is controlled by the CCS and not by any agent system or designer, the event is uncontrollable Computing: After the successful establishment of the liaison, the CCS computes the transaction and the related components to check the stability with reference to constrained network and coordinates of the assembly model Event Ci characterises such an activity This action is by the CCS, hence uncontrollable Location and relocation: After the computation terminates successfully, the event Li takes place and the part gets associated with the model With occurrence of the event Li, the signal to carry out the transactions for the next part in sequence is sent If the computation does not terminate successfully, the part is dissociated from the assembly and the relocation takes place The complete cycle is an iterative cycle until a stable assembly complex is achieved The relocation event is characterised by Ri Figure establishes the relation between different components of the event set It describes the sequential arrangement of the events Moving into a more detailed view, we shall refer to the indices assigned to the parts to explain each of the liaisons We fix the Doublet2 with index as the global reference point The coordinate system of the reference 370 K Mishra et al Figure Liaison graph for the process 4.3 Supervisory function architecture The supervisory functions are the primary objectives that need to be achieved by the CCS In order to execute the transactions in a sequential order, certain control specifications need to be implemented to avoid any miss placement of parts After the successful completion of a particular assembling task, the supervisor function calls for the next liaison in the sequence These supervisory functions are derived from the precedence constraint table obtained from the liaison graph After the successful completion of all liaisons in the liaison graph, the liaison is said to be complete Figure Process plan diagram CAD system is fixed as per Doublet Now, we are left with the transaction of the remaining four parts From the liaison graph and the process plan diagram in Figures and 7, we obtain the transactions associated with the liaison of the remaining parts X ¼ fA11 ; A12 ; A13 ; C1 ; L1 ; R11 ; R12 ; R15 g X X ¼ fA22 ; A24 ; C2 ; L2 ; R22 ; R24 g ¼ fA33 ; A36 ; C3 ; L3 ; R33 ; R36 g X ¼ fA46 ; A45 ; A44 ; C4 ; L4 ; R44 ; R45 ; R46 g X : ¼ Ui ¼ X i The automaton reads each component of S and accepts it, if the terminating state is a stable state e1 < e2 ð1Þ e2 < e3 ð2Þ e3 < e4 ; e5 ð3Þ e4 and e5 < e6 ð4Þ e4 < e5 ð4aÞ e5 < e6 ð4bÞ The ‘5’ defines that the liaison in the left hand side must be completed before the beginning of liaisons in the right hand side From each equation represented, we get the supervisory functions and display them as automata All the liaisons were done obeying the precedence constraints and the data locking rule (Song and Chen 2004) as shown in Figure All these five automata were then combined to get the control specification S: ¼ Ui4¼1 Ai Figure shows automata for completeness specifications 4.4 The CCS computation After the establishment of a successful transaction, the CCS calls for the next liaison In this process, pattern and figure recognition properties installed in the CAD International Journal of Computer Integrated Manufacturing software of the reference system were used to get the geometric configuration and texture of the faces of the assembled parts and the part Pi The CCS randomly chooses a face Fi of the part Pi The selected face Fi was paired with all the faces of the assembly one by one in a sequential manner For each pair (Fi, Ai), a stability set S is generated and compared with the standards given as input by the manufacturer or customer If the constraints not match as in Figure 10, then the relocation step follows and another Figure Control specification automata Figure Automata for completeness specification 371 set of constraints is tried Figure shows the condition when no constraints match Figures 10 and 11 display the conditions of partial matching and complete matching, respectively Results and discussions The logic and algorithm for the CCS can be defined in compiling environments such as MATLAB (Miller 1995) and Turbo Cþþ The compiled program can be Figure 10 Pseudo code for stability index checking 372 K Mishra et al added as add-on to the CCS With the help of this logic, the CCS will automatically assemble the parts in the most optimised sequence We had used Autodesk (Autodesk Mechanical Desktop, 2008), Solidworks (Solidworks, 2008), CATIATM V5 (Catia v6, 2008) in the agent systems, and Pro-Engineer (ProE wildfireV2, 2008) was used in the reference system From the liaison graph and the process plan diagram, we are able to obtain the most likely editing events for each liaison The precedence constraint equations were obtained from the process plan diagram The control automata were designed based upon the liaison graph and the automata models Figure 11 Invalid constraints Figure 12 Partially constrained The pseudo code displayed focuses on the CCS decision-making system, i.e the automated coordinator The computation class compares index of each class with the stability index The stability index is set containing various parameters as per the requirement of the manufacturer For the illustrated example, we have considered the following way of getting the stability index (1) Surface property Flat surface is given the index 0, cylindrical surface is given the index and spherical index is given the index 2.(SP) (2) Surface texture Smooth surface is given index 0, threaded surface is given the index International Journal of Computer Integrated Manufacturing Figure 13 373 Fully constrained 1, vertical grooves are given the index and horizontal grooves are given the index 3.(ST) (3) Axis Horizontal axis is given the index and vertical axis is given the index 1.(AH) (4) Solid property If the object is hollow then index is assigned and if the object is a solid then index is assigned.(SPRO) (5) Diameter The diameter of the surfaces were taken as per their absolute value.(d) When the faces were compared with the assembly, these properties were arranged in pairs as shown Assembly is given the symbol A and part is given the symbol P The index for any pair is arranged as set of ordered pairs in the following manner just for the illustrated case {(ASP,PSP), (AST,PST), (AAH,PAH), (ASPRO, PSPRO), (AD,PD)} It is then compared with the ideal stability index shown below (1) Second Doublet and Spacer {(0,0),(0,0),(1,1),(1,1),(3,5)} (2) First Doublet and Spacer {(0,0),(0,0),(1,1),(1,1),(5,7)} (3) Second Doublet and Lock Ring {(0,0),(0,0),(1,1),(1,1),(3,3)} (4) First Doublet and Sub Assembly {(0,0),(0,0),(1,1),(1,0),(7,7.5)} (5) Lock Ring and Sub Assembly {(1,0),(0,0),(1,1),(1,0),(7.5,7.5)} attachment: attachment: attachment: attachment: attachment: The snap shots below show the events occurring during the second doublet and spacer attachment Conclusion In this article, an automated assembling system is proposed, which is realised by automated coordinators of the CCS The platform developed by this system is linked with a multi-agent network with dissimilar CAD systems The proposed model tends to maximise the designer benefits and reduces conflicts during assembling The mechanism involved in data conversion and transfer of information involves the translation of the geometric information and coordinates constraints into neutral machine codes These machine codes are again converted by the NMC-SMO translators installed in the central coordinating system into reference system compatible code Transfer of data and information is mostly in the form of data packets, which ensures a faster rate of information sharing The proposed system executes a mechanism that follows the established automata theory This theory aptly specifies the control specifications for a discrete event system like CAD assembling The control specifications displayed play a regulatory role during the functioning of the 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