A virtual enterprise architecture for logistics service - Springer Link

Inf Syst Front (2008) 10:503–518 DOI 10.1007/s10796-008-9106-3

Complex service design: A virtual enterprise architecture for logistics service Charles Møller & Sohail S. Chaudhry & Bent Jørgensen

Published online: 17 June 2008 # Springer Science + Business Media, LLC 2008

Abstract This paper presents an interdisciplinary systems approach to service modeling, design and deployment. The study is based on a longitudinal case study of the development process of a complex logistic service system based on an advanced logistics model. The case examines the development of a Virtual Enterprise Architecture (VEA) for an automatic high-speed transport and sorting system applied in airports for baggage handling. The study traces the evolution of the system from the early conceptual phases to a successful commercial service deployed at Changi, Singapore Airport. The study is conducted using a retrospective analysis of the case using a design science research approach. The paper evaluates and discusses the issues of creating and designing a new complex logistics service, distinct from the physical product, based on an advanced discrete event-based simulation model. The paper concludes by presenting a generalized and validated conceptual framework for a VEA based on an advanced simulation model. The paper thus contributes to the field of

C. Møller Center for Industrial Production, Aalborg University, Fibigerstraede 16, 9220 Aalborg, Denmark e-mail: [email protected] S. S. Chaudhry (*) Department of Management and Operations/International Business, Villanova School of Business, Villanova University, Villanova, PA 19085, USA e-mail: [email protected] B. Jørgensen Simcon A/S, Ellemosen 9, 8680 Ry, Denmark e-mail: [email protected]

service systems and service management by identifying a novel approach to effective design of a new service. Keywords Case study . Design science research . Service modeling . Virtual enterprise architecture

1 Introduction In the majority of developed countries the main valueadding activities have moved away from the manufacturing of the physical goods towards services and highly personalized goods (Bretthauer 2004). This phenomenon has caused an emerging stream of service related research including the National Science Foundation’s (NSF) program on service enterprise engineering. Service enterprise engineering in this context deals with the design, planning, and control of operations and processes in commercial service enterprises (National Science Foundation 2006). This NSF program has funded a variety of projects such as healthcare, procurement auctions, sequencing human tasks, financial engineering, designing and managing logistics, and communications networks, among others since 2001. This new integrated perspective challenges the traditional industrial engineering approaches in several ways, and thus requires new approaches to the provisioning of products and services (Morelli 2006). Zang and Fan (2007) improved operational performance by proposing and implementing architecture for a complex event processing in enterprise information systems. Some research communities define the network of different stakeholders as solution oriented partnerships (SOP) and, the mix of material and immaterial components which satisfy the requirements of each of the stakeholders, is defined as a

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product service system (PSS; Morelli 2006). This paper deals with the engineering of a particular PSS in a SOP. The backbone of every major airport operation is a baggage handling system. The baggage handling system is often a particular implementation of a more general automatic high-speed transport and sorting system (ATS). An ATS can be considered a PSS aimed at providing logistics service, distinct from its physical components. This ATS is developed in a complex interplay between the component suppliers, system vendors, contractors and the organization at the receiving site. Overall, the baggage handling system facility is customized and configured to provide the final customer, the airline passengers with a fast and reliable logistics service (Andreatta et al. 2007). Quite often flights or passengers are delayed and thus, the dynamic performance and in particular, the robustness of the overall system is the critical issue. In many cases these requirements are examined using static constructs such as the level of service standards proposed by the international air transport association but increasingly stochastic analysis is used for this type of analysis (Morelli 2006). This paper explores the relationship between an advanced simulation model and the design of a complex service. The paper is a case study of the development of a VEA based on an advanced simulation model (Møller and Jørgensen 2000). This architecture is instantiated through an ATS as applied in airports for baggage handling, but can also be demonstrated in the context of other sites such as JYSK, Uldum, Changi Terminal 3, and Carlsberg Apollo. The study examines the process of evolution and the diffusion of the idea from the early conceptual phases to a successful commercial service applied at sites such as the airports in Arlanda Stockholm, Sweden, Changi, Singapore, and Guangzhou, China. The theoretical perspective of the papers is the design of an innovative PSS as a case of systems development in information systems research (Nunamaker et al. 1991). There is an emerging tradition in the information systems field to emphasize the design of the artifact in the confluence of people, organizations, and technology. This paradigm is referred to as design science research and is the methodological framework employed for studying this case (Hevner et al. 2004). Thus, this paper contributes to model driven approaches to information systems design (Huang et al. 2005), and also aims at providing a contribution to the more general theoretical design debate, through formulating a set of design propositions for developing a PSS in solutions oriented partnerships. The paper is organized as follows. First, the concept of service management, modeling, and design is reviewed in order to provide a consistent conceptual framework for the paper. In Sections 3 and 4, the methodological issues and

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the study design are addressed. The case study is then presented and the particular findings are analyzed and discussed. In the final section, the more general conclusions and further research perspectives are presented.

2 Background Perhaps the most critical challenge to most enterprises is the need for constant change. Markets and technologies are rapidly evolving, and in response companies are reorganizing, engaging in new collaborations and developing new products and processes. This necessitates the integration of the enterprise operation and the development of a discipline that organizes all knowledge that is needed to identify the need for change in enterprises and to carry out that modification expediently and professionally. This discipline is known as enterprise engineering (IFIP–IFAC 1999). The field of industrial engineering has been dealing with the issues of enterprise engineering for several years, and often these methodologies are combined into standardized frameworks. The advantage of using such development methodologies have often been illustrated in large complex industrial applications. Recently, the need to define a generalized architecture caused the IFAC/IFIP Task Force on Architectures for Enterprise Integration to develop an overall definition of a generalized architecture. The proposed framework is known as Generalized Enterprise Reference Architecture and Methodology (GERAM; IFIP– IFAC 1999). GERAM is about those methods, models, and tools which are needed to build and maintain an integrated enterprise, be it a part of an enterprise, a single enterprise, or a network of enterprises, for example, virtual enterprise or extended enterprise (Kass-Pedersen et al. 1998; Tølle and Bernus 2003). 2.1 Complex service design The PSS has been proposed as an overall design based business strategy. A PSS can be defined as the result of an innovation strategy, shifting the business focus from designing and selling physical products only, to selling a system of products and services which are jointly capable of fulfilling specific client demands (Manzini and Vezzoli 2002). The partnerships for delivering a PSS are often created as a convergence of partnerships between different stakeholders and such a network is sometimes referred to as solution oriented partnerships (Morelli 2006). SOP are about the conception and development of solutions (Cook et al. 2006). More precisely, these partnerships are about industrialized, contextualized, and sustainable solutions that are produced and delivered by networks of partners and thus, the concept resembles the virtual or the extended

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enterprise concept (O’Neill and Sackett 1994). So if a SOP producing PSS is equivalent to an extended enterprise, obviously the question arises is whether a complex service can be designed using the concepts and tools from the GERAM methodology? The GERAM enterprise architecture framework defines a set of process oriented concepts. Business processoriented modeling aims at describing the processes in the enterprise capturing both their functionality (that is what has to be done) and their behavior (that is when things are done and in which sequence). In order to achieve a complete description of the processes, a number of concepts have to be recognized in the guiding methodology. The process-oriented concepts defined in GERAM include enterprise entity life-cycle and life-cycle phases, life history, enterprise entity types, and enterprise modeling with integrated model representation and model views. 2.2 Advanced simulation model Global manufacturing has changed the environment in which goods are produced. Meanwhile, the rapid development of electronics and communication technologies has required design and manufacturing to keep pace. Computer Aided Design (CAD)/Computer Aided Engineering are technologies that can reduce lead times within the overall process as well as eliminate stages (Hill 2004). These technologies have become an integral part of engineering and thus, also the engineering of products and service systems. Provided that the PSS is a logistics service, industrial engineers should be able to design the service system just like a product designers developing a new product. Discrete event simulation has been proposed as a tool for computer-aided production engineering (Klingstam and Gullander 1999). Most commercially available simulation tools are based on discrete event simulation. In the discreteevent model, the simulator imitates the behavior of entities when an event occurs at a distinct point in time. Entities are the components of a system, described by variables. The moment in time when variables change are called events. Occurring events drive the simulation and the simulation clock. Between events nothing happens. Thus, time in a discrete event system does not proceed linearly but in irregular intervals. The strength of the discrete event simulation model is the availability of strong standardized tools for modeling and simulating event based models. Among the notable features of these packages include a strong mathematical formalism in set theory and probability calculus, general programming languages, and advanced three-dimensional (3D) visualization technologies. The disadvantages of using standardized simulation tools are that the simulation model is embedded in the package.

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Servers, queue, routing rules, and programming logic are usually integrated into the model. This makes modularization and reuse cumbersome, and consequently a new architecture is needed. 2.3 Virtual enterprise architecture In this section we present our Virtual Enterprise Architecture. In literature, when geographically dispersed independent organizations create a temporary alliance to accomplish a common goal, then such a network is known as a Virtual Enterprise (VE). The organizations use their core competencies and interoperate to provide service to their customers. Once the objective is achieved, the VE is then dissolved (Chen et al. 2007; Assimakopoulos and Dimitriou 2006). In order to achieve complete integration and coordination of the business processes among the partners in the network, researchers have developed different approaches to model the situation. Presley et al. (2001) proposed high-level virtual enterprise architecture which was used to assist in the design process of the VE. Barnett and Miller (2000) presented architecture within a VE that was based on the High Level Architecture developed by the Department of Defense to show how several supply chain models can be combined to support distributed simulation. Vesterager et al. (2003) developed a Virtual Enterprise Reference Architecture to model the complexity associated with the various functions of the business partners in the VE. Their architecture was based on the modeling framework of and related concepts of GERAM. The generic VEA can be hypothesized from the previous discussion. The SOP is the extended or virtual enterprise. The resulting logistics service and products is modeled using an advanced simulation model. This simulation model is based on a standardized discrete event simulation package. The simulation model mediates information between the stages of the systems lifecycle. The lifecycle of the system is represented using the GERAM framework. The general lifecycle model consists of seven phases and they are identification, concept, requirement, design, implementation, operation, and decommission. The VEA should support integration engineering activities during these phases. The advanced simulation model is reused (Robinson et al. 2004) as a mediator between the various actors in the service oriented partnership instantiated in the lifecycle of the resulting system and thus is the Virtual Enterprise.

3 Methodology The methodology used in this paper is an interdisciplinary systems approach to service modeling, design, and deploy-

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ment. The study is based on a longitudinal embedded case study of the development process of a complex service system. The premise is that an advanced simulation model holds the potential to increase the overall value of the system by leveraging services throughout the system lifecycle. The VEA in Fig. 1 illustrates this relationship and is thus, a conceptual framework for the study. This paper argues that if this relationship can be instantiated in a real situation, then we have strong evidence for the argument that this architecture is an effective service design approach. The study is conducted using a retrospective analysis of the case of developing a baggage handling system using a design science research approach. In this section, first, we briefly review the design science approach. This is followed by the formulation of the research questions. 3.1 Design science research Design science and design science research are relative new approaches in the field of information systems. The design science paradigm has its roots in engineering and in what Herbert Simon (1996) entitled “science of the artificial”. This work identifies design science research as a methodology distinct from natural and social science with an axiological emphasis on utility. A distinct feature of this methodology is the complexity of the design task and consequently, the contingency of design. This leads to formulating solution searching and evaluation, the iterative approach that characterizes design science research. The premise is that research contributions can result from systems development, experimentation, observation, and performance testing of the systems under development and that all of these research approaches are needed to investigate different aspects of the research question (Nunamaker et al. 1991). Design theory in information systems is a prescriptive theory which integrates normative and descriptive theories into design paths intended to produce more effective information systems (Walls et al. Fig. 1 Virtual enterprise architecture

1992). An example of such a design theory is found in (Markus et al. 2002). Markus et al. (2002) created a design theory for systems that support emergent knowledge processes. A design theory in this context is a causal chain from the characteristics of the knowledge process to the objective, namely, an effective support system. More generic theory for design and action purports how to do something. It is about the principles of form and function, methods, and justifying theoretical knowledge that are used in the development of IS (Gregor 2006). The early systems development approach (Nunamaker et al. 1991) provides both a research framework, an outline of the research process, and a multi-methodological approach. Design science in information systems seeks to create innovations, or artifacts, that embody the ideas, practices, technical capabilities, and products required to efficiently accomplish the analysis, design, implementation, and use of information systems. Research in this area has produced four types of artifacts (Hevner and March 2003; March and Smith 1995):

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Constructs provide the language in which problems and solutions are defined and communicated. Models use constructs to represent a real-world situation—the design problem and its solution space. They aid problem and solution understanding and frequently represent the connection between problem and solution components, enabling exploration of the effects of design decisions and changes in the real world. Methods define solution processes. They can range from formal, mathematical algorithms that explicitly define the search process to informal, textual descriptions of “best practice” approaches. Instantiations show how to implement constructs, models, or methods in a working system. They demonstrate feasibility, enabling concrete assessment of an artifact’s suitability to its intended purpose. Researchers can use instantiations to learn about the

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Service 2

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SOP ...

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real world, how the artifact affects it, and how users appropriate it. The Design science research process has been further developed and elaborated from its original contribution (Nunamaker et al. 1991). Peffers et al. (2006) develops a model for producing and presenting information systems research. This model provides a nominal guideline for designing the case study. Design science research deals with the dichotomy of theory and action. Theorizing and theory building play slightly different roles in design science research (Venable 2006) but in general theorizing is an indisputable element of the design science research framework (Nunamaker et al. 1991; Walls et al. 1992; March and Smith 1995). In a recent taxonomy of IS theory, theories for design and action has its own category (Gregor 2006). Design is action in design science research but action is also the design process. Action is related both to the process of building the artifact and to the design research process itself. Action research addresses the social system change that is at once a means of effecting change and generating knowledge about the modification (Cole et al. 2005). Design science research and action research (Davison et al. 2004) share a number of similarities which can be used to evaluate design science research. Evaluating design science research constitutes a particular issue. The issue has been addressed by (Hevner et al. 2004) researchers who are proposing an information system research framework and a set of guidelines for design science in information systems. This study is evaluated using these guidelines in combination with criteria from action research. 3.2 Research questions The central issue in this study is to theorize on the value of an advanced simulation model in the case of the automated transportation and sorting system. For example, how is the business value influenced by the advanced simulation mode through the architecture of the solution? This is driven by putting forward a set of propositions on the design of service based on advanced simulation technology. There are numerous unresolved issues in how to determine the value or the success of an information system (DeLone and McLean 2003). One of the pragmatic arguments is that information systems should be understood in the context of the work systems that they enable (Alter 2006). However, when it comes to the value of a technology there is no simple business process to use as a reference due to the networked nature of the SOP (Manzini and Vezzoli 2002). A simpler but also a more overall measure is the diffusion of the technology into the industry (Schilling 2006). Diffusion of the VEA into an industry is a

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measure of success from a market perspective. In this case, this is reinforced by the fact that the VEA constitutes an architectural innovation (Henderson and Clark 1990) to the ATS industry. The second question to study is the leveraging of services. Again, the market perspective is a useful perspective. Provided an actor can conduct a commercially viable business based on services previously not feasible, this service is considered to be leveraged by the new architecture.

4 Case study: Baggage handling system In order to address the research questions, we present and analyze a case of designing a complex logistics service based on the VEA. Globman21 was a program of the European Commission initiative European Strategic Program on Research in Information Technology in the field of information technology (Kass-Pedersen et al. 1998). In Globman21, the product lifecycle is chosen as a central modeling perspective of a virtual enterprise. This and many other contributions on virtual/extended enterprises aim at discrete products. But what happens if the product essentially is a logistics service? In this case, we must be able to establish a dynamic model of the product which is analogous to the logistics system and the control systems producing the service in our case study. This was the initial idea that spurred the development of the VEA and the baseline for this case study. The case study evolves around a Danish manufacturer of material handling solutions. These material handling solutions are often used for customized baggage handling facilities in airports. These facilities are developed by several different vendors, and the resulting facility is later a platform to provide the airport with different services. The focal point of the study is the simulation model and the simulation technology. The VEA is embodied in the simulation technology, and the architecture is also validated through other projects besides the baggage handling system, for example, automated material handling in distribution centers. The case study details the progression of a 7-year development process of the VEA. It provides details from the initial conceptual phase, to the concept projects at a Danish university, through various prototypes, and toward the development of a standardized commercial product. The case study design is thus a longitudinal embedded case study as described by Yin (2002). The authors of the papers have been involved in various phases of the development process and thus, a multitude of quantitative and qualitative data are available for the study. The study is structured using design science research and the inquiry into the research question is

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validated using a pattern matching strategy (Alter 2006). The development process progressed according to the process for systems development research (Nunamaker et al. 1991). In the remaining part of this section, we present the case study structured as the design science research model (Peffers et al. 2006). 4.1 Problem identification and motivation In 1999 Crisplant A/S was a project-oriented and orderproducing company with two business areas, ATSs and liquefied petroleum gas filling systems. Within these two areas, Crisplant developed, produced, and installed customer-tailored system solutions globally. Crisplant was listed on the Danish stock exchange, had 800 employees and a €160 million turnover. Crisplant was later acquired by the international engineering group FKI plc. and is now, a business unit in FL Logistix International (FKLI). FKLI is a leading provider of automated material handling solutions, supplying its customers with an integrated set of leading-edge technologies in high-speed sortation, conveyor systems, carousels, palletizing, paperless pick products, automated storage and retrieval systems, crossdocking, baggage handling and security, controls, order processing software, radio-frequency identification implementation, warehouse control systems, and total material handling automation. The movement and sorting of goods is a demanding process in many modern enterprises. Not only do companies have to be concerned with efficiently getting goods from point ‘A’ to point ‘B’, they need to track valuable information related to the individual products as well as the existing position in the handling process. Sorting is a process of dividing objects into specified groups according to specified criteria, but also value added services between picking and sorting such as packaging, labeling, bar-coding and so on. Companies want to find suppliers who can handle the sorting, systematically and efficiently in accordance with modern logistic demands of their portfolio of diverse products that require complex sorting criteria. The four basic elements of this system include one or more feeder sections where items enter the system, one or more chute sections where an item exits the system, one or more tracking section where items are identified, for example, weight, volume, or barcode, and a conveyor section (trays or belt) where items are transported between feeder and chutes. Figure 2 depicts a typical ATS with these basic elements. The entire sorting process is illustrated with the numbering scheme of Fig. 2. Items are induced in the feeder (1). Items are measured and weighted (2). Items enter a tray according to induction algorithms (3). The items are scanned using the bar code (4). Depending on the bar-code, the items are sent to the appropriate chute

Fig. 2 A typical automatic high-speed transport and sorting system

according to sorting algorithms (5). From the chutes, the items are sent to a picking location (6), and the sorted items can now be transferred to their destination (7). The conveyor section of an ATS includes several conveyor segments. Each section and segment of the ATS is an independent unit, but tracking and sorting information is managed in a central control system. The central control system handles sorting algorithms, sorting plans, the overall data storage and handling, and interfaces with other external computer systems such as the warehouse information systems, the order execution systems, and so forth. The low-level functions for handling items on the sorter are organized by a machine controller. The machine controller distributes the control tasks to decentralized input/output control units for the execution of items such as inducting an item onto the sorter, reading bar codes at an overhead scanner, or tilting a tray at a chute. Initially, the communication between the machine controller and the equipment was based on a high-speed ISD network, but in the new generation of ATS, all communications are based on Transmission Control Protocol/Internet Protocol (TCP/ IP) and Programmable Logic Controllers (PLC’s). The implication of this is that all controller modules communicate via standard socket. A socket is a communication point through which applications can send and receive data. Models can communicate through sockets on the same processor, on multiple processors running on the same machine, and/or on multiple machines connected by a network. The operators of the system interact with an ATS through a graphical interface on a workstation. The workstation, the machine controller, and the central control system are referred to as an automatic high-speed transport and sorting control system.

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Furthermore, an ATS is integrated with storage facilities, equipment for batch and item picking, and conveyors between storage, picking locations, and the feeder stations. Thereby, the ATS constitutes a complete integrated logistics solution. Most of the ATSs are in the high performance business segments and consequently, the system is often mission-critical to the end-users. FKLI as a system integrator and is able to function as main contractor as well as a supplier of after sales services, which is a growing business. Throughout the years, FKLI has adapted the capacity and optimized a number of products, both in regards to function and price. Targetoriented work has also taken place with renegotiations of framework agreements with a number of key suppliers leading to positive results. A project has been carried out to strengthen production for the future with the result that for example, core technologies have been chosen, make-or-buy analyses have been performed and the organization form established. The production division set fixed prices for the project divisions in a similar way as other suppliers. The production strategy takes starting point in a high extent of own production of key components, cooperation with central key suppliers and efficient project purchases. The fluctuations unavoidably appearing in a project-oriented company are sought prevented through the use of sub-suppliers and a flexible and dynamic production organization. At the start, FKLI took a conventional manufacturing approach to the ATS business by selling products to the customer. But after the customer installs a reliable logistics solution, the understanding and transformation of the logistics requirements into an operational ATS solution and beyond is becoming a core business process for FKLI. Therefore, a phased project management model with welldefined gates has been adapted as a tool to plan and control development projects from the call for tenders to after-sales service in best possible conformance to customer requirements (Cooper 1990). Within such a context, the University of Aalborg, a sub-supplier, and a simulation consultant established a project aimed at improving system integration and to explore new simulation technology in the ATS case. 4.2 Objectives of a solution An ATS is easily contemplated through the VEA as described earlier. The SOP is the consortium headed by the main contractor, and it includes hardware and software vendors as

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well as automation consultants. The PSS is the physical ATS plus the logistics services. The important feature of an ATS is the logistics performance in the customer organization. To model this dynamic system a simulation model is needed. FKLI has been using simulation models for various applications. These models have been developed for very specific purposes, like testing complex sorting algorithms or to produce cool animations for boardroom sales meetings. The experiences from these projects were positive but the cost of developing the models needed to be justified. The problem to be investigated is what will happen if the simulation model was the backbone of the complete customer project and perhaps even in the ATS development process? Could we break the barrier of development cost and what would the benefits be of this approach? So the first question to explore was how to use modelbased development and what kind of model is needed to represent this system? The model should then be able to represent the system from the first quotation phase to the after-sales phase. The requirements of the model and the level of details would then gradually shift over time. A modular architecture was needed to allow the different real sub-systems to be plugged into the model to represent their functionality as soon as they are developed. The need for a modular architecture challenges the traditional approach of building discrete simulation models of ATSs. Traditionally, simulation models are built in dedicated software packages which offer a suite of functions and objects with the prime goal of supporting a fast and efficient modeling process that allows a model builder to develop all aspects of a simulation model within the same software package. Event calendars, queues and resources, random number streams, distributions, statistical reporting of utilization, objects in 2D or 3D are among the elements of software package for discrete event simulation. The model building approach is based on a combination of traditional programming in a simulation specific high level language and drag and drop objects and templates which may reflect mechanical functions, work processes, controls logic, system and object status and statistical reporting. This approach favors fast model building but compromises the modular architecture that opens for integration of real sub-systems into the model. The AutoMod example in Fig. 3 reflects programming for directing one bag on a sorter from one position to the next destination based on the actual location and status of the bag.

if AS_Status "Cleared" and AI_Position = 20 then set AI_Destination = oneof(75:31, 25:32) else set AI_Destination = oneof(25:41, 25:42, 25:43, 25:44) /* final destination */ travel to conv:chute_(AI_Destination)

Fig. 3 Sample code: traditional modeling approach

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The attribute “AS_Status” and “AI_Position” contains the current status and position (bar code reader) of a specific bag. The next destination is selected by means of the stochastic distributions “one of ()” which is a 75% and 25% split between the two valid destinations; chute #31 and 32 or an evenly split between four valid destination; chute #41, 42, 43 and 44. The transport command to the selected destination is performed by the traveling action “travel to…”. In VEA, the simulation model is divided into a sorter sub-model for the mechanical equipment (sorter) and a control logic module that stores the actual status of the bag and makes decision of next destination. The real TCP/IP based message communication between the mechanical sorter module system and controls software is applied to the simulation model for later connection to the real controller software. In the early design phases, the controller logic is modeled in a separate simulation sub-model or by means of a controller modeling application like the Simcon ghost controller dedicated for modeling controls logic. The AutoMod example in Fig. 4 reflects the programming for opening a socket connection in the initialization of the model as well as the looping of bags on the sorter and sending the message “GetDest” associated with passing a bar code reader in the sorter and requesting the next destination. The build up of the message and the sending actions are embedded in a function call, which is not shown in the example. The programming for receiving the responding message and updating the destination of the bag is not shown in the table below. Applying VEA requires more programming effort as well as a modeling approach that more closely reflects the actions and functionality of the real system. This seems on one hand to compromise fast and efficient modeling building. On the other hand, the modular architecture and in particular, the separation of mechanical equipment, controls logic and communication opens for new perspective such as auto generation of simulation model code from CAD drawings which brings two important benefits; a fast

modeling process and precise models of especially large scale and complex systems. Experience from case studies will be outlined in Section 4.4. The tool AutoMod from BrooksSoftware (www.autosim. com) was chosen because of its strong ability to model conveyor system (LeBaron and Thompson 1998), its graphical and communication features, but mostly because of the prior experience with AutoMod at the company. Furthermore, AutoMod supports composite sub-model structure which facilitates separation of the simulation model into modules and sub-model in accordance to the ATS architecture for an advanced simulation model. We choose to focus on the high level controller software as an area to test the architecture because part of the software development and customization was outsourced to a sub-supplier and hence, a strong need to coordinate the work of this supplier with the customer projects. All ATS are based on the same core for the high level sorter controller, but the customization of the controller and the development of customer specific features is a time consuming activity. Furthermore, modeling and verifying the controller is complicated and this is due to the complexity of total system, a number of errors can only be identified under operating conditions or when the ATS is physically realized. The role of the simulation model would be to emulate the total ATS, and gradually, as the modules are developed, replace the modules of the simulation mode with the real components. In the area of operations management, we are familiar with the use of simulation in creating a model of a real system for the purpose of learning more about the behavior of that system by performing ‘what if’ analyses. On the other hand, emulation is the process of re-creating an actual system for the purposes of testing integration with other systems. Care should be taken as simulation and emulation are similar enough to cause some confusion (Roher 1998). Examples include the emulation of computer hardware by

/* The line below is placed in a model initialization function and calls a communication module that supports threaded connection, keep-alive, re-connection etc to a surrounding communication framework that handles the communication to the control application or sub-module. */ call FOpenSocket("5555","1.1.127.1") /* socket and ip-address */ . /* below all bags are looping on the sorter until the correct destination is reach */ /* when a bag reaches the barcode reader at position 20, message "GetDest" is send to the controller */ /* in a message received procedure the AI_Destination of the bag is updated automatically */ while AI_Position AI_Destination do begin /* check if correct destination is reached */ if AI_Position = 20 then call FSendMessage(AI_SystemID, "GetDest", AI_BaxID, AI_Position) set AI_Position = AI_Position + 1 /* select next logical position on sorter */ if AI_Position = VI_MaxPosition then set AI_Position = 1 /* back to first position */ travel to conv:chute_(AI_Position) end

Fig. 4 Sample code: new modeling approach

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computer software, and the emulation of actual material handling equipment with software. 4.3 Design and development The project was initiated because a new communication module—The Model Communications Module—emerged as an extension to the simulation package AutoMod (AutoSimulations 1998). This simulation package was used by FKLI, but only for limited purposes. Overall, the aim of the project was to generate knowledge about new possible areas of applications of advanced simulation. A demonstration model of the system was implemented in the spring of 1999. Based on the experience of this demonstration model, the development of a full scale model was launched in 2000. Here, we will concentrate on the system architecture and the key learning and the perspectives of the ATS. The VEA has led to the idea of a generic architecture of an ATS based on a simulation model. The same model architecture will be used throughout the entire lifecycle of an ATS plant. Based on the first contact with the customer, an initial rough model is generated using a configurator. This model serves as a reference model for the development project and thus it is: &

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a vehicle for the dialogue between the customer and sales. Specific requirements may be reflected in the model, and the model may illustrate different sorting solution principles. a vehicle for the dialogue between the sales and engineering. Customer requirements expressed, as a sorting solution, will be formulated in technical terms. a vehicle for the dialogue between engineering and the suppliers. Supplier requirements and the overall impact on the system are exposed.

Fig. 5 Automatic high-speed transport and sorting system (ATS) architecture based on an advanced simulation model

&

used for development test and verification of customer specific features in the control system. Quality assurance can be performed at system level before the factory acceptance test.

Before the ATS is handed over to the customer, the reference models can be developed to document the entire system and delivered as part of the project. The reference model can be used to train the operators at the customer site before the plant is operational. In addition, the reference model could be used to establish a virtual training environment at the customer site. The training function should be able to force the operators to handle exceptional situations, which often causes breakdowns in the early phase at a plant. This will significantly reduce the startup cost of the customers as well as the financial penalty covered by the manufacturer. When the ATS has been operational for some time, the reference model may be used for revitalizing the plant, for example, to tune the parameters to match the actual sorting tasks. This requires that the data acquired in real-time are aggregated into model data. To establish this kind of functionality, we establish an architecture of models that enables us to plug the actual components into when the development has finished. The complete architecture of the virtual enterprise illustrated in Fig. 5 embodies six different sub systems or modules, to be explained in the following. The Configurator Module is the tool to construct the ATS. The number and type of the feeders and chutes, layout of the transportation system, and sorting algorithms, the configurator module generates the models behind the transport and sorter module. The configurator creates a complete model representation of the transport and sorter module in a text file format, which is then imported into AutoMod.

Advanced Simulation Model Configurator Simulator Controller Transport and Sorter

Data Acquisition

Emulation Framework

User Interface

Animation Training

Real World ATS Central Control System (CCS)

Central Work Station (CWS)

Central Machine Controller (CMC)

Programmable Logic Controller (PLC)

Conveyor Segment

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The Controller Module simulates the functionality of the central control system and interacts with the transport and sorter module using the same signals and interfaces as the physical system. The real central control system controller is used for this purpose. The Transport and Sorter Module is a simulation model of the physical entities. This model represents the logistics of the ATS, i.e., it is a simulation model of the sorter itself and the functionality of the low level PLC functions that controls detail mechanical components such as motors, tilt mechanism bar code readers, and the like. This module is the core model of the architecture. The Transport and Sorter module is a conventional AutoMod simulation of the ATS with the exception that the high level execution logic is outside the simulation model. The interface is programmed using standard sockets, and thereby, the simulation model is unaware if the signals are processed by another simulation module or by the real controller. The Data Acquisition Module collects the history of simulated or real signals and presents them to the operator in a comprehensive way by means of the user interface. The data acquisition module must interface with the Central Machine Controller and aggregate the signals into model parameters. The User Interface Module is a visual 3D model of the physical system as well as the real Central Control System. The operator has the ability to control the ATS on the Central WorkStation interface, for example, to start or stop specific chutes or to choose different sorting algorithms. The Training Module generates simulated situations to be used in a training session of the operators. This module also facilitates the testing of specified requirements and functions. In addition, this module can handle different scenarios, for example, different product mix and flows, and specific exceptions like breakdown and alternative flows. These scenarios should be used to train the operators much like in a flight simulator. The Emulation Framework has interface to all modules via a database, file exchange, or a standard TCP/IP protocol. The modules are not bound to the same computer as the operational system. Typically, there will be different requirements to computers in real-time operational environment and computers to use in classroom training. The emulation framework handles communication between the simulation model and the high level execution logic. The emulation framework is on one side connected to the model by a simple bi-directional threaded socket communication using a simple readable application protocol. Although the model may simulate the functionality of several PLC’s, this interface is very simple and easy to apply for a simulation modeler. On the other hand, the emulation framework is connected to the variety of high

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controls, applying the correct interface as in real life operation. Each connection between the high level controllers is defined in the framework by means of the correct transport and application protocol and handled by a multithreaded programming technology. This means that all controllers are connected to the simulation model/framework via the real interface, believing that they are controlling the real system. The simulator and animation module are standard modules. This project has provided us with useful experience on the application of simulation in a virtual enterprise. First of all, the project has verified that a present standard simulation package can be applied together with industrial control systems. Secondly, we have established the knowledge required developing networked software solutions at the department, and third, the project has established the foundation for further research into simulation models applied in a virtual enterprise. 4.4 Demonstration: Singapore Changi Airport Changi Airport in Singapore opens in 2007/08 a new terminal 3 and a new automated baggage handling system (Logistex 2006). The contract for the baggage handling system was granted to FKIL in 2002. An AutoMod bases simulation model has been applied in the design phase and is currently in use during the implementation phase for testing parts of the central control system. This section describes the simulation model and outlines activities as well as important issues addressed over time. The baggage handling system controls the baggage from check-in and transfer passengers in terminals 3 and has direct transport connections to the baggage handling system in the existing terminals 1 and 2 for fast transportation of baggage between the terminals for transfer passengers. The model reflects all mechanical equipment for automatically handling of baggage, and consists of two major systems; a traditional tilt-tray sorting system and a high speed CrisBag system. The sorting system consists of two large tilt-trays sorters, a number of check-in lines, and equipment for baggage security screening, manual coding stations for handling baggage with damaged or non readable barcodes, and a series of chutes for discharging baggage to containers for the departing flights. This system conveys baggage from check-in lines to the tilt-trays sorters and baggage are discharged from the tilt-trays sorter to the chute assigned for the departing flight. During this transport process the baggage passes through the different level of security screening. Baggage with non-readable barcode is discharged from the sorters to conveyors lines with manual coding station for identification and automatically inserted back onto the sorters.

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The CrisBag system is a tote based baggage handling system composed by approximately 10,000 standard conveyor elements of an average length of more than 2m. The CrisBag system handles baggage from transfer passengers arriving to terminal 3 and leaving by other flights departing from Terminal 3. If the passenger is arriving in one terminal and departing from another terminal, the CrisBag system secure fast transportation of baggage between the terminals. The CrisBag system also consists of early baggage storage for luggage that for some reason has arrived too early and will be leaving the airport at a later point in time. Every single piece of baggage is top loaded onto a tote and conveyed from the top loader section to the destination which is a tilt section for discharging the baggage into a chute connected to buffer conveyor as shown in Fig. 6. The baggage will be loaded from the buffer conveyor into containers, which will be transported to and stored in the departing flight. The empty tote returns on the CrisBag system to empty tote stackers place close to the top loaders. The long distances transport between the terminals is on high speed section with a speed of up to 7m/s, including a tunnel under the highway connected to the Changi Airport terminals. The simulation model is an AutoMod model, which opens for a composite sub-model structure facilitating the separation of the simulation model according to the ATS architecture. Transport and sorter module and configurator The simulation model was used for verifying the system design. The transport and sorter module consisting of approximately 10,000 conveyor elements was generated automatically from engineering drawings in AutoCAD. These drawings are part of the core documentation of the system and are used for several engineering purposes, including generating the system topology for the control software. The transport and sorter module is normally built by drag and drop of elements in the 3D building environment

Fig. 6 Conveyor elements: baggage loaded on totes (used with permission from FKLI)

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of AutoMod, and implementing approximately 10,000 elements in 3D is quite a time consuming task. In a similar but smaller project, baggage handling system for the Brussels International Airport, the traditional modeling work took 5-weeks. The auto generation of the transport and sorter module from the computer-aided design documentation by means of a java-based configuration application took around a day to complete. Beside time savings, the auto generating of the transport and sorter module also ensured correct model building. The CAD data for the model and for the system topology of the control software were the same, which ensured complete coherence. This is of great importance for the later emulation process. Transport and sorter module and controller During design simulation, a simplistic sorter controller was built by employing a ghost controller using real protocols. This opens for direct re-use of the transport and sorter module model during system emulation for test and quality assurance of the real control software. This concept of separating the controls module and material handling equipment module has been applied to other simulation models for different projects such as the Bershka, Arlanda, and Brussels International airports. Furthermore, this approach will be applied to future projects with the prime goal of using the same material handling equipment module in both design verification and later in the system emulation for quality assurance of controls software. Data acquisition module The data acquisition module collects the history of simulated or real signals and presents them to the operator in a comprehensive way, for example, using the user interface. This includes a complete log of the flow, status, and timing of each and every piece of the baggage. The data acquisition module also collect important statistics concerning the performance of the system such as utilization, point to point traveling time statistics, flow rates, and so on. The data is stored in database or an excel format for further analysis and for direct import into work reporting documents. Training module The training module serves as an interface for generating different scenarios to be used in a training session of supervisors and operators. This module also facilitates the testing of specified requirements and functions. This module can handle different scenarios that may include different product mix and flows, and specific exceptions like breakdown and alternative flows. Full training set-up is offered to end-users for facilitating inhouse training. For example, this training was provided to the end-users at the Brussels International and JYSK installations.

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Fig. 7 An enhanced animation from the advanced simulation model (used with permission from FKLI)

The animation from an advanced simulation model in Fig. 7 emulates the real world ATS by interfacing the control system and the physical conveyor. Conversely, the model can generate scenarios for test and training in the real system. 4.5 Evaluation and communication A modern ATS is a complex system that cannot be understood by simulation alone. Factors like network latency or database access, etc. makes these high performance systems difficult to assess from the components alone. Therefore, the architecture developed in this study enables providers to perform more realistic simulations to test the overall flexibility and robustness of the system. But perhaps most important of all, a systematic modeling approach to the development of an ATS provides a language that facilitate the dialogue between the actors in the development process. The VEA is essentially a combination of simulation and emulation models. Building the simulation models into emulation models brings the re-use of models into a complete new perspective with a wide range of benefit for software development, commissioning as well as in the operation and optimization of automated material handling system. The parts of the simulation model which reflect the control logic are removed from the model, so the model only reflects the physical equipment, and communication modules and middleware are connected to the real controls software. When using real industrial protocols and applying realistic scenarios, the control software can not distinguish

between running the real system or the emulation model, which brings a high degree of realism and confidence into the process. If the controls software can not control the model, it can not control the real system. The main purpose is to optimize bug finding and test during software development phases and to reduce the workload during commissioning and installation on site. Integrating the model with the real controls software opens for test and training set-up, where future operator and supervisors can be trained on the real graphic interfaces of the control software using realistic scenarios. This facilitates the operation of the new system under dynamic situations, reducing the risk of operator error and problems during ramp up and initial production. The VEA also enabled analyzing the benefit and potential of applying advanced control algorithms to industrial systems. In one case (Hallenborg 2007), the VEA were used to optimize performance and robustness using multi-agents based control principles. Performance analyses, like in Fig. 8, verified that the outcome of

Fig. 8 Simulated results of a test scenario with and without the saturation management strategy (Hallenborg 2007)

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applying new principles to saturation management had positive outcome. Multi-agents are considered an advanced but also a high risk approach which might never have been commercially approved without solid validated robustness. This could also have been done using traditional simulation but the difference is that the model is already developed and dynamically validated and that the simulation study will be highly reliable. The experience from this project shows that incorporating simulation as a tool in the various stages of the development process results in adding value to an ATS in many different areas. Another important lesson learned is that the knowledge required is wide spread in the organization, and therefore, the cost of developing the models was underestimated. However the modeling effort turned out to be an important activity from a knowledge management perspective. One of the advantages of using this particular VEA is that the models need only to be developed once. Once a reference model has been established, it can be used throughout the entire lifecycle of a plant, and thereby, the cost should be gained over a longer period. Also, the modeling cost will decrease for each new project since the core of the control systems are the same, and thus, the developed models have been reused at other projects. Finally, disadvantages were observed during the development process including redundancy. For example, it was observed that certain similar modeling activities took place at FKLI internal software development group as well as at one of the suppliers of software. Both parties developed a simulation system in order to verify and validate the control software as part of their internal quality assurance. These costs could have been avoided by jointly using the same advanced simulation models. The main values from using the VEA were found in the following areas: & & & & & &

Testing and bug finding under controlled testing environments Validation of controls software for automation—early identification of errors and actions taken early in the development process. High visibility of quality state of the software during the development process—reveal problems and opens for action-taking in the early process Continuous test in parallel with installation while the real system is occupied by other activities Simulation of future operations scenarios (year 201X), which is difficult to set up in real life operation Low cost of future developments and changes

Evaluation showed that the major costs savings of using the VEA and the advanced simulation models were realized in the quality assurance of controls software. Testing realistic operation of a large automation system in a model

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set-up that reflects complex flow and interaction between hundreds or even thousands of elements is costly and error prone. This new approach saves time and resources by finding and correcting errors in the early stages of the development process. Thus, this approach makes the process of developing controls software visible and manageable. If the controls software can not control the model using the real protocols, then the control software will not be able to control the real system. If the control software fails, the scenario can be analyzed using the message logs and animation of the models set-up. Testing complex flow is normally performed on the life system late in the commissioning phase. Experiences from several automation projects shows that software problems are normally revealed very late in the development stage that results in very costly and time consuming corrections. This causes the delay in handing-over the system to the customer and thus, decrease in productivity is realized during ramp-up. The cost of delays can be very high for both the system supplier and the end-user of the automated handling system. Using the advanced simulation models as system emulator for quality assurance of control software for automation has tremendous benefits, which were not, anticipated when the VEA project was developed. Experience from Danish projects shows that applying system emulation provides benefits such as saving several months of testing work; ensured hand-over of a system ahead of schedule; and available for on-site testing of software set-up prior to site acceptance test. Experience from the projects where the VEA were used show that test work on site are reduced by up to 3-months (according to the system vendors) and the experience from one project showed that the handover of the system could be done 4-week before scheduled. Based on these experiences, VEA is now required in the tender offer and the methodology has been applied to large scale automation projects, such as JYSK Uldum, Changi Terminal 3, Carlsberg Apollo.

5 Findings and discussion From the design science research perspective, there are two levels that the development process described in the case can be contemplated. First, there is the development of the VEA concept, and secondly, there is the development of the ATS architecture. The ATS architecture can be interpreted as an instantiation of the VEA concept, and thus, the VEA concept can be considered a generic ATS architecture. The relationship between the two concepts in the design science research view is illustrated in Table 1 below.

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Table 1 The research outputs: Vaishnavi and Kuechler (2004/2005), adapted from March and Smith (1995) Research output VEA

ATS

Description

Constructs

SOP PSS Advanced Simulation Model System lifecycle

The conceptual vocabulary of a domain

Models

The VEA concept

Methods Instantiations

Enterprise engineering Automatic transport and sorting concept Complex service design

Configurator Controller User interface Training simulator Data acquisition Automatic transport and sorter concept Simulation, emulation and animation Simcon emulation framework

A set of propositions or statements expressing relationships between constructs A set of steps used to perform a task—how-to knowledge The operationalization of constructs, models and methods

Virtual enterprise architecture

Artifact construction as analogous to experimental science

Better theories

Based on this interpretation of the case, we conclude that the successful application of the ATS concept in practice warrants the validation of the VEA concept. Therefore, we assume that the findings from the ATS case may be generalized into the VEA domain, and finally, the VEA concept may be an instantiation of the extended enterprise concept. The findings are summarized by proposing a set of design propositions for complex services. 1) Conceptualization. The use of an advanced simulation model improves the understanding of the final product and service. Design theories often emphasize the designers role in the process, but Schrage (Schrage 1999) argues that the innovative prototype create innovations, not the designer. This argument is quite simple that the process of working (and playing) with the prototype models is more important than the design process. Furthermore, the work illustrates how leading enterprises master this modeling process and are able to transform the learning into innovative products on the market. The message is simple: you need to be able to model your ideas, play with the models, and learn by doing (Schrage 2004). Consequently, the advanced simulation model plays the role of a prototype in service design. 2) Integration. The advanced simulation model takes the role as a boundary object (Wenger 1999) in the development process. The communication between the actors in the SOP is dramatically improved. This is well known during the technical engineering phases, but the ATS concept also illustrated the value of engaging the end-user in early training or revitalization. This is similar to test pilots flying an aircraft simulator before the actual physical construction. This is a new business opportunity for the baggage handling system. 3) Reuse. A critical issue is the cost of modeling. Since most of the effects of the advanced simulation model are network externalities in the SOP, there must be an

isolated business case for developing the models. The full potential for the advanced simulation model appears when the level of reuse is sufficiently high. This is achieved in the ATS case through small scale experience accumulated as well as tool development as is the case of the Simcon emulation framework. Design science research which was used as a methodological framework in a 7-year development project is not without flaws. There are many changing conditions and priorities, and the process was sometime more action research oriented and other times driven by pure coincidence. Referring to the research guidelines proposed by Hevner et al. (2004), the research rigor is not optimal. However, the main value driver for this project is the business value and consequently, the relevance of the work is high. The successful diffusion of the concept into the industry can be taken as evidence of the value of the ATS architecture and the VEA concept. The fact that a business can be made from offering new services based on the ATS concept can be taken as evidence that service advanced simulation model can leverage the business value of the services offered.

6 Conclusions In this paper, we presented and analyzed the case of a VEA as applied in the domain of automatic transportation and sorting applied to baggage handling in airports. Based on the successful diffusion of the VEA concept into the industry, it is concluded that the model is valid and valuable to practice. Based on the fact that the VEA facilitates that a number of new services effectively can be delivered, we conclude that the advanced simulation model can leverage the value of the concept. One of the side effects is reduced modeling cost through the reuse of

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models and integration and communication between actors in the development process has been improved. The advanced simulation model in the VEA can be considered analogue to a product model in product manufacturing. The paper used the extended enterprise perspective as a theoretical lens. Using the concepts and modeling methodologies from the enterprise engineering field proved to be useful, and based on this experience, we concluded that it is possible to make an analogy from product management to service management. We can conclude that an advanced simulation model can play the role as a reference model in service design. This is the main and most important contribution in this paper. The case study was analyzed using design science research as a methodological framework. Although the work is not entirely consistent with this framework, design science research was useful for organizing and presenting the study. In a 7-year perspective, there are many different factors influencing the research process and hence, it is inevitable that this case study was not completely controlled as a well-structured process. The methodological experiences can further be used in the design of a process innovation laboratory (Møller 2007). The experiences gained from this study also have implications for the future. Although service industry is emerging as an important sector in many developed countries, there is little research on service design where service is considered as a special case of physical products. The approach can be adapted to service design in other areas such as model driven EIS design (Barjis 2007). The development of the generic modeling tool, instantiated in the Simcon emulation framework could evolve into a new breed of modeling tools which could be used for designing complex services. Acknowledgement The research of Sohail Chaudhry was partially funded by the Sabbatical leave from Villanova University.

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Inf Syst Front (2008) 10:503–518 Charles Møller is a Professor in Business Process Innovation at the Center for Industrial Production, Aalborg University teaching and researching the management of enterprise information systems in a supply chain context. The chair is co-sponsored by Danfoss-IT. His current research interest is Business Process Management and the purpose of his work is to develop methods and tools for developing effective systems in industry. He has published more than 110 papers. He is the first author on 86 papers, and his publications include 21 papers in Danish and international journals, 37 refereed conference papers, and 26 chapters or books. He was recently awarded the Emerald Literati Network Highly Commended Award. In addition, he is a member of the editorial board for Enterprise Information Systems and has served as “ad hoc” reviewer for several journals. Also, he has being involved in organizing workshops in his field of research as well as serving on the organization committee of several international conferences. Previously, Dr. Møller was affiliated with Aarhus School of Business, University of Aarhus, Department of Production, Aalborg University, Interconsult Management, Brüel & Kjær, and IBM. Furthermore, he is an external lecturer at University of Southern Denmark and University of Aarhus.

Sohail S. Chaudhry is a Professor in the Department of Management and Operations/International Business at Villanova University. He received his Ph.D. in Industrial Engineering and Operations Research from Columbia University. He has published numerous papers in refereed journals such as Computers and Operations Research, Decision Sciences, Expert Systems, European Journal of Operational Research, International Journal of Production Research, Journal of the Operational Research Society, Management Science, OMEGA, and Systems Research and Behavioral Science. He serves as an Associate Editor for Enterprise Information Systems journal, as an Area Editor for the International Journal of Operations and Quantitative Management, and is a member of the Editorial Review Board of Production and Inventory Management Journal. His teaching interests are in the areas of Supply Chain Management, Operations Management, Management Science, and Quality Management. Dr. Chaudhry is also the Program Coordinator of the Business Track of the study abroad program for Villanova University in Geneva, Switzerland. His previous teaching positions have been at Columbia University, Loyola University Chicago, Mosul University, University of Wisconsin at La Crosse, Denmark’s International Study Program, Affiliated with University of Copenhagen, Temple University Japan, and European Business School in Oestrich-Winkel, Germany. In addition, he has lectured at Beijing Jiaotong University, Beijing, China and the Institut Suprieur de Gestion de Sousse, Sousse, Tunisia.

Bent Aksel Jørgensen is CEO at Simcon A/S and has been involved with simulation of manufacturing and material handling systems for several years. His current focus is on system emulation or model based testing of controls software for automation, connecting the real controls software to the 3D simulation models for off-line validation prior to commissioning and operation. In his position at Simcon A/S, he has developed methods and commercial software for testing controls software at different levels of automation including PLC controls, PC-based controls software, ERP/SAP and their associations to the execution systems. These methods and software have been applied in several automation projects in distribution centres, manufacturing lines, and baggage handling systems around Europe and Asia. Dr. Jørgensen received his B.Sc. in Mechanical Engineering from Technical University of Denmark and his Ph.D. in the Industrial Researcher Program jointly administered between Aalborg University and Bang & Olufsen A/S.