Abstract Code: 011-0533 Designing a Simulation Laboratory Environment for Service-Oriented Supply Chain Information Management Stephen C. Shih1 Chikong Huang2
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Associate Professor & Associate Director, School of Information Systems and Applied Technologies Southern Illinois University Carbondale, Illinois 62901-6614, USA E-mail:
[email protected], Phone: (618) 453-7266 2
Professor, Department of Industrial Management, Institute of Industrial Engineering and Management, National Yunlin University of Science & Technology, 123 University Road, Section 3, Touliu, Yunlin, Taiwan 64002, R.O.C. E-mail:
[email protected], Phone: 886-5-5342601 ext. 5336
POM 20th Annual Conference Orlando, Florida U.S.A. May 1 to May 4, 2009
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Designing a Simulation Laboratory Environment for Service-Oriented Supply Chain Information Management
Abstract The primary objective of this research is to propose a simulation laboratory environment to examine the interactions and information exchanges between various levels of the serviceoriented supply chain network. The research involves the exploration of three areas: (1) the essential characteristics and associated requirements underlying various service-driven supply chain transactions, (2) business assumptions underlying conventional service-driven supply chain models, and (3) modeling theories adopted to redefine the new models. Furthermore, the laboratory will be used as a viable base to develop fundamental theories for improved network communications and increased operational efficiency. An important thesis of this research is to explore the synergy between service supply chain logistics and information exchange. Ultimately, this research has the potential to extend simulation techniques and information technology to an important economic sector—the the service sector—that has long been overlooked in the past.
Key Words: service-driven supply chain management and logistics, service supply chain information management, and simulation.
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1. Introduction The primary objective of this research is to propose a framework and information technology architecture for the design of a simulation laboratory environment to examine the interactions and information exchanges between various levels of the service-oriented supply chain network. The proposed simulation laboratory, Integrated Service Enterprise Systems Laboratory (ISES Lab), will provide an environment for researchers and students in various disciplines, including production and operations management, operations research, industrial engineering, and information systems, to conduct research in a number of areas related to service supply chains (e.g., exploration of the essential characteristics underlying service-driven supply chain transactions). The ISES Lab will be further used as the viable bedrock for development of theories on service supply chain network communications and information management. The research conducted in the laboratory involves the exploration of three areas: (1) the essential characteristics and associated requirements underlying various service-driven supply chain transactions, (2) business assumptions underlying conventional service-driven supply chain models, and (3) modeling theories adopted to redefine the new models. The lab research projects will help explore the synergy between service supply chain logistics and information exchange. This synergy has presented an unprecedented territory of innovation and challenge. Ultimately, the research work and resulted nourished from the lab has the potential to extend simulation techniques and information technology to an important economic sector, the service sector, that has long been overlooked in the past.
2. Service Enterprise Systems and Service Supply Chains Due to global expansion and the need for close collaboration with business partners, many
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companies have grown in the direction of enterprise systems extension by including service operations. Most of the manufacturing enterprises have historically focused mostly on the supply chain management of designing and manufacturing physical products while paying little attention to the so-called “forgotten supply chain” of their services and post-sales businesses. Post-sales businesses represent all the maintenance, repair, and overhaul (MRO) services that are either not included in the original equipment sales or not delivered by the original equipment manufacturers (OEMs). For the post-sales service intensive enterprises (e.g., Boeing and Otis), the profit margin of their MRO businesses is usually much greater than that of original goods or equipment sales. With combined higher net margins and decreased capital requirements of the post-sales service operations, greater financial value can be significantly created. Complexity is inherent in most real-world service enterprise systems which are usually characterized as a super-hybrid system. Service enterprise system modeling itself is a complex task not only from the effort involved in mapping out all the information paths and business processes in the service supply chain and the simulation models but also due to the complex taxonomy of object types involved in the enterprise system. A generic learning tool for designing and modeling an enterprise-level super-hybrid environment requires substantial innovative learning mechanisms. Service quality is typically a significant competitive factor for those service-intensive industries that provide planning, monitoring, maintenance, and repairing services for their aftersale equipment (e.g., elevators). The ability to plan, schedule, and manage the service activities is crucial to the service-based companies. Nevertheless, in modern service industries, there are no consistent methods for service-related tasks, such as workforce planning, scheduling, and resource allocation. For global operations, most service-intensive organizations are still reliant
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on a great number of different business processes and sub-optimized approaches to support each individual regional operation. Even though numerous analytical techniques have been developed for the scheduling of various manufacturing systems, only relatively few studies were targeted at the service sector. Among them, Ahituv and Berman (1988) have developed techniques that can be used in the management of various service networks including ambulance, police, fire, and courier services; Kolesar and Blum (1973), Smith (1979), Hambleton (1982), Agnihothri and Karmarkar (1992), Hill, March, Nachtsheim, and Shanker (1992) have studied the field service territory planning problems that involve balancing the number of technicians and the size of the geographic region they will service in order to achieve some performance objectives; Dzubow (1972), Panson (1983), Agnihothri and Karmarkar (1992), Hill (1992) have considered various dispatching rules for the Traveling Technician Problem (TTP); Haugen and Hill (1999) have proposed three scheduling procedures to maximize field service quality in the TTP and the comparison of these scheduling procedures against three dispatching rules in a simulated TTP scenario has shown domination of all scheduling procedures in all four service quality criteria. Most of these studies were too restrictive in their assumptions such as all service calls were of the same service class and had the same repair time distribution (i.e., ignoring the factors of various technician skill levels, parts/tools availability, planned regular maintenance vs. emergency repair, etc), constant travel speed (i.e., ignoring the factor of various traffic congestion levels at different times and locations), and do not account for the complexity of the real-life field service scheduling problem. Also, there are quite a few field service management software packages available in the market (Albright, 2002); however, their planning and scheduling algorithms are proprietary and undisclosed.
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3. Service Enterprise Systems Education The trend of service enterprise systems evolution has urged many multinationals to view and manage the entire operations from a whole new perspective. Specifically, this trend has prompted companies to start focusing on dynamic, team-oriented organizational structure. As more companies strive to coordinate activities seamlessly, it is imperative that their employees be equipped with cross-functional knowledge and skills. Given that students often acquire knowledge and skills in different functional areas, many attribute deficiencies mentioned previously to the lack of systematic, cross-functional integration in the curricula. In other words, academic disciplines including information systems, POM, and other curricula still follow the “stovepipe” approach to educating their students (Albrecth and Sacks, 2000; Corbitt and Mensching, 2000; Gorgone et al., 2002). The leveling of organizational structure and the resultant changes in the workplace call for an interdisciplinary curriculum. A review of the current service enterprise systems education programs shows a lack of collaboration across the boundaries of academic disciplines. In addition, research has shown that the cross-functional approach to enterprise systems design is rarely the foci in the curricula (Trauth, 1993; Denis et al., 1995; Bandow, 2005). Such educational settings are not conducive to developing interdisciplinary knowledge and skills in a horizontal working environment. Consequently, many recent graduates were criticized for not being productive team players in a cross-functional team setting due to their lacking of extended enterprise perspectives and interdisciplinary problem-solving skills.
4. The Framework of ISES Lab The proposed Integrated Service Enterprise Systems Laboratory (ISES Lab) pinpoints several
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important trends that could profoundly shape and revolutionize the modern service enterprise systems education, including enterprise systems evolution, lean manufacturing, and horizontal organizations. Associated with each trend, major themes and focuses for advanced service enterprise systems education are identified and highlighted in this paper. The research projects and instructional lab assignments are unique in that it combines several service enterprise systems topics with proven pedagogical methods for conducting research projects and instructional assignments in service supply chain management. One of the foremost themes of the ISES Lab is to underline the importance of an extended enterprise system (Davis and Spekman, 2003) to go beyond its organizational boundary by including its service supply chain partners in collaborative planning and execution of its aftermarket business operations. With the notion of extended enterprise, the proposed laboratory environment emphasizes the importance of seamless integration and multi-directional service supply chain coordination. Second, the lab assignments fosters critical knowledge and practical applications of “lean thinking” (Womack and Jones, 2003) in enterprise systems design to create a lean enterprise (Henderson et al., 1999; Kennedy, 2003). Based on a holistic, value-stream approach, a lean enterprise is the application of lean principles and techniques to an extended service enterprise and supply chains. Moreover, another key tenet of conducting lab research projects and assignments in the laboratory is “interdisciplinary” that underscores the significance of intra-organizational collaboration and integration among business units to ensure a successful extended service enterprise system implementation. The laboratory environment, along with the practical set of lab assignments, provides a robust interdisciplinary framework for integrating service enterprise systems education for students from the disciplines of information systems (IS) and production and operations management (POM). Finally, the proposed laboratory incorporates several
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experiential learning and instructional methods, such as web-enabled collaborative learning, scenario-based simulation and discovery learning, and student-driven evaluation. Several courses and project assignments are designed to offer the students practical application opportunities on lean service enterprise systems improvement through establishing baselines and target metrics for key business processes. In the wake of changing organizational structure, the lab environment is created to help students develop cross-discipline knowledge and skills needed in horizontal organizational structures and develop an integrated service enterprise perspective by immersing them in a learning environment that closely simulates real-world business scenarios.
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Web-enabled, simulation-based, collaborative learning laboratory environment
To support the implementation of the ISES Lab, a web-enabled, simulation-based, collaborative computing environment is set up as the foundation for both the instructional modules and the laboratory projects. In a snapshot, this technology-enabled learning laboratory serves multiple purposes:
The laboratory forms the basis of an ideal collaborative learning environment where students can learn from hands-on experience via various assignments in real-life business contexts.
The ISES Lab provides a variety of software tools in the areas of computer simulation and modeling (e.g., ProModel and ILOG), business applications development tools (e.g., Microsoft Visio Studio), database management systems (e.g., Microsoft SQL Server), and knowledge discovery and data mining (e.g., Oracle Express and SAS Enterprise Miner) to support the entire service enterprise systems development process. Specifically, ProModel is recommended for developing the simulation models, while ILOG CPLEX should be installed
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to develop C and C++ programs for linear programming, mixed integer programming, other mathematical programming problems. In addition, ILOG Solver can be used to solve problems in production planning, resource allocation, optimization, and management.
To simulate information sharing and transaction exchanges with business partners in a supply chain, an ERP system (e.g., Microsoft Dynamics) is provided to support such learning tasks as systems analysis, process mapping, system configuration, and testing.
Next, as an important tool in the simulated environment of intra and inter-organizational collaboration and communication, an service enterprise portal is incorporated into the laboratory to demonstrate how a web portal can facilitate more effective group decisionmaking, information sharing, and collaborative planning and design among different team members.
The ISES Lab includes a collaboration mechanism, shared reference space, for collaborative learning. This multimedia-enriched cognitive support tool allows students to visually conceptualize, analyze and communicate their analyses of complex service enterprise systems problems, and apply lean principles in different settings of information exchange. The shared reference space support both synchronous and asynchronous communications. When students are present at the same time over the service network, the collaborative learning support should assisting students in brainstorming ideas and exchanging information to co-develop enterprise system solutions. This collaborative learning continues when they are not working in the same workspace or time, since all the ideas, comments, findings, and changes to the solution are tracked and recorded in the shared reference space. Collaborative learning offers a viable, effective approach to deal with the dynamic, demand-responsive nature of service enterprises.
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The ISES lab also offers handicapped accessible laboratory facilities and other necessary computing equipment (e.g., electronic smart board) for web-enabled collaborative teaching and learning. A small-scale enterprise database is used as the underlying data source for the project. Using a web-based information sharing mechanism and an integrated enterprise database, students can perform the tasks mentioned above by extracting and aggregating information and knowledge from different functional areas in an enterprise‟s value chain, such as engineering design, manufacturing, logistics, and services.
4.2 Laboratory projects and assignments Through completing laboratory projects and assignments, students are imbued with important concepts in analyzing and improving a service enterprise and its supply chain, such as business workflow and production processes, balancing demand and supply (capacity), global supply chain optimization and integration, infrastructure of information systems and computer networks, interaction and information sharing among internal and external environment (i.e., upstream suppliers, downstream dealers, retailers, and customers, as well as the third-party inbound and outbound logistics providers), and implementation and deployment strategies. The lab also provides a viable environment for developing fundamental theories to improve service supply chain network communications and collaborative efficiency. Specifically, the lab projects and assignments were designed to test several hypotheses of complex behaviors in service-oriented supply chain operations. For instance, three tested hypothesis are shown as follows: Hypothesis # 1 – The size and complexity of a service-oriented supply chain can evolve dramatically with characteristic time-constants, such weeks and months. The dynamic,
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demand-responsive nature of many services makes the modeling and decision making under uncertainty significantly more critical than in manufacturing settings. Hypothesis #2 – Transactions and interactions in a supply chain network may track up to thousands of supply chain units and each unit may further contain hundreds of maintenance actions. Each maintenance action can represent a unique supply chain unit including spare parts, repair processes, technical skills, logistics and distribution, etc. Hypothesis #3 – The object types in a supply chain may constitute a complex taxonomy ranging from material properties, inventories, technical skills, to transportation dynamics.
It has been realized that substantial service improvements in quality as a result of implementing visually rich simulation systems. The proposed laboratory environment applies this idea to testing the hypotheses mentioned above via laboratory simulation and experimentation. Laboratory simulation and experimentation involves the creation of an artificial environment for isolating and better controlling of potentially confounding variables (Hersen & Barlow 1976, Jarvenpaa et al. 1984, 1985, Jarvenpaa 1988, Benbasat 1990a, 1990b, DeSanctis 1990). A simulation-driven decision support laboratory will be developed to examine the explosion in distributed supply chain network and information exchange activities. A set of simulation models are developed and tested on a hypothetic or real-world demonstration problem against an established baseline and the three hypotheses mentioned above. In addition, scenariobuilding is adopted to gather new insights into relationships among transactional variables in a service supply chain network.
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4.3
Implementation principles and learning methods
The following paragraphs depict how the lab project is carried out through the application of three fundamental implementation principles: (1) collaborative and just-in-time learning via multidisciplinary product team formation and role-playing and (2) scenario-based simulations and discovery learning. Collaborative learning has long been promoted by numerous educators and researchers (Johnson and Johnson, 1989; Kuhn, 1993). The ISES Lab supports collaborative learning by creating a simulated, shared environment in which students can collectively investigate realworld business problems in case studies of model companies and recommend viable enterprise systems solutions. Moreover, to promote just-in-time learning, both the IS and OM teams are encouraged to continually develop their competencies by learning new knowledge and skills from their own disciplines as well as from other disciplines in an iterative cycle of learning and application. To assist students in successfully carrying out the laboratory projects, the “blended learning” approach is adopted by combining instructor-led lectures with scenario-based simulation learning and discovery learning methods (Bruner, 1961, 1966; Leutner, 1993; Hammer, 1997; Johnson et al., 2003). The scenario-based simulation learning method is recommended because computer simulation research has shown the effectiveness of computer simulation as a strategic planning tool for organizations to implement lean principles (Fripp, 1993; Towne, 1995; Aldrich, 2003). With scenario-based simulation, students learn to analyze and simulate different production scenarios, think hypothetically, and evaluate different potential solutions before making the final decisions. Systematic validation and statistical analysis of simulation results is an essential step of a
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successful simulation modeling and development. To ensure the validity of the developed simulation model, a validating process will be performed to examine if the model correctly represents the essential aspects of the system within the real-life context. To begin with, a number of validation criteria will be defined to facilitate fully understanding of a successful simulation. The modeling assumptions and sensitivity will be examined by testing and changing various parameters identified in the model. Statistical tests (e.g., goodness-of-fit tests) will be performed on all inputs and internal processes. The model will be assessed to verify whether the simulation model is built with respect to requirements and performance criteria established during the requirements definition stage and further examine if it is a viable representation of the real-life service system. Furthermore, an empiricist‟s approach (Law, 1991) will be adopted for supply chain model validity checking on the initial set of modules and a rationalist‟s approach for the newly defined model components. With the empiricist‟s approach, the performance results generated by the scenario simulation model are then compared with historical data in the real-life environment. Hypothesis tests are used to determine if the disparity between the various performance results is statistically significant between the real model structure. For the new model development, historical data is unattainable. The newly developed model is then closely examined and the assumptions are properly updated and justified. To fully seize the essence of the behavior of the system, experimentations will be indispensable. Formulating pertinent experimental conditions and simulation scenarios under which the model behavior is examined. Given that statistical analysis of the results of each experiment is appropriately conducted and the results are properly evaluated and compared, the experimentation process will not only lead to a greater comprehension of the behavior of existing system but also figure out the ways to enhance the system with desirable performances.
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As shown in Figure 1, students are required to role-play as employees in two organizational units at any typical organization as they were in the real world setting: Department of Information Systems (IS) and Department of Production and Operations Management (POM). In addition, students take turns playing the role of the “end customers” who work with the product teams to provide insight from the customers‟ perspectives. By playing different roles and working with students from different disciplines, both the IS and POM teams can acquire cross-functional knowledge and skills. Essentially, the laboratory project requires the students to carry out multidisciplinary, collaborative tasks in the following areas:
Information processing support activities: The IS teams carry out information processing and related support activities. They are responsible for developing “right-sized” information systems to provide just-in-time ERP data and information
Primary production activities: The POM teams focus on the primary activities directly related to services and after-sale operations. In addition, they are in charge of organizing all the value-creating activities in the best sequence for a specific service along a value stream.
Continuous process improvement: While each team has its primary focus, both the POM and IS teams will collectively define and continually refine the business and manufacturing processes throughout the entire project.
5. A Generic Example of Service Enterprise Systems Simulation To assist in conceiving both the theoretical and practical significance of the lab projects, a generic example—field service planning and scheduling of after-sale equipment—is illustrated in this section. From a broader perspective, the primary sources of field service work include planned maintenance tasks, callbacks, repair work and open-order requests.
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Among them, planned maintenance and scheduled repairs are performed at regular intervals on the serviced units through service contracts. Callbacks or emergency service calls are unplanned service requests that are due to equipment failure and that arise randomly over time. These work types have very different scheduling requirements. One of the integral simulation models is to combine all the work types in one optimized scheduling solution to address some highly dynamic and challenging problems. In the simulation process, various real-life scenarios are to be set up and constraints on service work are to be defined, including different work types, spatially separated service geography, skills, parts, and other business rules.
Primary Production Activities (role-played by the POM Team)
Inbound Logistics
Outbound Logistics
Operations
Product Team X
Material Flows
Information Flows
Suppliers
The “Customer” Group
Product Team Z
Information Processing Support Activities (role-played by the IS Team) Databases Design
Programs Design
Interfaces Design
Product Team Y
Networks Design
Figure 1. Role-Playing and Interdisciplinary Learning for Collaborative Planning and Design
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To address the realism of the defined service demand assumptions and requirements, a field service planning and scheduling process for automated and real-time resource allocation and scheduling is to be simulated. This simulation task will be conducted based on a multidimension assignment of service jobs, required resources, and time slots. For initial schedule planning, an external Enterprise Resource Planning (ERP) system is connected for the provision of basic data, such as equipment history and usage data, types of service work, parts availability, etc. The planning module of the system is capable of developing an optimal plan by grouping the service jobs based on both the distance-proximity clustering and time-proximity clustering methods. For essential mapping information, an external Geographic Information System (GIS) will be integrated to provide a map database containing layout of roads, geocode information, direction and speed limits of roads, etc. As the heart of the simulation model, an automated and integrated scheduling engine will be built to continuously run for maintaining a real-time “optimal” or “near-optimal” solution across a rolling horizon as uncertain conditions, such as service times, travel times and arrival of callbacks, are realized. The scheduling engine is capable of integrating all service work types and the solution will faithfully abide by all the objectives (soft business rules) and constraints (hard business rules). Figure 2 depicts a conceptual architecture of the simulation environment mentioned above. The modules of the lab will be a simulation-based testing environment with two essential components: Visual Modeler and Simulator and Service Operations Optimizer.
Visual Modeler and Simulator. This component will be employed to create necessary simulation models to simulate various interactive and collaborative transaction and exchange scenarios in specific service supply chain implementations. This simulation environment setting will help design and valuate high performance service operations.
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Enterprise Resource Planning (ERP) Equipment History
Usage Data
...
Types of Parts Work Data
Field Service Geographic Information System
Business Objectives & Constraints
Planner
(GIS) Field Service Scheduler
Figure 2. Conceptual Architecture of the Field Service Simulation Environment
Service Operations Optimizer. This mechanism is to assist in optimizing complex stochastic problems in the systems via optimization modeling.
The simulation-based laboratory will help contribute to develop service-oriented supply chain and optimization theories for the service industries. Meanwhile, the results will provide guidelines to academic and business communities in the design of advanced supply chain software for service sector. In addition, the research effort will advance new capabilities in modeling, simulation, and optimization of service supply chain.
6. Conclusions The ISES Lab facilitates implementing a service enterprise systems educational plan in two aspects: (1) developing the after-sale service logistics and information management courses and 17
(2) implementing a new approach to enhancing the learning of modern service supply chain optimization and service enterprise systems integration. When it comes to broader impacts, the ISES Lab could help promote research that extends the application of optimization theories, simulation and modeling techniques, and information technologies to an important economic sector, the after-sale service sector. For instance, one of the research initiatives is the development of fundamental theories of condition-based maintenance (CBM) and field service management for improving the competitiveness and reliability of after-sale service enterprises. Additionally, this research will open a new area for teaching service planning and scheduling, service chain optimization, and e-service information systems design. It will provide the foundation for introducing after-sale service logistics optimization in the supply chain management course, which is becoming an important course in the curricula of many disciplines, such as industrial engineering, production and operations management, operations research, and management information systems. This research work can be incorporated into the teaching of both operations management and information systems at both the undergraduate and graduate levels. The educational plan potentially leads to significant enhancement in the learning of service supply chain and logistics management. The introduced courses will improve both theoretical and practical aspects of the service enterprise systems curriculum. Involving students in research not only trains them adequate problem-solving skills but also encourages them to continue graduate study. The proposed ISES Lab also supports educational enhancement for cross-disciplinary programs by incorporating various innovative learning methods for effectively engaging students in service enterprise systems design and management. The philosophy to innovative technology development consists of applying results from various sources. The proposed lab environment
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facilitates collaboration among multidisciplinary members (majoring in IS, POM, etc.) allowing the project teams to leverage their diverse experiences, knowledge, and skill sets. With the special designed lab projects and assignments, teaching and learning are significantly enhanced through the use of the scenario-based simulation exercises and discovery learning method. The ISES Lab provides an interdisciplinary and collaborative learning environment allowing students to greatly advance their cross-functional knowledge, problem-solving skills, and innovative thinking in lean enterprise systems design—which will well prepare them prior to their entering the job marketplace.
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