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A SIMULATION BASED GAME APPROACH FOR TEACHING OPERATIONS MANAGEMENT TOPICS

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Simulation games have been utilized as an educational tool in order to complement the traditional teach- ing methods. They have been widely applied in the teaching of different subjects such as business man- agement, nursing, and medicine. This paper
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   Proceedings of the 2012 Winter Simulation ConferenceC. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher, eds A SIMULATION BASED GAME APPROACH FOR TEACHING OPERATIONS MANAGEMENT TOPICS Francesco CostantinoGiulio Di GravioAhmed ShabanMassimo TronciUniversity of Rome “La Sapienza” Via Eudossiana 18 RM-00184 Roma, ITALY   ABSTRACT Simulation games have been utilized as an educational tool in order to complement the traditional teach-ing methods. They have been widely applied in the teaching of different subjects such as business man-agement, nursing, and medicine. This paper proposes a new simulation game which simulates a produc-tion system that consists of a set of machines, conveyors, and other components. The objective of the proposed game is to enhance the teaching of some concepts of operations management such as capacityutilization and maintenance planning. The game decisions are repeatedly made in two consecutive stepsof playing in order to enhance the learning of students. This framework of decision making can be utilizedto evaluate the progression of students learning and the educational effectiveness of the game. Studentsshowed a positive response to the game and learning through gaming in an evaluation conducted after  playing the game. 1   INTRODUCTION1.1   Motivation Students have difficulties to understand the complexity of the Operations Management (OM) subject. Themultiple and competing objectives that are linked to the OM decisions are not obvious to people and stu-dents that do not have experience in a company operations as students generally have not obtained yet thisexperience. The theoretical knowledge that OM students acquire in classrooms does not give them fullawareness of OM issues or the criticality of the decisions of operations managers (Ammar and Wright1999). In other words, lectures and explanations provide students with theoretical knowledge but fail to provide them with practical skills required for their future career as operations managers so that studentsfail to link between OM concepts and their applications. To overcome this situation, the teaching activityin OM is sometimes supported by videos and company tours. This helps but does not permit the typicaleducation approach where the student changes a parameter of the reality and sees what happens due tothis modification. The motivation of this paper resides in the opportunity that a simulation model will al-low students to easily see the effects of different OM decisions on production system performances. This paper proposes a new simulation game which simulates a production system that consists of a set of ma-chines, conveyors, and other components. The objective of the proposed game is to enhance the teachingof some concepts of OM such as capacity utilization and maintenance planning. 978-1-4673-4780-8/12/$31.00 ©2012 IEEE  Costantino, Di Gravio, Shaban, and Tronci 1.2   Literature Review The operations management field covers a wide range of subjects from specific areas such as inventoryand scheduling to much wider interrelated areas such as lean and supply chain management. Teaching of operations management usually depends on using the traditional teaching methods such as lectures, as-signments, and case studies. Although these teaching methods are appropriate for the dissemination of foundational and theoretical knowledge, they may not be appropriate to transfer practical skills to students(Ben-Zvi and Carton 2007). Furthermore, many studies have proved the differences between students’learning styles which lead to the need for different approaches to be adopted when teaching a subject(Chwif and Barretto 2003). Proserpio and Gioia (2007) emphasized also that the learning style of the new‘virtual generation’ (V-gen) is very different from that of former generations. It is much more visual, in-teractive, and focused on problem-solving (Pasin and Giroux 2011). Therefore, instructors have to adoptnew teaching aids in order to easily allow the students experience the issues involved in operating andmanaging a production system. Ahn (2008) argued that experiential learning can be characterized as alearning method that involves immersing learners in an environment in which they actively participate inacquiring knowledge. The required experiential learning can take place through a variety of in-class activ-ities that can be used to complement the traditional theoretical presentation methods (Ammar and Wright1999, Chang et al. 2009). The new teaching aids for operations management and business fields are pro- posed to help students gain a new understanding of real industries and enable them to employ theknowledge and theories that are obtained from classrooms in the real world (Chang et al. 2009). Game- based learning is one of these teaching aids where games have been revealed to be very useful pedagogi-cal methods for supplementing traditional teaching techniques (Adelsberger et al. 1999, Chang et al.2009).Bloomer (1973) as cited in Pasin and Giroux (2011) defines a game as any contest (play) among adver-saries (players) operating under constraints (rules) for an objective (winning). On the other hand, Simula-tion refers to a broad collection of methods and applications to mimic the behavior of real systems, usual-ly on a computer with appropriate software (Law and Kelton 1991). A simulation is not necessarily agame (Pasin and Giroux 2011) where simulation has been widely used to analyze systems and to compare proposed scenarios in order to improve the systems’ performances, but simulation can also be adapted toconstitute a game. Simulation Games consist of two components, a description and a simulation model(Adelsberger et al. 1999). The description is an introduction to the game, i.e., to the situation, basic rules,team structure, and various options. The simulation model is used to process the players’ inputs and to ob-tain the reports for each player. Pasin and Giroux (2011) defined a simulation game as challenging inter-active pedagogical exercises, wherein learners must use their knowledge and skills to attain specific goals, played within an artificial reproduction of a relevant reality. Simulation games can also be defined as acontest or a competition based problem solving in a virtual reality.Simulation games as a learning tool attempts to replicate various real world problems in the form of agame for various purposes of training, analysis, or prediction. Chapman and Martin (1995) argued thatsuch types of learning methods can assist in the development of more effective personal transferable skillssuch as team-work, problem solving techniques, and oral and written communication. Furthermore, simu-lation games can be used to help companies to identify technical and non-technical problems, and it canalso be used to evaluate the required changes in the business processes before the implementation(Forssén-Nyberg and Hakamäki, 1998). Simulation games indeed help students to learn and have funsimultaneously (Anderson 2008) and they have been applied in diverse areas; in training in the militaryand the aeronautics industry, and in the teaching of medicine, nursing, engineering, management, andseveral other fields (Pasin and Giroux 2011). For example, Stanley and Latimer (2011) evaluated the ef-fectiveness and suitability of ‘The Ward’ as a simulation game to promote and support nursing studentsunderstanding of decision making, critical thinking and team work in clinical practice situations.Deshpande and Huang (2011) reviewed the different simulation games that have been applied in the edu-cation of science and engineering.  Costantino, Di Gravio, Shaban, and Tronci Several simulation games have been developed for mastering business concepts in operations man-agement. The main purpose of business games or simulations is to mimic the real decision making pro-cess that players will be involved in the future, or they may be already involved in if the players are exec-utives. Haapasalo and Hyvonen (2001) reported that the history of business simulations in general ismore than 40 years long.   Faria et al. (1998) reported that, in a recent survey of accredited businessschools, fully 97.5% of them used simulation games in part of their courses. Sterman (1989) developedthe famous “Beer Distribution Game” at Massachusetts Institute of Technology (MIT) to study the dy-namics of supply chain and especially the bullwhip effect in which demand variability increases as onemoves up in the supply chain. The Beer Game mimics a single product serial supply chain in which each player is assigned to a supply chain position to manage both demand and inventory flows. Other research-ers have developed computerized and web-based versions of that Beer Game to study bullwhip effect (Ja-cobs 2000, Machuca and Barajas 1997). Since then, the Beer Game has been adopted universally as anefficient teaching tool in supply chain courses. In addition, other simulation games devoted for supplychain teaching have been developed such as Mortgage Service Game (Anderson and Morrice 2000) andBlood Supply Game (Mustafee and Katsaliaki 2010). Chang et al. (2009) developed a flexible simulationgame environment called SIMPLE (Simulation of Production and Logistics Environment) in order toraise teaching effectiveness and improve classroom teaching in different major business concepts, such asinventory management, capacity management, pricing determination and negotiation, and information-sharing between players. Bringelson et al. (1995) developed a computer simulation game ‘‘NCTB’’ tohelp engineering and business students learn about the inter-functionality of decision making, in an inter-disciplinary group. The game focuses on teaching four functional areas; namely, purchasing, production planning and control, quality control, and marketing. Battini et al. (2010) developed a simulation gamecalled “Logistic Game” to assess learning-by-doing and knowledge-sharing in Industrial Logistics andOM topics. The main body of their game is a discrete event simulation model developed for the internallogistics of an industrial plant. Similarly, other authors have adopted the discrete event simulation ap- proach to develop pedagogical simulation games, see, for instance, Haapasalo and Hyvonen (2001), andChwif and Barretto (2003).Based on the above literature, it can be concluded that simulation games can be used to improveclassroom teaching of OM subjects. This paper proposes a new operations management simulation gameto be used for students and professionals of OM. The main focus of the proposed game is to teach the stu-dents the main concepts of capacity utilization and maintenance planning.The rest of this paper is organized as follows: in the following, Section 2 introduces the proposedsimulation game, and Section 3 discusses the game organization. Section 4 presents the game evaluation. 2   PROPOSED SIMULATION GAME This game is devoted to teach and train the students and practitioners of operations management on themain concepts and theories of OM. Students need some way to directly experience the issues involved inoperating a production system (Ammar and Wright 1999). Computer simulation games, refined graphics,and multimedia can be developed to present engineering topics in ways that are not possible within thelimitations of the traditional lecture format (Deshpande and Huang 2011). The proposed game providesan environment of problem solving which simulates the reality that student would be involved, but in a playful manner. In this game, the players who may be either students or professionals will be asked tomake strategic decisions regarding the production system configurations (Figure 1) such as the selectionof machines capacities, maintenance strategies, and conveyors speeds. These decisions are classified asstrategic decisions that have to be made accurately during the design of the production system in order toassure the efficient performance of the system. The total cost for operating the production system dependson the decisions made by the players where each decision has its corresponding cost. For example, a teammay select to buy a machine of high capacity rate although the annual demand is not so high to buy it, sothat the team will encounter more cost than it would be to cover the demand. Generally, the objective of each team of players is to operate the production system at the lowest possible cost in comparison to the  Costantino, Di Gravio, Shaban, and Tronci other teams to win the game. This will allow students to realize the impact of their decisions on both thesystem performance and the total cost. In this game, the competition among the teams is based on the totalcost correspond to their decisions. Therefore, the team who achieves the minimum cost will be consideredas the game winner. It is expected that this game will be a helpful aid in order to complement the tradi-tional teaching methods of OM.Figure 1: Production system configuration. 2.1   Simulation Model The proposed game depends on the simulation of popular production system that consists of a set of ma-chines, conveyors, and storage areas. The production system is producing two types of products (Prod. Aand Prod. B); each product has its sequence of operations as shown in Figure 1. The simulation modelrepresents a virtual production system that has to be effectively managed in order to achieve the objec-tives from this system. The simulation model has been built using SIMUL8 software (see Figure 2). Thesimulation model is linked with an external spreadsheet in order to facilitate the entry of the players deci-sions. The production line starts with a large inventory of raw materials connected to the production sys-tem with a conveyor (see Figure 1). The machines in the production system are also connected together with conveyors (see Figure 1). It is also assumed that the system starts every week with initial raw mate-rials inventory, enough for the predicted weekly demand. The machines and conveyors are exposed tomaintenance stops for repairing where these stops may be due to planned or unplanned maintenance.Figure 2: Simulation model flowchart. 2.2   Game Decisions According to the objective of the game, the production system has to be effectively designed in order tosatisfy the weekly demand of each product at the least cost. The decisions that the players are allowed tomake and the decisions alternatives that they can select from are summarized in Table 1. The decisionabout the equipment (Machine/Conveyor) capacity rate may be either fast or slow. The cost of the equip-ment is the sum of the fixed cost and the variable cost; these costs depend on the equipment capacity rate  Costantino, Di Gravio, Shaban, and Tronci (see Figure 3). Furthermore, the player can select the equipment maintenance strategy to be either Planned Maintenance (PM) or Corrective Maintenance (CM). Each maintenance strategy has its relatedcost where in this game the maintenance cost is calculated as the number of stops multiplied by the costof each stop (see Figure 3). Accordingly, there are 2 14 combinations, i.e., 2 possible decisions, 9 convey-ors and 5 machines, of decisions alternatives that can be made by the players. Those combinations could be considered as a set of feasible solutions that can be made.The encountered costs in the production system are divided into fixed cost, operating cost and back-log cost; the operating cost includes both the variable cost and the maintenance cost. The variable cost isa function of the served number of products by each equipment. The backlog cost is encountered whenthe production system fail to satisfy the target demand. The unsatisfied amount of the two products isconsidered as backlogged demand. It is also assumed that each product has a different backlog cost. Thefollowing equations summarize the main costs of the production system that have to be optimized in order to achieve the least total cost.Table 1: Different decisions alternatives.   Equipment Equipment CapacityRateMaintenanceStrategy () i MachineMC   Fast/Slow PM/CM ()  j ConveyorConv  Fast/Slow PM/CM 5911 _ _  ijij TotalFixedCostFixedCostMCFixedCostConv = = = + ∑ ∑  (1) 5911 _ _  ijij TotalVariableCostVariableCostMCVariableCostConv = = = + ∑ ∑  (2) 5911 _ _  ijij TotalPMCostPMCostMCPMCostConv = = = + ∑ ∑  (3) 5911 _ _  ijij TotalCMCostCMCostMCCMCostConv = = = + ∑ ∑  (4) TotalOperatingCostTotalVariableCostTotalPMCostTotalCMCost  = + +  (5) ()()()()  Backlog CostTarget Demand_Prod. A - Actual Production_Prod. ABacklog Cost_Prod. ATarget Demand_Prod. B - Actual Production_Prod. BBacklog Cost_Prod. B = +  (6) = + + Total Cost Total Fixed Cost Total Operating Cost Backlog Cost   (7)Where 1 5 ito = and 1 9  jto = represent machine index and conveyor index, respectively.According to the above equations, it is clear that the poor decisions will lead to high cost of produc-tion and low utilization of the production system’s resources. Therefore, this game aims at learning the players how the poor decisions have a great effect on the production system performance and hence onthe total cost. 3   GAME ORGANIZATION The proposed game is played in teams of players; each team is a group of 3-4 players. The game is man-aged by the administrator who is responsible to constitute the teams, leads the Simulation Game, and the problem oriented coaching for the participants (Adelsberger et al. 1999). Generally, the essential task of the administrator is to adequately combine the simulation game and other teaching contents. The distribu-
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