Learn more, better and faster: computer-based simulation ... - CiteSeerX

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Int. J. Business Performance Management, Vol. 7, No. 1, 2005

Learn more, better and faster: computer-based simulation gaming of production and operations Timo Lainema Information Systems Science, Turku School of Economics and Business Administration Rehtorinpellonkatu 3, FIN-20500 Turku, Finland Fax: +358 2 481 4451 E-mail: [email protected]

Olli-Pekka Hilmola Logistics, Turku School of Economics and Business Administration Fax: +358 2 481 4280 E-mail: [email protected] Abstract: In this paper we highlight the need to provide education that represents a business process view of organisational functioning. Following three computer supported methodologies are evaluated first: • traditional simulation • systems dynamics simulation and • business gaming. Operations management decisions usually deal simultaneously with multiple products and production capacity considerations, and issues related to successful sales and efficient purchasing. These are issues which are hard to capture with tools that present momentary, stagnant views of business circumstances. We reason that the potential in transparent business process simulation gaming is probably higher for production and operations management than what is the potential in traditional business games or other computer supported methodologies. This is because transparent and continuously evolving learning environments present the process view of business functioning. Furthermore, we shortly present this kind of continuously evolving business simulation game. This game works in a local area network, where participant groups with their own manufacturing companies can compete against each other in an imaginary market environment. We also analyse, what kind of results continuous gaming has produced in two Production and Operations Management courses, given for the MSc students. Keywords: production and operations management education; simulation methods; simulation gaming; business process training; business gaming; continuous gaming. Reference to this paper should be made as follows: Lainema, T. and Hilmola, O-P. (2005) ‘Learn more, better and faster: computer-based simulation gaming of production and operations’, Int. J. Business Performance Management, Vol. 7, No. 1, pp.34–59. Biographical notes: PhD Timo Lainema is an acting Assistant Professor in Information Systems Science at Turku School of Economics and Business

Copyright © 2005 Inderscience Enterprises Ltd.

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Administration (TSEBA) in Turku, Finland. He has an experience of 16 years in using business games both in university education and in in-house company training settings. Lainema’s research interests include such topics as time in decision-making, experiential learning, complexity in learning environments, and data bases. Dr. Olli-Pekka Hilmola is a Research Fellow of Logistics at TSEBA in Turku, Finland. Before joining TSEBA, he received his M.Sc. and Ph.D. from the University of Vaasa, Finland. Currently Dr. Hilmola is also working as a Supply Chain Manager for electrical motor manufacturer Rotatek Finland. His current research interests include supply chain management, theory of constraints, system dynamics, and total productivity management. He has published around 40 international conference papers, 20 journal articles and a number of book chapters. He is also a member of the editorial advisory board of Industrial Management and Data Systems.

1

Introduction

The higher-level business education is in a state of change. The traditional business school curriculum does not seem to correspond with the business realities. In business schools, there is a clear move away from viewing programmes as solely concerned with the transmission of content, knowledge and skills, and a move towards developing deeper intellectual skills with the capacity and capability to think independently (Prince and Stewart, 2000). Selen (2001) argues that there is a lack of integration of all the ‘traditional’ functional areas (e.g., accounting/finance, marketing, operations, management) in relation to evolving overall business models and strategies. Walker and Black (2000) state that business schools and faculties have a linear, disciplinary focus on business education, which neglects the introduction of process perspective needed in the business curriculum. Leitch and Harrison (1999) note that the range of teaching techniques must be extended to include process-oriented approaches. Thus, business schools (as well as companies) need learning tools, which promote business process understanding, and how a business operates as a whole. Business schools are typically organised into functional departments. Walker and Black (2000) state that this happens because there is an assumption that the delivery of business education can be best accomplished by dividing the teaching effort according to areas of speciality. Business education has not provided a broad, integrating, or realistic experience in the business curriculum. The core curricula of most business schools focuses primarily only on two dimensions: • functional knowledge •

skills.

This view is not consistent with cross-functional process managed in organisations (Harvard Business Review, 1992). Walker and Black (2000) note that a business process is a vehicle for truly cross-functional thinking. Faculty members who are trained in specialised fields tend to interpret learning objectives from the perspective of their speciality. Walker and Black state:

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T. Lainema and O-P. Hilmola “The process approach delivers to students an integrated understanding of how businesses function from an organisational, instead of a functional perspective. This is accomplished by presenting the study of business as a series of integrated activities instead of largely independent functions. The process courses help identify and eliminate undesirable redundancies in the coverage of topical material because, the process courses force communication and cooperation between faculty of different disciplines when the courses are developed.”

This discussion is relevant also in the field of production and operations management. Naim et al. (2000) note that with the advent of logistics as an important industrywide discipline, there is a greater need than ever before to produce cross-disciplinarily aware professionals. The core competency within the field of logistics is systems thinking and the ability to discuss and analyse processes. But process orientation does not mean that the old functional divisions disappear. It is more a question of being able to handle two different aspects at the same time, instead of changing one for the other. Naim et al. (2000) believe that it is necessary for the functional teaching to be completed so that desired cross-functional and process effects are taken into consideration. What we are aiming in this paper is quite exactly what Naim et al. (2000, p.75) are describing: “A critical element to obtain a cross-functional perspective of a problemsolving task, is the team project. Thus, individuals with different core skills (or individuals assigned with specific functional roles) work together to develop an integrated approach to logistics problem-solving. It is envisaged that each team will have to solve the same problem and they are in active competition with each other. Suitable performance metrics are assigned to the task so that the teams are participating in an environment that simulates the real, highly commercial world. Each group is assigned to a company that has specified supply chain problems that need insight and recommendations for solutions.”

This paper is organised as follows: In the following section we introduce some modern conceptions of learning. After this, we introduce three present computer-supported methods relevant in teaching business processes. These computer-supported methods are also cross-compared with respect to their strengths and weaknesses. Analysis of these computer-supported methods, as well as issues arising from the learning context, is our primary motive to propose a new computer-supported game, named as Realgame. The philosophy behind this new game is presented, but also some of the results and reflections from a Production and Operations course are being examined. The paper provides new and interesting avenues for further research in this area.

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The learning context

Some of the issues related to learning should be clarified first, as different computerbased learning environments are going to be analysed later in this paper. We follow current views of learning, which acknowledge that you cannot convey understanding; learners could only construct understanding by themselves. Technologies are more effectively used as tools to construct knowledge with – not from, like in the way it is in programmed instruction. Jonassen and Land (2002) list several modern concepts of learning that share many beliefs and assumptions. These views are based on the belief that learning is neither a transmissive nor a submissive process, but rather a

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wilful, intentional, active, conscious, and constructive practice that includes reciprocal intention-action-reflection activities. Bednar et al. (1992) argue that effective learning environments should encourage construction of understanding from multiple perspectives. Effective sequencing or rigorous external control of instructional events simply precludes constructive activity and the possibility of developing alternative perspectives. The aim should be to facilitate situating cognition in real-world contexts and construction of multiple perspectives. By real-world contexts Bednar et al. (1992) mean following: •

The task is not isolated, but rather is a part of a larger context. We should create projects or environments that capture a larger context in which the problems are relevant. This is highly relevant when we want to facilitate business process understanding.

•

The reason for solving the problems must be authentic to the context in which the learning is going to be applied.

•

The environmental context is critical. Learning always takes place in a context and the context forms an inexorable link with the knowledge embedded within it. Thus, an abstract, simplified environment is not just quantitatively different from the real-world environment, but is also qualitatively different. Authentic learning environments may be expected to vary in complexity with the expertise of the learner.

Spiro et al. (1991), who summarise their own research, note that a common thread going through the deficiencies in learning is oversimplification. As an example, they state that compartmentalisation of knowledge components works as an effective strategy in well-structured domains, but blocks effective learning in more intertwined, ill-structured domains (where each example of knowledge application typically involves the simultaneous interactive involvement of multiple, wide-application conceptual structures), which require high degrees of knowledge interconnectedness. Well-structured domains can be integrated within a single unifying representational basis, but illstructured domains require multiple representations for full coverage. Spiro et al. (1991) have found that a single analogy may help at early stages of learning, but actually interferes with more advanced treatments of the same concept later on, when the knowledge domain is more intertwined and ill-structured, requiring high degrees of knowledge interconnectedness. Duffy and Cunningham (1996) present and justify their principles for learning environments in a paper often referred to in the field of education: •

All knowledge is constructed; all learning is a process of construction. Learning is a matter of changes in one’s relation to the culture to which one is connected.

•

Many worldviews can be constructed; hence there will be multiple perspectives. The engagement with others creates the awareness of multiple perspectives.

•

Knowledge is context dependent, so learning should occur in contexts to which it is relevant. However, the physical character of the environment is relevant only to the extent it impacts the character of the ‘thinking’ and skill requirements.

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•

Learning is mediated by tools and signs. All distinctly human instances of learning are constructions, situated within a context that employs some form of mediational means, tools, and/or signs.

•

Learning is an inherently social-dialogical activity. Knowledge, and thereby learning, is a social, communicative, and discursive process, inexorably grounded in talk. The way in which an individual (a student) comes to manifest the effective behaviour of a community is the way to speak with the voice of that community.

•

Learners are distributed, multi-dimensional participants in a socio-cultural process. Learning is not the lonely act of an individual, but a matter of being initiated into the practices of a community.

•

Knowing how we know is the ultimate human accomplishment. We are generally unaware of the beliefs we have adopted or created to live and teach by, but raising them to awareness can have salutary effects.

Computers can be tools to off-load the basic cognitive task (amplifying the teaching task we have always been doing). Thus, computers can also offer genuinely new representations or views of phenomena that would not otherwise be possible, and hence provide new understanding. Duffy and Cunningham (1996) suggest that technology should be seen as an integral component of the cognitive activity. The focus is not on the individual, but on the activity in the environment. The task of the learner is no longer seen as static – the computer is applied to the task – but rather it is dynamic. The computer opens new opportunities and makes available new learning activities (Duffy and Cunningham, 1996): ‘Success [of learning] will increasingly depend on exploring interrelationships in an information-rich environment rather than on accepting the point of view of one author who pursued one set of relationships and presents conclusions reflecting his or her implicit biases.’ Duffy and Cunningham (1996) take another step further and describe problem-based learning. The focus should be on developing the skills related to solving the problem as well as other problems somehow related to it. Skills are developed through working on the problem, i.e., through authentic activity. It is impossible to describe what is learned in terms of the activity alone or in terms of the content alone. Rather, it is the activity in relation to the content that defines learning, the ability to think critically in that content domain, to collaborate with peers, and the ability to locate information related to the issues. The teacher does not teach students what they should do/know and when they should do/know it. Rather, the teacher supports the students in developing their critical thinking skills, self-directed learning skills, and content knowledge in relation to the problem. Today, these learning principles (often called the constructivist view of learning) seem to be more or less established: Technologies can support learning, if they are used as tools that help learners to think. These learning principles give support to different computer-based business process learning environments. We will next assess three different business process learning tools.

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Traditional simulation, systems dynamics simulation, and business gaming

3.1 Simulation Simulation means (Dictionary of Computer Science, English–French, 1989) ‘the use of a data processing system to represent selected behavioural characteristics of a physical or abstract system. For example, the representation of air streams around airfoils at various velocities, temperatures, and air pressures. Different sources define the basis of simulation to be imitation of the behaviour of some existing or intended system.’ A simulation is a representation of the structure in action (Whicker and Sigelman, 1991). What is simulated is some of the critical features of the reality (Saunders, 1995). Naylor (1971, p.2) defines simulation of economic systems as follows: ‘A numerical technique for conducting experiments with certain types of mathematical models, which describe the behaviour of a complex system on a digital computer over extended periods of time.’ The principal difference between a simulation and a real world experiment is that with simulation the experiment is conducted with a model of the real system, instead of the actual system itself. Pidd (1998) notes that computer simulations are used when it is impossible or inconvenient to find some other way of tackling the problem. In such simulations a computer is used because of its speed in mimicking a system over a period of time. A point like similarity is the exception rather than the rule. Bunge (1973) mentions that an illusion of perfect formal analogy can be produced only in special cases, as in certain mechanical or hydraulic analogs of electric circuits. Computer simulation involves experimentation on a computer-based model of some system (Pidd, 1998). The model is used as a vehicle for experimentation, often in a ‘trial and error’ way to demonstrate the likely effects of various policies. Those policies, which produce the best results in the model, would be implemented in a real system (Figure 1). Figure 1

Simulation as experimentation (Pidd 1998)

The purpose of using simulations is to gather understanding of the original object by studying the behaviour of the simulation (Bunge, 1973, p.125): ‘Without analogy there might be no knowledge: the perception of analogies is a first step towards classification and generalisation.’ On the other hand, Bunge warns us about the inability to distinguish analogy from equivalence, which may lead to the classical, yet mistaken belief that analogy is the source of induction. Computer simulation methods have been developed since the early 1960s and may well be the most commonly used of all the analytical tools of management science (Pidd, 1998). Morecroft (1992) notes that in the past business computer models were thought of as technical tools for tightly structured problems of prediction, optimisation, or financial planning. Increasingly, models are seen to have a different and subtler role as

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instruments to support strategic thinking, group discussion and learning in management teams. One of the most frequent targets of simulation applications has been different manufacturing processes. These processes are a complex combination of machines, labour, bills of materials, bills of routing, maintenance, lot sizes (production and transfer), set ups, production control methods, etc. Manufacturing companies nowadays simply want to achieve their varying objectives (higher production volumes, rapid response, shorter lead-time, lower inventory investment, etc.) with rather small and simply implemented investments to the existing production processes, and simulation offers almost a sole answer to this need. Figure 2 represents exemplary results of a simulation concerning hub production (this very model is available among the MPX-simulation package; (Suri,1998)). As can be noticed, the lead-time reduction concerning four different end-items (Hubs ranging from one to four) is radical – manufacturing lead-times will decline from 15 to 34 days to less than three days. According to the MPX-simulation program (more information about MPX and a quick response could be found from (Suri, 1998) this could be accomplished with only a few changes: •

set up and cycle time reduction programme should be addressed for some selected group of machines, and after this

•

production and transfer lot sizes of different products should be radically decreased.

As always, and in this case too, simulation experiments will produce improvement proposals, which are a combination of ‘hard’ investments (‘doing right things’) and ‘soft’ managerial improvements (‘doing things right’). Figure 2

After several improvements, the lead-times of a manufacturer’s different products are going to decline very significantly (the end-items are hubs from one to four), according to the MPX-simulation program

In conclusion, our interpretation of simulation is that it is a tool for searching optimal solutions to a certain problem. It is not primarily meant as a tool to build understanding of the underlying relationships between different elements, and therefore, it requires plenty of pre-knowledge of the field. How the user otherwise could have trust on the

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results of this black box if she did not have a strong confidence in how things should work according to the theory? However, it should be noted that simulation methods are important and have been successfully applied since the early days of the method, especially in manufacturing environments. This topic has also been included in the curriculum of industrial engineering / management programmes as well as it represents a vital part in the business development laboratories of international manufacturing companies.

3.2 Systems dynamics simulation Klabbers (2000) discusses system dynamic models, their use as learning environments, and different modes of simulation. Traditional system dynamics models are closed as their decision rules are fixed. This means that all the information to explain a system’s behaviour is included in the model and the models are not self-organising in the sense of generating new model structures to cope with new circumstances. However, this weakness could also be identified as an opportunity to learn. Most often system dynamists are not interested in the reliability of the simulation results themselves (‘How accurate the simulation results are?’), but how the model builder has understood the structure of the studied phenomenon; especially the relations between different elements are under interest (e.g., Forrester, 1968). So, in system dynamics the aim is to be able to build as valid structures as possible – some of the structures are even considered to be ‘classics’. For example, Forrester’s (1958) beer game model is really not complex or a big one, but it describes very precisely the problems faced by logistics / supply chain management (e.g., Mason-Jones and Towill, 1999; Lee and Whang, 2000; Holweg and Pil, 2001). Figure 3 represents one classical model of system dynamics – intended to describe how speed acceleration of a car will develop (more about the basics of system dynamics can be found from Senge et al. (1994). In the model we have different elements – ‘Acceleration’ is the flow element, which is dependent on three different elements, called ‘Time to adjust speed’, ‘Speed’ and ‘Target speed’. This relation is described in the ‘Acceleration’ element with the following equation: Target speed – Speed / Time to adjust speed. As can be noticed, all the acceleration flow will eventually end to the ‘Speed’ stock (the level of stock is dependent on the initial speed, too). Figure 3

A system dynamics model for the speed acceleration of a car

Source: The Vensim-simulation package

The system dynamics model described in Figure 3 will produce results shown in Figure 4 (below). The Current scenario is a result from the following parameter values: ‘Time to

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adjust speed’ = 10, ‘Target speed’ = 80 and ‘Initial speed’ = 0. As can be noticed from the results, it will take around 50 seconds for a car to speed up itself to the speed of 80 miles per hour. However, if the researcher is interested in how a more efficient car could perform according to this model, ‘Time to adjust speed’ could be halved. According to the results, this change will cause dramatic improvement in the results; we are able to achieve the speed of 80 miles per hour in around half the time compared to the original situation. The main problem with system dynamics is learning; this will most often happen in the model-building phase. During the process of the simulation itself, the learning experience is not that strong. In a carefully reported questionnaire research (Sweeney, Linda and Sterman, 2000; Sterman, 2001), it has been shown that in the beginning students of various levels (BSc, MSc and PhD) have a hard time, when they try to understand flow models. They are not even able to sketch correctly the results of simple ‘near to real life’ situations. Also, the learning taking place during the simulation runs is problematic to a great extent. During the simulation run the users are often not able to modify the parameter values. Therefore, the results develop as ‘given’, and the users can only analyse what were the reasons for the results of the final parameter (like ‘Speed’). Figure 5 presents one exemplary management simulation programme, based on system dynamics modelling. As can be seen, a decision maker has four different delays to be controlled and two additional binary decisions to be made (Vendor Managed Inventory, so called ‘better visibility’ and a characteristic of demand change). By changing these values the user can see what are the possible results to the order of variation, and to the amount of inventory. Figure 4

Model outcome (an out of stock figure called ‘Speed’), acceleration of the current car speed and the faster alternative

Learn more, better and faster Figure 5

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A.T. Kearney’s supply chain simulator, which is based on system dynamics modelling

Source: http://web.mit.edu/jsterman/www/SDG/MFS/simplebeer.html

We think that system dynamics models need to integrate the ideas of a multi-factor simulation to the models, as proposed by Klabbers (2000). In a multi-actor simulation set up, actors communicate, share or withhold values, knowledge, and information to gain influence. Actors enact a system of interactions through rules. They develop strategies to control the resources mapped in the simulation model. The actors should have distributed access to the model, which means that they are only able to influence parts of the model. In addition, actors should have the possibility to intervene in the behaviour of the sub-system of resources. These intermediate interventions allow for adapting the strategies as the social system develops over time. For us, this sounds a lot like traditional business gaming. What Klabbers sees crucial here, is that the interventions in the learning environment have to deal with the design of new structures.

3.3 Business gaming Traditionally, business games have been batch-processed. This means that the game participants create plans for their companies for a certain period (typically a few months or a year) in the immediate future. After having finished these plans (e.g., budgets), the plans and their figures are entered in the simulation model. The simulation model then calculates the results from these plans and generates a historic report. Thus, the simulation model is a black box, of which the game participants have no internal view. To put it in another way, the decisions and the results from them have no explicit cause-relationship, but the forming of this relationship is left to be created by the limited ability of the learner. Figure 6 represents a decision-making screen of a typical batch-processed business game (note the period specific decisions like in a budgeting process; to be entered to the black box simulation).

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T. Lainema and O-P. Hilmola The decision-making application interface of a typical batch-processed business game

Source: http://www.markstrat.com/

Business games have not been connected to business process management discussion, unlike system dynamics models and simulation tools. However, we see some potential in business games to support the process perception of employees. This is possible, if the processing method of business games described, explicitly and continuously, the flow of time. Kueng and Kawalek (1997) note that amidst the many debates about various modelling formalisms, little attention is paid to the value of making goals explicit or to the fundamental purposeful nature of the system we are modelling. The advantage of business games in contrast to process simulation techniques is that business games are interactive. By this interactivity we mean that the participants are part of the business process and are in continuous interaction with the other participants, customers, suppliers and funding. Chiesl (1990) mentions that an interactive business simulation would offer the students a more realistic environment than the fixed-time format business games. Thus, participants experience a business environment that has the appearance of being true and real. Also, Patz (1990) notes that a simulation may run continuously with participants entering new decision rules at their discretion or, as indicated by current market conditions. Overall, this means that simulations may assume the day-to-day character of ongoing business while encouraging the development of long range strategies. But why these kinds of structures have not been constructed before? Patz (1990) may give us one possible explanation (p.164): ‘Simulation purposes, for the most part, are decided by coding convenience rather than pedagogical, conceptual, or theoretical relevance.’ Similar findings arise from information systems (IS) literature. Leidner and

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Jarvenpaa (1995) note that computers are merely used as vehicles for teaching procedural material, rather than teaching concepts. This is because it takes a tremendous time to redesign a course for computer-based teaching methods. Still, they note that interactive student use of computers is a prerequisite for effective computer-based teaching methods.

3.4 Synthesis As can be concluded from the three analysed simulation methods, all of these contain features, which facilitate learning. But also on the contrary, they might hinder experiencing of some areas, which constrain the learning in some vital areas. It can be generally concluded that all of these methods use more or less the so-called ‘black box’ approach to simulation, which does not support the learning process during the simulation run (See Table 1). In all of these cases, the users just need to wait for the computer to ‘crunch the used parameters’. Thereafter the user can only start to wonder, what the results really are. So, the understanding of the users mostly builds upon this analysing phase. This might be troublesome, in the used theory. Table 1

Comparing the strengths and weaknesses of different simulation methods, mostly with respect of user learning Traditional Simulation

Strengths / Application domain

To find an optimal solution to a decision problem. Very complex structures can be easily simulated Significant improvements, while combining hard and soft technology together. Widely used – and therefore understood among the users – related to some specific topic.

Weaknesses from the point of view of learning

No interaction between the participant and the simulation model during the simulation execution. ‘A black box model’. Requires knowing the theories related to the simulated subject: How these things should develop after changing some parameters?

Systems Dynamics Simulation

Business Gaming

Model building with different elements, enhances your understanding concerning the subject.

Often specific for some topic.

Understanding our world of problems through ‘stock-flow’ models.

A simulation program with technical details in a minor role.

Many fields of application, can be called ‘multidisciplinary’.

User won’t learn much by just running the simulation model. Only some of the parameters could be changed during the simulation run. Hard to understand for the first time users. A simulation is ‘simplification’, and contains only one product.

Working in a group and sharing ideas and point of views.

Problems can be complex and ‘soft’.

Traditional business games (= the vast majority of games) function according to the black box model. During the simulation the participants can’t intervene in the course of processes, but the processes are let to run interdependently until the end of the term.

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As can be concluded from Table 1, the highest potential grade for learning among the analysed approaches could be given to system dynamics simulation. The downside of this approach is the time needed in understanding how ‘stock-and-flow’ models really work. In system dynamics simulation the model building phase is the first step, which will facilitate a deeper learning concerning the subject. For example, some marketing students might benefit from just being able to sketch out different factors and their relations, with respect to the attraction to new customers. It is often argued that system dynamics models do not produce sufficient and precise results, but it should be noted that the used ‘equations’ and parameters within and between different factors are secondary. In system dynamics field it is understood that structures drive the results, and parameter selection is only a secondary matter.

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An alternative: Realgame

There have been some experiments, which include cross-functional educational goals and the use of simulation. Zack (1998) notes that attaining a practical level of knowledge about systems integration requires a sufficiently complex, real-world environment. Zack notes that when using simulations, the primary trade-off is between control and realism. Sending students to the field offers realism, but lack of control over content, context, and timing of the experience often results in problems. He further argues that the marketplace demands a more applied approach to teaching. Systems design requires managing complexity as well as ambiguity, and this aspect should be included in educational experience to afford real-world learning. As other benefits from using simulations, Zack mentions that students learn not only the technical material but also human relationships, collaboration, and communication skills. Furthermore, the role of sub-systems, their relationships, and the potential conflicts among them can be made visible to the participants (Zack, 1998). The simulation which Zack introduces, has a different primary aim than our proposal. We are aiming at giving a holistic business-process oriented view on business organisations as Zack is aiming at designing, developing, implementing, and operating information systems. Much of the efforts of business game designers seem to have been to introduce and cover new problem areas with safe technological solutions. Not as much research has been devoted to test new technological solutions, which might offer whole new ways of introducing the problem areas. In that sense, the field of business gaming has been the same now for already more than 40 years (from the end of the 1950’s). Burgess (1995) discusses how business gaming software could be improved. He demands more realistic games by increasing the level of complexity. However, any increase in game complexity tends to reduce the extent of participants’ learning. Burgess suggests the following aims:

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a

base games on real market scenarios

b

use multimedia

c

use continuous simulation, the so-called interactive approach, rather than the existing dominant method of discrete simulation.

Our research is connected to two of these suggestions: (a) and (c). The simulation game we are introducing, includes the possibility to be configured according to real world business environments. The central argument in developing the game has, however, been the use of continuous processing. When we speak about continuous processing, we specifically mean continuous processing in a game where there are several companies competing against each other, over a computer network. A term closely related to continuous processing is transparency. It is in connection to continuous processing as continuous processing reveals the process structure of business processes, thus showing how business operations and different events unfold. Here we refer to Machuca (2000) who states that traditional business games are of black box type, where the internal structure of the simulation that generates the results is not very well known (hidden). As a result (Machuca, 2000, p.233): •

the learning assumed is attained through a system of trial and error in which the player does not really know the origin of the results obtained, although he or she bases his or her decisions on these (the symptoms of the problem)

•

the basic structure of the simulation model might be erroneous, with no possibility of detecting this fact. This may lead to faulty learning with little chance of correction

•

adaptation with a view to modify the learning to changing conditions becomes practically impossible.

The continuous nature of Realgame (our case business game) means the market application of the game triggers the game’s internal clock in one-hour cycles, and the participants’ game applications follow this. The participants are able to see the game clock (hour, day, and month) on their computer screens. One game hour may take from 30 seconds (in the beginning of the game) to one real-world second (in the end of the game). The process described above is much like real-time processed video games for example, SimCity (http://simcity.ea.com/). The continuous processing method is described in more detail in Lainema and Makkonen (2003). In Realgame there are from six to eight companies competing against each other, and the markets, suppliers and funding sources are common to all participating companies. The companies are managed usually by groups of three participants. The customers in the game market server trigger demand according to the offers (sales price, term of payment, delivery speed), image (marketing, delivery certainty), and product quality (R&D) of the participating companies. The source of demand is the same for all the participating companies and the game operator can change the total market demand during the game.

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Figure 7 Realgame uses Local Area Network to create artificial markets, and to foster high level of interaction between different actors (four types of interaction; 1. between group members; 2. between the participants and their company application; 3. between the company applications and the market server application; 4. indirect interaction between different groups of participants)

Source: Nurmi and Lainema, 2002

The decision-making application for one participating company includes the major business functions of a manufacturing company (production, purchases, sales, marketing, deliveries, funding, financial reporting, and so on). Although we refer here to business functions, this does not mean that we regard the game as being a mechanistic view of a business entity with high differentiation of different functions, but rather as an open system of interrelated sub-systems, with tasks and individuals belonging to a larger whole (Morgan, 1997). Furthermore, these game companies are able to play an active role in shaping their future by making decisions about which products to offer and eventually, which markets to participate within. Morgan stresses that organisations are open systems, and best understood as ongoing processes rather than a collection of parts. This, we feel, describes quite well the functioning of Realgame. On the other hand, we feel that the traditional batch processing in business games represents the mechanistic, Taylorian view of business organisations with its budget making process, where the top management makes decisions on behalf of the whole organisation. Realgame starts by introducing the floor level business operations and then, as the participants develop their skills and knowledge about the business environment, proceeds step by step towards more holistic decisions. Figure 8 represents the Realgame user interface.

Learn more, better and faster Figure 8

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The Realgame user interface. The screen copy represents, for example, the current simulation time (the clock window); the online cash window; the production line window presenting production cells; the inventory window which includes all the online volumes of all inventory items; the sales offers window with sales terms; the income statement window (to be updated continuously).

As to last feature of Realgame we introduce its configurability. The game’s internal and external environment can be configured according to the case company’s real world environment. This configurability concerns, for example: •

The market structure: how many market areas there are; how many customers within each; what is the volume and purchase frequency of each customer; which factors the customers value when they make their purchase decisions.

•

The structure of the companies’ internal materials process: how many production phases, how many production lines in each phase, which raw materials are needed in each line, what is each line’s capacity, and so on.

•

The suppliers’ market structure: how many suppliers and which raw materials and/or components those suppliers supply, delivery lead time of different supplies, price and term of payment for each item.

•

Availability of external funding: available loans and interest rates.

•

Company balance sheet structure and internal cost structure.

•

The external environment: workers’ salaries, terms of notice, cost of new production capacity, and so on.

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Figure 9 represents a real world company configuration example (only the production line window). Figure 9

An example of Realgame configuration: The production line window of a configuration for a manufacturer of high-tech analysis systems (Please compare to the less complicated production line window in Figure 8, which has been used in courses of a university’s MSc program)

Through manipulating the configuration parameters either before or during the training sessions, the game operator is able to radically change the game environment. As a conclusion, what we are seeking for is something Trauth et al. (1993, p.299) quest for: “Learning about integration (of applications, data, and business functions) requires a sufficiently complex environment so that students can observe how disparate parts are brought together.”

We believe that certain types of business simulations can include characteristics that can solve most of the demands discussed above. We are aiming at a strong contextual orientation, which includes (Trauth et al., 1993, 299): “…a deep understanding of the business units within which they (IS professionals) will work, interpersonal skills necessary to work with the end users, and an ability to effectively apply technology in seeking solutions to business problems.”

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Continuous game processing can also be argued to be more realistic than batch processing. However, increased realism (fidelity, which usually also brings along increased complexity) as such is a doubtful argument when used alone to justify simulation gaming. Alessi (1988) notes that several sources state that transfer of learning (student being able to apply what is learned) is believed to increase as the similarity between the learning situation and the application situation increases. On the basis of a literature survey Alessi, however, notes that there are two primary explanations for failing to show an advantage for high realism (fidelity): •

High realism means higher complexity, which taxes memory and other cognitive abilities.

•

Instructional techniques, which improve initial learning, tend to lower realism.

As a solution, Alessi (1988) proposes that we should ascertain the correct level of realism based on the students’ current instructional level. As the students progress, the level of realism (fidelity) should increase. Some support for the use of different game phases and automating some game functions, when moving from one phase to another, can be found in Alessi (2000). Alessi discusses whether a simulation model should be opaque or transparent. He notes that the degree of the visibility of the model may change or depend on learner’s progress. Furthermore, he states that parts of the model may be hidden at some times and made visible at others, depending on particular needs and objectives. Alessi (2000, p.181) comments: ‘A simulation may combine procedural learning (teaching how to do something) and conceptual learning (teaching about something) […] in which case some parts of the model should be made visible for more expository instruction and other parts left invisible for student exploration and discovery.’ This description represents well the aim of using different phases in Realgame.

5

Findings from the business school’s production and operations management course

Realgame has been used in the basic course of Production and Operations Management, in Turku School of Economics and Business Administration (Turku, Finland) within four successive years (autumn 2001 to 2004). During these sessions, players of the game have mostly been second or third year students of the MSc programme, which should give some indication that their abilities to manage a manufacturing unit are at least satisfactory. Gaming sessions are played in groups of two or three students. Concurrently, a maximum of eight different manufacturing companies can be logged on the local area network. Before starting the first gaming session, all the students are given an appropriate introduction to the game controls and decisions under their influence. The settings of the first gaming session are made easier with less end items, and with less complex purchasing decisions. However, in the second gaming session the students are asked to plan in a more detailed manner their manufacturing strategy, and they had a higher variety of products to be offered to the markets, a more complicated manufacturing and purchasing process and plenty of more options in purchasing (e.g., the number of different suppliers has been increased).

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We have already reported some selected findings from the use of the appropriate manufacturing strategy and the absolute profit based on the gaming results (Hilmola and Lainema, 2002). According to our previous research, the most profitable companies share three following characteristics: •

have high market share in a particular product group (be in the TOP 3)

•

give focus on operations for this particular product group

•

focused products have appropriate profit margins (value added: after taking direct purchases off from the product price).

During autumn 2002 course, the students were asked to analyse further, what kind of decisions they made in the second gaming event (e.g., production mix, sales mix and purchasing), and what were the consequences on their profitability. Table 2 illustrates the results from four extreme observations; as can be noted, first three groups have achieved very high profitability, while one group had a really disappointing performance. Three first groups used some form of an order driven production (make-to-order or assembly-to-order), while the very disappointing profitability performance was caused by the ordinary cost minimising, make-to-stock strategy. All the successful student groups also realised the value of right purchasing decisions, and were able to manage the actual production flow in a efficient and effective manner, since machine utilisation was mostly based on the customer orders, rather than arbitrary make to stock decisions (in short-term, the latter one will lead to higher resource load and sudden demand changes could not be covered). Two highest performing groups were also able to further explain their success in the marketplace: Group I was using product mix flexibility as a competitive weapon, while Group II invested in a significant manner on production machinery, and was therefore able to achieve low inventory and short lead time production. All of the successful strategies could be explained with contemporary research in the area, since agile and flexible production are seen as key determinants of success in the real-life too (e.g., Suri, 1998; Mason-Jones and Towill, 1999; Lee and Whang, 2000; Holweg and Pil, 2001; Helo, 2000; 2001; Helo and Hilmola, 2003). After the second gaming session the students were asked also to fill in a two-page questionnaire on the gaming experience (n = 31). The first part of the questionnaire included a structured part with 15 questions, and the participants were asked to evaluate how well certain themes were represented in the game (seven point Likert scale: 1 [poor/disagree] – 7 [excellent/agree]). According to this questionnaire, Realgame receives very high grades from the following themes (please see Table 3): •

representing information demands and flows

•

enjoyability of playing

•

the importance of time in decision-making

•

representing sequential dependencies in operations.

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53

Three most profitable student groups of the two different gaming sessions Profit Margin (%)

Group I 26.3% (ROI 239%)

Main Cause

Asset Turns

9.1 Cost efficient and well planned purchasing: with long lead times and lower prices. Distribution of products with very short lead time and high costs. No overtime or additional shifts on production.

(ROI 142.3%)

Group III (ROI 110%)

Group IV (ROI –49%)

14.7%

Cost efficient and well planned purchasing (same as Group I).

Make-to-order based production strategy. Some of the purchased components were left unused in inventory, because of the rapid change in offered in product groups. Very short payment times, and very low sales receivables.

Being able to change focus of offered product groups according to the market development. Group II 22.2%

Main Cause

6.41

Other costs, like production, were, tried to be minimised with sudden layoffs, etc.

Significant investments to the production capacity (machines and workers), and therefore production lead times and inventories were relatively low.

Cost efficient and well planned 7.5 purchasing (same as Group I and II), but more emphasis on JIT/Lean based production.

Because of the assembly to orderbased production strategy, some of the capacity is still unused. Very low inventory investments.

Additional shifts used only on the production of semi-finished items.

Customers have longer payment times, and this was used as order winning criteria.

–25.9% Purchasing was planned so that very 1.9 large order quantities were used to get highest discounts. Very large investments to production capacity and therefore high costs, even if the layoffs and capacity reduction were implemented in the end of the gaming session.

Longer payment times were not used as order winning criteria, and therefore sales receivables were low.

Significant investments to the production capacity (machines and workers), and make-to-stock production strategy. Inventories were very high because of poor sales.

Besides of the theme ‘enjoyability of playing’, the other three themes are connected to cross-functionality in business organisations. ‘Time’ is the central factor in all business decision-making and, especially important in the business process context (processes evolve over time and timely actions are especially important in materials management). Processes consist of successive tasks and actions, and thus, ‘sequential dependencies’ are of central concern. Managing cross-functional processes requires informational support. In that sense the training tool ability to represent ‘information demands and flows’ is important. We assume that the modest grade from game ability to give feedback partly depends on the short time period used for gaming in both of the gaming sessions. Thus, the

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students were deeply immersed in managing the materials process, and that way did not have time enough to analyse the reports from the game. On the other hand, traditional teaching methods do not rehearse students to apply their knowledge in practice, and this invalidity could be part of the low grade here also: The students received a lot of information, but might have been confused about the meaning of the knowledge. The other questionnaire items with relatively low grade (‘Game process correspondence to reality’ and ‘Level of realism of the game’) both deal with realism of the game – this aspect of the game is about to be improved for the next time, when this course will be given. Table 3

Questionnaire items and their rating from gaming experience (Scale for questions 1–11: 1 [poor] – 7 [excellent]; for questions 12–14: 1 [disagree] – 7 [agree])

S. No.

Average

St. Dev.

St. Dev. %

Representing information demands and flows

5,9

0,9

16,0%

2

Enjoyability of playing

5,6

1,0

18,2%

3

The importance of time in decision-making

5,5

1,1

19,3%

4

Representing sequential dependencies in operations

5,5

1,0

18,8%

5

Ease of use of the game interface

5,3

1,2

23,5%

6

Fluency of gaming

5,3

0,9

17,7%

7

How realistic was the uncertainty in the game

5,1

1,2

23,7%

8

Representing a holistic view of a company

4,7

1,3

27,2%

9

Game ability to give feedback on decisions

4,4

1,3

29,7%

10

Game process correspondence to reality

4,3

1,1

25,0%

11

Level of realism of the game

4,0

1,1

28,3%

1

Item

12

The time used to play was too short

4,9

2,0

41,3%

13

It was easy to find information in the game

4,6

1,2

26,7%

14

Game gave enough feedback during the playing

3,8

1,2

31,4%

According to the further analysis of the questionnaire answers, it could be seen that students did not think that Realgame production environment was too complex, but what they really would have liked to have, was more feedback during the gaming event from completed decisions. Thus, they did not feel that they were not able to receive adequate information to make their decisions – ease of finding information was graded high. Besides grading some of the theme areas with quantitative scale, students were also asked to answer qualitatively to some short questions. Compared to earlier experiments with Realgame in different contexts, we were actually quite surprised on how many learning topics the students could name. Usually the students have been quite unconfident in expressing their learning. This may be because Realgame has been used at a more general level in the earlier training sessions. This time the learning was focused on production management, and the organisational supporting functions were left with less attention. Following is a detailed list of answers given to question: What do you feel to have learned during the game trainings? What do you feel were the most important things you learned?

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•

Logistics in general, the importance of raw material purchases, price/marketing speculations.

•

Be fast, no matter what you do as long as you react. Still, keep the strategy in mind and scan the markets.

•

Bullwhip effect; focusing on certain products or markets is profitable; manufacturing has many different faces.

•

Timely purchases, speeding up the production.

•

Planning is important + market information and its use.

•

Practical things! Everything depends on anything!

•

I learned especially about the materials process.

•

The importance of the use of time; purchases are important.

•

The importance of time (point of time of order, delivery time); for the basis of your decision-making you need better information (market surveys, accounting information).

•

Controlling the material flow is important; you should avoid inventories. The flow of information is important.

•

The importance of purchases, smooth and steady production.

•

Information and communication. Living according to plans, the flexibility of production.

•

The production process and taking into account several factors at the same time.

•

One should not be too excited, but should use common sense instead, especially in production. Changes made should not be too radical.

•

A holistic perception of this whole thing. It makes it easier to understand the lectures.

•

It’s no use hesitating with the decisions.

•

Everything affects everything. One erroneous decision in some phase and the whole thing blows up for a long time. Correcting the errors is very difficult (or then you just do not have the know how to do it).

•

One learned the importance of making plans and, in addition, that the future forecasts influence considerably on the operations of a company, and making decisions a priori based on these is important.

•

Underpricing and dumping do not affect as much as one would expect, it is the whole that matters.

•

Inventory management is a difficult job / it is difficult to estimate the actions of the other companies.

•

I have learned about inventory management, and production politics. Mainly that you need to be sensitive to the demand and not to fill up your inventory with materials or to think product focused. I also learned the importance of time in decision-making – some amount of anticipation is important.

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•

The most important result was to understand the importance of information needs.

•

The formation of key figures from the materials process.

•

The order-delivery-production process in practice.

•

People’s actions are human. You have to be able to analyse the environment in order to be able to function in the environment. In the game, the impossibility/difficulty of scanning the environment was a problem.

A certain theme that seems to repeat in the answers is the issue of a holistic view of a manufacturing enterprise, and also the influence of time in decision-making. Both of these issues are hard to cover in traditional lectures. We also believe that representing these issues in such a period of time, what we had in use here, would not have succeeded with other teaching tools. Complexity and a holistic structure could have been expressed also through building a systems dynamic model, but that would have required considerably much more time. A systems dynamics model or a simulation model would not have presented the time issue as well as a continuously processed business game, because the time dependent constant interaction in Realgame gives a totally different feeling of evolving time than a model that just runs from the beginning to the end, without any interaction in between.

6

Conclusions

As was shown in this paper, there exist several computer supported methods to be used in production and operations management education. Some of these have already well-established position in the curriculum of business schools, and the use of these methods is quite wide, also in other disciplines. However, all of these introduced methods lack some potential, when they are analysed through some modern learning principles. In this paper we introduced an alternative approach, called Realgame. We also argued that this game includes special potential with respect of learning: It is simply motivating for the students to play against each other in real time with the use of local area network. We also argued in this paper that students will focus on the problem areas of a manufacturing company much better, while developing their own strategies. As the situations change in real time within this game, students are also able to identify what kind of effects their actions trigger inside a manufacturing company and/or a marketplace. Information transparency in all areas of this game as well as the real time developments will foster process-based thinking for its players, instead of the functional approach. According to our understanding, this process knowledge is a necessity for industrial companies to survive in today’s highly competitive marketplace. Based on the questionnaire answers, the working during the exercise proved to be very intense and engaging. Realgame seemed to maintain the task-orientation of the participants well over the exercise. The continuous processing element of Realgame helped the participants to see how the different business processes elaborated, emerged and linked together. Our results clearly give support to continuous processing, which represents business processes and real world complexity more authentically than batch processing. The attitude of the participants clearly supported this. The participants thought that the game very well represented information flows and demands, sequential dependencies in operations and a holistic view of a company.

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As a concluding comment we state that Realgame was found to be a very useful tool to be used on the case course. The rationale for its usefulness comes from the participants’ positive feedback answers and the gaming results. Participants regarded Realgame training as a very rewarding and interesting experience. Realgame seemed to be able to introduce the complex nature and interdependencies of the functioning of the manufacturing company. In other words, the game was able to deliver the process-based view to the participants.

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