THE "SQUARE MODEL" ARENA APPLICATION

“Arena‖ is a process modeling and simulation software developed by Rockwell Automation. The software is widely used to model and simulate industrial processes and supply chains. The major value of Arena is to help employees anticipate the implications of complex processes by observing simulated performance without first incurring the costs of actually building and implementing physical facilities. Arena produces Markov-system simulations based on discrete events and probability distributions for the entry of entities into the system and for the duration of events. The software generates reports reflecting the performance of the simulation. It is taught at over 900 universities globally, primarily within Industrial Engineering and Management Science programs.

The Rockwell Arena application is designed to allow the simulation of a simple communication network such as might be found among upper echelon individuals overseeing first responders in an emergency situation. This particular network has just four active nodes and six communication connections as represented in Figure 1, below:



Figure 1: Pattern of communication among four team members

Actors (nodes) 1, 2, and 3 are team members who each observe situations or facts occurring in a dynamic environment created by an unspecified emergency. They can either pass an observation on to one of the other actors they are connected to, or they may choose not to pass the observation on at all. This decision-making process is controlled by probabilities attached to each of the communication channels. Actor 4 shown here is the unit ―head‖ or ―decision-maker‖. As life would have it, members 1, 2 and 3 are located in ―the field‖ where an emergency occurs, while member 4, who makes all the important decisions, is safe in her/his office and knows only what is communicated to him by members 2 and 3.

When the simulation is over, a report is generated which gives information on the movement of observations, the number of facts that reach ―the boss‖, and the number of times the various actors received more information than they could handle. A summary of the number of environmental events, and the

149

throughput of observed facts to member 4, appears on the screen of the simulation itself. A detailed report is also produced as a text file.

The program is loaded into Arena by simply double-clicking on the .doe file or choosing File/Open from the Arena menu. The program is then executed by choosing Run/Go from the menu system. The first item the student sees is an input form shown in Figure 2, below:



Figure 2: Form used to set initial parameters for simulation

Under ―Number of Observations‖, the student can select how many environmental events to which responders 1, 2, and 3 will be exposed. The box labeled ―Transition Probabilities‖ allow the user to assign probabilities to the communication links. The numbers in each row must not exceed one. Under ―Cognitive Overload Cutoffs‖ the user can set the maximum of observations which each actor can hold in active (human) memory. The ―Randomize‖ check box allows the student a degree of control over the randomness of the simulations. Normally it should remain checked, which means everything in the simulation happens randomly.

―Events‖ are created by the environment and able to be observed by members 1, 2, and 3. As observations move through the simulation as reported events, they have different colors reflecting their origin. When an observation reaches one of the members (either by direct observation or communication), it is placed in a queue waiting to be dealt with. The size of these queues determines whether an actor is suffering from cognitive overload. Suppose, for example, an

150

actor has his/her cognitive overload cutoff set to three and his/her queue contains three observations. If a fourth observation comes his/her way, the oldest observation in his/her queue will be deleted (it disappears from the simulation) before the new observation is added to the queue. In other words, when members become cognitively overloaded, complete knowledge fails to flow through the social network. Facts that member 4 could use in making important decisions never reach him.

During the simulation class, the instructor reviewed the concept of knowledge management and the relevance of social networks to knowledge management. He explained knowledge as being information in the mind of a human. He explained that while information management is largely about the storage and retrieval of data and documents from computer systems, knowledge management is about facilitating communication among employees with the hope that when decisions are made decision makers have access to relevant knowledge that resides within the organization. The instructor then made brief reference to the National Incident Management System (NIMS) and the importance of knowledge being shared efficiently and completely, especially in teams of first responders.

Each student was required to submit an individual data sheet based on the in-class assignment plus a short written essay about the assignment and what they learned. Figure 3, below, is a pictorial representation shown to each student:



Figure 3: Visual representation of the team members in the model and simulation

The assignment was completed during one class period conducted in an instructional computer lab. The students had already been introduced to the distinction between information management and knowledge management and

151

Neubauer, Rogers and Yang

Using a projector, the instructor demonstrated how to open Arena and to navigate the animated graphic. Referring to the visual, he explained that team members one, two and three are at the site of an unspecified emergency and that member four makes all of the most important decisions. He makes his decision from an inside office unable to directly observe what is happening at the site. Member one is in two-way communication with members 2 and 3. Members 2 and 3 do not communicate with each other. Members 2 and 3 report observed facts to member 4, but member 4 does not send information to any of the other three members. The instructor explained to the students that each member has a bounded ability to hold facts in active memory and that when a member's "cognitive cutoff is exceeded the older observations are lost from the organization. Each of the first three members has a propensity to communicate, called a "transition probability" in the software. The environment in the model can be set to generate a range of numbers of new facts to be observed by members 1, 2 and 3. Using the projector, the instructor demonstrated how the values of parameters in the model could be set and the model run as a simulation. The students reported that they understood the parameters and how to run the model.

The class then went through a series of iterations by following the instructions on the printed data forms. The steps of each iteration were as follows.

• 
  Identify the set of parameters to be adjusted.
  
• 
  Set the other parameters to the default values.
  
• 
  Write a hypothesis regarding what is expected.
  
• 
  Enter the parameters into the form that opens upon preparing to run the model as a simulation.
  
• 
  Running the simulation and observing the resulting simulated knowledge throughput to member 4.
  
• 
  Considering the outcome in light of the hypothesis.
  

152

The second iteration of the assignment involved substantially increasing the number of events in the environments while leaving the cognitive cutoffs and the transition probabilities at their default values. The students were asked to write a hypothesis regarding what would happen. Most of the students anticipated that system knowledge throughput would decrease. However, when they ran the simulations, they generally found that system throughput was about the same as in the first iteration.

In the third version students were instructed to substantially reduce cognitive cutoffs for all four members while using the default values for the number of environmental events and the transition probabilities. Most of the students anticipated that knowledge throughput would decrease. Throughput decreased less than expected.

In the fourth run students were instructed to increase transition probabilities while leaving the other parameters at the default values. Most of the students anticipated that system throughput would increase. The resulting information system throughput to member 4 (the boss) did, in fact, increase substantially.

The class discussion that followed the simulation involved multiple themes and insights. For example, the model assumes that members 1, 2, and 3 correctly interpret their observations of events in the environment. The model further assumes that when facts are communicated there are no misunderstandings among members. Moreover, the specific roles of each member are not defined. Therefore, the model cannot take into account members' applying "need to know" when deciding what to communicate and to whom.

One unexpected discussion topic was triggered by the visual representation of the team related to the gender and appearance of team member 3. The genders and appearances of the four members were not intentionally selected for the purpose of creating classroom discussion. However, some students were strongly persuaded that men do not listen to women, suggesting that member 4 ("the boss") would likely discount (or not even perceive) the communications from member 3, visually represented as a young blond woman. The student‘s observation generated a lively discussion about how personal bias and other attitudes can affect the extent to which knowledge and information flows through social networks.

The assignment was a component of an information systems course in the Master of Public Administration program at Albany State University, Albany, Georgia. The course was designed to help students understand knowledge management and gain insights into how organizations use modern information systems and social networks. Since the qualitative study involved only the

153

behavior and responses of students of this one course, the capacity to generalize the findings is limited.

Students were successful in using the software. They were able to clearly understand the concept of cognitive overload as well as transition probabilities (the propensity of members to share facts with one another). The visual aspects of the simulation supported the importance of complete and unhindered information flow in the role of decision making in an organization. Based on student questions, their responses to the instructor‘s question, their reports, and the comments offered by students, the classroom exercise enhanced student understanding of knowledge management in an organization, defined in terms of system knowledge throughput. Moreover, the students appreciated the ―hands on‖ value of the activity as the simulation allowed them to actually see the importance of social networks and open, clear communication channels in organizational decision making. One key to the simulation‘s use by other instructors is to make the needed resources available in a convenient package. Thus, the simulation should be packaged with a supplemental textbook or as ancillary material associated with a textbook on information systems or organization learning.

Note: With permission from Rockwell Automation, Arena can be installed in a campus computer lab, presently without charge. As of November 2009, the Arena software is in version twelve and runs on Windows XP and Windows Vista. Currently, there are at least five university textbooks about Arena in print (Seila, Ceric & Tadikamalla, 2003; Altiok & Melamed, 2007; Seppanen, Kumar & Chandra, 2004; Kelton, Sadowski & Sturrock, 2007; McLaughlin & Hays, 2008). The student version of Arena is packaged with each of these textbooks.

REFERENCES

Altiok, T. & Melamed, B. (2007). Simulation Modeling and Analysis With

Harvard Business Press. Ichijo, K. & Nonaka, I. (2006). Knowledge Creation and Management: New

(4th ed.) New York, NY: McGraw Hill Higher Education. Mclaughlin, D. B. & Hays, J. M. (2008). Healthcare Operations Management.

154

Journal of Business and Behavioral Sciences

MA: Harvard Business School Press. Seila, A, Ceric, V., & Tadikamalla, P. (2003). Applied Simulation Modeling.

Pacific Grove, CA: Duxbury Press. Seppanen, S. K., Kumar, S., & Chandra, C. (2004). Process Analysis and

Improvement. New York, NY: McGraw-Hill/Irwin. Vygotsky, L. S., Cole, M., John-Steiner, V. & Scribner, S. (1978). Mind in

Society: Development of Higher Psychological Processes, (14th ed.) Cambridge, MA: Harvard University Press.

155

School of Management, Gautam Buddha University, India

Performance with reference to an organization is an art in which the actions of an individual or a group at a particular place and in a particular time constitute the work. Beach (1980) has defined performance appraisal as, ―Performance Appraisal is the systematic evaluation of the individual with regard to his or her performance on the job and his potential for development.‖

Heyel (1993) says "Performance Appraisal is the process of evaluating the performance and qualification of the employees in terms of the requirements of the job for which he is employed, for purpose of administration including placement, selection for promotions, providing financial rewards and other actions which require differential treatment among the members of a group as distinguished from actions affecting all members equally."

In a workgroup, members, consciously or unconsciously, make opinion about others. The opinion may be about their quality, behaviour, way of working, etc. Such an opinion becomes basis for interpersonal interaction. In the same way, superiors form some opinions about their subordinates for determining many things like salary increase, promotion, transfer etc.

Journal of Business and Behavioral Sciences

In the present highly competitive environment, organizations have to ensure peak performance of their employees continuously in order to compete at the market place effectively. Traditionally, this objective was attempted to achieve through employee performance appraisal, which was more concerned with telling employees where they lacked in their performance. Though this served the purpose to some extent, it was not considered enough to raise the employee performance at the most desirable level.

Performance management has overcome this problem to some extent. An organisation‘s performance appraisal system is an important, but often neglected, tool for managing the effectiveness and efficiency of employees in the workplace (Armstrong & Baron, 1998) and there is wide spread contention that these are those employees that create competitive advantage. As Drucker (1994) puts it, employees are our most valuable assets, and they can determine the success and survival of an organisation. Appraisal is one way in which the efforts of those employees can be aligned with the aims of an organization and those employees can be motivated and supported (Desimone, Werner & Harris, 2002). Despite this, many organizations and managers fail to give appraisal the attention and support it deserves (Aminuddin, 2001; Armstrong, 1998).

PERFORMANCE APPRAISAL IN BANKS

Among the various industries, banking underwent greater transformation in the liberalization and globalization era. Right from prudential accounting norms and classification of non-performing assets up to the mass computerization, including ATMs, the industry has undergone many dramatic changes. Opening of more private sector banks, increased operation of some multinational banks, the closing and merging of branches and banks all have happened rapidly and drastically.

Human Resource Management in banks has assumed greater significance this is because it is through motivated people and their collective efforts that organizational goals are achieved. HRM strategies in banks should therefore be people oriented so as to enable employees to contribute in greater measure to the organizational growth and development. HRM policies and practices in banks should also aim at bringing about a positive attitudinal re-orientation among employees so that they identify themselves with organizational goals and contribute their best in achieving the said growth and development of employees and transparent and objective personnel policies and practices and biasness free appraisal system not only foster a positive work culture and team spirit among employees but also paves way for creating in them a sense of belonging and commitment. Therefore in the emerging scenario and in the backdrop of managerial autonomy accorded by Government of India, there is a greater need to have an effective workforce with special emphasis on building newer skills

157

and capabilities among employees as well as devising an appraisal system with least possibility of biasness.

To keep pace with these changes, HR policies, especially the performance appraisal system of our employees because they are the ones which shall take up to the competitive advantage. It is very much essential to have a right system of evaluating their performance and finding the potential for future. In order to implement the performance appraisal systems have been designed.

The idea behind introducing Performance Appraisal System in banks is to make people effective by enabling them to acquire skills and capabilities. The Performance Appraisal System is focused on the individual to enable a person to think about his/her role in terms of those dimensions, which are unique to their roles and provide a base for performance planning.

Performance Appraisal is a system, which involves a person in identifying achievable goals through a process of discussion with his superior. It helps in generating meaningful data about the person through analysis and review of his performance which can help in taking placement, promotion and training decisions.‖

The Performance Appraisal System also aims at bringing about growth and development of individuals in the bank. Performance Appraisal System gives clarity of the role to a person. It also involves the person in goal-setting process. It gives an opportunity to an individual for analyzing his own performance. It also gives him feedback on his performance. It helps in identifying his strengths and weaknesses. The performance evaluation is not based on traits but on individuals work. This helps to provide better relationship between boss and subordinate. Performance Appraisal System also generates data for training, placement and promotion decisions.

In the process of goal accomplishment the person uses his existing skills, sharpens them further and most importantly acquires certain new skills. When new learning takes place, growth becomes the result of that process. The major problem in the performance appraisal is of performance rating errors.

COMMON RATING ERRORS

Performance appraisals require the rater to objectively reach a conclusion about performance. The use of ratings assumes that the rater is reasonably objective and accurate. These biases produce rating errors, or deviations between the ―true‖ rating an employee deserves and the actual rating assigned. Rating errors reduce the reliability, validity, and utility of performance appraisal systems. Biases in performance ratings manifest themselves in many forms. Some of the most common types of rater biases are:-

158

Journal of Business and Behavioral Sciences