The Dynamics of Service Delivery Management by ดร.ศานติ โบดินันท์

I TABLE OF CONTENTS

Chapter 1 Overview of the research 1.1 Background 1.2 Contracted hiring in airline industry 1.3 Significance 1.4 Purposes 1.5 Overview of dissertation

Chapter 2 Literature review 2.1 Introduction 2.2 Labor cost and service capacity 2.3 The dynamics of service delivery 2.3.1 The labor structure 2.3.2 The capacity structure 2.3.3 The cost structure 2.3.4 The service delivery structure 2.4 Conceptual model

Chapter 3 Research methodology 3.1 Introduction 3.2 Model formulation 3.2.1 Model boundary 3.2.2 Model structure

Chapter 4 Model validation and sensitivity test 4.1 Introduction 4.2 Model validation methodology 4.2.1 Model validation in Operation Research/Management Science 4.2.2 Model calibration as validation strategy 4.2.3 Calibration strategy 4.3. The research site: THAI domestic passenger check-in 4.3.1 Passenger check-in characteristics 4.3.2 Data collection 4.4 Sensitivity test

Chapter 5 Model applying in policy analysis 5.1 Introduction 5.2 Policy analysis of standard run 5.3 Alternative policy analysis 5.4 Future research recommendations

References

EDITOR'S NOTE This thesis is 111 pages long

The Dynamics of Service Delivery Management: An Investigation of the Impacts of Hiring Contracted Agents on Service Capacity and Cost.â&#x2C6;&#x2014; Sawat Phoomphuang, BA. (Public Adm.), MA. (Organizational Adm.) Chapter 1 Overview of the research 1.1 Background Hiring contracted agents is crucial to both public and private organizations which made through firm (De Loria, 2001) or individual (Adair, 2000) contract. Public organizations employ this strategy to solve manpower crisis (Lane et al., 2002). Washington State Department of Personnel (2003) announced the Personnel Reform Plan to be effective in the year 2005. Private organizations, on the other hand, hire contracted staff to various type of business: hotels (Hallam & Baum, 1996), health service (Vining & Bloberman, 1999), as well as airline (ABN-AMRO, 2002). In spite of capital and labor intensive business (Air Transport Association, 2003), airlines hire contracted agents to almost activities: information, sale, maintenance, cargo, accounting, catering, and ground service (Doganis, 2001).

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A Doctoral Thesis submits to Intercultural Open University (IOU), Netherlands, 2004.

1.2 Contracted hiring in airline industry Airline deregulation in 1978 causes airline industry to be restructured that stem to liberalization and started opening skies in 1992 (Aharoni, 2002; Ash, 2002) and spread out through other region. The United Kingdom and United Arab Emirate for example, was signed in October 2002 (ABN-AMRO, 2002). Moreover, in Asia-Pacific region, airline is strongly supported by the Government, tends to more liberate airline business (Heunemann & Zhang, 2002). These phenomena cause many airlines: All Nippon Airways (2000), Delta Airline (Burns, 2002), and Lufthansa Airline (HĂ¤ tty, 2002), employ hiring contracted agents in their firm to strengthen competitiveness and reducing cost, labor cost in particular. In United States airlines pay more than 36 percent to labor expense (Heimlich, 2002), Meanwhile, Canada is slimly lower, 32 percent (Oum & Yu, 2001). Airline in Europe and Asia have expenditure in labor force 34 and 19 percent respectively (Doganis, 2001). The same as other airlines, Thai Airways International is directly affected by the world airline restructuring. After the organizational structure had reformed in 2001, traditional model to aviation business unit model according to Doganis (2001), partly contracted hiring staff was introduced to domestic passenger check-in in 2002. Even though the aggressive implementation of contracted hiring in various businesses, the studies concerned such a matter is not the same direction. None of those studies focused to explore its impacts to capacity and cost: one only focused to organizational commitment (De Loria, 2001), one

explored implementation obstacles (Vining & Globermann, 1999), or one was only the attitude survey about introducing it to the firm (Hallam & Baum, 1996). Even the totally employed contracted staff to various activities like airline business (Doganis, 2001) was only a survey report of using contracted hiring in many airlines through out the world (ABN-AMRO, 2002). Those studies finding were not enough to conclude its impacts to capacity and cost. In explaining the interactions among any variables clearly and systematically, System Dynamics Model, explain how things change through time (Forrester, 1961; 1969; 1971; Coyle, 1971; 1996; Sterman, 2000) is needed. Therefore, to explain the impacts of hiring contracted staff to capacity and cost, dynamics behavior of service delivery system must be understood. Moreover, tool must be comprised of four qualifications: formal, abstract, dynamic and non-linear, which is completely equipped in System Dynamics Model (Commission of The European Communities, 1998). For those reasons, the author is willing to investigate the impacts of hiring contracted agents on service capacity and cost using System Dynamics Model to seek the answers for the following questions: (1) How hiring contracted agents affect service capacity and cost in passenger check-in? (2) How contracted hiring feedback impacts firm hiring policy? And (3) what is the optimum number of hiring contracted agent and permanent employment in passenger check-in?

1.3 Significance The System Dynamics approach of this research is contributed to both managerial and theoretical implication. In managerial context, labor cost is the major expenditure that determines competitiveness of the firms especially in service industry. Therefore, effectively manage labor force is directly affect organization competitiveness. The System Dynamics approach in this research is going to enhance managersâ&#x20AC;&#x2122; perspective to view their firm as a whole, which is the crucial guideline to strengthen competitiveness. In theoretical, on the other hand, System Dynamics itself is base on system thinking which is able to deal with complexity of the system (Forrester, 1999). Therefore, by using this study approach, the answers to the questions mentioned above are going to be explored clearly and precisely, finally stem to strong theory or even law at the end. 1.4 Purposes To investigate the impacts of hiring contracted agents on service capacity and cost in passenger check-in on System Dynamics approach, two main purposes will be explored: Firstly, system interrelation among hiring contracted agents, service productivity, and cost by using System Dynamics Modeling. Lastly, simulate the impacts of hiring contracted agents on service capacity and cost.

1.5 Overview of dissertation The research work described in this dissertation can be grouped into three distinct stages: 1) formalization of the service delivery management model, 2) empirical validation of the model, and 3) provide simulated alternative policies for managers. The next paragraphs briefly describe the three stages stated above and its location within the text of the dissertation. Service delivery management model drawn upon System Dynamics originated by Jay W. Forrester (Forrester, 1961) briefly reviewed in Chapter 2. The model consists of four structures: labor, service capacity, cost, and service livery which are constructed from previous works of various scholars in System Dynamics approach, also detailed in Chapter 2. This Chapter also presented conceptual framework of this dissertation. Then, Chapter 3, Research Methodology is fully devoted for model formulation that is comprised of Stock and Flow Diagrams and Model Equations. The second stage, empirical validation of the model is presented in Chapter 4. Prior to validation process, epistemology of Operation Research/ Management Science (OR/MS) is discussed ranging from Vienna Circle to Forrester Approach. Later, model calibration is chosen as method of validation. Finally, the third stage, alternative policy simulation is discussed in Chapter 5. Three alternative policies namely: Moderate Agents, Maximizing Agents, and Minimizing Time are drawn and simulated as well as discussion for each policies. Moreover, this Chapter also discusses avenues of future study. This dissertation also includes References and the Authorâ&#x20AC;&#x2122;s briefly biography.

Chapter 2 Literature review 2.1 Introduction The literatures reviewed in previous chapter explored hiring contracted backgrounds and importance. The following chapter is concerning labor cost, service capacity, and the dynamics of service delivery. 2.2 Labor cost and service capacity. According to resource base view theory (Penrose, 1959), labor cost is generated both direct and indirect way that comes from salary and lost opportunity to produce goods or services respectively. Calculating the former source cost is not only uncomplicated, by summing up benefits paid to all employees (Worland & Wilson, 1988; Bauer, 2003), but also easy to access data (McGuckin et al., 1995). Unlike direct cost, indirect cost comes from various sources: job rotation, absenteeism, even smoking (Johanson et al., 1998). Calculating those cost method is different in detail (see in Cascio, 1987). Labor cost in airline business is close related to productivity, determiner of capacity. Reducing cost may be done either by reduce wage directly or remain wage but increase labor productivity (Alamdari, 1998). Moreover, labor cost reports in airline business is quite deferent from other businesses; presents cost per output as ATK or RPK that some report preferred ATK (Alamdari, 1998; Doganis, 2001 or some

preferred RTK and RPK (Oum & Yu, 2001). The former reports base on capacity but the latter base on revenue. Service capacity is determined by function of total labor and labor effectiveness (Oliva, 1996). Money incentive may stem to work capacity according to Fitzsimmons (2002). Unit of service capacity in airline business is difficult to count because in one-passenger concerns many activities before flying (Bureau of Industry Economics, 1994), that consistent to Doganis (2001) approach. Work capacity in new management era is on the basis of multi skill approach, products of total activities done and total output divided by total labor (Van Oyen & Michael, 2002). In conclusion, labor cost is caused by direct and in direct expenditure. Service capacity, on the other hand, is determined by number of labors, their effectiveness, as well as money incentive. 2.3 The dynamics of service delivery Invented by Forrester (Glenn, 1994; Mills & Bishop, 2000; Dooley, 2002; Gilbert & Troitzsch, 1999), System Dynamics is not only categorized in one of the futures research methods (Glenn, 1994; Mills & Bishop, 2000), but also categorized in one among many simulation researches methods (Dooley, 2002; Gillbert & Troitzsch, 1999). Base on Information Feedback Control Theory, System Dynamics was first developed to explain dynamics behavior of industry (Forrester, 1961) at MIT Sloan School of Management. Eight years later, Forrester had expanded his model to explain interrelation between industry and urban (Forrester, 1969)

before completed his model to explain dynamics behavior of the Earth (Forrester, 1971). After that, Forresterâ&#x20AC;&#x2122; s works aim to explain dynamics behavior of learning in educational management (Forrester, 1994; 1996; 1999). Business and industry dynamics however, is remaining attracted to many other scholars (Coyle, 1977; 1996; Sterman, 2000; 2001; Morecroft, 1999; Oliva, 1996; Kim, 1998; Kirkwood, 1998; Hishida, 1999; Anderson & Morrice, 2000; Morrison, 2003; Schillinger, Zock & GrĂśĂ&#x;ler, 2003; Dudley, 2003 etc) Literature review in this sector concern the dynamics behavior of service delivery applied directly from service literature and indirectly from industry literature. For example: effects of hiring delay, trailing, absent, and turnover. Even though the dynamics behavior of industry is different from service; focus on raw materials and equipment, but the same focus is labor (Scholl, 2002). Therefore the author believe that both literature can be blended to explain the dynamics behavior of service delivery being presented in this study. The dynamics of service delivery management will be comprised of four feedback structures: labor structure, capacity structure, cost structure, and service delivery structure. 2.3.1 The labor structure Literatures related to labor structure on System Dynamics approach emerged from many scholars both service and industry field for example: Sterman (2000); Oliva & Sterman (2001); Barlas, Cirak & Duman (1999); Hustache, Gibellini & DeMatos (2001); Bhattarai & Neupane (2002); Gans & Zhou (2001); Weidenmier (2002); Warren (1998; 2000); and Repenning & Sterman

(2000). Those scholarsâ&#x20AC;&#x2122; models are differentiated by different variables. The labor structure is close related to the capacity structure. Barlas et al. (1999), Sterman (2000), and Oliva & Sterman (2001) focused on number of employee, which determine capacity of the system. Recruitment process (Hastache et al., 2001) and training (Bhattarai & Neupane, 2002) have to be done before those employees could get in to the system. The stated two steps cause delay for increasing staff (Gans & Zhou, 2001; Caufield & Mai, 2002). Required staff is determined by service demand in the system (Oliva, 1996; Sterman, 2001). The gap between required staff and actual staff is the number of new employee to be hired in to the system. In summary, the labor structure comprises required staff, determined by service demand, and actual staffs, come from recruitment and training process. The gap between those two numbers determines new staff to be hired. The labor structure is close related to the capacity structure as shown in figure 1. actual staff +

hiring

required staff

staff gap

new staff

+ serivce demand

Figure 1: The labor structure

2.3.2 The capacity structure The capacity structure of the system describes labor capacity in the system. The capacity of the system is function of number of employee and employee effectiveness (Oliva 1996). The number of employees is depending on derivative of hiring rate and attrition rate (Warren, 1998; GrĂśĂ&#x;ler, Notzon & Shehzad, 1999; Barlas & Diker, 1996; Sterman, 2000; Kirkwood, 1998). If hiring rate is higher than attrition, growth behavior of the system can be expected. Decay behavior of the system, on the other hand, can be expected if attrition rate is higher than hiring rate. The employee effectiveness depends on training (Bhattarai & Neupane, 2002; Mellet, 2002; Sveiby, Linard & Dvorsky, 2001) and experience Mallis, 2002; Akkermans & Van Oorschot, 2002). More training cause more effectiveness especially on the job training by internal instructor (AragonSanchez et al., 2003), as well as more experience. In macro view however, the capacity structure includes birth rate, death rate, migration rate, and reading rate of population in to the system for explaining national production capacity (Saeed, 1996) or agricultural capacity (Kopainsky, Buchli & Rieder, 2003a; 2003b). The above variables will not be brought in to this study, because it focuses to micro view of one business only. Moreover, those variables are beyond boundary of the micro system (Sterman, 2000). In summary, the capacity structure comprises one main stock variable, number of employees, and one main rate variable, employee effectiveness. The

stock depends on derivative of hiring rate and attrition rate. Meanwhile the employee effectiveness is the variable determines stock capacity, which stem from training and experience as shown in figure 2. service capacity -

staff

training

+ effectiveness

Figure 2: The capacity structure 2.3.3 The cost structure The cost structure of the system describes expenditure caused by the labor and the capacity structures. There are two groups of scholars, one treat cost as rate while other treat it as level. This study agrees with the former group of scholars. Nishida (1999), Gans & Zhou (2001), GrĂśĂ&#x;ler (2000), and Dudley (2003) argued that cost should be rate variable, because at least it will be a constant rate in one period of time. Meanwhile, Morecroft (2000), Homer, Keane, Lukiantseva, & Bell (1999), Hustache, Gibellini & De Matos (2001), Warren (2000), Millis (2002), Dubelko (2002) and Bhattarai & Neupane (2002) argued that cost is able to accumulate depending on the derivative of flow in and flow

out of expenditure in the system. In spited of differentiated by the approach, the variables in their model are almost the same name. The former argument viewed the system comparing with real business while the latter viewed the system comparing with real world. Real businesses have to fix one value that manager can compute but real world things change over time. The constant model use to manage real business accompanied by the level model use to shape manager think systematically. This study aim to formulate the model to help managers in real business management. Therefore, cost in this study will be treated as constant caused by direct and indirect source according to the RBV theory of Penrose (1959). The common directs source of labor cost included in the System Dynamics Model is number of employee (Homer, Keane, Lukiantseva, & Bell, 1999). Cost for training (Mallis, 2002) and time used in recruitment (Hustache, Gibellini & De Matos, 2001) is also main sources of labor cost. The indirect sources, on the other hand, including absenteeism and turnover are not the direct source of labor cost but they accelerate hiring to increase employee to fit the system. Therefore, labor cost will increase indirectly through absenteeism and turnover. In summary, the cost structure of the system in this study comprised of indirect sources: number of employee and training and time to recruit, and indirect sources: absenteeism and turnover. While cost in this model will be treated as rate constant caused by above variables to help managers planning their real business as shown in figure 3.

employee +

+ labor cost

recruit & hiring

service pressure hiring

+ turnover & absent

Figure 3: The cost structure 2.3.4 The service delivery structure The scholars studied in this structure are almost the same group as scholars who study the capacity structure. Because of the close interrelation between those two structures, they begin to explore the capacity sector as the starting point, but deviate in to different managerial field. Mellett (2000), Oliva & Sterman (1996), Sterman (2001), Cresswell, Black & Luna (2002), and Homer, Keane, Lukiantseva, & Bell (1999) applied to use in service sector. Meanwhile, Coyle (1996), Repenning & Sterman (2000), Groößler (2000), and Scholl (2002) used it to explain production of industry. The stock name of the service delivery structure was named “service backlog” from scholars in service field (Oliva, 1996; Anderson & Morrice,

2000). Industry field scholars, on the other hand, named it as â&#x20AC;&#x153;work in processâ&#x20AC;? (Coyle, 1996; Scholl, 2002; Anderson Jr. et al., 1995). Flow-in of this stock is customer or order coming to the system (Morrice, 2000; Morecroft, 1999; Sterman, 2000), which is treated as the exogenous variables and changing over time. Service backlog or work in process, the stock of the service delivery structure, depending on customer flow or order rate and service completed rate or order fulfillment rate (Oliva, 1996; Anderson & Morrice, 2000; Scholl, 2002; Jr. et al., 1995). Accumulations of stock stem from derivative of flow-in and flow-out at the points of time. In conclusion, the service delivery structure describes accumulations of the customers accumulated in the system. The derivative between flow-in, customer flow rate, and flow-out, service completed rate, determine stock level. As shown in figure 4, negative feedback is the dynamics behavior of this structure.

service backlog

service completed

service demand

service pressure

service satisfaction

Figure 4: The service delivery structure 2.4 Conceptual model Literatures reviewed in this chapter describe the dynamics model of service delivery management. It comprises of three subsystems: labor, capacity, and cost. Each sub system displays difference system behavior. The negative feedback is shown in the labor sector (figure 1). The more actual staff the less staff between actual and required staff. Further, it results in the less new staff, the less hiring, and the less actual staff in final. Unlike the labor sector, the positive is shown in the capacity sector (figure 2). High service capacity causes training to be reduced. Then, reduce training stem to low staff effectiveness. Finally, to increase service capacity, more staff needs to be added in to firm. The cost sector, on the other hand, two feedback loops displayed in this sub system (figure 3): positive, and negative feedback. The former feedback stems from many employee causes: high labor cost, hiring to be reduced, but

high service pressure, turnover rate, recruitment and training to replace turnover staff. Finally, increase labor cost. Meanwhile, the latter feedback stem from many employees reduces service pressure, turnover rate, recruitment and training, and number of employee at the end. The service delivery sector, however, is the sector affected by those three stated sectors above. To reduce service backlog, labor sector, capacity sector, need to be strengthening, which is result in high cost. Negative feedback loop is expected in this sector as shown in figure 4. The stated four subsystems above can be integrated in to conceptual model of the dynamics system of service delivery management as displayed in figure 5. Labor Hiring process Turnover Experience Total Labor Effective Labor Fraction

Effect of work pressure on turnover

Service Capacity

Labor Cost

Effect of work pressure on capacity

Passenger Demand

Direct Cost Indirect Cost

Effect of work pressure on cost

Service Backlog Work pressure Service completed rate Service Delivery

Figure 5: Model Subsystems

Passenger Demand

In conclusion, as shown in figure 4, the same set of variable, labor and service delivery subsystem, affect to both service capacity and labors cost. Service delivery -work pressure- causes changing in labor subsystem through turnover. Moreover, turnover result in service capacity and labor cost.

Chapter 3 Research methodology 3.1 Introduction Many contemporary researches based on System Dynamics approach, Coyle (1996), Sterman (2000; 2001), Oliva (1996), Oliva & Sterman (2001), Nishida (1999), Repenning & Sterman (2000), for instance maintain the hearth of method originated in Forrester (1961; 1968) works. Their works can be grouped in to three: Forrester, mathematics, and man language. The work of Coyle (1996), and Kim (1998) are the examples of work presented on Forresterâ&#x20AC;&#x2122;s language know as DYNAMO. Mathematics language, however, is difficult for non-calculus background. The work of Oliva (1996), Oliva & Sterman (2001), and Nishida (1999) are the examples of papers based on this language. The last group, man language, is the most interesting among those three. It is not only able to understand easily, but also calculus background is not necessary. Those works can be found in the papers of

Sterman (2000), Kim (1998), Bang (1998), and the present paper. In spite present in distinct language, steps of presentation are almost the same, only the term used is difference, most of them comprise of five steps. The first step of System Dynamics approach is problem articulation according to Sterman (2000), or problem recognition termed by Coyle (1996). The next step is formulation of dynamics hypothesis, termed by Sterman (2000), Meanwhile Colye (1996) named it as problem understanding and system description. The two steps mentioned were presented in chapter 1 and chapter 2 respectively. The three remaining steps, model formulation, model testing, and policy design and evaluation will be presented in chapter 3, chapter 4, and chapter 5, respectively. Feedbacks from one step, as suggest by Sterman (2000) and Coyle (1996), are useful to improve previous step. 3.2 Model formulation The same as the first step, various names were termed for this step. Coyle (1996) named this step as ‘construction of a simulation model’, while Kim (1998) and Ford (1995) term this step as ‘model description’. The latest named, termed by the latest authority of System Dynamics approach, however, John D Sterman (Sterman, 2000), is ‘formulation of a simulation model’. Even though there are many terms for this step, its objective is the same: to exhibit model behavior by explaining model detail and to test dynamic hypotheses by simulating model (Coyle, 1996; Sterman, 2000). Testing dynamic hypotheses in some cases, however, may violate to moral and human

right in the real world. Therefore, they can be tested morally, legally in the virtual world as done in the simulation method (Sterman, 2000). Model formulation in some works, Kim (1998), Oliva (1996), for example, presented only its equations. Meanwhile, some scholars (Bang, 1998; Ford, 1995) presented both its equations and its stock and flow diagrams. The stated two styles usually followed by description of model. Its equations, in the most works for example: Coyle (1996), Ford (1995), Kim (1998), Bang (1998), and Sterman (2000) were written on Forresterâ&#x20AC;&#x2122;s language, DYNAMO. The mathematics language, however, were used by a few scholar, Oliva (1995) and Nishida (1999), for example. In model formulation for this paper, to enhance its usefulness to many managersâ&#x20AC;&#x2122; as much as possible, VENSIM language, originated from DYNAMO, a computer simulation written on man language, feature of useful mathematic functions (Ventana Systems, Inc., 2003), will be applied to formulate the model equations. A long with VENSIM equations, to ease model imagination, it will be accompanied by the stock and flow diagrams. Before representation of stock and flow diagrams, as suggest by Oliva (1996) and Ford (1995), its will be proceeded by model boundary and variables definition. In conclusion, model boundary and its variable definitions are starting point of the model formulation in this study. Next, it will be followed by model structure, which is comprise of stock and flow diagrams, model equations, and model descriptions for each structure: labor, capacity, cost, and service delivery. 3.2.1 Model boundary

The main idea of model boundary is to limit its area containing behaviors of the system, which is limited and emphasized by model boundary as displayed in figure 6 and its variable definition. The figure exhibits model boundary in three categories of variables: endogenous, exogenous, and ignored variable. In this study, the author assumes that there are four variables: unemployment rates, economic growth rate, fatigue, and job satisfaction, out of model boundary, or ignored, in the other word.

Jobsatisfaction

Fatigue

Ignored Unemployment rate Exogenous Hiring Training Attrition Service effectiveness Salary Benefits

Economic growth rate Endogenous Labor Service capacity Cost Service delivery

Figure 6: Model boundary The first two ignored variables, unemployment rates and economic growth rate, the author assumes that there are no effects to the model studied, even the first variable seem to have positive impact on employment period, the higher unemployment rate the longer employment, because of new job shortage.

The same as the former variable, only its impact is distinctive, negatives, economic growth rate. The higher economic growth rate, the shorter employment period, because of many new jobs available. Even though those variables have to be included in model boundary for macro model as suggested by Sterman (2000) and Saeed (1996), but in this model the author believe that those variables is beyond service capacity management in the firms. The latter two variables, job satisfaction and fatigue, on the other hand, the author agree with prior study done by Oliva (1996). Oliva found that fatigue have negative impacts on turnover, as well as the study done by Weidenmier (2002), he found that job satisfaction have positive impacts on effectiveness. The reason for excludes these two variables out of the model is the author assumes that there are no vary for them. The author believes that the most recognition firm like national airline is taking a good care of its people. Job satisfaction and fatigue are assumed to be static or no distinctive among employee. Next to ignored variables are exogenous variables and endogenous variables, which is included in this model. Their definition will be given to clarify model boundary. Exogenous variables, the variable determines state of the stock in the model, or rate variable, in this model comprises of six main variables: hiring, training, attrition, service effectiveness, salary, and benefits. The first three exogenous variables: hiring, training, and attrition, hold the same unit of measurement: person per time unit. Hiring means the act of

taking new employee in to the firm. Meanwhile, attrition is the opposite actions, absent, leave, rising, and retire, for example. Training, on the other hand, is on the job training instructed by in house instructor. These three variables determine state of the labor and service capacity structure. The last three exogenous variables, however, are distinctive in unit of measurement. Service effectiveness, determiner of service delivery and service capacity, is the table function of employee experience, measured by 1 unit per employee. Meanwhile, salary and benefits, direct and indirect expenditure respectively paid to hire employee, determiner of cost structure, measured in Baht per employee per time unit. The most inner boundary of the model, endogenous variable, the variable shown status of the structure, or stock variable, contains four stock: labor, service capacity, cost, and service delivery. Stock named as follow: total labor for labor structure, capacity per time unit for service capacity, Baht per time unit for cost, and service backlog, number of passenger in the system, per time unit for service delivery. In conclusion, model boundary and its variables definition is given to use as guideline for constructions of the model presented in the next section. 3.2.2 Model structure This section contains a formal description of the service delivery management formulated as a System Dynamics Model. The model consists of two types of equations: stock variables and table functions. Along with model equations, it will be proceeded by model descriptions and its diagrams. The four subsystems:

labor, service capacity, cost, and service delivery will be presented in order stated. Labor subsystem, the first structure of the Service Delivery Management System Dynamics Model, SDM2, models the hiring, training, and turnover of the labor force. There are two sources of labor forces: permanent staff and contracted agent. The specific assumptions are made for hiring, training, and turnover distinctively to each labor force source. A key assumption for hiring is that permanent staffs hiring are zero, only overtime hiring is made, which cost 150 or 200 per cent of normal wage. Unlike permanent staff, contracted agents are only source of labor force hired newly in to the system: new contracted made every two-year, new hiring substitute resignation. Training, on the other hand, is not necessity for permanent staff. Meanwhile, two months of on the job training is needed for contracted agent. Finally, turnover, its assumption is that discrepancy turnover rate between two labor force is expected. Permanent staff assumed 10 per cent leave per day, while contracted agent assumed no leave, but 5 percent of resignation per month is expected. Figure 7 displays flow in and flow out of the labor force in this subsystem.

agent attrition

staff attrition

staff hiring

total staff staff vacancy passenger demand

Contracted Agent

Permanent Staff

required staff

total agent

agent hiring

agent vacancy

time to fill agent vacancy

required agent

Figure 7: Labor stock and flows diagram The diagrams above show stock and flow of labor force in service delivery system. The model equations formulated in VENSIM language format, describe its structure, alphabetically presented as follow: (01) agent attrition=0.25 (02) agent hiring=agent vacancy/time to fill agent vacancy (03) agent vacancy=total agent-required agent (04) Contracted Agent= INTEG (+agent hiringagent attrition,20) (05) FINAL TIME = 24 The final time for the simulation. (06) INITIAL TIME = 0 The initial time for the simulation. (07) passenger demand=180000

Units: person/Month Units: person/Month Units: person/Month Units: person Units: Month Units: Month Units: passenger

25 (08) Permanent Staff= INTEG (+staff hiringstaff attrition,1800) (09) required agent=20 (10) required staff=passenger demand/3000 (11) SAVEPER = TIME STEP The frequency with which output is stored. (12) staff attrition= 600 (13) staff hiring=staff vacancy (14) staff vacancy= total staff-required staff (15) TIME STEP = 1 The time step for the simulation. (16) time to fill agent vacancy=2 (17) total agent=Contracted Agent (18) total staff=Permanent Staff

Units: person Units: person Units: passenger/person Units: Month Units: person/Month Units: person/Month Units: person Units: Month Units: Month Units: person Units: person

Close interrelated to labor subsystem, service capacity, is stock of this system. Its source, flow in is function of number of labor force, training, and experience which result in service effectiveness. Meanwhile, its sink, flow out is attrition of both permanent staff and contracted agent. Capacity per month is unit of measurement for this system. A key assumption for this system is that service effectiveness for the first two months, on the job-training period, is equal to zero. After on the job training, their effectiveness increases to 50 in the next four months, six month from beginning. One year of experience it increases to 100, and maximum assume to 200 after two years experience. Figure 8 exhibits its diagrams.

service effectiveness

Service Capacity

staff hiring

capacity attrition resign rate

contracted agent

actual staff

required capacity

required staff service pressure

Figure 8: Service capacity stock and flows diagram The diagrams above show stock and flow of service capacity in the system. The same as labor subsystem, its model equations are formulated in the same language to describe its structure as follow: (01) (02) (03) (04) (05) (06) (07) (08) (09)

actual staff=1800 capacity attrition=resign rate*100 contracted agent=20 FINAL TIME = 24 The final time for the simulation. INITIAL TIME = 0 The initial time for the simulation. required staff=service pressure/200 required capacity=180000 resign rate=0.25 SAVEPER = TIME STEP

Units: person Units: dmnl Units: person Units: Month Units: Month Units: dmnl Units: capacity Units: person/Month Units: Month

27 The frequency with which output is stored. (10) Service Capacity= INTEG (+service effectiveness-capacity attrition,180000) (11) service effectiveness= (contracted agent*100)+(staff hiring*200) (12) service pressure=required capacity- Service Capacity (13) staff hiring=actual staff-required staff (14) TIME STEP = 1 The time step for the simulation.

Units: capacity/Month Units: dmnl Units: capacity/Month Units: person Units: Month

Stem from both subsystems mentioned, cost subsystem. It consists of one main stock, labor cost. It is generated by number of employee new hired because of turnover and absents which is caused by firm try to reduce cost by decrease number of employee. Meanwhile, labor cost can be reduced by labor effectiveness. Labor cost in this system will be measured in cost per one passenger. The assumption for this system is that target resource is limited. Therefore, target cost is settled to make system equilibrium. This system works like a gauge of the whole system. Figure 9 exhibits its stock and flow diagrams.

passenger demand Labor Cost

hiring

firing

normal wage labor force

overtime

cost pressure

target cost

absent

Figure 9: Labor cost stock and flows diagram The diagrams above show hiring, source or flow in of labor cost. Because of absent result in additional labor force to be hired due to firm try to keep its cost as target cost settled. In other word, labor cost can be by reduced directly by firing over cost labor force. The model structure is formulated as the following equations:

(01) absent=100*cost pressure/1800 (02) cost pressure=Labor Cost-target cost (03) FINAL TIME = 24 The final time for the simulation. (04) firing= cost pressure (05) hiring=labor force*normal wage*overtime

Units: dmnl [0,10] Units: cost Units: Month Units: dmnl Units: baht

29 (06) INITIAL TIME = 0 The initial time for the simulation. (07) Labor Cost= INTEG ( (hiringfiring)/passenger demand,100) (08) labor force=absent (09) normal wage=30000 (10) overtime=1.75 (11) passenger demand=200000 (12) SAVEPER = TIME STEP The frequency with which output is stored. (13) target cost=90 (14) TIME STEP = 1 The time step for the simulation.

Units: Month Units: dmnl Units: person Units: baht Units: dmnl Units: passenger Units: Month Units: dmnl Units: Month

The service delivery subsystem is the last system of model studied. It monitors service delivery status in the system. Service backlog is the main stock, indicator of service delivery status of this system. Flow in or source of its stock is passenger demand or service demand, while service completed rate is flow out or sink. Service satisfaction or passenger satisfaction is caused by service pressures that results in passenger demand and service backlog at the end. A key assumption is that service pressure and service satisfaction is nonlinear exponential growth function, while service demand is exponential decay. As long as they are no service pressures, passenger satisfaction is full as well as passenger demand. Figure 10 shows stock and flow of it structure.

passenger demand

passenger satisfaction

Service Backlog

service completed

service pressure

service capacity

Figure 10: Service capacity stock and flows diagram The diagrams show passenger satisfaction as the cause of flow in, passenger demand, of the system. Meanwhile, passenger satisfaction is function of service pressure, gap between service capacity and capacity required determined by service backlog, and service capacity. Model structure is formulated to describe relationship among the variables in the system as following equations:

(01) FINAL TIME = 24 The final time for the simulation. (02) INITIAL TIME = 0 The initial time for the simulation. (03) passenger demand=Service Backlog*passenger satisfaction

Units: Month Units: Month Units: dmnl

31 (04) passenger satisfaction=(service capacityservice pressure)/service capacity (05) SAVEPER = TIME STEP The frequency with which output is stored. (06) Service Backlog= INTEG (passenger demand-service completed,180000) (07) service capacity=180000 (08) service completed= Service Backlog/service capacity (09) service pressure=Service Backlog/service capacity (10) TIME STEP = 1 The time step for the simulation.

Units: dmnl Units: Month Units: passenger Units: capacity Units: dmnl Units: capacity Units: Month

The subsystems above show behavior of the system in disaggregate manner. Before simulating model, it will be aggregated in to single model, the fifth order model; consists of five stocks in the one model. Its stocks and flows diagram as exhibited below.

resign rate agent attrition agent Contracted Agent agent hiring

Labor Cost

overtime

cost pressure

required agent agent vacancy

capacity attrition passenger satisfaction

Permanent Staff staff hiring

required capacity

service pressure

passenger demand

staff attrition leave rate

time to fill agent

Service Capacity service effectiveness

Service Backlog

control cost

staff

required staff

service completed

Figure 11: Service Delivery Management stocks and flows diagram

The aggregate model equations were written in the same way as disaggregate one; only some equation was combined together to make model structure tightening. The aggregate equations exhibit as following: (01) agent= Contracted Agent*0.25 (02) agent attrition=resign rate (03) agent hiring=agent vacancy/time to fill agent (04) agent vacancy=required agent-Contracted Agent (05) capacity attrition=(staff attrition*1)+(agent attrition*0.5) (06) Contracted Agent= INTEG (+agent hiringagent attrition, 40) (07) control cost=100 (08) cost pressure= control cost-Labor Cost (09) FINAL TIME = 24 The final time for the simulation. (10) INITIAL TIME = 0 The initial time for the simulation. (11) Labor Cost= INTEG (agent+overtime+staff)/Service Backlog,100) (12) leave rate=(control cost-cost pressure)/control cost (13) overtime=required staff*1.75 (14) passenger demand=Service Backlog*passenger satisfaction

Units: Baht Units: person Units: person/Month Units: person Units: pax Units: person Units: Baht Units: Baht Units: Month Units: Month Units: Baht

Units: dmnl Units: Baht Units: dmnl

(15) passenger satisfaction=(Service Capacityservice pressure)/Service Capacity (16) Permanent Staff= INTEG (+staff hiring-staff attrition,80) (17) required agent=40 (18) required capacity=Service Backlog/1 (19) required staff=service pressure (20) resign rate=0.25 (21) SAVEPER = TIME STEP The frequency with which output is stored. (22) Service Backlog= INTEG (passenger demand-service completed,100) (23) Service Capacity= INTEG (+service effectiveness-capacity attrition,100) (24) service completed=Service Backlog/Service Capacity (25) service effectiveness= (Permanent Staff*1)+ (Contracted Agent*0.5) (26) service pressure=required capacity/Service Capacity (27) staff=Permanent Staff*1 (28) staff attrition= leave rate (29) staff hiring=required staff (30) TIME STEP = 1 The time step for the simulation. (31) time to fill agent=2

Units: dmnl Units: person Units: person Units: person Units: person Units: 1/Month Units: Month Units: pax Units: pax Units: dmnl Units: pax Units: person Units: Baht Units: dmnl Units: person Units: Month Units: 1/Month

The proposed model behavior is nonlinear dynamics as exhibit in table one through tables four: the labor sector, the service capacity sector, the cost sector and the service delivery sector, respectively. Its behavior will be compared with the empirical data for its validation presenting in the next chapter. The Labor Sector 41 40,000 0.4 0.4 2 2

person person person person/Month dmnl dmnl

40 20,000 0.3 0.2 1.5 1.5

person person person person/Month dmnl dmnl

39 0 0.2 0 1 1

person person person person/Month dmnl dmnl

4 2

4 4 1 2

1 3

3 2

2 2

0 Contracted Agent : Baserun Permanent Staff : Baserun agent attrition : Baserun agent hiring : Baserun 4 5 leave rate : Baserun 6 staff attrition : Baserun

6 5

1 2 4

4 5

6 5

3 4

5 6

2 3

4 5

1 2

3 4

6 5

2 3

4 5

6 5

1 2

3 4

1 2

3 4

1 2

6 5

12 Time (Month)

1 3

6 5

2 3

6 5

1 2

6 5

1 2

6 5

5 6

person person person person/Month 5 dmnl 6 dmnl

Table 1: The labor sector base run. Behaviors of labor sector consists of goal seeking for stock of contracted agent and agent hiring. Meanwhile, stock of permanent staff behavior is exponential growth as shown in table 1. Service capacity sector, on the other hand, that shown in table 2, most of its variables are exponential growth, except

capacity attrition is constant value. Meanwhile, cost sector behaviors in table 3 consist of all behaviors: goal seeking, exponential growth, and constant. The last sector, service delivery behaviors consist of exponential growth for most of its variables except passenger satisfaction as shown in table 4. The Service Capacity Sector 2 600 M 80,000 10,000 40,000 10,000

pax person pax dmnl pax person

1.5 300 M 40,000 5,000 20,000 5,000

pax person pax dmnl pax person

1 0 0 0 0 0

pax person pax dmnl pax person

4 6 3

6 5 2 4 3

4 3

4 3 2

6 5

3 2 4

5 6

3 5 2 4

2 6

3 5 4

6 2

3 4 5

3 6 2

4 6 5

3 2

4 6 5

12 Time (Month)

3 2

1 4 6 5

4 3 2

1 6 5

4 3 2

6 1 5

5 2 1

1 1 1 1 1 1 1 1 1 1 1 capacity attrition : Baserun 1 pax 2 2 2 2 2 2 2 2 2 2 required capacity : Baserun 2 person 3 3 3 3 3 3 3 3 3 3 3 Service Capacity : Baserun pax 4 4 4 4 4 4 4 4 4 4 4 service completed : Baserun dmnl 5 5 5 5 5 5 5 5 5 5 5 service effectiveness : Baserun pax 6 6 6 6 6 6 6 6 6 6 6 person service pressure : Baserun

Table 2: The service capacity sector base run

The Cost Sector 600 M 0 110 20,000 11 40,000

pax Baht Baht Baht Baht Baht

300 M -1 105 10,000 10 20,000

pax Baht Baht Baht Baht Baht

0 -2 100 0 9 0

pax Baht Baht Baht Baht Baht

6 4 1

2 5

5 4

2 3

3 4 1

6 1

6 4

4 6

2 4 1

2 4 6 1

12 Time (Month)

4 2 6 1

6 2

4 6

2 1

3 1

1 1 1 1 1 1 1 1 1 1 1 Service Backlog : Baserun 1 2 2 2 2 2 2 2 2 2 2 2 cost pressure : Baserun 3 3 3 3 3 3 3 3 3 3 3 Labor Cost : Baserun 4 4 4 4 4 4 4 4 4 4 4 4 overtime : Baserun 5 5 5 5 5 5 5 5 5 5 5 5 agent : Baserun 6 6 6 6 6 6 6 6 6 6 6 6 6 staff : Baserun

pax Baht Baht Baht Baht Baht

Table 3: The cost sector base run The Service Delivery Sector 600 M 600 M 10,000 1 10,000 80,000

pax dmnl dmnl dmnl person pax

300 M 300 M 5,000 0.9 5,000 40,000

pax dmnl dmnl dmnl person pax

4 4 3 5 6 4

6 4

1 3 5

0 0 0 0.8 0 0

pax dmnl dmnl dmnl person pax

3 5

4 5 6

4 4

3 2 1

6 5 1

3 2

6 5 1

5 3 2

6 2 1

5 3

6 2 1

5 3

6 3 2 1

6 5

3 2 1

5 6

12 Time (Month)

3 2 1

5 6 1

1 2

3 3

5 6

1 2

1 1 1 1 1 1 1 1 1 1 1 Service Backlog : Baserun 1 pax 2 2 2 2 2 2 2 2 2 2 passenger demand : Baserun 2 dmnl 3 3 3 3 3 3 3 3 3 3 3 service completed : Baserun dmnl 4 4 4 4 4 4 4 4 4 4 passenger satisfaction : Baserun 4 dmnl 5 5 5 5 5 5 5 5 5 5 5 service pressure : Baserun person 6 6 6 6 6 6 6 6 6 6 6 Service Capacity : Baserun pax

Table 4: The service delivery sector base run

Summary, in this chapter, we have presented the dynamics model of service delivery management both disaggregate: labor, service capacity, cost, and service delivery, and aggregate manner: Service Delivery Management, as well as its structure presented in VENSIM language equations for each disaggregate structure. The aggregated one will be use for this study. In the next chapter, it will be tested by empirical data observed by the author. Then, the model will be used to simulate the impacts of hiring contracted agent on service capacity and cost presenting in the following chapter.

Chapter 4 Model validation and sensitivity test 4.1 Introduction This chapter describes a study validate empirically the proposed model of service delivery management. A detailed argumentation of the validation methodology is first presented with an evaluation of its strengths and limitations. Next, the research site is described after a brief explanation of the selection criteria. The core of the chapter is dedicated to the calibration and adjustment of the model to the characteristics of the research site; longitudinal data, as well as semi-structured interviews and participant observation were used during the process. To assess whether we can increase our confidence in

the proposed model the chapter concludes with an evaluation of the adequacy of the model for describing the structure and behavior of the research site. 4.2 Model validation methodology In this section, after clarifying the different criteria for model validity in the Operation Research/Management Science literature, describes the general strategy followed to test empirically the model proposed in the previous chapter. 4.2.1 Model validation in Operation Research/Management Science Logical empiricism, logical positivism, emerged from the Vienna Circle, group of philosophers that met during the 1920s and 1930s at the University of Vienna, and dominated science during the first half of the century (Oliva, 1996; Kjellman, 2003). Logical empiricism regards models, theories, as canonical objective reflections of factual observations. Hence, a model is valid only if grounded in empirical observation, and if the terms used to describe it is stated in a formal logico-mathematical language free from ambiguity. This orthodox view of science is the underpinnings of the pioneering work in Operation Research/Management Science, OR/MS, (Barlas & Carpenter, 1990; DĂŠry, Landry & Banville, 1993). Under this perspective, the criteria put forward to judge model validity by the OR/MS community were: 1) the modelâ&#x20AC;&#x2122; s correct representation of the real system (Graybeal & Pooch, 1980), or structural and replicative validity (Churchman, Ackoff & Arnoff, 1957), and 2) the modelâ&#x20AC;&#x2122;s predictive power (Naylor & Finger, 1967).

While being adopted as epistemological foundation for OR, the formal and algorithmic search for the ‘canonical form’ embraced by logical empiricism came under attack in the second half of the century. The empirical component of logical empiricism was contested by research in sociology and psychology that showed that humans do not have access to an objective reality, and that perception is social, relational, and constructed (Cicoured, 1974; Lakoff, 1987). However, the major flow of logical empiricism was its principle of theory verification. The implication made by theory, T, or model is that if it is true, certain data, D, would be observable, T!D. To claim that a theory, T, is verified, true, because empirical data math a predicted outcome, D, is to commit the fallacy of affirming the consequent. Popper (1959) analyzed this difficulty and suggested the principle of falsification as a way to advance scientific knowledge. Under falsificationism, refutationism, empirical observations must be framed as deductive consequences of a theory, a refutable statement. If observations are shown to be true, then the theory is confirmed by those observations. If the theory fails to match observed data, then there is a certainty that the theory is untenable. The greater the number and diversity of confirming observations, the more probable it is that the conceptualization embodied in the model is not flawed. Falsificationism shifted the focus from theory verification to theory confirmation, and to a methodological improvement of theories through ‘natural selection’. During the 1960s and 70s, Kuhn (1970) and Lakatos (1978) showed that there are strong social and historical forces understanding theory selection

under falsificationism. They provide historical evidence that scientists tend to discount data that refute their theories, and that they prefer to work with a theory that has proven to be imperfect than not to have a theory at all. Their work has led to a historical-critical perspective to explain what is valued by scientists and hence makes a scientific theory valid. Under the historical-critical perspective, theory validation becomes the process of building confidence in a theory, either through falsification of a functional perspective of theory usefulness. There is evidence that the OR/MS community is currently shifting its formal views on model validity to the more functional perspective dominant in the philosophy of science literature (Gass, 1983; Miser, 1993; Mitroff, 1972; Roy, 1993; Smith, 1993; Sargent, 1998) Within the system dynamics approach, Forrester (1961; 1968) argues that validity of a simulation model cannot be discussed without reference to a specific purpose, and has identified (1973) two groups with different interpretations of model validity emerging from their objectives for model building. He notes that most professionals, operators, take validity as relative usefulness, while academics, observers, see validity as a formal logical concept. Forresterâ&#x20AC;&#x2122; s utilitarian interpretation of model validity is also finding its way into the OR/MS literature (Landry, Malouin & Oral, 1983) 4.2.2 Model calibration as validation strategy The process of estimation the model parameter to obtain 2 match between observed and simulated distributions of a dependent variable is known as model calibration. Calibration of a model to an empirical setting will attest to the

modelâ&#x20AC;&#x2122;s potential relevance to managers, and the generalizability of the proposed structure to other settings, external validity (Cook & Campbell, 1979). Although it is impossible to verify a model, insofar as the formulations proposed in the previous chapter are capable of capturing the behavior observed in a service setting I can augment my confidence in the model. Since the model was specified before entering the research site, and it has been shown that it is capable of generating diverse reference modes with different parameter values, the calibration exercise constitutes a test, in the Popperian sense, for the model. The previous chapter presented evidence that the proposed model is grounded in empirical observations: the model was developed from behavior observed in a service setting, the THAI passenger check-in domestic case, and the key relationships hypothesized in the model have been independently observed in empirical research. Furthermore, the formal description of the model and the simulation results ensure that the model is internally consistent and capable of providing a causal explanation of the observed behavior, thus providing a refutable causal model with multiple â&#x20AC;&#x2DC;points of testingâ&#x20AC;&#x2122; (Bell & Bell, 1980; Bell & Senge, 1980). To test the coherence of the model as a whole, it is necessary to assess whether these individual hypotheses, micro decisions, are simultaneously in place in a particular setting, and if their interactions, structure, are capable of replicating the observed behaviors of the service setting, macro behavior. Chapter 2 and 3 described the proposed hypotheses using theory and empirical knowledge available from the literature. The present chapter describes the empirical verification of these assumptions, and formally compares the

output generated by the simulation model to the behavior observed in the service setting. The process, however, has some limitations. No model is entirely confirmed or refuted by observational data; this is particularly evident with complex hypotheses. Working in an empirical setting entails the risk that not all the data required for the calibration process are available, or that some of the hypothesized relationships are not active in the specific situation under study. Under these circumstances, the attempts to falsify the model cannot be fully developed, i.e. no final binary decision can be made about model validity. Thus, validation is used as an inherently partial assessment of the degree of usefulness of the model (Oreskes, Shrader-Frechette & Belitz, 1994). Finally, the validity of the theory under the utilitarian criterion, whether managers involved in the field research believe that the model is useful and decide to implement its recommendations in their operations, was partially addressed in a report and presentation to the managers of the research site. However, the results of the implementation, what Forrester calls the ‘system improvement test,’ are beyond the scope of this thesis. 4.2.3 Calibration strategy The thrust of the empirical work presented in this chapter will be the estimation of the parameters driving the behavior of the formulations proposed in Chapter 3, i.e., the model calibration process. Forrester (1961) distinguishes between two types of decisions in System Dynamics models. He calls ‘overt’ decisions those consciously made by people as part of the management or economic

process, and ‘implicit’ decisions those that arise inexorably from the current state of the system. …production serves to emphasize the distinction between over and implicit decisions. Actual, present production rate is usually the result of an implicit decision function that shows how productions rate is consequence of employment, available equipment, and materials. … The accompanying overt managerial decisions are the decisions to attempt to hire people and to order equipment and materials (Forrester, 1961, pg.102)

The distinction between overt and implicit decisions was used to develop a calibration strategy. Calibration of ‘implicit’ decisions, or the parameters that drive them, is limited to identifying, through observation or interviews, the physical attributes of the workflow in the research site. Alternatively, the majority of the calibration efforts are focused on the statistical estimation of the parameters describing the model’s ‘overt’ decisions and the information processing capabilities of the agents in the service setting (Graham, 1980; Mass & Senge, 1980; Peterson, 1980; Senge, 1977). The non-linear functions proposed in the formal articulation of the model are part of the overt decisions. For each decision or set of parameters of interest, ‘detailed data’ i.e., data specific to the relationship under study, were collected from the field site, and the parameters or shape of the relationships estimated. Three outcomes are possible from the estimation process: 1) evidence found in the research site permits estimation of parameters, and confirms the hypothesized relationship

and formulation, 2) evidence found in the research site leads to the rejection the proposed formulation or the hypothesized relationship, and 3) not enough data are available in the research site to test the hypothesized relationship. For the first two cases, the relationships were integrated into the model as specified by the data. In case of lack of field data to test a micro relationship, I adhered to the System Dynamics paradigm and incorporated in the model the best estimate available from the existing literature and previously available empirical research (Forrester, 1975). As shown in the previous chapter, most of these links have been tested independently in the marketing, human resource, and operations management literature, and there is some empirical evidence to support all of them. The data gathering process was initially driven by the calibration requirement. However, when data were not directly available, or a particular formulation was not capable of generating the desired observed behavior, the process became an iterative cycle of observation, assessment, design, and model modification. To the extent these iterations were necessary, the exercise served not only as model calibration but also as theory refinement. 4.3. The research site: THAI domestic passenger check-in Although the proposed model is capable of generating multiple modes of behavior, I believed that confirmation effort would be more effective if the fieldwork was conducted at a site that showed the reference mode of service delivery management. In this case, the advantage is given to my, because I had

worked for THAI domestic passenger check-in almost two years, before moved to Load Control Department. In fact, I have been working for THAI for more than 8 years. Therefore, the research site had been well observed by myself. Mentioned in the beginning of this paper, THAI started hiring contracted agents, approximately 20 per cent of total permanent check-in staff, varies from 85-100 persons, to domestic passenger in 2002 as the strategy to reduce labor cost which is the major source, 70 per cent, of customer service expenditure. In the same time, labor is not only the major source of cost but also the major source of its capacity. Therefore, how to maintain its service capacity in the optimum cost is challenging THAI passenger check-in managers. The following reasons were considered for the site selection. First, domestic passenger checkin process needs skillful person. Second, service capacity depending on number of check-in staff and their effectiveness. Third, data is available since the author is participant observer. And lastly, passenger check-in is a good example for many others customer services. 4.3.1 Passenger check-in characteristics The main task of check-in staff is issuing boarding pass to airline passenger. It look like a piece of cake, but before one, permanent staff, could do that job, one month in class room with satisfied score in final test is needed after passing selection process. Next, they have to walk along their mentor in the next three month, on the job training, before walking alone but closely supervised. Their effectiveness will be full in at least one year. Contracted agent, in contrast, only one day in orientation room followed by one to two months on the job training.

Therefore, their effectiveness will never full, assumed 50 per cent of permanent staff. Back to issuing boarding pass task, the flight will open for check-in 3 hours and closed for check-in half an hour prior to departure time in normal case. Number of passenger for domestic flight range from 149 to 405 per flight, 100 percent load factor, depending on season. There are approximately 48 flights a day. Roughly say, flights start departing from 0600hrs to 2200hrs, 16 hours, flights per hour, 8000 passenger a day, 500 passenger per hour, which means 8 passenger per minute. Estimate passenger distribution shows in table 5. All of those values are estimated in normal situation, which not quite correct, because passenger start crowded 45 minutes before departure time. Therefore, staff effectiveness, skillful, is needed. These characteristics are guideline for data gathering in model testing. 1600

1400 1200

1000 800

600

400 200

0 0600-0759

0800-0959

1000-1159

1200-1359

1400-1559

1600-1759

1800-1959

2000-2159

Table 5: Passenger distribution in each hour periods 4.3.2 Data collection The data requirements for the calibration process can be broadly grouped in four areas. Those consist of: (1) service delivery consists of service backlog, number of flow in, flow out passenger, (2) labor consists of permanent staff, contracted agent, hiring rate, and turnover rate, (3) service capacity consists of service effectiveness, (4) labor cost consists of expenditure paid for permanent staff and contracted agent. Data concerning finance and number of passengers will be conversed to 100 in the starting month to protect business interest of THAI. In service delivery management model testing, service backlog, number of passenger derivative in the system, is used to validate proposed model. The dynamics hypothesis for this structure is that service backlog increase if service capacity reduce. It is logical hypothesis, but in the real system of passenger check-in passenger is not possible to increase, flow in must equal to flow out. Only service capacity, in the real system will be increased. Therefore, turnover and overtime will be used as inputs to compare data generated by propose model and empirical data. The following tables illustrate data comparing for each variable.

Permanent Staff 200,000 149,500 99,000

48,500

2 2 2

-2,000

1 2 2 1

2 1 1 2

2 1

Permanent Staff : observe Permanent Staff : propose

2 1

1 2

10 12 14 Time (Month)

1 2

2 1

1 2

person person

staff hiring 60,000 2

45,000 2

30,000 2

14,999 2 2

-0.2 0

2 1

1 2

2 1

staff hiring : observe staff hiring : propose

1 2

2 1

1 2

2 1

10 12 14 Time (Month) 1

1 2

2 1

1 2

person person

staff attrition 200 150 100

1 1 1 2

0 0

1 2

staff attrition : observe staff attrition : propose

1 2

10 12 14 Time (Month) 1

1 2

24 dmnl dmnl

required staff 60,000 2

45,000 2

30,000 2

14,999 2 2

-0.2 0

2 1

1 2

2 1

required staff : observe required staff : propose

1 2

2 1

1 2

2 1

10 12 14 Time (Month) 1

1 2

2 1

1 2

person person

leave rate 200 150 100

1 1

1 2

leave rate : observe leave rate : propose

1 2

10 12 14 Time (Month) 1

1 2

24 dmnl dmnl

Contracted Agent 2

20 10

1 1 1

0 0

Contracted Agent : observe Contracted Agent : propose

10 12 14 Time (Month) 1

1 2

person person

agent hiring 10

1 1

5 0

1 1

-5 -10 0

agent hiring : observe agent hiring : propose

1 2

10 12 14 Time (Month) 1

1 2

person/Month person/Month

agent attrition 1

7.5 5 2.5 0 0

2 1

agent attrition : observe agent attrition : propose

10 12 14 Time (Month) 1

1 2

person/Month person/Month

agent vacancy 20

1 1

10 0

1 1

-10 -20 0

agent vacancy : observe agent vacancy : propose

10 12 14 Time (Month)

1 2

person person

Service Capacity 400,000 285,000 1

1 1

170,000

1 1

2 1 2

55,000

2 2

10 12 14 Time (Month)

-60,000

Service Capacity : observe Service Capacity : propose

1 2

24 pax pax

capacity attrition 6,000 4,500

1 1

3,000

1 1 1

1,500

0 0

capacity attrition : observe capacity attrition : propose

10 12 14 Time (Month)

1 2

24 pax pax

service effectiveness 200,000 100,000

2 2

1 1 2 2

1 2 2 1

2 1

2 2 1 1

1 1 1 1

-100,000

1 1

-200,000 0

service effectiveness : observe service effectiveness : propose

10 12 14 Time (Month)

1 2

24 pax pax

service pressure 60,000 2

44,995 2

29,990 2

14,985 2 2

-20 0

2 1

1 2

2 1

1 2

service pressure : observe service pressure : propose

2 1

1 2

2 1

10 12 14 Time (Month) 1

1 2

2 1

1 2

person person

Service Backlog 20 B 15 B 10 B 2

4.999 B 2

-200,000

2 1 1 2

1 2 2 1

Service Backlog : observe Service Backlog : propose

1 2

10 12 14 Time (Month)

1 2

2 1 1 2

1 2

2 1 1

2 1

1 2

pax pax

passenger demand 20 B 15 B 10 B 2

5B 2

passenger demand : observe passenger demand : propose

1 2

10 12 14 Time (Month)

1 2

24 dmnl dmnl

service completed 800,000 400,000 1

1 1

2 2

1 1

1 2

2 2

2 1 1

-400,000

1 1

-800,000

service completed : observe service completed : propose

10 12 14 Time (Month)

1 2

24 dmnl dmnl

required capacity 20 B 15 B 10 B 2

4.999 B 2

-200,000

2 1 1 2

1 2 2 1

required capacity : observe required capacity : propose

1 2

2 1 1

2 1

1 2

10 12 14 Time (Month)

1 2

2 1 1 2

1 2

person person

Labor Cost 2,000 1,500 1

1,000

1 1

500 1

1 2

Labor Cost : observe Labor Cost : propose

1 2

10 12 14 Time (Month) 1

1 2

Baht Baht

agent 600,000 450,000 1

300,000

150,000 1 1 1

2 2

agent : observe agent : propose

1 2

10 12 14 Time (Month) 1

1 2

1 2 2

1 2

Baht Baht

1 2

staff 4M

1 2

2 1

1 2

-7 M

1 1 1 1

-18 M

1 1 1

-29 M

1 1

-40 M 0

staff : observe staff : propose

1 2

10 12 14 Time (Month) 1

1 2

24 Baht Baht

overtime 100,000 73,000 2

46,000 2

19,000

2 2 2 1

-8,000

2 1

2 1 1 2

1 2 2 1

2 1

overtime : observe overtime : propose

10 12 14 Time (Month)

1 2

Baht Baht

1 2

cost pressure 2 1

2 1

1 1

-500 -1,000

1 1

-1,500 -2,000 0

cost pressure : observe cost pressure : propose

10 12 14 Time (Month)

1 2

Baht Baht

Table 6-27: Data comparisons between propose and observe model The tables demonstrate that the proposed model is totally invalid. Line 1 and line 2, representative of observed and proposed data respectively, is completely difference. Therefore, the model need to be reconstructed as said in previous chapter that model construction is an itineration process. The stock and flow diagrams and equations are reconstructed and rewritten as the following presentation.

agent attrition agent

Labor Cost

Contracted Agent agent hiring

overtime

cost pressure

required agent agent vacancy

capacity attrition

Permanent Staff staff hiring

required capacity

service pressure

passenger demand

staff attrition leave rate

time to fill agent

Service Capacity service effectiveness

Service Backlog

control cost

staff

required staff

service completed

Figure 12: Reconstructed Model

(01) agent= Contracted Agent*10972 (02) agent attrition=0+STEP(10* Contracted Agent/100, 2) (03) agent hiring= agent vacancy/time to fill agent (04) agent vacancy=required agent-Contracted Agent (05) capacity attrition=(staff attrition*100)+(agent attrition*50) (06) Contracted Agent= INTEG (+agent hiringagent attrition, 40) (07) control cost=10 (08) cost pressure= control cost-Labor Cost (09) FINAL TIME = 24 The final time for the simulation. (10) INITIAL TIME = 0 The initial time for the simulation. (11) Labor Cost= INTEG ( (agent+overtime+staff)/Service Backlog,10) (12) leave rate=(control cost-cost pressure)/control cost (13) overtime=required staff*1.75*25000 (14) passenger demand=240000 (15) Permanent Staff= INTEG (+staff hiring-staff attrition,85) (16) required agent=25 (17) required capacity=Service Backlog

Units: Baht Units: person/Month Units: person/Month Units: person Units: pax Units: person Units: Baht Units: Baht Units: Month Units: Month Units: Baht Units: dmnl Units: Baht Units: dmnl Units: person Units: person Units: person

63 (18) required staff=service pressure/100 (19) SAVEPER = TIME STEP The frequency with which output is stored. (20) Service Backlog= INTEG (passenger demandservice completed,24000) (21) Service Capacity= INTEG (+service effectiveness-capacity attrition,240000) (22) service completed=Service Capacity (23) service effectiveness= (Permanent Staff*100)+(Contracted Agent*50) (24) service pressure=required capacity/Service Capacity (25) staff=Permanent Staff*25000 (26) staff attrition= leave rate+8 (27) staff hiring=required staff (28) TIME STEP = 1 The time step for the simulation. (29) time to fill agent=2

Units: person Units: Month Units: pax Units: pax Units: dmnl Units: pax Units: person Units: Baht Units: dmnl Units: person Units: Month Units: 1/Month

The results of reconstructed model run are represent in the following figure 13-17. These figures illustrate behavior of the passenger check-in system. Moreover, simulation run also illustrates causes strip for each sector as shown in figure 18-22.

The Service Delivery Management 2M 400 40 100 400,000

pax Baht person person pax

4 4 5

3 5

4 5

5 4 5 2

-100,000 -40 0 -400 -60,000

pax Baht person person pax

2 4 5 1 4 2 1 3 3 5

2 2

3 2 3 1

1 2

3 1

2 3

10 12 14 Time (Month)

1 1 1 1 1 1 Service Backlog : reconstructed pax 2 2 2 2 2 2 Baht Labor Cost : reconstructed 2 3 3 3 3 3 Contracted Agent : reconstructed person 4 4 4 4 4 Permanent Staff : reconstructed 4 person 5 5 5 5 5 Service Capacity : reconstructed 5 pax

The Labor Sector 40 100 10 10 60 2

person person person/Month person/Month dmnl person

0 -400 0 -10 0 -0.6

person person person/Month person/Month dmnl person

3 4

4 3

1 2 4 5

5 2

2 6 1

6 5

0 Contracted Agent : reconstructed Permanent Staff : reconstructed agent attrition : reconstructed agent hiring : reconstructed staff attrition : reconstructed staff hiring : reconstructed

1 3

2 3

3 4

5 6

20 1

2 3

1 2

5 6

1 4

5 6

5 6 2

10 12 14 16 Time (Month)

3 4

2 3

1 2

5 6

6 1

person person person/Month person/Month dmnl 5 6 person

The Service Capacity Sector 400,000 6,000 40,000 200 2M 2

pax pax pax person person person

1 1

3 3

-60,000 0 -40,000 -60 -100,000 -0.6

pax pax pax person person person

Service Capacity : reconstructed capacity attrition : reconstructed service effectiveness : reconstructed service pressure : reconstructed required capacity : reconstructed required staff : reconstructed

4 6

10 12 14 Time (Month)

1 2 3 4

3 4

4 5

24 1 pax

4 6

1 3

5 6

2 3

1 2

2 1 3 4 6 5 3

4 6 5

2 3

2 4

5 6

pax pax person person person

The Service Delivery Sector 2M 400,000 2M 400,000 200 400,000

pax dmnl person dmnl person pax

-100,000 200,000 -100,000 -60,000 -60 -60,000

pax dmnl person dmnl person pax

4 6 4 6 3

4 6 1 5

1 3

1 3 4 6

4 5

5 6

24 1 pax

3 4

2 3

20 1

2 3

1 2

4 5

1 3

4 5

10 12 14 Time (Month)

2 3

1 3

1 2

2 4

1 3

0 Service Backlog : reconstructed passenger demand : reconstructed required capacity : reconstructed service completed : reconstructed service pressure : reconstructed Service Capacity : reconstructed

dmnl person dmnl person pax

The Labor Cost 400 600,000 80,000 4M 40 10

Baht Baht Baht Baht Baht Baht

-40 0 -40,000 -8 M -400 8

Baht Baht Baht Baht Baht Baht

6 5 5

5 4

4 4 1 3

3 1

1 2

0 Labor Cost : reconstructed agent : reconstructed overtime : reconstructed staff : reconstructed cost pressure : reconstructed control cost : reconstructed

1 2

10 12 14 Time (Month) 1

2 3

1 2

3 4

Baht Baht Baht Baht Baht

2 3

4 5

24 1 Baht

1 3

5 6

2 3

1 2

5 6

Figure 13-17: Reconstructed model run reconstructed

Contracted Agent 40 35 1

1 1

20 agent attrition 4 3

1 1

2 1 0 agent hiring 4

-2

1 1

-5 -8

12 Time (Month)

67 reconstructed

Permanent Staff 400 200 1

-200

-400 staff attrition 60 45

1 1

0 staff hiring 0.04

0.028

0.016

1 1

0.004 -0.008

reconstructed

12 Time (Month)

Service Capacity 400,000 300,000 1

200,000

100,000

0 capacity attrition 6,000 4,500

3,000

1 1

1,500

0 service effectiveness 40,000

20,000 1

-20,000 -40,000

12 Time (Month)

68 reconstructed

Service Backlog 400,000 1

200,000

1 1 1

-200,000

-400,000 service completed 400,000 300,000 1

200,000

1 1

100,000 0

12 Time (Month)

passenger demand reconstructed: 240,000

reconstructed

Labor Cost 400 285 170 55 -60 Service Backlog 400,000 200,000 0 -200,000 -400,000 agent 600,000 500,000 400,000 300,000 200,000 overtime 2,000 1,400 800 200 -400 staff 6M 3M 0 -3 M -6 M 0

1 1

1 1 1

1 1

1 1 1

12 Time (Month)

Figure 18-22: Causes strip for each sectors

4.4 Sensitivity test Before applying the model in policy analysis, it has to know the degree to which those analyses might change as reasonable alternative assumptions are built in the model. How sensitive is the model to changes in parameter values and apparently minor variations in the formulation of the equations? Sensitivity test in this study will run on numerical sensitivity by changing 25 per cent of constant value to examine resulting output for change in values, and to robustness constructed model (Sterman, 2000. The sensitivity procedure used in this study is based on multivariate sensitivity simulation (MVSS) or Monte Carlo Simulation (Ventana System, 2003). Figure 23-26 illustrates its results.

sensitivity 50% 75%

95%

100%

Service Capacity 4M

-2 M

-4 M

12 Time (Month)

70 sensitivity 50% 75%

95%

100%

Labor Cost 40,000

20,000

-20,000

-40,000

sensitivity 50% 75%

95%

12 Time (Month)

100%

Service Backlog 4M

-2 M

-4 M

12 Time (Month)

71 sensitivity 50% 75%

95%

100%

overtime 1M

500,000

-500,000

-1 M

12 Time (Month)

Figure 23-26: Numerical sensitivity on Monte Carlo simulation Summary, this chapter describes the procedures of model testing. Scientific method is discussed, followed by model calibration, the processes of comparing data generated by propose and observe model. In this study, the proposed model is totally invalid, therefore model improvement is also discussed in this chapter. The model is reconstructed and simulated as well as it robustness, numerical sensitivity test, by Monte Carlo simulation. Now, the model is ready to apply for the investigation of the impacts of hiring contracted on capacity and cost discussing in the next chapter.

Chapter 5 Model applying in policy analysis 5.1 Introduction In Chapter 4, the System Dynamics model of service delivery management for passenger check-in system in THAI was executed through 24 months of simulation time. The results from simulation of model validation base run result in model improvement and sensitivity test in that chapter indicate that this model is reasonable to demonstrate the general dynamic behavior of the real system in passenger check-in of THAI domestic case. The researcher further analyzed the results from strand run in order to answer the question of the impacts of hiring contracted agents on service capacity and cost. Eventually, the model will be executed under conditions introduced as the policy or alternative in order to inspect the responses of system to those changes and to discover the policy which able to propose the optimum scenario to those managers. 5.2 Policy analysis of standard run The results of implementing standard policy: 25 contracted agents, cause service capacity to be stable in the first 12 months, then slimly rising until the 20th month, in the last stage, it keeping dropping until the end of the simulation. Figure 28 illustrate the causal strips of its behavior: decreasing, capacity attrition, of service capacity keep rising to the end of simulation result in the service effectiveness and service capacity to behave in the opposite of it attrition, falling to the end, except its effectiveness.

Standard Policy 40 6M 20 B 100 B 2,000

person person pax pax Baht

3 2

3 2 2

5 4

20 -40 M -200 B -100 B 0

person person pax pax Baht

4 5

5 4

4 5

3 4

10 12 14 Time (Month)

1 1 1 1 1 1 person Contracted Agent : standard run 2 2 2 2 2 2 person Permanent Staff : standard run 3 3 3 3 3 3 pax Service Capacity : standard run 4 4 4 4 4 Service Backlog : standard run 4 pax 5 5 5 5 5 5 Labor Cost : standard run Baht

Figure 27: Standard policy simulation standard run

Service Capacity 20 B

1 1

-35 B -90 B

-145 B 1

-200 B capacity attrition 400,000

300,000 200,000 1

1 1

100,000 1

0 service effectiveness 20 B -5 B

1 1

-30 B

-55 B -80 B

12 Time (Month)

Figure 28: Causes strip of service capacity

Labor cost, in contrast, as illustrated in figure 27, keep rising to the 24th month. Figure 29 illustrate causal strips of this behavior. Service backlog is the main determination of labor cost. It causes firm to hire more employees to reduce its backlog. The main source of labor cost stem from overtime payment for permanent staff. standard run

Labor Cost 2,000 1,500 1,000 500 0 Service Backlog 100 B 50 B 0 -50 B -100 B agent 400,000 350,000 300,000 250,000 200,000 overtime 4e+012 2.85e+012 1.7e+012 550 B -600 B staff 200 B -50 B -300 B -550 B -800 B 0

1 1

1 1 1 1

12 Time (Month)

Figure 29: Causes strip of labor cost This policy displayed high labor cost because of high overtime payment for permanent staff range from 150 to 200 per cent of normal salary. In the next sector the author will simulate the three alternative policies: scenario 1 moderate hiring contracted agent, scenario 2 maximum contracted agent, and scenario 3 reducing time to fill agent, training time, and reducing 50 per cent

turnover rate. The results of those alternative policies will be discussed in the next section. 5.3 Alternative policy analysis Three alternative policies namely: scenario 1, scenario 2, and scenario 3 will be analyzed and discussed in this section. Moderate contracted agent is the main suggestion of the first scenario. There are three levels of moderation: 50, 75, and 100, contracted agents namely a, b, and c, respectively. The simulation results illustrate in figure 30 â&#x20AC;&#x201C; 32. Theses figures illustrate that increase contracted agents to 75 persons are the most effectiveness among those three alternative policies. Even though it has no impacts on labor cost, but it help stabilizing service capacity more than the other two as illustrate in figure 33 and 32. Scenario 1a 60 4M 20 B 100 B 2,000

person person pax pax Baht

50 -18 M -90 B 0 1,000

person person pax pax Baht

3 3 2

3 2

2 3

2 5 4 1

4 5

4 3

5 4 2

40 -40 M -200 B -100 B 0

person person pax pax Baht

5 1

1 3

Contracted Agent : scenario 1a Permanent Staff : scenario 1a Service Capacity : scenario 1a Service Backlog : scenario 1a 5 Labor Cost : scenario 1a

5 1

1 2

3 5

2 3

4 5

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

12 Time (Month)

1 2

3 4

1 2

3 4

4 5

person person 3 pax 4 pax 5 Baht

Scenario 1b 80 4M 20 B 200 B 2,000

person person pax pax Baht

2 3

70 -18 M -190 B 0 1,000

person person pax pax Baht

2 5 4

4 5

3 4

60 -40 M -400 B -200 B 0

person person pax pax Baht

5 1

5 5

Contracted Agent : scenario 1b Permanent Staff : scenario 1b Service Capacity : scenario 1b Service Backlog : scenario 1b 5 Labor Cost : scenario 1b

1 2

3 4

1 2

3 4

12 Time (Month)

1 2

4 5

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

2 3

4 5

person person 3 pax 4 pax 5 Baht

Scenario 1c 100 4M 40 B 200 B 2,000

person person pax pax Baht

90 -18 M -180 B 0 1,000

person person pax pax Baht

3 2

2 3

3 2

3 2 4

5 4

4 5

5 4

5 1

80 -40 M -400 B -200 B 0

person person pax pax Baht

1 5

1 2

Contracted Agent : scenario 1c Permanent Staff : scenario 1c Service Capacity : scenario 1c Service Backlog : scenario 1c 5 Labor Cost : scenario 1c

1 2

Figure 30-32: Scenario 1 simulation run

2 3

4 5

1 2

3 4

1 2

4 5

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

1 2

3 4

12 Time (Month)

1 2

3 4

1 2

3 4

24 person person pax 4 pax 5 Baht

Service Capacity 40 B

2 1 3 1 2 3

2 1 3 1 2

3 1 3 2

2 1 3 1 2

3 1 3 2

2 1 3

2 1 3 1 3 2

1 2 2 1 3 3 1

-70 B 3 2

-180 B 2 1

-290 B -400 B 0

Service Capacity : scenario 1c Service Capacity : scenario 1b Service Capacity : scenario 1a

10 12 14 Time (Month)

1 2

2 3

24 pax pax pax

Labor Cost 2,000 1,500 1,000 3 2

2 1

3 2 2 1 3 1

3 2 1 1

3 2

500 3 2

0 0

2 1 3 1

3 2 1 1

3 2

3 1 2

Labor Cost : scenario 1c Labor Cost : scenario 1b Labor Cost : scenario 1a

3 1

10 12 14 Time (Month) 1

2 3

2 1

1 2

Figure 32-33: Scenario 1 comparative simulation

2 3

24 Baht Baht Baht

Maximum contracted agents, hiring agent to serve all passenger demand, scenario 2, by dividing passenger demand with agent effectiveness, assuming 1,500 per person/month. Maximum number of contracted is 160 person. The system behaves indistinctively from scenario 1, only behavior details are discrepancy. As illustrate in figure 35-36, service capacity in scenario 1 is higher than the counterpart scenario 2 from the 21st month of the simulation, but labor cost, on the other hand, keep higher from the beginning. Therefore, scenario 2 is more effective than scenario 1, the illustration in figure 37-38, visualizing this conclusion. Scenario 2 200 6M 40 B 200 B 2,000

person person pax pax Baht

150 -27 M -180 B 0 1,000

person person pax pax Baht

3 2

2 3

3 3

2 3

1 1

1 5

5 1

4 5

5 5 4

4 1

1 2

100 -60 M -400 B -200 B 0

person person pax pax Baht

5 5

12 Time (Month)

1 1 1 1 1 1 1 1 1 1 1 1 Contracted Agent : scenario 2 1 person 2 2 2 2 2 2 2 2 2 2 2 2 2 Permanent Staff : scenario 2 person 3 3 3 3 3 3 3 3 3 3 3 3 3 Service Capacity : scenario 2 pax 4 4 4 4 4 4 4 4 4 4 4 4 4 Service Backlog : scenario 2 pax 5 5 5 5 5 5 5 5 5 5 5 5 5 5 Baht Labor Cost : scenario 2

Figure 34: Scenario 2 simulation run

Service Capacity 40 B

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

-70 B 2

-180 B 2

-290 B

-400 B 0

Service Capacity : scenario 2 Service Capacity : scenario 1b

10 12 14 Time (Month)

1 2

pax pax

2 1

1 2

Labor Cost 2,000 1,500 2

1,000 2

2 1

500 2

2 1

Labor Cost : scenario 2 Labor Cost : scenario 1b

2 1

10 12 14 Time (Month) 1

1 2

Figure 35-36: Scenario 1 and 2 comparative simulation

24 Baht Baht

80 scenario 2 scenario 1b

1 2

Service Capacity 40 B

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

2 1

1 2

1 2 2 1

-70 B

2 1

-180 B

-290 B

-400 B capacity attrition 400,000 300,000 2 1

2 1

1 2

2 1

1 2

2 1

200,000 100,000 2 1

1 2

2 1

0 service effectiveness 20 B

2 1

1 2 2 1

-35 B

2 1

-90 B

1 1

-145 B -200 B

scenario 2 scenario 1b

1 2

12 Time (Month)

1 2

2 1

Labor Cost 2,000 1,000 0 Service Backlog 200 B 0

2 1

1 2

-200 B agent 2M

0 overtime 8e+012

1 2

3.5e+012

1 2

-1e+012 staff 200 B

2 1

1 2

2 1

-900 B -2e+012

12 Time (Month)

Figure 37-38: Causal strips comparison

2 1

1 2 2 1

2 1

The last alternative policy, scenario 3, is focusing on reducing time to fill contracted agent, in the other word, minimizing training period as short as possible, and reducing torn over rate. This policy assume that time to fill agent reduce to 1 month, turn over rate reduce to 5 per cent, agent not allow to quit before 3 month. The scenario 3 simulation, as illustrate in figure 39, displays low labor cost in the first 10 month, then jumping to high cost in the next month. Service capacity, on the other hand, behaves in the same pattern as the first 2 scenarios. Focusing in detail for service capacity and labor cost, as illustrate in figure 40-41, there is no distinction between scenario 1 and 3 for service capacity. Exception labor cost, scenario 2 is the most effective policy for reducing labor cost. Scenario 3 80 4M 20 B 200 B 2,000

person person pax pax Baht

3 2

2 3

3 2

75 -18 M -190 B 0 1,000

person person pax pax Baht

70 -40 M -400 B -200 B 0

person person pax pax Baht

5 5 4 1

4 5

5 4

4 4

3 4

1 5

5 1

12 Time (Month)

1 1 1 1 1 1 1 1 1 1 1 1 Contracted Agent : scenario 3 1 person 2 2 2 2 2 2 2 2 2 2 2 2 2 Permanent Staff : scenario 3 person 3 3 3 3 3 3 3 3 3 3 3 3 3 Service Capacity : scenario 3 pax 4 4 4 4 4 4 4 4 4 4 4 4 4 Service Backlog : scenario 3 pax 5 5 5 5 5 5 5 5 5 5 5 5 5 5 Baht Labor Cost : scenario 3

Figure 39: Scenario 3 simulation run

Service Capacity 40 B 3 2 1

3 2 1

2 3 1

3 2 1

3 1 2

2 3 1

3 1 2

-70 B 1 3

-180 B 3 1

-290 B

-400 B 0

Service Capacity : scenario 3 Service Capacity : scenario 2 Service Capacity : scenario 1b

1 2

12 Time (Month) 1

2 3

1 2

2 3

pax pax pax

Labor Cost 2,000

1,500 1

3 3

1 3

1,000

3 2

500

1 3 2

3 1 2

2 3 1

3 1 2

Labor Cost : scenario 3 Labor Cost : scenario 2 Labor Cost : scenario 1b

1 2

1 3 2

1 2

12 Time (Month) 1

2 3

1 2

Figure 40-41: Scenario 1-3 comparative simulation

1 2

Baht Baht Baht

In conclusion, this chapter presented an analysis hiring contracted agentâ&#x20AC;&#x2122;s policy and the others alternative policies namely: scenario 1, scenario 2, and scenario 3. Summarizing, this policy (Figure 27) is more cost effective than permanent scenario policy (Figure 42). Standard policy, hiring contracted agents, cost start at the lower point than the permanent one. Service capacity, in contrast, has no significant distinction. It could be implied that whether hiring contracted agent or not hiring, the main focus for manager is providing service capacity to serve passenger demand. Therefore, there is no discrepancy among those policies simulated, only cost will determine what policy is going implemented. Further more, this chapter simulated three alternatives hiring contracted policy to investigate the most effective solutions for this policy. Permanent Scenario 10 2M 10 B 40 B

Baht person pax pax

3 2

1 3 2

3 2

2 3

3 2

2 3

3 1

-995 -9 M -45 B 0

2 2 3

Baht person pax pax

4 1

1 2

4 2

2 4

-2,000 -20 M -100 B -40 B

Baht person pax pax

12 Time (Month)

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Baht Labor Cost : scenario PM 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Permanent Staff : scenario PM 2 person 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 Service Capacity : scenario PM pax 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 Service Backlog : scenario PM pax

Figure 42: Permanent policy simulation run

Resulting from comparative simulation run, there is no significant distinction among those three alternatives policy namely: scenario 1, scenario 2, and scenario 3, but cost displayed an interested simulation. Scenario 2, maximizing contracted agents, is the most cost-effective policy. It is not difficult to explain; contracted agents are much lower paid than permanent staff. In spite of sound effectiveness policy, carefully implementation is needed. 5.4 Future research recommendations The findings and limitations of this study point to two separated areas where future study should be carried out. 1) Further validation of model formulation, in formulating cost pressure on absent, further study should explore the new way to formulate model equations to find its validation. Further more, increasing confidence and/or update this model structure by replicating the calibration analysis in other service settings. 2) Extend existing model, the model boundary needs to be expanded to include other structural characteristics of service settings and causal relations that were excluded from this study. Among other changes, it could be extended to formally introduce into the impacts of agentâ&#x20AC;&#x2122;s satisfaction and economic growth rate on turnover rate. Further more, further study should expand to the impacts of fatigue on service effectiveness which is assumed no effect in this study.

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Authorâ&#x20AC;&#x2122;s Biography

Before my face came to appear on this page, many things had happened to my life. I try to recall it as much as possible. Here is my tale based on the true story. Get closer, so you will know more about me. Born in 1971, in the northeast of Thailand, Surin province, I was named Sawat Phoomphuang. After I had graduated from local hometown primary school, Baan Noan Sa Bai Primary School, my life was shifted to Buddha arena. I had been there for three years, graduated secondary school from nonsystematic education (tele-education), graduated Buddhist Study (Nag Dham Ake), then I migrated to city of angel, Bangkok, where my adventure began, while I was fifteen years old. Room mated with older cousin, Choochart, and others 4 persons in 16 square meters rental room, I works for Star Bowling Center at 10.00 am after finished a type writing class. In the first five years in Bangkok, I had completed the first step, high school certificate, of course, the same means as my secondary one. The first step that made me eligible to follow my dream, to be a university student.

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Moved to hotel jobs: Royal River Hotel, Chao Phya Park Hotel, I spent four year to obtain a college degree from Ramkhamheang Open University in the same time as earning money from hotel works. Here I was certified bachelor of arts (public administrations) in 1996. In my graduation ceremony, another good news came to my life: I was accected to a new home, Thai Airways International, the most desirable job for Thai young graduated at that time. In this new house, I started chasing another dream after I had passed probation period, a master degree student. Krirk Private University is the place where my second dream came true. At Krirk, my paradigm was shifted. I was introduced to the new knowledge: Appropriate Technology, The Third Wave, Turning Point, especially the lecture of Prof. Dr. Teerachote (Banpote) Veerasai: Seminar on Organizational Administration. This lecture totally changed my world view and resulted in my first paper: Tao Te Ching In Managerial Context, printed in Krirk University Journal Vol. 17 No. 1. The remarkable paper that made me accomplished master degree in 1998 was my thesis. It was supervised by Prof. Dr. Payorm Wongsarnsri who was not only my thesis chair person, but olso the crucial figure for my research in practice which was first introduced in science, theory of knowledge, and philosophy by the late Prof. Dr. Sawaeng Ratanamongkolmart whom I will never forget. My thesis, well supported by Khun Boontan Tontong, research site director, was concerning work stress in electronics manufacture. I had prepared my self to study in doctoral degree before I decided to choose the thesis plan for my master degree. I though that is enough to entry

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doctoral class, but I was wrong; two times fail in oral examination for Mahidol University where was my first effort and Bangkok University was the second one. I took another effort to Mahidol again, then UTCC, both disappointed me. My energy for chasing doctoral degree had been lowing since 1998 until Ramkhamheang University gave me a chance in 2002. Two years at Ramkhamheang University, I had learned much from Prof. Dr. Suchart Prasitrath-sint. In here, my learning was unlimited; knowledge comes from every where through out the world via ciber space. I proposed Service Marketing Management Model to the University Dissertation Committee, but it fails. Then, I had begun studying System Dynamics Management Model before the University Ph.D. Committee collapsed. It was the valuable two years, but I was not certified Ph.D. even my knowledge told me that. In the long lasting night at Ramkhamheang University, I finally discovered the dawn with an encouragement of my Ph.D. class mate: Dr. Krichit, Dr. Itthikorn, and Dr. Nivet, as well as Dr. Sudawan, at The Intercultural Open University in 2004 where knowledge is centered in students. My System Dynamics Management Model was accomplished here. This place made me to be appeared under the name: sdmlab.com, my new life arena, where I am ready to start a new adventure with you, my new challenging.