A UNIFIED FRAMEWORK FOR THE DESIGN OF SERVICE SYSTEMS: A CO-PRODUCTION APPROACH by Truong Hong Trinh A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Industrial and Manufacturing Engineering Examination Committee: External Examiner: Nationality: Previous Degree: Scholarship Donor: Prof Voratas Kachitvichyanukul (Chairperson) Dr Huynh Trung Luong Dr Do Ba Khang Prof Yon-Chun Chou Institute of Industrial Engineering National Taiwan University Vietnamese Master of Science in Industrial Engineering Asian Institute of Technology, Thailand Ministry of Education and Training (MOET), Vietnam – AIT fellowship Asian Institute of Technology School of Engineering and Technology Thailand August 2012 ACKNOWLEDGEMENTS The author would like to take this opportunity to express his sincere gratitude to those individuals who have provided tremendous influences during his graduate study at Asian Institute of Technology (AIT), Thailand Firstly, the author would express sincere gratitude to his advisor, Professor Voratas Kachitvichyanukul for sound advice, reliable guidance and the influential encouragement throughout the doctoral program Grateful acknowledgements are also extended to his dissertation committee members Dr Huynh Trung Luong and Dr Do Ba Khang for their valuable comments and enthusiasm assistance He would send sincere thanks to Professor Yon-Chun Chou as an external examiner for the review of this dissertation The author would like to express his high appreciation to scholarship donors of Ministry of Education and Training (MOET), Vietnam - AIT fellowship for providing financial support to enable him to continue the doctoral program at AIT Thank to all of his colleagues from the University of Danang, Vietnam for their support and encouragement Thanks are also extended to faculty members and staffs in AIT for their kindness and guidance Finally, the deepest thanks to his family members and to his beloved wife Mrs Le Thi Hai, without her support and understanding he would never have completed his challenge Thanks to all of his friends ii ABSTRACT The design of service system is an important issue in service organizations and is also one of the most interested topics in service operations management However, a major challenge in earlier literature is on how to find general principles to guide the design process The purpose of this research is to propose a co-production approach and to develop a unified framework for the design of service systems The co-production approach views customer as co-producer with the presence of customer inputs in a service process, and conducts strategic tradeoffs between firm and customers In this research, the co-production approach is employed to develop a framework for the design of service systems that includes theoretical models such as service strategy triad, service positioning strategy, and service delivery strategy, in which interrelationships among market target, service concept and service delivery system design are explored by defining service classification based on two dimensions of service and process, and integrating service strategy into service delivery strategy The design of service system actually requires theoretical and analytical models to guide managerial decisions For that reason, a bi-level multi-objective model is developed for service delivery strategy from strategic level to operational level For the upper level, the co-production approach is employed to develop channel strategy that conducts an objective tradeoff between total cost and total utility For the lower level, the DEA (Data Envelopment Analysis) method is used to plan allocation policy with capacity constraints, in which output-oriented CCR models are employed to measure customer efficiency and firm productivity In order to deal with multi-objective problem, Multi-Objective Particle Swarm Optimization Algorithms (MOPSOs) are developed using the Object Library for Evolutionary Techniques, (ET-Lib) to identify objective tradeoffs (Pareto Fronts) An experimental study with a hypothetical service system is carried out to verify theoretical typology on service co-production through the analytical models, and conduct strategic approaches and allocation behavior in service delivery systems The experimental result indicates that the co-production function is feasible under economic and institutional considerations; the co-production approach extends and generalizes both the firm approach and the customer approach; and there exists strategic tradeoffs between firm and customers in service delivery systems These findings provide managerial indicators for the design of service delivery systems This research not only contributes a theoretical background on co-production and a unified framework for the design of service systems, but also creates rich opportunities for application of analytical methods in service operations Keywords: co-production approach, co-production function, service system design, service strategy triad, service positioning matrix, service delivery strategy, DEA method, customer efficiency, firm productivity, strategic tradeoff, multi-objective optimization iii TABLE OF CONTENTS CHAPTER TITLE PAGE TITLE PAGE ACKNOWLEDGEMENTS ABSTRACT TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS i ii iii iv v vi vii INTRODUCTION 1.1 Background 1.2 Objectives of the Research 1.3 Contributions and Publications 1.4 Organization of the Research 1 LITERATURE REVIEW 2.1 Co-production Concept 2.2 DEA Method 2.3 Reviews on Service System Design 4 METHODOLOGY 3.1 Co-production Approach 3.2 The Design of Service System 7 10 THE CONCEPTUAL MODELS 4.1 Channel Strategy 4.2 Allocation Policy 4.3 Bilevel Multi-objective Model 16 16 19 21 EVOLUTIONARY METHODS FOR MULTI-OBJECTIVE PROBLEMS 5.1 Framework for MOEAs 5.2 Multi-objective Evolutionary Algorithms 5.3 Elite Optimality Procedure 24 THE EXPERIMENTAL STUDY 6.1 Experiment Design 6.2 Analysis and Results 35 35 36 CONCLUSIONS AND RECOMMENDATIONS 7.1 Summary and Conclusions 7.2 Recommendations for Further Research 43 43 44 REFERENCES 45 iv 24 26 32 LIST OF TABLES TABLE TITLE 6.1 6.2 The Data of the System with the Current Allocation Efficiency Approach for Resource Allocation with the Same Capacity Productivity Approach for Resource Allocation with the Same Capacity Comparison of Two Approaches in the Same Capacity System Parameters and Strategic Approaches New Allocation with the Efficiency Approach New Allocation with the Productivity Approach Selected Cases of Production Capacity from the Upper Level 6.3 6.4 6.5 6.6 6.7 6.8 PAGE v 35 37 38 39 40 41 41 42 LIST OF FIGURES FIGURE TITLE PAGE 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 5.1 5.2 5.3 5.4 5.5 5.6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 Economic Constraints under Different Approaches Service Process Matrix Service Strategy Triad Strategic Positioning Matrix Analytical Framework for Service Delivery Strategy Strategic Options for Service Delivery System Approaches for Developing Channel Strategy Technical Change, Efficiency Change and Productivity Change Flowchart of MOEAs Solution Representation for MOPSO Algorithm Solution Representation for MODE Algorithm Reference Point Approaches for Multi-objective Optimization Flowchart of Elite Optimality Process Elite Optimality Process Average Cost and Economies of Scale Pareto Frontiers under Economies of Scale Pareto Frontiers under Decreasing/Increasing Returns to Scale Efficiency Approach for Resource Allocation Productivity Approach for Resource Allocation Approaches for Developing Channel Strategy Approaches for Planning Allocation Policy Non-dominated Solutions of Production Capacity Set of Non-dominated Solutions of Resource Allocation vi 10 11 12 15 16 19 25 29 31 32 33 34 36 36 37 38 39 40 41 42 42 LIST OF ABBREVIATIONS ACO BCC CCR DE DEA DMUs EAs EMO ER ET-Lib GA GD GDP MOACO MODE MOEAs MOGA MOPSO MS NSGA II PFknown PFtrue PSO SOM SP SPA SP/SP TFP UST Ant Colony Optimization Banker, Charnes, and Cooper Charnes, Cooper, and Rhodes Differential Evolution Data Envelopment Analysis Decision Making Units Evolutionary Algorithms Evolutionary Multi-objective Optimization Error Ratio Objective Library for Evolutionary Techniques Genetic Algorithm Generational Distance Gross Domestic Product Multi-Objective Ant Colony Optimization Multi-Objective Differential Evolution Multi-Objective Evolutionary Algorithms Multi-Objective Genetic Algorithm Multi-Objective Particle Swarm Optimization Movement/Mutation Strategy Non-dominated Sorting Genetic Algorithm II A known Pareto Front A true Pareto Front Particle Swarm Optimization Service Operations Management Spread Service Process Analysis Service Process/Service Package Total Factor Productivity Unified Service Theory vii CHAPTER INTRODUCTION 1.1 Background Service industry plays an important role in many nation economies with increasing contributions to Gross Domestic Product (GDP) In developed country, about 70% of GDP comes from service sectors which employ about 80% of the total labor force In addition, a trend toward the integration of goods and service into a single offering implies that the production is to create a combined product of goods and services (Johansson and Olhager, 2006) In response to the emerging trends, many researchers have attempted to develop service typologies but relatively few studies have been done in service operations management (SOM), in which a major challenge is to find operations management principles that guide the design of service systems The design of service system is important issue in service organizations since it allows a firm to transform its strategy into operational decisions (Roth and Menor, 2003) In literature, there are generic approaches to service system design including production line approach (Levitt, 1972; Levitt, 1976); customer contact approach (Chase, 1978; Chase, 1981); and unified service theory (Sampson, 2001; Sampson and Froehle, 2006) Even though many earlier researchers have attempted to develop conceptual frameworks for the design of service systems, but gaps between theory and practice still remain due to lack of a theoretical background that provides general principles to guide the design of service systems The challenge is to create innovative ideas that can be used in this effort The design of service system is also one of the most interested topics in service operations management involving both service design and service delivery design While service design refers to “what should be delivered”, service delivery design refers to “how the service should be delivered” (Chen and Hao, 2010) Since customer is co-producer in a service process (Parks et al., 1981; Wikström, 1996; Ojasalo, 2003), the problem is how to achieve strategic tradeoffs between firm and customers in service delivery systems Building on the synthesis of existing literature on service co-production, researches on coproduction approach in service operations have an important role in attracting researchers’ attention 1.2 Objectives of the Research For this motivation, this research proposes a co-production approach as an extension of operations management principles, in which service operations principles are simply an extension of manufacturing operations principles The co-production approach provides general principles on strategic behaviors of firm and customers in service delivery systems Meanwhile, the design of service systems needs theoretical and analytical models to make managerial decisions The objective of this research is to develop a unified framework for the design of service systems under the co-production approach This objective will be achieved by completing three main studies:  First, the study builds a theoretical background on co-production, in which coproduction function assumes well-defined functional form with the presence of inputs from both the firm and the customers The co-production approach has two important features of “customer as co-producer” and “the product is the process” In addition, the co-production approach conducts strategic tradeoffs between firm and customers in service delivery systems  Second, the study develops a unified framework for the design of service systems The framework includes theoretical models such as service strategy triad, service positioning matrix, service delivery strategy Building upon published literature, this study explores interrelationship among components of service strategy triad by defining service classification based on modified service process matrix, aligning between service concept and service delivery design  Third, the study employs analytical techniques and tools for service operations Analytical models are developed to make managerial decisions on channel strategy and allocation policy DEA method is employed to measure customer efficiency and firm productivity In addition, Multi-objective Particle Swarm Optimization Algorithms (MOPSOs) are developed using the Object Library for Evolutionary Techniques (ETLib) (Nguyen et al., 2010) for solving the multi-objective problems in service delivery systems that provides strategic options for the design of service delivery systems 1.3 Contributions and Publications The main contributions highlighted in this research include theoretical background on the co-production approach; framework for service system design; and technique and tool for service operations  Theoretical background on the co-production approach serves general principles on behaviors of firm and customers as an extension of operations management principles The co-production approach views customer as co-producer, and conducts strategic tradeoffs between firm and customers The co-production approach is presented in Chapter  Framework for service system design unifies the prior conceptual frameworks including models of strategic service alignment, service classification schemes, and service design models The unified framework explores service strategy triad and aligns service strategy with service delivery strategy The framework with theoretical and analytical models is presented in Chapters and  Technique and tool for service operations explore parametric and non-parametric methods for capital planning and allocation policy, and multi-objective optimization for strategic tradeoffs between firm and customers Analytical models and methods are presented in Chapters and Several research manuscripts were produced as a result of this research The list is given as follows: Trinh, T H., Kachitvichyanukul, V., and Khang, D B (2012) The co-production approach to service: a theoretical background Journal of the Operational Research Society Under Review This paper explores co-production concept and develops a theoretical background on the co-production approach as an extension of operations management principles The co2 production function assumes well-defined function with both firm and customer inputs The paper indicates that the co-production function is feasible under economic and institutional considerations, and the co-production approach generalizes both the firm approach and the customer approach The paper contributes a theoretical background on the co-production approach that provides general principles on service operations management Trinh, T H., and Kachitvichyanukul, V (2012) An analytical framework for the design of service delivery systems: a co-production approach International Journal of Operational Research In Press This paper develops an analytical framework for the design of service delivery systems The framework employs the co-production approach that conducts strategic tradeoffs between firm and customers A bilevel multi-objective model is developed for service delivery strategy The paper contributes the analytical models for service delivery strategy from strategic level to operational level that provides the important guidance for the design of service delivery systems Trinh, T H., Kachitvichyanukul, V., and Luong, H T (2012) A tradeoff between customer efficiency and firm productivity in service delivery systems Industrial Engineering & Management Systems In Press This paper proposes non-parametric methodology that provides approaches for studying allocation behaviors of firm and customers including efficiency approach and productivity approach The paper findings are existing tradeoffs between customer efficiency and firm productivity that provides strategic options for allocation policy in service delivery systems 1.4 Organization of the Research This research is organized into main chapters as follows: Chapter introduces the background, motivations and objectives of the research Chapter reviews literatures on co-production concept, DEA method and service system design Chapter presents methodology that includes co-production approach, and framework for service system design Chapter develops conceptual models for service delivery strategy including channel strategy and allocation policy Chapter presents framework for MOEAs, multiobjective evolutionary algorithms (MOPSO and MODE algorithms), and Elite optimality procedure Chapter focuses on experimental study for hypothetical service system Chapter summarizes the main findings and recommendations for the further research CHAPTER THE EXPERIMENTAL STUDY 6.1 Experiment Design The experimental study proposes a hypothetical single channel service system (c = 1) that has customers (DMUs) with single input and single output Table 6.1 presents data of the system with the current allocation The output-oriented CCR model is used to measure efficiency scores of the current system Table 6.1: The Data of the System with the Current Allocation DMU i A B C D E Total Current Input Xt 25 Current Output Yt 25 Efficiency Score 1.00 1.50 1.67 2.80 2.25 Functions are used in analytical models as follows: Co-production function: Oc  Ac  K cαc  H cc  Lcc t Utility function: U   wS Oc  wL Lc  c 1 t Cost function: F   wK K c  wH H c  wL Lc  c 1 The hypothetical service system assumes the co-production function with mean productivity of (Ac = 1) and constant returns to scale (α + β + γ = 1) with α = β = 2γ in experiments Customers have the same unit cost of customer input (wL = 2) and unit value of customer output (wS = 5) In addition, unit costs of firm capital (wK) and firm employee (wH) are 10 and 3, respectively In order to identify objective tradeoffs (Pareto fronts), the experiment uses MOPSO algorithm from ET-Lib: Objective Library for Evolutionary Techniques (Nguyen et al., 2010) Key parameters of MOPSO algorithm used in experiment are as follows:     Population size (number of particles) is 50 particles Personal/global/local/neighbor acceleration constants (cp/cg/cl/cn) are 1/1/1/1 Number of iteration is 500 The maximal/minimal inertia weights (wmax/wmin) are 0.9/0.4 Movement strategy used to identify Pareto fronts is “explore solution space with mixed population of particles” that takes advantages of different search strategies in the algorithm toward a high quality Pareto fronts (Nguyen and Kachitvichyanukul, 2010) 35 6.2 Analysis and Results The objectives of the experiment are to: (1) conduct correlations between economy of scale and the co-production function; (2) investigate allocation behavior under the efficiency approach and the productivity approach; and (3) identify strategic tradeoffs between firm and customers in service delivery systems Experiment is to conduct correlations between economy of scale and the co-production function The experiment is designed with respect to three cases of economies of scale including increasing returns to scale (α + β + γ > 1), constant returns to scale (α + β + γ = 1), and decreasing returns to scale (α + β + γ < 1) For each case, the experiment analysis will be conducted with different values of these parameters (α, β, γ) For simplicity, it assumes that α = β = 2γ for cases Figure 6.1: Average Cost and Economies of Scale Figure 6.1 illustrates the relationship between average cost and total output under economies of scale The relationship represents the growth rates of total output and total cost Increasing returns to scale indicate that growth rate of total output is greater than this of total cost Decreasing returns to scale indicate that growth rate of total output is smaller than this of total cost As a result, average cost may tend to increase in case of decreasing returns to scale or to decrease in case of increasing returns to scale For constant returns to scale, the output growth and cost growth are almost the same rate Thus, average cost seems to be constant with any level of total output The experimental result reveals that economies of scale depends on parameters (α, β, γ) of the co-production function similar to traditional Cobb Douglas production function Figure 6.2: Pareto Frontiers under Economies of Scale 36 Figure 6.2 presents Pareto frontiers under economies of scale of co-production function These Pareto frontiers represent tradeoffs between total cost and total output, total cost and total utility Case stands for decreasing returns to scale with α + β + γ = 0.5, Case stands for constant returns to scale with α + β + γ = 1, and Case stands for increasing returns to scale α + β + γ = 1.5 The experimental result indicates that there are correlations between total cost with total output as well as total utility under economies of scale The shape of Pareto frontier is concave or convex toward the origin depending on economies of scale as showed in Figure 6.3 Figure 6.3: Pareto Frontiers under Decreasing/Increasing Returns to Scale For the decreasing returns to scale, Case 11, Case 12 and Case 13 are for α + β + γ = 0.25, α + β + γ = 0.5 and α + β + γ = 0.75, respectively The lower value of parameters, the Pareto frontier is more concave and total utility is less sensitive with changing in total cost Likewise, the higher value of parameters of increasing returns to scale (Case 31 for α + β + γ = 1.25, Case 32 for α + β + γ = 1.5, and Case 33 for α + β + γ = 1.75), the Pareto frontier is more convex and total utility is more sensitive with changing in total cost Experiment is to investigate allocation behavior under the efficiency approach and the productivity approach The experiment is designed to investigate resource allocation with the same capacity (total input of 25, and total output of 25) Experiment analysis determines new allocation and compares efficiency change and productivity change between the two approaches The experiment uses output-oriented CCR models to measure efficiency scores In addition, values of input (X) and output (Y) are proposed in range of (lower bound) to (upper bound) Table 6.2: Efficiency Approach for Resource Allocation with the Same Capacity DMU i A B C D E Total Current Current Xt Yt 25 25 New Xt+1 25 New Yt+1 25 37 Current New Efficiency efficiency efficiency change 1.00 1.00 1.50 1.50 1.67 1.67 2.80 2.80 2.25 2.25 9.22 Table 6.2 presents current allocation and new allocation under the efficiency approach with the same capacity The experiment result indicates that most customers (DMUs) allocate their resource (X and Y) via improving efficiency score While only customer A is efficient in the current allocation, all five customers are efficient (efficiency score equal to 1) in the new allocation Even though the new allocation improves almost all customer efficiency scores with the sum of efficiency change of 9.22, technological progress is reduced as in Figure 6.4 Slope of CCR frontiers illustrates the technological progress for the system with single input and single output The technical change from the current allocation to the new allocation is 0.5 Figure 6.4: Efficiency Approach for Resource Allocation Table 6.3 presents the productivity approach for resource allocation with the same capacity Since the objective function is to maximize productivity change, customer resource will be allocated so as to maximize the productivity change The result indicates that the productivity approach improves technical change (4.50) rather than efficiency change (4.78) as in Table 6.4 In fact, efficiency score of customers may decrease with the new resource allocation Only customers B and D are more efficient with efficiency change of 1.50 and 2.80 respectively, the remaining customers are worse-off in terms of both efficiency score and efficiency change It notes that customer A is efficient in the current allocation, but it is inefficient in the new allocation Table 6.3: Productivity Approach for Resource Allocation with the Same Capacity DMU i A B C D E Total Current Current Xt Yt 25 25 New Xt+1 9 25 New Yt+1 9 25 Current New Productivity efficiency efficiency change 1.00 81 0.06 1.50 6.75 1.67 81 0.09 12.61 2.80 2.25 2.03 21.53 Figure 6.5 shows the current allocation and new allocation under the productivity approach The increasing slope of new CCR frontier reveals that technological progress 38 (technical change) is improved By observing the current allocation and the new allocation, it seems that allocation of customers A and C are improperly since their allocations are in inefficient region The efficient region is defined by lower and upper bounds where a new allocation of X and Y is non-dominant or better than the current allocation Figure 6.5: Productivity Approach for Resource Allocation Table 6.4 gives a comparison of two approaches in terms of efficiency change, technical change and productivity change The efficiency approach allocates resource via maximizing efficiency change (9.22), while the productivity approach allocates customer resource on the best way of productivity change (21.53) These changes provide service firm about customer behavior as well as policy feedback Further, it also determines useful boundaries for improving individual customer efficiency and productivity performance as a whole Since there is an existing tradeoff between the efficiency approach and the productivity approach, it provides managerial indicators on allocation policy in service delivery systems Table 6.4: Comparison of Two Approaches in the Same Capacity DMU i A’ B’ C’ D’ E’ Total Efficiency approach Productivity approach Efficiency Technical Productivity Efficiency Technical Productivity change change change change change change 1.00 0.50 0.50 0.01 4.50 0.06 1.50 0.50 0.75 1.50 4.50 6.75 1.67 0.50 0.83 0.02 4.50 0.09 2.80 0.50 1.40 2.80 4.50 12.61 2.25 0.50 1.13 0.45 4.50 2.03 9.22 0.50 4.61 4.78 4.50 21.53 Experiment is to identify strategic tradeoffs between firm and customers in service delivery system The objective of the experiment is to conduct strategic approaches and to identify objective tradeoffs in service delivery strategy Thus, experiment designs are set up for three main experiments: (1) strategic approaches for developing channel strategy, (2) objective tradeoff between customer efficiency and firm productivity for planning allocation policy, (3) Strategic tradeoffs for the design of service delivery systems 39 The experiment is to conduct study on strategic behavior in planning production capacity Table 6.5 shows system parameters and strategic approaches Baseline model stands for the current production with (Kc, Hc, Lc, Oc) = (25, 25, 25, 25), in which the co-production function Oc  Ac  K cαc  H cc  Lcc with assumptions of Ac = and α = β = 2γ = 0.4, total utility (U) and total cost (F) are 75 and 375, respectively Table 6.5: System Parameters and Strategic Approaches Channel strategy Firm capital (Kc) Firm employee (Hc) Customer input (Lc) Customer output (Oc) Total utility (U) Total cost (F) Unit cost/value wK = 10 wH = wL = wS = Baseline system 25.0 25.0 25.0 25.0 75.00 375.00 Customer approach 25.0 25.0 10.5 21.02 84.09 346.02 Firm approach 10.0 33.3 25.0 19.44 47.21 250.00 The customer approach requires channel strategy to maximize total utility (U = 84.09) upon customer behavior This approach assumes no changes in policies about firm capital (Kc = 25) and firm employee (Hc = 25) Production capacity is set up as total inputs (Lc = 10.5) and total output (Oc = 21.02) Meanwhile, the firm approach assumes no changes in customer inputs (Lc = 25) There are only changes in firm capital (Kc = 10.0) and firm employee (Hc = 33.3) that minimize total cost (F = 250) Figure 6.6 illustrates different approaches for service delivery strategy Pareto frontier represents non-dominated feasible solutions from the co-producer approach Since the coproducer approach assumes that all production inputs (Kc, Hc, Lc) may be changed within the objective tradeoff between maximum of total utility and minimum of total cost, the approach provides a wider range of capacity planning comparing with the traditional approaches Figure 6.6: Approaches for Developing Channel Strategy The following experiment is to identify the objective tradeoff between the efficiency approach and the productivity approach The experiment assumes that the production capacity (Kc, Hc, Lc, Oc) remained the same as the current capacity (25, 25, 25, 25) 40 Table 6.6 presents the new allocation with the efficiency approach Even the efficiency allocation policy improves scores of customer efficiency with the sum of efficiency change of 9.22, but technological progress is reduced (0.5) In contrast, the productivity allocation policy intends to maximize productivity change (21.53), in which technological progress is improved (4.50) significantly as in Table 6.7 Table 6.6: New Allocation with the Efficiency Approach DMU i A’ B’ C’ D’ E’ Total New input Xt+1 25 New output New Efficiency t+1 Y efficiency change 1.00 1.00 1.00 1.50 1.00 1.67 1.00 2.80 1.00 2.25 25 9.22 Technical change 0.50 0.50 0.50 0.50 0.50 0.50 Productivity change 0.50 0.75 0.83 1.40 1.13 4.61 Table 6.7: New Allocation with the Productivity Approach DMU i A’ B’ C’ D’ E’ Total New input Xt+1 9 25 New output New Efficiency t+1 Y efficiency change 81 0.01 1.50 81 0.02 2.80 5 0.45 25 4.78 Technical change 4.50 4.50 4.50 4.50 4.50 4.50 Productivity change 0.06 6.75 0.09 12.61 2.03 21.53 Figure 6.7 illustrates non-dominated solutions representing the objective tradeoff between customer efficiency and firm productivity, where two extreme solutions stand for the efficiency approach at (9.22, 4.61) and the productivity approach at (4.78, 21.53) The experiment indicates the existing tradeoff between the efficiency approach and the productivity approach This result provides strategic insights about allocation policy in service delivery systems Figure 6.7: Approaches for Planning Allocation Policy 41 In order to identify strategic tradeoffs, the bilevel multi-objective model as in Chapter is developed for the design of service delivery systems For the upper level, the coproduction approach is employed for planning production capacity Table 6.8: Selected Cases of Production Capacity from the Upper Level Case Kc 12 14 19 23 26 Hc 16 27 51 71 80 87 Lc 11 10 10 11 13 17 Oc 10 16 22 29 34 39 U 28 60 90 122 143 160 F 130 221 313 425 496 555 There are various options for production capacity as a result of the objective tradeoff between total cost and total utility For more simplicity, the experiment selects six cases on Pareto frontier for planning allocation policy at the lower level as describled in Table 6.8 Figure 6.8 illustrates non-dominated solutions of production capacity and selected cases The experiment indicates that each case of production capacity at the upper level provides non-dominated solutions (Pareto frontiers) of allocation policy at the lower level The experimental result reveals that non-dominated solutions of mixed case have the best objective tradeoff as illustrated in Figure 6.9 Moreover, the existing tradeoffs between firm and customers provide strategic options for the design of service delivery systems Figure 6.8: Non-dominated Solutions of Production Capacity Figure 6.9: Set of Non-dominated Solutions of Resource Allocation 42 CHAPTER CONCLUSIONS AND RECOMMENDATIONS 7.1 Summary and Conclusions The tradition of operations management is rooted in manufacturing Most operations management is focused on manufacturing perspectives and principles that not drive most service processes The fact is that most business processes operating in the developed world are not manufacturing processes, but are service processes This research provides a theoretical background on co-production approach as the extension of operations management principles, in which the co-production function is expanded to heterogeneous and multiple inputs/outputs processes Since the production unit is a process, process inputs and service outcome are defined by physical units with assigned prices This definition has enhanced the remarkable power of both parametric and non-parametric approaches in studying strategic behaviors of firm and customers in service delivery systems The co-production approach not only views customer as co-producer with the presence of customer inputs, but also conducts strategic tradeoffs between firm and customers Moreover, the strategic tradeoff highlights opportunities for value co-creation, and the shift toward co-production as a means to enhance a view of efficiency and perceptions of value seems entirely reasonable As a result, the view of efficiency has been changed from the firm view (the firm’s benefits versus firm costs) to the customer view (the customer value versus customer costs); and the perspectives of value is that customer is creating value with the firm as opposed to the firm creating value for customer While the theoretical background on co-production provides general principles for service operations, the design of service system requires theoretical and analytical models to guide managerial decisions Combining insights of the earlier service literature with the coproduction approach, this research develops a unified framework for the design of service systems The co-production approach extends for the underlying approaches, such as customer contact approach (Chase, 1981; Chase and Tansik, 1983) and unified service theory (Sampson, 2001; Sampson and Froehle, 2006), and the proposed framework unifies the prior conceptual frameworks including models of strategic service alignment (Goldstein et al., 2002; Roth and Menor, 2003); service classification schemes (Kellogg and Nie, 1995; Tinnilä and Vepsäläinen, 1995; Collier and Meyer, 1998) and service design models (Edvardsson and Olsson, 1996; Johnston and Clark, 2005) The unified framework includes theoretical models such as service strategy triad, service positioning matrix and service delivery strategy The service strategy triad provide integrated model for service system design that emphasizes on strategic service alignment of target market, service concept, and service delivery system design The service process matrix is helpful for service classifications and service strategy Meanwhile, the service positioning matrix is used to realize the alignment between service strategy and service delivery strategy In addition, analytical models are developed for service delivery strategy that conducts strategic tradeoffs between firm and customers in service delivery systems To deal with multi-objective problems, ET-Lib platform is used to develop MOEAs that provide sets of non-dominated solutions (Pareto fronts) The quality of Pareto fronts is conducted under MOPSO and MODE algorithms with Movement/Mutation strategies Moreover, this research also proposes a procedure for Elite optimality process that transforms a known Pareto front (PFknown) into a true Pareto front (PFtrue) As a result, it supports managers for making more reliable decision Since the experiment is the best way 43 to verify the theoretical typology through the analytical models, a hypothetical service system is proposed for the experimental study From the experimental results, the research findings can be summarized as follows:  The co-production function with the presence of customer inputs is feasible under economic and institutional considerations  The co-production approach extends and generalizes the traditional approaches including the firm approach and the customer approach  The existing tradeoffs between firm and customers provide strategic options for the design of service delivery systems 7.2 Recommendations for Further Research These findings provide general principles in service operations and managerial insights for the design of service systems However, some limitations remained and should be explored for future research  The co-production function assumes well-defined functional form (technically feasible function) In further research, the co-production function with different functional forms should be studied under economic and institutional considerations  The measurement for customer inputs and service outcome remains a major challenge that requires reasoning customer perception of service experience, and approaching different measurements to different types of customer inputs  The co-production theory is still far from completed that requires various additional empirical studies to test the reality of the co-production approach and to suggest its shortcomings to the framework for the design of service systems In conclusion, this research contributes a theoretical background on co-production approach and creates rich opportunities in dealing with big ideas in service operations management (Chase and Apte, 2007): (1) transference of manufacturing principle to service context, (2) framework for service design and management, and (3) tools and techniques to evaluate service processes 44 REFERENCES Ai, T J (2008) Particle Swarm Optimization for Generalized Vehicle 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Chase and Tansik, 1983) emphasizes the physical presence of the customer in service operations The customer contact approach is regarded as the. .. maximized at a given level of inputs Meanwhile, parametric methods measure economic efficiency that is broader than technical efficiency in which it covers an optimal choice of the level and structure of inputs and outputs based on reactions to market prices DEA (Data Envelopment Analysis) is a mathematical programming based on nonparametric technique that is designed to compare and evaluate the relative... influential approach in service paradigms (Cook et al., 1999; Chase and Apte, 2007) The approach divides service system into the front office and the back office The front office has been described as “where the customers are”, while the back office is not directly involving the customer The customer contact approach aims to improve service quality in the front office and system efficiency in the back office,... notice an immediate reduction in the production variety, thereby affecting the customers’ notion of quality received The production line approach would enable a redesign of the service performance itself and promote the creation of new tools, processes and organizational models A service taking this production line approach could gain a competitive advantage with a cost leadership strategy However, the. .. system parameters, but also the way to positioning service strategy and service delivery strategy (Pullman et al., 2001; Metters et al., 2003) The service concept relates to the characteristics of service offered to the target market that plays a significant role in competitive services and market positioning Sasser et al (1978) first described the service concept as the bundle of goods and services... of service customization, this quadrant is labeled “professional service Service strategy is cost leadership for service factory that is based on low-cost inputs and efficiency It takes the advantages of learning through repetition, non-divergence, and 11 economy of scale In contrast, firm adopts a differentiation strategy for professional service that intends to provide a service in a different way... Decision Making Units (DMUs) using DEA and Malmquist indices The bank branches are clustered in different groups based on their managerial strategies and environmental conditions Felthoven et al (2009) investigated the presence of heterogeneous production, and measure heterogeneous capacity and capacity utilization The measure defines capacity as the maximal feasible output that can be produced with the. .. Differentiation Service Factory Mass Service Cost Leadership Focus Low Low High Customer Participation Figure 3.4: Strategic Positioning Matrix The lower left quadrant labeled service factory”, contains firms with a low degree of customer participation and a low degree of service customization The low customer participation and standardized service allow service firm in this quadrant to operate similar to