A unifield framework for the design of service systems a co production approach

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

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: Prof Voratas Kachitvichyanukul (Chairperson)

Dr Huynh Trung Luong

Dr Do Ba Khang

External Examiner: Prof Yon-Chun Chou

Institute of Industrial Engineering National Taiwan University

Nationality: Vietnamese Previous Degree: Master of Science in Industrial Engineering

Asian Institute of Technology, Thailand

Scholarship Donor: 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

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

2.2 DEA Method 2.3 Reviews on Service System Design

4

6

5 EVOLUTIONARY METHODS FOR MULTI-OBJECTIVE

PROBLEMS

24

5.2 Multi-objective Evolutionary Algorithms 26

LIST OF TABLES

6.1 The Data of the System with the Current Allocation 35 6.2 Efficiency Approach for Resource Allocation with the Same

6.4 Comparison of Two Approaches in the Same Capacity 39

6.8 Selected Cases of Production Capacity from the Upper Level 42

LIST OF FIGURES

3.5 Analytical Framework for Service Delivery Strategy 12

4.2 Technical Change, Efficiency Change and Productivity Change 19

5.4 Reference Point Approaches for Multi-objective Optimization 32

6.3 Pareto Frontiers under Decreasing/Increasing Returns to Scale 37

6.9 Set of Non-dominated Solutions of Resource Allocation 42

LIST OF ABBREVIATIONS

ACO Ant Colony Optimization

BCC Banker, Charnes, and Cooper

CCR Charnes, Cooper, and Rhodes

DE Differential Evolution

DEA Data Envelopment Analysis

DMUs Decision Making Units

EAs Evolutionary Algorithms

EMO Evolutionary Multi-objective Optimization

ET-Lib Objective Library for Evolutionary Techniques

GA Genetic Algorithm

GD Generational Distance

GDP Gross Domestic Product

MOACO Multi-Objective Ant Colony Optimization MODE Multi-Objective Differential Evolution

MOEAs Multi-Objective Evolutionary Algorithms MOGA Multi-Objective Genetic Algorithm

MOPSO Multi-Objective Particle Swarm Optimization

MS Movement/Mutation Strategy

NSGA II Non-dominated Sorting Genetic Algorithm II

PFknown A known Pareto Front

PFtrue A true Pareto Front

PSO Particle Swarm Optimization

SOM Service Operations Management

SPA Service Process Analysis

SP/SP Service Process/Service Package

TFP Total Factor Productivity

UST Unified Service Theory

CHAPTER 1 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 co-production 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 production, in which production 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-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 (ET-Lib) (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 3

 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 3 and 4

 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 4 and 5

Several research manuscripts were produced as a result of this research The list is given as follows:

1 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 co-

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

2 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

3 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 7 main chapters as follows: Chapter 1 introduces the background, motivations and objectives of the research Chapter 2 reviews literatures on co-production concept, DEA method and service system design Chapter 3 presents methodology that includes co-production approach, and framework for service system design Chapter 4 develops conceptual models for service delivery strategy including channel strategy and allocation policy Chapter 5 presents framework for MOEAs, multi-objective evolutionary algorithms (MOPSO and MODE algorithms), and Elite optimality procedure Chapter 6 focuses on experimental study for hypothetical service system Chapter 7 summarizes the main findings and recommendations for the further research

CHAPTER 2 LITERATURE REVIEW 2.1 Co-production Concept

Customers have always been the prime focus for marketing activity but the way that the service firms view this relationship is changing Kotler (1991) notes that a shift of paradigm is emerging within marketing theory where the focus in the future will be on long-term relationships instead of on short-term exchange transactions The shift in attitude changes from ‘making a sale’ to ‘gaining a client’, and to probably “co-producer”

In literature, the role of the customer as a “co-producer” has a long history in service operation management Whitaker (1980) proposed co-production models used in the field

of public policy management where citizen participation is commonly viewed as attempts

to influence the formulation of public policy Moreover, customers, important resources for the firm, can actively participate in firm’s activities as service co-producers (Lengnick-Hall

et al., 2000) Some recent researchers concerns on the formulation of service production function Lindenberger (2003) employed derivation method for formulating production function designed to service industries Xue et al (2007) formulated service co-production function applied in retail banking, in which the co-production function involves two independent portions of firm inputs and customer inputs

In fact, the traditional production function is difficult to apply to heterogeneous process and multiple inputs/outputs process Meanwhile, heterogeneity is one of important characteristics of service that is primarily caused by heterogeneity in process inputs, especially customer inputs (Sampson, 2001) Some economists and operations researchers (Farrell, 1957; Charnes et al., 1978; Banker et al., 1984) addressed this problem by using a non-parametric method, known as DEA (Data Envelopment Analysis) that does not need

to specify a mathematical form for the production function Moreover, the DEA method can handle multiple inputs and outputs with any measurements

2.2 DEA Method

Since customer is co-producer, customer efficiency has influence on firm productivity in service delivery systems Many different approaches reported in literature to measure efficiency include non-parametric methods and parametric methods Non-parametric methods measure technical efficiency which looks at the levels of inputs or outputs Being technically efficient means that the inputs are minimized at a given level of outputs, or the outputs are 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 parametric technique that is designed to compare and evaluate the relative efficiency of a number of Decision Making Units (DMUs) DEA was initiated by Charnes et al (1978) demonstrated how to change a fractional linear measure of efficiency into a linear programming format

non-Consider n DMUs to be evaluated, DMUo (o = 1 n) consumes amounts X o = {X oj } of inputs (j = 1 m) and produces amounts Y o = {Y ok } of outputs (k = 1 s) Charnes et al

(1978) were first to introduce the primal CCR model that is used for measuring the efficiency (o) of a particular DMUo as follows:

Fractional programming CCR model:

s

k

ok ok o

X Y

oj 0,  1

s k

k

ok

1 1

oj 0,  1

s k

of weights of inputs and outputs, respectively

Based on the primal CCR model, various theoretical extensions have been developed Banker et al (1984) extended application of DEA for variable returns to scale (BCC model) Based on measurements on efficiency from Farrell (1957) and productivity from Caves et al (1982), Färe et al (1992) developed a Malmquist productivity index where the production technology exhibits constant returns to scale Grifell-Tatjé and Lovell (1999) and Balk (2001) extended formulation of Malmquist TFP (Total Factor Productivity) index for situation where the production technology exhibits variable returns to scale

Many recent researchers have used non-parametric methodology to measure efficiency and productivity in service delivery systems Most previous studies measure the Malmquist TFP index with historical production set to evaluate productivity change in hospitals, universities and banks However, there are still no studies that discuss on tradeoffs between customer efficiency and firm productivity The concept of customer efficiency was first introduced by Xue and Harker (2002), and the DEA method is used to measure customer efficiency in E-shopping Camanho and Dyson (2006) developed measures for comparing groups of 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 given level of technological, environmental and economic conditions The contribution of the previous studies allows extending DEA method with capacity constraints to use the Malmquist productivity index as a planning tool

2.3 Reviews on Service System Design

The design of service system is one of the most interested topics in service operations management This design can be approached in three generic approaches: production line approach (Levitt, 1972; Levitt, 1976); customer contact approach (Chase, 1981; Chase and Tansik, 1983); and unified service theory (Sampson, 2001; Sampson and Froehle, 2006)

2.3.1 Production line approach

Levitt (1972; 1976) suggested that service firms may improve their quality and efficiency

by adopting technocratic approach rather than a humanistic approach By restricting the human factor, service firms were to 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 production line approach failed to take the very essential component of change (Bowen and Youngdahl, 1998) and the ability to quickly adapt to change in the market (Lashley, 1999) into considerations

2.3.2 Customer contact approach

The customer contact theory (Chase, 1981; Chase and Tansik, 1983) emphasizes the physical presence of the customer in service operations The customer contact approach is regarded as the most 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, in which the system efficiency is a function of the degree of customer contact from high contact to low contact In particular, the lower the customer contact, the greater the system efficiency Conversely, the higher the customer contact, the smaller the system efficiency Even though the customer contact implies the physical presence of customer in the service operations that subsequent researchers have employed to define service process dimensions

in their service typologies, these dimensions are generally hard to measure and interpret

2.3.3 Unified service theory

Sampson and Froehle (2006) proposed a unified service theory (UST) that defines a production process with the presence of customer inputs as a service process The UST considers the unit of analysis to be the process, and the production system includes service processes with customer inputs and non-service processes without customer inputs Therefore, service processes are fundamentally and managerially different from non-service processes Although the UST provides a framework for analyzing design issues for service processes through value-creation input/output model with the presence of customer inputs, the UST has neither defined relationship between service outcome and production inputs, nor conducted strategic tradeoffs between firm and customers in service processes

CHAPTER 3 METHODOLOGY 3.1 Co-production Approach

The earlier approaches are still in the domain of traditional operations management and lack of theoretical background on service operations management Thus, it is necessary to address a co-production approach as an extension of operations management principles The co-production approach not only treats customer as co-producer with the presence of customer inputs, but also conducts strategic tradeoffs between firm and customers in service systems The co-production approach has key features as follows:

3.1.1 Customer as co-producer

Co-production involves a mixing of the productive efforts of firm and customers that may occur directly with coordinated efforts or indirectly through related efforts Co-production occurs as a result of technological, economic and institutional influences (Parks et al., 1981)

There are two types of co-production forms where co-production is technically feasible if

the co-production form is a function of both firm and customer inputs, at least over some range of values of these inputs The first co-production form is that firm inputs and customer inputs are substitutes under the form of Qa f bg  The second co-production form of a  b 

g f

c

Q   indicates that firm inputs and customer inputs are interdependent Where, f  and g  are portions of firm inputs and customer inputs, respectively The view of the co-production approach adopts that both firm and customers have influence on service outcomes interdependently, and substitute relationship is used to relax strict interdependence in service production

In addition, production is always subject to economic constraints For the traditional approach, economic constraint is a cost function under the form of

, g

f For the co-production approach, economic constraint is a tradeoff between cost function and utility function, in which the utility function is the total customer utility under the form of U w QQ w g g  Where, Q  is amount of service outcomes, and w Q is value unit of service outcome The co-production approach identifies various efficient combinations  * *

,U

F

Figure 3.1 illustrates economic constraint under the traditional approach and the production approach While the traditional approach considers economic constraint through a tradeoff between firm inputs and customer inputs, the co-production approach considers economic constraint through an objective tradeoff between firm and customers

If there exists the correlation between economy of scale and production function,

co-production is then economically relevance In addition to this, if the co-co-production function

is considered under additional constraints with market and non-market arrangements, and

there exist non-dominated feasible solutions, co-production is institutionally feasible

Figure 3.1: Economic Constraints under Different Approaches

There is no given mathematical functional form for a production function The best known

of these functional forms is the so-called Cobb-Douglas production function which has the form as follows:

H K A H

K

f

O ,   α

Where, O is production output, K is firm capital and H is firm employee A, α, β are

predetermined parameters (constants)

The popularity of the Cobb Douglas production function is due to a number of mathematical advantages in connection with empirical analyses Since customer is treated

as co-producer in service production, besides firm capital (K) and firm employee (H), customer input (L) should be included in the following co-production function

L H K A L

g()

f()

* 1

F

* 1

region

3.1.2 The product is the process

A service can be described as an outcome, “what a customer receives”, a service can be described as a process, in which the service outcome was delivered to the customer (Mohr and Bitner, 1995) Key feature of service comparing to manufacturing is that “for service, the product is the process” (Fitzsimmons and Fitzsimmons, 2001), which means that it is not possible to deliver a service without active customer participation

The service process matrix is used to classify service types upon the nature of the service The matrix with two dimensions of the degree of labor intensity and the degree of interaction and customization (Schmenner, 1986) yields a four-way service classification including service factory, mass service, service shop, and professional service Customer interaction represents the degree to which the customer can intervene in the service process involving design, transformation, production and consumption, in which transformation process uses firm inputs and customer inputs (labor, property, and information) to produce the service outcome However, customization can occur interdependently of interaction, and labor intensity is an antecedent of customer inputs (Sampson and Froehle, 2006) Therefore, the dimensions of the service process matrix (Schmenner, 1986) needs modification As a result, the classification characteristics are based on the degree of service customization and the degree of customer participation as given in Figure 3.2

Figure 3.2: Service Process Matrix

Source: Maister and Lovelock (1982) and Schmenner (1986)

The degree of service customization is considered as the most important variable in classifying service systems (Chen and Hao, 2010) Following Apte and Vepsäläinen (1993), customized services are typified by numerous configurable parameters, and require close customer relationship Standardized services are characterized by limited configurable parameters, and a transaction-based customer relationship strategy The degree of customer participation is defined as the effort paid by customers when they are involved in the production process (Wang et al., 2007) According to Sampson and Froehle (2006), customer participation in a production process is classified into a service process with customer inputs (labor, property, and information) or a non-service process without customer inputs (service design, selection and consumption) Bitner et al (1997) argued that the extents of customer participation from low to high in service production defines different customization levels from standardized services to customized services

Service Shop

Hospitals Auto repair shops Restaurants

Professional Service

Doctors/Lawyers Accountants

Architects

Service Factory

Fast foods Hotels/ Motels

Airlines

Mass Service

Retail firms Wholesale firms Schools

3.2 The Design of Service System

The literature on service system design emphasizes the importance of conceptual models of strategic service alignment (Ponsignon et al., 2011) Recently, Roth and Menor (2003) synthesized an integrated model of service system design - Service Strategy Triad that reconciles two distinct perspectives of marketing and operations

3.2.1 Service strategy triad

The service strategy triad (Roth and Menor, 2003) considers the strategic alignment of three components: (1) the target market, (2) the service concept, and (3) the service delivery system design as illustrated in Figure 3.3

Figure 3.3: Service Strategy Triad

Source: Roth and Menor (2003)

The target market defines ‘who are the customers’ The choice of target markets effects not

only to 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 sold to customer and the relative importance of each component to the customer” The process of service delivery ensures that the expected service outcome is received by the customer (Goldstein

et al., 2002) Thus, service system design requires service firms to pay attention to aligning between service concepts and service delivery design

The service delivery system design addressed the question of “how” the service concept is

delivered to target customers (Tax and Stuart, 1997) Collier and Meyer (2000) argued that the configuration of a service delivery system is defined by service classifications to meet customer requirements Roth and Menor (2003) proposed architecture of service delivery systems that is organized around three major interrelated and dynamic components of service delivery systems: (1) strategic service design choices, (2) service delivery system execution renewal and assessment, and (3) customer perceived value of the total service concept

How is the service concept delivered?

The service strategy triad is useful for emphasizing the need for alignment between the service concept and service delivery process, but the triad provides little assistances in specifying the design characteristics to realize the alignment Meanwhile, service process matrix is helpful to classify different types of service based on alignment between two dimensions of service and process Even though the service process matrix is useful in investigating the strategic changes in service operations, the matrix does not define and specify on how to approach service strategy

3.2.2 Service positioning matrix

Many earlier researchers attempt to develop models and frameworks for service positioning strategy The service process matrix (Schmenner, 1986) analyzes effects of the various positions that can be useful to investigate the strategic changes in service operations However, two dimensions of the matrix are difficult to distinguish and interpret In addition, the service process analysis (SPA) model (Tinnilä and Vepsäläinen, 1995) and the service process/service package (SP/SP) matrix (Kellogg and Nie, 1995) investigated the most efficient positions on the diagonal of the matrix, while the need for control would be greater for positions above or below the diagonal Thus, the strategic positioning matrix is developed with two modified dimensions of service customization and customer participation as in Figure 3.4

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 factories that can take advantage of economy of scale The lower right quadrant is for a standardized service and high customer participation, this quadrant is labeled “mass service” The upper left quadrant with a customized service and low customer participation

is labeled “service shop” Finally, the upper right quadrant contains firm with a high degree

of customer participation and a high degree 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

economy of scale In contrast, firm adopts a differentiation strategy for professional service that intends to provide a service in a different way from its competitors Service strategy for firms in quadrants of mass service and service shop should be focused strategy to identify customer groups with the similar customer inputs and expectations, and then design the service process around those inputs and expectations

3.2.3 Service delivery strategy

Service delivery strategy involves both channel strategy and allocation policy The overall objective of service delivery strategy is not only to identify opportunities to make strategic tradeoff between customer utility and firm cost, but also to enhance customer efficiency and firm productivity The challenges require service firms to face varieties of choices in such tradeoffs

In the traditional approaches, service delivery strategy is developed on the basis of either the firm approach (minimum of total cost) or the customer approach (maximum of total utility) The customer approach requires service delivery strategy to maximize total utility based on customer behavior The firm approach based on firm behavior develops service delivery strategy so as to minimize total cost Meanwhile, the co-producer approach conducts an objective tradeoff between maximum of total utility and minimum of total cost In fact, the co-producer approach extends both the customer approach and the firm approach since the co-producer model provides a wider range of non-dominated feasible solutions (Pareto frontier)

Since the service delivery strategy is developed under the co-production approach that conducts objective tradeoffs between firm and customers, it provides strategic options for the design of service delivery systems Figure 3.5 presents an analytical framework for service delivery strategy

Figure 3.5: Analytical Framework for Service Delivery Strategy

The concept of customer efficiency was first introduced by Xue and Harker (2002), DEA was used to measure scores of customer efficiency through the primal output-oriented CCR model as follows:

Efficiency & Productivity

Efficiency Allocation

Maximum

Efficiency change

Productivity Allocation Maximum

Productivity change

Primal output-oriented CCR model

oj m

k

t ok t

1 1

Consider n DMUs (Decision Making Units) to be evaluated in period t, DMUo (o = 1 n)

consumes amount of  t

oj t

X  of inputs (j = 1 m), and produces amounts of  t

ok t

Y

Y0 

of outputs (k = 1 s) The set of production possibilities (technology) of DMUo in period t

can be written as:

 

 t t t t

t

Y X

Y

X

Färe et al (1994) followed Shephard (1970) to define the output distance function in

period t and t +1 as:

Malmquist TFP index was first introduced by Caves et al (1982), the method used distance functions in defining the TFP index as follows:

              2

1

change Technical

1 1

1 1

1 1

change Efficiency

1 1 1

change

ty

Productivi

1 1

,

,,

,,

,,

,,

t t t o t

t t o

t t t o t

t t o

t t t o t t t

t

o

Y X D

Y X D Y

X D

Y X D Y

X D

Y X D Y X Y

X

Where, efficiency change and technical change between periods of t and t+1 are measured

by the output-oriented distance functions in below:

 t t

t o

t t t o

Y X D

Y X D

,

,change

Efficiency

1 1

1 1

1 1

,

,,

,change

t t o

t t t o

Y X D

Y X D Y

X D

Y X D

(3.8)

Following Färe et al (1992), the production set is known for both periods t and t+1, the

four distances which make up equation (3.6) can be estimated via programming techniques These distance functions are estimated from the primal output-oriented CCR models as follows:

The dual (envelopment) model

n

i

t oj t ij t

n

i

t ik t oi t

oj m

j

t oj t

t t

D

1 1

k

t ok t

1 1

on t

o2 t o1 t

oi λ λ , ,λ

λ  is a vector of weights The primal (multiplier) model is used

to estimate the distance function of t t t

X

n

i

t oj t ij t

n

i

t ik t oi t

ok t

oj m

j

t oj t

t t

D

1 1 1

1 1

1 1

k

t ok t

1 1

To compute the mixed-period distance function, t  t t

D 1 , , the t and t+1 superscripts

must simply be reversed Malmquist TFP index is mostly used to analyze efficiency

change and productivity change between periods of t and t+1

In addition, Zomerdijk and Vries (2007) emphasizes that contingency variables, such as the service being delivered, have influence on the design of service delivery systems In addition, Ponsignon et al (2011) states that the design of the service delivery system should support the realization of the service concept and different service concepts require different approaches to the design of service delivery systems Figure 3.6 presents strategic options for service delivery system that adapt to service concepts

Figure 3.6: Strategic Options for Service Delivery System

As mentioned earlier, service factory provides standardized service with low customer participation that allow service firm to adopt a cost strategy similar to factories The service strategy can take advantage of economy of scale and may employ less expensive unskilled workers Since service delivery system design adapts to service strategy, service delivery strategy requires minimizing total cost and maximizing efficiency change In contrast, professional service with customized service and high customer participation allows service firm to adopt a differentiation strategy that intends to provide unique a different service or a service in a different way from its competitors Therefore, it requires service delivery strategy to maximize both total utility and productivity

Service factory and professional service are considered as extreme service types, in which other service types also exist, like mass service and service shop Mass service has low customized service in combination with high customer participation Service shops provide various types of customized services with low customer participation similar to job shop type of manufacturing operations

Service delivery strategy

CHAPTER 4 THE CONCEPTUAL MODELS 4.1 Channel strategy

The traditional approaches develop channel strategy based upon either customer behavior

or firm behavior While the customer approach is to maximize total utility and the firm approach is to minimize total cost, the co-producer approach develops channel strategy that simultaneously considers both customer and firm behaviors so as to maximize total utility and minimize total cost Figure 4.1 presents different approaches for developing channel strategy in service systems

Figure 4.1: Approaches for Developing Channel Strategy 4.1.1 Customer approach

The customer approach assumes no changes in policies about firm capital (K c) and firm

employee (H c ) Customers intend to allocate resource (customer input - Lc and service

output - O c) so as to maximize their utility

Consider a customer i can use t different service channels, indexed by c (c = 1 t) to conduct service transaction, in which unit cost of customer input (L ic ) is w L, and unit value

of service output (O ic ) is w S Thus, utility u of a customer i in using service transaction at ic channel c is:

t c L w O

S O w L w

w F

S O w L w

w F

1

Kc, Hc, Lc: Variables

“Co-producer” approach

Where, w L and w S are predetermined parameters The total utility is the sum of the utility of all customers (i=1 n) obtained in all service channels Total utility U is given by:

L

1

This problem yields a set of first order conditions in which the marginal product of

customer input equals the unit cost of customer input (w L):

c

c

w

w L

O

L

By differentiating the co-production function (O c ) with respect to customer input (L c)

c c c

c c c

c

H K L

A L

c

L

S c c

c c

w

w H

K A

1 1

1 1

1

c c c c c c c

L S

c c

Thus, customers will utilize each channel to produce service output (O c*) as a function of

productivity of input (A c ) at channel c, unit cost of customer input (w L), unit value of the

service (w S ), and the firm inputs at channel c, (K c , H c)

4.1.2 Firm approach

The firm approach assumes that there are no changes in customer input (L c), at least in the

short-term (noting that all production inputs (K c , H c , L c ) will change in the long-term) The changes of firm capital (K c ) and firm employee (H c ) have influence on service output (O c) and the firm may vary both so as to minimize total cost The co-production function with firm inputs and customer inputs can be expressed as:

c c c c c c c

By using the least-cost combination of production inputs, cost function F(O c ) of each

channel can be determined as follows:

 O c w K K c w H H c w L L c

Where, c denotes index of service channel (c = 1 t) w K and w H are unit prices of firm capital and firm employee, respectively w L is a unit cost of customer input

Find the values of K c , H c , L c and λ that minimize the Lagrangian:

L c c c c

c

c c K

K K

L f

L c c c c

c

c c H

H H

L f

c H

c

L c c K

c

L c c

c

w

w L

w

w L

Thus, service firm produces service output (O c*) that is a function of productivity of input

A c at channel c, unit costs of firm inputs (w K , w H ) and customer input (w L), and the

customer inputs at channel c (L c)

4.1.3 Co-producer approach

The co-producer approach requires channel strategy to achieve a strategic tradeoff between

firm and customers The co-producer approach assumes that all production inputs (K c , H c ,

L c ) will change so as to maximize total utility and minimize total cost The co-producer

approach extends for both the customer approach and the firm approach since the producer model provides a wider range of non-dominated feasible solutions (Pareto frontier) The Pareto-based method is of much interested in solving multi-objective problem where the strategic tradeoff between firm and customers is conducted under the following co-producer model

co-The co-producer model:

S O w L w

w F

1

Subject to

1 tc

K c H c L c, c1 t

Indices of the co-producer model:

c: Index of channel (c = 1 t)

Parameters of the co-producer model:

wK: Unit price of firm capital

wH: Unit price of firm employee

wL: Unit cost of customer input

wS: Unit value of service output

Ac: Mean productivity of inputs

α c: Output elasticity of firm capital

β c: Output elasticity of employee capital

γ c: Output elasticity of customer input

Variables of the co-producer model:

Kc: Firm capital at channel c

Hc: Firm employee at channel c

Lc: Customer input at channel c

The co-producer model is used to identify a strategic tradeoff between total cost and total utility, in which the co-production function represents relationship between service

outcome (O c ) and allocation of firm inputs (K c , H c ), and cuatomer inputs (L c)

4.2 Allocation Policy

The primal output-oriented CCR model is used to develop allocation models under the efficiency approach and the productivity approach The productivity approach allocates resource so as to maximize productivity change instead of efficiency change as in the efficiency approach As mentioned earlier, productivity change involves both efficiency change and technical change A customer which allocates resources on the boundary of this set is called technically efficient Technical change means that the set of feasible combinations expands or contracts, while efficiency change means that the customer moves closer to or further away from the boundary Figure 4.2 illustrates technical change and tradeoff between efficiency change and productivity change

Figure 4.2: Technical Change, Efficiency Change and Productivity Change

Efficiency approach

Productivity approach

Output

Current CCR frontier

Input

Productivity

Efficiency

Productivity CCR frontier

Efficiency CCR frontier

Pareto frontier

4.2.1 Efficiency approach

The efficiency approach allocates customer resource (inputs and outputs) within existing capacity so as to maximize efficiency change In order to conduct new allocation,

constraints of capacity (total inputs – L, and total outputs - O) will be added in the

allocation model as follows:

Efficiency allocation model

ok m

j

t oj t oj

s

k

t ok t ok m

j

t oj t oj

Y v

X

Y v

X

1

1

1 1 1

1 1

1 1





Subject to

L X n

Y

m

j

t oj t ij s

k

t ok t

1 1

Y

m

j

t oj t ij s

k

t ok t

1

1 1 1

j

t oj t

X

1 1

j

t oj t

X

1

1 1 1

1

1  are current and new efficiency

scores of customer o (o = 1 n) Superscripts t and t+1 denote current allocation and new

allocation, respectively Notes that the above allocation model is a non-linear programming model since vectors of weights  t 1

between periods of t and t+1

4.2.2 Productivity approach

The productivity allocation model is the same as the efficiency allocation model except for the objective function that maximizes productivity change The objective function of the productivity allocation model is formulated from both sources of efficiency change and technical change as shown in equation (3.6)

Productivity allocation model

ok m

j

t oj t oj s

k

t ok t

ok m

j

t oj t oj

s

k

t ok t ok m

j

t oj t oj s

k

t ok t ok m

j

t oj t oj

Y v

X Y

v X

Y v

X Y

v X

1

1

1 1 1

1 1 1

1 1

1

1

1 1

1 1

Y

m

j

t oj t ij s

k

t ok t

1 1

Y

m

j

t oj t ij s

k

t ok t

1

1 1 1

4.3 Bilevel Multi-objective Model

The bi-level multi-objective model is developed to conduct the strategic tradeoffs between firm and customers that provide strategic options for the design of service delivery systems In order to deal with the objective tradeoff problems, multi-objective partial swarm optimization (MOPSO) algorithm is used to identify sets of non-dominated feasible solutions (Pareto fronts)

Bilevel multi-objective model

Upper level: (Channel strategy)

S O w L w

w F

1

Cost function

Subject to

1 tc

K c H c L c, c1 t