Towards Smart Service Networks : An Interdisciplinary Service Assessment Metrics

Yan Wang, Yehia Taher, Willem-Jan van den Heuvel

European Research Institute in Service Science

Tilburg University

Tilburg, The Netherlands

{Y.Wang13, Y.Taher, W.J.A.M.vdnHeuvel}@tilburguniversity.edu

Abstract—Service Networks (SNs) are open systems accom-modating the co-production of new knowledge and servicesthrough organic peer-to-peer interactions. Key to broad successof SNs in practice is their ability to foster and ensure ahigh performance. By performance we mean the joint effortof tremendous interdisciplinary collaboration, cooperation andcoordination among the network participants. However, dueto the heterogeneous background of such participants (i.e.,business, technical, etc.), different interpretations of the sharedterminology are likely to happen. Thus, confusion may appearin the multi-disciplinary communication of SNs participantswhich in turn may lead to performance anomalies. To dealwith such a problem, we propose a novel framework of bi-dimensional (business vs technical) performance metric indica-tors built on the basis of a systems thinking mindset. By usingour framework, a holistic picture of the multiple dimensionsand structure of SNs is provided, so that the interdisciplinaryservice participants have a correct understanding of the servicescope and required resources in operation. Moreover, and mostimportantly, it provides a way to examine the performancetraceability of the services within a SN.

Keywords-service networks; systems thinking; multi-levelinteractions; performance measurements

I. INTRODUCTION

The Service Networks (SNs) are considered as systems

of service systems that are open, complex and fluid,

accommodating the co-production of new knowledge and

services through organic peer-to-peer interactions [1].

Enterprises from different industrial divisions are involved

in the SNs, and demand innovative service systems to

advance their business in the increasingly complex and

dynamic environment [2]. The overall performance of SNs is

resulted from a joint effort of tremendous interdisciplinary

collaboration, cooperation and coordination among the

network participants [3].

Researchers from various scientific disciplines, such as

marketing [4], organizational study [5], and computer sci-

ence [6] etc., have advocated exploring the SNs. The at-

tention on SNs taken from business perspective focuses

on the strategic planning and overall business performance

management [7], whereas computer scientists research into

the engineering of SNs, considering how to design, program,

test, deploy and provision software services into aggregated

software services [8]. However there is seldom a balanced

research that is able to analyze, tune and reconcile both

business and technical considerations in SNs. When there

are performance anomalies found in the SNs, it is hard to

trace and track its root cause. This refers to the fact that the

multiple dimensions of SNs have been separately captured

by different communities, and there are striking challenges

in bridging them.

The research presented in this paper aims at leveraging

disciplined methods and tools for examining the essence

of SNs and optimizing its performance. Our main objec-

tive is to obtaining a correct understanding of the multi-

dimensional (strategy, business performance, process etc.)

SNs, and have an effective mechanism for studying the

performance aggregation in SNs. In particular, we propose

a framework of bi-dimensional (Business vs. Technical) per-

formance metrics with a systems thinking mindset that focus

on the interactions between multi-level service interactions.

The main contributions of our interdisciplinary framework

can be summurized as follows: (1) providing a diagnostic

mechanism that will reconcile the business and technical

perspectives, (2) revealing a holistic picture of the SNs,

(3) generating a set of performance measurements which can

be further applied in simulation for performance analysis

and prediction. Those will allows the SNs structure to be

viewed with less conceptual and terminological confusion

and improved performance traceability.

In the remainder of this paper, we firstly introduce our

SNs scope, research motivation and problems in section II.

Then our conceptual research framework is presented in

section III, in which we make use of the systems thinking

as our steering philosophy. In section IV, we present results

of a bi-dimensional investigation on SNs, by which we

clarify the confusions in the shared service terminology

and the different service categorizations held by business

and technical worlds. Based on the investigation results,

a metamodel for assessing SNs service is presented (sec-

tion V), from which assessment metrics are defined and a

set of performance components is derived. Next, we apply

the proposed assessment metrics into a well documented

case study (section VI), and conclude our work at last

(section VII).

2012 IEEE 16th International Enterprise Distributed Object Computing Conference Workshops

978-0-7695-4786-2/12 $26.00 © 2012 IEEE

DOI 10.1109/EDOCW.2012.22

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II. MOTIVATION AND PROBLEM STATEMENT

In this section, we aim at reaching an agreement on the

definition of the service network and existing problems in

current research. We briefly browse related works on the

same subject, then present our scope and specification of

the service network in section II-A. In section II-B, we

illustrate the research problems we are tackling and our

research direction.

A. Service Networks (SNs)

The concept of SNs appears in diverse semantics and

syntax according to different research domains. In general

business oriented sense, SNs is the business collaboration

among different firms that constitute the most general form

of economically motivated cooperation [9]. In the eyes of

engineers, SNs is a semantic relation based software service

infrastructure [10]. If looking into the correlations among

the services and network participants, SNs can be viewed

as an instantiation of business ecosystem at the time of

composite service consumption [11]. If focusing on the

value perspective, SNs can be considered as a networked

value chain where the business value is co-created through

interactions between the service provider and customer while

offering or consuming services [12].

A great number of works has been done with respect

to different aspects of SNs. For instance, strategic man-

agement [13] has been widely used to specify the enter-

prise’s mission, vision and objectives at different levels [14].

Benchmarking [15] provides comparison of one’s business

performance with industry bests [16]. Several promising

modelling techniques [17] and tools [18] have been pro-

posed and implemented in specifying business process as

workflow [19] and the interactions with Web services [20],

or simulating the business process [21].

Holistically, the SNs can be abstracted into a layered

architecture (see Figure 1), addressing concerns from two

main perspectives, business and technical. In particular, it

can be divided into four conceptual layers, namely strategy

layer, business performance layer, end-to-end processes layer

and service-enabled processes layer. The SNs participants,

regarding the specific domain on each layer, could be indi-

viduals, a group of people, organizational units or software

applications.

Strategy layer specifies the networked partners’ interests,

missions, visions and long-term business objectives, such as

market share. Policies and plans are defined to achieve these

objectives by launching projects and programmes. Resource

allocation in implementing the policies and plans, projects

and programmes is also addressed in this layer. The business

objectives specified in this layer are reflected in terms of

key performance indicators (KPIs) in business performance

layer. The time horizon for planning in strategy layer is about

five years or even longer.

Bu

sin

ess

Tech

nic

al

Strategy

Business Performance

End-to-End Process (Service Choreography)

Service-Enabled Process Flow (Service Orchestration)

Figure 1. Conceptual Service Network Stack

Business performance layer addresses the mutual agree-

ments among the networked partners, defines the expected

service level and stipulates the protocols. The performance

of the service provision in this layer is measured by moni-

toring and analysing the behaviour of the projects and pro-

grammes, such as throughput employee turnover, cashflow,

costs, revenues and return on investment (ROI). This layer’s

time horizon is one to two years.

In the end-to-end processes layer, the partner services are

choreographed in compliance with the service level agree-

ments (SLAs) in the actual business process. The interaction

protocol between those services is defined from a global

perspective across the SNs participants. The performance

of such service composition is measured by system level

indicators, such as web service response time, web service

availability, security, reliability. This layer’s time horizon is

about months.

Service-enabled processes layer describes the logical se-

quence and timing of service invocations. Compared with

the global perspective in end-to-end processes layer which

involves multiple participants, the process specified in this

layer is from the local viewpoint of one single participant.

The time horizon of dealing with service invocation may be

hours, minutes or seconds.

Clearly, these four layers correlate with each other and

have strong influence over the SNs performance. The busi-

ness strategy specifies the desired business value and KPIs,

and the business performance relies on the performance

of the actual end-to-end process, which is composite of

service invocations. Contrariwise, the performance of every

single service invocation collectively determines the end-

to-end process performance. The business performance, as

an accumulation of the end-to-end process performance over

time, tells how well the business objectives are achieved. No

matter taken top-down or bottom-up approach in examining

the SNs performance, the interactions between the different

layers play a crucial role in revealing the performance

structure of SNs.

95

B. Problem Statement

Although we have seen numerous works on each of

the above mentioned SNs conceptual layers, there is still

a lack of a holistic framework managing the connections

and interactions (denoted by those arrows in Figure 1)

among the presented SNs layers. This issue is crucial to the

performance management of the entire network. We need

to find an effective solution to the SNs development and

performance optimization, which will align the disciplinary

research efforts and complement each other, so that it will

ensure the network works effectively and efficiently in the

sense of both business and technical perspectives.

Any analysis on the SNs should be based on a correct

mutual understanding of essence and characteristics of the

network. The SNs performance relies on harmonious com-

munication between the involved participants and is deliv-

ered through multiple interactions across the SNs conceptual

layers. In practice, there are gaps or grey areas between these

conceptual layers, which could be caused by organizational

boundaries, different domain languages, or disconnected

information exchange links, etc. [22]. The SNs participants

need to have a clear understanding and exchange information

effectively with each other, regarding the scope, objectives

and tasks. There should be no conceptual and terminological

confusion among the involved SNs participants. Here it

raises our first research question:

1) How can we understand and reveal the essence

of SNs correctly from both business and technical

perspectives?

After obtaining the essence of SNs, we need a noval way

to examine and manage the correlations between different

parts of the SNs business process. For this purpose, the

business process model is required to encompass diverse

operations and performance measurements (e.g. decision

making at various levels, business and technical perfor-

mance indicators, etc.) throughout the network. The causal-

ity between various performance indicators - across the

different time horizons at each SNs layer - needs to be

identified. More importantly, the quality of the business

process model of the SNs should be guaranteed before being

implemented. Simulation techniques come to the light given

its cost-effective virtual environment for scenario testing [21]

and performance prediction [23]. Then our next two research

questions are:

2) What is an effective simulation based modeling

framework for SNs?

3) How can we validate the proposed SNs frame-

work?

In this paper, we only focus on the first research question,

exploring the SNs essence and providing correct baseline

information for the SNs performance analytics. The SNs

performance should be properly analyzed, modelled and as-

sessed in supporting the decision-making and the definition

of strategies and partnerships at the business level [24]. Both

functional and non-functional business requirements should

be interpreted correctly and consistently into technical sys-

tem requirements. Both business and technical performance

components should be clearly understood, identified and

measured. The answers to the first research question is

fundamental to further SNs research.

III. CONCEPTUAL FRAMEWORK CONSTRUCTION

The striking standpoint of this paper is to take a balanced

view on both business and technical concerns and their

involvement in the SNs. Given the complex interdisciplinary

interactions in SNs, we employ the systems thinking as our

steering philosophy [25] throughout this work. We firstly

take a close look at the applications of systems thinnking

in SNs research in section III-A, to show its relevance in

our research. Then the conceptual framework of our work

for exploring the essential SNs information is presented in

section III-B.

A. Systems Thinking in SNs research

Systems thinking is a process of understanding a complex

system, by understanding how system elements influence

each other and examining the impact of their interactions on

the overall system [26]. It is not new to see its application

in service systems, given the characteristics of systems

thinking that are addressing the problems of complexity.

Systems thinking, with the ability to analyze the feedback

effects, has been suggested for strategic business planning

for network services [27], that a model using system dynam-

ics methodology is developed, to capture the interactions

in the network service provision and the behavior of the

business processes. System theory has also been applied in

the reference architecture proposed in [28], which adds the

feedback loop in capturing the dynamism of complex system

and continuous system optimization.

Systems thinking also addresses the interrelations between

the technical and social parts of systems, which is considered

as one of the major problems in organizations [29]. The

coordination and orchestration of services involves multiple

service stakeholders at both business and technical levels

in the SNs [30], as the service systems are essentially so-

ciotechnical systems [2]. Besides taking care of the technical

part, the interrelations among the sociotechnical system enti-

ties, including the people, group of people and sub-technical

systems, should be taken into account for managing the qual-

ity of SNs. As researched in [31], human provided services

and software services are highly interrelated and influence

each other in large-scale enterprise service networks, which

raise challenges, such as determining the relations between

human and non-human participants, the coordination and

composition of the network services.

In our SNs research, systems thinking is considered as the

steering mindset that keeps our scope at a holistic SNs level

96

and makes us being aware of the relations and influence

between the network elements.

B. Conceptual Framework

Figure 2 depicts the scope and path we are taking to

develop a diagnostic mechanism that provides interdisci-

plinary service assessment criteria which allows us to reveal

the essential picture of SNs and identify bi-dimensional

(Business vs. Technical) performance metrics.

This research takes place with a balanced view on both

business and technical domains, and has the mindset for

distinguishing and understanding the SNs essence from both

sides. It is a three-phase development, and starts with an

investigation into the concepts and structures of SNs, which

includes two building blocks Shared Service Terminology

and Service Categories. The investigation is taken from

two dimensions, business view and technical view. For

concepts, we look into the shared service terminology being

understood and adopted by SNs participants. For structures,

we look into existing service taxonomies to learn how the

services are categorized in different SNs dimensions.

With the investigation results, the research moves onto

the second phase: metamodeling of assessment metrics for

SNs. In this phase, we combine the core concerns from

both business and technical domains to the service operation

in SNs, and propose the interdisciplinary metamodel for

assessing the service essence and identifying performance

components.

At last, the proposed assessment metrics will be applied in

a well documented case study. This allows for revealing the

SNs structure and laying a foundation for SNs performance

optimization is evaluated.

Business

Technical

Bi-dimensional

Investigation in SNs

Shared

Service

Terminology

Service

Categories

Metamodeling of

Assessment Metrics

for SNs

Service

Assessment

Criteria

Performance

Components

Application

Evaluation

Case Study:

Car Repair

Service

Network

Systems Thinking as the Steering Philosophy for the Framework

Figure 2. Conceptual Framework for the Research Plan

IV. BI-DIMENSIONAL SNS

The SNs optimization has been one of the top priorities in

service science research [3] and has been separately captured

from several aspects by different disciplines. In this section,

we perform an investigation of SNs with a combined view

from two major disciplines, namely business science and

computer science. The investigation is made up of two

parts, the shared terminology in SNs (section IV-A), and

the service categories (section IV-B).

A. Confusions in Shared Service Terminology

When looking into the literatures on service science, it

is not surprising to be confused with the interpretations of

the shared terminology by different research communities.

The implementation of Service Oriented Architecture (SOA)

is considered to be able to align the business and IT in

the service delivery process. However the term of Service-

Orientation causes confusions among business and technical

parties that involved in the SNs much more than what

they are already aware. It is common to encounter different

interpretations of the concepts, such as service, among

business holders, software vendors and application providers

and so on.

We present in Table I a comparison of the different

perceptions of the most fundamental concepts that are shared

in service science, held by economics/marketing and service

computing communities. Clearly to see, the shared service

terminology has different interpretations in business science

and computer science respectively. It is caused by the

fundamental distinction in the definition of the service held

in these two domains. In business science, service is an

economic activity in which people are the major participants

and perform the key interactions. In computer science, it is

not necessary to involve human operation in carrying out the

service.

It is important to mention that, both business and technical

worlds recognize that there are different types of services

and tend to expand their vision in service provisioning

process. However, they do not reach an agreement in defin-

ing the business service and technical service, due to their

inherently different scope. This calls our extra attention in

studying the SNs, where participants from different domains

may be involved in the same service and communicate

intensively.

B. Service Categories

Given the mixed types of services found in the SNs [31],

service taxonomy is very helpful in differentiating the char-

acteristics of different types of services and identifying the

key drivers for successful development. There are several

service taxonomy propositions from various perspectives,

and each has its own focus with specific goals. Chen et

al. [42] develop a business service taxonomy for business

service innovation while taking into consideration the de-

gree of client interaction and the degree of standardization.

Bieberstein et al. [43] classify business services according

to their strategic importance and organizational ownership.

97

Table ISHARED SERVICE TERMINOLOGY IN BUSINESS SCIENCE AND COMPUTER SCIENCE

Economics / Marketing Service Computing

(Business Science) (Computer Science)

Service Orientation Service-Dominant Logic: an alternative view of economicexchange and value creation [4];

Design paradigm for separating concerns in building com-puter software with a suitable technology platform [32];

Service An application through deeds, processes and performances[33], which includes all economic activities whose output isnot a physical product or construction, and provides addedvalue that are essentially intangible concerns of its firstpurchaser [34];

Individule units of automation logic that conform to aset of principles that allow them to evolve independently,while maintain a sufficient amount of commonality andstandardization [32];

Processes of social and economic exchange containingactivities with benefit for another party [4]

A mechanism to enable access to one or more capabilities,where the access is provided using a prescribed interfaceand is exercised consistent with constraints and policies asspecified by the service description [35];

Service System Value co-creation configuration of people, technology,value propositions and shared information [36];

Software application, business unit or composition of ser-vice systems within and/or cross organizations [37];Social-technical system [38];

Service Network Business networks that constitute the most general form ofeconomically motivated cooperation among different firms[39];

A semantic relation based Web service infrastructure [10];

Business Service Business activities provided by a service provider to aservice consumer to create value for the consumer [9];

The most fundamental building block that encapsulates adistinct set of business logic within a well-defined func-tional boundary [32];

A specific set of actions performed by an organization [5];

Technical / IT Service Technical support, an activity or process of providingtechnological assistance [40];

Service that contains logic derived from a solution or tech-nology platform, and aims to provide reusable functions[32];Software service describes part of an application systemthat can be reused and composed based on business needs[41];

In an SOA environment with the purpose to create dif-

ferent SOA configurations, services can be abstracted into

hybrid application services, utility application services, task-

centric business services, entity-centric business services

and process services [32]. In the case of coordinating

the components of enterprise architecture, services can be

categorized into process service, capability service, core

service, utility service and Infrastructure service [44]. Sit-

hole et al. [45] categorizes the software services based on

the service functionality, the relationships, the interfacing

and runtime properties, the deployment, and the execution

strategies in order to derive performance components from

the service taxonomy.

C. Summary

Suffice it to say, the services perceived in business and

technical worlds are different in essence. From business

perspective, human operations form the major activities in

service provisioning, and are highly connected with each

other via the consumption of technology, shared information

and value propositions. Customers are heavily involved and

have strong influence on the stakeholders’ decision-making

over the capacity allocation.

On the other hand, from technical perspective, the major

focus is the functional construction of services. The services

mainly appear in the form of software applications or are

enabled by technological capacity. The business logic is

considered as a target instead of an enabler.

V. METAMODELING OF ASSESSMENT METRICS IN SNS

According to our investigation results, the standpoints

held in business and technical worlds have strong influence

on their perception and understanding of SNs. In order to

provide a balanced assessment, we propose a metamodel

(section V-A) for examining the essence of services in SNs.

This metamodel helps to define a set of criteria regarding

different SNs dimensions. Furthermore, a set of performance

components (section V-B) that comprises both business and

technical measurements can be identified for assessing the

service performance.

A. Metamodel of Service Assessment Metrics

If we put on the hat of systems thinking, the complexity

in SNs comes from the ambiguity concerning the semantic

interpretations of various types of services and service

resources, and the multiple interactions of the service partici-

pants. Regarding the results of our investigation (section IV),

the SNs resources includes service participants, personnel

capacity, technical capacity, and service productions, while

the service participants could be individuals, a group of

people, organizational units or software applications.

A metamodel (Figure 3) with bi-dimensional considera-

tions is generated for assessing the service essence in SNs.

Generally speaking, one instance service performs certain

functionality involves two types of participants (customer

and provider), and requires two types of capacity (personnel

and technical) in accomplishing the provisioning process

98

with producing some production. The service activity is

conducted through interactions between the participants.

The service provider owns the required capacity and is

responsible for allocating the capacity according to the

service requirements. Depending on the instance service, the

provider may provide the personnel capacity which comes

from human operation, the technical capacity which relies

on the quality of software services, or both. In SNs, multiple

services are interconnected. A production of one service

could be the input for another one. The provision of required

capacity in one service could be the assumption of another

service. For one specific participant, the role of customer or

provider is relative. For one specific service, its functionality

may be viewed with different scopes and visibilities.

Service

-functionality

+interactWith()

ParticipantCapacity

Customer

-satisfaction

+allocateCapacity()

Provider

-responsibility

-ownership

Personnel Capacity

-workHour

Technical Capacity

-QoS

is related with

*

*

1..*1..*

1..*

1..*involves requires

1..*

0..*

1..*

0..*

provides

Production

0..*1..*produces

/uses

provides

Figure 3. Metamodel of Service Assessment Metrics in SNs

In order to further describe our assessment metrics, we

generate a set of assessment criteria from the above meta-

model description and highlight them as follows:

* Customer involvement: who is the customer, how

much the end customer is involved in the service

operation. The extent of customer involvement indicates

on which SNs level the service operation is carried

out. The higher level the service is in the network,

the more the customer is involved. This criterion also

helps to identify the most influential factors in customer

satisfaction, since the latter is determined directly by

the service operation that a customer is involved in.

* Participant interaction: Who are involved? And how

intensively the involved service network participants

coordinate the service operation? The identification of

service provider and customer clarifies the responsibil-

ity and ownership in a service operation. In addition,

the intensity of the interaction implies potential network

performance bottleneck and extra capacity allocation.

* Human operation involvement: the degree at which

human operation is involved in executing the service

operations, such as service delivery and usage. The aim

of this criterion is to identify the required personnel

capacity in operating the assessed service.

* Software service involvement: the degree at which

software application is involved in executing the service

operations, such as service delivery and usage. The aim

of this criterion is to identify the required technological

capacity in operating the assessed service.

* Dependency: the degree at which one service relies

on each one of the other services from constructional

point of view. Checking the (inter)dependency of a

service provides us with a clear picture of its relations

with other services. Furthermore, it helps to form an

overview of the service operation structure in the SNs.

* Granularity: a relative measure of how broad a

required piece of functionality must be in order to

address the need at hand [46]. The involved partici-

pants may perceive different granularities of the same

service, due to the extent of the service operation is

visible to them, which reflects the discrepancy between

their interpretations of the same service. This criterion

allows participants to compare the composition level

of the same service from both business and technical

perspectives, so that the different perceptions held by

top-down and bottom-up approaches will be revealed.

B. Performance Components Identification

Based on the assessment criteria in section V-A, we

further derive a set of performance components (Figure 4)

that comprises both business and technical measurements

of service performance. The criteria Granularity and Depen-

dency are excluded here, because they are mainly concerned

about the structural aspects of SNs instead of observable or

measurable service performance.

Customer

involvement

Participant

interaction

Human

operation

involvement

Software

service

involvement

Personnel

capacity

Technical

capacity

Customer

satisfaction

Network

bottleneck /

capacity

allocation

e.g.

QoS aspects

Response time

Latency

Availability

Reliability

Security

etc..

e.g.

Service cost

Delivery time

Product-quality

Personnel-

service

e.g.

#service re-use

Response time

Pe

rform

an

ce

com

po

ne

nts

Crite

riaM

ea

sure

me

nts

e.g.

#working hour

#certification

Figure 4. Assessment Criteria and Performance Components

From the remaining four criteria, namely customer in-

volvement, participant interaction, human operation involve-

ment and software service involvement, four correspond-

ingly performance components are identified:

99

* Customer satisfaction: is determined by the differ-

ence between customer’s expectations and perceived

services [47]. During the service provisioning process,

it is influenced by the perceived service performance,

may including the cost, service time, production quality

and the interaction with service providers.

* Network bottleneck/capacity allocation: can be

checked by examining the intensity of participant inter-

actions. On one hand, network bottleneck slows down

the service provisioning process, where the interactions

between participants may become less smooth and en-

counter long response time. On another hand, the partic-

ipant interaction may become quite intensive, however

it is caused by repetition of the same service operation

due to the low successful rate at the bottleneck. Both

situations indicate that there is a need for extra capacity

allocation.

* Personnel capacity: is related with the dedicated per-

sonnel efforts and competence, which may be quantified

by the number of working hours and the number of

certified qualifications.

* Technical capacity: the quality attributes elicited in

existing Quality of Service (QoS) metrics for software

services [48] can be adopted as measurements for

illustrating the technological capacity.

VI. EXEMPLARY CASE STUDY

We apply the presented metrics to a car repair service

network case (Figure 5) in the automobile industry. All the

services in the network will be assessed from six dimen-

sions, by which the results will show the essential service

characteristics and the identification of a set of performance

measurements.

This service network case has been well studied in [49],

and includes four types of participants: an Original Equip-

ment Manufacturer (OEM), OEM-franchised Car Dealers,

Third Party Parts Suppliers (TPS), and Clients. At business

network level, there are three business services fulfilling

business transactions, namely catalogue management ser-

vice, parts and repair service and customer support service.

The business service is elaborated in service processes

with supports from services at the firm level. The service

processes are fulfilled by the design and execution of atomic

service at one level deeper, the business process level.

Atomic services can either be software services (SaaS) or

human services (from technician or parts manager).

We zoom into the Parts and Repair service, in which the

OEM-franchised car dealers deliver car parts and repair cars

for clients. The clients discuss with automotive engineers

about their needs. Then the engineers inspect the car and

diagnose and report the car service requirements. Based on

the car diagnosis, a cost estimate is computed and then com-

municated with the clients for authorization. With the clients

authorization, the automotive engineer will scrutinize failure

Figure 5. Overview of the car repair service network

symptoms, detect faulty parts, order parts and perform the

repair.

Due to the page limit, we only present detailed assessment

of the Diagnose Problem service. The diagnose problem

service takes client’s need as input, and identify the fail-

ure problem. Figure 6 shows the business process of the

complete diagnose problem service, note that this business

process contains atomic services at business process level,

and together they form up the diagnose problem service at

firm level. Table II presents the assessment result of the firm

level service, diagnose problem.

Figure 6. Business process of Diagnose Problem service

Based on the assessment, we have identified two influ-

ential factors to customer satisfaction, namely the perceived

customer service and repair price. The service granularity is

perceived differently if standing from different service net-

work levels, because the visibility of the service construction

varies at different levels. OEM-franchised car dealer is the

service provider, has three information exchange with the

customer. He also need to take care of personnel capacity

allocation in four operations, and technological capacity

allocation in one operation. The information exchange and

more detailed operations are executed in three atomic ser-

vices. The structure of the service processes and atomic

service is obtained from the dependency analysis.

100

Figure 7. Business process of Parts and Repair Service

Table IIASSESSMENT OF DIAGNOSE PROBLEM SERVICE

Diagnose Problem Service

Customer

involvement

Customer is directly involved in discussing theirneeds and authorizing the cost estimate. The per-ceived customer service and the repair price havestrong influence on customer satisfaction.

Participant in-

teraction

Service provider: OEM-franchised car dealer; ser-vice consumer: customer; intensity: 3 informationexchanges.

Granularity Seen from firm level: Low; seen from businessprocess level: High, because it can be further de-composed into atomic services: car diagnosis, costestimate and and identify failure problem.

Human opera-

tion involvement

Personnel capacity is required in discussing with cus-tomer about their needs, inspecting the car, request-ing customer authorization, identifying the failureproblem.

Software service

involvement

Technological capacity is required in computing thecost estimate.

Dependency Rely on the accomplishment of atomic services: cardiagnosis, cost estimate and identify failure problem.Provide input to the process of order parts service.

Again for space reason, we only present the derived per-

formance measurements for customer satisfaction (Table III).

Figure III depicts the complete process of Parts and Repair

Service. As we could see, customers are only involved

in initiating the repair request, negotiating the repair cost,

achieving the payment and picking up the repaired car. They

are excluded from the operations at car dealer’s back office

with OEM and TPS. What customers face directly are the

front desk of the customer service, the repair price, the

Table IIIIDENTIFICATION OF PERFORMANCE MEASUREMENTS

Influential factors Performance Measure-ments

CustomerSatisfaction

Perceived customer ser-vice

customer service time(number of minutes orhours)

Repair price Repair price (euro)Waiting time for car re-pair

car repair time (numberof days)

Repair quality number of skill certifica-tion

total waiting time for the car repair and the repair quality.

Therefore, we have the following measurements to evaluate

customer satisfaction: customer service time, repair price,

car repair time, and number of car dealer’s skill certification.

These measurements provide us a quantitative presentation

of the qualitative customer satisfaction.

VII. CONCLUSION

The essence of services are in the eyes of the service par-

ticipants. In the complex and highly dynamic network envi-

ronment, there are multiple perspectives taken for capturing

particular dimensions of SNs. Confusions can be seen via the

communication during the service provisioning process, due

to the different interpretations of the shared terminology in

the network. In this paper, we presented an interdisciplinary

assessment metrics to solve this problem with the aim to

have a correct understanding of the multi-dimensional SNs

and a mechanism towards SNs performance analysis.

101

Regarding the bi-dimensional perspectives held in our

assessment, the added value is twofold. Firstly, it provides a

holistic picture of the multiple dimensions and structure of

SNs so that the interdisciplinary service participants have a

correct understanding of the service scope and required re-

sources in operation. In addition, it also offers a mechanism

for eliciting the most influential performance indicators in

each derived performance components. Thus together with

the SNs structure, the said assessment metrics provide a way

to examine the performance traceability of the services in

SNs.

Our future plan is to apply this assessment metrics in

studying the interaction centric performance aggregation of

SNs in real world case. We will take the service assessment

results as a base document, adopt system dynamics to

measure the elicited performance indicators in simulation,

and reveal the impact of the dynamic interactions on the

SNs performance.

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