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Design of a Global Decision Support System for a manufacturing SME:Towards participating in Collaborative Manufacturing

Hao W. Lin a,n, Sev V. Nagalingam b, Swee S. Kuik b, Tomohiro Murata c

a Harbin Institute of Technology, Shenzhen Graduate School, Rm. 424, Bld. D, HIT Campus, Xili University Town, Shenzhen, Guangdong 518055, P.R. Chinab School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Australiac Graduate School of Information, Production and Systems, Waseda University, Japan

a r t i c l e i n f o

Article history:Received 15 February 2007

Accepted 5 July 2011Available online 22 August 2011

Keywords:

Collaborative Manufacturing

Collaborative networks

Global Decision Support System

System interoperability

Multi-objective optimisation

a b s t r a c t

This paper discusses the conceptual design of a Global Decision Support System for a manufacturingSmall or Medium Enterprise (SM/E), which actively participates in Collaborative Manufacturing. In

order to implement the proposed concept, a Web Services based system architecture is proposed to

offer maximum interoperability between all the distributed participants of a Collaborative Manufactur-

ing Network (CMN) and their management information systems. Furthermore, this conceptual design

utilises a Collaborative decision-support model that effectively interacts with the decision-makers and

the management information systems/tools exist in the network, and provides appropriate support to

all necessary decision-making steps towards the attainment of the network’s strategic goals, while

making full benefits of the network resources.

& 2011 Elsevier B.V. All rights reserved.

1. Introduction

In recent years, many manufacturing enterprises that are

operating worldwide show an interest for Collaborative Manu-

facturing (CM). This new business strategy offers manufacturers

the critically needed competitive advantages (Camarinha-Matos

and Macedo, 2010; Chung et al., 2004; Johansen et al., 2005).

CM is a concept that involves the establishment of Collaborative

Manufacturing Networks (CMNs) in order to fully exploit the

core competencies of every manufacturer within a network.

The strategy is aiming to achieve best possible fulfilment of

customer demands and improvement of their overall net profit,

agility, and competitiveness towards the global market (Danilovic

and Winroth, 2005; Kuik et al., 2010). However, CM heavily relies

on improved data, information, and knowledge transparency

typically a commonly recognised decision-making approach toachieve balanced profits, costs, and risks among the participants

(D’Amours et al., 1999; Lagerstrom and Andersson, 2003; Li and

Lai, 2005; Zhang et al., 2004). This reliance suggests that an

integrated manufacturing decision-support infrastructure is

essential for a CMN to successfully deliver the positive outcomes.

Enhancing the existing capabilities on supporting the manage-

ment and production activities are traditionally restricted to

in-house operations and department-oriented operations. Advan-

cing to the CM era, a corresponding new generation of manufac-turing systems must also expand their features to administrate

collaborative activities between the local enterprise and its

business partners within the CMN (Chiu and Lin, 2004; Cil et al.,

2005; Perrin et al., 2003).

Since, collaborative activities are highly complex and dynamic

(Cil et al., 2005; Perrin et al., 2003; Xu et al., 2009), adequate

interoperability between manufacturing systems that are distrib-

uted across the CMN is essential for the success of this network.

To a certain extent, this interoperability issue is not properly

addressed by most of the conventional integrated manufacturing

systems (Chiu et al., 2006; Lin et al., 2009). Especially these

systems are established by closely coupling computer systems

with inflexible interfaces that are hard-coded to accommodate

the purpose of a Business-to-Business (B2B) relationship. Under acustomised interface, these systems fulfil the objectives of infor-

mation sharing, and they proved adequate in sustaining the

automation of most pre-defined business operations. However,

hand-coded interfaces are not readily adaptive to the frequent

changes as experienced within a CMN. As a result, participants

within the network must invest invaluable resources in perform-

ing substantial updates just to maintain the operation of their

existing systems. In order to conform to these integration archi-

tectures, the system must be commonly endorsed by all business

partners to ensure smooth transaction of collaborative manage-

ment activities. Furthermore, the schema of shared information

and knowledge must be updated accordingly whenever the CMN

Contents lists available at SciVerse ScienceDirect

journal homepage: www.elsevier.com/locate/ijpe

Int. J. Production Economics

0925-5273/$- see front matter & 2011 Elsevier B.V. All rights reserved.

doi:10.1016/j.ijpe.2011.07.001

n Corresponding author. Tel.: þ 86 755 2603 3148.

E-mail addresses: [email protected] (H.W. Lin),

[email protected] (S.V. Nagalingam),

[email protected] (S.S. Kuik), [email protected] (T. Murata).

Int. J. Production Economics 136 (2012) 1–12

http://www.elsevier.com/locate/ijpe


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changes its formation. The maintenance cost of these systems is

therefore a critical drawback.

The current manufacturing integration architectures for CM

business activities are facing heavy technical and financial bur-

dens (Brunnermeier and Martin, 2002; Chiu et al., 2006). These

issues are significantly apparent for Small and Medium Enter-

prises (SMEs) due to scarce financial resources and limited

technical abilities. Instead of rapidly expanding the manufactur-

ing capabilities and capacities to cope with the highly dynamicglobal market, CM usually constitutes lower risks and is more

financially justified for manufacturing SMEs to achieve the same

effects of internal expansion (Danilovic and Winroth, 2005;

Johansen et al., 2005; Lin et al., 2005; Loeser, 1999; Nadvi,

1995; Wang et al., 2004; Wheelen and Hungar, 2000). Further-

more, a survey of Australian Manufacturing Industry in 2005

demonstrated the willingness of the organisation to collaborate

with other organisations (Intelligent Manufacturing Systems,

2005). In our work, the development of a Global Decision Support

System (GDSS) enables optimised decision-making via facilitating

interactions amongst the stand-alone manufacturing systems,

and the adoption of a generic collaborative decision-making

model. Subsequently, the GDSS is critical in building up the

willingness for networked collaboration.

This paper is organised as follows: in Section 2, the back-

ground on manufacturing SMEs, CMN, and business process (BP)

modelling are presented. In Section 3, the system architecture for

the GDSS is discussed. In Section 4, a process-based collaborative

decision-support model (CDSM) is proposed. The CDSM depicts

the execution of global decision-making processes within a Small

or Medium Manufacturing Enterprise (S/MME) and its business

partners. Section 5, a real case example is given to illustrate the

conceptual design of the GDSS. Finally, the concluding remarks

and future work are discussed in Section 6.

2. Background

The authors of this paper have performed extensive literature

reviews and on-site business analysis of an enterprise’s business

activities in association with CM. The outcomes of these studies

demonstrated the need of an improved GDSS for supporting the

current CM business phenomena by more readily attaining

successful decision-making outcomes.

2.1. Critical success factors for manufacturing SMEs

The definition of manufacturing SMEs varies among countries,

but generally the classifying parameters being the number of

employees (under 200 employees in Australia) (Australian Bureau

of Statistics, 2002) and the annual turnover (maximum of

40 million euros in Europe) (Small Business Service, 1996). These

two parameters have defined manufacturing SMEs characteristics

that are considerably different to large enterprises. Huin (2003)

surveyed the characteristics of manufacturing SMEs among

30 companies by conducting 95 interviews with executives, and

identified the key strategic and operational characteristics of an

S/ME. Based on Huin’s findings (2003) and our previous work

(Nagalingam and Lin, 2000), it is concluded that there are three

key business objectives that are critical to the success of manu-

facturing SMEs. Firstly, management activities throughout every

organisation unit of the S/ME must be integrated to the max-

imum, so that the workflow across all functional and manage-

ment boundaries are better aligned with the strategic goals of the

enterprise. Furthermore, since external factors are highly influen-tial to an S/MME, business partners must also participate in

relevant decision-making activities whenever appropriate. Sec-

ondly, SMEs should adopt knowledge management as an essential

activity so that work transition can be accomplished smoothly in

events of task transferring and unexpected staff turnover. Thirdly,

the S/MME must be proactive with decision-making, so that the

organisation is capable of confronting forecast distortions, unex-

pected events, and demanding customers in an effective manner.

2.2. Participating in a Collaborative Manufacturing network

From the perspectives of a manufacturing oriented S/ME, a

CMN is formed when the S/ME establishes highly transparentcollaborative relationships with its business partners, who

include customers, suppliers, and contractors such as illustrated

in Fig. 1. The key objective of the collaborative strategy is to

coordinate all the resources of the S/MME, suppliers, and con-

tractors to best fulfil the demands of the existing customers and

Manufacturing S/ME(Internal Business Processes)

Supplier 1

Supplier 3

Supplier 2 Supplier y Contractor 2

Contractor 1

Contractor z

Contractor 3

Analyse, create and

fulfill customer

demands

Procure materials and other manufacturing supplies from

suppliers that demonstrate

maximum synergy with the

manufacturing S/ME

Outsource production processes to

contractors that demonstrate

maximum synergy with the

manufacturing S/ME

Customer 1

Customer 3

Customer 2 Customer x

Supplier NetworkContractor Network

Customer Network

Collaborative Manufacturing Network

Fig. 1. CMN topology.

H.W. Lin et al. / Int. J. Production Economics 136 (2012) 1–122

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continuously expand the customer network ( Johansen et al.,

2005; Kuik et al., 2010).

Due to the globalised market trend, most SMEs no longer have

partners–customers confined within the local market, but are dis-

tributed worldwide (Binder and Clegg, 2007). By incorporating the

partners as part of the CMN, the manufacturing SMEs can engage in

more proactive management strategies so that partner and consumer

demands can be better understood, and trading issues can be made

more apparent (Binder and Clegg, 2007; Chung et al., 2004). With

more attentions given to customers (and consumers), manufacturers

can substantially increase business opportunities by attracting new

customers and also retaining the existing ones. The S/ME-to-con-

sumer relationship is classified as downstream or forward vertical

collaboration in the CMN topology (Lin et al., 2005). As for the S/ME-to-supplier relationship, the key role of suppliers is to supply raw

materials or other Original Equipment Manufacturer (OEM) goods to

the S/MME. All services must satisfy the requirements set out by the

S/ME such as quality assurance, delivery time reliability, and costs in

order to attract business opportunities. This type of the relationship is

classified as upstream or backward vertical collaboration in CMN

topology (Lin et al., 2005). Considering the roles of contractors, they

are generally to provide extended manufacturing and other suppor-

tive services to the S/ME. Examples of organisations that provide the

services include product packaging, Information Technology (IT),

logistic, legal consulting, and others. This type of S/ME-to-contractor

relationship is classified as a horizontal collaboration in the CMN

topology (Lin et al., 2005). All outsourced processes must not be any

of the SME’s core competencies, as they are critical in characterisingthe competitiveness of the SME. The actual driving force behind

outsourcing is to maintain a lean enterprise in order to avoid the risks

involved in expanding capabilities that are new to the S/ME.

2.3. Business process management modelling

In recent years, the concept of business process management

(BPM) modelling is becoming an increasingly popular manufac-

turing modelling technique. This technique provides managers

with the visibility, flexibility, and agility needed to manage their

businesses (Butler et al., 2002). In BPM modelling, the formation

of BPs is executed at the enterprise level, so that every stake-

holder is aware of the relationships between the processes, and is

clear about their roles (Perrin et al., 2003). The BP model has a

typical hierarchical structure, where each entity is empowered to

execute decisions made by the local experts or managers. As a

rule, the decision outputs of upper processes would always define

the constraints or the desired targets for their lower processes,

and the decision outputs of the lower processes would express their

commitments towards the upper processes (Aguilar-Saven, 2004). In

our research, BPM refers to the administration of complex relation-

ships between all the BPs in a CMN. BPM guides every available

activity in doing their parts to satisfy customer demands at minimal

resource utilisation. With this management model, the performance

of each process is transparent to the top management level, which

implies business performance can be evaluated more extensively and

that manufacturing bottlenecks can be easily identified. A key concept

of BPM is to empower, to operate and optimise their own processes,while satisfying the strategic goals set by the senior managers and

constraints presented by all stakeholders of the CMN (Butler et al.,

2002). This decentralised management strategy verifies that the BPM

concept can be adapted to establish the suitable management

technique for a CMN.

Extending from the customer oriented business model sug-

gested by McCormack and Rauseo (2005), the CMN oriented

business model examined in our study is shown in Fig. 2. The

model depicts the hierarchical relationships between the business

collaboration lifecycle of the CMN, the core-BPs of each member

of the CMN, and the key supporting BPs whose roles are to ensure

the fulfilment of core business activities.

3. System architecture

The system architecture of GDSS is made up of the hardware

infrastructure and software packages. Firstly, an information

framework is suggested based on a rationale that constitutes a

suitable information interoperability foundation for the GDSS.

Then the architectural design of the GDSS is introduced in detail.

3.1. Information framework for GDSS

The GDSS must adopt a widely accepted interoperable infor-

mation framework in the sense that those distributed systems

belonging to different members of the CMN can be integrated

seamlessly. A platform is provided to enable collaborative

Business

collaboration

life cycle

Core

businessprocess

Supporting

business

process

Search for suitable

collaborative business

partners

Evaluate, define, and

establish collaborative

processes

Execute collaborative

processes in accordance to

business requirements

Analyse, maintain, and

update collaborative

processes

Product

research and

development

Marketing

Business

relationship

management

Sales

management

Financial

management

Production

planning

Quality

assurance and

business

partner support

Local

shop-floor

scheduling

Capacity

planning

Inventory

planning

Production

monitoring

LogisticsOrder picking

Purchasing Outsourcing

Business

opportunitycreation

Business

opportunityconversion

Order

fulfillment

Support &

Service

Fig. 2. CMN oriented business model for manufacturing SMEs.

H.W. Lin et al. / Int. J. Production Economics 136 (2012) 1–12 3

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decision-making without confronting the issues related to legacy

systems. The rationale suggested by Chiu et al. (2006) is used to

select an interoperability framework for implementing the GDSS

that facilitates CM operations:

Business connectivity: the framework should ensure afford-

able connections with CMN participants’ systems for rapid

implementation and ease-of-use capabilities.

Business benefits: the framework should facilitate coherence of knowledge management across all members of a CMN and seek

to achieve convergence through voluntary compliance on mini-

mising the business impact while maximising the benefits.

Financial considerations: the framework should provide a

technology roadmap for choosing and implementing reliable,

scalable, and secure systems that are able to meet the rate of

technological changes.

E-government standards: existing government standards

should be adopted wherever they are available and

appropriate.

Forward outlook: the framework should grow in parallel with

the technological developments and establish a process to

grow the framework.

Usage limitations: the framework should provide a high level

or minimum basis for interoperability within the CMN without

any imposed standards, but rather collects and reflects stan-

dards in use.

Product support: the framework should provide flexibility in

the selection of vendors, preferably being vendor and product

neutral.

Technical support or staff experience: ideal selection of an

appropriate framework depends largely on the technical sup-

port or staff experience available however, these factors

impact heavily on the SMEs in reality.

Three interoperability framework architectures, namely: Elec-

tronic Data Interchange (EDI) (Hartley, 1993), Common Object

Request Broker Architecture (CORBA) (Vinoski, 2004), and Web

Services Architecture (WSA) (Cabrera et al., 2004) have been

reviewed by Chiu et al. (2006). The performance of the architec-

tures are analysed based on the above rationales. As an outcome

of this analysis, WSA is considered as the most appropriate

interoperability framework for the development of the GDSS.

Web Services (WS) through ease of integration, flexibility, and

support of Extensible Markup Language (XML) and HyperText

Transfer Protocol (HTTP) provide a suitable interoperability frame-

work to realise the GDSS for a CMN. In terms of business connectivity,

a CMN can reach vast number of business partners through the

Internet. The XML transformation and other associated technologies

facilitate system level integration of services and business partners.

The technical standards of WS are widely endorsed by the software

vendors, such as Sun Microsystem’s Java 2 Platform Enterprise Edition

(J2EE) and Microsoft .Net framework, and governments (AustralianGovernment Information Management Office, 2003) worldwide.

Standards-based systems are the choice of manufacturing SMEs that

desire to move to the future rather than reinvent the past. Every

application and computer–human interaction can be modelled as a

WS to facilitate collaborative knowledge management across mem-

bers of a CMN. This high degree of system flexibility helps minimise

the cost of switching among alternative requirements or partners, and

enables the indispensable features of CMN that include indepen-

dence, equality of the partners, and fluid boundaries.

3.2. GDSS architectural design

This section introduces the architectural design, which is

depicted in Fig. 3, towards the implementation of the GDSS. As

shown in Fig. 3, the system architecture design consists of

4 modules: (i) the database and knowledgebase, (ii) the web-

based client application, (iii) the collection of decision-support

WS that enables the functionalities of the GDSS, and (iv) the

softwares-to-WS interfaces that enable standalone information

systems to interoperate with the GDSS.

3.2.1. Database and knowledgebase module

The database and knowledgebase module focuses on thedesigning of a relational database that is capable of storing loosely

structured decision-making data, information, and knowledge in a

highly systematic table-based information system environment.

In our research project, Microsoft SQL 2000 database server

(Rankins et al., 2003) has been selected to implement the

database. It is highly scalable, reliable, and flexible to maintain

due to its functional rich user interface for the target audience,

the SMEs. Unlike transactional systems, an ideal decision support

system requires data and knowledge to be organised in a

simplified structure that maximise the efficiency of analytical

queries. Furthermore, the timely update of operational data and

information has a similar importance in maximising the accuracy

of quality of a decision. It is essential to ensure that at the point of

decision analysis, all relevant decision parameters have beenstrategically maintained (either manually or pre-programmed)

in order to establish a reliable representation of the actual

problem.

For our study, knowledge can be summarised as a depository

of know-hows on the interpretation and reasoning of decision-

attributes towards achieving the objectives of a decision. Knowl-

edge can be gained through educations, learning, and experience,

and it can be classified as tacit (knowledge exist in the state of

decision-makers’ minds) or explicit (knowledge that have been

formally documented). One of the critical challenges in our study

is to assist a CMN in the conversion of tacit knowledge to explicit

knowledge so that it can be formally stored in the knowledgebase.

For the GDSS, it can be generally summarised that four types of

knowledge are stored in the knowledgebase such as (i) businessprocess definition, (ii) business rules and policies, (iii) operation

know-hows, and (iv) historical experience (Lin et al., 2005, 2009).

3.2.2. Web-based client application

The web-based client application is implemented using Micro-

soft’s ASP .Net programming language in our research project.

This application allows decision-makers to interact with the GDSS

with minimum equipment requirements and highest flexibility.

Most web browsers of different vendors are capable of displaying

the interface and supporting all functionalities. In this project,

however, Microsoft Internet Explorer version 6 and later can best

support the dynamic features and maintain the correct displaying

format of the interface.

In GDSS, the key roles of the client application is to displayconcise information in relation to the decision and to guide

decision-makers through a pre-defined decision-making work-

flows as described by the CDSM introduced in Section 4. Using the

Web Services paradigm, all related information and knowledge

are made readily accessible to the decision-maker throughout

every step of the decision-making sequence. A template of the

web-based client application is depicted in Fig. 4.

3.2.3. Decision-support WS

The collection of WS can be further categorised into five

groups, with each serving a unique feature of the GDSS. These

groups are: (i) BPM Services, (ii) Knowledge Management

Services, (iii) Decision Analysis Model Management Services,

(iv) Information Management Services, and (v) Computer-based


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Fig. 3. Architectural design for GDSS.


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Delphi Process Services (Lin et al., 2005, 2009). These services

merely constitute the basic requirements of the GDSS. Due to the

scalability of the WSA, new services can be created indepen-

dently, and seamlessly interoperate with the existing services,

thus expanding the functionality of the GDSS. Each decision-

support WS is designed to address a specific feature or function

of the GDSS. Being implemented as a WS, the web-based client

application could dynamically and rapidly bind with these

features in any sequence, to create a highly intelligent decision-

support package that best address the requirements for decision-

making in a CMN. Furthermore, any of the features may be

selected to interoperate with the business partners’ WS-enabledsystem using the existing network infrastructure that is enabling

the Internet, and consequently expanding the capabilities for the

local system. Overall, a highly complex system can be achieved in

the CMN by using these functionally focused WS features.

3.2.4. Softwares-to-WS interfaces

Often, decision-support WS are required to extract information

and learn new knowledge from standalone or dedicated information

systems such ERP, SCM, Management Information System (MIS),

and Customer Relationship Management (CRM). Other times, they

are required to interact with decision analysis tools and models such

as mathematical optimisation solver software applications. In these

situations, an intermediate interface must be created to enable

interoperability between the GDSS and the standalone information

systems (Chiu et al., 2006; Lin et al., 2005). For example, Visual Basic

for Application (VBA) programme can be written to inter-connect an

Excel-based decision model with the GDSS via a particular Decision

Analysis Model Management Service.

4. Collaborative decision-support model

This section focuses on orchestrating all elements of the entire

collaborative decision-making progresses throughout a CMN.

Our research project has lead to the development of a CDSM,which provides a systematic way of supporting rational decision-

making in SMEs using quantitative methods.

4.1. Decision-making concepts

In CM, the essence of decision-making is to identify the unique

objectives, capabilities, constraints, and commitments of all

functional units of the CMN toward a manufacturing process,

and decide on how to optimally dispose the overall available

resources to fulfil the business objectives. Under such business

phenomena, decision-making is highly complex due to large

number of distributed but interrelated manufacturing variables,

as well as the large number of alternatives for achieving the

objectives. A systematic decision-making process is required to

Fig. 4. Web-based client application interface.


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address this overwhelming complexity. Simon (Marakas, 1999)

proposed a four phase decision-making model (Turban et al.,

2005) that is the most concise and yet completes characterisation

of a rational decision-making approach. The four decision-making

phases as suggested by Simon (Turban et al., 2005) are namely

intelligence, design, choice, and implementation. The intelligence

phase is used to simplify and make knowledgeable assumpt-

ions about the real world problem, so that decision-makers could

comprehend the situations and correctly define the potential

problems and or opportunities. The design phase involves the

selection of an appropriate model to analyse the decision and

thus finding the potential decision alternatives for the most likely

Scan for environmental

parameters and performance

indicators that deviate from

their desired values

Identify problems and/or

opportunities and construct

formal decision statements

Select appropriate decision

alternatives and scenario, and

forward the information to the

associated functional units

Analyse decision using local

models, and formulate goal

function(s) based on the

decision alternatives









Collect goal functions from the

functional units and ensure the

functions are valid

Solve the meta-goal

programming modelOutcome satisfied?

Implementation of

the decision outcome

No

Yes

Define meta-goals for all goal

functions under consideration

Activate computer-based

Delphi process and refine

previous phases as required

1 2

3

4.1 4.2 4.n

5 6

7

8

910

Decision-maker(s) approve(s)

meta-goals and establish

priority for each meta-goal

Group-based

interactive process?

No

Yes

Collect performance

feedbacks on the

implementation of decisions

11

Fig. 6. Collaborative decision-support model.

Intelligence Design

ChoiceOutcome

* Generally the starting point of a decision-making process

Implementation

Failure

SuccessSolution testing

Model validation

Problem

statement

Alternatives

Solution

Simplificationand

assumptions

* Identify objectives,

capabilities, constraints,

and commitments of all

functional units of the

CMN

Fig. 5. Decision-making model for Collaborative Manufacturing (adapted from Small Business Service, 1996).


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scenario. The choice phase focuses on using algorithms to solve

the model and find the solutions from the decision alternatives.

The implementation phase verifies the performance of the solu-

tions obtained. This model is conceptually illustrated in Fig. 5.

4.2. Collaborative decision-support modelling

CDSM is usually in a continuous loop. However, the scanning

for business performance parameter deviations is usually con-sidered as the first step of any decision-making process. During

any stage of the decision-making process, the decision-makers if

necessary can retrace to any of the previous stages to refine their

results. By projecting CDSM to Simon’s model (Turban et al.,

2005) of decision-making, Steps from 1 to 2 are categorised as the

intelligence phase, Steps from 3 to 4 belong to the design phase,

Steps from 5 to 9 are in the choice phase, and finally Step 10 is the

implementation phase. The CDSM is depicted in Fig. 6.

Conforming to the BPM concept, the CDSM assumes local

empowerment to each entity of the CMN. A critical challenge

however, is to best orchestrate local decisions toward the attain-

ment of strategic goals for the entire CMN. Stepping up to the

challenge, the CDSM employs Interactive Meta-Goal Program-

ming (IMGP) technique and Computerised Delphi Process torespectively, enlighten issues associated with human-to-model

interaction and conflicting of interests between entities. IMGP is a

mathematical optimisation technique proposed by Caballero et al.

(2006). Building on the fundamental ideas of Goal Programming,

IMGP offers a multi-level modelling structure that is highly

suitable for representing the hierarchical structured organisa-

tional decision problems in a CMN environment. The CDSM

suggests that each entity is empowered to build Goal Program-

ming models to express its local decision problem. The local

models are then collated and that meta-goals are designed to

dictate the level of attainment for each original goal function with

respect to the high level strategic objectives. Meta-goals are goals

of the original goal functions, and they provide a concise and yet

consistent manner to evaluate the quality of sub-decisions withrespect to the performance of the entire CMN. By adjusting the

meta-goal objectives, decision-makers can effectively interact

with the model and conduct ‘‘what-if’’ analysis to discover

optimal overall decision. Also, meta-goals are commensurable

performance parameters between all entities of the CMN, which

conveniently form the common reference point for multiple

decision-makers in resolving conflicting interests during Delphi

Processes. Similar to what is depicted by Turoff and Hiltz (1995),

the CDSM employs a computerised Delphi Process to establish

consensus decisions amongst all entities of the CMN. It is

expected that each entity is interested in optimise its local

performance, and that its respective contribution towards the

overall CMN performance is represented by the commensurable

meta-goals. In group decision-making, various configurations are

proposed by each entity, and that any conflicts must be resolvedin a manner that guarantees the aspiration of the CMN as a whole.

Under the Delphi Process, conflicts are resolved over numerous

rounds, with each round trying to anonymously establish a

consensus for disputing factor that contribute the most variation

towards the CMN performance. Consequently, the IMGP and

Delphi Process enabled CDSM can effectively facilitate group

decision-making in a CMN, and this concept is demonstrated

using a case example in Section 5.

5. Research methodology and discussion

This section illustrates an example on a decision-making process

that can be constructed and applied in a practical situation within

the GDSS platform. The results of the example is analysed, and the

performance of the conceptual design for the GDSS are discussed.

5.1. Background of a small manufacturing enterprise

In this section, the decision-making process for a production-

planning task of a small manufacturing enterprise that manufac-

tures medical accessories is illustrated. Due to business confiden-

tiality, most of the data used in this example are hypothetical. In

this example, we considered that three different functional units

participated in a decision-making process: the Sales and Fore-

casting unit that set goals to customer demands and business

profits; the operational planning unit that set goals to the utilities

of the available production capacity; the scheduling unit that setgoals to the utilities of the current machine group formation.

5.2. Consolidate information and data

The problem considered is in this example is to decide the

production quantity for 10 different types of suture for the following

Table 1

Technology coefficients obtained from different functional units.

Suture

type 1

Suture

type 2

Suture

type 3

Suture

type 4

Suture

type 5

Suture

type 6

Suture

type 7

Suture

type 8

Suture

type 9

Suture

type 10

Production quota (box of dozen) 200 150 180 130 230 50 90 85 130 255

Expected profit ($/box) 20 22 25 30 15 28 18 23 22 26

Manufacturing cost ($/box) 25 26 28 30 20 25 22 20 24 28

Production time on swaging machine

group 1 (h)

0.1 – – 0.2 – – 0.1 – – 0.2


group 2 (h)

– 0.1 – – 0.1 – – 0.15 – –


group 3 (h)

– – 0.15 – – 0.2 – – 0.15 –

Packaging time (h) 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

Quality cheque time (h) 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025

Needle type 1 consumption (unit) 1 – – 1 – – 1 – – 1

Needle type 2 consumption (unit) – 1 – – 1 – – 1 – –

Needle type 3 consumption (unit) – – 1 – – 1 – – 1 –

Thread type 1 consumption (cm) 20 20 20 – – – – – – –

Thread type 2 consumption (cm) – – – 20 – 20 – – – –

Thread type 3 consumption (cm) – – – – – – 20 20 20 –

Thread type 4 consumption (cm) – – – – 20 – – – – 20

‘‘–’’: empty fields imply technology coefficients not applicable for the corresponding suture type.


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week. The suture production process consists of three major steps,

which are swaging (joining the thread to the needle), packaging, and

quality checking. The production lead-time for different types of

suture varies, as finer needles are more difficult to handle than the

coarse ones. Since there are three different types of needles available

(with different diameters), the 10 available swaging machines are

divided into three groups, where machines of the same group are set

up to produce a particular needle size. Although, any machine can set

up to produce other needle sizes, it is not desirable by the productionworkers, as setting up is a time consuming process, and the slightest

miss-set-up would destroy the costly die in the swaging mechanism.

The technology coefficients for the functional units are summarised in

Table 1. The constraints to this problem are the available stock of the

needles and the threads, which are supplied by the inventory

management unit. These constraints are summarised in Table 2.

The multi-objective problem is then modelled by the following Goal

Programming equations, and the model is analysed using the algo-

rithm proposed by Caballero et al. (2006). The model is solved by

What’sBest!, a Microsoft Excel plug-in optimisation tool developed byLindo Systems Inc. (Lindo Systems Inc., 2006).

Parameters

i Types of suture considered in production planning

Decision variables/alternatives

x ¼ ½ x1, x2,::: xi (Production quantity for suture type i, i ¼ 1,:::,10)

Goals proposed by the sales and forecasting unit

x1 þd1 d þ

1 ¼ 200 (Production quantity for suture type 1)

x2 þd2 d þ


x3 þd3 d þ


x4 þd4 d þ


x5 þd5 d þ


x6 þd6 d

þ6 ¼ 50 (Production quantity for suture type 6)

x7 þd7 d þ


x8 þd8 d þ


x9 þd9 d þ


x10 þ d10d þ


20 x1 þ22 x2 þ25 x3 þ30 x4 þ15 x5 þ28 x6 þ18 x7 þ23 x8

þ22 x9 þ26 x10 þd11dþ

11 ¼ 30,000(Profit target)

Goals proposed by the operational planning unit


þ24 x9 þ28 x10 þd12dþ

12 ¼ 50,000(Budget utility)

0:1 x1 þ0:1 x2 þ0:15 x3 þ0:2 x4 þ0:1 x5 þ0:2 x6 þ0:1 x7

þ0:

15 x8 þ0:

15 x9 þ0:

2 x10 þd13dþ13 ¼ 350

(Swaging utility)

0:04ð x1 þ x2 þ x3 þ x4 þ x5 þ x6 þ x7 þ x8 þ x9 þ x10Þ þd14d þ

14 ¼ 70 (Packaging utility)

0:025ð x1 þ x2 þ x3 þ x4 þ x5 þ x6 þ x7 þ x8 þ x9 þ x10Þ þd15dþ

15 ¼ 35 (Quality cheque utility)

Goals proposed by the scheduling unit

0:1 x1 þ0:2 x4 þ0:1 x7 þ0:2 x10 þd16d þ

16 ¼ 175 (Swaging machine group 1 capacity)

0:1 x2 þ0:1 x5 þ0:15 x8 þd17dþ


0:15 x3 þ0:2 x6 þ0:15 x9 þd18d þ


Constraints


þ22 x9 þ26 x10Z10,000

(Profit-break-even point)

x1 þ x4 þ x7 þ x10r650 (Needle type 1 constraint)

x2 þ x5 þ x8r500 (Needle type 2 constraint)

x3 þ x6 þ x9r350 (Needle type 3 constraint)

20ð x1 þ x2 þ x3Þr10,000 (Thread type 1 constraint)

20ð x4 þ x6Þr3500 (Thread type 2 constraint)

20ð x7 þ x8 þ x9Þr6500 (Thread type 3 constraint)

20ð x5 þ x10Þr10,000 (Thread type 4 constraint)

In this model, the undesired deviations for the functional goals are

d1 , d

2 , d3 , d

4 , d5 , d

6 , d7 , d

8 , d9 , d

10,d11,d þ

12,dþ13,d þ

14,d þ15,d þ

16,dþ17,d þ

18


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According to the IMGP algorithm, firstly the payoff matrixmust be calculated to determine the boundaries of the solution

when the meta-goals are optimised one at a time. The payoff

matrix result is summarised in Table 3. The decision-makers use

this solution as a guide to prioritise the existing goal functions

and evaluate the tradeoffs between the outputs obtained from

different priority schemes. Realistically, the decision variables

denote quantity, and thus should be calculated in terms of

integers. To simply the evaluation process however, non-integer

values are used, and the final results are rounded off to the

nearest integers.

Based on the payoff matrix solution, the decision-makers can

analyse and compare different decision outcomes by suggesting

their preference on the degree of meta-goal achievements.

As an example, if a decision-maker has assigned all of the goal

functions to the same priority level, and suggested the meta-goal

achievements as listed in points below, the solution of this meta-

goal model is summarised in Table 4

i. Aggregate undesired deviations of all the goal functions

cannot be more than 0.3.

ii. Maximum undesired deviations of all the goal functions

cannot be more than 0.07.

iii. Number of unattained goals cannot be more than 5.

Table 2

Goal targets and hard constraints.

Equation type Description Target/constraint value Undesired deviations

Goals Production target for suture type 1 200 (box of dozen) Slack

Production target for suture type 2 150 (box of dozen) Slack




Production target for suture type 6 50 (box of dozen) SlackProduction target for suture type 7 90 (box of dozen) Slack




Profit achievable from the current production plan 30,000 ($) Slack

Budget utilisation for the current production plan 50,000 ($) Surplus

Total swaging capacity for the current production plan 350 (h) Surplus

Packaging capacity for the current production plan 70 (h) Surplus

Quality cheque capacity for the current production plan 35 (h) Surplus

Group 1 swaging machine capacity 175 (h) Surplus



Constraints Profit-break-even point that the manufacturer must achieve ¼ 10,000 ($) –

Stock availability for needle type 1 ¼ 650 (unit) –



Stock availability for thread type 1 ¼ 10,000 (cm) –Stock availability for thread type 2 ¼ 3500 (cm) –

Stock availability for thread type 3 ¼ 6500 (cm) –

Stock availability for thread type 4 ¼ 10,000 (cm) –

‘‘–’’: empty fields imply choice of slack or surplus deviation is not applicable, as hard constraint equations must be strictly satisfied.

Table 3

Meta-goal payoff matrix solution.

Objective Achieved score Decision-

makers’

preference

Aggregate

undesireddeviations

Maximum

undesireddeviation

Number of

unattainedgoals

Aggregate

undesired

deviations

0.24 0.1 4 ¼0.3

Maximum

undesired

deviation

0.471 0.057 9 ¼0.07

Number of

unattained

goals

0.645 0.323 2 ¼5

Table 4

Solution for single priority level model.

Goal functions Value

Meta-goals

Aggregate undesired deviations 0.3

Maximum deviation 0.076Number of unattained goals 5

Goals

Produ ct ion q uant it y f or s ut ur e ty pe 1 1 85 ( box of d ozen)




Produ ct ion q uant it y f or s ut ur e ty pe 5 2 30 ( Box of Dozen )

Produ ct ion q uant it y f or s ut ur e ty pe 6 5 0 ( box of dozen)

Produ ct ion q uant it y f or s ut ur e ty pe 7 9 0 ( box of dozen)

Produ ct ion q uant it y f or s ut ur e ty pe 8 8 5 ( box of d ozen )


Produ ct ion q uant it y f or s ut ur e ty pe 1 0 2 55 ( box of d ozen)

Profit achievable 32,594.65 ($)

Budget utility 36,472.64 ($)

Swaging utility 207.48 (h)

Packaging utility 58.29 (h)

Quality cheque utility 36.43 (h)Machine group 1 capacity 102.52 (h)

Machine group 2 capacity 49.97 (h)

Machine group 3 capacity 55 (h)

Underlined goals imply targets not met.


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So far in this example, the analysis focused only on the goal

priority preference set by a single decision-maker. To incorporate

multiple preferences, the CDSM suggests that Delphi Process should

be activated to deduce a consensus priority preference scheme that

can be used towards the IMGP analysis. Furthermore, through Delphi

Process, the goal attainment preferences proposed by different

functional units can be updated accordingly in order to achieve better

decision outcomes. For example, the analysis outcome of the example

presented here indicates that the quality checking capacity is the

bottleneck of the manufacturing process, which negatively affects the

fulfilment of production quota. To improve this situation, the quality-

checking unit is to be consulted on the ability to increase its output

capacity. Once an updated quality-checking capacity is obtained, the

corresponding payoff matrix of the new model can be calculated and

that meta-goal targets can be adjusted accordingly. In this approach,

the Meta-Goal Programming process and Delphi Process are iterated

until a solution that satisfies all associated decision-makers is found.

Below, we describe a possible refined meta-goal analysis model

that follows the Delphi Process. The goal functions are allocated into

four priority levels, and that goal functions per priority level are

analysed by a unique meta-goal such as described in the following

points. This IMGP model can be easily evaluated using What’sBest!

Plug-in, but the solution is not included here.

Priority level 1: Finance unit demands, the profit and

budget goals are to be in this priority.

These goals are also closely monitored and

supported by the enterprise’s high-level

management.

Priority level 2: The target defined by the sales and

marketing unit is considered as the second

priority to ensure customer satisfaction.

Priority level 3: Production, packaging, and quality goals

are in this priority level.

Priority level 4: The production-scheduling unit and

machine group formation that minimises

machine re-setup are placed in the lowest

priority while satisfying the meta-goals of higher priority levels.

5.3. Results, outcomes, and performance

The feasibility of the conceptual design for the GDSS was

analysed based on the results of our study. Our analysis have

identified that our CDSM has the following strengths:

i. Data, information, and knowledge are shared via the WSA

based interoperability framework, which is highly efficient in

establishing system integrations on the application-to-

application level.

ii. Data, information, and knowledge can be acquired from

stand-alone systems within the CMN at timely manner, thusdecision-makers are well supported towards making informed

decisions.

iii. Decision-making tools can be developed individually and

incorporated to the GDSS to further enhance the capabilities

of the CDSM.

iv. Knowledge coherence is achieved, since the business objec-

tives of the CMN are transparent to all participants of the

network.

v. The CDSM ensures that all decision-making activities are

appropriately orchestrated and synchronised toward the

achievement of optimal decisions.

vi. The GDSS is highly adaptable as new data, information, and

knowledge can be incorporated into the system at any time

to support new management requirements.

vii. The local desired goals are collected and analysed by the

IMGP model, and it ensures that the actual final decisions can

be practically fulfilled across all functional units under the

current manufacturing environment, and that the decision is

close to global optimal as much as possible.

viii. The meta-goal approach enables decision-makers to effi-

ciently analyse and compare alternate solutions, and identify

which manufacturing resources may be adjusted in order to

improve the overall outcome.ix. The Delphi Method provides a mediating environment for

decision-makers to quickly identify issues that prevent

decision consensus, and hence management resources could

be focused on eliminating those issues.

6. Conclusion

This paper analysed current business environment within a

CMN setting in considering for the perspectives of a participating

S/MME, and argued that a GDSS is necessary to enable the

manufacturing oriented S/ME in near optimal decision-making

within the CMN. A conceptual design of the GDSS is provided in

this paper as a key contribution. In our approach, firstly, Microsoft

.Net WSA is selected to design the system architecture for theGDSS. Fundamentally established on the existing Internet proto-

cols and other cross-platform standards, the WSA delivers max-

imum information system interoperability on all levels ranging

from a standalone simply application to a suite of software.

Furthermore, the WSA-based GDSS design ensures that maximum

system scalability and re-useability are readily achieved.

Secondly, a CDSM is proposed to guide the process of optimised

decision-making within the CMN. The model enables different

functional units to analyse the decision under consideration

separately and propose goals that are in favour of their respective

functional units’ performance. These goals are collected and then

analysed using the IMGP approach, which allows decision-makers

to concisely interact during the process of reaching a solution and

quickly converge to a solution acceptable to all the decision-

makers. The conceptual design of the GDSS is justified by a

simulated case study on an Australian manufacturer. In our study,

we have found that the GDSS is capable of integrating the existing

distributed information systems within the local manufacturer

and its CMN. This enables the managers of the local manufacturer

to efficiently conduct collaborative decision-making activities in

relation to other participants of the CMN. In addition, the GDSS

enables an S/MME to realise the benefits of a CMN, without

extensive alteration to their existing computer network architec-

ture and software systems. The next step of our research project is

to model other decision-making processes in compliance with the

CDSM for the core BPs. Example of these BPs for sustainability in

manufacturing include collaborative product design where design

objectives of individual functional units are considered at the

CMN level, and production order allocation problem whereinteger-based Meta-Goal Programming process is used to opti-

mally distribute production orders amongst a group of manufac-

turers. Thus, the GDSS establishes a fundamental platform for

future applications of decision-support approaches that can be

continuously added in supporting the dynamic management

processes for achieving sustainability in manufacturing in a CMN.

Acknowledgement

Authors acknowledge the funding provided by Australian

Research Council for this research project, the support provided

by industry partner, Dynek Pty Ltd., South Australia, Australia,


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and the review comments for this article by Mr. B. Crook,

Managing director of Dynek Pty Ltd.

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http://www.agimo.gov.au/publications/2003/08/framework


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http://www.lindo.com/

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http://www.lindo.com/










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