Business Intelligence And Supply Chain Agility

Description
Business Intelligence And Supply Chain Agility

Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
BUSINESS INTELLIGENCE AND SUPPLY CHAIN AGILITY
Mohammad Moniruzzaman
Department of Computing and Information Systems
The University of Melbourne
Parkville, Victoria, Australia
Email: [email protected]
Sherah Kurnia
Department of Computing and Information Systems
The University of Melbourne
Parkville, Victoria, Australia
Email: [email protected]
Alison Parkes
Department of Computing and Information Systems | Taylor’s Business School
The University of Melbourne | Taylor’s University
Parkville, Victoria, Australia | Selangor, Malaysia
Email: [email protected]
Sean B. Maynard
Department of Computing and Information Systems
The University of Melbourne
Parkville, Victoria, Australia
Email: [email protected]
Abstract
Supply Chain Agility is vital for organisations wanting to remain competitive in today’s dynamic
business environment. There is increasing interest in deploying Business Intelligence (BI) in the
Supply Chain Management (SCM) context to improve Supply Chain (SC) agility. However, there is
limited research exploring BI contributions to SC agility. In this research-in-progress paper we
propose a model based on a conceptual analysis of the literature showing how BI can help
organisations achieve SC Agility by supporting the key areas of SCM (plan, source, make, deliver, and
return). In the next stage of this project, we will conduct a series of case studies investigating how
organisations use BI when managing their SC activities and how BI contributes to SC Agility. The
result of the study will help organizations deploy BI effectively to support SCM and improve SC agility.
Keywords: Business Intelligence, Agility, Resource-based View, Supply Chain, Supply Chain
Management
1 INTRODUCTION
A single organization is unlikely to have the full capability of bringing products or services to the
market to meet changing customer demands. Organizations are frequently forced to form a network to
work collectively to meet customer demands. Christopher (1999) refers to such a collective network as
a Supply Chain (SC), which is responsible for the planning and management of all activities involved
in acquisition, conversion, and delivery of products/services to consumers. SC participants need to
work closely together to meet consumer demands rather than competing against each other.
Therefore, the nature of competition has shifted from organization against organization to Supply
Chain against Supply Chain (Ketchen et al. 2007). In today’s dynamic environment, flexibility is
critical in managing SC so that organisations can deal with market changes and convert these changes
into opportunities (Agarwal et al. 2007). The concept of Agile SC has been introduced to define the SC
capability that enables an organization to respond to unpredictable changes and uncertainties in
dynamic business environment. Achieving SC agility is challenging and has become a research topic of
increasing interest over the last decade.
Whilst some research exists (Swafford et al. 2008; Wu et al. 2006; Yusuf et al. 2014a) that investigates
the contributions of IT tools in achieving agility in SC, how agility in SC can actually be achieved by
organisations through the use of IT is still unclear. As most studies explore IT holistically, not enough
evidence of the utility of specific IT components is provided. In particular, studies assessing the
contribution of Business Intelligence (BI) in helping organisations achieve SC agility are lacking.
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Furthermore, the majority of studies (Christopher 2000; Yusuf et al. 2004) focus on either ‘agility
enablers’ or partial aspects of agility in SC to consider IT contributions, whereas agility is a complex
concept with multiple dimensions. This level of complexity requires a thorough study of all aspects of
agility to enhance the current understanding. In most research SCM is being considered only at a high
level instead of addressing the key areas of planning, sourcing, making, delivering and returning
goods. Achieving agility requires integrated management of all aspects of SC activities, a complete
understanding of BI contributions can only be obtained by considering all aspects of SCM in one
study. Furthermore, while some studies investigate the role of BI in the context of SCM performance
(Oliveira et al. 2012; Trkman et al. 2010a), none has assessed BI contributions to Supply Chain Agility.
Finally, most existing studies report survey data which limits the richness and understanding of how
BI can contribute to SC agility. Additional rich case studies would improve current understanding of
this complex and important area.
To address current knowledge gaps this study explores the contributions of BI use in SCM towards
achieving agility in SC. It examines the use of BI in SCM key areas and explores whether this BI use
can help improve agility in SC. The overall research question for this study is:
How does BI-enabled Supply Chain Management improve Supply Chain Agility?
In this research-in-progress paper, we review the literature in the areas of Supply Chain Management
and Business Intelligence to identify how BI can potentially contribute to SC agility. We adopt a
Resource Based View and use Organizational Information Processing Theory to conceptualize possible
mechanisms to achieve SC agility; we build in these foundations and present a conceptual research
model and propositions.
The outline of this paper is as follows. In the next section, we explain our key concepts and establish
the theoretical basis of the work. Next we present our conceptual research model and propositions. To
conclude we outline the next steps of the study and identify the expected contributions of this
research.
2 BACKGROUND
This section reviews three key areas of literature: Supply Chain Management, Supply Chain Agility,
and business intelligence. It then presents literature that has discussed SC agility and SCM, before
presenting the identified knowledge gaps in the literature.
2.1 Supply Chain Management
New et al. (1995) defines Supply Chain as a collection of organizations participating in a network to
ultimately meet market demands and illustrates the SC cycle as depicted in Figure 1. A typical Supply
Chain activity starts from raw material extractions from nature to produce raw materials for
component manufacturer. Final product manufacturer produces final product using components and
wholesale to retailer to ultimately deliver to final consumers. Distribution and warehousing functions
support the whole chain in appropriate stages.
The
Earth
Mineral/
Raw material
Extractions
Raw Material
Manufacturers
Component
Manufacturer
Final Product
Manufacturer
Warehouses Retailers
Final
Consumer
Recycling
Physical Distribution &
warehousing

Figure 1: Supply Chain processes (New et al. 1995)
There are various business processes within a typical Supply Chain that share the common objective
of satisfying changing customer demand. Therefore, the concept of Supply Chain Management (SCM)
has been introduced since 1990s (Lambert et al. 1998). SCM involves a collection of integrated
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
processes responsible for the acquisition of raw material, production and distribution of final
products/services to satisfy end user need (Houlihan 1985; Mentzer et al. 2001). Based on the Supply
Chain Operations Reference (SCOR) model, supply chain processes can be categorized into five
groups; Plan, Source, Make, Deliver and Return (Apics 2015). Business processes across these five
areas need to be coordinated and managed to ensure products and information flow efficiently within
Supply Chains. Within the context of SCM, IT contributions have been well acknowledged to help
organisations improve information flow and coordinate various activities efficiently and effectively for
the benefits of all Supply Chain parties (Kurnia et al. 2001; Simchi-Levi et al. 2008).
2.2 Agility in SC
SC agility is the ability of an organization to reconfigure, adjust, and change resources for the key SC
processes (Plan, Source, Make, Deliver and Return) to satisfy the demand of a changing environment
(Agarwal et al. 2007; Lin et al. 2006a; Yusuf et al. 2014a). Extant research (Braunscheidel et al. 2009;
Christopher 2000; Giachetti et al. 2003; Lin et al. 2006a; Lin et al. 2006b; Xun et al. 2009) identifies
four key elements of SC agility: Flexibility, Responsiveness, Competency and Quickness, these are now
described in detail.
Flexibility is the ability of a SC to cope with change. Christopher (2000) identified flexibility as an
important feature of agility that is needed to fulfil highly uncertain and varied market demand.
Flexibility describes the SC’s ability to cope with both certain and uncertain changes by reconfiguring
resources and processes to meet changes in customer demands (Braunscheidel et al. 2009; Gong et al.
2012; Xun et al. 2009).
Responsiveness refers to the SC’s ability to respond to a dynamic market. Unexpected demand
requires a quick assessment of organization capabilities to determine if demand can be meet and
provide feedback to consumers within short timeframes (Xun et al. 2009). An integrated view of the
overall supply status is critical to be able to respond to customers in a timely manner (Braunscheidel
et al. 2009; Qrunfleh et al. 2013).
Competency is the SC’s ability to achieve its goals. Ngai et al. (2011) identify 3 components of SC
competence which include: “IT Integration and flexibility”, “operational competence” and
“management competence”. IT Integration facilitates information flows within SC to forecast change
and address change (Zhang et al. 2013). Operational Competence refers to the ability of a SC to utilize
its resources to deliver according to market demand (Braunscheidel et al. 2009). Finally, Management
Competence refers to the role and vision of top management and the level of employee skills.
Quickness is the ability of a SC to execute activities within the shortest possible timeframe. The SC
needs to continuously adjust and restructure its relevant processes and strategies to respond quickly
to market changes (Agarwal et al. 2007). Increasing sophistication in customer preferences means
highly customized products are often required (Chen et al. 2011a; Power et al. 2001) so a SC needs to
be able to deliver customized products within short lead times without compromising quality or cost
(Lin et al. 2006a; Ngai et al. 2011; Stratton et al. 2003).
Through this study, we explore how BI can support SCM practices and achieve the above four
characteristics of Supply Chain Agility.
2.3 The potential of Business Intelligence (BI)
Isik et al. (2011) defines Business Intelligence (BI) as a mix of business and technical elements that
pull historical data from internal/external sources and transform it into meaningful information for
management decision making. Business use of BI refers to a tool that analyse data and support
decision making (Trkman et al. 2010b) while the technology aspects deal with how required BI
infrastructure can be made available. This study considers BI as a query and reporting tool that is
relevant for decision making in dynamic market environment.
Researchers identify three major uses of BI. First, BI integrates a wide range of data into a single
repository, providing a single enterprise-wide view via management dashboards and reports. (Long et
al. 2012). Second, BI transforms data into information that is valuable for managers to evaluate
options (Acar et al. 2010). Third, BI delivers the capacity to identify the root cause of problems by
providing information drill down (Hocevar et al. 2010).
BI provides insights, based on data analysis, to assist managers in decision making within highly
changing environments (Isik et al. 2013). Shanks et al. (2010b) established that BI use in an
organization assists managers in taking value creating actions by relying on facts or information
provided by BI query and reporting. Although there have been some studies assessing the impact of BI
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
use in SC performance (Trkman et al. 2010a), there has been limited systematic and detailed research
showing how BI assists organizations in Supply Chain Management.
2.4 Existing studies related to BI, SCM and SC Agility
This section synthesizes relevant literature and analyses gaps in the existing literature. To find
existing literature around agile SC, we used Discovery and Google Scholar, searching using different
combinations of keywords: “Agile Supply Chain”, “Information Technology”, “Business Analytics” and
“Business Intelligence”. Our search identified 357 papers mentioning Agile SCM. We also explored
related literature using forward citations in the key papers. We read through the titles and abstracts of
all papers to identify those papers that discuss IT/BI contributions in agile SC, resulting in twenty
papers (Table 1).
Table 1 summarizes five specific aspects addressed in the shortlisted prior studies in the area of agile
SC. None of the 20 papers summarised in table 1 address all of key concepts of Business Intelligence,
Supply Chain Management, and Supply Chain Agility. Five of the papers address at least one of the
four SC agility characteristics, while another two consider at least one of the supply chain
management key process areas. Four papers consider the relationship between one or more of the
SCM key process areas and SC agility characteristics, another four papers consider relationships
between one or more of the SC management key process areas and Business Intelligence. The
remaining five papers are included as they contain general perspectives on the relationship between
SC agility and SC management. However, none of these papers specifically considers Business
Intelligence.
Relevant
Literature
Business
Intelligence
focus
SCM areas SC Agility
Research
Method
Location
(Ngai et al.
2011)
Not BI specific SC in general Flexibility,
Competence
Multiple case
studies
Hong Kong
Findings: Considered IT as an important component of SC Agility. SC Agility
positively impacts organization’s performance.
(Swafford et
al. 2008)
Not BI specific SC in general Flexibility,
Quickness
Survey USA
Findings: Positive role of IT integration in SC Flexibility. SC Agility directly
improves competitive performance.
(Liu et al.
2013)
Not BI specific SC in general Responsiveness,
integration
competency
Survey China
Findings: IT capability affects organization’s performance through impacting
“absorption capability and SC Agility”.
(Cheng et al.
2011)
Partial Make Competence Survey Hong Kong
Findings: IT integration positively impacts organization’s performance.
(Chakraborty
et al. 2011)
Not BI specific SC in general Flexibility,
Responsiveness
Survey India
Findings: Identified 7 key factors of SC Agility: Technology, partnership, quality,
education, market, competence, and team building.
(Dowlatshahi
et al. 2006)
Not BI specific Make Agility enablers Survey USA
Findings: As enabler of Agile manufacturing, both Virtual enterprise and IT
positively impact organization performance.
(Hasan et al.
2012)
BI focus Make Agility enablers Single Case
Study
India
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Relevant
Literature
Business
Intelligence
focus
SCM areas SC Agility
Research
Method
Location
Findings: Positive contribution of Analytical Network Processing in Agility in
Production.
(Wu et al.
2006)
Not BI specific SC In general Agility in general Survey USA
Findings: Positive Impact of IT in SC Agility and organization performance.
Yusuf et al.
(2014b)
Not BI specific SC in general Agility enablers Survey UK
Findings: Identified dimensions and attributes of Agile SC.
Yusuf et al.
(2014a)
Not BI specific SC in general Agility enablers survey USA
Findings: Positive role of cluster strategy in Agility
Qrunfleh et
al. (2013)
Not BI specific Sourcing Responsiveness Survey USA
Findings: SC responsiveness impact organization’s performance
Bottani
(2010)
Not BI specific SC in general Agility enablers Survey Europe
Findings: 8 Agile attributes and 4 Agile enablers has been identified
Trkman et al.
(2010a)
BI focus Plan, Source,
Make,
Deliver
N/A Survey USA, Europe,
Canada, Brazil,
and China.
Findings: Positive impacts of analytic type of BI in SCM with mediating impact from
information systems and process orientation.
Oliveira et al.
(2012)
BI focus Plan, Source,
Make,
Deliver
N/A Survey World-wide
Findings: Impact of analytic type of BI in SCM is influenced by the business process
maturity of an organization.
Yusof et al.
(2013)
BI focus Make N/A Literature
review
N/A
Findings: Affirms positive contribution of BI in manufacturing processes.
(Braunscheid
el et al. 2009)
Not BI specific SC in general Responsiveness
Competency
Flexibility
Survey USA
Findings: Beside flexibility, integration among participants in a supply chain plays
vital role in achieving Agility in SC.
(Xun et al.
2009)
Not BI specific SC in general Dimensions of
SC Agility
Survey USA
Findings: Identified six dimensions to measure SC Agility: strategic alertness,
strategic response capability, operational alertness, operational response capability,
episodic alertness and episodic response capability.
(Chen et al.
2011b)
Not BI specific Return,
Deliver
N/A Analytic
model
N/A
Findings: Return can be managed through Supply Chain configuration considering
manufacturer and retailer agree on buy back prices for unsold inventory and customer
return.
(Xiao et al.
2010)
Not BI specific Return,
Deliver
Responsiveness,
flexibility
Analytic
model
N/A
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Relevant
Literature
Business
Intelligence
focus
SCM areas SC Agility
Research
Method
Location
Findings: Refund amount derived from return plays an important role in decision
making and profitability of SC participant.
(de Brito et
al. 2009)
Not BI specific Return,
sourcing
Flexibility,
responsiveness
Analytic
model
N/A
Findings: Information quality plays critical role in return forecast using return
information system and inventory control.
Table 1: Summary of selected studies on SCM and SC Agility
2.5 Research Gaps
Based on the literature analysis and synthesis conducted, five research gaps have been identified in
the existing studies around BI/IT, SCM and SC Agility.
First, an analysis of coverage of the four Supply Chain Agility characteristics (Flexibility,
Responsiveness, Competence, and Quickness) showed that 11 papers did not address these specific
characteristics, focusing instead on agility enablers (seven papers) or not considering agility (four
papers). Of the nine papers which addressed the supply chain agility characteristics none considered
all four. Two papers looked at only one characteristic, six papers considered two, and one paper
considered three of these four dimensions. Coverage of the characteristics was also uneven with six
papers considering Flexibility and six including Responsiveness. Four papers considered competence
while only one paper looked at Quickness. We argue that to understand contributions of SC agility on
competitive advantage in the market, a complete study focusing on all key characteristics of agile SC
need to be considered (Yusuf et al. 2014a).
Second, considering the Supply Chain Management key process areas (Plan, Source, Make, Deliver
and Return) 10 of the 20 papers did not address any of these key process areas, concentrating instead
on more generic views of SCM. Of the 10 papers which did address these key process areas none
considered all five areas. Five papers considered only one process area, three papers considered two
process areas, and the remaining two papers considered four of the five process areas. In terms of
coverage of each process area two papers considered Plan, four included Source, 6 considered Make,
four included Deliver and three involved return. To achieve agility, end to end SC needs to perform in
an integrated way. Hence, the role of all key SCM process areas need be considered in SC agility
related research (Huang et al. 2009; Lin et al. 2006a).
Third, Business Intelligence coverage was similarly sparse with only four papers focusing directly on
BI and one paper partially addressing it. The remaining 15 papers did not consider business
intelligence specifically instead they looked at information technology generically. Again, 5 papers
invested the role of BI in the context of Supply Chain Management, none of them has assessed BI
contributions to the achievement of Agile Supply Chain. Trkman et al. (2010a), for example, studied
SC operations in different organizations and found that Business Intelligence contributes to SC
performance with moderating effect from Information systems and business processes. However,
Trkman et al. (2010a) did not relate BI with SC agility components. On the other hand, de Brito et al.
(2009) focused on return forecasting and acknowledged the value of information in managing the
Return process. However, their research didn’t include BI as a potential tool to support return data
analysis. Xiao et al. (2010) and Chen et al. (2011b) recognise that SC Flexibility and Responsiveness
can be achieved through the coordination between the manufacturer and the retailer with respect to
managing risk due to returns. However, neither study acknowledged that the analysis of the integrated
view of return historical data analysis could be delivered by a BI tool (Davenport 2006; Sahay et al.
2008).
Fourth, a wide range of research methods were used in the paper reviewed. Fourteen papers used
surveys, one paper was not empirical, and three used analytical modelling techniques. The remaining
two papers contain case studies, one paper analysed multiple cases and the other contained a single
case. Most of the studies employ a survey approach with limited studies using a qualitative approach.
Though a survey helps researchers to have data from a number of different organizations, the data
may not provide a complete understanding about SC agility as the concept of agility in SC is relatively
new and perception based (Braunscheidel et al. 2009). On the other hand, case studies allow for in-
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
depth understanding to be obtained regarding how BI contributes to the achievement of SC agility
through direct interactions with subject organization resources (Yin 2011). Moreover, a case study is
the most appropriate method for how and why type of research questions (Yin 2013).
Fifth, as shown in Table 1, most of the studies are based on data from the USA, China, or India. Little
work has been done in Australian or New Zealand contexts. We argue that due to the geographic
location of Australia/New Zealand, the distance to global suppliers, logistic costs and lead time are
different to other parts of the world. Therefore, how BI can contribute in SC agility in an Australian or
New Zealand context may be different to other countries.
Based on the knowledge gaps identified, our study aims to specifically explore how BI creates SC
agility by considering the use of BI in the main areas of SCM. To gain an in-depth understanding, we
will conduct a qualitative study involving multiple case study organisations in the Australian and New
Zealand region.
3 Theoretical foundation
The Resource Based View (RBV) offers an appropriate lens to this study to understand how BI
supported SCM can improve supply chain agility. The effectiveness of BI also depends on how BI is
used within SCM processes. Organization information processing theory (OIPT) shows that
information resources can be implemented in an organization design to deal with uncertainty. Thus
RBV and OIPT will be used to build a solid framework for this study to explore how BI enabled SCM
can influence agility in SC. Each theory is explained in more detail in following subsections.
3.1 Resource Based View (RBV)
The RBV considers organizations’ assets, processes, information, know-how, capabilities, etc. as
resources. These resources contribute to competitive advantage if they possess two attributes:
resource heterogeneity and resource immobility (Barney 1991). Barney (1991) explains heterogeneity
attribute of organization’s resource(s) using two terms: valuable and rare. Valuable attribute of a
resource refers to the ability to outperform competitors or reduce organization’s own weaknesses.
Rare refers to exceptional or uncommon feature(s) of a resource so that competitor can’t conceive the
same. Valuable and rare resources of an organization can be a source of competitive advantage as long
as competitors are unable to obtain such resources. Barney (1991) refers to this condition as
‘Organization Resource Immobility’ which has two aspects of resources: in-imitable and Non-
substitutable.
A number of studies confirm that BI capability is a valuable resource for organizations, as BI can
improve organizational performance (Davenport 2006; Sahay et al. 2008; Trkman et al. 2010a). BI
capability embedded in business processes can become a rare capability of an organization, as
organization specific processes are not easily available for competitors in the industry (Barney 2001;
Isik et al. 2013). BI embedded business processes are hard for other organizations to duplicate as they
are specific to the culture and management skill set of an organization. This complies with the in-
imitable requirement of a resource as suggested by RBV. Thus, with the support from RBV theory, this
study argues that BI can be a Valuable, Rare, In-imitable and Non-substitutable resource for an
organization, particularly in SC functions. Davenport (2006) further argues that organizations using
BI may experience a performance increase, but not necessarily achieve competitive advantage.
Therefore, it is important to explore how BI can be effectively used in business processes to gain
competitive advantage in the market.
3.2 Organization Information Processing Theory (OIPT)
OIPT was proposed by Galbraith (1973) in the context of organizational design strategy. OIPT explains
the linkage between information as resource and the use of information resources as an important
factor for organization performance (Fairbank et al. 2006). In the course of organization design,
Galbraith (1973) views organization as a collection of tasks and subtasks that need to be integrated to
the whole tasks to ensure organization performance. Task owners within organizational hierarchy
need to deal with ‘task uncertainty’ due to the novelty of tasks involved or inadequate knowledge to
perform the tasks (Tatikonda et al. 2000). To deal with task uncertainty Galbraith (1973) proposed
two sets of non-mutually exclusive strategies: (1) reducing the need for information processing and (2)
increasing the capacity to process information. An example of the first strategy is to invest in buffer
stock to deal with demand/supply uncertainty in Supply Chain (Premkumar et al. 2005). An example
of the second strategy is to build BI capability to make relevant information available to task
performer so that appropriate decision can be made to deal with task uncertainty.
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
IS research provides empirical evidence in favour of the increased information processing strategy
suggested by OIPT. Fairbank et al. (2006) conducted a longitudinal research study within the U.S
life/health insurance industry. Based on 403 responses, they found that organizations which using
information processing can perform better than compitotors in the market. Premkumar et al. (2005)
conducted a study involving 169 managers from publicly traded organizations finding that ‘Supply
Chain Intelligence’ and ‘Marketing Intelligence’ play a critical role in the organization’s performance
in new product development.
Information processing need is correlated with the level of task uncertainty. If planned targets are not
met at a management function level then additional decisions may be required at higher levels of the
management hierarchy. Hence, more cross functional information is required to be processed at the
higher level of hierarchy. Thus OIPT proposes that organizations need to be “structured around
information and information flows in an effort to reduce uncertainty” (Fairbank et al. 2006). Based on
our understanding of the OIPT, we argue that a BI capability needs to exist in each task / subtask /
hierarchy of SCM processes so that the required data is available to respective task owners to make
decisions and optimize performance. Such implementation of BI capability may offer respective
organization a unique bundle of resources that are in-imitable and un-substitutable.
4 RESEARCH MODEL AND PROPOSITION DEVELOPMENT
In this section we present a research model and propositions, based on RBV theory and the OIPT,
showing how BI can be used within the five areas of SCM to improve SC agility. SC Agility is a
capability considered as a strategic resource to help an organization achieve competitive advantage
(Christopher 1999; Ivanov 2010; Qrunfleh et al. 2013; Stratton et al. 2003; Yusuf et al. 2014a). Our
conceptual research model is based on the theoretical foundations of RBV and OIPT as discussed
previously. Figure 2 shows our proposed research model and Table 2 provides the definition of each
construct in the model:
Supply Chain
Management
BI Capability
Agility in
Supply Chain
Flexibility
Responsiveness
Quickness
Competency
Plan
Source
Make
Deliver
Return
Reduces task
uncertainty in
Leads to
Figure 2- Conceptual research model
In the conceptual research model, BI is considered as a capability of an organization while SCM
activities are considered as organization processes. Applying the RBV, this model posits that BI can be
integrated in SCM key process areas such as Plan, Source, Make Deliver, and Return to form a
resource bundle which can support Supply Chain managers in decision making. BI supported SCM
potentially can help organizations in achieving another capability - SC Agility, noting that integration
of BI may have many forms. With the support from OIPT, we argue that BI can be integrated in SCM
processes according to information need and ‘task uncertainty’ involved at different levels of
organization hierarchy.
Definition References
BI Capability: The ability of an organization to
integrate multiple data sources and present
summarized information providing insight to users. BI
capability also supports what-if analysis and predictive
analysis based on historical data patterns.
(Davenport 2006; Isik et al. 2011;
Ramakrishnan et al. 2012; Ranjan 2008;
Sahay et al. 2008; Sharma et al. 2010).
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Definition References
Supply Chain Management: A set of integrated
processes that is responsible for acquisition of raw
material, production, and distribution of final
products/services to satisfy end user need.
(Christopher 1999; Mentzer et al. 2001; Soni
et al. 2013; Zhou et al. 2011)
- Plan: A SCM function that deals with preparing
actions for effective source, make, deliver and
return processes.
(Danese 2011; Gupta et al. 2003; Ivanov et al.
2012)
- SC Source: A SCM function that acquires
resources required for the ‘make’ processes.
(Christopher 1999; Tan 2001; Zhou et al.
2011)
- SC Make: A SCM function that transforms raw
materials into product to satisfy market
demand.
(Cagliano et al. 2004; Vinodh et al. 2013;
Yusof et al. 2013; Yusuf et al. 1999)
- SC Deliver: A SCM function that hands over
ordered products to customers.
(Christopher 1999; K et al. 2009; Yang 2013)
- SC Return: A SCM function that collects used or
defective products from customers.
(Chen et al. 2011b; de Brito et al. 2009; Xiao
et al. 2010; Yang 2013; Zhou et al. 2011)
Agility in SC: The capability of a Supply Chain to
respond to dynamic market conditions with the
following four characteristics.
(Christopher 2000; Christopher et al. 2001;
Huang et al. 2009; Lin et al. 2006a; Lin et al.
2006b; Liu et al. 2013; Power et al. 2001;
Swafford et al. 2008; Yusuf et al. 2004)
- Flexibility: the capability to reconfigure
resources and processes quickly to satisfy
customer demand.
(Giachetti et al. 2003; Gong et al. 2012;
Swafford et al. 2008; Wadhwa et al. 2008;
Xun et al. 2009)
- Responsiveness: the capability to predict
uncertainty and respond before competitors.
(Braunscheidel et al. 2009; Power et al. 2001;
Yusuf et al. 2004; Yusuf et al. 1999)
- Quickness: the capability to perform an activity
within the shortest possible timeframe.
(Chakraborty et al. 2011; Christopher 1999;
Christopher et al. 2001; Liu et al. 2013)
- Competency: the capability to achieve SC goals. (Huang et al. 2009; Lin et al. 2006a; Naim et
al. 2011; Ngai et al. 2011)
Table 2: Key concepts definitions for the current study
BI has the potential to reduce task uncertainties by providing insights in the form of multidimensional
sales forecast / analysis reports, multidimensional delivery analysis reports, and cost analysis
reporting. This functionality supports make or buy decisions, demand forecast reporting to facilitate
production scheduling, and so on (Davenport 2006; Ranjan 2008). Next we explain how BI can be
used to support the key SCM process areas including Plan, Source, Make, Deliver, and Return.
The first SCM key area, ‘plan’, involves a series of actions for sourcing, making, delivery and return (Li
et al. 2011; Long et al. 2012). BI transforms existing data into insights for better planning in all key
SCM areas of an organization. In a dynamic market, organizations can assess possible impacts of
changing demand - supply conditions and then update existing planned actions based on “what-if
analysis” and analytical insights supported by BI (Zhou et al. 2011). BI can also report on historical
data such as market growth patterns, customer behaviour changes, and sales trends which help SC
managers to plan (Sharda et al. 2013). BI supported SCM planning enables managers to become more
proactive and efficient in fulfilling uncertain customer needs and thus gain agility in the SC (Shanks et
al. 2010a). SC managers can better respond to changes where those changes can be predicted in
advance. BI enabled SCM planning helps managers to be more responsive to market demand by
predicting uncertainties and reacting to them before their competitors (Yusuf et al. 2014a). BI enables
SC managers to plan the appropriate course of action to deal with change.
The second area of SCM is ‘source’. Sourcing involves make or buy decisions, supplier selection,
supplier performance evaluation, and inventory management (Luo et al. 2009). BI provides data to
managers to analyse supplier key performance indicators and inventory consumption trends during
sourcing decision making (Ranjan 2008). In a joint SC network scenario, BI offers integrated data
repositories and tools to present such data in a meaningful presentation format to visualize relevant
information across the network (Gessner et al. 2009). BI can also be used for ABC analysis and
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
inventory aging analysis to help managers achieve efficiencies in warehouse management. BI
supported sourcing helps managers to fulfil customer needs quicker than competitors. In a complex
SC network, participating organizations need to know required information about other participating
organizations (Power et al. 2001). For example, in order to satisfy a sudden bulk export order, a
manager would need to be able to access key information such as ready stocks on hand in finished
goods warehouse instantly. If no ready stocks are available, that manager would need to know the
stock on hand at the raw material supplier’s warehouse and the time required to source those raw
materials to determine time to market. Any delay in information availability will impact the SC’s
quickness (Xun et al. 2009). An integrated real time information dashboard is the key in this scenario.
BI can process data from different sources and provide visibility across the SC to respond to the
market and act quickly to source products to meet customer demand (Sahay et al. 2008).
The third SCM function, ’make’, transforms raw materials into products that meet market demand. (Li
et al. 2011). ‘Make’ processes deal with capacity management, production scheduling, material flow,
manufacturing, packaging and so forth. A central view of demand forecasts, raw material availability
and production capacity analysis supported by BI tools helps managers to be well prepared for large
scale production (Yusof et al. 2013). BI embedded ‘making’ supports an organization to become more
flexible and to be able to reconfigure resources easily to satisfy customized demands. Some scenarios
require flexible production technical flow options to increase productivity. BI can provide data
evidence about historical performance of various production configurations (Yusuf et al. 1999). Thus,
BI can offer insights for managers to derive and evaluate alternatives when producing goods for the
market (Zhang et al. 2013). Therefore, it is expected that the use of BI in this area of SCM improves
the ability of SC managers to be flexible and competent in responding to the unexpected changes in
the market (Isik et al. 2013).
The fourth SCM area, ‘delivery’ hands over finished products to the customer. Delivery manages
order processing, warehousing, transport management, delivery schedule, delivery time etc. As
suggested by Trkman et al. (2010a), there is a practice of outsourcing delivery activities to external
logistic companies which forces organizations to have tools which can monitor and analyse the
outsourcing provider’s performance. BI can help SC managers to track picking performance, shipping
performance and delivery time records of the third party logistics company to gain operational
efficiency and cost effectiveness (Davenport 2006). BI supported decisions help SC managers to attain
faster delivery time, delivery cost savings, flexibility and effectiveness (Schläfke et al. 2013). As
delivery is a tail ended SC process, any deficiency in delivery process impacts overall SC performance.
Thus, decisions supported by BI improves agility not only in the delivery process but also in the
overall SC.
The final SCM function, ‘return’ deals with used or defective products returned by customers (Chen et
al. 2011b). Lawton (2008) suggests that only 5% of total returns are due to product defects, the rest
being due to not meeting customer tastes or expectations. Thus customer taste and preference data
analysis can be a critical input for SCM decision making (Chen et al. 2011b; Xiao et al. 2010). BI can
support such complex analysis based on historical consumptions data in different markets to indicate
insights on how to reduce returns (Phan et al. 2010). Phillips managed to reduce its returns by $100
million per year by utilizing an integrated system that collects and presents data for operational
decision making for SCM managers (Tony 2003). Returns also influence the wholesale and retail
prices as loss due to return needs to be considered in the product pricing process (Chen et al. 2011b).
BI supported return forecast reporting can assist SCM managers in pricing decisions (Bose 2009) and
in formulating effective return policy in association with other parties within the SC (Ding et al.
2008). Return has significant impact on SC inventory management as there is significant uncertainty
about when returns occur and about the quantity of return stocks. Such uncertainty can be reduced by
analysing historical sales and return data (de Brito et al. 2009). BI can deliver return analysis reports
to relate production defects with customer satisfaction (Xiao et al. 2010). Product performance
analysis, consumer behaviour analysis, and return cost analysis are some examples of BI reporting
that assists managers to take corrective actions (Ranjan 2008). BI helps managers to monitor return
performance to plan any possible product recall from market (Xiao et al. 2010).
Based on the above, we conclude with the following propositions:
P1: BI capability reduces task uncertainty within the key areas of Supply Chain Management (Plan,
Source, Make, Deliver, and Return).
P2: Reduced task uncertainty improves decision making in the key areas of SCM which in turn
results in improved SC Agility
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2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
5 Research Methodology
The selection of an appropriate research method together with effective design is the key for data
collection to derive meaningful conclusion. The current study is exploratory in nature as we want to
explore different uses of BI in SCM key areas especially in a dynamic market context. Existing
empirical research related to BI use in business has adopted either case studies (Shanks et al. 2010a)
or surveys (Dong et al. 2009; Oliveira et al. 2012; Trkman et al. 2010a) as a research method. As
Neuman (2005) suggested, a survey is more related to quantitative research where a number of data
samples are collected to explain the relationship, while a case study is an in depth examination of one
or multiple units over the periods (Neuman 2005).
This study will adopt a multiple case study approach. Justification for such selection is derived from
the argument made by Yin (1994) who suggests that case study research is appropriate when “a ‘how’
or ‘why’ question is being asked about a contemporary set of events, over which the investigator has
little or no control” (Yin 1994). A case study is selected for this research as it is interested in exploring
how BI can be integrated in key SCM processes. This requires in depth study of relevant events in an
organization. A multiple case approach has been chosen to explore for patterns in events across
multiple case sites.
6 Conclusion and Future Study
Achieving an agile SC is challenging because of the complexity involved in SCM. In this paper, we
discuss the potential for BI use in SCM processes to assist managers in reducing task uncertainty, and
making more informed decisions which ultimately assist in achieving SC agility. Based on our review
of the existing literature, we identified a number of gaps in the current understanding regarding how
SC agility is achieved. Some existing studies found that IT can be instrumental in achieving SC agility.
However, IT has been considered in a broad sense in most of those studies. Arguably, different IT
components such as ERP, BI, Internet and so on may have different effects on SC agility. In addition,
most of the existing studies cover partial aspects of SCM processes and SC agility. Hence, a
comprehensive study is required focusing on specific IT tools, all key SCM processes and all key
components of SC agility.
A conceptual research model has been proposed in this paper to guide future studies in addressing
these identified research gaps. Based on RBV, this study argues that organizations can use BI
strategically to create valuable, rare, in-imitable and non-substitutable resources to manage the key
areas of SCM that will help achieve SC agility. OIPT has also been used in this study to support our
argument that BI can be used in SCM key processes to reduce task uncertainty. Drawing on RBV and
OIPT, the proposed research model conceptualises how a BI capability enables organizations to
reduce task uncertainties through improved decision making in SCM areas, which in turn leads to
improved SC agility. The model also offers an extended scope of study by including five key SCM areas
and four possible components of SC agility.
We believe that our proposed research model will help researchers and practitioners to extend the
existing knowledge of how SC agility can be achieved. It also highlights the importance of developing a
BI capability effectively to reduce task uncertainties in SCM processes and ultimately improve SC
agility.
The current study doesn’t consider the maturity of BI (Cosic et al. 2015), SCM enabler and
organizational influences in the research model. Relative effectiveness of BI use in SCM areas is also
not within the scope of this research. These can be considered as limitations of this study and an
opportunity for further research.
As next step, case study research involving multiple participants from five organisations will be
undertaken. Within each organisation we will interview six to eight managers involved in managing
their SC, examine the BI capability within each organization to support the five key areas of SCM and
review relevant documentations that are relevant to examine the improvement in each organization’s
SC agility. Through these in-depth case studies, we will identify mechanisms for effective BI use to
create organizational capability to achieve SC agility.
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2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility

7 References
Acar, Y., Kadipasaoglu, S., and Schipperijn, P. 2010. "A decision support framework for
global supply chain modelling: an assessment of the impact of demand, supply and
lead-time uncertainties on performance," International Journal of Production
Research (48:11), pp 3245-3268.
Agarwal, A., Shankar, R., and Tiwari, M. K. 2007. "Modeling agility of supply chain,"
Industrial Marketing Management (36:4), pp 443-457.
Apics 2015. "SCOR Framework - The APICS Supply Chain Council," Apics.org.
Barney, J. 1991. "Firm Resources and Sustained Competitive Advantage," Journal of
Management (17:1), pp 99-120.
Barney, J. 2001. "The resource-based view of the firm: Ten years after 1991," Journal of
Management (27:6), pp 625-641.
Bose, R. 2009. "Advanced analytics: opportunities and challenges," Industrial Management
& Data Systems (109:2), pp 155-172.
Bottani, E. 2010. "Profile and enablers of agile companies: An empirical investigation,"
International Journal of Production Economics (125:2), pp 251-261.
Braunscheidel, M. J., and Suresh, N. C. 2009. "The organizational antecedents of a firm’s
supply chain agility for risk mitigation and response," Journal of Operations
Management (27) 1/1/2009, pp 119-140.
Cagliano, R., Caniato, F., and Spina, G. 2004. "Lean, Agile and traditional supply: how do
they impact manufacturing performance?," Journal of Purchasing and Supply
Management (10:4-5), pp 151-164.
Chakraborty, S., and Mandal, S. 2011. "Revisiting Supply Chain Agility from an IT
Perspective: An Empirical Study," The IUP Journal of Supply Chain Management
(VIII:2).
Chen, G., Zhang, G., and Pang, J. 2011a. "Study on Operating Mechanisms and Dynamics
Behavior of Agile Supply Chain," Journal of Software (6:5).
Chen, J., and Bell, P. C. 2011b. "Coordinating a decentralized supply chain with customer
returns and price-dependent stochastic demand using a buyback policy," European
Journal of Operational Research (212:2) 7/16/, pp 293-300.
Cheng, T. C. E., Lai, K.-h., and Wong, C. W. Y. 2011. "Value of Information Integration to
Supply Chain Management: Roles of Internal and External Contingencies," Journal
of Management Information Systems (28:3), pp 161-200.
Christopher, M. 1999. "Logistics and Supply Chain Management: Strategies for Reducing
Cost and Improving Service (Second Edition)," International Journal of Logistics
Research and Applications (2:1) 1999/04/01, pp 103-104.
Christopher, M. 2000. "The Agile Supply Chain : Competing in Volatile Markets," Industrial
Marketing Management (29), pp 37-44.
Christopher, M., and Towill, D. 2001. "An integrated model for the design of agile supply
chains," International Journal of Physical Distribution & Logistics (31:4), pp 235-
246.
Cosic, R., Shanks, G., and Maynard, S. B. 2015. "A business analytics capability framework,"
2015 (19) 2015-09-22.
Danese, P. 2011. "Towards a contingency theory of collaborative planning initiatives in
supply networks," International Journal of Production Research (49:4), pp 1081-
1103.
Davenport, T. H. 2006. "Competing on Analytics," Harvard Business Review) January
2006, pp 1-11.
de Brito, M. P., and van der Laan, E. A. 2009. "Inventory control with product returns: The
impact of imperfect information," European Journal of Operational Research
(194:1), pp 85-101.
Ding, D., and Chen, J. 2008. "Coordinating a three level supply chain with flexible return
policies," Omega (36) 1/1/2008, pp 865-876.
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Dong, S., Xu, S. X., and Zhu, K. X. 2009. "Information Technology in Supply Chains: The
Value of IT-Enabled Resources Under Competition," Information Systems Research
(20:1), pp 18–32.
Dowlatshahi, S., and Cao, Q. 2006. "The relationships among virtual enterprise, information
technology, and business performance in agile manufacturing: an industry
perspective," European journal of operational research (174:2), pp 835-860.
Fairbank, J. F., Labianca, G., Steensma, H. K., and Metters, R. 2006. "Information
Processing Design Choices, Strategy, and Risk Management Performance," Journal
of Management Information Systems (23:1) Summer2006, pp 293-319.
Galbraith, J. 1973. Designing Complex Organizations / J.R. Galbraith, (
Gessner, G., and Scott, R. A. 2009. "Using Business Intelligence Tools to Help Manage Costs
and Effectiveness of Business-to-Business Inside-Sales Programs," Information
Systems Management (26:2), pp 199-208.
Giachetti, R. E., Martinez, L. D., Sáenz, O. A., and Chen, C.-S. 2003. "Analysis of the
structural measures of flexibility and agility using a measurement theoretical
framework," International Journal of Production Economics (86:1), pp 47-62.
Gong, Y., and Janssen, M. 2012. "From policy implementation to business process
management: Principles for creating flexibility and agility," Government Information
Quarterly (29), pp S61-S71.
Gupta, A., and Maranas, C. D. 2003. "Managing demand uncertainty in supply chain
planning," Computers & Chemical Engineering (27:8-9), pp 1219-1227.
Hasan, M. A., Sarkis, J., and Shankar, R. 2012. "Agility and production flow layouts: An
analytical decision analysis," Computers & Industrial Engineering (62:4), pp 898-
907.
Hocevar, B., and Jaklic, J. 2010. "Assessing Benefits of Business Intelligence Systems--A
Case Study," Management (15:1), pp 87-119.
Houlihan, J. B. 1985. "International Supply Chain Management," International Journal of
Physical Distribution & Logistics Management (15:1) 01/01/Number 1/1985, p 22.
Huang, C. C., Liang, W. Y., and Lin, S. H. 2009. "An agile approach for supply chain
modeling," Transportation Research Part E: Logistics and Transportation Review
(45:3), pp 380-397.
Isik, O., Jones, M. C., and Sidorova, A. 2011. "Business Intelligence (Bi) Success and the Role
of Bi Capabilities," Intelligent Systems in Accounting, Finance and Management
(18:4), pp 161-176.
Isik, O., Jones, M. C., and Sidorova, A. 2013. "Business intelligence success: The roles of BI
capabilities and decision environments," Information & Management (50:1), pp 13-
23.
Ivanov, D. 2010. "An adaptive framework for aligning (re)planning decisions on supply chain
strategy, design, tactics, and operations," International Journal of Production
Research (48:13), pp 3999-4017.
Ivanov, D., and Sokolov, B. 2012. "Structure dynamics control approach to supply chain
planning and adaptation," International Journal of Production Research (50:21), pp
6133-6149.
K, A. K., Bakkappa, B., Metri, B. A., and Sahay, B. S. 2009. "Impact of agile supply chains'
delivery practices on firms' performance: cluster analysis and validation," Supply
Chain Management: An International Journal (14:1), pp 41-48.
Ketchen, D. J., and Hult, G. T. M. 2007. "Bridging organization theory and supply chain
management: The case of best value supply chains," Journal of Operations
Management (25:2), pp 573-580.
Kurnia, S., and Johnston, R. B. 2001. "Adoption of efficient consumer response: the issue of
mutuality," Supply Chain Management: An International Journal (6:5), pp 230-241.
Lawton, C. 2008. "The war on returns," Wall Street Journal (8:1).
Li, L., Su, Q., and Chen, X. 2011. "Ensuring supply chain quality performance through
applying the SCOR model," International Journal of Production Research (49:1), pp
33-57.
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Lin, C.-T., Chiu, H., and Chu, P.-Y. 2006a. "Agility index in the supply chain," International
Journal of Production Economics (100:2), pp 285-299.
Lin, C.-T., Chiu, H., and Tseng, Y.-H. 2006b. "Agility evaluation using fuzzy logic,"
International Journal of Production Economics (101:2), pp 353-368.
Liu, H., Ke, W., Wei, K. K., and Hua, Z. 2013. "The impact of IT capabilities on firm
performance: The mediating roles of absorptive capacity and supply chain agility,"
Decision Support Systems (54:3), pp 1452-1462.
Long, W. I., and Ching-Hui, C. 2012. "Using the balanced scorecard in assessing the
performance of e-SCM diffusion: A multi-stage perspective," Decision Support
Systems (52:2), pp 474-485.
Luo, X., Wu, C., Rosenberg, D., and Barnes, D. 2009. "Supplier selection in agile supply
chains: An information-processing model and an illustration," Journal of Purchasing
and Supply Management (15:4), pp 249-262.
Mentzer, J. T., DeWitt, W., Keebler, J. S., Soonhoong, M., Nix, N. W., Smith, C. D., and
Zacharia, Z. G. 2001. "Defining Supplychain Management," Journal of Business
Logistics (22:2), pp 1-25.
Naim, M. M., and Gosling, J. 2011. "On leanness, agility and leagile supply chains,"
International Journal of Production Economics (131:1), pp 342-354.
Neuman, W. L. 2005. Social research methods: Quantitative and qualitative approaches,
(Allyn and Bacon.
New, S. J., and Payne, P. 1995. "Research frameworks in logistics: three models, seven
dinners and a survey," International Journal of Physical Distribution and Logistics
Management (25:10).
Ngai, E. W. T., Chau, D. C. K., and Chan, T. L. A. 2011. "Information technology, operational,
and management competencies for supply chain agility: Findings from case studies,"
The Journal of Strategic Information Systems (20:3), pp 232-249.
Oliveira, M. P. V. d., McCormack, K., and Trkman, P. 2012. "Business analytics in supply
chains – The contingent effect of business process maturity," Expert Systems with
Applications (39:5), pp 5488-5498.
Phan, D. D., and Vogel, D. R. 2010. "A model of customer relationship management and
business intelligence systems for catalogue and online retailers," Information &
Management (47:2), pp 69-77.
Power, D., Sohal, A. S., and Rahman, S.-U. 2001. "Critical success factors in agile supply
chain management An empirical study," International Journal of Physical
Distribution & Logistics (30:4), pp 247-265.
Premkumar, G., Ramamurthy, K., and Saunders, C. S. 2005. "Information Processing View
of Organizations: An Exploratory Examination of Fit in the Context of
Interorganizational Relationships," M. E. Sharpe, p. 257.
Qrunfleh, S., and Tarafdar, M. 2013. "Lean and agile supply chain strategies and supply
chain responsiveness: the role of strategic supplier partnership and postponement,"
Supply Chain Management: An International Journal (18:6), pp 571-582.
Ramakrishnan, T., Jones, M. C., and Sidorova, A. 2012. "Factors influencing business
intelligence (BI) data collection strategies: An empirical investigation,"
Decision Support Systems (52), pp 486–496.
Ranjan, J. 2008. "Business justification with business intelligence," Vine (38:4), pp 461-475.
Sahay, and Ranjan, J. 2008. "Real time business intelligence in supply chain analytics,"
Information Management & Computer Security (16:1), pp 28-48.
Schläfke, M., Silvi, R., and Möller, K. 2013. "A framework for business analytics in
performance management," International Journal of Productivity and Performance
Management (62:1), pp 110-122.
Shanks, G., and Sharma, R. 2010a. "Creating Value From Business Analytics Systems: The
Impact of Strategy," 15th Pacific Asia Conference on Information Systems: Quality
Research in Pacific, PACIS 2011).
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Shanks, G., Sharma, R., Seddon, P., and Reynolds, P. 2010b. "The Impact of Strategy and
Maturity on Business Analytics and Firm Performance: A Review and Research
Agenda," 21st Australasian Conference on Information Systems, 2010).
Sharda, R., Asamoah, D. A., and Ponna, N. 2013. "Research and pedagogy in business
analytics: opportunities and illustrative examples," CIT Journal of Computing and
Information Technology:3), p 171.
Sharma, R., Reynolds, P., Scheepers, R., Seddon, P. B., and Shanks, G. 2010. "Business
Analytics and Competitive Advantage: A Review and a Research Agenda," Bridging
the Socio-technical Gap in Decision Support Systems (A. Respício et al. (Eds.)).
Simchi-Levi, D., and Kaminsky, P. 2008. "Designing and Managing the Supply Chain,"
Concepts, Strategies and Case Studies, 3rd ed., Boston, Mass).
Soni, G., and Kodali, R. 2013. "A critical review of supply chain management frameworks:
proposed framework," Benchmarking: An International Journal (20:2), pp 263-298.
Stratton, R., and Warburton, R. D. H. 2003. "The strategic integration of agile and lean
supply," International Journal of Production Economics (85:2), pp 183-198.
Swafford, P., Ghosh, S., and Murthy, N. 2006. "The antecedents of supply chain agility of a
firm: Scale development and model testing," Journal of Operations Management
(24:2), pp 170-188.
Swafford, P. M., Ghosh, S., and Murthy, N. 2008. "Achieving supply chain agility through IT
integration and flexibility," International Journal of Production Economics (116:2),
pp 288-297.
Tan, K. C. 2001. "A framework of supply chain management literature," European Journal
of Purchasing & Supply Management (7), pp 39-48.
Tatikonda, M. V., and Rosenthal, S. R. 2000. "Technology Novelty, Project Complexity, and
Product Development Project Execution Success: A Deeper Look at Task Uncertainty
in Product Innovation," IEEE TRANSACTIONS ON ENGINEERING
MANAGEMENT (47:1) February, pp 74-87.
Tony, S. 2003. "How Philips Reduced Returns," Supply Chain Management Review.
Trkman, P., McCormack, K., de Oliveira, M. P. V., and Ladeira, M. B. 2010a. "The impact of
business analytics on supply chain performance," Decision Support Systems (49:3),
pp 318-327.
Trkman, P., McCormack, K., Oliveira, M. P. V. d., and Ladeira, M. B. 2010b. "The impact of
business analytics on supply chain performance,"), pp 318–327.
Vinodh, S., Devadasan, S. R., Vimal, K. E. K., and Kumar, D. 2013. "Design of agile supply
chain assessment model and its case study in an Indian automotive components
manufacturing organization," Journal of Manufacturing Systems (32:4), pp 620-
631.
Wadhwa, S., Saxena, A., and Chan, F. T. S. 2008. "Framework for flexibility in dynamic
supply chain management," International Journal of Production Research (46:6), pp
1373-1404.
Wu, F., Yeniyurt, S., Kim, D., and Cavusgil, S. T. 2006. "The impact of information
technology on supply chain capabilities and firm performance: A resource-based
view," Industrial Marketing Management (35:4), pp 493-504.
Xiao, T., Shi, K., and Yang, D. 2010. "Coordination of a supply chain with consumer return
under demand uncertainty," International Journal of Production Economics (124)
1/1/2010, pp 171-180.
Xun, L., Goldsby, T. J., and Holsapple, C. W. 2009. "Supply chain agility: scale
development," International Journal of Logistics Management (20:3), pp 408-424.
Yang, X. 2013. "A Review of Distribution Related Problems in Logistics and Supply Chain
Research," International Journal of Supply Chain Management (2:4).
Yin, R. K. 1994. Case study research : design and methods, (Thousand Oaks, Calif. : Sage,
c1994.
2nd ed.
Yin, R. K. 2011. Applications of case study research, (Sage.
Yin, R. K. 2013. Case study research: Design and methods, (Sage publications.
Australasian Conference on Information Systems Moniruzzaman et al.
2015, Adelaide, South Australia Business Intelligence and Supply Chain Agility
Yusof, E. M. M., Othman, M. S., Omar, Y., and Yusof, A. R. M. 2013. "The Study on the
Application of Business Intelligence in Manufacturing: A Review," International
Journal of Computer Science Issues (10:1), pp 317-324.
Yusuf, Y. Y., Gunasekaran, A., Adeleye, E. O., and Sivayoganathan, K. 2004. "Agile supply
chain capabilities: Determinants of competitive objectives," European Journal of
Operational Research (159:2), pp 379-392.
Yusuf, Y. Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N. M., and Cang, S. 2014a.
"A relational study of supply chain agility, competitiveness and business performance
in the oil and gas industry," International Journal of Production Economics (147), pp
531-543.
Yusuf, Y. Y., M., S., and Gunasekaran, A. 1999. "Agile manufacturing: The drivers, concepts
and attributes," International Journal of Production Economics (62), pp 33-43.
Yusuf, Y. Y., Musa, A., Dauda, M., El-Berishy, N., Kovvuri, D., and Abubakar, T. 2014b. "A
study of the diffusion of agility and cluster competitiveness in the oil and gas supply
chains," International Journal of Production Economics (147), pp 498-513.
Zhang, J., and Chen, J. 2013. "Coordination of information sharing in a supply chain,"
International Journal of Production Economics (143:1), pp 178-187.
Zhou, H., W. C. Benton, J., Schilling, D. A., and Milligan, G. W. 2011. "Supply Chain
Integration and the SCOR Model," Journal of Business Logistics (32(4)), pp 332–
344.

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