Full Terms & Conditions of access and use can be found at
International Journal of Production Research
ISSN: 0020-7543 (Print) 1366-588X (Online) Journal homepage: https://www.tandfonline.com/loi/tprs20
Big data-driven supply chain performance
measurement system: a review and framework for
Sachin S. Kamble & Angappa Gunasekaran
To cite this article: Sachin S. Kamble & Angappa Gunasekaran (2020) Big data-driven supply
chain performance measurement system: a review and framework for implementation, International
Journal of Production Research, 58:1, 65-86, DOI: 10.1080/00207543.2019.1630770
To link to this article: https://doi.org/10.1080/00207543.2019.1630770
Published online: 17 Jun 2019.
Submit your article to this journal
Article views: 4637
View related articles
View Crossmark data
Citing articles: 38 View citing articles
International Journal of Production Research, 2020
Vol. 58, No. 1, 65â€“86, https://doi.org/10.1080/00207543.2019.1630770
Big data-driven supply chain performance measurement system: a review and framework for
Sachin S. Kamblea and Angappa Gunasekaranbâˆ—
aOperations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India; bSchool of Business
and Public Administration, California State University, Bakersfield, Bakersfield, CA, USA
(Received 5 September 2018; accepted 27 April 2019)
Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The
PMM improves the firmâ€™s performance by providing open and transparent communication between the various stakeholders
of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The
present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various
PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two
non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance
of the big data analytics capability and the second category represents 130 measures used for assessing the performance of
BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement
system is proposed which will guide the managers to have a robust performance measurement system in their organisation.
Keywords: big data; supply chain management; performance measures; predictive analytics; data analytics
Performance measurement is defined as the process of quantifying the efficiency and effectiveness of action. The set of
metrics used to quantify the efficiency and efficacy of action is referred to as a performance measure (Neely, Gregory,
and Platts 1995). The fundamental questions used to analyse the performance management systems are â€˜What performance
measures are used? What are they used for? How much do they cost? And what benefit do they provideâ€™ (Neely, Gregory,
and Platts 1995). Performance measures are believed to facilitate more open and transparent communication between the
stakeholders of an organisation, leading to a supportive work environment and hence improved organisational performance
(Gunasekaran and Kobu 2007).
Big data analytics (BDA) defined as collection of data, analytical tools, computer algorithms and techniques to derive
meaningful insights, patterns from the collected large data sets (Jeble et al. 2018; LaValle et al. 2011) is gaining wide
acceptance in the supply chain (SC) helping the managers to deliver sustainable value, improved business performance
and competitive advantage (Wamba et al. 2017). Manyika et al. (2011) point out that BDA may lead to a new movement
of productive growth by transforming economies. BDA is enabling novel ways of organising and analysing SC processes
to drive SC performance (Hazen et al. 2016; Waller and Fawcett 2013), create manufacturing capabilities and improve
customer satisfaction (Anwar, Khan, and Shah 2018). Studies have also found that the BDA has a positive influence on the
firm performance (Gunasekaran et al. 2017; Wamba et al. 2017), building competitive advantage (Chen, Preston, and Swink
2015) through cost reduction, improved decisions, and improvements in products and services (Matthias et al. 2017). The
supply chains adopting the BDA are referred to as big data-driven supply chains (BDDSC).
There have been numerous attempts in the SC literature to collate the performance measures and metrics (PMM) used
for evaluating the SC performance. The PMMs are classified based on multiple dimensions that include; qualitative or
quantitative (Beamon 1999; Shepherd and GÃ¼nter 2006); cost and non-cost (Gunasekaran, Patel, and Tirtiroglu 2001);
quality, cost, delivery, flexibility, strategic, operational, tactical (Gunasekaran, Patel, and Tirtiroglu 2001; Shepherd and
GÃ¼nter 2006); cost, quality, resource utilisation, flexibility, visibility, trust and innovativeness (Chan 2003); and the SC
process using SCOR framework (Lockamy and McCormack 2004; Shepherd and GÃ¼nter 2006). The previous studies argue
*Corresponding author. Email: [email protected]
Â© 2019 Informa UK Limited, trading as Taylor & Francis Group
66 S. S. Kamble and A. Gunasekaran
that design, development, and implementation of PMMs is not a one-time activity and has to be continuously monitored and
reviewed for accommodating the changes in the competitive environment (Beamon 1999; Bourne et al. 2000; Medori and
The PMMs used in traditional SC is based on historical information, isolated, static and are less efficient in delivering
information to the decision makers (Lapide 2010). The traditional performance measurement system (PMS) is concerned
to find what happened in the past and the reasons for the same and therefore, may not apply to manage the BDDSC performance which is fast-changing, highly volatile and are expected to be highly proactive, anticipating future performance
instead of reacting to the problems after they occur (Stefanovic 2015). It would be interesting to find whether the PMM of
BDDSC complements the PMM used in the traditional SC and does it provide additional information on what will happen
in the future, helping the decision makers to take corrective actions to prevent the occurrence (Bauer 2005). To the best
of our knowledge, no studies have explored the literature and collated the PMM considered in a BDDSC. It is necessary
to understand how the PMM used in BDDSC PMS are different from the traditional SC PMM. This knowledge will help
the performance managers to design, implement and monitor an active BDDSC PMS. The above reasons motivated the
researchers to conduct this study to explore and comprehend the PMM used to evaluate the BDDSC performance and propose a PMS. The study investigates how the BDA capability in a supply chain is conceptualised, what measures contribute
to the development of a strong BDA and how these measures complement the existing PMM used in traditional SC. We
used a systematic literature review suggested by Tranfield, Denyer, and Smart (2003) to seek information for the following
research questions (RQs);
RQ1: How is BDA conceptualized in a BDDSC?
RQ2: What PMM are used to evaluate BDDSC performance?
RQ3: How to implement a BDDSC PMS?
The study contributes to the SC management literature by identifying a set of PMM that can be used by the organisations to
evaluate their BDDSC performance. Based on the findings of the study, we also propose a BDDSC-PMS framework which
will guide the managers to implement PMS in their organisations.
The remaining of the paper is organised as follows: Section 2 provides background on PMM in traditional SC and
need for PMM in BDDSC. Section 3 presents the review methodology. The findings of the study are presented in section
4. Section 5 discusses the BDDSC â€“PMS framework based on the results of the review. Section 6 offers managerial
implications and future research directions. Section 7 presents the conclusions drawn from the study.
2. Performance measures in traditional and big data-driven supply chains
Performance management plays a significant role in the successful functioning of an organisation. The timely and efficient
evaluation of the SC helps in strategy formulation, implementation, and monitoring. The PMMs have undergone a considerable transformation from a very traditional PMM to advanced and balanced techniques (Mishra et al. 2018). Kaplan
and Norton (1992) proposed the use of a balanced scorecard approach that suggests the use of financial and non-financial
measures to achieve strategic alignment. The primary objective of the balanced scorecard approach was to provide managers with better decision-making environment that included a broader perspective of both tangible and intangible assets.
A significant contribution is in the form of SC operation reference (SCOR) framework that characterised those SC practices and processes which has a substantial influence on the SC performance. The various domains used for categorisation
included plan, source, make, deliver and return. Many studies have provided PMM taxonomy based on a critical review
of the literature. Shepherd and GÃ¼nter (2006) identified that SC activities such as process modelling, data integration, software support, and forecasting did not receive much attention in the SC performance management literature. Arzu Akyuz
and Erman Erkan (2010) also identified various aspects of PMM such as collaboration, agility, flexibility, and IT support
systems that were not considered for SC performance evaluation. The other significant contribution was from Gunasekaran,
Patel, and McGaughey (2004) in the form of a validated set of PMM.
Further, Gunasekaran and Kobu (2007) classified the various SC PMM using balanced scorecard, components of measures, location and nature of measures, traditional versus modern measures and decision levels. The review by Gopal and
Thakkar (2012) identified considerable scope for research to address critical issues in SC PMM, benchmarking, integration,
business intelligence, and collaborative decision-making. Lapide (2010) reports that the traditional SC PMM are based on
historical data, which are static and reactive. There is a competitive pressure on the SCs to enable proactive evaluation and
management (Bauer 2005).
International Journal of Production Research 67
In a BDDSC, the BDA capability is seen as a significant asset that helps the organisations to extract the hidden
knowledge from the collected information (Viet, Behdani, and Bloemhof 2018). Stefanovic (2015) identified that BDDSCPMS should provide the following benefits to the organisations, resulting in strong BDA capability and improved SC
(i) New knowledge for improved future projections on performance.
(ii) A standardised approach to SC performance measurement across all the levels in SC hierarchy.
(iii) Knowledge-based planning and strategy development.
(iv) Organisations ability to develop proactive SC strategies.
(v) Improved competitive advantage on the dimensions of better adaptability, reduced SC risk and improved
(vi) The collaborative and persistent performance monitoring system
These benefits inspired us to review the literature on BDDSC and explore the appropriate PMM to understand how a
BDDSC- PMS can be effectively implemented to monitor the SC performance.
3. Review methodology
The information to the RQs was obtained through a systematic literature review, based on the guidelines suggested by
Tranfield, Denyer, and Smart (2003). The review was conducted in three main stages that included; (i) Planning the review,
(ii) Conducting the review and, (iii) Discussion on findings and knowledge dissemination. The review is discussed below.
3.1. Planning the review
The primary objective of this research was to identify relevant PMM for evaluating BDDSC performance. Therefore the
SLR was designed to include the research papers that addressed the applications of BDA in SC management or established
the linkage of BDA with SC performance. Papers that did not cover these aspects or were outside the scope of RQs were
not considered for review. The procedure adopted for selection of the papers is discussed in the next stage.
3.2. Conducting the review
The present study is based on the research papers extracted from Scopus database, as it covers a superior number of journals
as compared to other leading online databases (Chadegani, Salehi, and Yunus 2013; Mishra et al. 2018). To ensure data
consistency and relevance across the selected papers the selection of the search keywords were guided by the objectives of
the study and included â€˜big dataâ€™, or â€˜data miningâ€™, or â€˜data analyticsâ€™, or â€˜data-drivenâ€™, or â€˜predictive analyticsâ€™, or â€˜supply
chain analyticsâ€™ AND â€˜supply chain performanceâ€™, or â€˜firm performanceâ€™, or â€˜organizational performanceâ€™, or â€˜business
performanceâ€™ or â€˜performance measuresâ€™ or â€˜performance metricsâ€™. Initial search with the use of the above combination of
keywords in the title, abstract, and keyword index resulted in a total of 1453 papers. In the second stage, we restricted the
articles to those published in journals up to the year 2018, excluding the articles those published in conference proceedings,
book chapters, doctoral work, white papers, editorial columns, etc. reducing the total count of papers to 587. The number of
papers were further reduced to 95 by limiting the search to the subject area â€˜Business, Management, and Accounting.â€™ The
authors read the introduction and conclusion/discussion section of each paper and removed the papers not aligning with the
review objectives, were highly technical or did not address the selected keywords. Total sixty-six (66) research papers were
retained for the final review. The list of journals selected for the study is presented in Appendix A.
3.3. Reporting of findings and knowledge dissemination
The results of the review are presented in this stage of the systematic literature review. The findings of the present study are
detailed in a narrative form, giving the contents of what has been written on a topic or a subject. The review findings are
discussed in the next section.
4. Review findings
4.1. Theories for BDA conceptualisation and its impact on BDDSC performance
A BDDSC- PMS should have the capability to collect, integrate, consolidate big data, and apply BDA tools to extract valuable insights from the hidden trends and patterns in big data. The findings from the review suggest that to study the impact
68 S. S. Kamble and A. Gunasekaran
Table 1. Different theories used to study the impact of BDA on BDDSC performance.
Theories Key references
Resource-based view Chae, Olson, and Sheu (2014a), Chae et al. (2014b), Gupta and George (2016), Barbosa et al.
(2017), Hazen et al. (2016), Tan et al. (2015), Waller and Fawcett (2013), Gunasekaran
et al. (2017), Popovic et al. ( Ë‡ 2018), MÃ¼ller, Fay, and vom Brocke (2018), Chavez et al.
(2017) Akter et al. (2016).
Knowledge-based view Akter et al. (2016), Chae et al. (2013), Chae, Olson, and Sheu (2014a), Chae et al. (2014b),
Ji-fan Ren et al. (2017), Raguseo and Vitari (2018), Hazen et al. (2016).
T-O-E framework Wamba et al. (2015), Chen, Preston, and Swink (2015), Tan et al. (2015)
Dynamic capability theory Chae and Olson (2013), Richey et al. (2016), Chen, Preston, and Swink (2015).
Scpm3 de Oliveira, McCormack, and Trkman (2012)
Organisational information processing
Zhu et al. (2018), Chen, Preston, and Swink (2015), Srinivasan and Swink (2018)
Information success theory Ji-fan Ren et al. (2017), Wamba et al. (2016), Huang, Wang, and Huang (2018)
of BDA on the firm or SC performance, BDA is conceptualised using different theories and frameworks. These theories
help us to understand the critical components of BDA capabilities required to achieve improved BDDSC performance. A
summary of the theories used to study BDA in SCM is presented in Table 1.
4.1.1. Resource-based view
Resource-based view is a managerial framework used to determine the significant strategic resources that can be exploited
by the organisations to achieve sustainable competitive advantage (Barney 1991). According to resource-based view, the
resources that are readily available for all the firms and can be traded in the market are the â€˜tangibleâ€™ resources, whereas the
resources without any clear and visible boundaries are the â€˜intangibleâ€™ resources (Barbosa et al. 2017; Gupta and George
2016). The transformation of the conventional SC systems to BDDSC creates new competitive advantages for the firm
through improved operational effectiveness (Manyika et al. 2011). BDA is considered a significant organisational and technological resource provides useful information and knowledge leading to the development of adequate competencies (Tan
et al. 2015; Waller and Fawcett 2013). BDA is a highly valuable, inimitable, and non-substitutable resource that combines
data management, IT-based planning, and performance management resources (Chen, Preston, and Swink 2015; SchlÃ¤fke,
Silvi, and MÃ¶ller 2013). BDA assimilation, which influences the SC performance is observed as a three-stage post-diffusion
process lead by organisational resources and top management support (Gunasekaran et al. 2017). The BDA, an organisational resource is moderated by the firmâ€™ s BDA capabilities and other organisational factors such as BDA strategy, top
management support, financial resources, and people engagement Popovic et al. ( Ë‡ 2018). Jeble et al. (2018) argues that the
resource-based view has a significant limitation of not identifying the conditions in which the firmâ€™s resources or capabilities provide a competitive advantage. However, the econometric study conducted by MÃ¼ller, Fay, and vom Brocke (2018)
analyses the direction, sign, and magnitude of the relationship between BDA and firm performance based on objective measurements of BDA assets that included technological and human capabilities possessed by the organisations. They found
that BDA investments in the BDA assets and the type of industry in which the company operates determines the impact of
BDA on the firmâ€™s performance improvement. Based on resource-based view, Chavez et al. (2017) investigated the linkages between the BDA, manufacturing capabilities, and customer satisfaction and found that BDA contributes positively
to the various dimensions of manufacturing capability dimensions. Akter et al. (2016) combined the resource-based view
with the entanglement view of socio-materialism and conceptualised BDA to consist of management, technology, and talent
capability influencing the firm performance.
4.1.2. Knowledge-based view
The knowledge-based view perspective builds upon and extends the resource-based view of the firm. Although the resourcebased view recognises the critical role of knowledge in firms in achieving a competitive advantage, the proponents of the
knowledge-based view argue that the resource-based view perspective does not go far enough. In knowledge-based view,
knowledge is considered the most strategically significant resource of a firm as it is usually difficult to imitate and socially
involved. The studies by Akter et al. (2016), Chae, Olson, and Sheu (2014a), Chae et al. (2014b), Hazen et al. (2016),
and Ji-fan Ren et al. (2017), conceptualised BDA on the knowledge-based view. BDA value is considered as a knowledge
resource, and its impact on firm performance is found to be dependent on the data quality (Hazen et al. 2016). High-quality
levels of data acquired by the firm enable strong BDA capability that are valuable, rare, imperfectly mobile, not imitable, and
International Journal of Production Research 69
Table 2. BDA capabilities and asset development.
Type of Assets BDAC
Human assets â€¢ Capacity to understand data
â€¢ Expertise in handling IT systems/software and the use of data
â€¢ Opinion plus data
â€¢ Actionable results
â€¢ Data visualisation skills
â€¢ The ability for the contextual recommendation
â€¢ Analysis paralysis
â€¢ Dedicated Business Intelligence /Insights teams
â€¢ New blood (leadership)
Technology Assets â€¢ Significant investments
â€¢ Buying other companies
â€¢ Incompatibility among multiple IT platforms and matching up disparate systems
â€¢ Legacy systems
â€¢ Compiling Metadata
â€¢ Creating ontologies/typologies
â€¢ Investing in a third party help
â€¢ Ability to access third-party data
Relationship assets â€¢ External help to adapt and integrate
â€¢ Strategic partners/suppliers of specialist services
not substitutable (VRINN). The firm that best acquires distributes and deploys their knowledge assets can create long-term
performance advantages that might be transferred, absorbed, and applied for improving financial performance.
4.1.3. Technology, organization and environment framework
T-O-E framework explains the influence of the technological, the organisational, and the environmental context on the
process of technology innovation adoptions by the firms (DePietro, Wiarda, and Fleischer 1990). The internal and external technologies (both equipment and procedures) are analysed in the technological context. The organisational context
describes the characteristics (firmâ€™s size, the degree of centralisation, the degree of formalisation, managerial structure) and
resources of the firm (human resources, amount of slack resources, and linkages among employees). The size and composition of the industry, the firmâ€™s competitors, the macroeconomic context, and the regulatory environment are included
in the environmental context (DePietro, Wiarda, and Fleischer 1990). Business value is an outcome of how well a sound
big data strategy is implemented (Wamba et al. 2015). Therefore, the organisations should examine the financial implications on enhancing the innovation and product design capabilities before BDA adoption in SCM (Tan et al. 2015; Wamba
et al. 2015). Chen, Preston, and Swink (2015) used the T-O-E framework to study the inter-relationships with specific T-OE elements and managerial factors, thus developing a complete model of organisational-level IT usage. The human assets,
technology and relationship assets of the firm have a direct link to the firm performance (Ramanathan et al. 2017). In Table 2
we summarise the various BDA capabilities required to build these assets.
4.1.4. Knowledge-based view
Dynamic capability refers to the capability of the organisation to align with the available resource base (Teece, Pisano, and
Shuen 1997). Dynamic capability is defined as â€˜the firmâ€™s ability to integrate, build and reconfigure internal and external
competencies to address rapidly changing environmentsâ€™ (Chae and Olson 2013). The focus of the dynamic capability view
is more on the issue of competitive survival in response to rapidly changing contemporary business conditions, rather than
emphasising on the sustainable competitive advantage as in resource-based view. Dynamic capabilities are significant for the
organisations as it supports them to create, modify, integrate, acquire, and discharge their resources. BDA is conceptualised
to be a dynamic capability, a particular kind of organisational resource in BDDSC (Barbosa et al. 2017; Richey et al. 2016).
4.1.5. SC performance management maturity model
The SC performance management maturity model developed by de Oliveira, Ladeira, and McCormack (2009) comprises
thirteen groups of hierarchically interrelated capabilities that are classified on five levels of maturity. The brief description
of different levels is given in Table 3.
70 S. S. Kamble and A. Gunasekaran
Table 3. Snapshot of SC performance management maturity model.
I (Foundation) â€¢ Focused on building an underlying structure.
â€¢ Planning, order management, fulfilment, and procurement are not the focus area.
II (Structure) â€¢ Distribution and demand planning processes get structured
III (Vision) â€¢ Planning, source, make, deliver are viewed distinctly
â€¢ Focus on the development of cross-functional teams
IV (Integration) â€¢ Firms focus on establishing long-term collaborative relationships with all their SC
â€¢ Strategic planning team focus on selecting SC partners and building long-term relationships with suppliers and customers
V (Dynamics) â€¢ SC attains a high level of integration
â€¢ Firmâ€™s take control of demand and capacity constraints by establishing a pull system
through the development of a close relationship with customers
Table 4. BDA investments in SC processes.
SC Maturity level
Investments needed in the
following SCOR domains Reasons
Level I Plan, Source, and partly Make. â€¢ Poorly defined (ad hoc) processes
â€¢ Focus on planning operations on deciding on which
resources to be exploited
Level II Deliver â€¢ Defined processes
â€¢ Focus on fulfilling the orders of its customers.
Level III Make â€¢ Focus shifts on aligning make the process to supply highvalue products and services to customers.
Level IV and V Source â€¢ Focus on cooperation and collaborations with customers
â€¢ Strategic partnership/alliances
Table 3 confirms that the SC performance management maturity model is a process-oriented model, with information as
an essential driver for success. The process maturity in the SCs influences the information processing needs, which in turn
affects the relationship between BDA and performance results (de Oliveira, McCormack, and Trkman 2012). de Oliveira,
McCormack, and Trkman (2012) argued that the SC maturity level decides the information processing needs and capabilities
of an organisation. Their study identifies the level of investments to be made for developing BDA capability in the different
SCOR areas (see Table 4). BDA investments in the plan area are required to be kept minimum as BDA is meant to be used
for executing the strategies in the source, make and deliver areas and not for planning purpose.
4.1.6. Organisation information processing theory
The organisational information processing theory states that the firmâ€™s information processing performance is an outcome
of the information processing needs and the information processing capability possessed by the firm. Zhu et al. (2018)
argues that uncertainties in the SC can affect the relationship between information processing capabilities and related outcomes (Chen, Preston, and Swink 2015; Wong, Potter, and Naim 2011). Therefore, it becomes essential for organisations
to develop the capability to proactively engage in communication with stakeholders for improved visibility and traceability in SC operations. Zhu et al. (2018) used the organisational information processing theory within the SCOR framework
to investigate the impact of BDA on the firmâ€™s information processing capabilities and SC processes. The organisational
information processing theory was used by Srinivasan and Swink (2018) to study the influence of BDA on firm performance.
4.1.7. Information success theory
The IST is a comprehensive model that examines the information system success based on the relationship between six of
the most critical dimensions of success viz., information quality, system quality, service quality, system use/usage intentions,
user satisfaction, and net system benefits (DeLone and McLean 1992). BDA system quality that includes the concerns like
adaptability, accessibility, response time, privacy and information quality play a significant role in deciding the business
International Journal of Production Research 71
value; a firm might obtain from BDA deployment (Ji-fan Ren et al. 2017). Organisations with BDA implementations have
better financial and market performance (Huang, Wang, and Huang 2018).
The above theories suggest that BDA provides improved SC visibility, transparency, and performance. It is necessary
that organisations invest in developing their BDA capabilities. However, it is imperative that they evaluate their current
BDA capability for better utilisation of their investment. We identify and collate twenty-five performance measures from
the literature that may be used by the organisations to evaluate the effectiveness of their current BDA capability and plan
for future improvements.
4.2. Performance measures and metrics for evaluation of BDA capability
Neely, Gregory, and Platts (1995) suggests that performance measurement is used for quantifying the efficiency and effectiveness of action and should consider the benefit they provide to the organisations. As BDA is conceptualised as a significant
organisational resource having a positive influence on the SC and the firm performance, it is essential to have a quantified
list of measures to evaluate the BDA capability. Table 5 presents a list of twenty-five performance measures classified on
the dimension of cost versus non-cost and qualitative versus quantitative measures. The cost measures are related to the distribution costs of products, return on investment, information-processing, investments made on developing BDA capability,
and the non cost measures are based on the dimensions of quality, time, flexibility, and innovativeness (De Toni and Tonchia
2001; Gunasekaran, Patel, and Tirtiroglu 2001; Gunasekaran and Kobu 2007). Both the cost and non-cost measures can be
either qualitative or quantitative. For example, the data quality cannot be quantified regarding incremental influence it has
on the firmâ€™s financial performance.
It is observed from Table 5 that 95 percent of the measures are non-cost based with only one measure (BDA investment)
based on cost dimension. The majority (70 percent) of the non-cost based measures represented quality dimension, five
measures the flexibility dimension and two measures the time dimension. None of the measures represented the innovativeness dimension. Eighty-three percent of the measures were qualitative. Further BDA contributes to all the dimensions of SC
processes such as plan, source, make, deliver and return.
4.3. Performance measures used in big data-driven supply chain processes
The PMM presented in the above section relates to the measures used to develop the BDA capability. The literature suggests that the PMM in a BDDSC are fast-changing, highly volatile and are expected to be highly proactive, anticipating
future performance instead of reacting to the problems after they occur as compared to a traditional SC, where the PMM is
based on historical information that is isolated, static and are less efficient in delivering information to the decision makers
(Lapide 2010). Therefore we reviewed selected papers to identify the PMM that are used to measure the BDDSC processes.
The primary objective was to explore the new PMM that complements the existing list of PMM that are used in traditional
SC. Table 6 presents a list of PMM that were identified from the review of selected papers. The PMM is classified using
the SCOR framework in addition to the cost/non-cost and quantitative/qualitative dimensions used to classify the PMM for
BDA capability. SCOR framework is one of the most rigorous SC performance evaluation and strategic decision-making
tool when aligned with SC visibility strategy, help the organisations to achieve improved efficiency, measurable and actionable outcomes (Ntabe et al. 2015). The SCOR is structured around five key SC processes (viz., plan, source, make, deliver,
and return) and all the information and physical flows are represented according to these domains (Huang, Sheoran, and
Keskar 2005; Hwang, Lin, and Lyu 2008; Kasi 2005; SC Council 2008). A BDDSC analysed using a SCOR framework
provides reliable and useful information to the decision makers on each domain and helps the organisation to plan their
BDA investments in alignment with their performance objectives (Trkman et al. 2010). SCOR framework has been previously used in classifying the PMM and found to identify and rectify the performance problems persistent in respective SC
processes (Gunasekaran, Patel, and Tirtiroglu 2001; Shephard and GÃ¼nter 2006).
Table 6 presents a list of 130 performance measures identified from the BDDSC literature. These measures are arranged
according to the SCOR framework and classified on the dimensions of cost/non-cost and qualitative/quantitative dimensions.
The results indicated that most of the studies focused on developing the PMM for the plan process (44%) followed by make
(23%), return (13%), deliver (15%) and source process (10%). The findings on what these performance measures intended
to measure were found to be imbalanced with the quality measures receiving the highest representation (68%) followed
by cost (28%), flexibility (13%), time (9%), and innovativeness (04%). Our findings deviate from the previous studies on
traditional SC, where the cost-based measures were used to a high distinction (Beamon 1999; Shepherd and GÃ¼nter 2006).
However, the lack of measures on flexibility and innovativeness was in line with the findings of the previous studies. The
summary statistics of performance measures are presented in Table 7.
72 S. S. Kamble and A. Gunasekaran
Table 5. BDA Performance Measures.
BDAC performance measures Definitions
Cost (C) Time (T)
Quality (Q) Flexibility
(F) Innovativeness (I)
(QL) or Quantitative(QN)
BDA Investments This includes the investments made on the technology,
training human resources to develop BDAC.
Accessibility The extent to which the BDA platform is accessible to the
Adaptability The extent to which the BDA platform can be adapted to
meet various needs in changing situations.
Compatibility The compatibility of BDA technologies with existing
Integration The capability to integrate the data from different formats
such as audio, text, video, and images.
Modularity The flexibility offered by the BDA platform to add, modify,
or remove features from, or to the model.
Analytical tools skill-set The userâ€™s knowledge of using appropriate analytical tools
Business domain knowledge The expert business knowledge and deep understanding the
BDA users have on industry rules, regulations, policies,
Connectivity The connectivity between the various SC partners for data
sourcing and sharing in real-time.
Control The amount of control the firms have on their BDA
Co-ordination The coordination between the analysts and the line
managers in the decision-making process.
Data Currency The perceptions of the user on how the latest is the data.
Data accuracy The perception of the user that all the information captured
by the BDA system is highly accurate.
Data completeness The perception of the user that the BDA system captures all
the necessary information.
Data-driven culture The use of BDA to manage all the SC processes. Q QL
Data format/consistency The perception of the user on data format and its consistent
use on different platforms.
Relational domain knowledge The interpersonal skills and the ability to work closely with
professionals from different functions
Reliability The extent to which the BDA platform can be relied upon
to be free from any form of disruption or interferences.
Security and Privacy Data security and privacy at different stages of collection,
storage, processing, and sharing.
Technical domain knowledge The expert technical knowledge and deep understanding,
the BDA users have on the various aspects of BDA
Top management commitment The degree to which the top management understands
supports and appreciates the BDA capabilities.
Volume of data The ability of the BDA platform to collect, store, analyse
and share a large size of data.
Real-time data The capability of the BDA system to collect, analyse and
share the information in real-time.
Response time The timeliness and promptness of the BDA platform to
respond to any queries or requests.
Further, the study identified a new set of performance measures that were not used in traditional supply chains. These
measures are associated with the predictability of the occurrence of events in the different BDDSC processes and include
predictability of machine failure, quality, maintenance requirements, procurement frauds, and customer expectations. The
other set of new performance measures that compliments the performance measures used in the traditional SC are the
customer-oriented measures in the return domain and includes the number of customer queries, reviews, ratings, etc.
The performance measures identified for the evaluation of BDA capability and BDDSC processes, as listed in Tables 5
and 6, may be used by the performance managers to evaluate their BDDSC. In the following section, we present a BDDSC
PMS framework that will guide the performance managers to implement and monitor the PMS in their organisations.
International Journal of Production Research 73
5. Big data-driven supply chain performance measurement system (BDDSC-PMS)
The performance measures used to evaluate the SC processes in a BDDSC provide organisations with the ability to predict
future performance. It is implied from the review that in a BDDSC the performance relies on strong BDA capability, compelling the organisations to develop strong BDA capabilities. BDA is conceptualised as a significant organisational resource,
and therefore it needs to be utilised economically. The BDDSC-PMS framework, as shown in Figure 1 is conceptualised on
the organisational information processing theory. According to this theory, the firmâ€™s information processing performance
is based on information processing needs and information processing capability (Trieu 2017). We also felt it appropriate to
extend the SCOR framework for the proposed BDDSC- PMS, as we already have a ready reference of performance measures classified on the dimensions of SCOR dimensions (see Table 6). The use of SCOR framework in BDDSC PMS will
allow all the SC partners to communicate in a common language providing a standardised definition for processes, process
elements, and metrics (Huang, Sheoran, and Keskar 2005). SCOR model is found to improve the SC efficiency offering
measurable and attainable outcomes when aligned with the SC visible strategy (Ntabe et al. 2015). As shown in Figure 1
the BDDSC-PMS has three antecedents leading to the predictive performance management that are discussed below.
5.1. BDDSC performance planning
BDDSC performance planning, the first component of the BDDSC-PMS deals with identifying the information needs of the
organisation. It is implied that the performance managers should identify their information requirements, aligned with the
strategic objectives of the organisation. Once the information requirements are identified, the managers must define their
priorities regarding financial (cost) and non-financial (time, quality, flexibility, and innovativeness) measures. These priorities should be assigned based on the extent to which these measures contribute to achieving the strategic objectives. Next,
the manager is required to set performance targets on the selected measures (refer Table 6 for selection of measures). The
selected measures could be quantitative and qualitative and should be able to translate the strategies into specific objectives.
It is implied that the managers must have a thorough understanding of how the different SC processes influence the firmâ€™s
objectives and accordingly set realistic performance targets. These performance targets are required to be communicated
to all the functional managers and SC partners to guide their operational decisions. In this stage, the managers will also be
Figure 1. A framework for BDDSC performance measurement system.
74 S. S. Kamble and A. Gunasekaran
Table 6. Performance measures for BDDSC processes. BDDSC Process
Cost (C) Time (T)
Quality (Q) Flexibility
(F) Innovativeness (I)
(QN) Key References
Plan Ability to learn new technology I QL Fernando, Chidambaram, and Wahyuni-TD
Accounts receivables C QN Huang, Wang, and Huang (2018)
Accounts receivable turnover C QN Huang, Wang, and Huang (2018)
Accurate demand forecasts Q QN Fernando, Chidambaram, and Wahyuni-TD
Asset utilisation C QN Gunasekaran et al. (2018), Chen, Preston, and
Average year on year sales
C QN Chen, Preston, and Swink (2015)
Buyers flexibility F QN Hofmann (2017)
Cash flow C QN Anwar, Khan, and Shah (2018)
Cash-to-cash cycle time C QN Chen, Preston, and Swink (2015)
Collaborative inventory planning Q QL Stefanovic (2015)
Cost reduction C QN Chavez et al. (2017), Lee (2017), Zhan and Tan
Decision-making lead times T QL Hazen et al. (2016), Hofmann (2017), Richey
et al. (2016)
Demand shaping Q QN Chae (2015)
Direct control of the downstream
Q QL Hofmann (2017)
Employee satisfaction Q QL Anwar, Khan, and Shah (2018)
Environment support Q QN Gunasekaran et al. (2018)
Inter-departmental relations Q QL Stefanovic (2015)
Inventory cost C QN Chavez et al. (2017), Kwon, Lee, and Shin (2014)
Inventory turnover C QN Chen, Preston, and Swink (2015)
Labor expenses C QN Huang, Wang, and Huang (2018)
Labor productivity C QN Huang, Wang, and Huang (2018)
Low product price C QN Srinivasan and Swink (2018)
Market expansion F QN Chen, Preston, and Swink (2015), Ren et al. 2017,
Raguseo and Vitari (2018)
Market sensing Q QN Chae (2015)
Market share C QN Gunasekaran et al. (2018), Chen, Preston, and
Meeting customer demand C QN Chen, Preston, and Swink (2015)
New product development F QN Matthias et al. (2017), Ji-fan Ren et al. 2017,
Raguseo and Vitari (2018)
New product development
F QN Schoenherr and Swink (2015), Ji-fan Ren et al.
(2017), Raguseo and Vitari (2018)
New service propositions F QN Matthias et al. (2017)
Order flexibility F QN Bock and Isik (2015)
Order fulfilment Q QN Chae, Olson, and Sheu (2014a), Chae et al.
(2014b), Srinivasan and Swink (2018)
Order fulfilment lead time T QN Hofmann (2017), Srinivasan and Swink (2018)
Order processing lead time T QN Hofmann (2017), Srinivasan and Swink (2018)
Order taking lead time T QN Chavez et al. (2017), Srinivasan and Swink
Overhead costs C QN Chavez et al. (2017)
Process innovation I QN Fernando, Chidambaram, and Wahyuni-TD
(2018), Krishnamoorthi and Mathew (2018),
Gunasekaran et al. (2018), Tan et al. (2015)
Product Innovation I QN Tan (2018), Chae (2015), Tan et al. (2015), Zhan
et al. (2017)
International Journal of Production Research 75
Table 6. Continued. BDDSC Process
Cost (C) Time (T)
Quality (Q) Flexibility
(F) Innovativeness (I)
(QN) Key References
Product mix flexibility F QN Chae, Olson, and Sheu (2014a), Chae et al.
Profit C QN Anwar, Khan, and Shah (2018), Gunasekaran
et al. (2018), Zhan and Tan (2018), Akter
et al. (2016), Wamba et al. (2017), Bag (2017),
Gravili et al. (2018), Ji-fan Ren et al. (2017),
Raguseo and Vitari (2018), Song et al. (2018),
St-Pierre and Delisle (2006)
Profit margin C QN Huang, Wang, and Huang (2018)
Quality of work Q QN Chen, Preston, and Swink (2015)
Reduced management planning
C QN Kwon, Lee, and Shin (2014)
Reduced management planning
T QN Kwon, Lee, and Shin (2014)
Resource use and availability C QL Fernando, Chidambaram, and Wahyuni-TD
Return on asset (ROA) C QN Anwar, Khan, and Shah (2018),
Return on human resources C QN Huang, Wang, and Huang (2018), Gunasekaran
et al. (2018), Gravili et al. (2018)
Return on Equity (ROE) C QN Huang, Wang, and Huang (2018)
Return on investment (ROI) C QN Akter et al. (2016), Wamba et al. (2017), Gravili
et al. (2018)
Revenue C QN Tan et al. (2015)
Sales growth C QN Gunasekaran et al. (2018), Akter et al. (2016),
Chae (2015) Wamba et al. (2017), Ji-fan Ren
et al. 2017, Raguseo and Vitari (2018),
Satisfaction with inventory
Q QL Chae, Olson, and Sheu (2014a), Chae et al.
(2014b), Stefanovic (2015)
Satisfaction with shop floor
Q QL Chae, Olson, and Sheu (2014a), Chae et al.
Satisfaction with the cost
Q QL Chae, Olson, and Sheu (2014a), Chae et al.
SC visibility Q QN Zhu et al. (2018), Yadegaridehkordi et al. (2018),
Srinivasan and Swink (2018)
Support to community Q QL Gunasekaran et al. (2018)
Technology innovation I QN Fernando, Chidambaram, and Wahyuni-TD
Transaction costs C QN Hofmann (2017)
Unit product cost C QN Srinivasan and Swink (2018)
Source Flexibility to change supplier
T QN Fernando, Chidambaram, and Wahyuni-TD
Flexibility to change as per
F QL Fernando, Chidambaram, and Wahyuni-TD
Innovative supplier selection
I QL Fernando, Chidambaram, and Wahyuni-TD
Procurement flexibility F QL Bock and Isik (2015), Huang and Handfield
(2015), Srinivasan and Swink (2018)
Predictability of procurement
Q QN Matthias et al. (2017)
Relationship with suppliers Q QL Huang and Handfield (2015)
Responsiveness to supplier needs T QL Fernando, Chidambaram, and Wahyuni-TD
Responsiveness to supplier
T QN Fernando, Chidambaram, and Wahyuni-TD
76 S. S. Kamble and A. Gunasekaran
Table 6. Continued. BDDSC Process
Cost (C) Time (T)
Quality (Q) Flexibility
(F) Innovativeness (I)
(QN) Key References
Supplier trust Q QL Fernando, Chidambaram, and Wahyuni-TD
Supply visibility Q QL Srinivasan and Swink (2018)
Make Manufacturing cost C QN Brinch (2018), Kwon, Lee, and Shin (2014)
Capacity flexibility F QN Ali, Ghoniem, and Franke (2014), Stefanovic
Detection of machine bottlenecks Q QN Yu and Matta (2016)
Equipment health Q QL Kumar, Shankar, and Thakur (2017)
High-performance products Q QN Chavez et al. (2017)
Low quality losses Q QN Juuso and Lahdelma (2013)
Maintenance costs C QN Kumar, Shankar, and Thakur (2017)
Maintenance team availability F QL Arya et al. (2017)
Make rapid product mix changes F QN Chavez et al. (2017)
Manufacturing flexibility F QN Chae et al. (2014b)
Manufacturing quality Q QN Kumar et al. (2016)
Prediction of product quality Q QN Kim et al. (2018)
Predictive fault finding Q QN Juuso and Lahdelma (2013)
Predictive machine failure Q QN Ghattas, Soffer, and Peleg (2014), Juuso and
Produce customised products F QN Chavez et al. (2017), Lee (2017), Matthias et al.
Product features flexibility F QN Chavez et al. (2017), Srinivasan and Swink
Product lifecycle costs C QN Kumar et al. (2016)
Product Quality Q QN Anwar, Khan, and Shah (2018), Chavez et al.
Production efficiency C QN Kumar et al. (2016)
Production makespan T QN Ali, Ghoniem, and Franke (2014)
Production visibility Q QL Brinch (2018)
Rapid change in production
F QN Chavez et al. (2017)
Reduced cycle inventory stock Q QN Wong, Potter, and Naim (2011)
Reduced inventory C QN Kwon, Lee, and Shin (2014), Wong, Potter, and
Reduced manufacturing cycle
T QN Kwon, Lee, and Shin (2014)
Reduced material processing cost C QN Kwon, Lee, and Shin (2014)
Reduced obsolete inventory
Q QN Wong, Potter, and Naim (2011)
Reduced safety inventory stock Q QN Wong, Potter, and Naim (2011)
Scalability of the production F QN Stefanovic (2015)
Deliver Reduced distribution costs C QN Kwon, Lee, and Shin (2014)
Delivery lead time T QN Chavez et al. (2017)
Delivery performance Q QN Arya et al. (2017)
Delivery reliability Q QN Chavez et al. (2017), Chae, Olson, and Sheu
(2014a), Chae et al. (2014b)
Improved warehouse operations Q QN Pang and Chan (2017)
Late deliveries Q QN Jain et al. (2014)
Responsiveness to customer
Q QN Fernando, Chidambaram, and Wahyuni-TD
Responsiveness to urgent
F QN Brinch (2018)
International Journal of Production Research 77
Table 6. Continued. BDDSC Process
Cost (C) Time (T)
Quality (Q) Flexibility
(F) Innovativeness (I)
(QN) Key References
Service delivery Q QL Fernando, Chidambaram, and Wahyuni-TD
Total delivered cost C QN Srinivasan and Swink (2018)
Returns Customer feedback Q QL Fernando, Chidambaram, and Wahyuni-TD
Customer complaints Q QN Fernando, Chidambaram, and Wahyuni-TD
Customer Loyalty Q QL Anwar, Khan, and Shah (2018), Gunasekaran
et al. (2018)
Customer response time T QN Tan et al. (2015), Fernando, Chidambaram, and
Customer Retention C QN Akter et al. (2016), Wamba et al. (2017), Ji-fan
Ren et al. (2017), Raguseo and Vitari (2018),
Customer ratings Q QL Wamba et al. (2016)
Customer reviews Q QN Wamba et al. (2016)
Customer satisfaction on the
Q QL Wamba et al. (2016)
Customer satisfaction Q QL Anwar, Khan, and Shah (2018), Song et al. (2018)
Customer service level Q QN Lee (2017), Fernando, Chidambaram, and
Customer service quality Q QL Anwar, Khan, and Shah (2018)
Customer trust Q QL Fernando, Chidambaram, and Wahyuni-TD
Customers opinions of the
Q QL Wamba et al. (2016)
Flexibility to change as per
F QL Fernando, Chidambaram, and Wahyuni-TD
(2018), Song et al. (2018)
Market visibility Srinivasan and Swink (2018)
Quick refund Q QN Wamba et al. (2016)
Predicting customer reactions Q QL Wamba et al. (2016)
Volume of customer reviews Q QN Wamba et al. (2016)
Table 7. Summary of performance measures used in BDDSC measures.
BDDSC Process No. of measures Cost/Non-cost measures Qualitative or Quantitative
Plan 58 (44%) C: 26 (46%), F: 5 (09%), I: 4 (07%), Q: 16 (28%), T: 05 (09%) QL: 11 (19%), QN: 47 (81%)
Source 10 (07%) Q: 03 (33%), T: 3 (33%), F: 2 (22%), I: 1 (11%), QL: 06 (67%), QN: 03 (33%)
Make 31 (23%) C: 07 (22%), F: 08 (26%), Q: 14 (45%), T: 2 (07%) QL: 04 (13%), QN: 27 (87%)
Deliver 15 (11%) C: 01 (09%), F: 1 (09%), Q: 08 (73%), T: 1 (09%) QL: 01 (07%), QN: 10 (93%)
Return 17 (13%) C: 01 (06%), F: 01 (06%), Q: 14 (82%), T: 01 (06%) QL: 10 (59%), QN: 07 (41%)
Abbreviations: C-Cost, F- Flexibility, I- Innovativeness, Q-Quality, T-Time, QL-Qualitative, QN-Quantitative.
required to set the performance targets for the BDA requirements, so that the relevant information required to evaluate the
BDDSC performance is available with the managers without any disruption.
5.2. BDA platform
Information processing needs drive the information processing capability of an organisation. The information processing
capability is represented by the second component, â€˜BDA platformâ€™ in Figure 1. A strong BDA capability helps the organisation to achieve improved SC visibility which is defined as â€˜the extent to which actors within the SC have access to the
timely and accurate information that they consider to be key or useful to their operationsâ€™ (Barratt and Barratt 2011; Barratt
78 S. S. Kamble and A. Gunasekaran
and Oke 2007). The BDA platform should provide the organisational support to collect, store, analyse, and share the data
as planned in the performance planning stage. The BDA platform consists of tangible and intangible resources such as data
collection devices, storage database, internet connectivity, and data analysts. The BDA performance of the organisation
should be evaluated against the relevant performance measures listed in Table 5.
5.3. BDDSC performance monitoring
Performance-monitoring stage access the information generated by the BDA platform and performs a real-time performance monitoring of the BDDSC processes on the selected measures. Any deviations in BDDSC performance should be
highlighted in the performance monitoring stage so that the organisations can take proactive actions to overcome the performance problems. In contrast to having regular performance meetings in traditional SC to monitor the performance, in
BDDSC, we propose to have a highly specialised team of performance managers who will be monitoring and measuring the
performance in real-time, sharing the shortcomings with the functional line managers for proactive decision making. The
performance managers should possess business, technical and relational domain knowledge and have good coordination
with the managers and SC partners responsible for the performance of BDDSC processes. These teams should continuously
monitor and review the progress of the selected performance measures and take necessary actions in real-time. The main
advantage of BDDSC-PMS over traditional SC PMS is the availability of information on all the measures in real-time. This
advantage will help the organisations to overcome the problems of delays in conducting performance reviews due to lack
of knowledge or inadequate analytical skills. The main obstacles if any, during the monitoring and review of BDDSC could
be the poor BDA capability. However, appropriate measures for evaluating the BDA capability would ensure a robust BDA
platform. In the next section, we illustrate the use of BDDSC-PMS.
5.4. Illustration on implementing the BDDSC PMS
5.4.1. Strategic objective
To broaden the existing product offerings.
5.4.2. Sample BDDSC performance measures
The organisation may select the sample performance measures such as; Product Innovation, New product development, New
product development success rate, Manufacturing flexibility, Customer feedback, Customer reviews, Customer opinions,
5.4.3. Performance targets
The managers would be required to set the performance targets for the above performance measures. These may include the
number of product innovations to be done in a year, the number of new products launches to be done in a year, the number
of successful product launches, the level of manufacturing flexibility required regarding no. of products to be manufactured
at a given facility. It will also include the targets for the number of customer feedback to be obtained at each stage of product
development, the number of positive reviews on product usage, type of opinions expected and so on.
5.4.4. Sample BDA capability performance measures
The above-planned performance measures for the BDDSC processes are from the SCOR domain of plan, make and return.
These measures will drive the BDA capability, and hence, the performance managers are required to strengthen their BDA
capability to connect with the SC partners from these domains. The sample BDA performance measures may include; BDA
investments, information access to the product designers, analytical skill sets required by the data analysts, and security
level for the consumer data.
5.4.5. Performance targets for BDA capability
Next, the managers will be required to set the targets for the selected BDA capability measures such as level of BDA
investments for a specific domain to provide the relevant data, the level of accessibility to be provided, and level of security
International Journal of Production Research 79
5.4.6. Performance monitoring
The significant outcome of a BDDSC-PMS monitoring process is real-time updates and revisions in the performance
measures and the targets. The performance review teams may perform any of the following actions based on the review
outcomes. The real-time updates from the customers in the form of reviews, opinions, feedbacks, and reactions will help
the designers to be more efficient and expedite the new product development process resulting in successful innovations
and product launches thus, improving the SC performance. The BDA capability will help them to predict the success of the
product in the future. If at any point it is felt by the organisations that they will not be successful with the product launch they
can terminate the project and save losses to the organisation. The performance managers may come across the following
(i) No change in the performance measures and targets: This is the ideal situation for the performance managers
where the selected measures and targets contribute to achieving the strategic objectives without any deviations.
(ii) Removing the non-contributing or insignificant measures: Performance managers during the review process
may identify that some measures are not contributing as expected towards the strategic objectives or the strategic
alternatives may change because of competitive pressures influencing the team to take this decision.
(iii) Revision in existing measures through replacing them with more appropriate measures: This action is an
outcome of improved BDDSC processes and BDA capability of the organisation offering them a new set of
performance measures that were not available to the team before.
(iv) Revising the performance targets: The performance team may like to review the targets considering the market
dynamism. As the BDDSC-PMS is capable of predicting the future performance, some of the variables (measures)
may be expected to contribute highly during the performance monitoring process, and hence the performance team
may like to increase the weight assigned to such variables and revise the performance targets.
It is believed that the framework will support the organisations to predict performance problems and be prepared with
proactive solutions to overcome them. In the next section, we present the implications and future research directions from
6. Implications and future research directions
The review focused on understanding how the BDA is conceptualised in the SC performance studies, what are the various
performance measures used for evaluating the BDDSC performance and how to measure the BDDSC performance. Having
reviewed the literature, we find that BDA plays a significant role in influencing the SC performance. The findings reveal that
both the performance of BDDSC process and BDA platform are critical in deciding the BDDSC performance, compelling
the organisations to develop PMS integrating them. This section presents the managerial implications and the agenda for
future research in the field of BDDSC performance measurement.
â€¢ The literature highlights that BDA is regarded as an information and knowledge resource that should be exploited
for analysing the BDDSC. Therefore, the performance managers should focus on understanding the complexity
of the data, monitor, and control the data quality for effective decision-making. The SC managers should take an
integrative view of the available analytical or data management tools and techniques instead of focusing on the
stand-alone tools. It is required that the future studies should consider promoting a collaborative interdisciplinary
approach by involving experts from different domain who would help on maximum utilisation of this resource to
have maximum impact on the BDDSC performance.
â€¢ Even though the BDA has a positive impact on the BDDSC performance, the success of the organisation depends
on the level of BDA capability they possess. The role of top management is found to be highly significant in
developing BDA capability (Popovic et al. Ë‡ 2018). The other factors that moderates the influence of BDA on the SC
performance includes the strategic alignment between BDA and business (Akter et al. 2016), the adoption stage of
BDA (Huang, Wang, and Huang 2018), supplier base complexity level (Jeble et al. 2018), and supply uncertainty
(Zhu et al. 2018). SC practitioners should focus on addressing these issues while developing their BDA capability.
This will ensure tight alignment between analytics efficiency and strategy. Therefore, the practitioners should
ensure that their BDA capability aligns with their strategy. The future research should focus on identifying more
number of moderating variables that may affect the relationship between the BDA and the SCP.
â€¢ It is found that majority of the performance measures for evaluating the level of BDA capability are quality measures. BDA investment was the only cost measure used by the organisations. The primary motivation for the
organisations in making investments in developing BDA is the improvements in their decision-making performance. Compared to other IT assets, BDA is considered as a productive investment, promising lucrative returns.
80 S. S. Kamble and A. Gunasekaran
It is therefore implied that the SC practitioner should quantify the magnitude of the potential SC improvements
before deciding to make BDA investments (MÃ¼ller, Fay, and vom Brocke 2018). The BDA and BDDSC process
measures presented in this paper will guide the practitionerâ€™s to select the appropriate measures to decide on the
expected level of BDA and BDDSC performance. Future research studies should focus on identifying industryspecific performance measures that will further assist the SC practitioners in determining the level of investments
they should make on developing their BDA capabilities.
â€¢ The impact of BDA is not equal in all the BDDSC processes and varies according to the process maturity level of
the SC. For example, de Oliveira, McCormack, and Trkman (2012) reports that the BDA has a low impact on the
planning, manufacturing, and delivery process, whereas very high impact on the sourcing, in the level IV and V of
SC process maturity level. It is therefore crucial that the SC practitioners should assess their present SC process
maturity before making their BDA investments. The process maturity levels impact the information processing
needs and thus affects the relationship between BDA and performance results. Future research should focus on
identifying the inter-relationships between the BDDSC processes when BDA is deployed within the organisations.
For, e.g. the studies should focus on studying the impact on SC visibility and transparency in the source, make,
deliver, and return processes when high investments in BDA are made to improve the visibility and performance
of planning process.
â€¢ It is found that majority of the BDA performance measures represented quality dimensions. Very few measures
represented time and flexibility dimensions. While implementing the measures, the practitioners should consider
each of the SC processes (planning, source, make delivery and return) separately and evaluate the impact it has
on the SC performance. For example, while evaluating the performance of BDA on accessibility, the firm should
assess to what extent the BDA system is available over time for making decisions related to planning, sourcing,
make, deliver, and return domains. Similarly, the measure â€˜system integrationâ€™ refers to the ability to integrate
various sources of data coming from different SC process domains to produce meaningful insights. More studies are required to be undertaken to investigate the impact of different BDDSC processes on building the BDA
â€¢ Response time is the only time measure used to evaluate the BDA performance. This measure evaluates how fast
the BDA system responds to the requests of the various users in the organisation. The practitioners should use this
measure for assessing the promptness of the BDA platform. However, more such measures will be required in the
future for evaluating BDA performance. For example, how much time is taken for developing and implementing a
BDA platform for PMS can act as an important performance measure to assess the speed of implementation. Future
research studies may focus on identifying more performance measures, as presently we find the existing measures
inclined more on measuring the quality.
â€¢ Data-driven culture is an outcome of the organisation culture built over a period and differs across companies.
The performance of the BDA on this measure will help the organisations to realise the full potential of big data
(Jeble et al. 2018). The practitioners should arrange for a regular meeting and develop transparent communication
systems to interact with their SC partners, to sensitise the importance of BDA for the improved SC performance.
This will help to overcome any barriers that may arise due to behavioural and political factors. Future studies
should focus on identifying strategies that may be implemented to develop a sound data-driven culture in the
â€¢ It is found that none of the measures focused on the evaluation of the BDA on innovativeness dimension. This
may be because enhancing the innovation, and product design capabilities of the firm are seen as an outcome of
BDA implementation (Chen, Preston, and Swink 2015; Ramanathan et al. 2017; Wamba et al. 2015). Therefore,
the performance measures representing the innovativeness dimensions are included in the BDDSC process and
not under BDA measures. The practitioners should consider the above point while evaluating the performance of
BDDSC on innovativeness measures.
â€¢ The performance measures for BDDSC processes as presented in Table 6 apply to both manufacturing and service
industries. However, the importance attached to these performance measures may vary across companies. For
example, an online company (e-tailer) may assign more weight for return domain (customer satisfaction) while
a manufacturing firm may not consider this measure for evaluation. The practitioners are required to select the
relevant measures carefully. Future studies will be needed to identify more industry-specific performance measures
so that the practitioners can use them as ready reckoners.
â€¢ There is an emergence of new measures as an outcome of the big data predictive analytics capability. The predictive
analytics capability will allow the firms to go beyond understanding what has happened in the past by providing
the best estimations of what may occur in the future. The predictive analytics capability has given rise to many
new measures, which we have identified in our study to evaluate the BDDSC processes. Few of the measures
International Journal of Production Research 81
include predicting accurate forecasts, machine failure rates, detecting frauds, and predicting vehicle arrival rates.
The practitioners should include these measures for improving the performance of BDDSC. Future research studies
should focus on developing new predictive analytics based applications for logistics and SCM. With the increasing
development in the field of machine learning techniques and artificial intelligence, it is expected that big data
predictive analytics will be providing more business value to the organisations. The focus of the researchers should
be on developing such performance measures.
â€¢ Our study also identifies new performance measures in the return domain. The previous studies on traditional
SC have identified customer satisfaction or dissatisfaction as a significant performance measure. However, with
the rise of social media which is a substantial source of big data, a new set of measures based on social media
analytics are presented in our study. These measures will provide valuable insights to the SC practitioners and
help them to improve their marketing performance by incorporating the real-time customer insights into their
strategies. However, more research studies are required to be conducted in the future on investigating how the
insights received from social media analytics can be used by the organisations to improve the firmâ€™s performance.
Presently, very few studies deal with examining the relationship between the BDA and its influence on SC performance for an electronic commerce company. Some performance measures are required to be developed in this
â€¢ The practitioners after identifying the relevant performance measures should classify or rank the measures based
on the degree of its importance. They should continuously monitor the changes in the environment and update
the significance assigned to these performance measures. The performance measures are found to evolve based on
changing business environments. For example, our study explores new performance measures in the return domain,
which otherwise were few before the advent of BDA. More studies are required to examine such measures from
time to time. The present study also identified a list of measures to evaluate sustainable SC performance. This
indicates that the practitioners are acknowledging the role of BDA in achieving sustainable SC performance. The
practitioners can track their green gas emissions, contribution towards the poverty reduction or savings on fuel
consumptions to evaluate their sustainable SC performance.
â€¢ More studies will be required to be conducted in future on BDDSC with the focus of identifying innovativeness,
flexibility and time measures, as presently they lack in number, compared to the cost and quality measures.
7. Conclusions, limitations, and scope for future studies
A BDDSC is different from the traditional SC as it is concerned to find what events are occurring in the real-time and what
will happen in the future. A BDDSC has a futuristic view and can offer meaningful insights on measuring the predictive
performance of the organisations. The review suggests that the BDA is conceptualised as an organisational resource which
is required to be used effectively and economically. It is therefore essential to evaluate how the organisations perform on
the BDA capability.
Furthermore, the review indicates that the performance measures used for BDDSC processes are different from the
traditional SC measures. The BDDSC measures are required to be monitored and tracked in real-time, demanding actions
from the performance managers without much delay. The findings of the study reveal that to achieve predictive BDDSC
performance; the organisations should have an integrated performance measurement system that combines both the performance measures of BDA and BDDSC processes. Our study identified and collated 130 measures for BDDSC processes
and 25 measures for BDA capability. The BDA and BDDSC performance measures were classified on the dimensions of
cost/non-cost measures and quantitative/qualitative measures. Additionally, the SCOR domains were used for organising
the performance measures for BDDSC processes. The proposed BDDSC-PMS framework will help the practitioners in
implementing the PMS in their BDDSC.
The research papers for the review were extracted from the Scopus database. The selection of the keywords was made
considering the scope of the study. However, it is possible that it might not have included some significant papers relevant
to the scope of the study. Studies in the future may review the search and selection criteria used in this study and validate the
findings. Further, the BDA and BDDSC performance measures identified in this study need to be validated by the industry
practitioners. In the future, industry-specific case studies on determining the industry-specific performance measures may
be conducted. In this study, we have used pre-determined classification schemes available from the literature to categorise
the defined performance measures. The future studies may survey industry practitioners and use factor analysis for validating the categories used in this study. Studies are also required to be conducted to validate the BDDSC-PMS framework
proposed in this study. The outcomes of such studies may be incorporated in revising the framework and making it more
82 S. S. Kamble and A. Gunasekaran
No potential conflict of interest was reported by the authors.
Akter, S., S. F. Wamba, A. Gunasekaran, R. Dubey, and S. J. Childe. 2016. â€œHow to Improve Firm Performance Using Big
Data Analytics Capability and Business Strategy Alignment?â€ International Journal of Production Economics 182: 113â€“131.
Ali, A. I., A. Ghoniem, and A. Franke. 2014. â€œEvaluating Capacity Management Tactics for a Legacy Manufacturing Plant.â€ Journal of
the Operational Research Society 65 (9): 1361â€“1370. doi:10.1057/jors.2013.82.
Anwar, M., S. Z. Khan, and S. Z. A. Shah. 2018. â€œBig Data Capabilities and Firmâ€™s Performance: A Mediating Role of Competitive
Advantage.â€ Journal of Information and Knowledge Management 17 (4), doi:10.1142/S0219649218500454.
Arya, V., P. Sharma, A. Singh, and P. T. M. De Silva. 2017. â€œAn Exploratory Study on Supply Chain Analytics Applied to Spare Parts
Supply Chain.â€ Benchmarking: An International Journal 24 (6): 1571â€“1580.
Arzu Akyuz, G., and T. Erman Erkan. 2010. â€œSC Performance Measurement: A Literature Review.â€ International Journal of Production
Research 48 (17): 5137â€“5155.
Bag, S. 2017. â€œBig Data and Predictive Analysis is Key to Superior Supply Chain Performance: A South African Experience.â€
International Journal of Information Systems and Supply Chain Management 10 (2): 66â€“84. doi:10.4018/IJISSCM.2017040104.
Barbosa, M. W., A. Vicente, M. B. Ladeira, and M. P. Oliveira. 2017. â€œManaging SC Resources with Big Data Analytics: A Systematic
Review.â€ International Journal of Logistics Research and Applications, 21 (3): 177â€“200.
Barney, J. 1991. â€œFirm Resources and Sustained Competitive Advantage.â€ Journal of Management 17 (1): 99â€“120.
Barratt, M., and R. Barratt 2011. â€œExploring Internal And External Supply Chain Linkages: Evidence from the Field.â€ Journal of
Operations Management 29 (5): 514â€“528.
Barratt, M., and A. Oke 2007. â€œAntecedents of Supply Chain Visibility in Retail Supply Chains: A Resource-Based Theory Perspective.â€
Journal of Operations Management 25 (6): 217â€“1233.
Bauer, K. 2005. â€œPredictive Analytics: The Next Wave in KPIs.â€ Information Management Magazine. http://www.informationmanage
Beamon, B. M. 1999. â€œMeasuring SC Performance.â€ International Journal of Operations & Production Management 19 (3): 275â€“292.
Bock, S., and F. Isik. 2015. â€œA New Two-Dimensional Performance Measure in Purchase Order Sizing.â€ International Journal of
Production Research 53 (16): 4951â€“4962. doi:10.1080/00207543.2015.1005769.
Bourne, M., J. Mills, M. Wilcox, A. Neely, and K. Platts. 2000. â€œDesigning, Implementing and Updating Performance Measurement
Systems.â€ International Journal of Operations & Production Management 20 (7): 754â€“771.
Brinch, M. 2018. â€œUnderstanding the Value of big Data in SC Management and its Business Processes: Towards a Conceptual
Framework.â€ International Journal of Operations & Production Management. doi:10.1108/IJOPM-05-2017-0268.
Chadegani, A. A., H. Salehi, and M. Yunus. 2013. â€œA Comparison Between Two Main Academic Literature Collections: Web of Science
and Scopus Databases.â€ Asian Social Science 9 (5), doi:10.5539/ass.v9n5p18.
Chae, B. K. 2015. â€œInsights From Hashtag SC and Twitter Analytics: Considering Twitter and Twitter Data for SC Practice and Research.â€
International Journal of Production Economics 165: 247â€“259.
Chae, B. K., and D. L. Olson. 2013. â€œBusiness Analytics for Supply Chain: A Dynamic-Capabilities Framework.â€ International Journal
of Information Technology and Decision Making 12 (1): 9â€“26. doi:10.1142/S0219622013500016.
Chae, B. K., D. Olson, and C. Sheu. 2014a. â€œThe Impact of Supply Chain Analytics on Operational Performance: A Resource-Based
View.â€ International Journal of Production Research 52 (16): 4695â€“4710. doi:10.1080/00207543.2013.861616.
Chae, B., C. Yang, D. Olson, and C. Sheu. 2014b. â€œThe Impact of Advanced Analytics and Data Accuracy on Operational Performance: A Contingent Resource Based Theory (RBT) Perspective.â€ Decision Support Systems 59 (1): 119â€“126.
Chan, F. T. 2003. â€œPerformance Measurement in a SC.â€ The International Journal of Advanced Manufacturing Technology 21 (7):
Chavez, R., W. Yu, M. A. Jacobs, and M. Feng. 2017. â€œData-driven Supply Chains, Manufacturing Capability and Customer Satisfaction.â€
Production Planning and Control 28 (11-12): 906â€“918. doi:10.1080/09537287.2017.1336788.
Chen, D. Q., D. S. Preston, and M. Swink. 2015. â€œHow the Use of Big Data Analytics Affects Value Creation in SC Management.â€
Journal of Management Information Systems 32 (4): 4â€“39.
de Oliveira, M. P. V., M. B. Ladeira, and K. McCormack. 2009. â€œThe Statistical Analysis of SCM Process Maturity Levels and Practices.â€
In 26th German logistics congress, Berlin.
de Oliveira, M. P. V., K. McCormack, and P. Trkman. 2012. â€œBusiness Analytics in SCsâ€“The Contingent Effect of Business Process
Maturity.â€ Expert Systems with Applications 39 (5): 5488â€“5498.
De Toni, A., and S. Tonchia. 2001. â€œPerformance Measurement Systems-Models, Characteristics and Measures.â€ International Journal
of Operations & Production Management 21 (1/2): 46â€“71.
DeLone, W. H., and E. R. McLean. 1992. â€œInformation Systems Success: The Quest for the Dependent Variable.â€ Information Systems
Research 3 (1): 60â€“95.
International Journal of Production Research 83
DePietro, R., E. Wiarda, and M. Fleischer. 1990. â€œThe Context for Change: Organization.â€ In Technology and Environment, Vol. 199,
151â€“175. Lexington, MA: Lexington Books.
Fernando, Y., R. R. M. Chidambaram, and I. S. Wahyuni-TD. 2018. â€œThe Impact of Big Data Analytics and Data Security Practices on
Service Supply Chain Performance.â€ Benchmarking: An International Journal 25 (9): 4009â€“4034. doi:10.1108/BIJ-07-2017-0194.
Ghattas, J., P. Soffer, and M. Peleg. 2014. â€œImproving Business Process Decision Making Based on Past Experience.â€ Decision Support
Systems 59 (1): 93â€“107. doi:10.1016/j.dss.2013.10.009.
Gopal, P. R. C., and J. Thakkar. 2012. â€œA Review on SC Performance Measures and Metrics: 2000-2011.â€ International Journal of
Productivity and Performance Management 61 (5): 518â€“547.
Gravili, G., M. Benvenuto, A. Avram, and C. Viola. 2018. â€œThe Influence of the Digital Divide on Big Data Generation Within Supply
Chain Management.â€ International Journal of Logistics Management 29 (2): 592â€“628. doi:10.1108/IJLM-06-2017-0175.
Gunasekaran, A., and B. Kobu. 2007. â€œPerformance Measures and Metrics in Logistics and SC Management: A Review of Recent
Literature (1995â€“2004) for Research and Applications.â€ International Journal of Production Research 45 (12): 2819â€“2840.
Gunasekaran, A., T. Papadopoulos, R. Dubey, S. F. Wamba, S. J. Childe, B. Hazen, and S. Akter. 2017. â€œBig Data and Predictive Analytics
for Supply Chain and Organizational Performance.â€ Journal of Business Research 70: 308â€“317. doi:10.1016/j.jbusres.2016.08.004.
Gunasekaran, A., C. Patel, and R. E. McGaughey. 2004. â€œA Framework for Supply Chain Performance Measurement.â€ International
Journal of Production Economics 87 (3): 333â€“347.
Gunasekaran, A., C. Patel, and E. Tirtiroglu. 2001. â€œPerformance Measures and Metrics in a SC Environment.â€ International Journal of
Operations & Production Management 21 (1/2): 71â€“87.
Gunasekaran, A., Y. Y. Yusuf, E. O. Adeleye, and T. Papadopoulos. 2018. â€œAgile Manufacturing Practices: The Role of Big
Data and Business Analytics with Multiple Case Studies.â€ International Journal of Production Research 56 (1-2): 385â€“397.
Gupta, M., and J. F. George. 2016. â€œToward the Development of a Big Data Analytics Capability.â€ Information and Management 53 (8):
Hazen, B. T., J. B. Skipper, J. D. Ezell, and C. A. Boone. 2016. â€œBig Data and Predictive Analytics for SC Sustainability: A Theory-Driven
Research Agenda.â€ Computers & Industrial Engineering 101: 592â€“598.
Hofmann, E. 2017. â€œBig Data and SC Decisions: The Impact of Volume, Variety and Velocity Properties on the Bullwhip Effect.â€
International Journal of Production Research 55 (17): 5108â€“5126.
Huang, Y.-, and R. B. Handfield. 2015. â€œMeasuring the Benefits of ERP on Supply Management Maturity Model: A â€œbig Dataâ€ Method.â€
International Journal of Operations and Production Management 35 (1): 2â€“25. doi:10.1108/IJOPM-07-2013-0341.
Huang, S. H., S. K. Sheoran, and H. Keskar. 2005. â€œComputer-assisted SC Configuration Based on SC Operations Reference (SCOR)
Model.â€ Computers & Industrial Engineering 48 (2): 377â€“394.
Huang, C. K., T. Wang, and T. Y. Huang. 2018. â€œInitial Evidence on the Impact of Big Data Implementation on Firm Performance.â€
Information Systems Frontiers 1â€“13. doi:10.1007/s10796-018-9872-5.
Hwang, Y. D., Y. C. Lin, and J. Lyu Jr. 2008. â€œThe Performance Evaluation of SCOR Sourcing Processâ€”The Case Study of Taiwanâ€™s
TFT-LCD Industry.â€ International Journal of Production Economics 115 (2): 411â€“423.
Jain, R., A. R. Singh, H. C. Yadav, and P. K. Mishra. 2014. â€œUsing Data Mining Synergies for Evaluating Criteria at Pre-Qualification
Stage of Supplier Selection.â€ Journal of Intelligent Manufacturing 25 (1): 165â€“175.
Jeble, S., R. Dubey, S. J. Childe, T. Papadopoulos, D. Roubaud, and A. Prakash. 2018. â€œImpact of Big Data and Predictive
Analytics Capability on Supply Chain Sustainability.â€ International Journal of Logistics Management 29 (2): 513â€“538.
Ji-fan Ren, S., S. Fosso Wamba, S. Akter, R. Dubey, and S. J. Childe. 2017. â€œModelling Quality Dynamics, Business Value and
Firm Performance in a Big Data Analytics Environment.â€ International Journal of Production Research 55 (17): 5011â€“5026.
Juuso, E. K., and S. Lahdelma. 2013. â€œIntelligent Performance Measures for Condition-Based Maintenance.â€ Journal of Quality in
Maintenance Engineering 19 (3): 278â€“294. doi:10.1108/JQME-05-2013-0026.
Kaplan, R. S., and D. P. Norton. 1992. â€œIn Search of Excellenceâ€“der MaÃŸstab muÃŸ neu definiert werden.â€ Harvard Manager 14 (4):
Kasi, V. 2005. â€œSystemic Assessment of SCOR for Modeling SCs.â€ In System Sciences, 2005. HICSSâ€™05. Proceedings of the 38th Annual
Hawaii International Conference on, 87bâ€“87b. IEEE. doi:10.1109/ HICSS.2005.574.
Kim, A., K. Oh, J.- Jung, and B. Kim. 2018. â€œImbalanced Classification of Manufacturing Quality Conditions Using CostSensitive Decision Tree Ensembles.â€ International Journal of Computer Integrated Manufacturing 31 (8): 701â€“717.
Krishnamoorthi, S., and S. K. Mathew. 2018. â€œBusiness Analytics and Business Value: A Comparative Case Study.â€ Information &
Management 55 (5): 643â€“666.
Kumar, A., R. Shankar, A. Choudhary, and L. S. Thakur. 2016. â€œA Big Data MapReduce Framework for Fault Diagnosis in Cloud-Based
Manufacturing.â€ International Journal of Production Research 54 (23): 7060â€“7073.
Kumar, A., R. Shankar, and L. S. Thakur. 2017. â€œA Big Data Driven Sustainable Manufacturing Framework for Condition-Based
Maintenance Prediction.â€ Journal of Computational Science 27: 428â€“439.
Kwon, O., N. Lee, and B. Shin. 2014. â€œData Quality Management, Data Usage Experience and Acquisition Intention of Big Data
Analytics.â€ International Journal of Information Management 34 (3): 387â€“394. doi:10.1016/j.ijinfomgt.2014.02.002.
84 S. S. Kamble and A. Gunasekaran
Lapide, L. 2010. â€œPredictive Metrics.â€ The Journal of Business Forecasting 29 (2): 23.
LaValle, S., E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz. 2011. â€œBig Data, Analytics and the Path From Insights to Value.â€
MIT Sloan Management Review 52 (2): 21.
Lee, I. 2017. â€œBig Data: Dimensions, Evolution, Impacts, and Challenges.â€ Business Horizons 60 (3): 293â€“303. doi:10.1016/j.bushor.
Lockamy III, A., and K. McCormack. 2004. â€œLinking SCOR Planning Practices to SC Performance: An Exploratory Study.â€ International
Journal of Operations & Production Management 24 (12): 1192â€“1218.
Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. 2011. Big Data: The Next Frontier for Innovation,
Competition, and Productivity. San Francisco, CA: McKinsey Global Institute.
Matthias, O., I. Fouweather, I. Gregory, and A. Vernon. 2017. â€œMaking Sense of Big Data â€“ Can it Transform Operations Management?â€
International Journal of Operations and Production Management 37 (1): 37â€“55. doi:10.1108/IJOPM-02-2015-0084.
Medori, D., and D. Steeple. 2000. â€œA Framework for Auditing and Enhancing Performance Measurement Systems.â€ International Journal
of Operations & Production Management 20 (5): 520â€“533.
Mishra, D., A. Gunasekaran, T. Papadopoulos, and R. Dubey. 2018. â€œSupply Chain Performance Measures and Metrics: A Bibliometric
Study.â€ Benchmarking: An International Journal 25 (3): 932â€“967.
MÃ¼ller, O., M. Fay, and J. vom Brocke. 2018. â€œThe Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics.â€ Journal of Management Information Systems 35 (2): 488â€“509.
Neely, A., M. Gregory, and K. Platts. 1995. â€œPerformance Measurement System Design: A Literature Review and Research Agenda.â€
International Journal of Operations & Production Management 15 (4): 80â€“116.
Ntabe, E. N., L. LeBel, A. D. Munson, and L. A. Santa-Eulalia. 2015. â€œA Systematic Literature Review of the SC Operations Reference
(SCOR) Model Application with Special Attention to Environmental Issues.â€ International Journal of Production Economics 169:
Pang, K.-W., and H.-L. Chan. 2017. â€œData Mining-Based Algorithm for Storage Location Assignment in a Randomised Warehouse.â€
International Journal of Production Research 55 (14): 4035â€“4052.
Popovic, A., R. Hackney, R. Tassabehji, and M. Castelli. 2018. â€œThe Impact of Big Data Analytics on Firmsâ€™ High Value Business Ë‡
Performance.â€ Information Systems Frontiers 20 (2): 209â€“222. doi:10.1007/s10796-016-9720-4.
Raguseo, E., and C. Vitari. 2018. â€œInvestments in Big Data Analytics and Firm Performance: An Empirical Investigation of Direct and
Mediating Effects.â€ International Journal of Production Research 56 (15): 5206â€“5221. doi:10.1080/00207543.2018.1427900.
Ramanathan, R., E. Philpott, Y. Duan, and G. Cao. 2017. â€œAdoption of Business Analytics and Impact on Performance: A Qualitative
Study in Retail.â€ Production Planning & Control 28 (11-12): 985â€“998.
Richey, R. G., T. R. Morgan, K. Lindsey-Hall, and F. G. Adams. 2016. â€œA Global Exploration of Big Data in the Supply Chain:
Global Exploration of Big Data.â€ International Journal of Physical Distribution and Logistics Management 46 (8): 710â€“739.
SC Council. 2008, March. â€œIntroduction to GreenSCOR: Introducing Environmental Considerations to the CSOR Model.â€ In proceedings
of the North America conference and exposition March (pp. 17â€“19).
SchlÃ¤fke, M., R. Silvi, and K. MÃ¶ller. 2013. â€œA Framework for Business Analytics in Performance Management.â€ International Journal
of Productivity and Performance Management 62 (1): 110â€“122. doi:10.1108/17410401311285327.
Schoenherr, T., and M. Swink. 2015. â€œThe Roles of SC Intelligence and Adaptability in New Product Launch Success.â€ Decision Sciences
46 (5): 901â€“936.
Shepherd, C., and H. GÃ¼nter. 2006. â€œMeasuring SC Performance: Current Research and Future Directions.â€ International Journal of
Productivity and Performance Management 55 (3): 242â€“258.
Song, P., C. Zheng, C. Zhang, and X. Yu. 2018. â€œData Analytics and Firm Performance: An Empirical Study in an Online B2C Platform.â€
Information and Management 55 (5): 633â€“642. doi:10.1016/j.im.2018.01.004.
Srinivasan, R., and M. Swink. 2018. â€œAn Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics:
An Organizational Information Processing Theory Perspective.â€ Production and Operations Management 27 (10): 1849â€“1867.
St-Pierre, J., and S. Delisle. 2006. â€œAn Expert Diagnosis System for the Benchmarking of SMEsâ€™ Performance.â€ Benchmarking 13 (1-2):
Stefanovic, N. 2015. â€œCollaborative Predictive Business Intelligence Model for Spare Parts Inventory Replenishment.â€ Computer Science
and Information Systems 12 (3): 911â€“930.
Tan, K. H. 2018. â€œManagerial Perspectives of Big Data Analytics Capability Towards Product Innovation.â€ Strategic Direction 34 (8):
Tan, K. H., Y. Z. Zhan, G. Ji, F. Ye, and C. Chang. 2015. â€œHarvesting Big Data to Enhance SC Innovation Capabilities: An Analytic
Infrastructure Based on Deduction Graph.â€ International Journal of Production Economics 165 (2015): 223â€“233.
Teece, D. J., G. Pisano, and A. Shuen. 1997. â€œDynamic Capabilities and Strategic Management.â€ Strategic Management Journal 18 (7):
Tranfield, D., D. Denyer, and P. Smart. 2003. â€œTowards a Methodology for Developing Evidence-Informed Management Knowledge by
Means of Systematic Review.â€ British Journal of Management 14: 207â€“222.
International Journal of Production Research 85
Trieu, V. 2017. â€œGetting Value From Business Intelligence Systems: A Review and Research Agenda.â€ Decision Support Systems 93:
Trkman, P., K. McCormack, M. P. V. De Oliveira, and M. B. Ladeira. 2010. â€œThe Impact of Business Analytics on Supply Chain
Performance.â€ Decision Support Systems 49 (3): 318â€“327. doi:10.1016/j.dss.2010.03.007.
Viet, N. Q., B. Behdani, and J. Bloemhof. 2018. â€œThe Value of Information in Supply Chain Decisions: A Review of the Literature and
Research Agenda.â€ Computers & Industrial Engineering 120: 68â€“82.
Waller, M. A., and S. E. Fawcett. 2013. â€œData Science, Predictive Analytics, and Big Data: A Revolution That Will Transform SC Design
and Management.â€ Journal of Business Logistics 34 (2): 77â€“84.
Wamba, S. F., S. Akter, A. Edwards, G. Chopin, and D. Gnanzou. 2015. â€œHow â€˜big Dataâ€™can Make big Impact: Findings From a Systematic
Review and a Longitudinal Case Study.â€ International Journal of Production Economics 165: 234â€“246.
Wamba, S. F., S. Akter, H. Kang, M. Bhattacharya, and M. Upal. 2016. â€œThe Primer of Social Media Analytics.â€ Journal of Organizational
and End User Computing 28 (2): 1â€“12. doi:10.4018/JOEUC.2016040101.
Wamba, S. F., A. Gunasekaran, S. Akter, S. J.- Ren, R. Dubey, and S. J. Childe. 2017. â€œBig Data Analytics and Firm Performance: Effects
of Dynamic Capabilities.â€ Journal of Business Research 70: 356â€“365. doi:10.1016/j.jbusres.2016.08.009.
Wong, H., A. Potter, and M. Naim. 2011. â€œEvaluation of Postponement in the Soluble Coffee Supply Chain: A Case Study.â€ International
Journal of Production Economics 131 (1): 355â€“364. doi:10.1016/j.ijpe.2010.08.015.
Yadegaridehkordi, E., M. Hourmand, M. Nilashi, L. Shuib, A. Ahani, and O. Ibrahim. 2018. â€œInfluence of Big Data Adoption on Manufacturing Companiesâ€™ Performance: An Integrated DEMATEL-ANFIS Approach.â€ Technological Forecasting and Social Change
137: 199â€“210. doi:10.1016/j.techfore.2018.07.043.
Yu, C., and A. Matta. 2016. â€œA Statistical Framework of Data-Driven Bottleneck Identification in Manufacturing Systems.â€ International
Journal of Production Research 54 (21): 6317â€“6332. doi:10.1080/00207543.2015.1126681.
Zhan, Y., and K. H. Tan. 2018. â€œAn Analytic Infrastructure for Harvesting Big Data to Enhance Supply Chain Performance.â€ European
Journal of Operational Research. doi:10.1016/j.ejor.2018.09.018.
Zhan, Y., K. H. Tan, G. Ji, L. Chung, and M. Tseng. 2017. â€œA Big Data Framework for Facilitating Product Innovation Processes.â€
Business Process Management Journal 23 (3): 518â€“536.
Zhu, S., J. Song, B. T. Hazen, K. Lee, and C. Cegielski. 2018. â€œHow SC Analytics Enables Operational SC Transparency: An Organizational Information Processing Theory Perspective.â€ International Journal of Physical Distribution & Logistics Management 48
86 S. S. Kamble and A. Gunasekaran
Appendix A: Journal wise distribution of papers
Journal Name No. of Publications
International Journal of Production Research 8
Decision Support Systems 6
International Journal of Production Economics 5
International Journal of Operations & Production Management 3
Production Planning & Control 3
Benchmarking: An International Journal 2
Information Systems Frontiers 2
Information and Management 2
International Journal of Information Management 2
International Journal of Logistics Management 2
International Journal of Physical Distribution & Logistics Management 2
Journal of Business Research 2
Journal of Management Information Systems 2
International Journal of Productivity and Performance Management 2
Business Process Management Journal 2
International Journal of IS Modeling and Design 1
Journal of Business Intelligence and Data Mining 1
Journal of Organization and End User Computing 1
Business Horizons 1
International Journal of Logistics Research and Applications 1
International Journal of Computer Integrated Manufacturing 1
International Journal of Information Systems 1
International Journal of Information Technology & Decision Making 1
Journal of Business Logistics 1
Journal of Computational Science 1
Journal of Enterprise Information Management 1
Journal of Information System and SCM 1
Journal of Information and Knowledge Management 1
Journal of Intelligent Manufacturing 1
Journal of Operation Research Society 1
Journal of Organizational and End User Computing 1
Journal of Quality in Maintenance Engineering 1
Production and Operations Management 1
Strategic Decisions 1
Technological Forecasting and Social Change 1
Information System Research 1
Are you busy and do not have time to handle your assignment? Are you scared that your paper will not make the grade? Do you have responsibilities that may hinder you from turning in your assignment on time? Are you tired and can barely handle your assignment? Are your grades inconsistent?
Whichever your reason is, it is valid! You can get professional academic help from our service at affordable rates. We have a team of professional academic writers who can handle all your assignments.
Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.
Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.
While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.
Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.
In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.
Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.
We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!
We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.
Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.
We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.
Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.
There is a very low likelihood that you won’t like the paper.
Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.
We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.
You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.
We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.
You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.
Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.
You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.
The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.Read more
Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.Read more
Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.Read more
Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.Read more
By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.Read more