Modeling big data enablers for operations and supply chain management

• Pages: 629-658
• Published date: 14 May 2018
• Date: 14 May 2018
• DOI: https://doi.org/10.1108/IJLM-07-2017-0183
• Author: Kuldeep Lamba,Surya Prakash Singh
• Subject Matter: Management science & operations,Logistics

Modeling big data enablers

for operations and supply

chain management

Kuldeep Lamba and Surya Prakash Singh

Department of Management Studies,

Indian Institute of Technology Delhi, New Delhi, India

Abstract

Purpose –The purpose of this paper is to identify and analyse the interactions among various enablers

which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Design/methodology/approach –Fourteenenablers of big data in OSCM have beenselected from literature

and consequent deliberations with experts from industry. Three different multi criteria decision-making

(MCDM) techniques,namely, interpretivestructural modeling(ISM), fuzzy total interpretivestructural modeling

(fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify

driving enablers.Further, common enablersfrom each technique, their hierarchiesand inter-relationshipshave

been established.

Findings –The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management

commitment, financial support for big data initiatives, big data/data science skills, organizational structure

and change management program are the most influential/driving enablers. Across all three different

techniques, these five different enablers has been identified as the most promising ones to implement big data

in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and

storage and data security and privacy have been commonly identified across all three different modeling

techniques as the most dependent big data enablers for OSCM.

Research limitations/implications –The MCDM modelsof big data enablers have beenformulated based

on the inputs from few domainexperts and may not reflect the opinion of whole practitioners community.

Practical implications –The findings enable the decision makers to appropriately choose the desired and

drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM.

The most common driving big data enablers can be given high priority over others and can significantly

enhance the performance of OSCM.

Originality/value –MCDM-based hierarchical models and causal diagram for big data enablers depicting

contextualinter-relationships hasbeen proposed which is a new effort for implementation of big data in OSCM.

Keywords India, Decision-making, Modelling, DEMATEL, Big data, Interpretive structural modelling,

Management research, Operations and supply chain management (OSCM), Fuzzy-TISM, Enablers

Paper type Research paper

1. Introduction

The intense market competition has made organizations to think past traditional decision

making based on past experiences and intuition to a more precise and informed decision making

backed by big data analytics to stayaheadincompletion(Zachariaet al., 2011). The usefulness

of big data-driven decision making is now gaining widespread acceptance across organizations

and there is an increased enthusiasm among operations and supply chain managers for “big

data”which essentially is huge volumes of data captured from variety of sources in real/near

real time (Lamba and Singh, 2016). Big data has unleashed the data-driven paradigm and has

paved the way to challenge new classes of problems which were not solvable in the past

(Song and Zhu, 2015). Utilizing the capabilities and hidden potential of big data to make better

decisions can lead to improved profitability for an organization (Demirkan and Delen, 2013).

Someofthebenefitsthatcanberealizedbytheuse of big data in operations and supply chain

management (OSCM) are optimized inventory and productivity, shorter cycle times, better The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 629-658

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-07-2017-0183

Received 13 July 2017

Revised 1 September 2017

Accepted 8 September 2017

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

The authors are thankful to the anonymous referees for their critical comments and constructive

suggestions which has improved the manuscript.

629

Modeling big

data enablers

for OSCM

customer satisfaction, effective decision making and quick resolution of customer issues.

Big data can greatly facilitate in driving insights that cannot only transform the entire OSCM

but also help in edging past the rivals (Wamba et al., 2015). Various studies have argued that

best business decisions are taken when the decision makers are well-equipped with data and the

necessary technical expertise to gain insights from it (Dubey and Gunasekaran, 2015). Owing to

its high strategic and operational potential, particularly in business value creation, big data has

recently caught the attention of academia and corporates alike. The big data has been identified

as the “next big thing in innovation”(Gobble, 2013) and considered to be the “the fourth

paradigm of science”(Strawn, 2012). It has also been identified as “the next frontier for

innovation, competition, and productivity”(Manyika et al., 2011) and the next “management

revolution”(McAfee and Brynjolfsson, 2012). A recent study by Capgemini in 2012 highlighted

the benefits of making business decision using big data and reported 26 percent improvement in

performance of firms in the last three years and expected 41 percent performance improvement

in the next three years. A similar kind of fact is corroborated by McAfee and Brynjolfsson (2012)

who found that “companies in the top third of their industries in the use of data driven decision

making were, on average, 5% more productive and 6% more profitable than their competitors.”

Manyika et al. (2011)alsoconductedastudyandfoundthatbyefficientlyusingbigdata,

national health care expenditures in the USA can be reduced by about 8 percent thereby

creating an annual value of 300 billion dollars. For developed economies of Europe, they

estimated savings of more than $149 billion in operational efficiency by using big data.

Furthermore, the personal location data has been estimated to generate consumer surplus of 600

billion dollars Amazon, claims to generate 30 percent of its sales by its recommendation engine

“you may also like.”

In spite of thesebusiness advantages, however,the actual use of big data in OSCM is much

less and a fairlylarge number of firms fail to unveilits business value (Pearsonand Wegener,

2013). It can be attributed to many factors such as unavailability of data science experts who

can draw meaningfulbusiness insights fromhuge pool of structured and instructeddata, lack

of appropriate tools and technologies for analysis, data storage capacity and retrieval

limitations, etc. Recent works by researchers (Addo-Tenkorang and Helo, 2016; Akter et al.,

2016; Comuzzi and Patel, 2016; Kaur and Singh, 2017; Tayal and Singh,2016; Fosso Wamba

et al., 2017; Kacheand Seuring, 2017; Lamba and Singh, 2017) havehighlighted the possibility

of integrating big data with OSCM. Fosso Wamba et al. (2017) havestudied the effects of big

data analytics capability (BDAC) model on a firm’s performance (FPER). They have also

highlighted the effect of process-oriented dynamic capabilities on the relationship between

BDAC and FPER. Their study suggests that BDAC has a direct impact on a FPER. Lamba

and Singh (2017) have reviewed the available literature on big data in the context of OSCM.

Their findingsclearly highlight the paucity ofwork pertaining to integrationof big data with

OSCM. They have also suggested some framework for integrating big data in some thrust

areas of OSCM, namely, facility layout, procurement and joint procurement problems.

However, the integration of big data for improving OSCM processes being a rather new

domain (Zhou et al.,2014), not much work has appeared in literaturehighlighting the enablers

of big data successin OSCM. Keeping this as a motivation,the present paper seeks to identify

and model the enablers for the success of big data in OSCM. A systematic model showing

hierarchy and inter-relationships amongthe enablers, and classifying the enablers into cause

and effect groupswill be extremely useful for the decision makers to strategically utilize their

resources for successfully embedding big data in OSCM. In particular, the paper seeks to

achieve below mentioned objectives:

•identifying the enablers for success of big data in OSCM;

•establishing hierarchy and inter-relationship among enablers usi ng interpretive

structuralmodeling (ISM) and fuzzytotal interpretivestructural modeling(fuzzy-TISM)

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