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Big data and its impact on digitized supply chain management

  • Jashore University of Science and Technology
  • Jashore University of Science and Technology


Day by day the customers demand changing rapidly, it is very difficult for a company to identify demand. Big data used in supply chain management helps to create better customer demand planning strategies by collecting, documenting & analyzing data in a real situation. Big data combined a set of data usually referred as large and complex datasets; enables the company to review real-time data flows. Digitized supply chains have the potential to dramatically lower costs, reduce lead time and increase product availability. Digitization makes the supply chain more effective, agile and responsive by sharing knowledge and collaborating complex supplier networks. As a result, this research work helps a manager to identify the current situation of their business and it will help them to take timely, fast and better decision.
Big data and its impact on digitized supply chain management
Md. Tahiduzzaman*1, Mustafizur Rahman1, Samrat Kumar Dey1, Md. Sumon Rahman1,
S. M. Akash2
Md. Tahiduzzaman
Faculty, Department of Industrial and Production Engineering
Jessore University of Science and Technology
Jessore-7408, Khulna, Bangladesh
Mobile: +8801925325265
Mustafizur Rahman
Faculty, Department of Industrial and Production Engineering
Jessore University of Science and Technology
Jessore-7408,Khulna, Bangladesh
Mobile: +8801911673229
Samrat Kumar Dey
Faculty, Department of Industrial and Production Engineering
Jessore University of Science and Technology
Jessore-7408, Khulna, Bangladesh
Mobile: +8801911291231
Md. Sumon Rahman
Faculty, Department of Industrial and Production Engineering
Jessore University of Science and Technology
Jessore-7408, Khulna, Bangladesh
Mobile: +8801917834751
S. M. Akash
Student, Department of Industrial and Production Engineering
Jessore University of Science and Technology
Jessore-7408, Khulna, Bangladesh
Mobile: +8801948213303
IJRDO-Journal of Business Management
ISSN: 2455-6661
Volume-3 | Issue-9 | September,2017 | Paper-6
Day by day the customers demand changing rapidly, it is very difficult for a company to identify
demand. Big data used in supply chain management helps to create better customer demand
planning strategies by collecting, documenting & analyzing data in a real situation. Big data
combined a set of data usually referred as large and complex datasets; enables the company to
review real-time data flows. Digitized supply chains have the potential to dramatically lower
costs, reduce lead time and increase product availability. Digitization makes the supply chain
more effective, agile and responsive by sharing knowledge and collaborating complex supplier
networks. As a result, this research work helps a manager to identify the current situation of their
business and it will help them to take timely, fast and better decision.
Keywords: Big data; Supply chain management; Digital transformation.
1. Introduction
Today’s increasing proliferation of data, on everything from material flows to customer
preferences, companies highlighting the strong need for enhanced data management and
analytics [russell]. The data play a very significant role on the various decisions related to supply
chain and logistics operations of the business. To identify the needs and wants of the customers,
companies are relying on data. The data that are used now-a-days in supply chain management
are voluminous, versatile, fast as well as sensitive. These types of data are known as Big Data
Big data acts as a disruptive technology in today’s supply chains. Big data analytics features a
variety of applications within the supply chain, its impact is so great. Big data offering supplier
networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence
shared across supply chains [colombus]. Amazon uses big data to observe, track and secure 1.5
billion items in its inventory that are laying around two hundred fulfilment centers around the
world, and then relies on predictive analytics for its ‘anticipatory shipping’ to predict when a
customer will purchase a product, and pre-ship it to a depot near the ultimatedestination [ritson].
Wal-Mart handles more than a million customer transactions each hour & generates 2.5 petabyte
(1 petabyte approximately 1015 bytes) of customer transaction data every hour [sanders].
Furthermore, if Wal-Mart operates Radio Frequency Identification (RFID) on the item level, it is
expected to generate 7 terabytes (1 terabyte approximately 1012 bytes) of data every day
[zaslavsky]. UPS deployment of telematics in their freight segment helped in their global
redesign of logistical networks [davenport].
SCM organizations are immersed with data, so much that McAfee and Brynjolfsson [mcafee]
mentioned “commercial enterprise accumulates additional data than they realize what to do
A digital supply chain is a supply chain whose basis is constructed on internet-enabled
capabilities. A digital supply chain has techniques that monitor real-time inventory levels,
customer interactions with items, provider locations, and equipment and make use of this
information to assist plan and execute at increased levels of overall performance. Digital supply
chain technologies are supporting some companies obtain a step change in performance in more
complicated areas. Amazon, as an example, offers the Dash Button, an internet-enabled gadget
that customers presswhile not having to log on to an accountto reorder laundry detergent,
diapers, and other basic grocery items [generiwall].
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Digitization has touched upon all elements of organizations, along with supply chains and
working models. Today, technologies like RFID, GPS, location based informationand wireless
sensors networks have enabled organizations to convert their existing hybrid (mix of paper-based
and IT-supported strategies) supply chain structures into greater flexible, open, agile, and
collaborative digital models. Unlike hybrid supply chain models, which have led to inflexible
organizational structures, inaccessible information, and dividedrelationships with partners;
digital supply chains allow business process automation, organizational flexibility, and digital
management of corporate resources [capgemini].
The overall intention of this studies is to close the understanding gap between data science and
supply chain management area, linking the records, technology and useful knowledge in big data
applications throughout procurement, marketing, transportation and distribution centeroperations
[I v roza].
Applications of big data have been visible in numerous fields including retail, drugs, nance,
manufacturing, logistics, and media communications (Feng et al. 2013). Researchers (Wamba et
al. 2015; Chen et al. 2012; Wang et al. 2016) have tried to discover distinctive dimensions of big
data and seize the potentialbenefits to supply chain management (SCM). It is vital for supply
chain managers tocomprehend the role of big data in enhancing the efficiency and profitability of
a firm.
2. Digital Supply Chain Management
Supply Chain Management is outlined by Christopher (2011) as the management, across and
inside a community of upstream and downstream corporations, of both relationships and
streamsof material, information and assets.
Digital supply chain is a smart value driven network that leverages new procedures & techniques
with technology and information analytics to make value and revenue [digital Accenture].Digital
supply chains have the capability for broad data accessibility and advanced collaboration that
leadto enhanced responsibility, agility and effectiveness [digital capegedamini].
The ultimate intention of the digital supply chain is to enable insights for enhanced efficiencies,
removing waste and facilitating greater earnings. Companies with a digital supply chain are
highercapable of move people, property, assets and inventory to where they are required at any
given time with a purpose to lessen costs by responding proactively to transportation and
production risks. The potential payoffs of a completely acknowledged digital supply chain
incorporate savings in every area, from assets, time, and money to a discounted environmental
footprint [what is].
Digitization brings into question the basic commandments of how we contend in commercial
enterprise. Preferably, a digital supply chain has approaches that monitor real-time inventory
levels, customer collaborations with items, transporter locations, and equipment and uses this
information to assist design and execute at improved stages of overall performance [rouse].
Digitization of the supply chain has the capability to dramatically decrease costs, growth product
availability, and even create new markets unknown or unavailable prior to the provision of key
technologies [shrener].
Digital business flows are ongoing and outside-in. Traditional supply chain processed are inside-
out and based on historic transactions. As outlined in table 1, there are some vital distinctions.
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Table 1. Comparison of Traditional supply chain process and digital supply chain process
Basis for comparison Characteristics of traditional
supply chain process Characteristics of digital
supply chain process
Data Structured Structured & unstructured
Data latency Data latency of hours, days &
weeks Real time data; they are
updated on regular basis.
Deployment Within the organization Cloud based to synchronize
inter and intra-enterprise flows
Process flows Inside out Outside in
Visualization Rows, columns and graphs Simulation and digital
representation of outcomes
Response Response based on history Sensing based on internet of
Source: Lora Cecere, Embracing the Digital Supply Chain.
3. Benefits of digitally transformed supply chain
Leading companies are utilizing digital supply chain technologies to upgrade their working
models and go-to-market techniques in order to generate enormous growth in revenues and
margins [bcg three]. With the right organizational layoutand governance, they can enable
advanced collaboration and communication across digital platforms leading to enhanced
reliability, agility and effectiveness. This performance distinction will force organizations with
traditional supply chains to adjust to the brand new digital realities or run the risk of falling
behind competition [capemini]. Digitization of information & material flows allows real time
analysis of the company. Nowadays, 9 out of 10 users buy online.We’re already seeing disruption
inside the supply chain. Examples of winning digital disruption: Uber: world’s largest taxi
company owns no taxis; Airbnb: world’s largest accommodation provider owns no real estate;
Alibaba: world’s most valuable retailer owns no inventory and many more.
4. Big data
Data are flooding in at rates never seen beforedoubling every 18 monthsas a result of
greater access to customer data from public, proprietary, and purchased sources, as well as new
information gathered from web communities and newly deployed smart assets. These trends are
broadly known as “big data” [bughin mckincy].
Manyika et al. (2011) defined Big Data as the “datasets whose size is beyond the ability of
typical database software tools to capture, store, manage and analyses”. This definition is not
confined to data size, since data sets will increase in the future. It highlights the necessity of
technology to cope up with the rapid growth in available data. Big data has recently emerged as
“the next big thing” in management [wamba]. Some researchers and practitioners even
recommend that big data analysis is the “fourth paradigm of science” [strawn], or may be “the
next frontier for innovation, competition, and productivity” [maniyaka], or the “new paradigm of
knowledge resources” [hagstom]. McAfee and Brynjolfsson (2012) viewed Big Data as an
approach that transforms decision making processes by enhancing the visibility of firms’
operations and improving the performance measurement mechanisms.
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Companies rely on massive set of extensive versatile and rapid data for their prompt steps in the
zone of supply chain and logistics management. In case of E-commerce giants like Amazon,
Flipkart, Snapdeal and so forth need to accumulate lot data related to customers, orders,
inventory etc. The success of the E-commerce companies depends a lot on how those companies
capture, store and utilize that information in an efficient manner.
In 2012, The Human Face of Big Data accomplished as a global project, which is centering in
real time collect, monitor and analyze massive amounts of data. According to this media project
many statistics are derived[sagi].Facebook has 955 million monthly active accounts using 70
languages, 140 billion photos uploaded, 125 billion friend connections, every day 30 billion
pieces of content and 2.7 billion likes and comments have been posted; about 48 hours of video
are uploaded each minute on YouTube; Google support many services as both monitories 7.2
billion pages per day and processes 20 petabytes (1 petabyte: 1015 bytes) of data daily also
translates into 66 languages;Twitter users generate more than 1 billion tweets every 72 hours,
571 new websites are created every minute of the day [wamba][sagi]. This presents the
importance of big data in the current research world.
4.1 Defining Big data via 3v’s
Big data depicts a method of gathering, handling and analyzing massive amounts of data.
Therefore, big data is mostly characteristics with the three Vs. They are defined as volume,
velocity and variety.
The volume characterizes the large amounts of data stored within the IT infrastructure. Data is
now vast in the amount such as Petabyte (1 PB: 1015bytes), Exabyte (1 EB: 1018 bytes),
Zettabyte (1 ZB: 1021 bytes) and Yottabyte (1 YB: 1024 bytes) etc.
Velocity describes the large amounts of data that generated at an excessive speed. Velocity is
needed not only for big data, but also all processes.
Varietymeans big data comes from a great variety of sources and generally has in three kinds:
structured, semi structured and unstructured. Structured data inserts a data warehouse already
tagged and easily sorted. Unstructured data is random and hard to analyze. Semi structured data
does not conform to fixed fields but includesvarious structure ofinformation handled within the
big data surroundings.
[Mishra agri] Besides the “3Vs”, three other characteristics, that is, veracity, variability and value
have been introduced.
The value of big data is reflects the financial benefits from big data [forrester];related to the
reality that the data needs to be processed and analyzed for further usage.
Veracityconsists of two aspects: data consistency (or certainty) and data reliability. White (2012)
indicates that veracity deals with data quality and its significance, as well as the level of trust
accorded to a source of data.
Variability refers to data changes during processing. Increasing variety and variability
additionally increases the attractiveness of data and the potentiality in presenting unexpected,
hidden and valuable information.
5. Data sources
Big data comes from a wide range of sources:
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-Today’s technologies and social platforms allow businesses to get direct customer feedback in
the form of ratings, reviews and blog comments.
-Data from mobile, social platforms and e-commerce are being integrated with data from
enterprise systems.
-Manufacturing is changing from event-based planning to real-time sensing with the introduction
of the Internet of Things and machine-to-machine communication.
-Evolved sensor technology provides real-time equipment and product conditions data resulting
in automated maintenance and process adjustments.
6. Big data in SCM
[Mishra] It has been argued that the competition is no longer between firms, but between
wholesupply chains. As a final result of increasing attention on SCM, managers are now
pressured to reassess their competitive strategies (Zacharia et al. 2011). Since both technology
and data are available; it is essential for companies to decide how to use them to win (Hopkins et
al. 2010). Supply chain managers are getting increasingly dependent upon data for gaining
visibility on expenditure, identifying trends in prices and performance, and for supporting system
manipulate, inventory tracking, manufacturing optimization, and process improvement efforts.
As a matter of fact, there are several companies that are flooded with data and try to capitalize on
information analysis in an attempt to achieve competitive advantage (Davenport 2006). Having a
capacity to exploit data, firms like Google, Amazon outperform their competitors by developing
potential business models. Barton and Court (2012) highlighted that via big data, firms can
change the way they work together and deliver performance gains similar to the ones achieved in
1990s when companies updated their core processes. They also pointed out that the adoption of
data-driven strategies will soon emerge as a significant point of competitive differentiation.
McAfee and Brynjolfsson (2012) observed that productivity rates and profitability of companies
can be enhanced by 56 % if they consolidate big data into their operations.
[Isacc] Big data solves problems in a variety of business domains, but sales and operations are in
the lead. The study conducted by Forrester Research Inc. On How Forrester Clients are Using
Big Data, September 2011. Indicated some of the key points about business domains where Big
Data has the greater influences. This view is indicated in the table 1.
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The potential of big data is not limited to manufacturing companies; retailers, service providers,
healthcare professionals, and governments, among others, also see big data potential. McKinsey
Global Institute specified the potential of big data in five main topics:
- Healthcare: clinical decision support systems, individual analytics applied for patient
personalized medicine, performance based pricing for personnel, analyze disease patterns,
improve public health.
- Public sector: creating transparency by accessible related data, discover needs, improve
performance, customize actions for suitable products and services, decision making with
automated systems to decrease risks, innovating new products and services.
- Retail: in store behavior analysis, variety and price optimization, product placement design,
improve performance, labor inputs optimization, distribution and logistics optimization, web
based markets.
- Manufacturing: improved demand forecasting, supply chain planning, sales support,
developed production operations, web search based applications.
- Personal location data: smart routing, geo targeted advertising or emergency response,
urban planning, new business models.
Following the Gartner survey [20], nowadays around 26-28% of manufacturing companies and
retailers invest in big data solutions. In the transportation sector only 20% of the
asked companies have already invested, but with 50% there is the highest value of planned
invests within the next two years. The problem addressed with big data (summarized over all
industries) is about 32% in improving risk management.
Table 2: Business domain big data
Business Domain Big Data Contribution (%)
Marketing 45
Operations 43
Sales 38
Risk Management 35
IT Analytics 33
Finance 32
Product Development 32
Customer Service 30
Logistics 22
HR 12
Other 12
Brand Management 8
Source: Forrester Research Inc. How Forrester Clients are Using Big Data, September 2011.
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7. Levers in the big data driven supply chain
Big data can tremendously affecteach of the supply chain levers (mentioned below) and can add
value to the overall supply chain operations using improving operating efficiency based on the
result of analysis.For instance, marketing captures and tracks demand through Point of Sale
(PoS) data,transportation creates records from GPS transponders, RFID data identifies stored
goods and electronic data interchange sends automatic buying orders.
Marketing- Marketingis the traditional supply chain lever seen with big data which can
transformed customer understanding into an agile system that sends hugeamount of information
flowing upstream in the chain (Jüttner et al., 2010). Intimacy with customers can be
accomplished by analyzing customer data, and at this lever, data sources that encompass social
media, mobile apps, or loyalty programs can be found; all of them are the enablers for the
sentiment analysis, location based marketing and in-store behavior analysis.
Procurement- Procurement manage the relationships at the upstream supply chain. Data
complexities on this side may emerged from globalized purchasing strategies with thousands of
transactions. In this lever, a strong connection with internal finance reporting led to adopt
measures on spend visibility data, to gain granular levels on aggregated procurement patterns.
The procurement needs to activatethe data sources not only for spending data management
process, but also for the wholeprocurement function.
Warehouse management- (Especially inventory management) has been greatly modifiedby
modern identification systems after successful introduction of RFID. Within this group, the
largest groups of data are associated with an automated sensing capability, particularly as the
Internet of Things(IoT) and extended sensors, connectivity and intelligence to material handling
and packaging systems applications evolved.
Manufacturing- A number of company’s report using big data analytics for inventory
management, optimization of stock ranges, maintenance optimization, and some in facility
location. Some are considering use in the workforce productivity assessment in addition to study
of capacity constraints.
Transportation-Transportation analysis making use of operational research models has been
greatly utilized for location, network design or vehicle routing using origin and destination
(OND) and logistics networktopology.
8. How digital scm use big data
Davenport mention, “Businesses across many industries spend millions of dollars employing
advanced analytics to control and improve their supply chains. Organizations look to analytics to
help with sourcing raw materials more efficiently, enhancing manufacturing productivity,
optimizing inventory, minimizing distribution cost, and other related goals.” As business leaders
recognize, the speed at which businesses operate today is frequently measured in minutes or
seconds. Manual processes simply can’t perform at those paces; which is the reason cognitive
computing systems are being utilized at the heart of digital supply chains.
Noha Tohamy argues in a recent Gartner Research report that it is ‘progressively unrealistic’ for
supply chain organizations at big companies to think they can function without advanced analytic
solutions. Only with these types of solutions will companies be able to examine huge sets of
structured or unstructured data to acquire deep insights, make expectations, or generate
recommendations.” Deborah Abrams Kaplan clarifies, utilizing large data sets for analysis and
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planning purposes, those in the supply chain can react quicker to changes at different points
along the chain. There are many ways big data and advanced analytics are being used today to
make digital supply chains a reality. They are:
Real-time monitoring-Add in big data originating from social sources (e.g., Facebook, Twitter),
news, events and weather, businesses can better predict and plan future inventory instead of
relying on ancient data. For example, a store running a weekend promotion can track sales on a
real-time basis, versus once daily. Taking into account current sales, along with social media
responses to the promotion and potential weather events, the company can quickly alter their
supplies and warehouse shipping plans.
Supplier sourcing- Maintaining large data sets enables companies to more easily track their
suppliers and make adjustments quickly.
Customer segmentation- Using big data, companies can segment their buyers and markets,
offering each store with specific items of interest to their customers.
Knowledge sharing- Big data is changing the nature of supply chain management by creating
opportunity for knowledge sharing. Rather than depending on a linear chain of knowledge, we
now have access to 360° data from sources in every industry and in every geography in
actual time.
Forecasting demand- Companies can now integrate fast-moving data from clients, machinery,
suppliers, environmental factors and contextual factors, such as pricing, competitor activity and
geopolitical impacts. BCG [] estimates that companies who are able to predict the future more
precisely can diminish their inventory by 20-30% while expanding the ‘fill rate’, which is the
effectiveness of inventory to satisfy demand, by 3-7%.
Simplifying distribution- With such broad analytical capabilities, big data can possibly
breakdown company’s distribution networks and can feature areas to streamline them.
9. Benefits of big data driven supply chain
Big data is more useful than many people fully realize.Companies wanting to increase efficiency
and profitability in supply chain execution should take note of big data[sander].
Improved visibility across supply chain- Planning and scheduling are perhaps the most crucial
part of any supply chain. So much money can be lost or expended with scheduling and planning.
Using big data, firms can truly optimize this process. With the use of big data firms can gain end
to end visibility so that managers know that where itemsare at all times, firm can also attain high-
quality decisionsupport which can be crucial if something goes wrong a splitsecond decision
does not have to go without support.
Improve customer experience- Analysis of more different data types, including social
media data, can be used to improve the customer experience.
Increase accuracy in demand forecasting- Another benefit is that a firm can really predict
and satisfy demand. With big data, helps to predict and determine whatitems are going to be
needed as it pertains to demand.
Better manufacturing efficiencies- Big data helps to expedite order picking and order
fulfillment by analyzing data from different sources likehistorical orders, item inventory,
warehouse layout andhistorical picking times. It also improves product and
servicetraceability. Identification of potential problem suppliers aswell as identify problems
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for suppliers executed in better way.It uncovers defects in products/services in the supply
chain,give early warning and avoid recalls Minimize inventory andsupply chain risk using
big data analytics.
Opportunities to solve more complex distribution network problems- Most complex
distribution networks have developed organically over time into an almost impenetrable web
of factories, warehouses and distribution hubs which can struggle to adapt quickly to
changing patterns of demand. Companies can deal with this complexity more easily than in
the past with the use of big data analysis. Big data provides the opportunity to solve much
more complex distribution network problems by modelling outcomes in more detailed
scenarios than ever before.
Better inventory planning & development- this is another benefit as big data allows users
to plan, forecast, and truly optimize their inventory so that they do not waste space or waste
money with items that may or may not be working the way they should.
Develop greater collaboration in supply chain stakeholders- Big data helps to better
visibility which can translate into better collaborations with vendors, suppliers, carriers,
distributers, warehouses, and customers.
10. Challenges of big data driven supply chain management
The successful utilization of big data techniques presents great advantages in economy
transformation, but also raises many challenges, including, among others, troubles in data
capture, storage, searching, analysis and visualization. These challenges need to overcome with a
purpose to exploit capabilities of big data. Moreover, the amount of information increases
exponentially. This has a massive impact on limitation of real-time values discovery from big
data. Another challenge associated with the big data analysis includes data inconsistence and
incompleteness scalability, timeliness and informationsecurity. Reskilling workforce, handling
integration of new types data is also considering important challenge in big data driven supply
chain. Hence, data must be correctly constructed and a number of preprocessingtechniques, for
example, data cleaning, data integration, data transformation and data reduction need to be
implemented in order to alleviate noise and correct inconsistencies.
11. Conclusion
This study explored the impact of big data in supply chain management. The use of big data in
digitized supply chain management is promising and novel; research in these areas is still a work
in process. More efforts from diverse fields of expertise need to be concernedin order to more
effectively exploit all hidden values in vast datasets. There are numerous difficulties and
challenges to address, such as data quality, privacy, technical feasibility, among others, before
big data can obtain widespread influence in the supply chain management. In the long run, these
difficulties are likely to be solved, considering venturesome character of big data. With the help
of big data timely, rapid and effective decisions can be taken in businesses. Organizations can
make better decision with the help of precise analysis of data. Thus, better decision means
greater operational efficiencies, improve customer experience and reduced risk.
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... Today, technologies like RFID, wireless sensors networks, and location based information have enabled organizations to switch their existing hybrid supply chain structures into greater collaborative digital models, flexible, open, and agile. (Tahiduzzaman et al., 2017). ...
Smart technologies can help to get a clear data of the condition, location, and environment of goods, and processes at anytime, anywhere, also to make intelligent decisions and take corrective actions so that the supply chain can run more efficiently. This work aims at exploring the state of art on the smart solutions such as Big Data Analytics, Cloud Computing, Internet of Things (IoT) and Blockchain in Logistics and Supply Chain Management (SCM). This paper explains the potential applications as well as the impact of smart techniques on SCM. Furthermore, a smart model for supply chain management is proposed.
Full-text available
Big Data Analytics (BDA) is one of the most digital innovations for supporting supply chain firms' activities. Empirically, multiple benefits of BDA in Supply Chain Management (SCM) have been demonstrated. The study aimed to investigate the relationship between technical, organizational, and environmental factors and supply chain firms' performance using the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovation (DOI) theory. This study was conducted at medium-large supply chain firms in Saudi Arabia, the sample size reached 700 firms recognized by Saudi Arabia's Ministry of Commerce and Industry in different domains. In this study, a questionnaire was used to collect primary data. The collected data are analyzed using SPSS version 26.0. SPSS is used to describe respondents' demographic profiles. The percentage of respondents to the questionnaire reached 57%. In addition , to test hypotheses and accomplish research goals, PLS-SEM version 3.0 is used to examine the relationship between independent and dependent variables. From the PLS results, the study reported that complexity (β = 0.097, t = 2.817), security (β = 0.222, t = 3.486), IT expertise (β = 0.108, t = 1.993), and external support (β = 0.211, t = 3.468) were positively related to firm's performance; in contrast, relative advantage (β =-0.006, t = 0.200), compatibility (β =-0.020, t = 0.314), top management support (β =-0.046, t = 0.386), organizational resources (β =-0.065, t = 1.179), competitive pressure (β =-0.011, t = 0.199), and privacy (β =-0.05, t = 0.872) were negatively related to firm's performance.
Conference Paper
Full-text available
Advancements in telecommunications and computer technologies have led to exponential growth and availability of data, both in structured and unstructured forms. The term Big Data mainly refers to enormous datasets containing large amount of unstructured data that require more real-time analysis. Great potential and very useful values are hidden in this huge volume of data. The influence of Big Data is recognized in logistics services, turning large-scale data volumes into a unique asset capable of boosting efficiency in areas of the business. This paper analyses benefits and opportunities of Big Data in logistics systems. Challenges and risks that logistics systems, affected with this phenomenon deal with, are highlighted. The paper also proposes some efficient ways of exploiting the value of Big Data in logistics systems.
Full-text available
As Big Data has undergone a transition from being an emerging topic to a growing research area, it has become necessary to classify the different types of research and examine the general trends of this research area. This should allow the potential research areas that for future investigation to be identified. This paper reviews the literature on ‘Big Data and supply chain management (SCM)’, dating back to 2006 and provides a thorough insight into the field by using the techniques of bibliometric and network analyses. We evaluate 286 articles published in the past 10 years and identify the top contributing authors, countries and key research topics. Furthermore, we obtain and compare the most influential works based on citations and PageRank. Finally, we identify and propose six research clusters in which scholars could be encouraged to expand Big Data research in SCM. We contribute to the literature on Big Data by discussing the challenges of current research, but more importantly, by identifying and proposing these six research clusters and future research directions. Finally, we offer to managers different schools of thought to enable them to harness the benefits from using Big Data and analytics for SCM in their everyday work.
Conference Paper
Full-text available
The main objective of this study is to provide a literature review of big data analytics for supply chain management. A review of articles related to the topics was done within SCOPUS, the largest abstract and citation database of peer-reviewed literature. Our search found 17 articles. The distribution of articles per year of publication, subject area, and affiliation, as well as a summary of each paper are presented. We conclude by highlighting future research directions where the deployment of big data analytics is likely to transform supply chain management practices.
Full-text available
Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
Conference Paper
Today's business is highly turbulent due to high-end competition. Every company wants to survive and continue to strive for higher market share. Companies are continuously trying to meet the expectations of the customers. Customers' needs and wants are fast changing. Companies are investing heavily in creating Information Technology support and platform so as to facilitate the businesses. Logistics and Supply Chain are the vital wings of any business. Big Data in different aspect is helping the businesses in the areas of Logistics and Supply Chain. Through Big Data businesses are in a position to collect, update, store, use versatile set of data relating to different processes and activities of the business. Timely, fast and effective decisions can be taken in businesses with the help of Big Data.
The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) – that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.