Evaluating Big Data's Role in International Supply Chains: Domino's

This report critically evaluates the application of big data analytics within international supply chains, using Domino's Pizza as a case study. It explores how big data is utilized to predict market demand, optimize distribution, and gain a competitive advantage. The report delves into the concept of big ...Read More

International Supply Chain Management
University
A CRITICAL EVALUATION OF THE ROLE OF BIG DATA IN
INTERNATIONAL SUPPLY CHAINS AND ITS IMPACT ON
SUPPLY CHAIN MANAGEMENT: A CASE STUDY OF
DOMINO’S PIZZA
Student
ID
Unit title
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Lecturer’s name
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Executive summary
This study evaluates the contribution of big data analytics in the management of international
supply chains. Evaluation of the data has availed the understanding that the huge amount of data
that supply chains like Domino’s acquire from their stakeholders might be evaluated and utilised
with the use of big data analytics, for predicting, the market demand and appeal of their products
and services. For this, the business organisation might utilise SCOR model and combine it with
the elements of big data analytics for generation of competitive advantage.
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Table of content
Introduction......................................................................................................................................4
Findings and analysis.......................................................................................................................4
Conclusion.....................................................................................................................................16
Reference list.................................................................................................................................17
Appendix........................................................................................................................................20
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Introduction
In the current business environment of global competition, optimisation of the supply chain has
become essential for the multinational corporations and big data analytics, due to its capability of
evaluating and comparing homogenous data has immerged as the preferred measure for
assuming the company strategies and predicting the market trends (Forbes.com, 2017).
Utilisation of big data emerged as an amiable way for generating value in the functional areas
such as product development, perdition of market demand, optimisation of distribution and
feedback acquisition from customers for global supply chains (Russom, 2011). Since a decade,
multinational supply chains, such as, UPS, DHL and Maersk have found big data analytics to be
suitable for logistics and transportation management. Business experts identified it as capable of
saving considerable expenses of the company through identifying the shortest and most direct
path for the company operations and by predicting the potential determinants of the success of
the company in market, assisting it to identify the market opportunities. Such in-depth
understanding of the market trends and varied functional paths helps the business corporation to
identify the most profitable strategic options for conducting the organisational operations in
international market environment.
According to a recent report, Domino’s has already utilised the big data analytics for managing
its international supply chain activities which has supposedly improved the organisational sales
and increased its revenue in considerable manner (Forbes.com, 2017). Dominos is the largest
pizza supplier across the globe, which has accepted that it uses big data analytics for evaluation
of the organisational data and predicting its business prospect. The organisation, applying big ata
analytics has recently transformed its marketing and sales strategy in considerable manner and
emphasised more on the online transaction and complete virtual sales experience. This has
increased customer preferences concerning the company’s product, which might be identified
increase in company’s sales and reduced sales of its closest competition, Pizza Hut by 2%. In this
particular research report, the researcher has investigated the role that big data plays in
management of international supply chains with examples from Domino’s Pizza.
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Findings and analysis
Concept of Big Data analytics
NASA first used big data, as a term in 1997 for explaining the challenging faced in visualisation
of the issue or prospects due to the presence of huge sets of data (Hazen et al. 2014). Big data
has the potential of contributing to the enhancement of sales, loyalty of customers and reduction
of organisational expenses and risks through optimisation of real-time routes (Waller and
Fawcett, 2013). From this, it can be deduced that application of big data analytics might become
mandatory for the multinational supply chain companies in future due to the huge amount of
information that they are stored in their database for evaluation. Evaluation of the concept of big
data analytics from the resource-based view might be useful in further evaluating its necessity in
managing multinational supply chains.
Resource-based view (RBV) previewed the physical infrastructure concerning the information
technology as the tangible resource of a business organisation, while the competency and
knowledge of the employees along with their experience are considered intangible assets (Kwon
et al. 2014). These intangible resources of the business organisation are developed through the
external evaluation and internal investment. Therefore strengthening such tangible and intangible
assets in the RBV is crucial for enhancement of the general IT capability of the company, which
might be a significant determinant of the competitive advantage of the organisation. Dubey et al.
(2016) opined that the richer a company is in terms of such resources, more chances it has for
generating further value for itself. Incorporation of big data analytics in the data management
and analysis process of the firm ensures heterogeneity of the firm in terms of approach towards
the market and customer needs, which is crucial for maintaining sustainability of the company
within the competitive market. Forbes reported that information from 85,000 different sources is
collected and added to the database of the company every day (Forbes.com, 2017). This includes
both structured as well as unstructured data. Therefore, it is essential for the company to utilise
big data analytics to acquire positive IT capability and utilise such data. Apart from RBV, the
isomorphism is also capable of explaining the importance of big data in supply chain
management.
As per the neo-institutional isomorphism, the basic purpose of a business organisation behind
any innovation is not only enhancement of their level of efficiency, but also increasing functional
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and operational similarity with the performance benchmark (Demirkan and Delen, 2013). Often
the intention or willingness of a company behind the acquisition of IT measures is triggered by
the fact that some other market entities have already generated profit through acquisition of the
measure. Similarly, the acquisition of big data analytics has enabled companies such as UPS that
operate in the segment of logistics to acquire considerable level of efficiency (Davenport and
Dyche, 2013). As per Davenport and Dyche, (2013) application, big data analytics in the supply
chain management process of the company has enabled it to save 8.5 million gallons of fuel and
cut its daily route by 85 million miles in 2011. As per the company record, by saving 1 mile a
driver per day the organisation saves $30 million. It has also planned to utilise the system to
optimise the level of efficiency of its 2000 aircraft flights every day, which is supposed to reduce
substantial amount of its expenses enhancing the efficiency of the company. Such inspiring
records have motivated the global supply chains like Domino’s pizza to utilise big data analytics
for enhancement of its efficiency level. The following record avails an insight to the influence of
big data on revenue of the company:
Sales Income Revenue
Expected
increase in
sales
Increase in
sales
Expected
increase in
income
(cent/share)
Increase in
income
(cent/share)
Expected
revenue
(Million)
Revenue
(Million)
9.40% 12.90% 90 96 $542.60 $5,66.70
Table 1: Consequence of application of big data analytics in Domino’s
(Source: Time.com, 2017)
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Expected revenue (Million) Revenue (Million)
Revenue
$530.00
$535.00
$540.00
$545.00
$550.00
$555.00
$560.00
$565.00
$570.00
$542.60
$566.70
Series1Axis Title
Chart 1: Consequence of application of big data analytics in revenue of Domino’s
(Source: Time.com, 2017)
From the table and chart presented above, it is evident that business performance of Domino’s
has improved considerably through the incorporation of big data analysis in its business
processes. As the big data, analytics avails the company an insight to the functional issues that
the company might face and the challenges that changing market environment might put before
the company, through evaluation of heterogeneous data, therefore the company achieves an
upper hand in combating the issue and retain its competitive advantage. Big data analytics
completes 5Vs of data analysis that develop strategies concerning management of business
operations.
Volume is the huge amount of data that is stored in the database of the multinational supply
chains every day that needs deep introspection for the strategy making (Levelling et al. 2014).
The second aspect of big data analytics is the variety of information. The vast number of sources
increases the challenge for global supply chains to categorise the data acquired from structured,
unstructured and semi-structured sources and evaluate them for sound decision-making. On the
other hand, velocity refers to the speed in which the data is collected. This determines the
reliability of the data and is dependent upon the efficiency of the data storage system of the
business organisation (Benabdellah et al. 2016). Veracity refers to the trustworthiness of the
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data, this process verifies the reliability of the sources from which the data is acquired and the
complexity of the data. It also evaluates the compliance of the data with the situation. Finally,
value is the measure taken to ensure that acquired data is not tampered with or exploited as these
data are valued assets of the company that assists its supply management and forecasting its
future strategic requirement (Brinch et al. 2017). These five aspects of the big data analytics
ensure exhaustive evaluation of the data available to the company for identification of suitable
strategy for the organisation. In the following section, the concept of supply chain and its
complexities are evaluated for acquiring a deeper understanding of the role of big data in
completing the complex task of supply chain management.
Supply chain and its complexities
The supply chain is the amalgamated entity of multiple business units that work in harmony with
each other and thrive to achieve a single goal through completion of their own responsibilities.
Management of the supply chain at its simplest form is the combination of three different tasks,
such as supply of the raw material to the manufacturer, production of the products distribution of
the product via different distribution channels (Christopher, 2016). While it appears to be a
simple to maintain coordination among these three basic tasks, in case of multinational
corporations such as Domino’s ensuring it is a complex task. This might be explained with the
help of the instance of Domino’s failure in the global market between 2006 and 2008.
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2006
2007
2008
2009
2010
-6 -4 -2 0 2 4 6 8 10 12
Sales growth in Domino's between 2006 and
2008 (%)
Chart 2: Sales growth in Domino's between 2006 and 2008
(Source: Exaltsolutions.com, 2017)
From the chart above it is evident that the sales Domino’s witnessed considerable downfall
between the period of 2006 and 2008, which after the alteration in its management measures of
global supply chain between 2009 and 2010 exhibited considerable growth. This avails an insight
to the importance of appropriate management of supply chain of multinational companies such
as Domino’s. Stadtler (2015) opined that along with the prospect of growth innovations also
bring the potentiality of loss, which in this particular case proved to be true. Sales of the
organisation between 2006 and 2008 faced downfall by 10% and the revenue of the company
dropped from US$1.5 billion to US$1.4 billion (Exaltsolutions.com, 2017). This provides an
understanding that, in case of multinational corporations such as Domino’s managing the
complexities of supply chain operations is essential for not just bringing development but also
for restraining loss.
The basic complexities, in the management procedures of supply chain, concern with the flow of
goods and information across the different units of the organisation. Fawcett et al. (2014) pointed
out that while, earlier the supply chain management required linear and sequential flow of
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information from the service provider or the company to the customers, in the current era of
globalisation and digital communication the path of information flow has not remained the same.
In the current era of data transparency, the information flow has become both way and the
communication takes place simultaneously among the supply chain partners, suppliers,
distributors, customers and the company. Presently the supply chain consists of several different
units that are either directly or indirectly linked to one another. This develops a complex web of
information flow. Some of these complexities are as follows:
Components of
the supply chain
Complexity of the supply chain depends on the number of the factors like
product, markets (Locations of operation), partners, processes, relations,
objectives (Carter and Liane Easton, 2011)
Domino’s operates in over 70 countries around the world through more than
10,000 outlets (BBC News, 2017)
It offers a vast variety of products and operates with multiple partners and
suppliers in the home and host market
Variety It refers to the dynamic responsibilities and behaviour of the supply chain
system and similar to any other multinational corporation the supply chain of
Domino’s also serves as the coordinator between its different departments,
interacts with its units and outlets in different countries, connects with
customers, investors and suppliers making its considerably complex (BBC
News, 2017).
Diversity This refers to the heterogeneity or homogeneity of the market (Rushton et al.
2014).
Domino’s operates in different countries all over the world, therefore its
market and internal culture face heterogeneity due to the cultural, political
and economic diversity of the target market making its supply chain complex
(Forbes.com, 2017).
Uncertainty Uncertainty, here, refers to the lack of cohesion within the supply chain,
which might be triggered due to lack of information or inappropriate usage of
the information (Wu and Pagell, 2011).
As has been mentioned in the earlier section Domino's receives a huge
amount of information concerning its functions and people's perception about
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its functions, therefore, lack of information is not an issue for the company
(Forbes.com, 2017).
Moreover, due to the application big data analytics proper analysis has also
been conducted on the acquired information that has led to considerable
growth of the organisation’s business.
Independency Complexity within the supply chain increases based on the increased
independence in terms of:
 Differentiation between products
 Supply chain partners
Table 2: Complexities of supply chain
(Source: Created by author)
From the evaluation above, it is evident that the vast operational sphere of Domino’s makes its
supply chain considerably complex, which requires a sound evaluative system for ensuring its
sustainability in the global market and growing its sphere. The vast data that Domino's deal with
regularly requires an efficient data analysis system. This refers to a system, which is capable of
evaluating homogenous data and predicting market or operational change requirements for the
company. Big data analytics has the capability to solve this issue through its 5Vs.
Expected increase in sales Increase in sales
Sales
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
9.40%
12.90%
Axis Title
Chart 3: Expected and acquired sales increase after applying big data in Domino’s
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(Source: Time.com, 2017)
From this table it is evident that the sales of the organisation increased in considerable manner
after the application of the 5Vs of big data analytics. The following segments avail an insight
into the scope for amalgamation of SOCR model and big data analytics for international supply
chain management.
Applicability of big data analytics in SCOR model
Supply chain operation reference model (SCOR), is the framework that avails an insight to the
manner in which performance metrics, business processes, practices and people skills can be
unified into a single operational structure for management of supply chain (Li et al. 2011).
Amalgamated application of these two measures might prove to be effective in generating value
for the global supply chains like Domino’s. SCOR model avails supply chain framework based
on five management processes that, together, simplify and increase efficiency of the management
process of supply chains (Zhou et al. 2011).
Figure 1: SCOR model
(Source: Zhou et al. 2011)
The plan is the very first stage of SCOR model, in which the information concerning the
customer requirement and availability of resources is acquired. Then the acquired information is
compared to identify the existing capabilities and gaps of the company (Georgise et al. 2012).
Incorporation of the big data analytics at this stage might prove to be highly beneficial in
evaluating such information and predicting operational requirements as per that. Source stage
concerns with the order and receipt of the good (Erkan and Bac, 2011). Domino’s has designed
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Plan Source Make delivery
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its app to track the time span for this entire session; therefore, evaluation of this data through the
big data might help in identifying the loopholes and availing solutions to enhance efficiency
level. This might be better explained through instance of GE that estimates that even 1%
improvement of efficiency in turbines in the global gas-fired power plants might save US$66
billion for fuel consumption (Davenport and Dyche, 2013).
Make refers to the period of development of the product while delivery concerns with the
process associated with reception to demand fulfilment stage (Erkan and Bac, 2011). Evaluation
of the feedback of customers through big data might avail an insight to the general opinion of
consumers concerning this. Finally, return refers to the reverse flow of the product, identifying
its reason is essential for the company to ensure rectification of the issue and improvement of
service quality (Benabdellah et al. 2016). As the entire process concerns with acquisition and
evaluation of information, therefore utilisation of big data might help the supply chains to
enhance its functional efficiency.
Big data application in SCM
Application of the 5Vs of big data analytics might be helpful in measuring the effectiveness of
this data analysis system in generating growth for the international supply chains such as
Domino’s.
Volume- As mentioned above Domino’s pizza acquires data from 85,000 different direct sources
every day (Forbes.com, 2017). The organisation has recently introduced its own mobile app for
availing the customers a complete digital experience of making order called AnyWare, which is
in effect in its outlets situated in over 70 different countries across the globes. The huge amount
of data generated through this app avails the organisation an opportunity to provide the
customers, offers and services as per their purchase behaviour and preferences. For effective
utilisation of such data, application of the big data analytics is essential. A variety of the data and
its categorisation is an equally important aspect for the big data analytics.
Veracity- while handling data from over 85,000 sources it becomes essential for the organisation
to evaluate the reliability of the data and their compliance with the issues that the organisation
might face (Forbes.com, 2017). Addressing this issue is essential for Domino’s for ensuring that
its operations generate maximum value for the customers. In encounter with Forbes Domino’s
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Pizza’s official, Djuric pointed out that the organisation has immerged as digital e-commerce-
operation since its acquisition of big data analytics for management and evaluation of its data
(Forbes.com, 2017). The official also added that this has enabled the organisation to lead the way
for the other similar companies towards generation of better purchase experience for the
customers.
Value- protecting the organisational data from manipulation and misrepresentation is essential
for any business organisation and Domino’s is no exception to this. Big data analytics has
availed the organisation a structured way of ensuring that.
Variety- In the current era of digitisation, large fast-food chains such as Domino’s receive bulk
amount of data from both online and offline sources that incorporate audio, visual, sensory and
statistical information that need to be decoded and evaluated through big data.
Velocity- Velocity or the speed of data collection, which is extremely high for Domino’s due to
its vast sphere of operation. Statistics show that around 45% of the sales of Domino’s are
conducted through the online measures that instantly avail record of the purchase, details
concerning the identity as well as preferences of the customers that automatically are added to
the organisational database (Exaltsolutions.com, 2017). Efficient management of such data is
essential for sustainability of the customers in the highly competitive global fast-food market.
The performance of dominos presented in the following table might be effective in proving the
effectiveness of such data management of the company.
Year Sales growth %
2012 3.1
2013 5.4
2014 7.5
2015 12
Table 3: Same store sales growth of Domino’s Pizza
(Source: Exaltsolutions.com, 2017)
The data presented in the table can be made further comprehensible through the following
diagram:
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2012 2013 2014 2015
0
2
4
6
8
10
12
14
3.1
5.4
7.5
12
Same store sales growth of Domino’s Pizza
Sales growth %
Chart 4: Same store sales growth of Domino’s Pizza
(Source: Exaltsolutions.com, 2017)
In the year 2013, the annual global online sales of the company were US$2 billion, in which 35%
of the revenue was generated through the mobile orders. For management of such huge data and
utilisation of them for the sustainability and growth of the company in the competitive market, it
is essential for the organisation to apply a sound measure for data evaluation. Moreover, as per
the report of Forbes, the organisation utilises big data analytics for forecasting the trends and
developing the strategy for supply chain management (Forbes.com, 2017). The success of such
measure can be inferred from the continuous growth in the sales of the company since 2013.
From the evaluation above, it is evident that acquisition of big data analytics has generated
considerable value for the Domino’s by enhancing the efficiency of its supply chain
management.
Although the evaluation of the given case study highlights the success of Domino’s decision of
incorporating big data analytics in its data management and evaluation process, there is no
evidence for the company to utilise it along with the SCOR model. Here it is noteworthy that
application of the big data analytics across the stages of SCOR model as discussed in the
previous section might help it to address each of the stages of supply chain management
separately. This could avail the company better scope of finding the flaws and scope for
improvement in its processes, which could add further accuracy in the organisational strategies
for supply chain management.
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Conclusion
Completion of this particular study has enabled the researcher to deduce that big data analytics in
the current business environment of extreme competition has become essential for the
multinational supply chains. From the examples of Domino's, it is evident that for the companies
that operate in the international market their database might be crucial source for competitive
advantage. Utilisation of big data for evaluation of that vast amount of data might help the
organisation to identify the market requirements and generate sustainable competitive advantage.
Although, currently around 45% of the sales operations of Domino’s are currently conducted
through online measures yet analysis of the source and delivery data with the help of combined
application of big data and SCOR model might avail its opportunity for further development.
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Appendices
Appendix 1
Figure: Most significant digital market activities in
(Source: http://blogs.brighton.ac.uk/md465/2016/05/02/what-is-big-data-and-how-did-a-pizza-
delivery-company-become-a-technological-leader-in-data-usage-and-analysis/)
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Appendix 2:
Figure: Changed performance of pizza chains between 2013 and 2014
(Source: http://www.pmq.com/December-2015/The-2016-Pizza-Power-Report-A-state-of-the-
industry-analysis/)
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Appendix 3:
Figure: Domino’s sales growth
(Source: http://www.exaltsolutions.com/blog/12-lessons-every-b2b-company-can-learn-
dominos-about-boosting-digital-sales)
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