1.
00989418
Imperial
College
London
Management
Report
Management Report
Imperial College London
August
15
Álvaro Llanza
CID: 00989418
Word Count: 4126
How does the emergence of “Big Data” and
business analytics affect the manufacturing
industries?

2.
00989418
Imperial
College
London
Management
Report
1
Abstract
Manufacturers have historically been considered the main drivers of innovation in
business development and value creation. Their efforts to bring the product to the
consumer, cut costs whilst increasing efficiency, have proven greatly successful in
the past, but to diminishing returns as of lately. Big data and business analytics is
thus welcomed as it offers that which the manufacturers have been seeking for so
long, having full technological and organizational transformative effects on every
single step of the manufacturing value chain. This report will thus look at how big
data and business analytics are influencing the different levers of the value chain and
to what extent they are doing so, with the help of examples.

3.
00989418
Imperial
College
London
Management
Report
2
Table
of
Contents
1.
Introduction
3
1.1
Methodology
4
2.
Manufacturing
Value
Chain
5
2.1
R&D
design
5
I.
Product
lifecycle
management
6
II.
Design-­‐to-­‐value
6
III.
Open
innovation
7
2.2
Supply
chain
management
8
I.
Demand
Forecast
8
II.
Logistics
9
III.
Inventory
9
2.3
Production
11
I.
Digital
building
11
II.
Internet
of
things
11
2.4
After-­‐Sales
Services
13
I.
Product
development
from
after-­‐sales
services
13
II.
Preventive
maintenance
and
outcome
optimization
13
2.5
Organizational
and
cultural
effects
15
3.
Conclusion
17
4.
Bibliography
18

4.
00989418
Imperial
College
London
Management
Report
3
1.
Introduction
Just as electricity, oil, or the Internet fundamentally changed the world and how we
go about doing business, the arrival of big data and business analytics can mean the
next great leap in innovation, productivity, and global growth. Spreading like a virus,
the influence of big data nowadays is felt throughout many industries and is helping
in the evolution of business and relationships by revolutionizing every step of value
creation. Essentially, in terms of the manufacturing industry, big data will help in
accelerating product development, use real time customer insights in order to come
up with better-suited designs, whilst reducing substantially manufacturing and
development costs.
In recent times, the manufacturing industry was going through a rough patch as it
saw how its once ground-breaking strategies produced ever-faster diminishing
returns: the promise of globalisation eventually led to higher wages in low-income
countries where manufacturing was once outsourced to; there was a lack of
technological innovation in the manufacturing industry; rising transportation costs;
ever-entangling supply chains; increasingly complex customer needs. All added up to
the stagnation of the industry, and thus the need for a revolution to re-energize the
dying behemoth.
Moore’s Law, updated recently by IBM, declares that, roughly every year, the number
of transistors in a chip doubles (The Guardian, 2015). This is a pattern that has been
replicated across the computing industry, applying to data and its relentless
expansion. According to IBM, we create around 2.5 quintillion bytes of data every
single day – meaning that 90% of all data ever created was done in the last two
years alone (IBM, 2015). And it originates from just about anywhere; uploading
pictures or videos to the Internet, walking around with your mobile location services
on, or through data-gathering sensors attached to car manufacturing robots.
Although data has been around and growing for a number of years now, it has
gained in importance and potential (only being realised now) due to being coupled
with lowering costs of computation and storage capabilities, as well as our newfound
ability to analyse and gain insights from it. Specifically, the appearance of cloud
technology has brought about a revolution in storage by allowing the outsourcing of

5.
00989418
Imperial
College
London
Management
Report
4
large amounts of data onto the Internet, and thus dramatically reducing costs and
physical space burdens.
Thus, it is only natural to think of big data and analytics as the next step that the
industry must follow and push for in order to regain its former glory. The quick
succession of these dramatic developments means that, in order to attain better-
informed decisions and market better products to their ever-demanding customers,
companies will need to delve profoundly onto the world of big data analytics, both in
a technological and organizational manner, as it becomes a powerful source of
competitive advantage.
1.1
Methodology
Having said this, the following report will try to better comprehend how big data
analytics are affecting the manufacturing industry. Specifically, the examination will
focus on the manufacturing value chain as described by McKinsey in Figure 1, and
the organizational revolution; R&D, supply chain, production, and after-sales service,
and how fundamental their transformation is in order to create further and better
value whilst driving costs down.
Figure 1: Big Data levers across the Manufacturing Value Chain (McKinsey, 2011,
p78).

6.
00989418
Imperial
College
London
Management
Report
5
2.
Manufacturing
Value
Chain
2.1
R&D
design
Innovation and production are interwoven and form the basis of manufacturing. In
order to innovate you have to know how to make your product inside out. The
importance of R&D is reflected by the fact that manufacturers in the US account for
more than two-thirds of private R&D, which ultimately has a spillover effect not only
on other manufacturers, but also on completely unrelated industries (Saving US
Manufacturing, 2011). But with the passing of the years, R&D, like other
departments, has also felt the blow of stagnating innovation, since this is a task that
has become harder and harder to carry out. The emergence of big data analytics as
a tool to revamp this crucial cog of the industry has been greatly felt in a number of
ways. Manufacturers now have the power to basically see into the future; R&D was a
science that created innovation without having precise knowledge of what the market
required, or what consequences would an ‘upgrade’ carry over others. Now, through
the use of big data’s predictive modelling and simulation tools, the analysis of
customer inputs, and open innovation appreciation, manufacturers are able to pursue
the decisions they know are backed up by the right data and won’t lead them into a
cul-de-sac, ultimately having important time and cost reductions down the line. This
has had profound repercussions in the areas of product lifecycle management,
design-to-value, and the aforementioned open innovation.

7.
00989418
Imperial
College
London
Management
Report
6
I.
Product
lifecycle
management
The implementation of IT systems to help figure out a product lifecycle have, with
time, faced the end of their runway as they found themselves trapped within their
own structure. Introducing an interoperable, cross-functional platform and powered
by big data capabilities can bring together datasets from multiple systems and allow
for quick and cheap data share to create digital modelling and simulation. This would
permit firms to test designs, choose the right suppliers further ahead, and predict
manufacturing costs more precisely, “this is specially useful because decisions made
in the design stage typically drive 80% of manufacturing costs” (McKinsey, 2011,
p.88). Thanks to their grasping of collaborative data share and sculpted pre-emptive
designs, Toyota, Fiat, and Nissan were able to cut development time by 30 to 50%.
Specifically, Toyota boasts having eliminated 80% of faults before even building the
first physical prototype (McKinsey, 2011, p.88).
II.
Design-­‐to-­‐value
Market research has taken a new dimension with the incorporation of big data.
Although many players in the field prefer not to share their customer data with
manufacturers due to the competitive advantage inherent to those that do wield it,
design-to-value is a most fundamental process in manufacturing subsectors where
slight product differentiation and evolving customer preferences separates the
hunters from the hunted. Customer feedback, point-of-sales, and willingness-to-pay
are superficial measures and simply the tip of the iceberg regarding market research.
Big data has unlocked the possibility of actually knowing what’s beneath the surface
by mining social media in search of the real picture, and incorporating data-gathering
sensors to products that specifically dictate what features are used most. These
measures help in the manufacturing of better-designed products, practically getting
inside the consumer’s head, and are able to cut down development time and costs
(McKinsey, 2011, p.79). Ford’s automated cars technology is an example of
consumer-centric research: by implanting sensors on Ford’s cars, the firm is able to
extract information on driving patterns and road architecture and ultimately introduce
these into its future driverless vision (Ford, 2015).

8.
00989418
Imperial
College
London
Management
Report
7
III.
Open
innovation
Innovation does not come only in technological terms as companies look for new
ways of homing in the future: innovating innovation. The ideas of the 21st
century
belong not solely within the research lab but also outside of it. Open innovation’s role
and importance within the idea-producing chain has gained in attractiveness as the
numbers of successes across the industry keep piling up. Inviting external
stakeholders to collaborate in the brainstorming process has yielded great results,
particularly through social media or web-based platforms such as Kickstarter. P&G’s
2004 launch campaign of their printed Pringles is a great example of this. When
coming up with ideas on how to print onto the crisps they bumped into the
insurmountable roadblock that was the unfeasibility of the project on a large scale,
never mind the technological requirements that would have extended the
development time and costs onto the red figures. Rather than follow the traditional
approach, P&G released a tech brief outlining the issues they had encountered with
it and distributed it across their global networks to find a solution. They finally
bumped onto an Italian bakery run by a professor that had produced an edible ink-jet
technique for printing onto pastries, which was finally adapted by P&G to great
success (Harvard Business Review, 2006).
The pattern that surfaces from the newfound big data capabilities in R&D is the need
for a fully sharable platform that can extract insights from anywhere, both from within
the research labs and out in the depths of the internet, and be able to put them to
work and into development as quickly as possible.

9.
00989418
Imperial
College
London
Management
Report
8
2.2
Supply
chain
management
In an industry where increasing efficiency and reducing costs at every level are the
foundations on which the entire system relies, manufacturing supply chains have
evolved into ever-more complex puzzles burdened with increasing offshore wages
and sky-high transportation expenses. Big data technology has thus been welcomed
with open arms as it offers streamlining and simplification of operations in key supply
chain areas such as demand forecasts, logistics, and inventory.
I.
Demand
Forecast
Customer preferences are in constant flux and that has a direct repercussion on
demand volatility. As a result, retailers expect manufacturers to show greater
flexibility and responsiveness when drastic changes in one form or another do occur.
When both groups team up, they can use a number of big data resources such as
predictive tools, real-time data, and customer feedback, in order to develop the right
pricing models, attain cost optimization, forecast the demand for a product at
different prices (Entrepreneurial Insights, 2015), and even shape supply and demand
in line with the store’s time-based discounts (McKinsey, 2011, p.80).
A sales forecast, thanks to real-time data and predictive analytics, can be done to
adjust pricing to meet established projections. Furthermore, when in times of high
market demand and low supply, dynamic pricing is used to appropriately adapt the
price to maximise revenue. In addition, demand forecasting can be coupled with real-
time data of inventory to streamline excess ordering and stockouts, thus increasing
liquidity. Data on returns can lead to insights on product defaults and how they can
be improved, driving greater reductions.“We find that companies that do a better job
of predicting future demand can often cut 20 to 30 percent out of inventory,
depending on the industry, while increasing the average fill rate by 3 to 7 percentage
points. Such results can generate margin improvements of as much as 1 to 2
percentage points” (BCG, 2015). Specifically, and according to Mckinsey, P&G was
able to save $300 million thanks to advanced optimization models in its supplier
bidding operations (McKinsey, 2012, p.92).

10.
00989418
Imperial
College
London
Management
Report
9
II.
Logistics
Transportation costs, complex distribution networks, and minding multiple business
unit deliverables simultaneously deserve better systems than the ones in place. The
most malleable ones go as far as planning the truck’s route using historical traffic
patterns (BCG, 2015). But schedule mishaps that chain down the line provoking
avoidable but costly bottlenecks get revamping benefits courtesy of big data
analytics. Advanced geoanalytical technology, cheaper and faster cloud-based
location data, and strong predictive tools are thrown into the mix to re-configure truck
routes, bringing fuel costs down and efficiency up. Deeper into the rabbit hole,
dynamic and real-time rerouting is the standard thanks to the combination of demand
forecasts, real-time truck monitoring, and live traffic feeds. BCG found that firms
implementing big data analytics into their logistics operations were able to cut
transportation costs by 15 to 20% (BCG, 2015). Coca-cola specifically has saved $45
million annually due to their daily-vehicle routing systems inspired by big data
(McKinsey, 2012, p.92).
III.
Inventory
Inventory management has become the epicentre of some of big data’s greatest
advancements in time and cost cutting measures. Specifically, within this
department, Tesco made considerable strides by incorporating automated data
collection software into their big data analytics operations to run predictions on
consumer behaviour and the effectiveness of their discounts and promotions in
stores. For instance, a statistical model was formulated that can predict, depending
on the weather forecast and historical buying data, how the costumer will react and
thus stack the stores with the appropriate items that they will want. This model takes
into account the weather (more barbecue food is sold when its hot), the context (a
hot day in Glasgow may be average in Brighton), and whether there’s been a drastic
change in temperature (a sunny day after a long cold spell) (Information Age, 2013).
“That means there is a 97% change of customers who come into the store finding
what they want, whereas other supermarkets might not have it" (RFgen, 2013). This
model alone saved Tesco £6 million in one year.

11.
00989418
Imperial
College
London
Management
Report
10
In order to understand the value of their offers and have just the right amount of
stock to meet demand, Tesco employs ‘controllers’ specific to each store’s stock. But
due to the growing number of promotions run by the firm and at many times doing so
simultaneously, controllers are understandably overblown and cannot do their job as
accurately as it is inherently required. To tackle the issue, Tesco gathered all its
historical data on promotions and dumped it into a predictive model in the hope that it
would smooth up operations. A few revealing insights surfaced; ‘buy one, get one
free’ suits the consumer better than the 50% discount for non-perishable goods (i.e.
cooking sauces), but works the other way around when the 50% discount applies to
fruit and vegetables. As a result, Tesco pushed its big data insights back onto the
stock controllers in the shape of a sales uplift forecast specific to each controller’s
own store, which they would then use to plan their orders accordingly and strategize
on the promotion item’s location in-store. The rewards were huge as Tesco was able
to remove £50 million-worth of unnecessary stock out of its inventory (Information
Age, 2013).
At its most essential, big data analytics offers the supply chain predictive insights,
greater real-time agility, and an understanding of the business from the bottom-up,
which down the line will lead manufacturers to better meet the customer’s
expectations and preferences - inventory is to be replaced with information if this is to
be attained (BCG, 2015).

12.
00989418
Imperial
College
London
Management
Report
11
2.3
Production
The actual production process is suffering from an efficiency revolution that, on a
similar note as the rest of the manufacturing value chain, is driving time and cost
reductions down whilst unearthing new insights onto business development both in a
technological as well as in an organizational sense. Specifically, simulation and
performance tracking techniques form the underlying foundation of the digital factory
and Internet of things (IoT) environment within the production process.
I.
Digital
building
By absorbing inputs from past performance, manufacturers can develop digital
models of the entire production process, from machinery to labour data, and simulate
the most efficient course of action for a specific product, for new factory floors, or
even entire plants before physically building them. Sophisticated modelling can
drastically cut waste, dig out unsolvable (or unknown) bottlenecks, and redirect the
chain onto greater results. Intel has incorporated big data simulation tools onto its
back-end assembly systems in order to sharpen them up, specifically, “digitization of
the manufacturing process pays dividends by allowing engineers to analyse
production steps at a unit level that had previously been seen only in batches. Yield,
uptime and quality all benefit, resulting in higher precision at lower cost” (Industry
week, 2015). For instance, a steel manufacturer was able to improve its reliability of
delivery by around 25% by simulating its entire portfolio of factories and quickly tests
the improvements (McKinsey, 2011, p.81). According to McKinsey, an automobile
assembly plant was built five weeks ahead of time (and no field overtime) by a 3D
model simulation that minded not-yet-installed systems and that required plenty of
space (McKinsey, 2013, p.78).
II.
Internet
of
things
Hype follows each new innovation like a curse that can only be removed when a
proper functionality is attached to it instead. Such is the case of the Internet of things.
By implanting networked sensors onto production processes and equipment,
manufacturers can make use of preventive maintenance by monitoring wear,
optimize process control maximising yield, and even unearth further innovation. Real-

13.
00989418
Imperial
College
London
Management
Report
12
time data is constantly being fed onto the systems, in a production journey in which
every single step is scrutinized. Efficiency is both the means and the end of the
production journey.
In order to monitor lime mud temperature, a clear indicator of calcination, a
manufacturer of mineral products installed data-gathering sensors in a kiln. The
resulting analysis showed that the flame’s intensity generating heat from within the
kiln could be optimized, eventually leading to a 5% production increase (McKinsey,
2013, p.78). Similarly, Tesla has taken preventive maintenance to the next level by
being able to ‘fix cars over the air’ in its consumer-facing Internet of things division.
“Last year it changed the suspension settings to give the car more clearance at high
speeds, due to issues that had surfaced in certain collisions” (Wired, 2014). They did
this via a software upgrade.

14.
00989418
Imperial
College
London
Management
Report
13
2.4
After-­‐Sales
Services
Throughout the manufacturing value chain, big data’s role gravitated around its
capacity to cut costs whilst incrementing efficiency. But once the product is sold and
in the hands of the consumers, analysis does not halt as it is conducted through the
use of equipped sensors. Manufacturers have then to figure out ways to improve
upon previous products for future innovation, and prevent future defects through
predictive maintenance.
I.
Product
development
from
after-­‐sales
services
Sensor-equipped products come out still warm from the oven and onto the
consumer’s hands, whose purpose is not only to pay for the items but also inform the
manufacturers through real-time data of usage and performance. Thus begins the
product-as-a-service (PaaS) stage, as consumers are unconsciously both receiving a
service through the product by generating data to prevent defects before they
happen (preventive maintenance), and providing a service as they inform
manufacturers of demand forecast capabilities and product development. Big data
thus stands at the centre stage of the transition between the selling of the physical
product, and the outcome delivery that was promised by the product.
II.
Preventive
maintenance
and
outcome
optimization
Ted Levitt once rightfully declared that “People don’t want quarter-inch drills – they
want quarter-inch holes” (HBS Working Knowledge, 2006). The importance of the
outcome in a world where customer loyalty stands at its lowest point means that, if
your product is not delivering on its promise, then the consumer will search
elsewhere. It is thus understood that the most sought after services for the after-
sales stage of the value chain are pointing towards maximizing uptime by not simply
repairing equipment, but eliminating the possibility of needing a repair altogether
(Digabit, 2015).
PaaS is quickly becoming a source of competitive advantage, as firms will outclass
each other by their ability to provide operational uptime and continuous performance
along with their products.

15.
00989418
Imperial
College
London
Management
Report
14
Tesla, a car manufacturer with slightly over a decade of life, wants to be the de facto
provider of vehicles by battling a gasoline-reliant world with electric-powered
machines. The fact that the gasoline providers dwarf the electric-charging
infrastructure 100 to 1 means Elon Musk has to dig deeper in order to attract interest
in his cars. Enter big data analytics; by collecting and analysing in-depth data of
every single car they sell, Tesla is able to produce an almost fanatic following by
vamping costumer loyalty onto unheard-of-levels in the automotive industry. “Tesla
often knows about a problem before the driver does” (Taylor Institute, 2015) means
that Tesla can see an issue coming and prevent it before it actually happens, saving
time, maintenance costs, and allow for building of stronger bridges with the
consumer. Add to this the fact that Tesla can now prevent repairs by software
update, thus eliminating the hassle that was the trip to the garage in the first place
(Wired, 2014).
Elon Musk uses big data analytics as a competitive advantage to garner consumer
loyalty but he also used it as a shield to defend himself and Tesla from the
accusations directed at their Tesla Model S in what could have been a disastrous PR
scandal. Tesla was able to fend off some of the false claims that stated that the
Model S had a battery fail during a trip from NY to Boston, and that the New York
Times reporter driving the car had to turn off the heat and drive at 45MPH to
conserve battery power, by bringing out their data from the trip (Taylor Institute,
2015). In the end, although there were many order cancellations due to the article
published, people trusted the data before the word of a reporter. The bottom line is
that, if Tesla didn’t collect data and analysed it on a continuous basis, their future
would have been put in great danger by a reporter’s false accusations (Taylor
Institute, 2015).

16.
00989418
Imperial
College
London
Management
Report
15
2.5
Organizational
and
cultural
effects
In order to extract value of the highest order from big data implementation, firms will
have to invest not only in technological change but also aim for full organizational
transformation. As aforementioned, out-dated product lifecycle IT are trapped within
their own structures, and at the same time, making use of big data requires access
from multiple sources within and outside an organization. This means that to bring
organizational change so that more value can be extracted by big data analytics,
firms will have to establish interoperable, cross-functional platforms that allow
sharing of data: best value and insights in R&D, for instance, comes from full fluidity
between product development, production, and after-sales services. Furthermore,
investment will also have to be directed to the standardization of interfaces and
protocols in order to further accentuate the need for effective data share (McKinsey,
2011, p.83).
Excessively bureaucratic management in addition to overlapping and redundant data
are obstacles in the way of the full fluidity necessary across the organization
(McKinsey, 2011, p.83). Thus, a cultural shift as well as a strong leadership are
fundamental to the organizational revolution; the fact that the technology is in place
and running does not mean that the people will promptly go about sharing their data
with rest of the firm’s departments (McKinsey, 2011, p.83). Fortune describes a story
in which a VP of distribution tasked an internal team with running an analysis. There
were two roads: if they chose the first option the distribution team would achieve its
goals; the second road would bring huge returns for the firm as a whole, but diminish
the distribution team’s influence and results in the outcome. “The VP said, ‘You will
implement the one that helps me.’” (Fortune, 2015). Politics also has a say and many
times it diverts the firm’s focus from extracting value and homing results that would
otherwise not happen with full organizational, cultural, and leadership transformation.
Some levers from also need data both from within the value chain as well as from
outside the firm – R&D needs help from crowdsourcing initiatives; supply chain
requires data from retailer’s inventory management; After-sales services need input
from the consumers. Thus, establishing lasting relationships and making the right
partnerships is crucial to manufacturers. Some retailers might not want to share their
data due to its meaning to competitive advantage. Privacy issues regarding the

17.
00989418
Imperial
College
London
Management
Report
16
consumer might restrain others. Tesla’s strategy, as aforementioned, is thriving due
to their consumers not minding them using their data to create better products or
provide better services in the after-sales stage. Therefore, manufacturers and
retailers must be proactive in how they address privacy, by informing the consumer
of their choices and data transparency policies available to them (McKinsey, 2011,
p.84).

18.
00989418
Imperial
College
London
Management
Report
17
3.
Conclusion
After the analysis and results presented in this report, a reconsideration of the title
question might not be completely off the mark. Therefore, how don’t big data and
business analytics affect the manufacturing industries? All levers of the industry’s
value chain and the organizational scheme have so far found a way of exploiting its
many diverse features, to great results. Big data has revolutionized the establishment
from the bottom-up and then sideways. Whereby once the chain was understood to
have a beginning and an end - from the idea-creation process in R&D to the moment
the product found its way to the consumer’s hands – we now think of the chain to
have gone full circle, as the product that reaches the costumer simply marks another
step in the manufacturing and improving of the future innovation. Big data might be
thought of as a vast array of numbers and percentages, each with its own
significance and value. But analytics does not only bring you down to the microscopic
level, but somersaults he who wields its power onto greater heights, whereby the big
and underlying picture is easily discernible. At its most basic, the pattern that comes
up again and again from its implementation onto the different levers of the chain is
the following: efficiency and returns go up; time and costs go down. And at the end of
the day, nothing could be more important.

19.
00989418
Imperial
College
London
Management
Report
18
4.
Bibliography
• McKinsey&Company. McKinsey Global Institute. (2011) Big data: The next
frontier for innovation, competition, and productivity. [Online] Available from:
http://www.mckinsey.com/insights/business_technology/big_data_the_next_fr
ontier_for_innovation (Accessed: 5th
August 2015).
• Figure 1 - McKinsey&Company. McKinsey Global Institute. (2011) Big data:
The next frontier for innovation, competition, and productivity. [Online]
Available from:
http://www.mckinsey.com/insights/business_technology/big_data_the_next_fr
ontier_for_innovation (Accessed: 5th
August 2015).
• McKinsey&Company. McKinsey Global Institute. (2012) Manufacturing the
future: The next era of global growth and innovation. [Online] Available from:
http://www.mckinsey.com/insights/manufacturing/the_future_of_manufacturin
g (Accessed: 5th
August 2015).
• McKinsey&Company. McKinsey Global Institute. (2013) Game changers: Five
opportunities for US growth and renewal. [Online] Available from:
http://www.mckinsey.com/insights/americas/us_game_changers (Accessed:
5th
August 2015).
• Saving US Manufacturing. (2011) The importance of R&D to the
manufacturing industry. [Online] Available from:
http://savingusmanufacturing.com/blog/general/the-importance-of-rd-to-the-
manufacturing-industry/ (Accessed: 5th
August 2015).
• Ford. (2015) Mobility experiment: Big Data drive, dearborn. [Online] Available
from:
https://media.ford.com/content/fordmedia/fna/us/en/news/2015/01/06/mobility
-experiment-big-data-drive-dearborn.html (Accessed: 5th
August 2015).
• Harvard Business Review. (2006) Connect and develop: Inside Procter &
Gamble’s new model for innovation. [Online] Available from:
https://hbr.org/2006/03/connect-and-develop-inside-procter-gambles-new-
model-for-innovation (Accessed: 5th
August 2015).
• Harvard Business School Working Knowledge. (2006) What customers want
from your products. [Online] Available from:
http://hbswk.hbs.edu/item/5170.html (Accessed: 5th
August 2015).
• BCG. (2015) Making big data work: Supply Chain Managament. [Online]
Available from:
https://www.bcgperspectives.com/content/articles/technology_making_big_da
ta_work_supply_chain_management/ (Accessed: 5th
August 2015).

20.
00989418
Imperial
College
London
Management
Report
19
• Entrepreneurial Insights. (2015) Big data, corporate processes: How to
optimize supply chain management with big data. [Online] Available from:
http://www.entrepreneurial-insights.com/how-to-optimize-supply-chain-
management-big-data/ (Accessed: 5th
August 2015).
• Information Age. (2013) Tesco saves millions with supply chain analytics.
[Online] Available from: http://www.information-
age.com/technology/information-management/123456972/tesco-saves-
millions-with-supply-chain-analytics (Accessed: 5th
August 2015).
• RFgen. (2013) Tesco improves supply chain with big data, automated data
collection. [Online] Available from:
http://www.rfgen.com/blog/bid/285148/Tesco-Improves-Supply-Chain-with-
Big-Data-Automated-Data-Collection (Accessed: 5th
August 2015).
• The Guardian. (2015) Moore’s Law wins. [Online] Available from:
http://www.theguardian.com/technology/2015/jul/09/moores-law-new-chips-
ibm-7nm (Accessed: 5th
August 2015).
• IBM. (2015) What is Big Data? [Online] Available from: http://www-­‐
01.ibm.com/software/data/bigdata/what-­‐is-­‐big-­‐data.html
(Accessed: 5th
August 2015).
• Industry Week. (2015) Mass customization and the factory of the future.
[Online] Available from:
http://www.industryweek.com/factory-of-future
(Accessed: 5th
August 2015).
• Wired. (2014) Tesla’s over-the-air fix. [Online] Available from:
http://www.wired.com/insights/2014/02/teslas-­‐air-­‐fix-­‐best-­‐example-­‐
yet-­‐internet-­‐things/
(Accessed: 5th
August 2015).
• Digabit. (2015) The secret to helping customers with after-sales service.
[Online] Available from: http://digabit.com/the-secret-to-helping-customers-
with-after-sales-service/ (Accessed: 5th
August 2015).
• Taylor Institute Blog. (2015) Tesla’s use of analytics. [Online] Available from:
http://blog.thetaylorinstitute.org/?p=503 (Accessed: 5th
August 2015).
• Fortune. (2015) Big data could Improve supply chain efficiency – if companies
would let it. [Online] Available from: http://fortune.com/2015/08/05/big-data-
supply-chain/ (Accessed: 5th
August 2015).