Intel Computing White Paper

RESEARCH
PAPER
Tentechnologiesthathelp
youpredictanddealwith
peaksindemand
A discussion of how analytical tools can help businesses plan
for and react to seasonal fluctuations
December 2014
Sponsored by

Tentechnologiesthathelpyoupredictanddealwithpeaksindemand
Executive summary p3
Knowledge is power p4
The top ten technologies p5
Optimising your supply chain p11
Predicting consumer behaviour p11
Conclusions p13
About the sponsor, Intel p13
Computing | research paper | sponsored by Intel2
CONTENTS
This document is property of Incisive Media. Reproduction and publication of this document in any form
without prior written permission is forbidden.

Computing | research paper | sponsored by Intel 3
Executive summary
Running a business is never a smooth ride, with seasonal peaks and troughs a prime cause of
uneven sales for many companies. In these cases, predicting and dealing with inconsistent demand
is a vital skill. In times gone by, gut feel and experience may have been the best – if not the only
– method of steering the ship through these choppy waters. However, 2015 offers a range of
technologies with the power to help identify and cope with seasonal variations in demand.
Computing undertook a significant research survey into the methods used by businesses to deal
with fluctuations in demand. The results show that, while experience and judgment are still
highly prized, a number of key technologies are gaining traction within businesses to help predict
and manage variations in sales.
This white paper reveals Computing’s top ten technologies to help your business deal with
fluctuating sales and demand, from Business Intelligence to cloud bursting for peaks in demand
for IT services.
We also look at the tools most favoured by survey respondents to optimise their supply chains
and predict customer behaviour, and examine the case for technologies, such as web analytics,
to use alongside the judgment of experienced people.

Knowledge is power
In business – and indeed in life – there is often more than one solution to a given problem. The
first figures from Computing’s survey on how businesses deal with fluctuating seasonal demand
certainly bear this out. Figure 1 shows that many businesses still rely on human resource –
staffing levels, overtime, flexi-working – to cope with seasonal fluctuations. Working out how
much of that human resource they need – and when – is a different matter. Here, the important
factors are analysis of past sales, performance, supply and demand, and much more – the
analytics that enable them to decide how much human resource they need and when.
Fig. 1 : How do you cope with seasonal fluctuations?
Of the people surveyed who said their businesses were seasonal, the key word in coping with
ebbs and flows was ‘planning’. Whether they are travel firms analysing booking patterns in order
to time key advertising for consumers’ decision times, farmers planning for the surge of activity
around harvest time, or even meteorological experts planning IT capacity for a massive peak of
internet traffic when snow is forecast, knowledge is power to all.
Hire/release temporary staff
Allow more overtime
Introduce flexible working hours
Use analytics to time sales and special offers
Focus on cashflow management
Take on new suppliers on a temporary basis
Releasing/taking on new premises
Burst into cloud services
Other
16%
4%
9%
“We have spikes in manufacturing and
manage vendor capacity through forecasting data to achieve the
output and scale back appropriately”
43%
37%
26%
19%
3%
25%
* Respondents could select multiple answers.

The top ten technologies
As can be seen in Figure 2, there are many powerful tools currently used by businesses to help
predict and deal with fluctuations in demand. Business intelligence (BI) is the most popular tool,
used by almost two-fifths of respondents.
Fig. 2 : Which of the following tools do you use?
The results show a broad range of uptake for different tools. Read on for our list of the top ten
technologies and how they can help your business plan for any eventuality.
Business intelligence
Website analytics
Social media analytics
Capacity planning tools
Web-based analytics
Mobile and geolocation tools
Supply chain management (SCM)
Open Data sources, e.g. weather, population,
age demographics
Social media analytics
Temporary use of cloud – “bursting” – to cope
with peak demand
Anomaly detection
Recommendation engines
None of these/don’t know
23%
19%
20%
37%
35%
26%
25%
18%
15%
29%
5%
7%
7%

1. Business intelligence
Business intelligence (BI) refers to tools and techniques that transform data into useful
information about business performance. Crucially, BI technologies are able to handle huge
swathes of data to identify, develop and create business strategies designed to give companies
competitive advantage and long-term stability.
BI is not a new technology but it is one whose definition and capabilities are changing fast
as innovations such as cloud and Hadoop come onstream, allowing for quicker deployment,
use by non-experts, and the ingestion of data from many new sources. BI is considered to
be most effective when a company crunches both data derived from the market in which a
company operates (external data) with data from within the company itself (internal data). The
combination of internal and external data creates a higher level of intelligence that cannot be
achieved with a single data set.
The John Lewis Partnership analyses its massive customer database across both John Lewis
and Waitrose to really understand its customers, enabling it to target specific campaigns with
accuracy. As CIO Paul Coby told Computing, understanding its customers, their likes and dislikes
and shopping habits is a strategic imperative for the company.
2. Website analytics
If you want to measure and analyse web data so that you can optimise your offering or gauge
the success of specific campaigns, a web analytics tool is what you need. As well as providing
information about the number of visitors to a website and certain pages on the site, and the
number of page views that site has got over a set period of time, applications will identify
popularity trends which can be really useful as a market research tool.
Recognising that 99 percent of its sales were booked via its website, low-cost airline Ryanair
decided to invest in web analytics to improve its offering to customers. The data it got back
meant that the company had better understanding of its customers and that meant it could make
much more strategic decisions. The changes it made to its site resulted in better conversion rates
and traffic to partner pages, and double the revenue generated from email campaigns.
www.computing.co.uk/ctg/discussion/2151887/business-intelligence-john-lewis
Read more about the success story on the AT Internet site:
www.atinternet.com/en/documents/ryanair-etravel-4

3. Social media analytics
Many businesses mount an advertising push ahead of busy times such as Christmas, or launch
new products and services to meet anticipated demand. Social media analytical tools enable
you to measure the effectiveness of your social media and other marketing activity extremely
quickly. Has your advertising spend been effective? Which of your posts created the most buzz
on Facebook? Do pictures encourage more interaction? Have sales gone up in line with your
social media activity? Are your tweets being retweeted by online influencers? Successful social
media takes time and resource so it is wise to make sure your effort is paying off.
When Barclays launched an app called Pingit earlier this year, it was able to react immediately
to feedback from users on social media and make improvements to its product in double-
quick time. The app allowed people to transfer money to other people using only their phone
number but the launch didn’t enable under-18s to get involved. It quickly became apparent from
feedback on social media that young people and their parents felt this was an error that needed
to be rectified – a sentiment that Pingit itself picked up allowing the bank to take action.
4. Capacity planning tools
Capacity planning (CP) and capacity management tools help you to determine the production
capacity needed by your organisation to meet changing demands for products or services. Once
the preserve of manufacturing businesses, CP software is now finding use in other sectors,
especially those with complex supply chains or distributed or multi-sectoral businesses.
Modeling operations and production capacity as part of an end-to-end supply chain design
practice can be the key to outperforming the competition as well as anticipating peaks in
demand, allowing businesses to optimise their supply chain to mitigate risk, reduce waste and
increase profitability.
Increasingly flexible and now extendable via apps for mobile devices, CP software also makes it
easier to plan for staffing and skills needs based on current and historic demand and strategic
plans. They can sometimes be linked with the HR function in order to allow for hiring during busy
periods, or training or relocating existing employees.
The NHS is an organisation which has to deal with peaks and troughs in demand. When wanting
to minimise waiting times for example, only by knowing the key factors which impact the service
a specific department provides can the teams hope to reduce patient journey times.
Social Times reports in full here: http://oursocialtimes.com/
how-to-use-social-media-monitoring-for-a-product-launch
www.institute.nhs.uk/quality_and_service_improvement_tools/quality_and_service_
improvement_tools/demand_and_capacity_-_basic_concepts.html

5. Mobile and geolocation tools
Geolocation tools detect the physical location of internet-connected computing devices. The
benefit to business users of knowing where customers are based is considerable, and the
practice is so commonplace today that consumers almost expect it.
Geolocation tools are not just country or continent specific. Depending on the method used, they
are able to detect very specific information, such as the latitude and longitude coordinates of a
connected device.
In terms of handling peaks and troughs, geolocation tools and associated devices such as retail
beacons can help businesses work out where customers are in real-time, what their numbers
are, and especially in tandem with other programs such as social media analytics, what they are
likely to do.
San Francisco Soup Company – a 17-branch chain of fast-food outlets – recently used geolocation
intelligence combined with data on its customers’ previous buying choices to create location-
specific offers that were texted to customers’ phones as they passed one of their outlets.
6. Supply chain management tools
In very basic terms, supply chain management (SCM) is the management of the flow of goods.
SCM synchronises the efforts of all parties (suppliers, manufacturers, distributors, dealers,
customers) involved in meeting a customer’s needs.
These days SCM relies substantially on technology to enable seamless exchanges of information,
goods and service across different companies. Frequently this means optimisation and
automation, with goods being ordered automatically from the supplier with the lowest prices,
the biggest stocks or the shortest ongoing chain.
When Whirlpool in the US went through a rapid growth its supply chain management suffered,
resulting in unhappy customers and retailers. However, when the company started managing its
supply chain using SCM tools, it quickly found a decrease in forecasting errors by 50 percent and
it began to save five percent on warehouse and transport costs. Its rate of delivering the right
product to the right place at the right time rose from 83 percent to 93 percent, and reached 97
percent after just five years.
Instant.ly has the full scoop on the story here: www.instant.ly/blog/2014/05/san-
francisco-soup-company-leveraging-ibeacon-to-personalize-mobile-marketing-efforts
From the University of San Francisco: www.usanfranonline.com/resources/supply-chain-
management/supply-chain-management-case-study-whirlpool/#.VIWMo4etqfQ

7. Anomaly detection
Anomaly detection looks at data that is seemingly homogeneous and identifies things that are
unusual within it. Anomaly detection is often used when detecting fraud or network intrusion, or
when looking at poorly functioning networks in order to identify the culprit.
However, anomaly detection software can also help to identify peaks in demand; for example,
increased internet activity in certain regions, before these become apparent to human observers.
A very high level use of anomaly detection is in the commercial aviation industry. Most
approaches to aviation safety are reactive (they are designed to react to an incident that has
already happened) but anomaly detection enables analysts to proactively detect potential safety
anomalies in very large databases from commercial fleets all around the world.
8. Recommendation engines
These tools have become extremely common in recent years, and are used in a variety of
applications. The ubiquitous Amazon, for example, uses recommendation engines to ‘rate’
its products, most commonly its movies, music and books. In simple terms, these tools filter
information in order to predict the ‘rating’ or ‘preference’ that a user would give to an item.
In more complex terms, they produce a list of recommendations in one of two ways – through
either collaborative or content-based filtering. Collaborative filtering builds a model from the
consumer’s past behaviour and the actions of other users. Content-based filtering uses a series
of discrete characteristics of an item to recommend other products with similar properties.
Many people believe that a combination of these two approaches – a hybrid system – is the most
effective approach and this can be seen in the Netflix model, which makes recommendations by
comparing the watching and searching habits of similar users as well as by offering movies that
share characteristics with films that a user has rated highly.
In terms of managing fluctuations in demand, the potential of recommendation engines in
promoting (or demoting) certain items to certain users is obvious. There is much to be read on
Netflix’s recommendation engine online, not least about the competition it launched to get
people to create the best recommendation algorithm.
Here is one study into Netflix’s success: www.digitalbusinessmodelguru.com/2013/01/
analysis-of-netflix-business-model.html

9. Open data sources
Open data is a term generally applied to sets of information that are available free for individuals
and businesses to make use of as they see fit. In the UK, the majority of open data comes via
data.gov.uk, an official portal set up to encourage access to open data previously owned by the
government.
Open data can consist of almost any type of information. From weather forecasts and maps, to
planned road works and social trends, there is a wealth of information available with potential
benefits to predicting fluctuations in demand for businesses.
The challenge is how best to make use of the data. Considerable resources may be required in
terms of developers, designers and project managers to create the simplest of custom apps to
mine the open data sets.
However, leveraging existing efforts to realise open data’s potential may prove more cost and
time effective for some companies, depending on their specific business areas and needs.
The Scottish government recently used open source Ordnance Survey data in order to create
a ‘greenspace map’ of Scotland. It provided Scotland with a consistent and up-to-date data
set for managing the country’s green spaces and it saved the time and money required in data
collection across Scotland’s 32 local authorities.
10. Cloud bursting
Cloud bursting is a way of dealing with peaks in IT demand that can’t be handled by a company’s
existing hardware setup. No organisation wants to spend extra money on equipment that will
sit idle for most of the year so the best option could be to buy enough hardware to manage
everyday demands and then rely on the cloud to cope with the spikes.
It sounds ideal in theory but in practice cloud bursting is trickier to nail. Different systems
can’t always talk to each other, distributed applications require re-architecting, and of course
business-critical internet traffic needs to be secure. The advantages of cloud bursting need to be
weighed against the extra demands placed on developers and administrators, in order to judge
whether it is appropriate for a particular business.
A study was carried out a couple of years ago using eBay’s traffic statistics and it showed that
if they provisioned for the average load and then used on-demand resources (the cloud) for the
peaks they could in theory save 40 percent in overall costs.
www.ordnancesurvey.co.uk/business-and-government/case-studies/
greenspace-scotland-map.html
http://natishalom.typepad.com/nati_shaloms_blog/2012/05/
making-cloud-bursting-a-practical-reality.html

Optimising your supply chain
While a third of people questioned claimed to not be using any business analytics tools, Figure 3
shows that of those who do, nearly half use proprietary tools and a quarter use a mix of web-
based BI tools and open source analytics software.
The power of knowing your supply chain inside and out is considerable and enables you to
optimise use of resources and labour to manage periods of high demand. It also means you can
improve shipping and inventory accuracy, even during seasonal extremes, and you are able to
respond quickly and effectively to market changes and customer feedback.
The ultimate goal of successful SCM is to ease communications to such an extent that the
businesses involved effectively communicate as one unit. No distributed enterprise can expect
to negotiate peaks in demand without some sort of SCM solution in place.
Fig. 3 : Which of the following business analytics tools do you use?
Predicting consumer behaviour
As may be expected, respondents use a mix of media to advertise their businesses during peak
times, with internet banners, social media and email adverts ranking highly online and good
old posters, magazines, newspapers, TV and radio still ticking the boxes for many in terms of
traditional advertising. As can be seen in Figure 4, more than two-thirds of businesses now use
email campaigns to advertise in peak demand times. Perhaps more surprisingly, more than half
still rely on printed posters for their seasonal campaigns.
Proprietary business intelligence tools (e.g. Oracle
Hyperion, IBM Cognos, SAP BusinessObjects)
Web-based BI tools (e.g. Jaspersoft, Birst)
Open source analytic tools (e.g. Talend)
Graphics tools (e.g. Tableau, QlikTech, Pentaho)
No SQL databases
Hadoop-based distributions
Other
None
7%
8%
4%
43%
14%
10%
9%
33%

Fig. 4 : What form does your Christmas/peak period advertising take?
When it comes to judging the success of an advertising campaign, Figure 5 shows that
businesses are still relying heavily on broad sales figures and customer feedback.
However, it does seem that other more exact methods are catching up. Almost three out of five
companies are now using web analytics to detect surges in demand around advertising activity
and changes in the demographics of visitors. Growing numbers are using advanced analytics
tools and big data techniques to measure success and shape future campaigns.
When data analysis is coupled with more qualitative research, such as customer feedback, the
business is fully armed with the knowledge it needs to plan for its future advertising spend.
Fig. 5 : How do you judge whether your advertising is successful?
Email shots 69%
Posters 56%
Social media 56%
Print media 51%
Internet banners 49%
Mail shots 44%
TV/radio 41%
Internet audio/video 23%
Mobile 13%
Sponsorships 13%
Telesales 8%
Sales figures 69%
Customer feedback 59%
Web analytics (page hits/demographics) 59%
Advanced analytics (data mining, BI) 26%
Big data analytics 15%
Gut feel 8%
N/A or don’t know 3%
Other 3%

Conclusions
Predicting and dealing with fluctuations in demand are vital components in the survival and
success of many companies. From farmers and supermarkets, to umbrella makers and clothing
manufacturers, the range of businesses affected by seasonal, climate and macro economic
fluctuations is vast.
The top ten technologies identified in this white paper require varying levels of expertise and
resources to implement correctly. Computing’s survey results show that while some of our
top ten technologies are not widely used at present, they are certainly gaining in popularity.
Predictably, some are more common in larger organisations, where smaller companies without
the same levels of IT resource are playing catch up.
More traditional technologies, such as BI and website analytics, are still used by the biggest
number but newer tools, for example social media analytics and big data, are gaining acceptance.
The current state of play appears to be a mix of technology and more traditional methods of
demand prediction, such as historical sales figures, trends elsewhere in the market, customer
feedback and so forth.
For the time being, while the adoption of the technologies outlined in this white paper will
undoubtedly continue to increase, the most successful companies in predicting trends in
fluctuating markets will be those who best marry this improving technology with good
old-fashioned business experience.
About the sponsor, Intel
Intel (NASDAQ: INTC) is a world leader in computing innovation. The company designs and
builds the essential technologies that serve as the foundation for the world’s computing devices.
Additional information about Intel is available at newsroom.intel.com and blogs.intel.com.

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