1. Balance your Supply Chain
with Big Data
Author: Manju Devadas
VP Solutions and Technology, Bodhtree
mdevadas@bodhtree.com
www.linkedin.com/in/manjudevadas

2. Let’s start by going back…way back from a tech perspective. In the 1840s Samuel Finley
Breese Morse, the American co-inventor of Morse code, envisioned laying cable across the
Atlantic to enable telephonic communication from US to Europe. The business benefit metric
of the solution was a reduction in message transmission time from 10 days to only a few
minutes. With this massive return, the initiative would seem like a “no brainer” from today’s
perspective where communication is at milliseconds speed from your cell phone; believe it or
not, the question commonly asked then was ‘Do we really need communication so fast?’ The
project ultimately took over 18 years to complete when US president James Buchanan finally
conversed with Queen Victoria over the transatlantic cable, hence demonstrating the first
business benefit. Let us call this the ‘Paradigm Shift Period’ for communication. Modern
businesses now rely on instant communication across the world with voice and data transfers
occurring at lightning speed. People, processes and technologies within business have all
evolved to conform to this new paradigm of global data interconnection.
In fact, the original challenge has now come full circle. Business and government have
become so efficient at capturing and transmitting data that getting the data is no longer the
core of the issue. The challenge and opportunity now lay in processing and interpreting the
terabytes, even petabytes, of available structured and unstructured data to influence effective
business strategy.
The chances are that you’ve been bombarded with Big Data buzz over the last year. But in
spite of all the noise, you’ve probably noticed that few of these descriptions contain focused
business use cases for applying Big Data technologies. I am the first to acknowledge and
agree with Gartner research that Big Data is riding a hype cycle that will likely peak sometime
in 2013. Between now and then a lot of mind share will go into figuring out if there is value
for your domain, your industry and your job. If you work in supply chain, irrespective of the
industry, continue reading to understand how Big Data is expected to bring both direct and
indirect impact. Some of these reverberations may fundamentally change the nature and
duties performed in supply chain jobs. In 2010 we have witnessed a ‘Paradigm Shift Period’
for Big Data Analytics with major players like SAP announcing the next generation of real-
time analytics as many ask a similar question to 170 years earlier, ‘Do we really need
analytics so fast?’ SAP is now seeing their Hana analytics customers grow rapidly, similar to
other big players like Oracle. We are witnessing an epic shift in supply chain data analytics
that will make the approaches of the last decade seem antiquated.

3. The Supply Chain Domain
The core of any supply chain strategy is maintaining an appropriate balance between the
supply and respective demand. Every other related model, including the well-known JIT (Just
in Time), really targets the same goal with different degrees of precision and timeliness. Every
time you enter the car repair shop and the mechanic mentions a part will take X days to
order, you get a prime, though frustrating, example of a supply-demand imbalance. It is
every organization’s goal to maintain a supply-demand balance by optimizing cost and
quality with operational efficiencies.
On a much larger scale, I have observed operations at a $40B Hi Tech manufacturer where
maintaining the supply-demand balance is a far more complex proposition. Everyday
employees and partners in this supply chain ecosystem are trying to find answers to key supply
chain questions, but their view is constrained to only a piece of the picture since reports rely
primarily on structured data. How fast the person can get accurate and relevant information
has a significant impact on the growth, profitability and productivity of the supply chain
function.
The following are some ballpark metrics for the annual activities involved in keeping supply
aligned with constant variation in market demand:

4. Does this look ugly? It is. But think about what these numbers will be after data volumes
grow 16X by 2016.
It’s a category 5 hurricane of data.
All of the above communication is related to one or more of the following four areas: Assess
the demand, Assess the supply, Fulfillment of demand, Delivery of the product/service. The
efficiency and success of these activities can be tracked through metrics such as lead-time
variance, forecast inaccuracies, on-time shipments and quality metrics to name a few.
Big Data for Supply Chain
NOW, let us bring Big Data into the picture and see how this outlook changes. A Big Data
problem exists if data Volume, Velocity and Variety become difficult or impossible to store,
process, and analyze using traditional technology and methods. With Big Data technologies,
the capability to find answers faster and cheaper has grown exponentially.
While we predict 16X growth in data volumes in just a few years, human ability to
comprehend does not keep the same pace. From the perspective of people, processes and
technology within supply chain management, improvements will need to catch up as you
implement Big Data technologies. The probability is high that Big Data technologies will
play a key role in handling your rapid data expansion, so gear up your people and processes
to match the potential of these technological innovations. Within the broad range of supply
chain roles, let us consider the role of planner to see how his/her activities change from
today’s traditional technologies vs. Big Data technologies of tomorrow.

5. Key Supply Chain functions Today – Traditional Technologies Tomorrow – Big Data Technologies
F orecasting Running reports and analysis on a daily Forecasting using real time dashboards,
basis (reports alone can take hours to eliminating the concept of running reports.
produce). Data is ready at lightning fast speeds with
the capability to capture snapshots of
analysis.
D em and Planning Mostly using human-fed structured data Demand Planning using structured and
unstructured data (e.g. web clickstreams,
Facebook likes, Twitter Feeds , Customer
reviews, news article mentions)
Su pply Planning Traditional reports and email Supply Planning using real time data with
communications deep insights to the news of vendors and
partners.
F u lfillment & D elivery Tracked through workflows and report Proactive delivery tracking to predict
status possible delays and correlated
interdependent events.
There is a fundamental shift from planners reading the data and recommending changes to
the machine recommending changes and planners managing the exceptions. This has been
the goal of many organizations for the last decade, but recent Big Data technology
innovations represent quantum-leap advances toward true strategy automation.
The traditional model makes local copies of data which the planner edits and writes back.
The read/write process might take anywhere from seconds to many hours depending on the
tasks. With Big Data, the turnaround becomes milliseconds. The natural reaction is, “Do I
really need information flow that fast?” The important question is not how fast the information
flows, but how quickly you can change your decision from A to B, capturing a time-sensitive
opportunity or averting a major cost. Cancelling a wrong work order or not considering all
available information for analysis could mean a poor decision in current model. Visualize the
planners viewing all the information they want to see in real time while the competition is still
updating data and processing reports.
Bringing the Supply Chain Contacts, Content and Context Together for decisions
The most critical factor for effective corporate decisions is to bring the contacts, content and
context closer to each other. For example, a supply chain company that knows a part defect
would potentially affect the assembly, which could in turn delay customer delivery and

6. eventually affect services. Predicting the occurrence of defects well in advance through
analysis of historical Big Data has huge ROI potential by enabling appropriate adjustments to
every event in this chain. Additionally, with Big Data recommending related content and
relaying all of this to the right contacts, the result is direct ROI in the form of improved quality
metrics, increased customer satisfaction and reduced maintenance costs for part replacement.
Today’s Big Data technologies have the capability to demonstrate how in the automobile
industry an alternator part data sheet (Content) can be analyzed against all cars sold
(Contacts) and reveal the root cause for battery replacements (Context), an issue which has
cost the company millions of dollars in repair services. Similar examples can be found in
many Big Data technology use cases across industry verticals.
All of these scenarios are primarily connecting the 3Cs, the Contacts (e.g. Customer
information or internal employee) and Content (Use case specific information e.g. Battery
failure) with Context ( How a battery replacement is due to alternator failure ) .
Much of a Planner’s time is spent searching for information across multiple tools, reports and
manual communication with traditional technologies. One gauge of an effective Big Data
technologies implementation is to reduce the number of reports to 1/10 the current volume.
Let the machines do the job of relating and correlating the huge flow of information, and put
the planner in the command seat to review recommendations and approve/disapprove. This
will directly increase the productivity of the planner as he/she has to focus on reviewing the
recommendations rather than searching for information.
Where to Start
All of this means that you need to first conduct an assessment of your supply chain ecosystem
with a specific use case in mind to which Big Data technologies will be applied. The specific
area targeted for improvement may be forecast inaccuracies, which in today’s model relies
mostly on structured data combined with massive exchanges of manual communication,
ignoring much of the available market feedback (unstructured data). Measure the baseline
and set realistic targets. Traditional Forecast/Demand planning fundamentally relies on a set
of numbers entered by internal and external users. It does not factor in some of the Big Data
elements such as sentimental analysis of the market, internal/external unstructured
communication (e.g. blogs, chats, Tweets, customer reviews). When the unstructured
information is correlated with structured data, new insights arise prompting better decisions.
1% improvement in your forecasting drives multi-fold improvements to your entire supply

7. chain based on empirical research. Upon realizing these early Big Data benefits, we can
then expand it to broader supply chain use cases.
ROI
Now, where do you initiate the change and get the quick ROI? Our recommendation is to
pick the top five supply chain reports you run on your traditional BI platform, analyze them
and assess whether Big Data technologies would bring in improved results. Consider
dimensions of accuracy, precision, and timeliness. For example, forecasting traditionally
depends on sales, BU or operations entering their forecasts and coming up with some form of
consensus. Inherent forecast inaccuracy exists, which are mitigated by a continuous
improvement process. Now, with Big Data you start feeding unstructured market information
into the analysis, casting more light on external reactions to your product. This insight
provides early indications of demand variations, allowing for corrections to forecasts.
Conclusion
The fundamental disruption in our supply chain eco system has begun through Big Data
technology capabilities impacting People, P rocess and Technology. Faster, better and
cheaper processing of Big Data will drive improvements in people’s behavior and actions,
bringing improved supply/demand balance. Similarly, process improvements learned from
various supply chain driven companies (e.g. automobile) will flow into other industries like Hi
Tech and Healthcare. The traditional daily job of a supply chain employee who reads and
writes Content relating it to a Context working with his set of Contacts will dramatically
change. Human-driven searching will fundamentally shift to machine-driven searching,
mapping relevant information for faster decision making with recommendations. Get started
with a use case which can be easily measured for ROI realization, then use this success as a
launch pad to expand Big Data insights across the organization.
Contributors: Ryan Madsen (Bodhtree)
References:
Real Customer Case Study
Gartner’s Hype Cycle for Emerging Technologies in 2012
Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011–2016
Wikipedia – “Transatlantic Telegraph Cable”