Balance your Supply Chain

with Big Data

Author: Manju Devadas VP Solutions and Technology, Bodhtree

mdevadas@bodhtree.com www.linkedin.com/in/manjudevadas

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.

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:

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.

Key Supply Chain functions Today – Traditional Technologies Tomorrow – Big Data Technologies

Forecasting Running reports and analysis on a daily

basis (reports alone can take hours to

produce).

Forecasting using real time dashboards,

eliminating the concept of running reports.

Data is ready at lightning fast speeds with

the capability to capture snapshots of

analysis.

Demand 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)

Supply Planning Traditional reports and email

communications

Supply Planning using real time data with

deep insights to the news of vendors and

partners.

Fu lfillment & Delivery Tracked through workflows and report

status

Proactive delivery tracking to predict

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

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

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, Process 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”