CSCMP 2014 :exploring scm big data cscmp

1. Understanding the What, How and
Why of Big Data in Supply Chain
Relationships:
A Structure, Process, and
Performance Study
University of Alabama
Robert “Glenn” Richey, Ph.D.
Tyler R. Morgan, Ph.D. Candidate
Mississippi State University
Frank G. Adams, Ph.D.

2. Defining Big Data
• The world seems abuzz with the discussion of the
importance of “Big Data.”
• Big Data is defined according to the three aspects that
differentiate it from other analytics: the volume of
information produced, the velocity at which it is created and
the variety of forms it takes (McAfee and Brynjolfsson 2012).
• Confusion exists over other potential dimensions - Variability
or Complexity or Veracity or ….
• Big data has been suggested to be useful in targeting
customer needs, eliminating service created waste,
improving forecasting, economizing reverse logistics,
improving partnering, etc. (McAfee and Brynjolfsson 2012).
There is no common definition!
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3. Research Questions
• What the “state of the art” is in Supply Chain Big Data?
– Where managers should look for specific types of data?
– What data sources & technologies support specific types of
performance?
• How are Big Data relationships governed?
– What level of safeguarding/transparency is being used and is
suggested for Big Data laden relationships.
– How do partnerships perform “better” through Big Data; can a Big
Data culture be created?
– How do global needs and implications of Big Data partnering differ
across different parts of the world.
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4. Method
• Processes-oriented approach: We have a substantial basis
on what Big Data is, but avoided forced definitions.
• A qualitative multiple case study approach with +8 (31)
cases (Eisenhardt, 1989; Eisenhardt & Graebner, 2007; Yin,
2009)
• Purposeful selection - CSCMP Organizations and
Nominations
• Validity from multiple sources: multiple levels of company,
multiple industries, and documentation audit trail
• Emergent and iterative 4 person coding and categorizing,
refining constructs from the literature, to identify patterns,
but sensitive to context of the firms (Welch et al, 2011)
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5. Going Native: A Qualitative Approach

6. Sample: n=31/ N=6
Country Sample Size SC Classification
China 3 Manufacturing
Germany 6 3PL/4PL/Consulting
Supplier/Distributor
Manufacturing
Transportation
India 3 Manufacturing
South Korea 3 3PL/4PL/Consulting
Retailing
Turkey 3 Manufacturing
Retailing
USA 11 Manufacturing
Retailing
Brazil 1 Manufacturing
South Africa 1 3PL/4PL/Consulting
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7. Results: Worldwide
Quote of the Country (QoC):
“So how do you define big data?”
Opportunities:
– More effective forecasting
– More effective production planning
– Cost reduction (logistics)
Obstacles
– Finding Meaningful Data
– Finding People (Scientists)
– Fear of Risk and Regulation
– Finding Storage
– Partner Transparency
– National Culture
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8. Results: USA (n = 11)
QoC: “so, I don't see this going away in the next 5 to 10 years.
I actually see it growing, and I see people who have the skill
set and have that knowledge, and who can bring some of
these answers to the table, I see that as a competitive
advantage for a company who can figure that out"
Opportunity
– Searching for competitive advantage
– Sharing of data for improvement
Obstacles
“If each party uses a different
system to collect and
process the Big Data, it will
be redundant and wasteful.”
– Resistance to new technology
– Protecting information
– Finding central and sizable storage
– Effective presentation and communication
– Cross functional inflexibility
– Not forward looking/Speed to irrelevance
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9. Results: Germany (n=6)
QoC“Big data does not create innovation, that comes from little
data”
Opportunity
– Little data improvements
– Measurement precision
– Expat facilitation
– Customer focus
Obstacles
– National culture and communication
– Time and time zones
– Ease of access/safeguarding
– One version of the truth
– System integration
– Competitive risk of sharing data
“Is there one version of the
truth? I think there is no big
end to this story. So, I think
the challenge is to make
sure that we limit the
number of systems in our
landscape to be sure that
we have quality of
information.”
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10. Results: Turkey (n=3)
QoC: “Don’t even think about sharing consumer data with
anyone, that’s crazy!”
Opportunities
– Technology based decision making (less guessing)
– Process improvement
• Delivery, reverse logistics, barcode use
– In company system integration
Obstacles
– Partner fit, Trust and disclosure
– Sharing with outsourced SBU
– No road map
– Top management understanding
“Having Big Data and
revealing hidden patterns
is so important if you want
to offer customized,
personalized service and
this creates an edge over
the competitors."
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11. Results: China (n=3)
QoC:“Market data yes, but Big Data, why???”
Opportunities
– Single systems
– Retail level forecasting
– Incremental innovation
– Logistics cost reduction
Obstacles
" I think with the Big Data, we will have the
opportunity to create something innovative,
not necessarily new generation in
technology, you know, but in better ways to
forecasting, to physically distribute our
products, to redesign the working terms of
distribution. Things like that will also be
possible.”
– Labor costs trump technological investment
– Weak collaboration
– National culture issue: risk and boundaries
• (we won’t tell you what we don’t share)
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12. Results: India (n=3)
QoC: “The sources can be anything that comes in, your
customer touch points, your business touch points, how your
industry data has a factor in your older customer [data], your
older business [data], your software.”
Opportunity
– Procurement forecasting
– New product/service development
– Trend management
Obstacles
– How to pull and use flexibly
– Level of understanding across firm(s)
– Fine grained vs. usefulness
– Long-term data management
“Obstacle, the prime
obstacle that we face
is the level of
understanding of the
various people who are
capturing the data.”
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13. Results: S. Korea (n=3)
QoC: “…using the quantitative data that have objective validity
will help partners make a more informed decision”
Opportunities
– Risk reduction
– Informed decision making
– Data refinement
– Overstock reduction
Obstacles
– Dirty data
– Lack of data scientists
– What is the ROI?
– Privacy laws
– HR and Systems
"With Big Data, we will
be able to clarify
consumers’ every need
and their attribute and
eventually make them
spend more money.”
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14. Results: Single Respondents
South Africa
Opportunities
– Quicker upstream data into assessment
– Responsiveness
Obstacles
– Big Bully
– Misaligned readiness
“Big Data is the data that gets generated by those systems that are
implemented, that I just described. Really, it's more about running those
systems, like the modern ERP or warehousing system or distribution
systems. So, as you generate data, it happens merely by existing and
running the operations that way....It's a by-product of what you normally
do.”
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15. Results: Single Respondents
Brazil
Opportunities
– Reduced cost of design
– Inspiration
Obstacles
– Creative laziness
– Strategic blindness
“Using a lot of Big Data, as a matter of design, can create a little
bit of blindness on being creative and follow the trend or creating
something that it's not, like from scratch. On the other hand, the
use of Big Data for production skills or machinery is amazing...”
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16. Takeaways
A practical definition and understanding of big data does not
exist internationally (period).
– “I would say that it is a large amount of data that has to be analyzed.
The sources can be anything that comes in…” USA, Engineering
– “Data that contain a wide variety. … Everyone needs their own
definition of big data in order to use big data in a productive way.”
South Korea, Automotive Research Institute
– “…the material compared to the limits. …These are the information
that we should get together and talk about.” Germany, Industrial
Consulting
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17. Takeaways
Sharing of data exists somewhere between operational
minimums and not at all. This is dependent upon the company,
relationship, and country.
– “…what we see is that the big bully keeps all or most of
the benefits for himself…” South Africa, Industrial
Consulting
– “… financial information is not available for everybody in
the organization, for example…there are different levels
of authorization.” Germany, Retail Distribution
– “For our company, we do not share Big Data with our
customer.” China, Electronics
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18. Takeaways
Opportunities currently seem focused on process improvement
and logistical cost reduction with a future hope of finding a
strategic use.
– “Who is not only making the most money, but is sticking to their
contractual obligations?” USA, Healthcare
– “…we do go and look at history and pull that out and try to determine
what to anticipate and where the future is going to come from.
Especially, in things like what things are costing.” South Africa,
Logistics Consulting
– “…number 1, for each of the systems and to make sure that we have
the right capabilities to make the individual system work more
efficiently. Number 2 is truly to integrate all the systems to the others
that we have, so we can make better use of the data available…”
China, Manufacutring
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19. Takeaways
Tremendous obstacles exist within companies, countries and
across countries. Companies need help!
– “Before we can start using some of the elements of [Big Data], you
need to have other things in your own company in place first.”
Germany, Consumer Packaged Goods
– “… we need a combination of all of the information ... so it's stressed
getting all that data. It is a challenge to get all of it, … By far, the trust
in getting the data is the biggest thing.” USA, Agricultural Products
– “we send the data as it is in English from here to Korea … the
translated data is sometimes not perfectly understood by Korean
readers. Also, the time difference in these countries is an obstacle.”
USA, Automotive Manufacturing
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20. Future Work
What do you need to know?
– Relationship Governance: type, trust, commitment, and
opportunism
– Knowledge/information sharing and safeguarding
– Risk, Privacy and Law
– HR and TMTs
– Transparency
– National Differences/Cultural Differences
– Performance: Logistics, ROI, Market, Partner/SCM
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21. Thanks for Your Time
Big Data in supply chain management should be
characterized as relationship-based information that is
unique to business because of its volume, velocity,
variety, and variability/veracity.
Questions?
Future Information:
Glenn Richey
richeyglenn@gmail.com
+001 205 310 5973
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