Understanding the What, How and Why of Big Data in Supply Chain

Relationships:A Structure, Process, and

Performance StudyUniversity of Alabama

Robert “Glenn” Richey, Ph.D.

Tyler R. Morgan, Ph.D. Candidate

Mississippi State University

Frank G. Adams, Ph.D.

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!1 of 20

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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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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Going Native: A Qualitative Approach

Sample: n=31/ N=6

Country Sample Size SC Classification

China 3 Manufacturing

Germany 6 3PL/4PL/ConsultingSupplier/DistributorManufacturingTransportation

India 3 Manufacturing

South Korea 3 3PL/4PL/ConsultingRetailing

Turkey 3 ManufacturingRetailing

USA 11 ManufacturingRetailing

Brazil 1 Manufacturing

South Africa 1 3PL/4PL/Consulting

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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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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– Resistance to new technology– Protecting information– Finding central and sizable storage– Effective presentation and communication– Cross functional inflexibility– Not forward looking/Speed to irrelevance

“If each party uses a different system to collect and process the Big Data, it will be redundant and wasteful.”

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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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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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Results: China (n=3)

QoC:“Market data yes, but Big Data, why???”

Opportunities– Single systems– Retail level forecasting– Incremental innovation– Logistics cost reduction

Obstacles– Labor costs trump technological investment– Weak collaboration– National culture issue: risk and boundaries

• (we won’t tell you what we don’t share)

" 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.”

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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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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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Results: Single Respondents

South AfricaOpportunities

– 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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BrazilOpportunities

– 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...”

Results: Single Respondents

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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

Takeaways

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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

Takeaways

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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

Takeaways

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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

Takeaways

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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

Future Work

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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

Thanks for Your Time

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