This document summarizes research on the use of big data in retail supply chains. The research included surveys of 174 supply chain managers and interviews with 18 senior managers. The research found that while big data use holds potential benefits, current use remains limited in scope and applications. Most companies are in the early stages of initiating big data programs using traditional transactional data from sources like POS and inventory systems. The research identified several factors that influence adoption of big data as well as capabilities needed for effective use. Companies making more advanced use of big data reported improved performance outcomes. Overall, the research suggests big data can provide rewards if companies develop the right mindset, processes, and capabilities to leverage it effectively.

1. Big Data Use in Retail
Supply Chains
Drs. Mark Barratt, Anníbal Sodero
and Yao Jin

2. Acknowledgements
• The researchers are grateful for the financial and collaborative support
of CSCMP for this research project.
• We appreciate the opportunity to partner with CSCMP and the CSCMP
Research Strategies Committee on this research endeavor.
• Additionally, we appreciate the support of the Supply Chain Alumni
Group at Miami University and the Supply Chain Management Research
Center at the University of Arkansas in helping us collect the research
data.
• Finally, we offer our sincere thanks to the individuals and firms that
participated in the research process, who were promised anonymity in
exchange for their participation.

3. Big Data search pattern

4. Big Data vs. Supply Chain
Management search pattern

5. Big Data vs. Supply Chain
Management

6. Big Data vs. Supply Chain
Management

7. Research purpose
How Managers see Big Data in retail supply chains
• What it is and its perceived level of use?
• Characteristics of firms implementing it.
• What it is doing for them?
• How well it is working?
• What are the barriers and benefits achieved?

8. What is Big Data?
“The nearest to real-time as possible gathering,
storage, analysis of, and decision-making based on
large sets of both quantitative and qualitative data in
structured (tabular) and unstructured formats”
Implies four dimensions of Big Data:
1. Volume: large amounts in terms of bytes,
2. Variety: many forms of structured and unstructured data
3. Velocity: real-time creation and use of data, and
4. Veracity: trustworthy, relevant, and useful data.

9. What is (and is not) Big Data?
What Big Data is Not
• Simply demand forecasting
• A lot of data in the ERP system (Small and Medium data)
What Big Data is …..
• Comes from multiple traditional and non-traditional sources
• Beyond B.I.- enables real-time decision making
• New software platforms and technology (e.g. Hadoop, NoSQL)

10. Overall Finding
Big Data use in Retail SCs still elusive!
Three States: Initiation  Adoption  Routinization
Initial and some significant cases of use, but mostly
using traditional, transactional data
• Point of Sale (POS) and on-hand inventory data
• Social media data but for marketing purposes only - better
understanding of consumer preferences

11. Big Data: Good News
As reported by firms in more advanced state
(i.e. routinization)
• More positive view of Big Data
• Success in recognizing and overcoming challenges in
implementation
• Success in recognizing and overcoming integrating Big
Data into planning and replenishment

12. Research Overview

13. Shifting Retail Landscape and Role
of BD
• Being efficient and becoming more effective
• Goal: right consumer, place, time, quality,
condition and price
• Task is much more difficult and complex
• Consumer behavior: new level of whenever and
wherever.
• Demanding more of an Omni-channel experience
• Enabling the SC to become more demand driven

14. Research Methodology
Phase 1 – Survey Questionnaires
• 174 managers in retail supply chain firms
• Identify factors that significantly contribute to, inhibit, and result
from Big Data use
• Derive insight regarding the state of Big Data use in firms
positioned across retail supply chains
Phase 2 – In-Depth Interviews
• 18 senior supply chain managers
• Obtain greater details regarding their Big Data use efforts

15. Factors that influence BD adoption
Knowing… Being able to…
• Analyze data
• Merge BD with traditional data
• Establish data-sharing
protocols
• External integration with
customers
• Invest necessary resources
• All sources of data
• Questions to ask of data
• What data to share
• Possible benefits versus cost
• Data trustworthiness
• Supply-driven versus
demand-driven supply chain

16. BD: Benefits and Success Factors
Direct Benefits – Critical Success Factors
• Improved quality of data
• Increased demand and supply visibility both internally and across the SC
• Re-designed shared inter-organizational processes
• Significantly enhanced data analytic capabilities
Strategic Benefits – Omni-Channel and Demand-Driven
Supply Chains
• Predictive analyses of consumer demand patterns
• Advanced insights into procurement and distribution operations
• Strategic questions to shape supply chains

17. GAP: Definition - Practice
Veracity
Velocity
Variety
Volume
Managerial Definition
Significant Data
Quality Issues
Little Evidence
POS &
On-hand Inventory
Practice

18. Demographics: Job title & Revenue
Other,
10%
Director,
47%
Planner/
Analyst,
25%
Presiden
t/VP,
17%
Less
than
$250
million,
28%
$251-
$500
million,
5%
Greater
than $10
billion,
24%
$1
billion -
$10
billion,
32%
$500 million - $1 billion, 11%

19. Acceptance and Purpose

20. Big Data: States of Adoption
Initiation
34%
Adoption
11%
Routinization
55%
Initiation Adoption Routinization

21. Functional Use of Big Data
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Security
Finances
HRM
SC Planning
Procurement
After Sales
Marketing
Adoption State Routinization State

22. Extent of Big Data Use
Dimensions
• Routinization: Volume, Velocity, and Variety
• Initiation: Veracity
Types of Data
• Use of transactional and environmental data significantly higher than
consumer data
• Firms are likely to be constrained and restricted to particular sources
of data
• Incorporating new sources of data remains an opportunity

23. Big Data: Perceived Usefulness
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Can increase job effectiveness
Can increase job efficiency
Necessary to get the job done
Initiation State Adoption State Routinization State

24. BD: Perceived Ease of Use
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Allows me to do what I want to do with it
Requires litle mental effort
Clear and understandable
Initiation Adoption Routinization

25. Organizational
Capabilities

26. Current Use of Technology
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
WMS
TMS
EDI
APO
ERP
Initiation Adoption Routinization

27. Current Data Capabilities
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Close work with technology service providers
Use of current data to the maximum effectiveness
Enough data storage capacity to use Big Data effectively
People with extensive data analysis skills
Initiation Adoption Routinization

28. Organizational
Environment and Design

29. Big Data: Market Uncertainty
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Core production and delivery technology often change
Marketing promotions of competitors are unpredictable
Performance of major suppliers is unreliable
Customer demand patterns change on a weekly basis
Initiation Adoption Routinization

30. BD: Supply Chain Integration
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Actively involved in activities to streamline the supply chain
Interlocking programs and activities with supply chain partners
Information sharing externally across supply chain partners
Information sharing internally across departments
Management of cross-functional processes
Extensive use of cross-functional teams
Initiation Adoption Routinization

31. BD: Supply Chain Agility
Short-term capacity increases as needed
Quick addressing of environmental opportunities
Resolute decision-making to deal with environmental changes
Quick detection of changes in the environment
2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60
Initiation State Adoption State Routinization State

32. Operational and Financial
Performance

33. Performance Outcomes vs. Major
Competitors
More efficient than competitors
Short order fulfillment lead-time
Consistent on-time delivery to major customers
3.00 3.20 3.40 3.60 3.80 4.00 4.20
Initiation Adoption Routinization

34. Financial Performance vs. Major
Competitors
Profit Growth
Return on Investment
Sales Growth
2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 3.70 3.80
Initiation Adoption Routinization

35. Conclusions

36. Conclusions I
Current Concept
Ill-defined and under-explored by retail supply
chain member firms
Current Use
Limited scope in terms of sources, formats, and
applications
Concurrent Use Collaboration, visibility, and integration

37. Conclusions II
Caution Big data use is a double-edge sword
Success is Not
Easy
New mindset and a business process design
based around Big Data
Substantial
Rewards
Firms at more advanced states of use are
significantly outperforming their competitors
Virtuous Innovation
BD use is an innovation that may act as both a
catalyst and a byproduct of success

38. Speakers
• Anníbal Sodero
– Assistant Professor, Department of Supply Chain Management
– Sam M. Walton College of Business, University of Arkansas
– Email: asodero@walton.uark.edu
• Mark Barratt
– Associate Professor, Department of Management
– College of Business, Marquette University
– Email: mark.barratt@marquette.edu
• Yao “Henry” Jin
– Neil R. Anderson Assistant Professor of Supply Chain Management
– Farmer School of Business, Miami University
– Email: jiny3@miamioh.edu

39. Don’t Forget to Complete the
Evaluation!
Download the conference app and rate this session.
Start by choosing: “Cornerstones and Tracks” on the main
menu and follow the prompts to this session’s landing page.