Big Data Use in Retail
Supply Chains
Drs. Mark Barratt,
Anníbal Sodero,
and Yao Jin
Acknowledgements
• The researchers are grateful for the financial and collaborative support
of CSCMP and FMI for this research project.
• We appreciate the opportunity to partner with 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.
Perspective
• 90% of the data in our digital universe was
created in the last two years
• The digital universe is doubling in size every two
years and will multiply 10 fold between 2013 and
2020 from 4.4 trillion gigabytes to 44 trillion
gigabytes
Sources: Venturesity
Perspective
• Today, the average household creates enough
data to fill 65 iPhones(32g) per year.
• In 2020, this will grow to 318 iPhones
• In 2013, 22% of the digital universe was
considered useful, but less than 5% was analyzed
• In 2020, 35% will be considered useful data Sources: Venturesity
Reference Measures
• Gigabyte, 1024 megabytes: 4.7g=One DVD
• Terabyte, 1024 gigabytes: 1t=two years worth of
non-stop MP3s
• Petabytes, 1024 terabytes: 1p=13 years of HDTV
• Exabytes, 1024 petabytes: 5e=All the words ever
spoken by mankind
Big Data vs. Supply Chain
Management
Big Data vs. Supply Chain
Management
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?
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.
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”
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)
Three States: Initiation Adoption Routinization
• Point of Sale (POS) and on-hand inventory data
• Social media data but for marketing purposes only - better understanding of consumer preferences
Overall Finding
Big Data use in Retail SCs still elusive!
Initial and some significant cases of use, but mostly using traditional, transactional data
Big Data: Good News
• 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
As reported by firms in more advanced state (i.e. routinization)
Research Overview
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
• 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
Factors that influence BD adoption
Knowing… Being able to…
BD: Benefits and 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
• Predictive analyses of consumer demand patterns
• Advanced insights into procurement and distribution operations
• Strategic questions to shape supply chains
Direct Benefits – Critical Success Factors
Strategic Benefits – Omni-Channel and Demand-Driven Supply Chains
GAP: Definition - Practice
Volume
Variety
Velocity
Veracity
Man
agerial Defin
ition
Practice
Significant Data Quality Issues
Little Evidence
POS & On-hand Inventory
Demographics: Job title & Revenue
Other, 10%
Director, 47%
President/VP, 17%
Planner/Analyst,
25%
Less than $250
million, 28%
$251-$500
million, 5%
$500 million -
$1 billion,
11%
$1 billion -
$10 billion,
32%
Greater than $10 billion,
24%
Acceptance and
Purpose
Big Data: States of Adoption
Initiation Adoption Routinization
Initiation 34%
Adoption 11%
Routinization 55%
Functional Use of Big Data
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Marketing
After Sales
Procurement
SC Planning
HRM
Finances
Security
Adoption State Routinization State
Extent of Big Data Use
• Routinization: Volume, Velocity, and Variety
• Initiation: Veracity
• 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
Dimensions
Types of Data
Big Data: Perceived Usefulness
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Necessary to get the job done
Can increase job efficiency
Can increase job effectiveness
Initiation State Adoption State Routinization State
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
Clear and understandable
Requires litle mental effort
Allows me to do what I want to do with it
Initiation Adoption Routinization
Organizational
Capabilities
Current Use of Technology
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
ERP
APO
EDI
TMS
WMS
Initiation Adoption Routinization
Current Data Capabilities
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
People with extensive data analysis skills
Enough data storage capacity to use Big Data effectively
Use of current data to the maximum effectiveness
Close work with technology service providers
Initiation Adoption Routinization
Organizational
Environment and
Design
Big Data: Market Uncertainty
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Customer demand patterns change on a weekly basis
Performance of major suppliers is unreliable
Marketing promotions of competitors are unpredictable
Core production and delivery technology often change
Initiation Adoption Routinization
BD: Supply Chain Integration
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Extensive use of cross-functional teams
Management of cross-functional processes
Information sharing internally across departments
Information sharing externally across supply chain partners
Interlocking programs and activities with supply chain partners
Actively involved in activities to streamline the supply chain
Initiation Adoption Routinization
BD: Supply Chain Agility
2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60
Quick detection of changes in the environment
Resolute decision-making to deal with environmental changes
Quick addressing of environmental opportunities
Short-term capacity increases as needed
Initiation State Adoption State Routinization State
Operational and
Financial
Performance
Performance Outcomes vs. Major
Competitors
3.00 3.20 3.40 3.60 3.80 4.00 4.20
Consistent on-time delivery to major customers
Short order fulfillment lead-time
More efficient than competitors
Initiation Adoption Routinization
Financial Performance vs. Major
Competitors
2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 3.70 3.80
Sales Growth
Return on Investment
Profit Growth
Initiation Adoption Routinization
Conclusions
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
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
Council of Supply Chain Management Professionals
Chris Adderton
Vice President
333 East Butterfield Road, Suite 140
Lombard, Illinois 60148
630 574 0985
CSCMP's 2015 Annual Conference is Supply Chain’s Premier Event™
September 27 - 30, 2015
San Diego Convention Center