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A study sponsored by CSCMP on the drivers and outcomes of Big Data use in retail supply chains.
Citation preview
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 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.
Big Data search pattern
Big Data vs. Supply Chain Management search pattern
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
Research Methodology
• 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
• 18 senior supply chain managers
• Obtain greater details regarding their Big Data use efforts
Phase 1 – Survey Questionnaires
Phase 2 – In-Depth Interviews
• 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
VeracityManagerial D
efinition
Practice
Significant Data Quality Issues
Little Evidence
POS & On-hand Inventory
Demographics: Job title & Revenue
Other; 10%
Director; 47%President/VP; 17%
Planner/Ana-lyst; 25%
Less than $250 mil-lion; 28%
$251-$500 mil-lion; 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
Initiation34%
Adoption11%
Routinization55%
Functional Use of Big Data
Marketing
After Sales
Procurement
SC Planning
HRM
Finances
Security
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
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
Necessary to get the job done
Can increase job efficiency
Can increase job effectiveness
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Initiation State Adoption State Routinization State
BD: Perceived Ease of Use
Clear and understandable
Requires litle mental effort
Allows me to do what I want to do with it
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Initiation Adoption Routinization
Organizational Capabilities
Current Use of Technology
ERP
APO
EDI
TMS
WMS
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Initiation Adoption Routinization
Current Data Capabilities
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
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Initiation Adoption Routinization
Organizational Environment and Design
Big Data: Market Uncertainty
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
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Initiation Adoption Routinization
BD: Supply Chain Integration
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
- 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Initiation Adoption Routinization
BD: Supply Chain Agility
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
2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60
Initiation State Adoption State Routinization State
Operational and Financial Performance
Performance Outcomes vs. Major Competitors
Consistent on-time delivery to major customers
Short order fulfillment lead-time
More efficient than competitors
3.00 3.20 3.40 3.60 3.80 4.00 4.20
Initiation Adoption Routinization
Financial Performance vs. Major Competitors
Sales Growth
Return on Investment
Profit 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
Conclusions
Conclusions I
Current ConceptIll-defined and under-explored by retail supply chain member firms
Current UseLimited 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 InnovationBD use is an innovation that may act as both a catalyst and a byproduct of success
Speakers
• Anníbal Sodero– Assistant Professor, Department of Supply Chain Management– Sam M. Walton College of Business, University of Arkansas– Email: [email protected]
• Mark Barratt– Associate Professor, Department of Management– College of Business, Marquette University– Email: [email protected]
• Yao “Henry” Jin– Neil R. Anderson Assistant Professor of Supply Chain Management – Farmer School of Business, Miami University– Email: [email protected]
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