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

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630 574 0985

cadderton@cscmp.org

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