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: 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

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.