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

Transcription

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 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.

3 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

4 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

5 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

6 Big Data vs. Supply Chain Management

7 Big Data vs. Supply Chain Management

8 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?

9 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.

10 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)

11 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

12 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

13 Research Overview

14 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

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

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

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

19 Acceptance and Purpose

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

21 Functional Use of Big Data 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 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 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 WMS TMS EDI APO ERP Initiation Adoption Routinization

27 Current Data Capabilities 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 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 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 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 Initiation Adoption Routinization

34 Financial Performance vs. Major Competitors Profit Growth Return on Investment Sales Growth 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 Council of Supply Chain Management Professionals Chris Adderton Vice President 333 East Butterfield Road, Suite 140 Lombard, Illinois CSCMP's 2015 Annual Conference is Supply Chain s Premier Event September 27-30, 2015 San Diego Convention Center