Analytics For Non-Analysts The Value Of Predictive Analytics For - - PowerPoint PPT Presentation

2018 MHI ANNUAL CONFERENCE

Analytics For Non-Analysts

The Value Of Predictive Analytics For Digital Supply Chains

rbradley@utk.edu @randyvbradley linkedin.com/in/randyvbradley +1-334-354-5966

Randy V. Bradley, PhD, CPHIMS, FHIMSS The University of Tennessee

2018 MHI ANNUAL CONFERENCE

Agenda Overview

• 1. Top supply chain challenges

2.The quest for and challenge of visibility

• 3. Linking analytics and Nextgen supply chain

4.Got data? Get insights -- that are actionable and impactful

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Top Supply Chain Challenges

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Means, Not Ends: Transparency and Visibility

VISIBILITY TRACEABILITY INTEGRITY TRANSPARENCY

SUPPLY CHAIN RISK MANAGEMENT SUPPLY CHAIN SUSTAINABILITY

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Attributes of NexGen Supply Chains

Digital 1 Connected Collaborative Always-on Forward-looking analytics 2 3 4 5 Transparent 6 Secure and trusted

Agile, adaptive, responsive

Effective and efficient Safe and sustainable 7 8 9 10

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We as a company didn’t go to bed one night and say, “We can’t be an industrial company anymore. We need to be more like Oracle. We need to be more like Microsoft.” It happened more on an evolutionary basis, really based on the industries we’re in and the technology we serve. So industrial companies are in the information business whether they want to be or not. This is going to happen in the industrial space.

~Jeff Immelt, General Electric Chairman and CEO

We want to treat analytics like it’s as core to the company over the next 20 years as material science has been over the past 50

• years. We need to...share outcomes with our customers...we have

to add technology, we have to add people, we have to change our business models

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What Are We Seeing?

• 10% growth in electronic linkages (EL) with customers
• Up to 25% growth in EL with suppliers
• Increase in and greater emphasis placed on real-time

access to data

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What Are We (Still) Seeing?

• Digital business journeys hampered
• Insufficient business-savvy IT and analytics staff
• SC personnel lack requisite current gen IT and analytics skills

and knowledge

• Top drivers of ERP (“operational backbone”) adoption
• Streamline and improve business processes
• Enhance data accuracy and consistency
• Improve SC efficiencies

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1,100 manufacturing and supply chain industry leaders on supply chain innovation

Survey Participants for 2018 MHI Annual Industry Report

47%

annual sales in excess

• f $100 million, and 10% reporting

$10 billion or more

75%

manufacturers, distributors

• r service providers

50%

CEO, Vice President, General Manager, or Department Head 10

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Key Survey Highlights

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Most Common Uses of IoT in the Supply Chain

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Big Data is Relative…Not Absolute

Big Data (Noun)

When volume, velocity, and variety of data exceed an

• rganization’s storage or compute capacity for

accurate and timely decision-making

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Current Analytics Approach: MST = OBL

Multiple Sources of Truth

One Big Lie

Analytics Engine

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The Role of Predictive Analytics: Truth Teller

Analytics Engine Multiple Sources of Truth

The Truth

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The Role of Predictive Analytics: Truth Teller

Analytics Engine The Truth

Various Insights Multiple Sources

• f

Truth

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“Prescriptive analytics provides us with what’s the best possible le act ction I I can take today in in lig light of what I I anticipate happening tomorrow. . But what good is is it it to predict what you cannot act upon?”

From Predictive to Prescriptive

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The Role of Prescriptive Analytics: Value Realizer

Analytics Engine

Various Insights

Analytics Engine

Experience & Expertise 19

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Analytics Value Limiters

• Data streams
• Questions
• Strategy

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How well does your organization manage capturing, processing, and integrating data streams from multiple sources?

• A. We have no formal mechanism for

measuring

• B. Poor
• C. Fair
• D. Good
• E. Excellent
• F. Are you kidding me…I have no idea!

2018 MHI ANNUAL CONFERENCE

What types of questions do you seek to answer?

• A. What happened?
• B. How many, how often, where…?
• C. What exactly is the problem?
• D. What actions are needed?
• E. Why is this happening?
• F. What will happen next?
• G. What if we try this?
• H. What’s the best that can happen?

2018 MHI ANNUAL CONFERENCE

Got Data…Get Insights

What Type of Questions Are You Asking?

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Types of Questions and Analytics

Descriptive Predictive Prescriptive

Questions What happened? What’s happening? What exactly is the problem? What actions are needed? Why is this happening? What will happen next? Why will it happen? What should I do? Why should I do it? What’s the best that can happen? What if we try this? Enablers

• Ad hoc Reports
• Dashboards
• Data Warehousing
• Alerts
• Data Mining
• Text Mining
• Web/Media Mining
• Forecasting
• Optimization
• Simulation
• Decision Modeling
• Randomized Testing

Outcomes Well defined business problems and

• pportunities

Accurate projections of the future states and conditions Best possible business decisions and transactions

2018 MHI ANNUAL CONFERENCE

Descriptive Diagnostic Predictive Prescriptive Descriptive

What happened?

Diagnostic

Why did it happen?

Predictive

What will happen?

Prescriptive

What should I do?

Got Data…Get Insights

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DEEP SURFACE FUTURE PAST

2018 MHI ANNUAL CONFERENCE

Does your organization have a defined analytics strategy?

• A. Yes
• B. No

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Data-Driven Decisions?

When you say or hear data-driven decisions, what do you mean/is meant?

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Data-Driven Decisions?

Do you mean… Data Drive Decisions?

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Data-Driven Decisions?

Do you mean… Data Should Drive Decisions?

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Data-Driven Decisions?

Do you mean… Data Systems Make the Decisions?

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Data-Driven Decisions?

Do you mean… Data Should Support Decisions?

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Data-Driven Decisions?

Do you mean… Data Should Support Decision-makers?

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Data-Driven Decisions?

Do you mean… Data Influence the Decisions?

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Data-Driven Decisions?

Do you mean… Data Inform Decisions?

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Data-Driven Decisions?

Do you mean… Data Should Help Analyze Decisions?

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Got Data…Get Insights

ISSUE INSIGHTS ACTION

▪ Product persistence ▪ Identify idle products ▪ Change production schedules ▪ Implement sales incentives ▪ Returns & trade management ▪ Monitor trades & returns ▪ Detect suspicious activity ▪ Impose returns authorization via authentication ▪ Prevent parallel trade ▪ Buyer & supplier engagement ▪ Gather simple and complementary purchasing trends ▪ Collect customer consumption data ▪ Target regions based on purchasing trends ▪ Develop solutions based

• n consumer response data

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Presents an

• pportunity to collect

data for better value chain insights Businesses can gain true-time insights from rich data:

• Identify product persistence
• Dwell time
• Product expiry
• Improve returns and trade management
• Increase customer and supplier engagement

Got Data…Get Insights

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THANK YOU!

Randy V. Bradley, Ph.D., CPHIMS, FHIMSS

rbradley@utk.edu @randyvbradley linkedin.com/in/randyvbradley

+1-865-974-1761 +1-334-354-5966