Somewhat or More Famil iar

59%

Extremely

Somewhat

Not Ver y

8%

Ver y16%

35%

41%

WHAT’S TO GAIN?

DESCRIPTIVEWHAT HAPPENED?

Get in touch with reality, a single source of truth.

Largely Reporting

PREDICTIVEWHAT WILL HAPPEN?

Understand the most likely future scenario, and it’s

business implications

PRESCRIPTIVEWHAT SHOULD WE DO ABOUT IT?

Collaborate for maximum business value, informed

by advanced analytics

ExternalSupply chain optimization

isn’t l inear, but the 1st & 2nd

generation of planning

solutions are built that way

Concurrent optimization in

combination with cognit ive

learning delivers greater

insights and f lexibi l i ty, but no

proven ROI

VOLUME+VELOCIT Y+VERACIT Y+VALUE

Because of DATA

Managers can now measure, and learn more about their business, in order to translate the new known knowledge into better and improved decision making

More data at a faster rate than ever before are being emitted through forms of: messages, updates, images, social networking, readings from sensors, GPS signals, cell

phone usage, and more.

SMART DATA EQUALS MORE REVENUE

Top ranking companies in their industr y using data-driven decision making

are on average, 5% more productive and 6% more prof itable

THAN their competitors

OPTIMIZATION

LEARNING

REPORTING

YIELDS BETTER...

Disparate systems make

gett ing to data dif f icult – 5-7

ERPs solutions and 2-3

instances

IT budgets – t ighter than ever

Struggling with massive data

complexity

Internal

Product traceabil i ty data 61%38%

55%36%

45%27%

42%26%

36%28%

33%20%

30%23%

21%24%

39%33%

36%20%

36%21%

Supply chain visibi l i ty

Geo-location and mapping data

Mobile applications

RFID transmission

Unstructured data in warranty

Temperature and product s treaming

Voice and video data

Internet of things (e.g. sensor y devices

Sentiment data from user -generated

Sentiment data from socialmedia

on equipment, vending machines. etc.

and quality logs

comments on ratings, reviews and blogs

(e.g. Facebook , Twitter, etc.)

DATA MOVING

FORWARD

ONLY 20% of companies feel their supply chains are working well

in the Supply Chain

SMART

Initiative in the Supply Chain

Data

Rules

Engines

Deployment

Process Flows

Database Structures

Structured

Simple rules: single “ifs” to single “thens”

Optimization based on a “known” function or mathematical outcome

Within the organization

Inside-out with a focus on the efficient response

Relational

Structured and unstructured

Adaptive rules: multiple “ifs” to multiple “thens”through cognitive learning

Concurrent optimization, cognitive learning and combinational math to drive discovery and new insights

Cloud-based to synchronize inter-and-intra- enterprise flows

Outside-in with a focus on sensing and adaptation

Relational and non-relational to mine data in lakes, streams and clouds

Current State Evolving Techniques

COGNITIVEWHERE IS THE NEXT STEP-CHANGE IN DRIVING AN INTELLIGENT RESPONSE?

Highly automated optimization solutions that get smarter over

time

CURRENT VS. EVOLVINGWHERE ARE THE IMPROVEMENT OPPORTUNITIES

SOURCE: http://www.information-age.com/technology/information-management/123458486/big-data-dead-rise-smart-datahttp://supplychaininsights.com/big-data-and-analytics-the-new-underpinning-for-supply-chain-success/https://hbr.org/2012/10/big-data-the-management-revolution/ar

HOW TO OBTAIN SMART DATA

Data-driven decisions are better decisions

PERFORMANCE USING T YPES OF DATA

=BIG DATA

DATA

FAMILIARIT Y WITH DATA

WHO SEES IT AS AN OPPORTUNIT Y?

>$500M revenue79%

SMART DATA

The purpose of smart data (veracity & value) is to filter out the noise and hold the valuable data, and when done effectively can be used

by the enterprise to solve business problems.

<$500M revenue40%

are manufacturers58% are retai lers

42%

DATA OUTLOOK

Data Init iat iveNo Init iative

If a businesss simply uses volume & velocity, that qualifies as a BIG DATA problem. However, a lot of this data comprises noise.