How to Cheat and Make Better Decisions with Predictive Analytics. Track 1 Session 3

How to Cheat and

Make Better

Decisions with

Predictive Analytics

2 0 1 2

&

•

.

.

2

Robert Heaney

-

Company Website:

www.Aberdeen.com

-

Phone: 860-752-6186

-

Email :

[email protected]

-

Company: Aberdeen Group

2 0 1 2

&

•

.

.

Abstract

Pass or punt? An acquisition? Another beer?

Hindsight confirms that success or failure is

simply a result of how well decisions are made

along the way. This nationally recognized

expert will help us understand how gathering

and applying data to your “gut feel” decisions

will wildly improve your win rate. We will learn

how the many analytical apps available today

can guide you to making brilliant foresighted

decisions, as well as hear what the next

generation of tools and processes means to the

business world

.

2 0 1 2

&

•

.

.

Agenda



Business Context- Gut Feel and Complexity



Tradeoffs / Strategies In Predictive Analytics



Aligning Predictive Analytics to Supply Chain

-

Strategies and Focus

-

Capabilities of Leaders

-

Technology Enablers – traditional to social media



Case Studies and Best Practices



Key Takeaways

2 0 1 2

&

•

.

.

“Gut Feel” Decisions and Supply Chain

Executives

5



25% rely on “gut feel” supported with supply

chain data to make strategic decisions



45% rely on “gut feel” supported with supply

chain data to make tactical decisions



With today’s Business and Predictive Analytics

tools these decisions can be better and more

informed



75% of Executives report that if armed with the

proper analytics they could make better

decisions

2 0 1 2

&

•

.

.

2 0 1 2

&

•

.

.

Conversion of linear supply chain into multi-enterprise networks

- Results in loss of control and visibility and predictability

- Lead-times are increasing due to expansion of the network

- Planning collapsing into execution

- Sustainability embedded into decision-making

- Risks are proliferating

2 0 1 2

&

•

.

.

Customers

Suppliers

Raw Material

Component

Factory

Retail Store

System

Factory

Enterprise

Demand Network - Buyer of

services and products

For e.g: Retailers, Distributors,

Value Added Resellers (VARs),

Supply Network –

System suppliers,

Contract Manufacturers, ODMs,

Raw material suppliers

Enterprise – Sales, Marketing,

Operations, Manufacturing,

Procurement

Logistics Network – 3PLs, Shippers, Carriers

Financial Network - Flow of physical goods, supply chain data and financial information

2 0 1 2

&

•

.

.

Raw Materials

Supplier

Component

Supplier

Ocean

Retail Store

Raw Materials

Supplier

Component

Supplier

Retail

Store

Customers

Global Air

Global DC

Home

Delivery

International visibility through satellites

and cloud technology

010101

DEMAND

SUPPLY

Global

DC

Raw Materials

Supplier

Component

Supplier

Ocean

Retail Store

Raw Materials

Supplier

Component

Supplier

Retail

Store

Customers

Global Air

Global DC

Home

Delivery

International visibility through satellites

and cloud technology

2 0 1 2

&

•

.

.

10

Globalization & Multi-Channel

Logistics

Transportation Modes and Distribution Nodes

35%

40%

56%

68%

70%

92%

0%

20%

40%

60%

80%

100%

Multi-modal

Rail

Parcel

Air

Ocean

Trucks (TL and LTL)

Percent of Respondents

We currently operate a

distribution center

We currently operate 2-5

distribution centers

We currently operate

more than 5 distribution

centers

We are 100% managed

by a third party service

provider(s)

We do not operate a

distribution center, but

are planning to

We do not operate a

distribution center and

have no plans to

22%

24%

15%

25%

11%

3%

We currently operate a

distribution center

We currently operate 2-5

distribution centers

We currently operate

more than 5 distribution

centers

We are 100% managed

by a third party service

provider(s)

We do not operate a

distribution center, but

are planning to

We do not operate a

distribution center and

2 0 1 2

&

•

.

.

11

CSCO Supply Chain Disruptions

Supply Chain Disruptions

Discrete

Increase in customer demand

47%

Supplier / carrier capacity did not meet our demand

_45%

Raw materials price volatility

_42%

Shipment delayed / damaged / misdirected

_34%

Commodities price volatility

29%

Reduction in customer demand

33%

Product quality issues leading to recalls

26%

Unfavorable change in currency exchange rate

19%



Complexity and Volatility and “Gut Feel” decision

making is mitigated by Big Data and Predictive Analytics

2 0 1 2

&

•

.

.

12

Energy and Globalization Drive Actions

8%

13%

16%

24%

35%

35%

55%

9%

15%

18%

27%

39%

42%

60%

0%

15%

30%

45%

60%

Best

-

in

-

Class

All Others

8%

13%

16%

24%

35%

35%

55%

9%

15%

18%

27%

39%

42%

60%

0%

15%

30%

45%

60%

Changing the way we market our products

Focusing on inbound supply chain initiatives

Integrating supply chain process across the

extended supply chain

Changing transportation / logistics strategies

Introducing more efficient / environmentally

friendly packaging options

Changing the way we manage our waste and

disposal

Improve energy efficiency or use alternate

sources of energy

2 0 1 2

&

•

.

.

Agenda



Business Context- Gut Feel and Complexity



Tradeoffs / Strategies In Predictive Analytics



Aligning Predictive Analytics to Supply Chain

-

Strategies and Focus

-

Capabilities of Leaders

-

Technology Enablers – traditional to social media



Case Studies and Best Practices



Key Takeaways

2 0 1 2

&

•

.

.

14

The PACE Framework

C

E

P

ressures:

External and internal forces that impact an organization’s

_{market position, competitiveness, or business operations.}

A

ctions

:

The strategic approaches that an organization takes in

_{response to industry pressures.}

C

apabilities:

The business competencies (organization, process, etc…)

_{required to execute corporate strategy.}

E

nablers:

The key technology solutions required to support the

_{organization’s business practices.}

2 0 1 2

&

•

.

.

15

Tough Tradeoffs – Growing

Globalization

5%

37%

28%

52%

26%

12%

22%

28%

46%

52%

0%

10%

20%

30%

40%

50%

60%

Increased regulatory compliance mandates

reduced supply material & capacity

Escalating demand for service from customers

Increased demand volatility

Rising supply chain management costs (e.g.,

total landed costs, fuel, labor costs)

Growing complexity of global operations (e.g.,

longer lead times and lead-time variability, or

increasing numbers of suppliers, partners,

carriers, customers, countries, logistics

Percent of Respondents

Best-in-Class n=58

All Others

2 0 1 2

&

•

.

.

Predictive Analytics Definition

16



Predictive analytics = mathematical model to

use as the basis of predictions



Today’s Supply Chain BIG DATA - hundreds

thousands of pieces of data to segment by...

-

Customer

-

Product

-

Logistics Channel and Profit

2 0 1 2

&

•

.

.

17

Predictive Analytics Priorities in

Customer Marketing

29%

45%

55%

0%

10%

20%

30%

40%

50%

60%

Build unique customer

profiles and personas

Obtain a 360º view of

the customer

Improve targeting of

marketing offers

2 0 1 2

&

•

.

.

Predictive Analytics Usage in

Marketing

18



24% of companies have adopted predictive

analytics for marketing



An additional 33% of respondents plan to

implement this approach within the next 12

months



Segmenting components include

-

Customer

-

Product

2 0 1 2

&

•

.

.

19

Predictive Analytics Capabilities in

Customer Contact Centers

19

20%

18%

37%

29%

32%

50%

0%

10%

20%

30%

40%

50%

60%

Ability to make

proactive

inbound offers

Predictive model

used to drive

decisions in real

time

Customer facing

skills understood

and grown / hired

2 0 1 2

&

•

.

.

Predictive Analytics Requires Data :

Structured, Unstructured and Social

20

24%

33%

71%

63%

41%

49%

78%

81%

0%

20%

40%

60%

80%

100%

Access to social

media data

Access to

internal

unstructured data

Access to all

customer

transactional data

Access to

customer

behavior data

Percentage of Respondents, n=112

Leaders

Followers

“To a large extent,

the quality of the

model is only as

good as the

2 0 1 2

&

•

.

.

Agenda



Business Context- Gut Feel and Complexity



Tradeoffs / Strategies In Predictive Analytics



Aligning Predictive Analytics to Supply Chain

-

Strategies and Focus

-

Capabilities of Leaders

-

Technology Enablers – traditional to social media



Case Studies and Best Practices



Key Takeaways

2 0 1 2

&

•

.

.

22

Predictive Analytics Focus Aligns with

Supply Chain Mission

7%

22%

20%

18%

33%

5%

10%

19%

27%

40%

0%

15%

30%

45%

Profit center - a

potential source of

new revenue/profit

Cost center necessary

to conduct business

Market strategy

competitive

differentiator

Customer service

competitive

differentiator

Cost savings

opportunity area

Percent of Respondents

Best-in-Class n=58

All Others n=106

Officer

2 0 1 2

&

•

.

.

23

Top Strategic Actions in Predictive

Analytics Taken by Companies –

Internal vs. External

30%

32%

35%

37%

44%

0%

20%

40%

60%

Create a tighter feedback loop from actual market

activity to demand assumptions and plans

Create a more predictable cost optimized network

Transform supply chain organization

Optimize end-end inventory based on predictive

customer demand or service levels

Improve internal integration process for creating

forecasts, pricing and promotion plans and making

mid-course corrections

2 0 1 2

&

•

.

.

Predictive Analytics Usage in Supply

Chain Organization is Growing

24



35% of companies have adopted predictive

analytics for supply chain segmentation



An additional 23% of respondents plan to

implement this approach within the next 12

months



Segmenting targets include …

-

Customer /supplier

-

Product

-

Logistics Channel

2 0 1 2

&

•

.

.

Predictive Analytics Enablers :

Adoption Levels in Demand-Supply

25

20%

27%

51%

59%

34%

39%

63%

71%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Supply Chain automation with

embedded predictive

analytics

Social media monitoring

and analysis tools

Demand management

software

Customer segmentation

tool

Percentage of respondents, n= - 112

Leaders

Followers

20%

27%

51%

59%

34%

39%

63%

71%

0%

10%

20%

30%

40%

50%

60%

70%

80%

embedded predictive

analytics

Social media monitoring

2 0 1 2

&

•

.

.

26

Leaders More Likely to Align and to Act

2 0 1 2

&

•

.

.

27

Agenda



Business Context- Gut Feel and Complexity



Tradeoffs / Strategies In Predictive Analytics



Aligning Predictive Analytics to Supply Chain

-

Strategies and Focus

-

Capabilities of Leaders

-

Technology Enablers – traditional to social media



Case Studies and Best Practices



Key Takeaways

2 0 1 2

&

•

.

.

Schwan Foods Adopts Predictive

Analytics, Tailors Delivery Assortments

28



Schwan's Home Service is the largest

direct-to-home food delivery provider in the United States



Home Service markets and distributes more than

400 products, yet only 78 can fit on a truck



500 sales-and-distribution centers located

throughout the United States with 5,700 delivery

vehicles



Home Service markets and distributes more than

400 products, yet only 78 can fit on a truck



Predictive Analytics and route/customer

2 0 1 2

&

•

.

.

Schwan Foods Adopts Predictive

Analytics, Tailors Delivery Assortments

29



Most popular item is vanilla ice cream, yet have 43

flavors of ice cream alone



After reviewing assortments by route and driver a

predictive analytics model was used



Determine which specific 78 items to place on each

daily route



Predictive analytics program is now over 7 years

old and is ingrained in inventory allocation, routing

and demand-supply planning

2 0 1 2

&

•

.

.

30

Users of Predictive Analytics Leverage

Customer Segmentation and Social

30

82%

68%

61%

45%

38%

39%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Customer behavior used

to segment and target

Deliver specialized

offers to high value

customers

2 0 1 2

&

•

.

.

Schwan Foods and Predictive

Analytics

31

“Using predictive analytics to support the sales and marketing

function is different from using predictive analytics to support

strategic planning. When sales and marketing initiatives are

supported by this technology, insight from the solution and

real-time social media trends can be injected into customer

interactions. That means that employees lower down the

management ranks and even in our delivery fleets must be

empowered to act on those insights and we have seen a 13%

increase in suggestive sales”

2 0 1 2

&

•

.

.

Predictive Analytics Usage in Supply

Chain Organization is Growing

32



55% of Leaders are using social network

analysis tools, compared with 36% of followers



Social network feeds provide a public database

of intentions and sentiment



Up to 60% of marketing departments are paying

closer attention to social data as a source of

2 0 1 2

&

•

.

.

Sullivan Tire on Segmented Predictive

Analytics

33

“By breaking customers up into different segments, I was able

to see which customers are loyal and strong, who only comes

in occasionally, and also identify who I can likely turn into a

loyal customer who uses more services and comes in more

frequently

.”

2 0 1 2

&

•

.

.

Agenda



Business Context- Gut Feel and Complexity



Tradeoffs / Strategies In Predictive Analytics



Aligning Predictive Analytics to Supply Chain

-

Strategies and Focus

-

Capabilities of Leaders

-

Technology Enablers – traditional to social media



Case Studies and Best Practices



Key Takeaways

2 0 1 2

&

•

.

.

35

Key Takeaways



25-45% of CSCO utilize “gut feel” decisions



75% of executives feel that they could make

better decisions with better analytics



Predictive Analytics assists

-

Segmentation of supplier/products/customers

-

Inbound to outbound decision making

-

Adding structure to Social Media and Big Data



Leaders are 2 – times as likely to segment data



Predictive Analytics can mitigate suboptimal

segmentations and decision making

2 0 1 2

&

•

.

.

Agenda



Business Context- Gut Feel and Complexity



Tradeoffs / Strategies In Predictive Analytics



Aligning Predictive Analytics to Supply Chain

-

Strategies and Focus

-

Capabilities of Leaders

-

Technology Enablers – traditional to social media



Case Studies and Best Practices



Key Takeaways

2 0 1 2

&

•

.

.