Driving Value Creation through the exploitation of Big Data€¦ · CRM EMI Six different data sources house relevant data for most analyses Integrated “one-stop” relational database

Driving Value Creation through the exploitation of Big Data

KCP Finance Transformation Journey

Rob Hughes – Professional Products business VP and Division CFO

1990 2000 2010 2015

Financial Analysis has been on a Slow Evolution over the past 2 decades

2Evolutionary timeline / transitions subject to company size and scale

Activity Based Costing

Enterprise Manufacturing Intelligence

Enterprise Resource Planning

“Back Office” Outsourcing

Budget / Forecast Optimization

“Big Data” Analytics

Data Visualization

Several major transformation

waves occurred for most companies

The Role of Financial Analysts has also evolved…but considerable opportunity exists

3Source: BCG's "Office of the CFO" team

Financial Demands continue to Increase in a rapidly evolving business environment

4

Global CustomersReal-Time Data

Compliance and Controls

Customer Consolidation

E-CommerceCurrency Volatility

Political Instability

Global Supply Chain

Better Insights

Shorter Response Times

Expectations of FinanceMarket Dynamics

Economic slowdown

Social Media Explosion

Cyber Security Virtual Workplace

Sustainability

Proactive Leadership

…while costs are expected to decrease

5

* Source: PWC Finance Benchmark Study 2011; CFO magazine May 2015.

Finance is Simultaneously in the Best and Most Challenging Position to Influence Business Change

6

• Management Reporting

• Business Planning / Forecasting

• Decision Support• Results Analysis• Strategic Analysis

Procurement

Value-Added Insights

Proactive Recommendations

Business Diagnostics

Product Supply

Quality

Marketing

Innovation

StrategyRisk

ManagementHuman

Resources

Sales

…the Quantity of Data is rapidly increasing and the timeframe for analysis is contracting…

7

Enterprise Resource Planning

Enterprise Manufacturing Intelligence

Customer Resource Management

External Market Intelligence

Traditional Data Sources

We must transform our operating model to proactively drive greater value!

Speed of Data / Analytical NeedsQuarterly Monthly Daily Real-

Time

Quantity of Information

The Case for Change in Kimberly-Clark Professional

8

More than 5,000 unique products globally…

Manufactured internally and externally

“Big Data” has been a challenge for us for many years

Sold through nearly 4,000 distributors (with multiple branch locations)…

Utilizing a distributor pricing model with potentially thousands of unique price points for a specific product…

Custom

er Re

quested

Ana

lysis

Rep

orting

Fo

recasting

Mo

nth-End A

nalysis

Proa

ctive (hig

her value) A

nalysis

0%50

%100

%

50%20% 15% 10% 5%

25%5% 8% 2%

60%

Allocation of Analyst Time

Current Future

A step-change in how we worked was needed to drive greater value within KCP finance

9

50% of reporting, month-end analysis, and customer-requested analysis driven by data manipulation, combination, and system

“waiting”

Too little time being allocated to proactive, high value analytics

A 2 pronged solution was developed to capture value

10

A “big data” technology solution was needed to reduce time on data manipulation, combination, and system “waiting”

We also needed to change analyst mindsets around the role of finance in proactively driving incremental value

ERP CRMRebate

Management

EMIIncentive

Management

Multiple disparate financial and

operational systems required combination

One-Stop Interactive Analytics Engine

3 Critical Components to Our “Big Data” Technology Solution

11

Integration of traditional financial, operational, and project-based data

Ability to rapidly filter, organize, and identify granular “hidden opportunities”

Must be able to accommodate > 1 billion data records

Toolset transition should be minimal to ensure analyst adoption

Each element critical to overall vision of reducing analyst time on reporting, ad-hoc analytics, and month-end analysis

Analyst Use Cases Informed Data Architecture and Solution Selection

12

Use Case One: Month-end, investigative reporting, and ad hoc customer request analysis

13

Analyst Use Cases Informed Data Architecture and Solution Selection, continued…

Use Case Two: “Hidden Opportunity” Exception / Insight Identification

Would we even investigate “price” as an opportunity

with traditional AOC reporting?

Would a traditional category or customer analysis truly

highlight the biggest actionable opportunities?

Source: Illustrative example; not based on Kimberly-Clark data.

Traditional Analysis of Change will likely miss considerable opportunities

14

Building on the prior example, is product 5 the biggest issue / opportunity?

• Overall price erosion is ~$150k (with 60% of prices lower)• However, most significant unique contract price reduction is

$47k

What about the “hidden” opportunities within smaller product variances?

• With price erosion of less than $40k, product 2 may never be investigated

• However, just one contract is driving more than $400k in erosion!

Considerable Exception / Opportunities likely “hidden” in our granular data

15

How many sizable opportunities are being missed due to data aggregation?

What is the total value of the “hidden” opportunities?

How do we effectively sift through up to 1 billion data records monthly to rapidly identify these exceptions, trends, etc.?

Co

ntra

ct 1

Co

ntra

ct 3

Co

ntra

ct 5

Co

ntra

ct 7

Co

ntra

ct 9

Co

ntra

ct 11

Co

ntra

ct 13

Co

ntra

ct 15

Co

ntra

ct 17

-10

1

Product 1 Price Variance ($MM)

Contract 1

Contract 4

Contra

ct 7

Contra

ct 10

Co

ntract 13

Co

ntract 16

Co

ntract 19

-1.5

-1-0

.50

0.51

1.5Product 52 Price Variance ($MM)P

roduct 1

Pro

duct 3

Pro

duct 5

Pro

duct 7

Prod

uct 9-10

1Asset 14 Waste ($MM)

-$0.60

-$0.91 -$0.74

-$0.71

(-$0.60-$0.91-$0.74-$0.71…)

Opportunity $MM

Product 52 price (contract 4)Product 52 price (contract 15)Asset 14 waste (product 6)Product 1 price (contract 11)

-$0.91-$0.74-$0.71-$0.60

How did we Achieve our “Big Data” Transformation – Part I: Technology?

16

Finance has led the project throughout Defined the 3 objectives and 2 use cases

All technology considerations / trade-offs evaluated against these objectives and use cases

Very clear that solutions being designed to make finance more effective on analytics – not creating reports / dashboards for customers

Moving from concept to execution

Project Management and Technology Selection led by Finance

17

Step One: Finance use cases developed to define needs

Step Two: Conceptual tools developed for each by finance defined specific data needs and calculations to manipulate / combine data existing data sources mapped to conceptual tools

Step Three: Speed and flexibility of technology solutions evaluated against original finance objectives

Finance coordinated “stress testing” of different technologies to validate “claims” on 3 criteria: 1). speed, 2). Reliability, and 3). simplicity to finance users

Step Four: Finance heavily involved in data structure development and use testing; led development of user tools

Interactive Analysis Tool Exception Identification Framework

Technology Architecture Framework

18

ERPEnd User Sales Mgt

Distributor Management

Dispenser Tracker

EMICRM

Six different data sources house relevant data for most analyses

Integrated “one-stop” relational database

Running MSBI SSAS on IBM Netezza servers

MSBI-based management reporting

Finance user tools (Interactive Analysis and exception reporting) running off an SSAS tabular model with 64bit configuration for Excel 2013

How did we Achieve our “Big Data” Transformation – Part II: Mindsets?

19

financial analyst user adoption to delivery of tangible value

Transitioning financial analysts to the new one-stop integrated analytical database was not

difficult

ERP

EU DB

Dist DB

Disp DB

CRM

EMI

Financial Analysts

Manipulation &

combination

Historical Analysis Technique

Vs.

Historical Analysis Technique

ERP

EU DB

Dist DB

Disp DB

CRM

EMI

Integrated “one-stop” relational database

How did we Achieve our “Big Data” Transformation – Part II: Mindsets, continued?

20

financial analyst user adoption to delivery of tangible value

Custom

er Requested A

nalysis

Reporting

Forecasting

Month-E

nd Analysis

Proactive (higher value) A

nalysis

0%10

0%

50%20% 15% 10% 5%

25%5% 8% 2%

60%

Allocation of Analyst Time

Current Future

How do we ensure that “free time” is effectively used to

drive value?

Tangible “Value Creation” Prioritized in Everything we do

21

Engaged high potential analysts to propose plan to drive greater

value

3 Part Transformation Plan

1). Change in Expectations

2). Change in Priorities / Work

3). Catalyst for Ideas…

Product 1

Product 3

Product 5

Product 7

Product 9

-10

1

Asset 14 Waste ($MM)We are on-track to deliver our

stretching value creation objective in 2015!

Wrap-Up

22

The constantly evolving global business and acceleration of “big data” truly represents a major opportunity for finance

To capture this opportunity, we must change how we operate to become more efficient and increase our influence

How we approach “big data” is a critical element of this solution

Development of tools (designed for finance) that integrate financial and non-financial data to reduce analyst time and proactively isolate exceptions have the potential to transform how we work

We must change the mindsets of our teams in addition to the tools to be successful!

Questions?

23