Speed to Insight: Rapid realization of business value using in-memory technology

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Speed to Insight:

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Summary

SAP are aggressively marketing their in-memory data platform, SAP HANA

• Radical performance gains by eliminating the need to access conventional storage

• Efficient use of memory using improved storage methods and data compression

HANA's true strategic potential lies in its use for rapidly identifying business value in

corporate data

• Data can be easily gathered from a broad range of IT systems and data base types,

including SAP business suite

• Rich and sustainable toolset to rapidly combine and analyze data for specific business

questions

• Ease of use for developers allows for fast-paced and agile (big) data projects

BCG has developed the "Speed To Insight" approach for HANA-based rapid analytics

• Hypothesis-driven approach focusing on business value opportunities

• Rapid development of HANA-based analytics pilot based on real-world client data

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HANA is one of SAP's rising stars

High Low Low High

G

row

th r

a

te

Market share

?

HANA is the 'next big thing'

for SAP

Traditional SAP suite (ERP): Market

leader in a maturing duopoly market

SAP suite

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HANA is creating a huge service market

22

19

17

15

0

20

40

60

80 SAP Partner Revenue

for Big Data Analytics ($Bn)

2015

+13%

50

57 2017

2016

45 2014

39 SAP is aggressively pushing HANA

into its huge customer base

HANA is creating a huge service

market

• Similar growth dynamics as with

SAP's R/3 introduction in the 1990's

Enormous growth opportunity

for BCG

• ~$15 Bn global market for SAP

HANA services in 2014

• Up to $ 22 Bn by 2017

SAP margins Hardware Add-ons Professional services Source: IDC

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HANA is an memory big data platform optimized for

in-tegrating, analyzing, and displaying data at high speeds

Retrieve data from

different sources

Close integration with

SAP ERP

Ability to pull data from

additional systems

• Different and potentially

incompatible data

structures

• Potentially contradicting

information

• Varying data quality

Consolidate data on

HANA platform

Integrated data view

• e.g., combination of

financial and operative

data

Data consolidated using

HANA toolset

• Matching of related data

across different sources

• Rule-based data

cleansing

Perform analytics

and reporting

Tight integration with broad

range of reporting and

analytics tools

• Versatile analytics library

incl. statistics ("R"),

pat-tern recognition, SAS

integration

• Multiple reporting and

charting tools for desktop

and mobile devices

• Rapid processing of large

data volumes in real time

HANA

Operations Sales Finance Maintenance Market SAP Non-SAP External

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SAP Landscape Transformation Replication Server can

replicate data to SAP HANA from broad range of sources

• MSFT SQL Server

Enterprise Edition

• Oracle Enterprise Ed.

• IBM DB2 LUW/

UDB (DB6)

• IBM DB/2 zSeries

• IBM DB2 iSeries

(former AS/400)

• IBM Informix

• SAP MaxDB

• Sybase ASE

• SAP HANA

Supported Databases

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S2I takes advantage of in-memory technology by taking

the project approach to the next level

BCG value

proposition

BCG project approach

Top-down approach

Value orientation

Rapid execution

S2I approach

From …

to …

"Typical" approach

Data collection mostly manual

• Client interviews to collect/ validate data

• Aggregated reports in various formats

• Manual data validation and fixes

Direct access to raw source data

• Unfiltered, unaggregated data

• "Single source of truth"

Data analysis on consultant's laptop

• Manual analysis on aggregated data

• Time-consuming data updates

Analysis on a professional data platform

• Secure and robust data storage/handling

• Large data volumes and complex analysis

Project deliverable is a PowerPoint deck

• One-off analysis, often not repro-ducible by client

• Resulting "tools" are rarely sustainable

Project deliverable includes tool and enablement

• IT tools on high professional level

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BCG's Speed-to-Insight is an iterative approach using

the SAP HANA in-memory platform

• Strategic perspective

• Business opportunities

• Technical feasibility

• Only 10 weeks for analytics pilot

• Implementation effort practically invisible

• "Good enough" mindset

• Each step optimised for speed

_•

_{Unified reporting &}

analytics capabilities

• Iterative roll-out

approach ("Fund

the journey")

• Using HANA as

integration platform

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New business

models

Increased

business

insight

Operational

efficiency

"Smart Data" with significant potential for business

innovations in operations and business model

Price and revenue optimization Effective maintenance Procurement performance Lean manufacturing Customer & market insight Predictive maintenance Cross-market product fertilization R&D-driven product innovation Monetize existing data base New service-based business models Partnering for information ecosystems Framework

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Example:

Data-driven topics for rapid value

creation at global CPG company

… and how S2I can create value

Example question …

How should we cluster markets in an

optimal way based on market

environment and consumer

preferences?

Market

setup

Identify cluster patterns by combining

sales data with geographical/

demographical data

Reduce trade spend

Which brands should we

promote in which markets?

Brand

portfolio

Analyze successfactors per brand

and market to identify new market

opportunities

 Drive top-line growth

Do we allocate efforts in

Salesforce in most efficient way?

Salesforce

effectiveness

Analyze effectiveness of CRM efforts by

combining CRM & financial (P&L) data

 Optimize allocation of efforts

Topic

What are the strongest trade

spend levers in different

consumers/markets

Trade

spend

Analyze correlation of trade spend and

actual P&L impact

 Reduce trade spend

4 weeks in raw data analysis,

followed by S2I pilot project on selected topic

Source: BCG analysis

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S2I Supply Chain framework uses supply chain

data for concrete optimization scenarios

Plan Actuals Quantity Date Date Quantity Date Quantity Quantity Date Quantity Date Date Quantity Date Quantity Quantity Date Quantity Date Quantity Date Quantity Date Quantity Date Pro- duction order Finished goods inventory Sales order Raw material inventory Raw material procurement Trans- ports1 Reduce raw material inventory

• Compare planned and actual inventory level at different data points

• Reduce net-working capital by lowering planned inventory levels

1

Reduce finished

goods inventory

• Compare planned and actual inventory level at different data points

• Reduce net-working capital by lowering planned inventory levels

2

Increase confirmation

date accuracy

• Compare customer requested date vs. confirmed date to customer vs. actual date

• Increase accuracy of confirmed dates

3

Increase demand forecast accuracy

• Compare actual vs. forecasted sales in different hierarchical levels

• Improve/eliminate forecasts from entities/products with significant deviations between plan & actual

4

Optimize demand forecast patterns

• Compare historic forecasts on different hierarchical levels e.g., region vs. sales rep/offices

• Eliminate low level forecasts w/o relevance for final submission

5

1. at various stages of supply chain Source: BCG

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Ability to develop new analytical solutions in

8–12 weeks ensures quick realization of benefits

Analytics

Calculation

mock-ups

Cockpit

mock-ups

Data base

HANA Sales Deck-Feb13-MS_v2_comments.pptx

Requirements

Rapid development

Hand-over

2–4 weeks 4–6 weeks 1–2 weeks

Develop IT solution

• Data model

• Calculation logic

• User-friendly frontend

Partnering with

third-party IT solution

provider

• Well-established

working mode from

previous cooperations

• BCG driving the

process and ensuring

results are delivered

Speed-to-insight

• Fast realization of initial

business benefits

Deep business insights

• Gained from

comprehensive and

consistent data view

Starting point for

corpo-rate harmonization of

reporting

• Roadmap for complete

redesign of technical

reporting landscape

Business benefits

Iterative implementation of "speed-to-insight"

enables clients to fund the journey

Source: BCG project experience

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Multiple data sources required for pilot project

Project structure, cost Milestones, resources Technical documents

Scope: All current and past projects

Project

initiation

Data types

Project phase

CCT

2001

2006

now

Prima-

vera

SAP ERP

DMP

Migration

tables from

pre-SAP

legacy

systems

O2O

O2C

1-4

1–4

4 x

Relevant for pilot (s)

Source: Client project briefing in August 2014; BCG analysis

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Client example:

S2I identified 7% procurement

savings potential

7.0%

1.9% Best cost sourcing

Supplier mgmt 1.2% 1.9% 2.1% Vendor bundling Total potential Process quality

8 weeks to create

trans-parency on cost drivers

and issues

Material procurement accounts for 30% of maintenance cost

• Low transparency and control

• Diversified materials portfolio

• Complex supplier management

• Decentralized purchasing

Value created by BCG: transparency on cost drivers and issues

• Replacing inaccurate reports

• Dealing with low data quality

704 690 690 690 608 458 915 0 500 1.000

Purchase orders (units) Unit price (EUR)

40 100 100 100 100 60 101 Vendor A Vendor B Vendor C Mat. no.: 952200012 10,000 30,000 20,000

Unit price (EUR)

Unknown buyer Buyer 2 Buyer 1 Buyer 3 No Yes Time Frame contract

Frame contract

compliance opportunities

Best cost sourcing

opportunities

A

• Ensure that higher volumes translate to lower unit prices

• Bundle all purchases with lowest price vendor

• Introduce frame agreements with high volume vendors B 1 2 2 3 1 2 3 • Address compliance/fraud issues with individual plants and buyers based on S2I findings

• Introduce frame contracts to enforce same low unit prices across all plants and buyers 2 1 Mat. no.: 551404703

A

B

1 2

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Client example:

Combining data from two databases

revealed major inaccuracies in the forecasting process

Correct estimation Over-estimated cargo volume Under- estimated cargo volume

4 out of 10 forecasts deviate less than 20% from the actual cargo volumes

Days before departure Vessel departure Days after departure

Every second forecast is more than 20% inaccurate

1 out of 10 forecasts are submitted after vessel departure

Forecasting accuracy analysis enabled identification of

lowest-performing agencies for targeted improvement measures

Forecast submitted after vessel departure Deviation >20%

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Created deep insight into customer behaviour across ticket types, sales channels & locations, e.g.,

• Quantified untapped cross-selling potential from seasonal ticket purchases in large train stations

• Identified train stations with too many or too few ticketing machines

Provided cutting-edge reporting tools to enable client to replicate insights in daily operations

Impact

Client example:

Created deep insight into customer

behaviour via cutting-edge reporting tools

Get geographical

overview of sales

Tap stations to

see sales channels

Swipe to see ticket types

Apply filters to only see subset

of data

Tap any graph to change focus,

double-tap to make full screen

Case example

Use slider to zoom on subset

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Ongoing Case Example:

Four alternative topics

for internal pilot

Potential pilot

Key questions/hypotheses …

… and analysis to create value

Project per-formance transpa-rency

Create overview of cost/schedule changes per project and identify characteristics of high deviation projects (e.g., small vs. large hydro)

 Unified view of cost/schedule deviations

1

How realistic are sales calculations and project risk assessments? Which impact

have actual cost deviations on future estimations ("closed loop")?

Cost forecast accuracy

Compare initial/intermediate/post project calculations ("as offered", V, H, 0) and derive patterns for systematic bias and variance

 Improved risk estimates on project cost

2

Which impact has discipline in early planning of key milestones on unplanned

cost/schedule deviations?

Project planning behaviour

Score projects by initial planning detail and time period for final milestone fixation1_{; analyse} correlation with cost/schedule dev.

 Improvement areas for planning process

3

Which projects contribute the most to lost or delayed profit due to unplanned

cost/schedule deviations?

To what degree do frequent changes and iterations of technical documents predict

unplanned cost/schedule deviations?

"Moving targets"

Rank projects by documentation volatility (i.e., document revision index) and correlate with cost/schedule deviations

 Improved "early warning" mechanism

for troubled projects

4

1. e.g., in which time period after project start do estimations for key milestones converge on stable dates and how accurately are these dates achieved later on Source: Client project briefing on 5th of August 2014; BCG analysis

Pilot to be selected prior start,

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188900-24-18Nov2014-NH-mb-FRA-ji.pptx Draft—for discussion only 17 C opy ri ght © 2014 by T he B os ton C ons ul ti ng G roup, I nc . A ll ri ght s r es er v ed. • Organizational drill-down capabilities

• Integrated budget and HR view during reorganization

• Real-time integration of production and maintenance • Improve make-or-buy decisions • Transparency on profitability per unit

• Ability for ongoing

optimization of production plans

Iterative HANA transformation to

"fund the journey"

Iterative business

benefits realization …

… by connecting

existing "data islands"

Material planning Mainte- nance plan HR Pro-duction plan Finance Sales Maintenance utilization optimization Advanced budgeting planning Marginal profitability optimization