headline verdana bold - deloitte united states · 2018-05-18 · pass open gr/ir to ml model get...

Headline Verdana BoldDigital Finance in practiceMarc Mertens – Frederik D’heerMay 18th, 2018

© Deloitte 2018 2

LIVE QUIZ

Visit www.menti.com and use the code 274284 to participate

Let’s start with a quiz…

WARM-UP

http://www.menti.com/

© Deloitte Belgium 3

• Ad-hoc build• Next competitive

advantage• Not stable yet

• Unique processes• Industry-specific• Competitive

advantage

• Stable• Compliant• Standardized

Three systems to visualize a target landscape: Record, Differentiation, InnovationThe Pace Layer Model

ERP

Core

Fin

an

ce &

Co

ntr

oll

ing

Pro

cu

rem

en

t

Sale

s &

Dis

trib

uti

on

HR

Maste

r

Pla

nn

ing

MD

M

Pro

ject

Man

ag

em

en

t

Acco

un

tin

g

Basic

In

ven

tory /

WM

Basic

Tim

e

Reg

istr

ati

on

Basic

CR

M

Exte

nd

ed

bu

dg

eti

ng

, p

lan

nin

g

an

d c

on

tro

llin

g

Exte

nd

ed

Pro

cu

rem

en

t

(in

cl.

so

urcin

g)

Exte

nd

ed

CR

M

So

ft H

R

Exte

nd

ed

Bu

sin

ess

Pla

nn

ing

Tim

e &

Exp

en

ses

Exte

nd

ed

IM

/W

M

Syste

m

Exte

rn

al

bu

dg

eti

ng

,

pla

nn

ing

& c

on

tro

llin

g

e.g

. Tag

eti

k, A

nap

lan

, B

PC

Exte

rn

al

Pro

cu

rem

en

t S

yste

m

Exte

rn

al

CR

M

e.g

. S

F,

SA

P H

yb

ris

,

MS

FT

Dyn

CR

M,

etc

.

Exte

rn

al

So

ft H

R e

.g.

Wo

rkd

ay,

SA

P S

F,

etc

.

Exte

rn

al

Bu

sin

ess

Pla

nn

ing

Exte

rn

T&

E

e.g

. E

xp

en

dit

ure,

Co

ncu

r,

Certi

fy,

etc

.

Exte

rn

al

IM

/W

M

Syste

m

ER

P E

co

syste

m

System of differentiation

System of record

Internet of

ThingsBlockchain Robotics

Machine

LearningEtc.Analytics Big Data

See Chien, Melody (Oct. 2017), “Applying Gartner’s Pace Layer Model to Business Analytics”, Gartner.

System of innovation

Basic

Pro

du

cti

on

Bil

lin

g

Exte

nd

ed

Pro

du

cti

on

Syste

m

Exte

rn

al

Pro

du

cti

on

Syste

m

Exte

rn

al

Qu

oti

ng

,

Ord

erin

g o

r

Tran

sp

orta

tio

n t

oo

l

Exte

nd

ed

Pro

ject

Man

ag

em

en

t

(o

perati

on

al)

Exte

rn

al

Pro

ject

Man

ag

em

en

t

Exte

nd

ed

Tran

sp

orta

tio

n

Man

ag

em

en

t




System of record

Technical debt can be a hindrance to pursuing itClean ERP is a path to the Pace Layer Model architecture

Standard Configuration

Custom

Reports

Standard

EnhancementsCustom Forms

Custom

ScreensCustom Tables Custom Fields

Modifications“Misused”

Configuration

Outdated

requirements

Nice to have

customization

Years of

break-fix

Pace Layer Model Architecture(Platform-driven Ecosystem)

Transition dynamic,

complex leading practices

to the system of

innovation platform

Eliminate

unnecessary

customization

Adopt and optimize core-

specific leading

practices in/on top of the

baseline ERP

Reduction of Core

Footprint

Baseline ERP

Existing ERP Investments

Heavy t

echnic

al debt

= h

igh T

CO

Baseline ERP

Cle

an E

RP=

Agile,

low

er

TCO

1

2

3

Existing model




System of record



Custom

Reports

Standard


Custom



Configuration

Outdated

requirements

Nice to have

customization

Years of

break-fix


Transition dynamic,


to the system of

innovation platform

Eliminate

unnecessary

customization


specific leading


baseline ERP

Reduction of Core

Footprint

Baseline ERP


Heavy t

echnic

al debt

= h

igh T

CO

Baseline ERP

Cle

an E

RP=

Agile,

low

er

TCO

1

2

3

Existing model

© Deloitte 2018 6

SAP S/4HANA is the latest ERP solution of SAP.

What is SAP S/4HANA?

• S stands for simple

• 4 stands for fourth generation

• HANA stands for In-Memory Real Time

© Deloitte 2018 7

The HANA solution evolved from an in-memory database to a simplified data model including an improved user-

interface and easier deployments

Introducing S/4HANA

• Simplified data model

• New user experience

• Advanced processing

• Choice of deployment

S/4HANA

Enterprise

Management

• In-memory platform • Real-time analysis

• Real-time reporting

• Real-time business

• OLAP and OLTP

together

• SAP HANA Enterprise

Cloud for SAP

Business Suite on SAP

HANA

• Instant financial insight

• No aggregate

• Single source of truth

SAP HANA

SAP Business

Warehouse

powered by SAP

HANA

SAP Business

Suite powered by

SAP HANA

S/4HANA Finance(SAP Simple Finance

powered by SAP HANA)

2011 2012 2013 2014 2015

© Deloitte 2018 8

Speed, efficiency, simplicity and transparency are some of the core values of S/4HANA

Introducing S/4HANA

The Digital Core

New User ExperienceSpeed in transaction

processing

Integrated Reporting &

AnalysisSimplified data model

© Deloitte 2018 9

The below picture shows a typical SAP landscape.

Introducing S/4HANA

SAP BI-BO SuiteSAP Lumira

SAP Fiori

SAP WEBI

SAP Design Studio

SAP Analysis for Office

User interface

Reporting Tools

SAP S/4HANA Enterprise Management

(SAP FI, CO, MM, SD, …)

SAP HANA database

Transactional System

Functionality

Application

Legend

SAP BW (embedded)

SAP BPC 10.1 (Optimized for S/4HANA)

Enterprise Data Warehouse

Planning and Consolidation Other SAP applications or Self-

made applications

Database

SAP HANA database

VDM* withCDS** views


Demo 2 - TBD

Carrousel warm-up - Fiori examples




System of record



Custom

Reports

Standard


Custom



Configuration

Outdated

requirements

Nice to have

customization

Years of

break-fix


Transition dynamic,


to the system of

innovation platform

Eliminate

unnecessary

customization


specific leading


baseline ERP

Reduction of Core

Footprint

Baseline ERP


Heavy t

echnic

al debt

= h

igh T

CO

Baseline ERP

Cle

an E

RP=

Agile,

low

er

TCO

1

2

3

Existing model

© Deloitte 2018 12

Robotics Process Automation (RPA), can greatly improve the speed and efficiency of repetitive tasks.

S/4HANA in combination with Robotics becomes even more powerful

Opening email and

attachments

Logging into web/ enterprise

applications

Moving files and folders

Copying and pasting

Filling in forms

Reading and writing to databases

Scraping data from the

web

Making calculations

Connecting to system

APIs

Extracting structured data from

documents

Collecting social media statistics

Following “if/then” decisions/rules

RPA is… RPA is not…

Computer-coded software

Programs that replace humans performing repetitive rules-

based tasks

Cross-functional and cross-application macros

Walking, talking auto-bots

Physically existing machines processing paper

Artificial intelligence or voice recognition and reply software

What it can do




System of record



Custom

Reports

Standard


Custom



Configuration

Outdated

requirements

Nice to have

customization

Years of

break-fix


Transition dynamic,


to the system of

innovation platform

Eliminate

unnecessary

customization


specific leading


baseline ERP

Reduction of Core

Footprint

Baseline ERP


Heavy t

echnic

al debt

= h

igh T

CO

Baseline ERP

Cle

an E

RP=

Agile,

low

er

TCO

1

2

3

Existing model

© Deloitte 2018 15

Deloitte ReimaginePlatform is a collaboration between Deloitte and SAP to serve the best of what SAP and

Deloitte have to offer in terms of next generation technologies.

The Deloitte ReimaginePlatform

Industry Leadership

Cloud & Technology

Alliances

Data Integration

Excellence

Finance Advisory

Digital Supply

Networks

Cognitive Fabric

Change

Management

Machine Learning

Blockchain

Data Intelligence

Big Data

Internet of Things

Analytics

Digital Business

Transformation

© Deloitte 2018 16

We currently have more than 50 digital innovation use cases in different business lines.

Many applications are not quite as farfetched as you imagined them…

Presentation title[To edit, click View > Slide Master > Slide Master]

Member firms and DTTL: Insert appropriate copyright[To edit, click View > Slide Master > Slide Master]

17

• Automate discrepancies resolution based on past vendor behavior

• Accounts payable and procurement

• Reduced Close time

• Predictive analytics• High value variance

analysis • Vendor behavior analysis

DIM

Business Driver

Finance teams are under tremendous pressure to clear GRIR at

month or quarter end to ensure correct representation of liability

and this may mean potentially time consuming analysis governed

by a manual clearing process. If goods receipts are not

consumed by invoices, liabilities are overstated resulting in

incorrect cash position

This process of backing out and re-posting / manually clearing

can be time-consuming. The objective here would therefore be to

develop a mechanism to automate the clearing process, thereby

reducing the time to close at period end and enable faster close

Solution Overview

Provide real time insights to GR/IR variances caused due to

vendor behavior patterns, 3-way matching discrepancies

Suggest next steps based on materiality of variances,

criticality of Vendor, PO value, material and other criteria

Automatically write-off or write-up based on historical age

thresholds and vendor invoicing patterns

Features

Understand the subtle delivery and invoicing behaviors of

vendors, and the triggers for matching discrepancies

Provide instant feedback to vendors on short deliveries,

inflated invoices and other discrepancies

Automated resolution to discrepancies based on past user

behaviors

Automated debit memo creation when an invoice is booked

outside of tolerance

Communicate GR/IR discrepancies to purchasing teams

immediately after occurrence.

Collect insights based on patterns – types of differences,

locations, vendors, materials etc.

Lights Out Finance – GR/IR Clearing

Cross-Industry

Value Levers:

Accounts Payable Organization:

Faster and easier prioritization of high value

variances with high impact to margin or inventory

Minimizing the time required for analysing open

GR/IR items, writing off or writing up high volume

PO line variances

Improve housekeeping of clearing account and

minimizes potential fraud

Increased visibility to repeat offenders, short

receipts, inflated invoice price

Faster close cycles

Real-time GRIR Aging

Treasury Organization:

Improved cash flow

More current accrued liabilities

Machine

learning

Block

chain

Data

Intelligence

Big Data

IoT

Analytics

Core SAP

Analytics

Machine Learning

Planned Launch Date: Q4 2017

Integrated Market Solution: Lights Out Finance

Function: Finance

Processes: GRIR

Demographic: Cross-industry with primary focus on

C&IP, Retail, Wholesale Distribution , Manufacturing

PMD Sponsor: John Steele

Use Case Owner: Smitha Chowdavarapu

© Deloitte 2018 18

Overview of the initial process for GR/IR clearing.

Compare the standard way of working…

Month End ClosingAccountant

Runs MR11

Analyze the MR11

Output

Clear the line items based on

user defined business rules

Line Items

for further

analysis

Resume other month

end closing tasks

Perform other required checks

in System/Outside

Check with warehouse team on

packing list/GR details

Check with AP team for

missing invoices - Error EDI

Idocs/Parked Invoices etc.

Investigate the high value line

items

YES

NO

Manual

Automated

Continuous Iterative Process

© Deloitte 2018 19

And the scenario when applying machine learning to the process.

To the machine learning enabled way of working

Manual

Automated

Month End ClosingDaily Auto

Run ZMR11

Pass open GR/IR

to ML model

Get ML’s decision

and confidence

Confidence

of clearing

GRIR

> 80%

Auto clear

Carry out Month end process

for AP

User approves/ rejects based

on ML categories

70 - 80%

Request approval

NO

YESTrain ML model

on historical data of

GRIR clearance

ML Model deployed on

SAP HANA

© Deloitte 2018 21

Accelerated hard close, soft close and predictive close

Closing aspects in SAP S/4HANA

Faster Insight on the basis of certain

Improvements

Elimination

Real-time Execution

Continuous Execution

Acceleration (automatic tasks)

Automation (manual tasks)

Higher efficiency (manual tasks)

Enable Faster Close with less effort

Hard Fast Close Soft Close

Continuously be able to close the books

Continuous Closing – enabling to run all

tasks fast and easy in an automated

series and eliminating period-end tasks

Profitability characteristics are derived

automatically during the posting so

management information is available

already during the period – no need to

wait until month-end settlement is done.

Live Insight is provided automatically

Predicted Close (upcoming)

Analysis on the basis of “Predicted

Actuals”

Information of processes in progress,

previous period data, manual

adjustments, statistical methods

“Predicted Actuals” are derived from

these processes to already be able to

predict and analyze the results at

period end during the period

Prediction on a stable ground

© Deloitte 2018 23

LIVE POLL

Visit www.menti.com and use the code 274284 to participate

And what about your company?

WRAP-UP

http://www.menti.com/

headline verdana bold - deloitte united states · 2018-05-18 · pass open gr/ir to ml model get...

Documents