Change in Controlling – Prescriptive Analytics using Trufa · 2018-11-20 · Big data report...

PRESCRIPTIVE ANALYTICS

Björn ReitzensteinRobert Bosch GmbH

16. CCS Controlling Competence Stuttgart | 22.10.2018

Intern | Powertrain Solutions | PS/CTG2 | 23.10.2018© Robert Bosch GmbH 2018. Alle Rechte vorbehalten, auch bzgl. jeder Verfügung, Verwertung, Reproduktion, Bearbeitung, Weitergabe sowie für den Fall von Schutzrechtsanmeldungen.

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Agenda

► Bosch Group – Technology to enhance quality of life

► Example for Digital Finance @ Bosch – Advanced Analytics

Bosch – a global network


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Four business sectors

Mobility Solutions Industrial Technology Energy & Building Technology Consumer Goods

Bosch – Corporate presentation


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A Global Network

402,166*Bosch associates make these solutions possible

440 regionalsubsidiaries

60*countries –

Including sales and service partners, Bosch’s global manufacturing and sales network covers nearly every country in the world.

Mobility Solutions

Industrial Technology

Energy & Building Technology

Consumer Goods

* As of 12.17

Four business sectors

Share of sales


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Key Figures 2017*

Leading in drive and control technology, packaging, and process technology

Industrial Technology

One of the world’s largest suppliers of mobility solutionsMobility Solutions

Leading supplier of power tools and accessories Leading supplier of household appliances

Consumer Goods

One of the leading manufacturers of security and communication technology Leading manufacturer of energy-efficient heating products

and hot-water solutions

Energy and Building Technology

Bosch Group 402,166associates

78.1 billion eurosin sales

61%

39%

* as of 12.17

Bosch

Intern | Powertrain Solutions | PS/CTG2 | 23.10.2018

Market and figures for 2017*

© Robert Bosch GmbH 2018. Alle Rechte vorbehalten, auch bzgl. jeder Verfügung, Verwertung, Reproduktion, Bearbeitung, Weitergabe sowie für den Fall von Schutzrechtsanmeldungen.6

78.1 billion eurosin sales

280manufacturing sitesBosch Group 402,166

associates

1 Including other countries

Europe

160

245,040

52%

Asia Pacific1

78

112,817

30%

Americas

42

44,309

18%

Manufacturing sitesAssociatesShare of sales* as of 12.17


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Agenda

► Bosch Group – Technology to enhance quality of life

► Example for Digital Finance @ Bosch – Advanced Analytics

Scope: Optimization of Net Working CapitalExisting Process for NWC control:- Different teams analyse and control NWC

elements and report separately to top management (Sales, Logistics, Purchasing, Finance)

- Little progress in NWC improvement - Limit transparency- High number of FTE involved in analysis

Target approach via Big Data Tool


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FinancialIndependence

- generating sufficient Cash Flow - high equity ratio

TOP-rating ensures free choiceof capital providers

NWC in % oS(+ CAPEX)

Capital employed

Cash/ Liquiditiy

Advanced Analytics


Digital Transformation – Big Data

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1.From Classical BI to Business Analytics

Business Analytics

Classical BI

Which data do we have?

Analytics

1. Descriptive Analytics

What happened?

2. Diagnostic Analytics

What has happened?

3. Predictive Analytics

What will happen?

4. Prescriptive

Analytics

How to make it happen?

Efficient controlling tool @ BOSCH


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Latency model

Storage: ~ ½ TB on data (Bosch POE) Timeframe:

3,75 complete years (01.01.2015 – today) Line items: 2 774 103 709 Information: 167 140 129 378 Ideas from bots: ~3.6m Calculated opportunities: 246 178

(increasing daily) Significant opportunities: 13 398

(increasing daily) Productivity of bots: ~8 000 calculated

opportunities / day w/o human input Bots are used to capacity

for the next 400 days

time

Valu

e ad

ded

business transaction

Data in IT systems available

analysis available measures defined

analyzed by TRUFA in real time

assessmentby experts

Value added

for enterprise

Day 1 Day 2 Day 3

Source: Hackathorn, R.: Current Practices in Active Data Warehousing, 2002.

benefit in time

benefitin

valueadd

analysis available (TRUFA)

measures defined

Presenter

Presentation Notes

Worin bestehen aus der Erfahrung des Projekts heraus konkret die Vorteile von Big Data Analytic Tools? Die Verwendung von unstrukturierten Daten bietet im Gegensatz zum klassischen BW Ansatz die Chance, über vom Tool erkannte statistische Korrelationen bisher unbekannte Erkenntnisse zu gewinnen. Darauf werden wir später im Rahmen der Projektpräsentation noch näher eingehen. Ein weiterer, nicht zu unterschätzender Vorteil sind mögliche Effizienzgewinne bei der Analyse von Geschäftsvorfällen. Ein sehr großer Aufwandstreiber bildet heute die Analyse der Geschäftsvorfälle. Oft werden diese erst mit erhebliche Zeitverzögerung im Rahmen von Monatsendanalysen durchgeführt, Wertgewinn für die Unternehmen findet damit auch verzögert statt oder kann teilweise nicht mehr voll ausgeschöpft werden. Big Data Analytics Tools nehmen uns die Analysearbeit weitestgehend ab und liefern die Ergebnisse quasi in „Nullzeit“ nach Verfügbarkeit der Geschäftsvorfälle im IT-System. Die Aufgabe von Crossfunktionalen Fachexpertenteams und uns Controllern fokussiert sich damit auf die Ergebnisbeurteilung, da nicht alle vom Tool identifizierten Korrelationen auch zu sinnvollen Ergebnissen führen sowie auf das Einleiten von Maßnahmen. Um Erfahrung mit dieser Arbeitsweise zu sammeln, haben wir ein Big Data Analytics Projekt aufgesetzt. Das eingesetzte Tool musste folgende Kriterien erfüllen: Am Markt etabliert sein Zu unserem ERP-System und der eingesetzten IT-Landschaft passen Für einen Konzern wichtig: skalierbar auf andere Konzerneinheiten Es soll möglichst viele Aspekte von Advanced Analytics abdecken Und last but not least von Controlling Mitarbeitern bedient werden können, die eine gewisse Affinität zu IT-Systemen aber keine Programmierkenntnisse mitbringen

Behind the black box


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The Big Advantage: No modelling, just data

Fine granular data and mass of data – no aggregated dataCollects and analyses most detailed data for the processes

source, make and deliverAutomatically tests and finds correlations between almost all

possible aspects in the data Automatically presents not only drivers/effects but also the

economic value in EUR and in likelihood to achieve

Robustness and Flexibility: Algorithms must be able to deal with all kinds of data and data in all states of quality.

Classification: Opportunities are classified and compared by value and likelihood.

Big Data Analyses formerly and today


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Use case: Spendings development and forecast

Cost Prognosis Project (2016)• Analysis of costs resulting from purchase or internal orders• Database created on basis 2008 – 09.2016

• Investment: 180h Controlling + 60h DataScientist

2016

Performance Management Machine• Analysis of costs resulting from purchase or internal orders• Database incl. in downloaded Data POE • No additional investment

Analyze time in tool: 5min + 10h Development

2018

Prescriptive Analytics using Trufa


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Data based analysis vs. personal knowlegde based analysisAnalysisPatterns

Simulation Clusters

Correlations

DriversResults

Known usecase(Problems/Potentials

are familiar)

AnalysisPatterns

Simulation Clusters

Results

SelectionSource (DPO)

Make (DIO)Deliver (DSO)

Data based analysis

Personal knowledge

based analysis

Focus, speed

DIO / coveragepotential gain

List of materials with inventory reduction potential due to too high coverage

Focus on materials with high potential gain by coverage reduction

Inventory reduction - Example


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Identification of inventory reduction potential on part number level

Gains Focus on part numbers with

highest inventory reduction potential by visualization with adaptive safety control

TRUFA-identified part numbers analyzed on-site Confirmed reduction of EUR 3.4m (EUR 5m proposed)

Evaluation of 30 part numbers in 4 plants

Next Steps Share focus part numbers

within all plants Implement standard procedure

Limitations Almost only FERT Special stocks not considered

(only explanation) Derisking stock (earthquake, strike) Life-time-stock …

visualizationSAP settings

inventory development

LOC tool adaptive safety control Analysis of historical inventory and

consumption data Visualization of inventory

development

Benefits Benefits

Prescriptive Analytics


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Use Case: Management of ReceivablesStarting Point:ABC analysis of defaulting customers (exceeded due date for payment) with highest working capital.

Analysis Result:Different paying behavior within one customer concern. Accounts receivable management can initiate specific measures.

Analysis of receivables showed pattern in different regions with reduction potential of > 10 mEUROne customer used payment terms to the disadvantage of Bosch; potential 2,2 mEUR



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First statements from potential users

There is no direct link between

potentials and reporting figures

I do not have any budget and

resources for this new approach

I am not familiar with the working process in logistics,

sales and A/R

Big data report looks different from

my existing data

I do not believe the results of the tool, because I do not

have the chance to recalculate

I am not an SAP process expert and don`t understand the SAP language



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The organization behind it

Trufa„OpCenter“

SalesControlling

LogisticsTeam

PurchasingControlling

Plants

Business tasks

Discount losses

Inventories

Payments, ….

Presciptive Analytics using Trufa

Intern | Powertrain Solutions | PS/CTG2 | 23.10.2018

What we learned

Don’t invent the things you can buy on the market

Don’t loose yourself in the fascination for the tool

Get the buy-in of your team

Find the sweat spot where value is created and push hard

Scale your success

© Robert Bosch GmbH 2018. Alle Rechte vorbehalten, auch bzgl. jeder Verfügung, Verwertung, Reproduktion, Bearbeitung, Weitergabe sowie für den Fall von Schutzrechtsanmeldungen.19

Controlling


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Quo Vadis ?

Controller@

PowertrainSolutions

Reporting=> Self-Service

Transaction=> Robotics

Business Analysis=> Diagnostics & Prescriptive

Forecasting=> Predictive ?


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Take away

Digitization will fundamentally impact the role of the Controller

Robotic Process Automation, Big Data Analytics and Prediction as well as Self-Service Reporting Platforms are the disruptors of our today’s work

Key for success is not up to the tool but the way it is implemented in the organization

Look at it as a disruption, not as an improvement. Act different !

Move now ! Learn while you are on the go ! Don’t waste time !


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Change in Controlling – Prescriptive Analytics using Trufa · 2018-11-20 · Big data report...

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Transcript of Change in Controlling – Prescriptive Analytics using Trufa · 2018-11-20 · Big data report...