terri clementson - using analytics to cut through complexity

8/12/2019 Terri Clementson - Using Analytics to Cut Through Complexity

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Leading global excellence in procurement and supply

Using analyticsto cut through complexity

Name: Terri Clementson +61 (0) 419 754 794

Date: Thursday 18 October 2012



How can analytics help me

manage complexity?

Wood for the trees

Inspire / re-engageAnticipate change

On top of moving parts

Agile – where to put effort / time

But what if analytics itself seems complex or ‘over the

rainbow…?

2

Scan Focus Act



Seminar stream 5c

Analytics to cut through complexity

What is data analytics really all about?

Where is procurement in the BIG data race?

What does analytics look like when applied to analytics – how does that help anyway?

(Where is my org. on analytics compared to others?)

What’s at the ‘bleeding edge’ of analytics?

How can I act on this practically – right now?

How do I grow this capability in my function?

3



Data analytics is lots of things to different folks

Using data to manage information and

performance, and make smarter

dec is ions – Deloitte Analytics

“A mix of business

intel l igence , visual izat ion ,

and math …”

“BI is more backward looking,

analy t ics is forward looking ”

“Using advanced m ath

techniques to help predict…”

“Focused on stat ist ics ,

predictions and opt imizat ion ”


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Procurement analytics is mainstreaming – talent?

Hardware• Faster processing power runs complex math in seconds

• Storage cost per megabyte has become 100x cheaper since

2000

Data• Proliferation of data provides access to analyze unstructured

relationships. Data sales are soaring.

• Higher quality data / easier cleaned

Tools • More user friendly tools simplify complex techniques• Tools allow greater emphasis on understanding the output



© 2012 Deloitte Touche Tohmatsu6

Existingdata

Dataoverlays

Analysis

Granularinsights

Action 1

Action 2

Action 5

Action 3

Action 4

New value

The basics out on the table…


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It isn't about tools – but they are an ingredient…



Seminar stream 5c

Analytics to cut through complexity

What is data analytics really all about?

Where is procurement in the BIG data race?

What does analytics look like when applied to analytics – how does that help anyway?

(Where is my org. on analytics compared to others?)

What’s at the ‘bleeding edge’ of analytics?

How can I act on this practically – right now?

How do I grow this capability in my function?

8



© 2012 Deloitte Touche Tohmatsu

Stage 5

AnalyticalCompetitors

Stage 4

Analytical Companies

Stage 3

Analytical Aspirations

Stage 2

Localized Analytics

Stage 1

Analytically Impaired

Enterprises are at all different levels of analytics maturity

9

Tom Davenport – Competing on Analytics




Stage 5

AnalyticalCompetitors

Stage 4

Analytical Companies

Stage 3

Analytical Aspirations

Stage 2

Localized Analytics

Stage 1

Analytically Impaired

P&SC Analytics is alive and well...in other functions

10

MarketingProfiling & Segmentation

(customer, end-user, supplier)Product / Service performanceAnalysis

Forecasting demand

Acquisition & Retention Modelling

Behavioural Modelling

Campaign Analysis

Targeted MarketingCustomer / product lifetime Value

SalesPrice Optimisation

Profitability Analysis

Territory Optimisation

Call Sequencing

Wallet Share Analysis

CRM leverage

OperationsNetwork optimisation

Product analysis

Risk rating & root cause

FTE allocation to audit

Asset maintenance & renewa

Contracts management

Throughput smoothing

Satisfaction monitoring

Site analysis

HRFraud

Workforce modelling

Labour costsbenchmarking

Safety analytics



Copyright © 2012 Deloitte Development LLC. All rights reserved.11

Suppliers are leveraging analytics faster than buyers

Source: http://www.patagonia.com/us/footprint/?src=043012_mi1

What value are youleaving on the table?




hindsight




Increasing compliance - spend ‘under control’

Review period from March ‘09 to February ‘10 inclusive

Number of vendors 42,474 (30,807 Active)

Vendors actively used 9,010

Number of invoices 12 months 253,491

Total Spend $8,163M

Spend excl. Govt & Intercompany $5,707M

Categorisable Spend $2,662M

Purchase Orders Value Volume

Non PO $ 300M 11% 19,737 11%

Valid PO $1,144M 43% 74,459 41%

Confirmation Orders $1,216M 46% 87,934 48%

Implications: A high number of vendors combined with over half of the

spend not under a valid PO provides opportunity for spend aggregation,

spend control and spend under management

DRAFT FOR DISCUSSION1

2,715 or 30%were created in

the period

<50% spend

under valid PO




Risk rating – products, suppliers, sites, buying groups…

15 Competing on Analytics10/19/2012




Stimulating engagement – visualising & simplifying

16




Grappling with dynamics of time – replenishment behaviour & ‘out of stock’ management

17

27 Nov 2009 31 Jul 2010

Stock out

Season codeinactive date except M/M+

Active date

PO

Lead time

Invoice Recd

Lead time 2 days

POPO

Stockout

InvoiceRecd

Stockout

Assumptions :

• Purchase order (PO) to be received = Lead days + 1

• Season code active = Between active date and latest PO date for all season code except M (managed) and M+

• Additional order = Current day on order - previous day on order, doesn’t match with shop keep final quantity. More

explanation




Reducing holding cost, dissipation & waste – Stock holdings quantities vs. production quantities(month and location)

• At location X, raw materials purchasing exceeds finished goods production in all months except June and November. Thisis contributing to the build up of stock at this site and the consequent increased stock holding costs.• Other sites manage a lower level of stock holding which will have associated cost savings.

- 19 -

Operationalefficiency gap: Canbest practice stock

holding levels beidentified?

Process Improvement opportunity:Raw material purchases exceedproduction driving up stock holding.Is this related to supplier contracts?



Listening to many – sizing sentiment to focus action




Helping which humans – margins, recoveries & WIP

Partner 1

Partner 2

Partner 3

Partner 6

Partner 7

Partner 9

Partner 10

Partner 11

Partner 12

Partner 13

Partner 4

Partner 5

Partner 8

-21-




Scanning for fraud -Public holiday expense claims by long term contractors

-22-




insight

ver ay ng ex erna a a can a gn your s ra egy moret l t th t t li ti l (b ’)



y g g y gyaccurately to the contract, supplier or operational (buyers’)

context

(Supply)Contracts

Management

Complaints /warrantiesenacted /returns

Contact ABNand address

details

ContractConditions

Set

Audit

ResultsVendor risk

index (inputs)

Contracts

History

Ordervibrancy

Purchaseorders

Social media

Email

traffic

Weather

data

Travel route

durations

TeamPerformance

data

Chep palletmovement

index

Sector &countryeconomicmodelsContracts

team auditand trainingschedules

Sentimenttraffic

Span ofcontrol team

profile

Employeeprofiles

Censusprojections

Global

Wellbeingindex

Cultural risk& fraud index

Productiondata

Programtracking

Humanfactors inputsCurrency &

commoditymodels




Adding data sets to your ERPThere are hundreds of 3rd party data providers

Profiling groups customising action resourcing accurately




0% 20% 40% 60% 80%

MID-STRENGTH

LIGHT

TRADITIONAL/REGULAR

PREMIUM IMPORTED

PREMIUM CRAFT

Recommended Portfolio Current Portfolio

Current and Optimal portfolioconfiguration for customer X

Profiling groups, customising action, resourcing accurately



35.0 years

27% HNW

4,288

Medical Fees

Accessibility

Medicine

90%

1%

82%

$320

$267

4%

0.4%

11%

$44

$41

Adaptability

Morbidity

1,433 per GP8.2%

92%16%Clinical style

77%19%Leadership

62%25%Relationship

Cluster 8: “Regional wealth”

154 POAs 0.6 Hospitals

6.2%

4.6%0.4%Growth

30.5 years

20% HNW

3,066

Medical Fees

Accessibility

Medicine

90%

1%

82%

$320

$267

4%

0.4%

11%

$44

$41

Adaptability

Morbidity

2,224 per GP62.5%

92%16%Clinical style

77%19%Leadership

62%25%Relationship

Cluster 13: “Remote indigenous youth”

35 POAs 0.8 Hospitals

1.4%

4.6%0.4%Growth




foresight

Forecasting – d d d t t it l t i t



“What-If” Scenarios Project Concentrations of Future Events

demand, product costs, currency, capital certainty

Disclaimer: Notional Data - For demonstration purposes only.

Vi li i k k ff t t t ti d i i



Facility Consolidation, Distribution Network Design, Transportation Mode SelectionModels, and Inventory Management all include a spatial component.

Visualising knock-on effects – stress testing decisions


Optimising networks – understanding complex flows




p g g p

Detail: Allocation in Greater Los AngelesDetail: Allocation in DC, Philadelphia, & NYC


Making choices of unequal risk value cost



Retailers can determine site location risk according to proximity to other key sitesand socio-demographic data, weather patterns, traffic patterns, security, proximity toproducers…

Making choices of unequal risk, value, cost


What’s at the edge



34

• Leveraging open source data

• Scraping social media – for early insight

• Indices – aggregating variables for league tables

• Cross cultural risk loading

• Archetypes for targeted risk management /resourcing

• Seamless data capture through smart tools & mobile comms.

• Real time self assessment / performance ‘gaming’

What’s at the edge...




1. Pennies under the sofa cushions

2. Is all OK? - check risk

3. Cut through complexity and hunt value4. Can you visualise it? - re-engaging for action

Four immediate value injections

Analytics saved over $1M – with just two days of analysis




Analytics saved over $1M with just two days of analysis

Context

• Sourcing and procurement project for an Agricultural and

Consumer Products Manufacturer

• Client lacked visibility into total cost of ownership including

maintenance costs

Result

• Tangible savings: $1M+ demand mgt., $700-900K sourcing, 8-

27% increase in residual benefits

• Understanding of maintenance impact on asset efficiency

AnalyticsUsed

• Simulated maintenance costs across range of scenarios

• Performed multivariate regression analysis of residual values

The team developed the capabilities identify




The team developed the capabilities identifymaterial disruptions and risk in the supply chain

Situation

• Complexity of multi-tier networks and greater reliance on the

supply base pressures companies to better understand their

supply chains

• Companies lack visibility into understanding potential supply

chain disruptions and risk areas

Result

• Minimize disruptions and create a more resilient supply chain

• Faster response time to get supply chain back to normal

operating conditions

AnalyticsUsed

• Trending, risk algorithms, historical performance & relationship

analysis

• Supply chain visualization utilizing to identify focus areas

Advanced analytics unraveled complexity in our client’s$




supply chain and drove $100M in inventory reductions

Situation

• Client was missing customer deliveries due to ballooning

product lead times and rampant material shortages

• Current efforts focused to solve issue only through capital

investments in inventory and machinery

Result

• 35% reduction in average lead times

• 20% reduction in overall inventory

• 12% reduction in unutilized capacity

AnalyticsUsed

• Descriptive statistics, correlations, and regressions

• Advanced simulation techniques

• Interactive visualizations and unique data formats

Visualizing trade flows – movements & implications



(CHEP Index)

Source: http://atlas.media.mit.edu /

Where – when – who ~ looking geospatially




Where when who looking geospatially

Data and Report Creation

Proof-of-Concepts/Demos Address Validation and Geocoding

Routing and Drive Time Analysis

There is no barrier – leverage help and build capability



There is no barrier leverage help and build capability

GranularInsight

Dataquality

Security anddata access

Datamountain

Speed / cost

Skills

Start now – quick wins generate capital to reinvest & grow




g g

Flush your data with

some rules = $

Connect the Dots

-datasets and patterns

Target action &

track impact

Stimulate stakeholders’

and repeat!

1 2

3 4

And develop a procurement analytics capability roadmap

Deloitte Analytics




Unparalleled Capability in P&SC analytics

P&SC

STRATEGY

TECHNOLOGY

& DATATALENT

Competitive

widescreen

Deep quants /

fast experts

Technicians to

extract, clean,

connect data

COLLABORATIVE

CULTURE

Iterative

modeling

Inspiring staff

RISK &

REGULATORY

Practical

roadmaps

External data

libraries

overlays)

Scorecard &

dashboard links

Gold IT vendor

alliances

- unbiased

Advanced

Analytics &

Modeling

Trainers

Compliance,

controls

experts

Visualization

tool suite

Forensic &

fraud data trail

Trusted auditor

Staged to your

budget

Global reach

Deloitte Access

Economics

PARTNERING

ON

CAPABILITY

Analytics

current state

assessment

Capability Dev

Roadmap

Links to a 5,000

practitioner

community



More than 200,000 people

In 140 offices globally5,000 analytics deep specialists120 locallyThe largest professional servicesfirm in the world.

‘...and different’.

3,000 peoplealigned with data

analytics globally

>70people in

Aus

45

terri clementson - using analytics to cut through complexity

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