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October 2016 Leveraging Data – A CFO’s Perspective Jim Fredholm, CFO Agility Logistics

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Page 1: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

October 2016

Leveraging Data – A CFO’s PerspectiveJim Fredholm, CFO Agility Logistics

Page 2: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

39 Years Is A Lot Of Experience

• 1978 University Of Texas: Double Major in Accounting and Finance

• 1981 Ernst & Young: Certified Public Accountant

• 1982-87 High Tech Industry – International Controller

• 1987-97 London based FMCG: Rise to the C Suite

• 1998-present Switzerland: Group CFO Danzas DHL, Adecco, Agility

• Retiring at the end of this year to pursue NEW PASSIONS……..

Page 3: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Market situation – BI is important to our Competitors

and Innovators

3

Silicon Valley company whose core business is building BI and analytics solutions for others:

Our competitors:

Page 4: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

The Role Of Finance Is Shifting Toward Leveraging Data

4

Page 5: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Working in a Dynamic, Legacy Systems Environment

Page 6: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Benefits – examples of how we can use this capability

Dashboard functionality: Full branch level dashboard with operational and financial KPIs (including data from

various systems) Customer dashboard – integrated with Agility Connects, interactive, flexible and

advanced –used to acquire new customers, retain and/grow existing relationships Customer dashboard (internal) Sales numbers, churn, AR -> gives sales team full visibility

on customer/customer group Internal benchmarks and on-going monitoring of performance – for branches, sales

personnel, facilities

Predictive analysis: Product support: Air hub usage/ Airport rerouting in high season; gateway effectiveness Rate and volume – what if analysis Trade lanes volumes seasonality

Sales opportunities: System generated sales leads based on customer analysis Identify customers to upscale and provide information to local teams Product cross-selling

Page 7: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Benefits – examples of how we can use this capability

Future vision of value added services to customers:

Provide market data to customers as value added service

Make suggestions to customers as to where they can extend their business with the right products from Agility to support them (e.g. transportation, customs, warehousing); “e.g. we find your customers” - we see you ship Auto parts to Africa, these 10 retailers could be potential customers of yours, we have local support of distribution, warehousing (based on our own data, external data)

Scenario analysis:

Hanjin example: team can quickly provide a new “view” on Agility connects that show if the customer’s shipment is on a Hanjin shipment, provide guidance to internal teams as to which shipments are currently on Hanjin ships and advice them to take action; call customer, etc.

Scenario example: the team identifies scenarios (e.g. carrier fail, natural disaster, port strike, etc.) and prepares analysis that are triggered when needed. Guidance on communication with customer, rerouting etc. is provided (created by BI team in cooperation with business).

Page 8: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Our Journey - Phased Approach

Ph

ase

1: p

re-I

nsi

ght

Legacy reporting systems (e.g. EIS)

No unified reporting solution, many local reporting solutions, reporting unstructured and chaotic

Multiple sources and business rules leading to confusion

Limited capabilities

Old technology

Manual manipulations to produce external reports and dashboards P

has

e 2

: In

sigh

t

Recognized global solution

Well established as single source of truth with Management, global and regional teams

Providing reporting and limited dashboard capabilities.

Good acceptance

Standard and consistent business rules

Data source limited to CONTROL + basic invoice information from WMS and Brazil

Very limited analytic capabilities P

has

e 3

: BI P

latf

orm

Expanding scope and functionality of current solution

Focus on people and engagement with business – Pivotal group

Governance, Data owners

More business engagement for requirements

Analysts (start with pivotal group members)

Data warehouse (FOCiS, Oracle, WMS, Sales, 3P (Seabury/IATA), other sources)

Advanced dashboard capabilities (for customer reporting e.g. Shell)

Customer differentiator

2012-2014 2015-2017 2017 onwards

Page 9: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

A Big Data Example: Enabling the loading factor of

linehaul to be forecasted a few days in advance

Forecast:

next Wednesday departure

Forecast:

next Thursdaydeparture

Forecast:

next Friday

departure

Unusedcapacity

Likely to have too much unused space: action needed

Should be OK, no need for action

Forecasted groupageorders via data mining

Likely to be overloaded, need to make

a contingency plan

Page 10: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Optimize capacity

via sales / customer

service actions

Once the expected future linehaul situation is known,

actions can be taken to improve load factors

Optimize capacity via

linehaul planning

actions

Re-book customer orders for ‘empty’ days

Look for LTL orders to fit empty capacity

From other divisions

From partners

From freight exchanges

Re-plan linehaul to increase / decrease capacity

Produce existing LTL orders via linehaul

Look for supplier / partner capacity for linehaul

Route via alternative hubs (reforwarding)

Unused

capacity

Goal of actions: 1) Eliminate unneeded linehaul capacity, by cancelling trips

+10% load factor 2) Win extra customer orders based on available empty

capacity

Page 11: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

An Example: Leveraging Big Data For Financial Return

Large transport company P&L structure (typical, source: Roland Berger strategy consultants)

100

Net Revenue

50

15 7

4

Cartage

Services

GrossProfit

DirectCosts

IndirectCosts

4

Net Profit

-5 +5

An increase in load factor of 10% can double the profit of a profitable

operation, or turn around a loss-making operation

11

35

Linehaul

Page 12: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L
Page 13: Jim Fredholm, CFO Agility Logisticstransmetrics.eu/2016/content/uploads/JimFredholmAgility.pdf · An Example: Leveraging Big Data For Financial Return Large transport company P&L

Thank you!