jim fredholm, cfo agility logisticstransmetrics.eu/2016/content/uploads/jimfredholmagility.pdf ·...
TRANSCRIPT
October 2016
Leveraging Data – A CFO’s PerspectiveJim Fredholm, CFO Agility Logistics
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……..
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:
The Role Of Finance Is Shifting Toward Leveraging Data
4
Working in a Dynamic, Legacy Systems Environment
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
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).
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
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
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
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
Thank you!