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Global trade analytics
Delivering business insights to manage risk and enhance global trade effectiveness
September 2014
Page 2 Global trade analytics Global trade analytics
Presenters
► Alex Kuperman, Ernst & Young LLP (moderator)
► Colm Halpin, Ernst & Young (Ireland)
► Ann McDonald-Hintze, General Motors
► Muir Macpherson, Ernst & Young LLP (Quest)
Page 3 Global trade analytics
Global trade challenges
Customs authorities
Broker(s)
Customer
Distributioncenter
Exportcompliance
Fulfillment
Manufacturing
Receiving
Supplier
Customsoperations
CRO
Exports
Imports
CFO
COO
operations center
ERP
Is full advantage taken of FTAs and customs regimes? Do we have metrics and/or has anyone ever tested for leakages?
Could the business be re-aligned to increase trade benefits, i.e., is company deriving the cost and competitiveness benefits from supplier-plant and plant-customer configuration?
Are distribution centers (DCs) adding to customs duty costs or losing free trade agreement (FTA) benefits?
Are procurement and purchasing teams able to factor in customs duty costs and savings potential for alternative sources?
Can finance/tax teams access quantitative data on customs and trade for duty risk controls and governance? Will OECD require new data and reports?
Is supplier trade data correct? Are sales and delivery consistent with contract, incoterms
Is trade data (which drives duty costs) recognized as master data? Is it accurate and consistent across locations and countries?
Do transfer pricing (TP) and tax team understand and appreciate the duty cost and risk impact of changes to TP model or TP adjustments?
Where are internal and external trade compliance resources most needed and how should these be managed?
How are brokers performing from a performance, cost and compliance perspective?
How much duty is paid globally? Is this an area where real and significant savings can be found?
Page 4 Global trade analytics
Analytics can deliver business insights
Thus, our work moves left to right, but our thinking must move from right to left.
To drive better decisions, we must first ask the right business questions and then seek answers in the data.
Organizations are looking to data analytics to drive actionable insights, improve business performance, drive better decision making and improve risk management.
Page 5 Global trade analytics
Business functions and trade related concerns
Global trade data analytics can provide enterprise-wide benefits
Customs and trade compliance
Operating model efficiency to support trade
Supplier and third-party trading strategies
Readiness for entering new markets
Country and specific legal alignment
Supply chain network strategy optimization
Tax and transfer pricing
Defining and prioritizing the objectives is the starting point
Duty minimization
Export control Supply chain security
Technology deployment
Broker management
Page 6 Global trade analytics
► Necessary IT framework to collect, protect and analyze data
► Visualization layer for global distribution across different functions and levels
► New data source integration ► Governance program to
improve data quality► Change management to
create long-term sustainability
► Standard and bespoke analytics
► Performance benchmarks and Key Performance Indicators (KPIs)
► Reporting dashboards
► Analytics strategy aligned to business imperatives andgoals
IT framework to collect, protect and analyze
layer for global different
New data source integration Governance program to
Change management to
Standard and bespoke
Performance benchmarks Key Performance
Reporting dashboards
aligned to business imperatives and
Analytic capabilities
Companies need to prioritize what is most important to improving their global trade operations. As capabilities advance, the level of complexity and associated time horizon increases.
Information infrastructure
Data management(acquisition, quality and governance)
Reporting and analytics
Strategy
Capabilities
Visibility
The ability to mine past data and report on transaction activity
(value, volume, timing and costs)
Report
Management
The ability to apply comprehensive analysis
to gain a better understanding of
program effectiveness, risk patterns,
processes, product level costs and risk
Analyze
Risk and duty modeling
The ability to utilize both past transactional data to proactively identify possible compliance
risks prior to the occurrence, optimize duty costs and supply
chain trade-offs
Predict
Operational integration
The ability to align, integrate and collaborate,
for known or unknown needs, through real-
time scenario planning and simulation across an extended network, resulting in a complete demand, supply and
finance model
SynchronizeFramework
Page 7 Global trade analytics
Technology considerations
Performance measurement
Source and repository technology
This layer will allow executives, management, analysts, and operations to effectively view and use information to make decisions.
The tools and processes within this layer will be standardized and improve the overall value of the data within the organization.
The most complex of layers, and data feeds will go through the Extract/Transform/Load(E/T/L) process and should be automated. Data warehouse/Data marts will provide effective decision support systems.
Decision support/analytics
Data warehouse
Management reporting
Programanalytics
Transactionanalytics
Predictiverisk
modeling
External data sources► Brokers► Carriers
Enterprise systems► Enterprise Resource
Planning (ERP)► Global Trade Management
System (GTMS)
Open data► Government► Industry
Effective analytics approaches require that each layer be addressed
Visualizationlayer
Decision and support analytics
Key considerations► Requires ongoing input
from both business and technology group
► The benefit of “real-time”
► Supply chain visualization
*Note: This framework is a conceptual model only. It is not meant to imply specific recommendations on architecture.
Page 8 Global trade analytics
A look inside GM’s data analytics program
Page 9 Global trade analytics
The starting point
► In June 2012, GM Global Customs initiated its Global Customs Business Transformation.
► Recognized the need for consistent, timely, cumulative global import data
► Developed methodology for obtaining and utilizing data from multiple business units and import systems
► Identified critical data elements and determined roll-out cadence starting with largest importing business units
► August 2013, established the Global Customs Data Warehouse
Page 10 Global trade analytics
Strategic drivers
► Support corporate vision by providing easy to understand information, insight and metrics to drive the right business decisions
► Collaborative work with organizations outside Global Customs require accurate, timely and reliable data
► Use of data analytics to identify potential duty and tax savings opportunities
► Established Centers of Excellence to enhance operational efficiency, recognize savings opportunities and identify potential compliance risk
Page 11 Global trade analytics
Global customs Entry data warehouse
Data elements ► Importing country/business unit
► Ship from name/DUNS
► Ship to name/DUNS/plant code
► Part #
► Description
► Quantity
► Value
► Country of origin
► Country of export
► Free trade agreement status
► Amount of duty paid
► Mode of transportation
► Category(Prod/P&A/VEH/M&E)
► Entry/invoice detail
► Import related taxes and fees
Of 44 countries of import in total, only 5 are outstanding and those are expected to be inducted by September 30, 2014.
27 unique data elements, with all value fields also converted and stored in common currency – USD
Data is received through three channels: ► Scheduled extraction from GM’s global import
system ► Scheduled extraction from other import systems ► Extractions from broker systems – manually
provided and reformatted for upload
Records to date > 15.5m
Dates of import covering 1 January 2013 to date
Page 12 Global trade analytics
Global cash duty paid report
Effective duty rate, entered value and duty paid are available at the global and country level within minutes, versus days.
Page 13 Global trade analytics
Year-over-year analysis
Various views of the data can provide visibility into our business.
Page 14 Global trade analytics
Import line count report by category
Page 15 Global trade analytics
Duty savings opportunity identification
Page 16 Global trade analytics
Duties paid by global purchasing teams
Tailoring the information for the “consumer” is critical.
Page 17 Global trade analytics
The role of the data miner
► Relational database knowledge is essential to join import data with other GM customs data, such as FTA eligibility and Harmonized Tariff Schedule (HTS).
► Master reporting tools – Tableau, MS Excel ► Assist in design and implementation of interim approaches
for non-standard data ► Extract data and build reports, tailored to various
audiences ► Reports ► Data ► Metrics
► There is a huge benefit if these individuals understand the data and can offer ideas and solutions.
Page 18 Global trade analytics
Lessons learned
► Resources to mine and analyze provide a level of operational visibility critical to our business success.
► Ensure appropriate systems/storage/data mining capabilities are available and utilized.
► Be mindful that collected data adds value, is useful and is readily understood by the users.
► Get the word out that the data is available, encourage other organizations to take an interest and be invested.
► Demonstrate the value of the data collection, dissemination and utilization process.
Page 19 Global trade analytics
Duty modeling using enterprise data
Page 20 Global trade analytics
Why an enterprise approach?
► Global trade costs and risks vary enormously from country to country. ► Duty rates (levels and variability)
and classification complexity
► Available inbound/outbound trade agreements, customs regimes
► Customs and trade environment (pitfalls and non-compliance costs)
► Volume and complexity of the business
Common challenges: ► How much duty do we pay by country?
► How much do we save?
► What trade compliance resources/tools are needed and where?
► Where would we likely find “duty leakages?”
► Are our sourcing and distribution supply chains duty optimized?
► How can we assess and manage business changes that have customs duty cost or compliance implications? Conversely, can we factor in customs duty costs to business change decisions?
Page 21 Global trade analytics
Using enterprise data to create duty models
► Flows can be mapped from ERP or similar data, e.g., purchasing database.
► Source data can include non-customs elements, e.g., sku, product group, business unit division etc.
► Outputs (by country and business unit etc.) can include: ► Duty costs, savings and trade compliance risk heat maps ► Stack ranked duty leakage and optimization opportunities ► Predictive duty data, e.g., for scenarios, change management
► Other users (in the organization) can include supply chain, procurement, tax, finance, planning, legal and internal audit.
Page 22 Global trade analytics
► Visual duty rates model► These models show duty rates by flow lane for each
material/product. ► Color-coding highlights sub-optimizations► Alternative flows (Available (AVL) and non-AVL) can be
toggled.
► “Scenario” source and manufacturing countries can be added by material/product, e.g., China, Vietnam.
► Visualization can be adjusted, e.g., from linear (as shown) to filled map, according to user group.
► Filters can include or Business Unit (BU), PG. Models can point to specifics of the FTAs and preferences.
► Duty cost and optimization model ► This model builds in the $ value of the flows (again,
color coding represents duty efficiency).► Model calculates the gross duty (red), net duty (if available
FTA savings are claimed) and excess duty (additional savings that could be realized)
► Highest realizable FTA savings and FTA opportunities stack ranked to check “leakages” and “optimizations”
► Flows with highest net duties can be checked for regime and tariff suspension opportunities, duty driver planning (valuation).
► Duty model supports focused savings projects (and provides business case cost/benefit).
Global / Regional / Loca(All)[Dbl click country to drill down] [Dbl click code to see all rates]
Active Supply Base
Material or Product Country Name HS6 Local Code Used Optimization Statutory Rate per WT
Rate as % Val
Basis Values UoM CN TH BR CL IN MA TN US ZA
Soya oil Australia 1507.90 AU-1507.90.00.13 MFN 5.0% 5.0% MFN Rate as % 5.0% 5.0% 5.0% 5.0% 5.0% 5.0% 5.0% 5.0% 5.0%Volume 10,107,181 15,160,768Excess v. Best Pref 5,053,590 7,580,385
PREF 4.0% 4.0% DCS Rate as % 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0%Volume 10,107,181 15,160,768Excess v. Best Pref 4,042,872 6,064,308
BESTPREF Free 0.0% AANZFTA Rate as % 0%Volume 15,160,768Excess v. Best Pref
ACI-FTA Rate as % 0%Volume Excess v. Best Pref
AUS Rate as % 0%Volume Excess v. Best Pref
TAFTA Rate as % 0%Volume 15,160,768Excess v. Best Pref
Enterprise dataExamples of results
Page 23 Global trade analytics
► Tobacco manufacturer global sourcing, manufacturing and distribution ► Key raw materials (leaf) to primary tobacco plants ► Intermediates from primary to product finishing plants ► Finished goods to end markets
► Duty optimization tool now in use by procurement/ planning teams for more than five years
► Benefits not disclosed, but understood to be very significant
► Energy (Electrical) Product Manufacturing Company ► Global duty model (based on aggregated
classification) to support compliance and cost reduction
► Country trade profiles by BU that identify the key areas for cost reduction
► Benefits estimation $10m in Year 1 ► Data aggregation and cleansing for SAP GTS
Enterprise data Examples of outcomes
Page 24 Global trade analytics
Some final comments
► Duty modeling can provide country, regional and global insights to customs duty costs, risk and opportunities relevant to one or many stakeholders
► The model allows targeting and drill-down to direct effort and resources to where they provide the best return
► Predictive modeling can support business change strategies and point to key trade impacts, e.g., supply chain or transfer price model changes and TP adjustments.
► Modeling and broker/customs analytics are essentially complementary – which one to use, and when, depends on the direction and role of the trade function within the business.
Page 25 Global trade analytics
Risk modeling – predicting risky transactions
Page 26 Global trade analytics
Defining the problem
► Facing significant pressures to limit new Full Time Employees (FTEs) in the trade compliance departments, while still being required to help grow business and regulatory risks
► Spending too much time on tactical activities detracts from addressing strategic issues
► Expansive “import/export” footprint is difficult to monitor ► Fees and fines resulting from lack of proper controls ► Growing threats to an organization and its risk and
compliance framework from unconventional elements ► Competitive business pressures and regulatory needs
requiring sectorized compliance perspective
Page 27 Global trade analytics
Unifying insights with advanced analytics
► Involves building economic/statistical models in order to understand key behavior drivers
► Greater precision to understand why something has happened or what will happen by relating data inputs to data outcomes ► Example: What is the probability this
shipment has a misclassification?
Individual global trade analytic insights can be powerful, but may not be a useful guide to action when multiple insights compete for attention.
Page 28 Global trade analytics
The benefits of advanced analytics
Enables periodic monitoring to detect errors early on before they become systemic
Quantitative, risk-based approach to compliance monitoring
“Multivariate” approach uncovers main behavior drivers and anticipates riskier transactions
Reasons for using advanced analytics
Free up current resources to work on business support or other more value-added activities
Improve compliance, operational efficiency
Tap into and access an experienced knowledge pool to provide insights and assist with process if possible
Improve the predictability of duty spend
Evaluate the effectiveness of compliance audits
Additional capability that enhances compliance management activities
Internal control testing – evaluate the sufficiency of process controls and identify additional (more specialized) process controls
Advanced analytics helps optimize your risk and compliance activities to help you govern your business and achieve strategic objectives with predictable cost.
Page 29 Global trade analytics
Case study in predicting tariff classification errors
Import date Export date CO POE Mode HTS Currency Shipper Product description
Daily line total
Daily unique codes Daily unique POE HTS
frequency Code frequency
Shipper frequency
4/5/2014 3/31/2014 SG 3901 40 9018903000 USD ACME, SG SWITCHBOARD 122 77 9 1.0% 0.1% 21.3% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR TRANSFER SET 122 77 9 41.6% 0.9% 30.2% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR MEDICAL DEVICE 122 77 9 41.6% 0.9% 30.2% 4/5/2014 3/31/2014 US 5271 40 9026104000 USD ACME, CR METER 122 77 9 0.05% 0.01% 30.2% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR HOUSING 122 77 9 41.6% 0.9% 30.2% 4/5/2014 3/31/2014 US 5271 40 9026104000 USD ACME, CR METER 122 77 9 0.05% 0.02% 30.2% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR PUMP 122 77 9 41.6% 0.9% 30.2% 4/5/2014 3/31/2014 US 5271 40 9032896040 USD ACME, CR CALIBRATOR 122 77 9 0.1% 0.01% 30.2% 4/5/2014 3/31/2014 AT 2720 40 3002100190 EUR ACME, AT SOLUTION 122 77 9 12.2% 0.04% 7.1% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR SECUREMENT 122 77 9 41.6% 0.1% 30.2% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR SYRINGE 122 77 9 41.6% 0.9% 30.2% 4/5/2014 3/23/2014 CR 4909 11 9018908000 USD ACME, CR BAG 122 77 9 41.6% 0.3% 30.2% 4/5/2014 3/31/2014 CH 4110 40 3002100190 EUR ACME, BE ADAPTER 122 77 9 12.2% 0.1% 10.4%
The number of unique items – each requiring a separate classification – increases complexity
HTS codes outside of the client’s regular import mix increase the probability of an incorrect classification
A commonly used shipper reduces uncertainty and lowers the chance of a data entry error or miss classification
Certain HTS codes(e.g., parts of, basket provisions, etc.) are inherently more challenging to determine
Page 30 Global trade analytics
Insights on key outcome drivers
► Days with many unique product codes had higher errors
► Low volume shippers had error rates close to 100%
► Infrequently used HTS codes accounted for nearly 80% of errors.
05
1015
Res
tate
men
ts
0 20 40 60 80Unique product codes per day
0.2
.4.6
.8P
erce
nt re
stat
emen
ts
0 10 20 30 40Frequency by HTS code
0.2
.4.6
.81
Res
tate
men
t rat
e
0 10 20 30Total shipments for each shipper
Page 31 Global trade analytics
Unifying insights with advanced predictive analytics
Shipper volume
Country of origin
HTS frequency
Unique products
HTS complexity
Statisticalm
odel
Predicted probability
Page 32 Global trade analytics
Predicting tariff classification errors
5 April 2014 Exporting country HTS number Shipper Product description
Predicted probability
SG 9018903000 ACME, SG SWITCHBOARD 81.50% CR 9018908000 ACME, CR TRANSFER SET 1.79% CR 9018908000 ACME, CR MEDICAL DEVICE 1.79% CR 9026104000 ACME, CR METER 92.49% CR 9018908000 ACME, CR HOUSING 1.79% CR 9026104000 ACME, CR METER 92.49% CR 9018908000 ACME, CR PUMP 1.79% CR 9032896040 ACME, CR CALIBRATOR 92.40% AT 3002100190 ACME, AT SOLUTION 2.23% CR 9018908000 ACME, CR SECUREMENT 1.79% CR 9018908000 ACME, CR SYRINGE 1.79% CR 9018908000 ACME, CR BAG 1.79% BE 3002100190 ACME, BE ADAPTER 12.01%
Effective risk management is about identifying potential issues before they turn into systemic errors. Advanced analytics can help identify which shipments represent the greatest risk of leading to a compliance error.
High-risk shipments are
flagged
Page 33 Global trade analytics
Predicting tariff classification errors
5 April 2014 Exporting country HTS number Shipper Product description
Predicted Probability
Error Y/N
SG 9018903000 ACME, SG SWITCHBOARD 81.50% Y
CR 9018908000 ACME, CR TRANSFER SET 1.79% N
CR 9018908000 ACME, CR MEDICAL DEVICE 1.79% N
CR 9026104000 ACME, CR METER 92.49% N
CR 9018908000 ACME, CR HOUSING 1.79% N
CR 9026104000 ACME, CR METER 92.49% N
CR 9018908000 ACME, CR PUMP 1.79% N
CR 9032896040 ACME, CR CALIBRATOR 92.40% Y
AT 3002100190 ACME, AT SOLUTION 2.23% N
CR 9018908000 ACME, CR SECUREMENT 1.79% N
CR 9018908000 ACME, CR SYRINGE 1.79% N
CR 9018908000 ACME, CR BAG 1.79% N
BE 3002100190 ACME, BE ADAPTER 12.01% N
“Back-testing” against actual tariff classification errors was found to have a high predictive validity.
85% of transactions correctly classified 100% of errors correctly identified
Both errors were correctly
identified
Page 34 Global trade analytics
Case study summary
► Total invoice lines: 1115
► Error rate: 32.7%
► Total invoices lines > 80% predictive probability: 146
► Total invoices lines > 90% predictive probability: 77
► Total invoices lines > 95% predictive probability: 23
0%10%20%30%40%50%60%70%80%90%
100%
33%
62%
73%
78%
82%
85%
87%
89%
91%
93%
94%
95%
96%
97%
97%
98%
98%
99%
99%
99%
100%Sh
are
of to
tal r
ecla
ssifi
catio
n
valu
e
Share of total transactions
78%
82%
51.6%
6.0%
15.9% 13.6% 12.9%
3.3%7.9%
23.6%
32.6% 32.6%
0%-20% 20%-40% 40%-60% 60%-80% 80%-100%
Transactions Restatements
Page 35 Global trade analytics
Key success factors
► Identify and align on the most critical business problems to solve and build the solution around it – identify the key questions or themes that the global trade function wants to get answers to, but has not had the capability to do historically
► Build the business case – articulate the value, what constitutes success and the high level roadmap to guide the journey
► Obtain executive commitment – and maintain it throughout the journey for success to be realized
► Collaboration and compliance – seek input from a broad range of functional areas and supply chain partners and obtain buy-in as far as information relay, update cadence, and accuracy/quality of data are concerned
► Conscious focus on data management – define and enable robust data management capabilities that support high data quality
Page 36 Global trade analytics
Circular 230 disclaimer
This presentation is provided solely for the purpose of enhancing knowledge on tax matters. It does not provide tax advice to any taxpayer because it does not take into account any specific taxpayer’s facts and circumstances. These slides are for educational purposes only and are not intended, and should not be relied upon, as accounting advice.
Page 37 Global trade analytics
Disclaimer
► EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young LLP is a client-serving member firm of Ernst & Young Global Limited operating in the U.S.
► This presentation is © 2014 Ernst & Young LLP. All rights reserved. No part of this document may be reproduced, transmitted or otherwise distributed in any form or by any means, electronic or mechanical, including by photocopying, facsimile transmission, recording, rekeying, or using any information storage and retrieval system, without written permission from Ernst & Young LLP. Any reproduction, transmission or distribution of this form or any of the material herein is prohibited and is in violation of U.S. and international law. Ernst & Young LLP expressly disclaims any liability in connection with use of this presentation or its contents by any third party.
► Views expressed in this presentation are those of the speakers and are not necessarily those of Ernst & Young LLP.
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