Global trade analytics: Delivering business insights to ... · PDF fileGlobal trade analytics Delivering business insights to ... established the Global Customs Data Warehouse . ...

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?


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.


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


► 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


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.


A look inside GM’s data analytics program


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


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


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


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.


Year-over-year analysis

Various views of the data can provide visibility into our business.


Import line count report by category


Duty savings opportunity identification


Duties paid by global purchasing teams

Tailoring the information for the “consumer” is critical.


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.


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.


Duty modeling using enterprise data


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?


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.


► 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


► 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


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.


Risk modeling – predicting risky transactions


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


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.


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.


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


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


Unifying insights with advanced predictive analytics

Shipper volume

Country of origin

HTS frequency

Unique products

HTS complexity

Statisticalm

odel

Predicted probability


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


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


Case study summary

► Total invoice lines: 1115

► Error rate: 32.7%

► Total invoices lines > 80% predictive probability: 146



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


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


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.


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.

Global trade analytics: Delivering business insights to ... · PDF fileGlobal trade analytics Delivering business insights to ... established the Global Customs Data Warehouse . ...

Documents