how to leverage data analytics to improve your bottom line_sf iia dec 2014

© 2014 SRI Interna/onal -‐ Company Confiden/al and Proprietary Informa/on

San Francisco IIA – Winter Seminar How to Leverage Data Analy/cs to Improve your BoJom Line December 5, 2014 Dan Samson, Exec. Director and CAE, Assurance Services, SRI Interna/onal Stephanie Gray, Senior Manager, Assurance Services, SRI Interna/onal


Agenda

•  Overview of SRI Interna/onal

•  Why Internal Audit Should Provide Data Analy/c Leadership

•  Value to the Enterprise

•  Examples of Data Analy/c Treasure Troves

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Overview of SRI Interna/onal

•  Who has heard of SRI Interna/onal? •  You may be familiar with some of our innova/ons…

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Siri

First VPA

Personal compu3ng

Computer Mouse Minimally invasive surgery

Telerobo/c Surgery

ARPANET -‐ TCP-‐based Internet transmission

Internet

Ultrasound

Health

Magne3c ink character recogni3on

Banking

Robo/cs

Surface-‐climbing robots

Preclinical therapeu3cs for heart, lung, and blood

Novel drugs


Overview of SRI Interna/onal

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Fundamental Science

Universi3es, Na3onal Labs

Corpora3ons

Basic Research

Applied Research

Product Development

Produc3on

SRI


Why Internal Audit Should Provide Data Analy/c Leadership

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The Challenge: Innovate or Die

•  Value Factor =

Perceived Customer Benefits

Perceived Customer Costs

•  Benefits and Costs are determined by the customer

(not by us!)

•  Who are your customers? Ø  Audit Committee Ø  Executive Management Ø  Functional Owners Ø  External Regulators Ø  External (paying) Customers


Value to the Enterprise

•  Insights to business ac/vity; transac/on paJerns including anomalies

•  Process leaning

•  System op/miza/on

•  Reducing the cost of opera/ons

•  Revenue recovery and op/miza/on

•  Compliance monitoring

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•  Data analy/cs empower audit teams to make transforma/ve change

•  Enables customers to understand their data in new and different ways

•  Drives process efficiencies

•  Delivers hard, measurable savings, cost avoidance, and revenue recovery

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•  No more random or judgmental sampling, capability of analyzing 100% of data

•  Perform data analysis during planning, before field work, to priori/ze scope

•  Present data profiles at Opening Mee/ng with customers

–  Value add during opening mee/ng.

–  How many opening mee/ngs tell the customer something they don’t know?

–  How ofen are opening mee/ngs staid and formulaic?

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Data Analy/c Treasure Troves

Every func/on, process, and ac/vity produces data. All have poten/al cost recovery, revenue recovery, or cost avoidance

poten/al.

The only limiter is your imagina/on!

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Data Analy/c Treasure Troves

Tradi3onal

•  Travel & Expense

•  Accounts Payable

•  Corporate Credit Cards

•  Accounts Receivable

•  Payroll

But Also…

•  Third Party Agreements

•  Office Supplies

•  Telecommunica/ons

•  General Ledger Op/miza/on

•  Inventory

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Example – Travel & Expense

•  Reasonableness of cost incurred in compliance with policy

•  Analysis of airfare booking /meliness •  Analysis of airfare credits (unused airfare credits from cancela/ons)

•  Double payments •  Cash vs. credit card use paJerns •  Top travelers – logical? •  Execu/ve spending •  Expenses just below requirement to provide suppor/ng documenta/on

•  Typical cost recovery / savings of 3% of travel expenditures (e.g. 3% of $10 million is $300,000).

•  Hotel stays at non-‐preferred proper/es, unreasonable hotel rates, unreasonable hotel rate types (suite, upgraded rooms, etc.), hotel rate packages (breakfast included yet reimbursed for breakfast)

•  Airfare booking /meliness. Booking within 7 days of travel brings a ~33% premium.

•  Excess alcohol, types of alcohol (premium champagne)

•  Analysis of meal reimbursement amounts by staff for paJerns

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Travel & Expense Con/nued

Reasonableness of Expenditures Objec/ve: Assess expenses for reasonableness, reclaim excep/ons •  Data extract from T&E System and any external travel provider.

Key data include employee name, employee ID, expense amount, vendor name, expense date, expense type, travel purpose, travel dates and loca/on/s, FX rates.

•  Join files on employee name or ID. •  Subtotal expenditures incurred by employee. ID top spenders.

Examine hotel rates. Determine median by city and look for outliers.

•  Limo services. •  Airline club memberships •  Meals. Alcohol. Other reimbursements. •  Airfare class service.

Airfare Credits Objec/ve: Iden/ty unused airfare credits for use before expira/on •  Data extract from external travel provider of unused airfare

credits, employee names, airline, $ dollar amount of credit, original travel date, credit expira/on date.

•  Calculate difference between outstanding credits and change fees for net available cost savings.

•  Analyze credits by employee for paJerns. For personnel with excess credits and / or high volume rebooking ac/vity, verify appropriate use of credits for business purposes.

•  Extract data charts for use by managers before airfare credits expire.

•  Certain airlines (United) will allow pooling of credits for use by other employees.

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Airfare Booking Timeliness Objec/ve: Assess reserva/on /meliness for cost avoidence

•  Data extract from travel provider including employee name / ID, date of reserva/on, date of travel, cost of airfare and fees.

•  Calculate number of days between date of reserva/on and date of travel. Stra/fy based on the number of resul/ng days.

•  Calculate median $ airfare for each grouping. •  Calculate variance between groupings (<7 days before travel, 7<14

days before travel, 14<21 days before travel, >21 days before travel) and average $ airfare in popula/on.

Expense PaJerns Objec/ve: Iden/fy poten/al fraud and misappropria/on of funds

•  Data extract as noted in Reasonableness •  Run Benford Law Analysis (three digit) on expense amount •  Extract anomalies for further inves/ga/on •  Analyze cost paJerns over the year for reasonableness


Example -‐ Accounts Payable

•  Duplicate Payments; especially at companies with mul/ple accoun/ng systems or divisions

•  Payment discounts lost (due date vs. paid date)

•  Payment amount > invoice amount •  Aging of open accounts payable invoices

•  Vendor credits •  Top vendors by $ and by volume (look at high and low volume)

•  Benford Analysis

•  Payments to employees

•  Payments to vendors not on the vendor master list

•  Round $ amounts (payments or invoice amount)

•  Splixng payments

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Accounts Payable con/nued

Duplicate Payments Objec/ve: Iden/fy duplicate payments •  Data extract of payments made from Accounts Payable

including vendor name, vendor number, payment amount, payment date, payment/check #, invoice net amount, invoice creator ID, invoice date, invoice due date, invoice #, payment loca/on, payment type, payment void date.

•  Analyze data for payments for the same invoice amount, vendor name, and date. Key is “invoice amount”, as payment could be for mul/ple invoices.

•  Analyze payments for different vendors with similar names. •  Analyze payments for different vendors with same address

Payment Discounts Objec/ve: Iden/fy lost vendor payment discounts •  Data extract as noted under duplicate payments as well as

vendor master file. •  Extract vendors with payment discount terms and join to

accounts payable file on vendor ID. •  Analyze payment date vs. due date to iden/fy those vendors

paid afer discount period. •  Calculate total discount lost by vendor for year.

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Vendor Credits Objec/ve: Iden/fy outstanding credits for inac/ve vendors

•  Use data extract to query balances by vendor name for credit balances.

•  Evaluate age of credits. •  Request refund checks for inac/ve vendors or greatly aged

credits.

Cash Flow (commercial enterprise) Objec/ve: Op/mize cash flow related to payments

•  Data extract as noted under duplicate payments and payment discounts.

•  Calculate (create new field) “# of days to pay” by vendor by comparing invoice due date to payment date.

•  Analyze delta between # days to pay and payment terms (10, 30, 45 days) by vendor for all payments.

•  The difference represents underu/lized cash flow. Consider adding interest rate factor.


Example -‐ Third Party Agreements

•  Zombie (evergreen) agreements such as subscrip/ons, services, etc.

•  Agreements with automa/c rate increases

•  Mul/ple agreements with the same vendor

•  Vendors billing at incorrect rates

•  Vendors providing wrong type / level of service vs. agreement

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Third Party Agreements con/nued

Sofware Licenses Objec/ve: Analyze sofware agreements for op/miza/on

•  Extract payment data for sofware providers/vendors. Determine top provider over a certain $ threshold

•  Request licensing data from sofware provider (non-‐enterprise-‐wide agreement) including licensee / IP address or other iden/fier.

•  Request log-‐on data from provider •  Analyze / compare log-‐on data vs. ac/ve licenses. •  Iden/fy licenses that are not used or infrequently used for

elimina/on

Volume Discounts Objec/ve: Iden/fy vendors with mul/ple agreements for consolida/on

•  Data extract of payments made from Accounts Payable including vendor name, vendor number, and payment amount.

•  Analyze for like vendor names. •  Sum spend by like vendor names. •  Analyze for renego/a/on poten/al. For example, a company

may have mul/ple agreements with a telecommunica/ons provider with mul/ple plans instead of one plan.

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Zombie Agreements Objec/ve: Iden/fy vendor agreements on auto-‐pilot for cost savings •  Data extract of vendor master file including vendor name,

vendor ID, and contract / agreement expira/on date. If not in vendor master file, extract from appropriate system. Data extract of payments made from Accounts Payable including vendor name and vendor ID. Join on vendor ID for those vendors with no agreement expira/on date AND payments in last 12 months.

•  Analyze spend for vendor agreements with no expira/on date for mul/-‐year period. Assess reasonableness of spend over /me.

•  Based on analysis select agreements for renego/a/on.

Your Example?


Example -‐ Office Supplies

•  Most companies nego/ate an agreement with their office supply company that includes deeper discounts for certain items

•  Supply chain func/ons ofen rely on the vendor to determine the most deeply discounted items

•  Internal Audit can perform data analy/cs to determine the op/mal “market basket” to minimize cost

Also look at…

•  Average cost per employee for office supplies, look for departments with significant outliers; this may indicate thef

•  Analyze for key words –  MacBook, computer, projector, LCD,

Bose, Bluetooth, phone, sofware

•  Analyze for shipping address

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Example – Telecommunica/ons (Mobile, Land Lines, and Internet)

•  Analyze usage to determine Company’s need and iden/fy efficiencies.

•  Do services match business needs (interna/onal vs. domes/c)?

•  Determine if service agreement is op/mized for business needs.

Also look at…

•  Unusual/added fees for services not needed for business use.

•  Analyze usage by employee and determine reasonableness based upon job func/on.

–  Does an employee that travels interna/onally once or twice per year need an interna/onal phone plan?

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Example -‐ Shipping

•  Analyze shipment types (land, air, priority, next day, overnight, etc.)

•  Inbound shipment cost analysis. What rate are you paying for inbound shipment? Leverage company shipper/agreement.

•  Analysis of overnight vs. second day shipments, land vs. air, etc.

•  Shipping cost analysis by region shipped to and received.

•  Op/mize shipping service agreement for business need.

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Example – U/li/es (Electricity, Gas, Steam, etc.)

•  Analyze usage by loca/on and period of /me.

•  Analyze peak (demand) charges and iden/fy root cause.

•  Review billing rates for reasonableness. Benchmark with other providers.

•  When possible, obtain gain of scale and use one provider for mul/ple loca/ons and services.

•  Iden/fy areas of waste of energy and gas.

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Other Processes -‐ Discussion

•  Name a func/on, process, ac/vity…

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Data Analy/c Resources

•  Remember…it’s not the tool it’s the thought process and design.

•  There are many good data analy/c tools.

–  Audit Control Language (ACL). hJp://www.acl.com/

–  SAS JMP. hJp://www.jmp.com/

–  Microsof Excel

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Thank You! www.linkedin.com/in/danielasamson/

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how to leverage data analytics to improve your bottom line_sf iia dec 2014

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