use of caats at daikin europe
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Use of CAATT’s at Daikin Europe
Erik Claes
•About Daikin Europe N.V.•Why start data analytics in audit?•How we got started•The flow of data•Tools used•SAP data extraction ; Why keep a copy?•Issues with data•Creating a process for requesting data•Example of a script•Cases
} Daikin is the largest A/c manufacturer in the world with 15 billion USD in turnover.
} Japanese company based in Osaka, Japan} Over 50.000 employees worldwide} Daikin Europe N.V. is headquartered in
Brussels and covers EMEA region} In Europe: 5 production facilities, 17 affiliated
companies, five sales offices and a whole network of independent distributors.
} Sampling does not provide sufficient assurance
} Ask more specific questions about an exception
} Cannot cover everything with small audit dept} Stories about duplicate payments recovering
millions of Euros} More automation on sampling (where full
testing cannot be done)
} Used MS Access in 2004 to compare some tables
} Found Access not sufficient and moved to ACL} Used external company to get standard scripts
(e.g. duplicate payments)} Mixed feelings
◦ Recovered 70K over a period of 3 years◦ Internal controls are working well
} Took about 2 months to get results for duplicate payments
} Credit control scripts were created} Started with downloading data from SAP more
frequently for analysis} More usage…
(SAP) Database
Extractor
Local copy
Analysis Scripts
Reports• Audit use
} Connection to SAP (e.g. login / password, RFC)} Extraction: DAB Exporter} Analytics scripts (both development and
execution) - Arbutus Analyser} Data format: Flat CSV files. Naming convention
is based on the SAP table names.} Reports: Arbutus format and Excel} Audit Management: Vision
} Getting data from SAP tables} Number and size was a big issue
◦ Which ones to take◦ Which fields to choose◦ How many records to download and how frequently◦ Are records updated in SAP or not (e.g. difference
between CD tables and other tables)
} Extraction using a tool called DAB Exporter} Slice generation, Formatting, archiving is done
through scripting in Arbutus (similar to ACL)} Size of disk issue – nearly 2 TB now} Achieving automatic downloads was a goal in
itself.} Data slices are automatically downloaded
every month} Time period to stabilise this took over a year
} Reading tables takes a lot of I/O (input/output)
} I/O on the server will affect business users} Daikin IT intends to implement archiving of
data older than 3 years of age.
} Extraction time is large} Only incremental extracts (monthly deltas)} No capability to roll back; } Regular tables have to be downloaded for long
time ranges (Current FY and two previous FY)} Flat files have no indexes} Speed of data access is slow because of large
volume (nearly 2 TB)
} Many aspects to think of even before starting to pull data◦ Which tables◦ Which fields◦ What are the “other” possibilities within the business
process◦ What do I want to see as a result (which tables and fields)◦ How to test the result I get
} Naming conventions used◦ Filing data requests◦ Result file names◦ Script names
} Example of a Data Request template
Request Information Name of audit: Category/chapter Requestor name: A similar request exists (also in SAP interesting transactions)?
Yes/no
Title of the request Period for data Company code Other filters Objective Control / Workplan step to be attached to
Output fields/columns that are mandatory: [order of fields may also be mentioned]
Additional information Does this have to be repeated
yes/no
Type of result Exception list/ sample (Population) Rollout for self-assessment
yes/no
Target date (only add dates do not delete)
Result Information
Name of analyser John Process Source files/tables Script Result files Result Location How long it will take Time taken for the request
Change log
-
Sign Off Accepted / stopped / rejected: explain Name and Date: To be completed by requestor
Template update: 28.08.2014
} To find accounting postings that were done in a closed period
} The result files} The script
◦ The period definition file in Excel◦ The steps inside◦ Split the file◦ Clean up and close
} Find people who are not authorised to change credit data for customers
} Extract all the changes to credit data (CDPOS and CDHDR tables with Object Class as ‘KLIM’)
} Extract all the people who made these changes} Extract people who are authorised to make
credit data changes (AGR_USERS table filter using the term “CREDIT”)
} Join the people who made changes with the authorised people (unmatched)
} Split the file by country code and move to required folder
} Manual postings are those that are not done via an automatic batch run
} Only took P/L accounts} Asked IT to identify manual postings à look
for records with “RFBU”. } Later found manual postings without “RFBU”} Looked at time stamps (for a particular user
and company code) : 1 second rule for automatic postings
} Removal of postings to G/L accounts with automatic flag
} Number of records is around 1.5 million} Removal of “not relevant” transactions by
manual selection} A few thousands per company} Refinement still in progress!
• Questions?• You may mail me at claes.e@daikineurope.com
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