shs group presentation on bi performance improvement at business intelligence sig october 2010
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The SHS Group are an SME company with
a very diverse and complex operating
model. The company has implemented a
highly integrated end to end solution with an
ECC, CRM, BI / IP, Portal footprint.
BW solution viewed as a burden rather than
an integral part of the SAP solution…
SAP User Group 19th Oct 2010
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• Formed in 1975
• Privately owned
• Group Turnover 2008 500 million Euro
• Group employs 800+ people
• Core geographical areas:
United Kingdom
Republic Of Ireland
Background
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The Core Business
The Company operates in the FMCG sector and provides a
strong financial base and central resource for ten
successful businesses operating in four distinct areas:
• Brand Ownership
• Sales & Marketing
• Corporate Services
• Manufacturing
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SHS Group - Companies
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SHS Business Model / SAP Landscape
Plan Sales /
Promotions
Capture
Orders
Deliver From
Stock
Report /
Analyse /
Re-plan
InvoiceSettle
Promotions
IP CRM ECC BI IP
Trade Promotion Management
Portal / Electronic Trading Platform
Brand Owner Portfolio
Market understanding
Brand development
Sales and promotion
Logistics
Financial settlement
Analysis / feedback
Impulse /
Convenience
Multiples
On-Trade
• 380 BW Users
• 23 BW Reports
• Business Critical
• Brand Owner relevant
• 25 IP Reports
• Forecasting & Planning
• Daily Loads from:
• ECC (Actuals)
• CRM (Promotions & Forecasts)
BW Infrastructure
• Loss of credibility – No faith in BW, increase in
offline/non BW reporting
• Reporting Runtimes – As the amount of data
increased, so did the report runtimes
• Data Loading – The daily load amounts stayed the
same, but the time taken for the updates to complete
was constantly increasing
• Tablespace – constantly having to increase table
space volumes month on month
• Inherited Bad Practice – Lack of housekeeping, cube
design, lack of Data Store Object’s
BW – Issues & Challenges
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Plan for resolution of issues was created based on Best Practice
Strategy recommended by De Villiers Walton
Split in to two separate phases of work
Phase 1 – Best Practice Strategy
Phase 2 – InfoCube Redesign
B.I. Performance
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B.I. Performance
Phase 1 – Best Practice Strategy
• Aggregates
• Compression
• Scheduled Jobs – PSA / Change Log / Compression
• Process chains updated and reordered for better data loading
• Deletion of dormant InfoCubes / Queries
Reduced data usage from 98%
capacity to 64%
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B.I. Performance – Aggregates
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B.I. Performance – Compression
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B.I. Performance – Process Chains
Clear Out PSA
Clear Out Change Logs
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Phase 2 - InfoCube Redesign
Multiproviders – Ensuring that all cubes have a multiprovider over them
SAP_INFOCUBE_DESIGNS – 9 InfoCubes identified for redesign
Redesign – change of cube structure/storing of data
Indexing – Indexes added to improve performance of InfoCube
Partitioning – Cubes partitioned by fiscal periods
InfoCubes by Year – Each cube designed to hold one fiscal year only
Report movement from InfoCube to multiprovider
Reloading of data to redesigned InfoCube
BW Performance – Phase II
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B.I. Performance – MultiProviders
B.I. Performance – SAP_INFOCUBE_DESIGNS
Shows the database tables of an InfoCube, the number of records in
these tables and the ratio of the dimension table size to the Fact table
size.
If dimension tables are too large then they can cause badly performing
table joins on database level. As the data volume grows and the data
allocation changes over the time, this check should be executed
regularly.
When loading transaction data, IDs have to be generated in the
dimension table for the entries. If you do have a large dimension, this
number range operation can negatively affect performance.
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B.I. Performance – Indexing
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B.I. Performance – Partitioning
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B.I. Performance – Partitioning
Increased query performance
Average query runtime down from 20 secs to 5 secs
Average database call time down from 12.5 to 3 secs
Examples,
Sales v Budget report 15 secs down to 5 secs
Gross Margin reports 30 secs down to 3 secs
Debtors Analysis 60 secs down to 6 secs
Increased tablespace capacity – was 98% usage, now 60% usage
Improved data load time – Recent load of a cube took 60 hours, after cube redesign was down to 6 hours (excess 20 million record – highly customised)
User Acceptance – Improved performance leading to satisfied users running reports quickly and efficiently - now getting the data they want in a timely manner.
BW Performance – Benefits Realised
BW Performance – Benefits Realised
Accuracy – “One version of the truth” – reduction in offline reporting,
reversion back to centralised report structure. Business is now
benefiting from quick and accurate reporting.
Credibility – No longer a question over the availability or the accuracy
of the data. People can now rely on their reports being there and
being accurate
Downtime – Outages now restricted to unforeseen errors (often
caused by end users). Outage resolution a lot faster due to the
increase of system performance, full weekends no longer required for
reloads.
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