SABMiller is a global brewing company with operations in 75 countries. Accenture provided application management support for SABMiller's systems managing 19,000 users. A problem was that discrepancies in customer credit exposures blocked new sales orders, costing 13 hours per incident to manually resolve. Accenture created an automated solution to identify and correct discrepancies in customer credit exposures, eliminating all manual effort and blocked sales orders.

1. • Client Overview: SABMiller plc is a global brewing and bottling company
headquartered in London, United Kingdom. It has operations in 75 countries
across Africa, Asia, Australia, Europe, North America and South America and
sells around 21 billion liters of lager per year.
• Project Overview: Accenture provides AM support for SABMiller Global
Template that comprises of 14 production systems managing around 19000
users across Europe, Africa, Australia, UK and LATAM markets.
• Problem Statement: Discrepancy in customer’s credit exposure affects their
credit limit. This blocks any new sales order being created in the system for that
customer thus affecting the SALES REVENUE for SABMILLER. Manual effort (6
hours from client and 7 hours from Accenture) is needed for releasing such
orders and additional monitoring is required for subsequent failures.
• Accenture Solution: Accenture created an automated solution to identify the
customers with discrepancy in their credit scores and correct the same.
• Benefits:
 Zero sales orders blocked for customers due to incorrect credit exposure
 13 hours per incident saved – 6 hours for client and 7 hours for Accenture
 100% reduction in manual effort
 Eliminated 8 tickets per month
• TEAM:
 Swamynathan Ravindran
 Sujith Raja
 Raunak Tongia
 Ramanathan Ramachandran
 Sreeju Abraham Jose
 Vignesh Subramanian
BEFORE AUTOMATION POST AUTOMATION
Customer’s credit exposure is cleared
allowing new Sales Order creation
STEP ELIMINATED
STEP ELIMINATED
STEP ELIMINATED
STEP ELIMINATED
STEP ELIMINATED
Customer’s credit exposure is cleared
allowing new Sales Order creation
Job Scheduled manually to execute the
correction programs
Variants created manually in the correction
programs to clear the credit discrepancy for
identified customers
End user validates the calculations and
confirms to correct the same
Multiple iterations of calculations performed
in excel to validate total affected customers
Incident raised for removing the discrepancy
in customer’s credit exposure
0
20
40
60
80
100
Before Automation Post Automation
100
00
100
%
E
f
f
o
r
t
Manual Effort
Automation