Reducing Operation Cost of Airtel Kenya

This presentation discusses the main objective of reducing the operation cost of Airtel Kenya by 20%. It explores the background of the department, industry highlights, and the use of SAS for data analysis. The findings of preliminary research, solutions, and recommendations are also provided.

Business Analytics
IP4
CTU
ABED AZZAM

TABLE OF CONTENTS
 ABSTRACT
 BACKGROUND OF DEPARTMENT
 INDUSTRY HIGHLIGHTS
 SAS
 FINDINGS OF PRELIMINARY RESEARCH
 CONCLUSION
 SUMMARY
 RECOMMENDATIONS
 REFERENCES

ABSTRACT
 Main objective of the project is to reduce the operation cost of
the Airtel Kenya.
 New vice president of operations want to reduce the operation
cost 20 %.
 Airtel Kenya is a leading communication service provider.
 The company provides cellular communication throughout the
country.

BACKGROUND OF DEPARTMENT
 I am Project manager of Airtel Kenya.
 I am working in the company since the last five years
 My roles and responsibilities
 Involves the process of design the operations
 Organize the needs for the operations
 Use the resource to make the final output
 Series of activities from supplier to customer

INDUSTRY HIGHLIGHTS
 Consider V-firm clothes manufacturing company.
 The company currently faces some issues
 Average output of clothes
 Higher production costs etc.
 Improve production rate and reduce the production cost by 20%

Solution
 Solutions they followed
 Automation process
 Adoption of the BI system and using the technologies for decision
making is more common.
 Use BI systems for data mining etc.
 Helps to the manager to make the right decision at the right time.
 Uses different data mining tools like SAS, SAP etc.

SAS DATA MINING
 SAS stands for Statistical Analysis System
 Used for advanced analytics, data management and multivariate
analysis etc.
 Helpful for analysing the dataset
 Excellent visualisation of the insights of the dataset

SAS TOOLS AND FEATURES
 Strong data analysis capabilities
 Highly flexible 4th generation programming language
 Supports different types of datasets for the analysis
 provides data security by data encryption algorithms
 Easy to learn interface

FINDINGS OF PRELIMINARY
RESEARCH
 SAS software is used for the data analysis
 Analyzed the previous year data related to the costs
 Contains information about the cost spent by the different
departments
 Results are shown as graphs and plots for easy understanding

Comparison of cost spent for
Labor wages and error repair
S a l e s Ma r ke ti n g Pr o d u c t
Ma n a g e m e n t
Or d e r p r o c e ssi n g T r a n sa c ti o n
p r o c e ssi n g
0.00
1,000,000.00
2,000,000.00
3,000,000.00
4,000,000.00
5,000,000.00
6,000,000.00
7,000,000.00
Labour costs error repair costs

Pearson correlation between
Process error and error repair
costs

Revenue generated by various
departments
61%17%
22%
Revenue
Sales Marketing Product Management Order Processing Transaction Processing

Who is responsible for cost?
Sales Marketing Product
Management
Order
Processing
Transaction
Processing
0.00
10,000,000.00
20,000,000.00
30,000,000.00
40,000,000.00
50,000,000.00
60,000,000.00
Spendings Revenue

Who is responsible for cost?
 Sales department spends huge money on employee salary
 The company has nearly 600 employees.
 The average salary of the employees is $500000
 They also spent 2 million dollars

Excess costs
 Inventory and error repairing costs
 Increase in these costs reduces the profit.
 Avoid these completely is not practically feasible.
 The reduction is the only way to resolve these issues.
 Among them, error repairing costs are more dangerous than
inventory costs.

Suggestions for sales department
 Strict rules and monitoring system deployment is suggested to
reduce the error
 Maximum number of errors per employee must smaller than 2
per month
 If it exceeds they must be punished.
 1st penalty is a verbal warning
 2nd Written warning
 3rd termination

SUMMARY OR CONCLUSION
 Company spends huge money for error repair costs.
 Most of the cost spent to the similar set of problem solving.
 A single department spent huge amount of money for error
repairing
 Suggested to conduct analysis & redefine the process and
operations,

RECOMMENDATIONS
 Automate & Refine business activities
 Outsourcing
 carry out make or buy decisions before taking decisions
 make decisions based on facts
 Always go with facts don't give room for emotions and other
kinds of stuff

REFERENCES
 Advanced visualization and visual analytics: general discussion.
(2014). Faraday Discuss., 169, 245-264. doi: 10.1039/c4fd90020k
 Dimauro, C., & P.P. Macciotta, N. (2010). A Tutorial for Microarray Data
Analysis with SAS-STAT Software. Current Bioinformatics, 5(2), 89-108.
doi: 10.2174/157489310791268441
 Gunst, R. (2003). Regression and ANOVA: An Integrated Approach Using
SAS Software. Technometrics, 45(2), 170-171. doi:
10.1198/tech.2003.s159
 Khademi, A. (2018). Complex Survey Data Analysis with SAS. Journal Of
Statistical Software, 84(Book Review 2). doi: 10.18637/jss.v084.b02
 Liu, W. (2010). Survival analysis using SAS software package. Journal Of
Chinese Integrative Medicine, 8(5), 489-493. doi:
10.3736/jcim20100515

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