Mantra for Process Excellence

Vignesh SwamiduraiAssistant ConsultantTATA Consultancy Services

Taming RTB using

Central Composite

Design for Process

ImprovementPMI National Conference – Mantra for Process Excellence

Table of Contents

1. THE BUSINESS CHALLENGE.......................................................................................................3

2. CENTRAL COMPOSITE DESIGN.................................................................................................4

3. INITIATION........................................................................................................................................5

4. APPLYING CCD TO TICKET OPTIMIZATION.............................................................................5

5. APPLYING CCD TO COST OPTIMISATION................................................................................8

6. SETTING UP THE GOALS FOR 2013..........................................................................................9

7. THE PATH TO OPTIMISATION...................................................................................................10

7.1 The Ticket Optimization Exercise.........................................................................................10

7.1.1 Ticket Reduction using Causal Analysis and Resolution Process Area.............................10

7.1.2 The Results –Ticket Optimization.......................................................................................11

7.2 The Cost Optimization Exercise............................................................................................12

7.2.1 Cost Optimization using Organizational Performance Management Process Area............12

7.2.2 The Results........................................................................................................................ 13

8. CONCLUSION................................................................................................................................14

9. ACKNOWLEDGEMENTS..............................................................................................................14

10. REFERENCES............................................................................................................................14

Taming RTB using Central Composite Design

ABSTRACT

The application of Central Composite Design (CCD) in Software estimation is an innovative approach to

determine the Optimum goals for a project. This combination of Statistical estimation and highly matured

process deployment can result in substantial benefits and reduction in operating cost for an Organization

and can be applied to any RTB project across any industry. CCD is a statistical methodology of identifying

the independent variables or factors that affect a product or process, and then studies their effects on a

dependent variable or response in order to find the optimum setting of factors. In this paper, the Authors

have evaluated the usage of CCD for Software Optimization Estimation to determine the optimum goals

for a project. As a case study for this analysis, the Authors have chosen an RTB project for a Retail

Banking Customer. The different project parameters required for optimal performance have been

assessed and these parameters have been applied to CCD model to arrive at optimized goals. The two

primary focus areas were Ticket Optimization and Cost Optimization. The optimum goals for the project

have been derived through research of historical data and applying them through the CCD model which is

explained in the paper. The following goals were set for the project for 2013 as an outcome of CCD and

these determined goals were achieved successfully by the project team.

• 50% reduction in the number of Production Failures / Resolution Days

• 0.5 Million USD total cost of application savings / Soft Dollar savings

Keywords: Process Improvement, Cost Savings, Ticket Management, Central Composite Design, CMMI,

Pareto, RTB

1. THE BUSINESS CHALLENGE

Businesses in the Industry vary daily due to market dynamics in the global economy. Organizations are

striving hard to cope up with the ever-growing business needs, in addition to facing challenges such as

slim operating margins, rapidly changing technologies, decreasing time to market and strong competition

with counterparts. Coping up with these increased dynamism, uncertainty and complexities are a huge

challenge faced by the organizations of the current era.

Organizations are looking for every possible opportunity to reduce expenses, conserve resources and

increase their stock prices. An ideal scenario expects the Run theBusiness (RTB) cost to remain constant

and at a minimum, thereby allowing organizations to concentrate more on Change the Business(CTB)

projects.

However in the current scenario, due to the market dynamics, the cost incurred by RTB is increasing

exponentially. Therefore the CTB cost has to be compromised, to keep the business running. This causes

a huge pressure to reduce the day-to-day operating cost to grow more efficiently. The ideal scenario and

current scenario sample is depicted in Fig.1 and Fig.2.

Figure 1: RTB Vs CTB - Ideal Scenario** Figure 2: RTB vs CTB–Current Scenario**

** - Graphs plotted with mocked data to show the distribution, does not include actual spread

To reduce the RTB cost, there is a need for a verified Optimization technique and a proven Management

methodology to achieve good results. This paper discusses about the modernistic approach of using a

well-established Statistical Optimization model – Central Composite Design (CCD) for Software Process

Optimization. The paper also describes the use of Management Art to achieve the set of determined

goals using the Scientific Optimization model in an RTB Project.

This innovative combination of statistical study and management principles has facilitated in reducing the

operating cost of RTB, thereby helping to invest more in CTB, which in turn benefits the organization to

stay competitive and Customer-focused.

2. CENTRAL COMPOSITE DESIGN

Central Composite Design (CCD) is an established design approach for Optimization, which is

predominantly used in Metallurgical and Pharmaceutical industries. It is a technique that revolves around

the study of the influence of different variables, based on the outcome of a process. It involves identifying

the independent variables or factors that affect a product or process, and then studies their effects on a

dependent variable or response. CCD can also be treated as an enabler, to find optimum setting of

factors.

The implementation of CCD involves identifying the following three key factors:

1) A Factorial Design in parameters, each having two different coding levels.

2) A set of Centre Point for each parameter whose values is a median of levels identified in the

Factorial design

3) A set of Axial Points, which considers values that are below and above the median of Factorial

Design Points.

To use CCD, a controlled environment is required. A Production environment is an ideal fit for such a

controlled environment, because the Production environment is a setting where the reliable software and

robust hardware configuration are available for commercial daily operations of applications. Hence, it

becomes easier to vary the required set of parameters and realize the results. In addition, RTB projects

have historical data and can be easily tapped to be used for CCD analysis.

In a conventional CCD, a set of controlled experiments is performed with identified factors. However, in

this case, some re-computation was performed with the historical data, to arrive at the results of a

process. For this purpose, Data Mining was performed for each category of Optimization and the factors

were determined. Using the factors determined, the Quadratic expression of CCD is used to obtain the

Optimum goal point.

3. INITIATION

CIOs find it challenging to mandate the reduction of the RTB cost, as much as possible and invest the

savings benefit into strategic CTB projects. Increased complexities with RTB cannot be managed easily

using legacy management principles and skills. As a result, the Authors of this paper have chosen the

approach of CCD, which is a proven statistical model for Optimization.

For CCD analysis, the Authors chose an RTB project for a Retail Banking Customer. To start with, the

focus areas have to be set for the project. The basic requisite for an RTB project is the stability of

applications and reduction of tickets. Based on the applications’ performance in the past, complexity of

applications and the nature of work, the following two primary focus areas were determined:

Ticket Optimization

Cost Optimization

The following are the limitations of the current setting in the project:

Cost budget is limited

Number of FTE is constrained

The two Optimization areas were considered for investigation with CCD technique to obtain optimum

levels that can be set as a goal for the year 2013.

4. APPLYING CCD TO TICKET OPTIMIZATION

Being an RTB project, the priority of the project primarily lies on ticket reduction. Tickets are created,

whenever there are any Production failures (known as Abends or Abnormal End in Mainframe).

Historical data from 2012 was collected for tickets that occurred during 2012, and they were scrutinized

for factors contributing to them. There are three primary factors that affect the numbers of tickets (from

the year 2012):

Number of Production failures(Abends) in a month

Recovery time for each Production failure1

Resolution time for each Production failure2

These factors were fed to the CCD technique, to get an optimum value of tickets that could be fixed with

current base resources in the year 2013. Number of resources available to provide break-fix installs and

baseline hours were almost the same for 2012 and 2013 and hence this factor was a constant. The

Authors used a standard CCD with five center points and alpha value of 1. The corresponding factor

properties were updated within the CCD model and each independent variable had three levels assigned

to it (– 1, 0 and +1), based on a predetermined range of tickets or time and categories.

Table 1: CCD Variable – Coded Level Matrix

Independent variables

Coded levels

-1 0 1

Top 5 categories 80 100 120

Time/abend (mins) 35 50 65

Time/resolution (days) 80 100 120

The study was carried out, according to the CCD, and the experimental points were used, based on data

points from 2012. The regression coefficients for the second order polynomial equations and results for

the linear, quadratic and interaction term are presented in Table 2.

1 – Recovery time of a ticket is the time taken to restore service after an incident has occurred. This may be a temporary solution or work-around to restore the Service

2- Resolution time of a ticket time taken to permanently fix the ticket, so that reoccurrence will not happen again.

Table 2: Variation of Parameters for Optimum Point

S.NO

Top 5

categories

Time/abend

(mins)

Time/resolution

(days)

No of tickets

that can be

resolved

1 80 38 88 51.11

2 83 45 80 53.3

3 120 56 93 50.09

4 105 35 99 52.2

5 119 55 119 48.33

6 114 60 120 52.11

7 103 65 111 49.6

8 109 37 86 48

9 94 44 94 38.88

10 117 35 90 49.3

11 96 39 86 49.09

12 112 49 90 49.7

GRAND

TOTAL 1252 1156 591.71

The second order polynomial formula for CCD was used, to arrive at the number of tickets that can be

resolved.

y = β0 + β1x1 + β2 x2 + β12x1x2 + β11x12 + β22x2

2

The contour and quadratic formula result indicates that the maximum benefit occurs when an average

(591.71/1252) tickets can be reduced. As the average is 47.26, 50% ticket reduction is considered, as a

result from the Optimization. The predicted maximum achievable result will be 50% ticket reduction in

2013, out of the total tickets from 2012.

5. APPLYING CCD TO COST OPTIMISATION

The major factor for the increasing RTB spend is the total cost of applications. By effectively managing

this cost, a good savings can be achieved, which can be turned into a profit for the organization.

The four major factors that impact the Cost Optimization are as follows:

Number of Production failures(Abends) in a month

Recovery time for each Production failure

Resolution time for each Production failure

Effort per cost saving task

These factors were introduced to the CCD technique to obtain an optimum Cost savings that can be

obtained in the year 2013, with current resources being the same during 2012 and 2013. The

corresponding factor properties were updated within the CCD model and each independent variable had

3 levels assigned which were – 1, 0 and +1 based on a predetermined range and categories.

Table 3: CCD Variable – Coded Level Matrix

Independent variables Coded levels

  -1 0 1

Number of Production Failures in

a month 80 100 120

Recovery Time per failure (hours) 35 50 65

Resolution Time per failure (days) 80 100 120

Effort per cost saving task 100 200 300

The study was carried out according to the central composite design and the experimental points were

used from project reports for 2012.The regression coefficients for the second order polynomial equations

and results for the linear, quadratic and interaction term are presented in below table.

Table 4: Variation of Parameters for Optimum Point

S. No

# of Prod

Failures /

month

Recovery

time / failure

(mins)

Resolution

Time / failure

(days)

Effort per

cost

saving

task

(hours)

Cost Savings (in

MM USD)

1 80 38 88 256 0.8

2 83 45 80 210.03 0.63

3 120 56 93 190 0.45

4 105 35 99 188 0.41

5 119 55 119 189.11 0.37

6 114 60 120 188.78 0.309

7 103 65 111 280 0.965

8 109 37 86 210 0.699

9 94 44 94 194 0.501

10 117 35 90 193.67 0.58

11 96 39 86 189 0.49

12 112 49 90 190.9 0.51

Grand

Total 6.714

Applying the second order polynomial equation to this data, the Cost Savings in MM USD was obtained:

y = β0 + β1x1 + β2 x2 + β12x1x2 + β11x12 + β22x2

2

The contour and quadratic formula result that the maximum Cost Savings obtained by the team could be

near 6.714 MM /12 = 0.56 MM. The determined achievable Savings Goal would be in this case as 0.5

Million USD in 2013.

6. SETTING UP THE GOALS FOR 2013

Based on the results from CCD Approach and after internal brainstorming within the teams, the following

goals were set for 2013:

50% reduction in number of tickets from Year 2012  

0.5 Million USD Total Cost of Application Savings / Hard Dollar Savings

7. THE PATH TO OPTIMISATION

After the goals were identified, the next step was to identify a good management approach to achieve

them. Based on constraints on the problem, various structured frameworks are available for solving

business problems and effective process execution. The Authors of the paper have chosen the well-

established and accepted model for implementation of goals – CMMi framework. TCS being at CMMI

Level 5, both the goals were targeted to be implemented with CMMI Level 5 areas for Support and

Process Management.

7.1 The Ticket Optimization Exercise

For the Ticket Optimization exercise, the authors chose the Causal Analysis and Resolution (CAR)

Process area of CMMI Level 5 model. As per this process area, the root causes of historical tickets from

2012 wereanalyzed systematically using Pareto Chart. Once the Causal Analysis for the occurrence of

the tickets was determined, they were systematically addressed to prevent their reoccurrence.

7.1.1 Ticket Reduction using Causal Analysis and Resolution Process Area

Based on the CCD Model described in previous sections, the goal for 2013 was set as 50% Reduction in

the number of tickets from previous year. The Ticket Reduction Exercise was done in 2 phases as per the

Specific goals outlined in CMMI Level 5 CAR Process area as below:

Phase I : Measure and Perform Causal Analysis

In this phase, the historical data of 2012 tickets were analyzed and causal analysis was done for each of

them. After this, similar causes were grouped together and Pareto principle was used to prioritize the

deployment actions.

Figure 3: The Pareto Analysis for Root Cause for 2012 Tickets

7.1.2 The Results –Ticket Optimization

Once the prioritized causes were identified, the next step was to implement the action proposals to avoid

the occurrence. For this, the second specific goal statement of CMMI Level 5 CAR Process area was

followed:

Phase II :Prioritize and Deploy action proposals

For causal analysis and resolution tracking, a new tracking approach, Ticket Tracking Sheet, was

introduced to keep a track of all tickets and causal analysis for each ticket. On analyzing the tickets, it was

seen that Top 3 causes attribute to 80% of the tickets. By fixing the Top 3 Causes, more than 50%

reduction of tickets could be achieved.

Proactive approach was followed to mitigate all the top 3 root causes. Automated tools were introduced to

track frequently occurring problems. Supplementary automated monitoring systems were introduced to all

critical interfaces to avoid data issues and thereby reduce mass incidents. False alert Optimization was

done to make sure that tickets are created only when there is really an issue in the Applications. This also

reduced the manual intervention on delays and false notifications. Bottom line of this exercise was "Every

ticket should have a follow up or action item" thereby to nail down the root cause at the first occurrence of

an issue. Meticulous reviews were conducted when a change was installed in Production from the

Development team, to reduce the issues arising due to Integrated Releases.

Figure 4: Ticket Reduction – Actual Vs Target

Due to quicker Root Cause Analysis, number of tickets reduced leading to quicker resolution for the

tickets and thereby leading to tasks getting completed at a faster pace. All of this contributed to more time

available for working on Value Additions. This leads to a Cyclic Effect, due to which the applications were

optimized to the maximum.

Figure 6: The CYCLIC Effect

7.2 The Cost Optimization Exercise

7.2.1 Cost Optimization using Organizational Performance Management Process Area

The purpose of Organization Performance Management (OPM) Process Area of CMMI Level 5 is to

manage the organization’s performance and meet the business objectives in a proactive manner. This

process area follows a very well defined sequence of procedural steps that can be used to realize a goal

to improve the Organization’s performance. The specific goals of this process area and how they were

realized is given below:

Phase I : Identify the Scope and areas for Process Improvement

In this phase, the scope of the problem is defined and the SME’s from the teams defined which

applications have opportunities to start with for Cost Optimization.

Phase II : Review and Validation of Selected improvements

In this phase, the current state of the problem is reviewed, all the relevant information is studied and to

identify if there are any bottle-necks for implementation. During this phase, the Proposals are submitted to

the Performance Management team and reviewed with them to identify the cost saving opportunity. The

estimated savings for the task is identified in this stage.

Some of the opportunities identified and proposed to the Performance Management team are as follows:

Deletion of unused datasets in Production regions

Purging of Obsolete data in tables

Replace Full updates with Delta Updates in Tables

Usage of new Utilities and newer version of software to reduce the cost

Program changes which will bring about Cost savings

Cost-Benefit analysis is done based on the benefits yielded by the improvement against the effort spent

on deployment. After this, the improvement is taken forward to the next phase.

Phase III : Verify, Execute and Evaluate the benefits

In this phase, the identified opportunities are prioritized and a draft roadmap is outlined. The required

changes to enable and sustain the improvements are done in this phase. The implementations of

identified opportunities are done and the actual cost savings are calculated and submitted to the

Customer for feedback. The feedback is collected and the cost savings are approved by the Customer.

7.2.2 The Results

As a result of the steps outlined by the Organization Performance Management Process Area of CMMI

Level 5, teams worked on different Process Improvement tasks in the year 2013. These tasks have

yielded good results and yielded substantial savings to the Customer.

Figure 8: The Cost Savings Achieved in 2013

8. CONCLUSION

Thus the CCD Optimization Technique is a good fit for Software Optimization Estimation also. This

technique can be used for any RTB project which has historic data. Using the CCD, realistic goals can be

determined. Skillful planning and meticulous approach to the determined goals can result in substantial

benefits and can reduce the Operational cost. This will balance the RTB and CTB cost and the

Organization can spend more in Strategic CTB projects.

9. ACKNOWLEDGEMENTS

We would like to sincerely thank Kannan Balamurugan (Delivery Head) for providing valuable inputs

about Central Composite Design and for reviewing the paper and providing constructive feedback which

helped us to keep progressing. Our thanks to Sivashankar Hariharan (Delivery Manager), for providing

his inputs on Management techniques in Process Improvement and also initiating the idea for the paper.

We would also like to thank Rejo Reghunadh(Senior Business Analyst) for his contribution towards the

project data analytics.

10. REFERENCES

[1] Myers, Raymond H. Response Surface Methodology. Boston: Allyn and Bacon, Inc., 1971

[2] Koch, Richard – The 80/20 Principle – The Secret to Success by achieving more with less, 1998

[3] N.S. Sreenivasan, V.Narayana – Continual Improvement Process, 2005

[4] Carnegie Mellon Software Engineering Institute, “The CMMI Version 1.2 Overview presentation 2007.

http://www.sei.cmu.edu/library/assets/cmmi-overview071.pdf

[5] CMMI for Development, “CMMI-DEV V1.3,” Technical Report, Software Engineering Institute,

Pittsburgh, 2010.