Attrition Analytics 151

Attrition Analytics-A MarkovAnalysis Attempt for Attrition-ratePrediction and StabilizationWHITE PAPER

Author : Suvro Raychaudhuri

Wipro TechnologiesInnovat ive Solut ions, Qual i ty Leadership

In a competitive arena, the advantage is taken by the first-mover – and for an environmentwhere Seth and Sisodia’s The Rule-Of-Three predominates, it is not just the first mover, butthe fast -mover who has it all.Every organisation, no matter how stable its quality and people processes, are bound to fallprey to the silent warfare of the fast-movers – which I would prefer to call Corporate SitzKrieg1 ;and Hertzberg’s “Satisfiers” are today’s HR nightmare – because nothing seems to work!

Thus today, HR as a strategic partner in any organisation has lots to do in terms of metrics,HR analytics, prediction of trends and quantifying Human Capital measures.Since attrition is one of the main problems for any organisation struggling to retain itsexpertise and knowledge base, an analytical approach to the same would also help inprediction and necessary remedies.

This paper aims to draw on the recent HR trend of referring to the employee as an “internalcustomer” and therefore assumes that manpower attrition is similar to customer switchingproblems in case of products, thus has used Markov Analysis as an Operations Researchtechnique to predict attrition, and therefore form a basis for manpower planning.

This white paper is aimed at a greater scope of having more thought provoking ideas in theHR Analytics arena and within its limited scope here, suggests an OR model as part ofmanpower inventory planning in general.

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Table of Contents

Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization

INTRODUCTION .................................................................................................... 3

THE KNOWLEDGE-HARVEST .............................................................................. 4

WHAT WE REALLY LOSE..................................................................................... 5

WHAT OTHERS ARE DOING ................................................................................ 7

THE VALIDITY OF ATTRITION DATA .................................................................... 8

THE MARKOV ANALYSIS ................................................................................... 10

CONCLUSION ..................................................................................................... 13

RELEVANT LINK ................................................................................................. 14

REFERENCES ..................................................................................................... 14

ABOUT THE AUTHOR ......................................................................................... 14

ABOUT WIPRO TECHNOLOGIES....................................................................... 15

WIPRO IN COLLABORATION AND KNOWLEDGE MANAGEMENT ................... 15

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Introduction

The Attrition Warfare

One of the greatest strategies of War had been the strategy of attrition warfare, defined inmilitary terms as “a strategy of warfare that pursues victory through the cumulative destructionof the enemy’s material assets by superior firepower.”Metrics like body counts and terrain captured measure the progress of battle. On the oppositeend of the spectrum is maneuver warfare. All warfare involves both maneuver and attrition insome mix. The predominant style depends on a variety of factors such as the overall situation,the nature of the enemy and most importantly, on attackers’ capabilities.

Though this paper deals with attrition with respect to the War for Talent in Corporate arena,the strategy involved is the same – and even the terminologies quite similar – if “body count”can be a parameter to measure effectiveness of attrition warfare, then in corporate recruitmentstrategies the similar parameter would perhaps be “acceptance to offer ratio” (from theattacker’s perspective).

The main point here is, that today, Human Resource professionals are under increasedpressure from a different kind of a Corporate Sitzkrieg – the silent firepower of attrition whichcauses no less harm to Human capital assets, as compared to “the enemy’s materialassets” as in the definition above.The concept of applying warfare terminologies has been an age-old concept amongstmarketers – and human resource professionals are coming to terms with such terminologylike strategic human resource management, and the employee as the “internal customer”as per the marketing concepts – this has something to do with the changing scenario of acompetitive environment, where strategies no longer are framed at the top, but evolves out ofthe environment, cascading through the entire organization and demanding concrete actionplans. The concept of what has been stated above can be put into a simple model as shownbelow. (fig1.)

Strategy

Technology Structure People

Culture

Organization

fig1

Environment


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The pressure of competition from the environment and the evolution of strategy are self-explanatory in the above figure. The point to note here is the extent of the impact, whichinvolves hitherto soft issues like culture and people, and this is the origin of strategic humanresource focus, the war for talent and the need to garrison the human resource capital asone of the strategic parameters.

The Knowledge-Harvest

APQC (American Productivity and Quality Centre) has made several recommendationsto raise awareness of the problem of knowledge attrition, which include

1. Identifying a burning platform or issue related to knowledge loss2. Looking for windows of opportunity through champions who are willing to try out

knowledge retention approaches.

AQPC has categorized three knowledge types that are under attack through attrition.

This includes

1. Cultural knowledge – This includes management practices, values, respect forhierarchy, and decision flows.

2. Historical knowledge – this includes the organization’s journey from the day it wasfounded till the present

3. Functional knowledge – this includes technical, operational, process and clientinformation

A more careful look at figure 1 indicates that there seems to be some good amount ofconvergence with respect to AQPC’s definition of the three types of knowledge and themodel given in figure 1 – particularly the fact that corporate attrition warfare is all aboutgaining (through head-hunting, strategic recruiting, internal job offers, etc) human assets,who bring along with them the three kinds of knowledge, and thereby attack the verystrategic base of the organization.

Thus from the attacker’s point of view, depending on which type of knowledge it needsform the competitor, the recruitment strategies are also sorted out accordingly. It is evidenttherefore, that attrition rate among junior employees (2-4 yrs) would be higher for thefunctional knowledge part – associated with technical and operational processes.At higher levels, the attrition warfare would be more for gaining historical knowledge(business portfolio changes down the years, etc) and cultural knowledge from thecompetitors.

From the organization’s point of view, the counter strategy is to predict attrition “zones”which depend on the criticality or type of knowledge that is at important to the organization,and thereby evolve plans to counter loss of human assets from those positions.

Once we realize this, the next step is to come out with concrete plans to prevent attrition,which can only be forecast using data and trends available. Some of the world’s bestpractice organizations have tried capturing data to predict attrition on the long run, anddone that in different ways.


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What We Really Lose

Attrition and Knowledge Management – Loss of Historicaland Cultural knowledge

From the attackers’ perspective, one of the parameters to measure effectiveness of corporateattrition warfare might be “acceptance to offer” ratios. But from the perspective of theorganization that has to cope up with this ever-growing problem, the problems associatedare larger.Attrition is a pain area in any organization that intends to have a knowledge managementsystem in place. In a famous article1 , attrition (through normal retirement or throughresignations) has been discussed as one of the pain areas in the field of KM, becausevacancy of a position might be easier to fill in through the proper people-sourcing approaches,but filling in the knowledge gap is not. This is particularly in context of a tough economywhere the concept of all-size-fits-all is no longer working, and vacancy of a position byattrition is basically vacancy of a knowledge-base, and this vacancy in knowledge basecannot be filled in by any person.

This is precisely what is referred to as tacit knowledge, which most organisations today aregrappling to capture and retain. This closely pertains to what AQPC referred to as the Culturaland Historical knowledge, in addition to the Individual or Proprietary knowledge that goes offwithout being codified and migratory, and therefore is never assimilated in the organisationas invisible knowledge. This can be exemplified better through the typical knowledge-cycleof an organisation as shown below, originally by Takeuchi and Nonaka.

The problem can be aptly stated through examples from the corporate world itself –

Corning, which had been experiencing knowledge loss through the large scale retirementsthrough 1990’s estimated that it lost around 2000 years of cumulative years of experience asa result of a retirement package offered in 1998 – and this exemplifies loss of knowledgedue to planned retirements alone – here we are talking of corporate SitzKrieg, where anemployee may walk into the office any morning to place his resignation letter and walk offwith the competitor – not just creating a vacancy, but taking some of the most vital knowledgequantum from the company to it’s competitor.

Codifiedknowledge

Migratoryknowledge

Invisibleknowledge

Discoveredknowledge

Attrition event

PROPRIETARY SHARED

EXPLICIT

TACIT


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However, organisations even with established knowledge management practices have notbeen able to come up with any substantial measure to check this knowledge loss, andtherefore an indicator of failure in capturing tacit knowledge bases.

Attrition and Call-centers - Loss of Functional Knowledge

The problem is more acute depending on the industry and the demographics of theemployees too, as in call centres. Here the knowledge drain is at a different level, and itcorresponds more to AQPC’s definition of functional knowledge.

Though it is a known fact that high turnover rates drain the cost effectiveness of call centres,unfortunately little is being done about it.In the article “Reducing Call Centre Turnover”1 , managers in call-centres normally tend tolook only at advertising costs, interviewing and training costs etc, but overlook the vital costsassociated with attrition.Merrill Lynch attempted to find out costs associated with call-centre attrition – which cameout to be around $9m per annum for a company with 1000 employees, and annual revenueof $100m.

This shows that retention alone can significantly bring up the bottom-line for a call-centre.

Organizations tend to spend huge sums of money on recruitment, for web-postings, jobfairs, ads, employee referral bonuses, etc, and end up with 50% employees leaving beforereaching any level of proficiency.

Proper testing and screening, training, introduction of the apprenticeship scheme, aptitudetesting (10%), realistic job previews (8%), structured behavioral interviews (3%) can helpprevent attrition by percentages shown in parenthesis.

According to the Forum Group, 65% of the external customers leave due to internal reasonsalone (45% for poor service quality, 20% due to lack of attention) – thus internal attrition candevastate call-centre effectiveness if not tackled properly.

Shown in the table below are the typical turnover rates of call centres.2


MEDIAN (%) AVERAGE (%) HIGHEST (%)

Part time inbound 20 33.6 300

Full time inbound 19 26 252

Part time outbound 15 35.5 480

Full time outbound 10 21.3 210

TABLE1

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What Others Are Doing

Organizations across the world and operating in different industry segments have tried tofind out means to measure business loss through attrition.

Schlumberger, for example, understands how important it is to link its knowledge sharingtechniques with its HR processes: the oil industry faces an attrition rate of 44% by 2010.1

Pfizer also takes preventive measures to combat knowledge-drain and promote betterknowledge transfer through its six-step knowledge retention process.

Best practice companies, according to AQPC, should conduct a thorough audit to determinewhat knowledge is worth capturing. Stated in another way, this would also indicate the“critical positions” in the organization, which can create a substantial problem to the companyincase it is vacated under competitor attack.The table below shows the practices that are followed by these organizations to collect datarelated to attrition:2

The importance for including the various ways companies worldwide are collecting data onattrition would be clearer in the subsequent sections.

A Hay Group survey1 reveals that what people want most is to feel that their careers aremoving forward.In their survey, “The retention dilemma: Why productive workers leave and seven suggestionsfor keeping them”, reveals that employees leave because of disillusionment of the companymanagement’s direction, and because of under-utilization.Two of the seven things Hay Group identified as “attrition-preventing” are clearly related totraining –

1. Measurement of soft skills – because gaps exist when the companies say theyvalue their people, and do something else

2. Fight attrition with smart training – taking a longer term perspective in training anddevelopment as a retention tool.


Siemens Corning World Northrop Xerox BestBank Grumman Connect Buy

Internal networks Y Y Y Y Y

Interviews Y Y

Videotaping Y Y Y

SME directory Y Y Y Y

Repositories Y Y Y Y Y

After action project Y Y

milestone reviews

Mentoring programme Y Y

Knowledge maps Y Y Y Y Y

Recruiting strategy Y Y Y Y

Retention strategy Y Y Y

TABLE2

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The relationship between job satisfaction and attrition as surveyed by Hay Group is shownas follows:

Satisfaction with Total percent satisfied Gap

Employees planning Employees planningto stay for >2Yrs (%) to leave in <2Yrs (%)

Use of my skills and abilities 83 49 34

Ability of top management 74 41 33

Company has clear sense 57 27 30

of direction

Advancement opportunities 50 22 28

Opportunity to learn new skills 66 38 28

Coaching and counseling 54 26 28

from one’s own supervisor

Training 54 36 18

Pay 51 25 26

However, few organisations have been able to tackle attrition in spite of using various typesof data-gathering instruments as shown in table 2.

Thus the problem is perhaps somewhere else.

The Validity of Attrition Data

In order to understand this, it is important to question the very validity of the data that is givenby the employees – it is only common sense that an employee would not reveal the correctreason for leaving the company at some point of time – thus any action taken by theorganization to prevent attrition by altering the factors as mentioned above does not have anyeffect, because perhaps the data itself is not valid.

The problem of the validity of the data from an attrition survey –The Social Exchange Theory8

We have seen above, that inspite of a great number of efforts, and the availability of a numberof instruments for collecting reasons as to why people are leaving, an organization is reallynot being able to do much about attrition – the primary reason of this could be the validity ofthe data.As to why employees would not/might not give the correct response to an attrition surveystems from the social exchange theory (Dillman, 1978). According to this, there is a socialexchange between the survey interviewer, who desires information possessed by therespondent, and the respondent, who decides how much information to convey. Dillmanposits that the respondent participates because the act of participation is expected to bringrewards that exceed the cost of participation. These rewards might include monetary payment,but more importantly would include intangible rewards that, to some extent, can be influencedby the design and implementation of the survey.

TABLE3


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Dillman argues that the willingness of an individual to participate in a survey dependscritically on the degree of trust that the expected terms of the social exchange describedabove will be fulfilled.

The social exchange model described above can be translated into an economic modeland, in its translated form, can be used to help generate some empirically testable hypothesesabout the determinants of survey participation, validity of the response and the data.

This paper only outlines the theory, leaving it for future scope of research on the subject.

According to social exchange theory, the individual’s willingness to participate in a surveydepends on a comparison of the benefits and costs of participation to him. Let the individual’sutility function be given by

URit = UR (Lit, Yit) + Eit …………………………………………(1)

where

UR (Lit, Yit) is the utility the individual receives from leisure, Lit, and income, Yit.Eit is the psychic value the respondent expects to experience by participating in the interview,Eit = 0 if the individual does not participate.

The individual’s money budget is

Yit =Vit + wit Hit + pit ……………………………………………(2)

whereVit is nonlabor income,wit is the market wage rate,Hit are hours of workPit is a respondent payment for participation in the tth wave of the survey.

The individual’s time budget,

T = Hit + Lit + lit, ………………………………………………………..(3)

is the sum of hours of work, hours of leisure, and time spent on the interview.

The individual obviously chooses his labor supply independently of the survey interview bymaximizing Equation (1) subject to Equations (2), (3) and Eit = lit = Pit = 02. This choice isdescribed by the labor supply function Hit = H(wit, Vit). Substituting the labor supply functionand the time and money budgets into Equation (1), the individual’s utility function is given by

UitR = UR [T - Hit(wit,Vit) - lit, Vit + wit Hit(wit, Vit) + pit] + Eit................(4)

Treating lit as a marginal loss of leisure and pit as a marginal gain in income, the net utilitygain, or loss from participation in the survey is given by

�U = -ULlit + UYpit + Eit

= (-witlit + pit)UY + Eit. ……………………………………………..(5)where


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UL is the marginal utility of leisure,UY is the marginal utility of incomewit = UL/UY is the shadow price of time in nonmarket uses which is equal to the market wagerate if the individual is working in the labor force.

The individual will participate in wave t of the survey if the rewards from doing so outweighthe costs according to the decision rule

Participate if Eit/UY > witlit - pit; otherwise, refuse ……………….(6)

Where Eit/UY is the monetary value of the psychic costs and rewards of the survey experience– the problem here being, that a person who is leaving an organization wants neither psychicutility nor rewards, and thereby his perceived-utility is low, therefore he is under no obligationto respond correctly/accurately to attrition surveys.

The Markov AnalysisOne of the most recent trends in HR is treating the employees as internal customers.Though Marketers won’t converge on the benefits of such a trend because that causessome confusion between external and internal customers and strategies, the main advantagehere is that enables a large number of strategies to be developed.

If we can consider employees as internal customers, then the next step is to considerattrition as a customer-switching problem – and once we can do that, attrition rate predictionmay be dealt with similarly as in customer switching problem in case of marketing.

The solution proposed here is the application of Markov analysis to customer switchingproblems – clearly stated, a Markov analysis to find out the attrition rate and prediction of itsstability within time period t, which would give HR people a relevant input in terms of theirmanpower planning and recruitments.

A Markov chain is a random process for which the future depends only on the present state;it has no memory of how the present state was reached. This simplifying assumption leadsto a family of systems having a mathematical theory, as well as many applications to modelingin more applied science. A central property of ‘nice’ Markov chains is that they settle downinto a (stochastic) equilibrium.

The basic method for solving this is to construct the transition probability matrix, which takesin attrition probability data by using instruments as mentioned in the TABLE2. The validity ofthe output would depend on the validity of this probability, which is a problem area, becauseof the inaccuracy of responses as mentioned in the previous section.

Here I propose to exemplify the construction of the transition-probability matrix as under:

In analyzing switching between companies, the reason for attrition, the organization needsto have data that is needed to form the transition probability matrix.As an example laid down below, say the probability that the employee stays in the organizationis 0.95. The corresponding probabilities of his/her switching to competitor companies 2, 3,and 4 are say 0.02, 0.02 and 0.01 respectively. The other figures put in the example are self-explanatory.


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Thus we construct our probability matrix as follows:

To company 1 2 3 4From comp 1 | 0.95 0.02 0.02 0.01 2 | 0.05 0.90 0.02 0.03 3 | 0.10 0.05 0.83 0.02 4 | 0.13 0.13 0.02 0.72

Say for the present time, say this month, the probability of switching to companies 2, 3, 4 are23%, 20% and 12%, and for staying in the company itself is 45%.

[The probability is calculated on various parameters that evoke switching, for example,competitors’ pay, work environment, perks, etc]

The Solution

Assumptions

1. While exemplifying through the matrix, it has been assumed that the strategic sourcinggroup of the organization aims to have a 75% target of the probability of employeeswanting to remain, that is, around 25% attrition rate.

2. The basic assumption of Markov analysis is also applied here, that the process is astochastic one, whereby any event would only depend on the preceding event, and nothingelse.

We have the initial system state s1 given by s1 = [0.45, 0.23, 0.20, 0.12] and the transitionmatrix P given by

P = | 0.95 0.02 0.02 0.01 | | 0.05 0.90 0.02 0.03 | | 0.10 0.05 0.83 0.02 | | 0.13 0.13 0.02 0.72 |

Hence after one month has elapsed the state of the system s2 = s1P = [0.4746, 0.2416,0.1820, 0.1018] and so after two months have elapsed the state of the system = s3 = s2P =[0.494384, 0.249266, 0.16742, 0.08893] and of course the elements of s2 and s3 add to one(as required).

[Please note that any since we are utilizing the Markov analysis process, which is a stochasticchain, any event therein would follow only from the event preceding it – thus s2 = s1 x P,and so on.]

Hence the employee demand elapsed after two months are 49.44%, 24.93%, 16.74% and8.89% for companies 1, 2, 3 and 4 respectively.

Assuming that in the long-run the system reaches equilibrium [x1, x2, x3, x4] where

[x1, x2, x3, x4] = [x1, x2, x3, x4]P and x1 + x2 + x3 + x4 = 1


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we have that

x1 = 0.95x1 + 0.05x2 + 0.10x3 + 0.13x4

x2 = 0.02x1 + 0.90x2 + 0.05x3 + 0.13x4

x3 = 0.02x1 + 0.02x2 + 0.83x3 + 0.02x4

x4 = 0.01x1 + 0.03x2 + 0.02x3 + 0.72x4

x1 + x2 + x3 + x4 = 1

Rearranging we get

0.05x1 = 0.05x2 + 0.10x3 + 0.13x4 (1)0.10x2 = 0.02x1 + 0.05x3 + 0.13x4 (2)0.17x3 = 0.02x1 + 0.02x2 + 0.02x4 (3)0.28x4 = 0.01x1 + 0.03x2 + 0.02x3 (4)x1 + x2 + x3 + x4 = 1 (5)

Now from equation (3) we have

0.17x3 = 0.02(x1 + x2 + x4)

and from equation (5) we have

x1 + x2 + x4 = 1 - x3

Hence

0.17x3 = 0.02(1-x3)i.e. 0.19x3 = 0.02i.e. x3 = (0.2/0.19) = 0.10526

Now subtracting equation (2) from equation (1) we get0.05x1 - 0.10x2 = 0.05x2 + 0.10x3 - 0.02x1 - 0.05x3

i.e. 0.07x1 - 0.15x2 = 0.05x3 (6)

Also substituting for x4 from equation (5) in equation (4) we have0.28(1 - x1 - x2 - x3) = 0.01x1 + 0.03x2 + 0.02x3

i.e. 0.28 = 0.29x1 + 0.31x2 + 0.30x3

i.e. 0.29x1 + 0.31x2 = 0.28 - 0.30x3 (7)

Multiplying equation (6) by 0.31 and equation (7) by 0.15 and adding we get

(0.31)(0.07)x1 + (0.15)(0.29)x1 = (0.31)(0.05)x3 + (0.15)(0.28) - (0.15)(0.30)x3

and since we know x3 = 0.10526 we have x1 = 0.59655

Hence from equation (6) we find that x2 = 0.24330 and from equation (5) that x4 = 0.05489

As a check we have that these values for x1, x2, x3 and x4 satisfy equations (1) - (5) (to withinrounding errors). Hence the long-run employee demands for the companies are 59.66%,24.33%, 10.53% and 5.49% for companies 1, 2, 3 and 4 respectively.

We need a long-run system state of [0.75, x2, x3, x4] where x2, x3 and x4 are unknown (but sumto 0.25) and we have a transition matrix given by


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P =| p1 p2 p3 p4 || 0.05 0.90 0.02 0.03 || 0.10 0.05 0.83 0.02 || 0.13 0.13 0.02 0.72 |

where p1, p2, p3 and p4 are unknown (but sum to one).

Hence using the equation[0.75, x2, x3, x4] = [0.75, x2, x3, x4]P

we have the equations0.75 = 0.75p1 + 0.05x2 + 0.10x3 + 0.13x4

x2 = 0.75p2 + 0.90x2 + 0.05x3 + 0.13x4

x3 = 0.75p3 + 0.02x2 + 0.83x3 + 0.02x4

x4 = 0.75p4 + 0.03x2 + 0.02x3 + 0.72x4

Together with x2 + x3 + x4 = 0.25 ; p1 + p2 + p3 + p4 = 1

Here we have six equations in seven unknowns and so to solve we need an appropriateobjective. In order to avoid having to change the transition probabilities too much a suitableobjective would be

Maximize p1

I.e. find the largest value for the transition probability from company 1 to itself such that therecruiter achieves the long-run employee demand of 75%.

Conclusion

The above approach through a Markov analysis is a proposed model. This model may befollowed and can be mapped to a much more complex data through the construction and thesolving of the probability matrix through a mathematical tool. The objective of the paper wasto propose a quantitative way to predict attrition rate in any industry and therefore take thenecessary steps to prevent it, or plan the manpower inventory accordingly.

Companies should project retirements and attrition over the next five years. List the internaland external forces that can contribute to the problem. Then take the worst-case scenario.The main approach to preventing attrition should be grooming leaders, rather than justtreating employees the way it is normally done.In fact, the companies with leading-edge retention programs address all the areas mentionedbelow. According to International Data Corp.’s1 guru on resourcing strategies, Michael Boyd,program elements can include the following:

• Ongoing education and training.• A mix of job assignments.• The organization of small groups and teams.• Peer group and mentoring programs.


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• Organized career counseling.• Flextime and other lifestyle benefits, including on-site day care, fitness clubs and

sponsored charity work.• Internal marketing and communication with employees.But in case nothing works, the best way is to predict it and act accordingly. Thus predictionbecomes vital.

Relevant Linkwww.wipro.com/b2e/i-desk

References

1 The German Word for “Propaganda” or “Silent Warfare”.

2 “Why attrition is a chance to prove the value of KM”, KM Review Briefings, Vol6, Issue1,March/April 2003

3 Drew Robb, Customer Interface March 2002 Issue, P-34,35

4 Purdue University Centre for customer driven quality

5 “Proactive strategies to combat attrition”, Rowan Wilson and Jennifer Wilson, KM Review,Vol 4, Issue 6, Jan/Feb2002

6 “Why attrition is a chance to prove the value of KM” KM Review Briefings, Volume 6 Issue1 March/April 2003, P-10.

7 “Hay Group Study Identifies Training as One of Top 7 Employee Attrition Fighters” IOMA’sreport on managing training & development, April 2002 issue, P-13

8 REDUCING PANEL ATTRITION , By: Hill, Daniel H., Willis, Robert J., Journal of HumanResources, 0022166X, Summer2001, Vol. 36, Issue 3.

9 TO KEEP YOUR BEST IT PEOPLE, KEEP THEM LEARNING , By: Gantz, John,Computerworld, 00104841, 7/3/2000, Vol. 34, Issue 27

About the Author

Suvro Raychaudhuri is working as an HR Process Consultant with i-Desk. Presently he isinvolved with e-HR initiatives of I-desk, in the capacity of a domain consultant. He holds aDegree in Mechanical Engineering and is a Post-Graduate in Personnel Management andIndustrial Relations from one of the premier Business Schools in India.


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About Wipro TechnologiesWipro is the first PCMM Level 5 and SEI CMMi Level 5 certified IT Services Company globally.Wipro provides comprehensive IT solutions and services (including systems integration, ISoutsourcing, package implementation, software application development and maintenance)and Research & Development services (hardware and software design, development andimplementation) to corporations globally.Wipro’s unique value proposition is further delivered through our pioneering OffshoreOutsourcing Model and stringent Quality Processes of SEI and Six Sigma.

Wipro in Collaboration and Knowledge Management

Wipro, recipient of Information Today’s KM World 2002 - KM Reality award, provides end-to-end Collaboration and Knowledge Management services to Global Corporate Enterprises.Wipro can help organizations develop KM applications such as Knowledge Portals, ExpertiseManagement Systems, Knowledge Repositories and Dashboards. KM Assignments typicallyinvolve a Proof-of-Concept to showcase quicker results at lower risks.

Wipro also provide expertise around Taxonomy Development, Knowledge Discovery usingAutomated Categorization Tools and Intelligent Agents. Wipro offers services for integrationof KM systems with other enterprise systems such as ERP, CRM, Content & DocumentManagement, etc. Wipro also has expertise in solutions around OpenText’s Livelink, MicrosoftSharePoint, eRoom, Groove and IBM Lotus. The services also include maintenance andsustenance around these tools and other legacy systems.

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