Attrition Analytics 131
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Attrition Analytics-A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
WHITE PAPER Author : Suvro Raychaudhuri
In a competitive arena, the advantage is taken by the first-mover – and for an environment where Seth and Sisodia’s The Rule-Of-Three predominates, it is not just the first mover, but the fast -mover who has it all. Every organisation, no matter how stable its quality and people processes, are bound to fall prey 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 its expertise and knowledge base, an analytical approach to the same would also help in prediction and necessary remedies. This paper aims to draw on the recent HR trend of referring to the employee as an “internal customer” and therefore assumes that manpower attrition is similar to customer switching problems in case of products, thus has used Markov Analysis as an Operations Research technique 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 the HR Analytics arena and within its limited scope here, suggests an OR model as part of manpower inventory planning in general.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
Table of Contents
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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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
Introduction
The Attrition Warfare
One of the greatest strategies of War had been the strategy of attrition warfare, defined in military terms as “a strategy of warfare that pursues victory through the cumulative destruction of the enemy’s material assets by superior firepower.” Metrics like body counts and terrain captured measure the progress of battle. On the opposite end of the spectrum is maneuver warfare. All warfare involves both maneuver and attrition in some 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 recruitment strategies the similar parameter would perhaps be “acceptance to offer ratio” (from the attacker’s perspective). The main point here is, that today, Human Resource professionals are under increased pressure from a different kind of a Corporate Sitzkrieg – the silent firepower of attrition which causes no less harm to Human capital assets, as compared to “the enemy’s material assets” as in the definition above. The concept of applying warfare terminologies has been an age-old concept amongst marketers – and human resource professionals are coming to terms with such terminology like 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 a competitive environment, where strategies no longer are framed at the top, but evolves out of the environment, cascading through the entire organization and demanding concrete action plans. The concept of what has been stated above can be put into a simple model as shown below. (fig1.)
Environment Strategy Organization
Technology
People
Structure
fig1
Culture
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
The pressure of competition from the environment and the evolution of strategy are selfexplanatory in the above figure. The point to note here is the extent of the impact, which involves hitherto soft issues like culture and people, and this is the origin of strategic human resource focus, the war for talent and the need to garrison the human resource capital as one of the strategic parameters.
The Knowledge-Harvest
APQC (American Productivity and Quality Centre) has made several recommendations to raise awareness of the problem of knowledge attrition, which include 1. 2. Identifying a burning platform or issue related to knowledge loss 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. 2. 3. Cultural knowledge – This includes management practices, values, respect for hierarchy, and decision flows. Historical knowledge – this includes the organization’s journey from the day it was founded till the present Functional knowledge – this includes technical, operational, process and client information
A more careful look at figure 1 indicates that there seems to be some good amount of convergence with respect to AQPC’s definition of the three types of knowledge and the model given in figure 1 – particularly the fact that corporate attrition warfare is all about gaining (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 very strategic base of the organization. Thus from the attacker’s point of view, depending on which type of knowledge it needs form the competitor, the recruitment strategies are also sorted out accordingly. It is evident therefore, that attrition rate among junior employees (2-4 yrs) would be higher for the functional 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 the competitors. 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 best practice organizations have tried capturing data to predict attrition on the long run, and done that in different ways.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
What We Really Lose
Attrition and Knowledge Management – Loss of Historical and Cultural knowledge
From the attackers’ perspective, one of the parameters to measure effectiveness of corporate attrition warfare might be “acceptance to offer” ratios. But from the perspective of the organization that has to cope up with this ever-growing problem, the problems associated are larger. Attrition is a pain area in any organization that intends to have a knowledge management system in place. In a famous article 1 , attrition (through normal retirement or through resignations) has been discussed as one of the pain areas in the field of KM, because vacancy 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 economy where the concept of all-size-fits-all is no longer working, and vacancy of a position by attrition is basically vacancy of a knowledge-base, and this vacancy in knowledge base cannot be filled in by any person. This is precisely what is referred to as tacit knowledge, which most organisations today are grappling to capture and retain. This closely pertains to what AQPC referred to as the Cultural and Historical knowledge, in addition to the Individual or Proprietary knowledge that goes off without being codified and migratory, and therefore is never assimilated in the organisation as invisible knowledge. This can be exemplified better through the typical knowledge-cycle of an organisation as shown below, originally by Takeuchi and Nonaka.
EXPLICIT
Codified knowledge
Migratory knowledge
Attrition event
TACIT
Discovered knowledge PROPRIETARY
Invisible knowledge SHARED
The problem can be aptly stated through examples from the corporate world itself – Corning, which had been experiencing knowledge loss through the large scale retirements through 1990’s estimated that it lost around 2000 years of cumulative years of experience as a result of a retirement package offered in 1998 – and this exemplifies loss of knowledge due to planned retirements alone – here we are talking of corporate SitzKrieg, where an employee may walk into the office any morning to place his resignation letter and walk off with the competitor – not just creating a vacancy, but taking some of the most vital knowledge quantum from the company to it’s competitor.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
However, organisations even with established knowledge management practices have not been able to come up with any substantial measure to check this knowledge loss, and therefore 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 the employees too, as in call centres. Here the knowledge drain is at a different level, and it corresponds 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 to look only at advertising costs, interviewing and training costs etc, but overlook the vital costs associated with attrition. Merrill Lynch attempted to find out costs associated with call-centre attrition – which came out to be around $9m per annum for a company with 1000 employees, and annual revenue of $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, job fairs, ads, employee referral bonuses, etc, and end up with 50% employees leaving before reaching any level of proficiency. Proper testing and screening, training, introduction of the apprenticeship scheme, aptitude testing (10%), realistic job previews (8%), structured behavioral interviews (3%) can help prevent attrition by percentages shown in parenthesis. According to the Forum Group, 65% of the external customers leave due to internal reasons alone (45% for poor service quality, 20% due to lack of attention) – thus internal attrition can devastate call-centre effectiveness if not tackled properly. Shown in the table below are the typical turnover rates of call centres.2 MEDIAN (%) Part time inbound Full time inbound Part time outbound Full time outbound 20 19 15 10 AVERAGE (%) 33.6 26 35.5 21.3 HIGHEST (%) 300 252 480 210
TABLE1
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
What Others Are Doing
Organizations across the world and operating in different industry segments have tried to find out means to measure business loss through attrition. Schlumberger, for example, understands how important it is to link its knowledge sharing techniques 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 better knowledge transfer through its six-step knowledge retention process. Best practice companies, according to AQPC, should conduct a thorough audit to determine what 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 company incase it is vacated under competitor attack. The table below shows the practices that are followed by these organizations to collect data related to attrition:2 Siemens Corning World Bank Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Northrop Grumman Y Xerox Connect Y Best Buy Y
Internal networks Interviews Videotaping SME directory Repositories After action project milestone reviews Mentoring programme Knowledge maps Recruiting strategy Retention strategy
Y
Y Y Y Y Y Y Y Y Y
Y Y Y Y Y
TABLE2
The importance for including the various ways companies worldwide are collecting data on attrition would be clearer in the subsequent sections. A Hay Group survey1 reveals that what people want most is to feel that their careers are moving forward. In their survey, “The retention dilemma: Why productive workers leave and seven suggestions for keeping them”, reveals that employees leave because of disillusionment of the company management’s direction, and because of under-utilization. Two of the seven things Hay Group identified as “attrition-preventing” are clearly related to training – 1. Measurement of soft skills – because gaps exist when the companies say they value their people, and do something else 2. Fight attrition with smart training – taking a longer term perspective in training and development as a retention tool. Page : 07 of 15
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
The relationship between job satisfaction and attrition as surveyed by Hay Group is shown as follows: Satisfaction with Total percent satisfied Gap
Employees planning to stay for >2Yrs (%)
Use of my skills and abilities Ability of top management Company has clear sense of direction Advancement opportunities Opportunity to learn new skills Coaching and counseling from one’s own supervisor Training Pay 54 51 50 66 54 83 74 57
Employees planning to leave in <2Yrs (%)
49 41 27 34 33 30
22 38 26
28 28 28
36 25
18 26
TABLE3
However, few organisations have been able to tackle attrition in spite of using various types of 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 given by the employees – it is only common sense that an employee would not reveal the correct reason for leaving the company at some point of time – thus any action taken by the organization to prevent attrition by altering the factors as mentioned above does not have any effect, 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 number of instruments for collecting reasons as to why people are leaving, an organization is really not being able to do much about attrition – the primary reason of this could be the validity of the data. As to why employees would not/might not give the correct response to an attrition survey stems from the social exchange theory (Dillman, 1978). According to this, there is a social exchange between the survey interviewer, who desires information possessed by the respondent, and the respondent, who decides how much information to convey. Dillman posits that the respondent participates because the act of participation is expected to bring rewards 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 influenced by the design and implementation of the survey.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
Dillman argues that the willingness of an individual to participate in a survey depends critically on the degree of trust that the expected terms of the social exchange described above will be fulfilled. The social exchange model described above can be translated into an economic model and, in its translated form, can be used to help generate some empirically testable hypotheses about 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 survey depends on a comparison of the benefits and costs of participation to him. Let the individual’s utility 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) where Vit is nonlabor income, wit is the market wage rate, Hit are hours of work Pit 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 by maximizing Equation (1) subject to Equations (2), (3) and Eit = lit = Pit = 02. This choice is described by the labor supply function Hit = H(wit, Vit). Substituting the labor supply function and 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 utility gain, or loss from participation in the survey is given by HU = -ULlit + UYpit + Eit = (-witlit + pit)UY + Eit. ……………………………………………..(5) where
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
UL is the marginal utility of leisure, UY is the marginal utility of income wit = UL/UY is the shadow price of time in nonmarket uses which is equal to the market wage rate 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 outweigh the 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 psychic utility nor rewards, and thereby his perceived-utility is low, therefore he is under no obligation to respond correctly/accurately to attrition surveys.
The Markov Analysis
One 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 causes some confusion between external and internal customers and strategies, the main advantage here 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 consider attrition as a customer-switching problem – and once we can do that, attrition rate prediction may 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 switching problems – clearly stated, a Markov analysis to find out the attrition rate and prediction of its stability within time period t, which would give HR people a relevant input in terms of their manpower 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 leads to a family of systems having a mathematical theory, as well as many applications to modeling in more applied science. A central property of ‘nice’ Markov chains is that they settle down into a (stochastic) equilibrium. The basic method for solving this is to construct the transition probability matrix, which takes in attrition probability data by using instruments as mentioned in the TABLE2. The validity of the output would depend on the validity of this probability, which is a problem area, because of 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 needs to 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 organization is 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 selfexplanatory.
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Thus we construct our probability matrix as follows: To company 1 2 3 4 From 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 are 23%, 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 sourcing group of the organization aims to have a 75% target of the probability of employees wanting to remain, that is, around 25% attrition rate. 2. The basic assumption of Markov analysis is also applied here, that the process is a stochastic one, whereby any event would only depend on the preceding event, and nothing else. We have the initial system state s1 given by s1 = [0.45, 0.23, 0.20, 0.12] and the transition matrix P given by P = | 0.95 | 0.05 | 0.10 | 0.13 0.02 0.90 0.05 0.13 0.02 0.01 | 0.02 0.03 | 0.83 0.02 | 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 stochastic chain, 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% and 8.89% 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 = 0.02 i.e. x3 = (0.2/0.19) = 0.10526 Now subtracting equation (2) from equation (1) we get 0.05x1 - 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 have 0.28(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 within rounding 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 sum to 0.25) and we have a transition matrix given by Page : 12 of 15
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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 equations 0.75 = 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 appropriate objective. In order to avoid having to change the transition probabilities too much a suitable objective would be Maximize p1 I.e. find the largest value for the transition probability from company 1 to itself such that the recruiter achieves the long-run employee demand of 75%.
Conclusion
The above approach through a Markov analysis is a proposed model. This model may be followed and can be mapped to a much more complex data through the construction and the solving of the probability matrix through a mathematical tool. The objective of the paper was to propose a quantitative way to predict attrition rate in any industry and therefore take the necessary steps to prevent it, or plan the manpower inventory accordingly. Companies should project retirements and attrition over the next five years. List the internal and 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 just treating employees the way it is normally done. In fact, the companies with leading-edge retention programs address all the areas mentioned below. 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 prediction becomes vital.
Relevant Link
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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 Issue 1 March/April 2003, P-10. 7 “Hay Group Study Identifies Training as One of Top 7 Employee Attrition Fighters” IOMA’s report on managing training & development, April 2002 issue, P-13 8 REDUCING PANEL ATTRITION , By: Hill, Daniel H., Willis, Robert J., Journal of Human Resources, 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 is involved with e-HR initiatives of I-desk, in the capacity of a domain consultant. He holds a Degree in Mechanical Engineering and is a Post-Graduate in Personnel Management and Industrial Relations from one of the premier Business Schools in India.
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About Wipro Technologies
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Wipro in Collaboration and Knowledge Management
Wipro, recipient of Information Today’s KM World 2002 - KM Reality award, provides end-toend Collaboration and Knowledge Management services to Global Corporate Enterprises. Wipro can help organizations develop KM applications such as Knowledge Portals, Expertise Management Systems, Knowledge Repositories and Dashboards. KM Assignments typically involve a Proof-of-Concept to showcase quicker results at lower risks. Wipro also provide expertise around Taxonomy Development, Knowledge Discovery using Automated Categorization Tools and Intelligent Agents. Wipro offers services for integration of KM systems with other enterprise systems such as ERP, CRM, Content & Document Management, etc. Wipro also has expertise in solutions around OpenText’s Livelink, Microsoft SharePoint, eRoom, Groove and IBM Lotus. The services also include maintenance and sustenance around these tools and other legacy systems.
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In a competitive arena, the advantage is taken by the first-mover – and for an environment where Seth and Sisodia’s The Rule-Of-Three predominates, it is not just the first mover, but the fast -mover who has it all. Every organisation, no matter how stable its quality and people processes, are bound to fall prey 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 its expertise and knowledge base, an analytical approach to the same would also help in prediction and necessary remedies. This paper aims to draw on the recent HR trend of referring to the employee as an “internal customer” and therefore assumes that manpower attrition is similar to customer switching problems in case of products, thus has used Markov Analysis as an Operations Research technique 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 the HR Analytics arena and within its limited scope here, suggests an OR model as part of manpower inventory planning in general.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
Table of Contents
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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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
Introduction
The Attrition Warfare
One of the greatest strategies of War had been the strategy of attrition warfare, defined in military terms as “a strategy of warfare that pursues victory through the cumulative destruction of the enemy’s material assets by superior firepower.” Metrics like body counts and terrain captured measure the progress of battle. On the opposite end of the spectrum is maneuver warfare. All warfare involves both maneuver and attrition in some 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 recruitment strategies the similar parameter would perhaps be “acceptance to offer ratio” (from the attacker’s perspective). The main point here is, that today, Human Resource professionals are under increased pressure from a different kind of a Corporate Sitzkrieg – the silent firepower of attrition which causes no less harm to Human capital assets, as compared to “the enemy’s material assets” as in the definition above. The concept of applying warfare terminologies has been an age-old concept amongst marketers – and human resource professionals are coming to terms with such terminology like 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 a competitive environment, where strategies no longer are framed at the top, but evolves out of the environment, cascading through the entire organization and demanding concrete action plans. The concept of what has been stated above can be put into a simple model as shown below. (fig1.)
Environment Strategy Organization
Technology
People
Structure
fig1
Culture
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
The pressure of competition from the environment and the evolution of strategy are selfexplanatory in the above figure. The point to note here is the extent of the impact, which involves hitherto soft issues like culture and people, and this is the origin of strategic human resource focus, the war for talent and the need to garrison the human resource capital as one of the strategic parameters.
The Knowledge-Harvest
APQC (American Productivity and Quality Centre) has made several recommendations to raise awareness of the problem of knowledge attrition, which include 1. 2. Identifying a burning platform or issue related to knowledge loss 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. 2. 3. Cultural knowledge – This includes management practices, values, respect for hierarchy, and decision flows. Historical knowledge – this includes the organization’s journey from the day it was founded till the present Functional knowledge – this includes technical, operational, process and client information
A more careful look at figure 1 indicates that there seems to be some good amount of convergence with respect to AQPC’s definition of the three types of knowledge and the model given in figure 1 – particularly the fact that corporate attrition warfare is all about gaining (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 very strategic base of the organization. Thus from the attacker’s point of view, depending on which type of knowledge it needs form the competitor, the recruitment strategies are also sorted out accordingly. It is evident therefore, that attrition rate among junior employees (2-4 yrs) would be higher for the functional 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 the competitors. 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 best practice organizations have tried capturing data to predict attrition on the long run, and done that in different ways.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
What We Really Lose
Attrition and Knowledge Management – Loss of Historical and Cultural knowledge
From the attackers’ perspective, one of the parameters to measure effectiveness of corporate attrition warfare might be “acceptance to offer” ratios. But from the perspective of the organization that has to cope up with this ever-growing problem, the problems associated are larger. Attrition is a pain area in any organization that intends to have a knowledge management system in place. In a famous article 1 , attrition (through normal retirement or through resignations) has been discussed as one of the pain areas in the field of KM, because vacancy 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 economy where the concept of all-size-fits-all is no longer working, and vacancy of a position by attrition is basically vacancy of a knowledge-base, and this vacancy in knowledge base cannot be filled in by any person. This is precisely what is referred to as tacit knowledge, which most organisations today are grappling to capture and retain. This closely pertains to what AQPC referred to as the Cultural and Historical knowledge, in addition to the Individual or Proprietary knowledge that goes off without being codified and migratory, and therefore is never assimilated in the organisation as invisible knowledge. This can be exemplified better through the typical knowledge-cycle of an organisation as shown below, originally by Takeuchi and Nonaka.
EXPLICIT
Codified knowledge
Migratory knowledge
Attrition event
TACIT
Discovered knowledge PROPRIETARY
Invisible knowledge SHARED
The problem can be aptly stated through examples from the corporate world itself – Corning, which had been experiencing knowledge loss through the large scale retirements through 1990’s estimated that it lost around 2000 years of cumulative years of experience as a result of a retirement package offered in 1998 – and this exemplifies loss of knowledge due to planned retirements alone – here we are talking of corporate SitzKrieg, where an employee may walk into the office any morning to place his resignation letter and walk off with the competitor – not just creating a vacancy, but taking some of the most vital knowledge quantum from the company to it’s competitor.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
However, organisations even with established knowledge management practices have not been able to come up with any substantial measure to check this knowledge loss, and therefore 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 the employees too, as in call centres. Here the knowledge drain is at a different level, and it corresponds 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 to look only at advertising costs, interviewing and training costs etc, but overlook the vital costs associated with attrition. Merrill Lynch attempted to find out costs associated with call-centre attrition – which came out to be around $9m per annum for a company with 1000 employees, and annual revenue of $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, job fairs, ads, employee referral bonuses, etc, and end up with 50% employees leaving before reaching any level of proficiency. Proper testing and screening, training, introduction of the apprenticeship scheme, aptitude testing (10%), realistic job previews (8%), structured behavioral interviews (3%) can help prevent attrition by percentages shown in parenthesis. According to the Forum Group, 65% of the external customers leave due to internal reasons alone (45% for poor service quality, 20% due to lack of attention) – thus internal attrition can devastate call-centre effectiveness if not tackled properly. Shown in the table below are the typical turnover rates of call centres.2 MEDIAN (%) Part time inbound Full time inbound Part time outbound Full time outbound 20 19 15 10 AVERAGE (%) 33.6 26 35.5 21.3 HIGHEST (%) 300 252 480 210
TABLE1
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
What Others Are Doing
Organizations across the world and operating in different industry segments have tried to find out means to measure business loss through attrition. Schlumberger, for example, understands how important it is to link its knowledge sharing techniques 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 better knowledge transfer through its six-step knowledge retention process. Best practice companies, according to AQPC, should conduct a thorough audit to determine what 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 company incase it is vacated under competitor attack. The table below shows the practices that are followed by these organizations to collect data related to attrition:2 Siemens Corning World Bank Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Northrop Grumman Y Xerox Connect Y Best Buy Y
Internal networks Interviews Videotaping SME directory Repositories After action project milestone reviews Mentoring programme Knowledge maps Recruiting strategy Retention strategy
Y
Y Y Y Y Y Y Y Y Y
Y Y Y Y Y
TABLE2
The importance for including the various ways companies worldwide are collecting data on attrition would be clearer in the subsequent sections. A Hay Group survey1 reveals that what people want most is to feel that their careers are moving forward. In their survey, “The retention dilemma: Why productive workers leave and seven suggestions for keeping them”, reveals that employees leave because of disillusionment of the company management’s direction, and because of under-utilization. Two of the seven things Hay Group identified as “attrition-preventing” are clearly related to training – 1. Measurement of soft skills – because gaps exist when the companies say they value their people, and do something else 2. Fight attrition with smart training – taking a longer term perspective in training and development as a retention tool. Page : 07 of 15
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
The relationship between job satisfaction and attrition as surveyed by Hay Group is shown as follows: Satisfaction with Total percent satisfied Gap
Employees planning to stay for >2Yrs (%)
Use of my skills and abilities Ability of top management Company has clear sense of direction Advancement opportunities Opportunity to learn new skills Coaching and counseling from one’s own supervisor Training Pay 54 51 50 66 54 83 74 57
Employees planning to leave in <2Yrs (%)
49 41 27 34 33 30
22 38 26
28 28 28
36 25
18 26
TABLE3
However, few organisations have been able to tackle attrition in spite of using various types of 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 given by the employees – it is only common sense that an employee would not reveal the correct reason for leaving the company at some point of time – thus any action taken by the organization to prevent attrition by altering the factors as mentioned above does not have any effect, 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 number of instruments for collecting reasons as to why people are leaving, an organization is really not being able to do much about attrition – the primary reason of this could be the validity of the data. As to why employees would not/might not give the correct response to an attrition survey stems from the social exchange theory (Dillman, 1978). According to this, there is a social exchange between the survey interviewer, who desires information possessed by the respondent, and the respondent, who decides how much information to convey. Dillman posits that the respondent participates because the act of participation is expected to bring rewards 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 influenced by the design and implementation of the survey.
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
Dillman argues that the willingness of an individual to participate in a survey depends critically on the degree of trust that the expected terms of the social exchange described above will be fulfilled. The social exchange model described above can be translated into an economic model and, in its translated form, can be used to help generate some empirically testable hypotheses about 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 survey depends on a comparison of the benefits and costs of participation to him. Let the individual’s utility 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) where Vit is nonlabor income, wit is the market wage rate, Hit are hours of work Pit 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 by maximizing Equation (1) subject to Equations (2), (3) and Eit = lit = Pit = 02. This choice is described by the labor supply function Hit = H(wit, Vit). Substituting the labor supply function and 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 utility gain, or loss from participation in the survey is given by HU = -ULlit + UYpit + Eit = (-witlit + pit)UY + Eit. ……………………………………………..(5) where
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
UL is the marginal utility of leisure, UY is the marginal utility of income wit = UL/UY is the shadow price of time in nonmarket uses which is equal to the market wage rate 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 outweigh the 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 psychic utility nor rewards, and thereby his perceived-utility is low, therefore he is under no obligation to respond correctly/accurately to attrition surveys.
The Markov Analysis
One 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 causes some confusion between external and internal customers and strategies, the main advantage here 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 consider attrition as a customer-switching problem – and once we can do that, attrition rate prediction may 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 switching problems – clearly stated, a Markov analysis to find out the attrition rate and prediction of its stability within time period t, which would give HR people a relevant input in terms of their manpower 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 leads to a family of systems having a mathematical theory, as well as many applications to modeling in more applied science. A central property of ‘nice’ Markov chains is that they settle down into a (stochastic) equilibrium. The basic method for solving this is to construct the transition probability matrix, which takes in attrition probability data by using instruments as mentioned in the TABLE2. The validity of the output would depend on the validity of this probability, which is a problem area, because of 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 needs to 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 organization is 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 selfexplanatory.
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Thus we construct our probability matrix as follows: To company 1 2 3 4 From 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 are 23%, 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 sourcing group of the organization aims to have a 75% target of the probability of employees wanting to remain, that is, around 25% attrition rate. 2. The basic assumption of Markov analysis is also applied here, that the process is a stochastic one, whereby any event would only depend on the preceding event, and nothing else. We have the initial system state s1 given by s1 = [0.45, 0.23, 0.20, 0.12] and the transition matrix P given by P = | 0.95 | 0.05 | 0.10 | 0.13 0.02 0.90 0.05 0.13 0.02 0.01 | 0.02 0.03 | 0.83 0.02 | 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 stochastic chain, 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% and 8.89% 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 = 0.02 i.e. x3 = (0.2/0.19) = 0.10526 Now subtracting equation (2) from equation (1) we get 0.05x1 - 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 have 0.28(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 within rounding 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 sum to 0.25) and we have a transition matrix given by Page : 12 of 15
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Attrition Analytics - A Markov Analysis Attempt for Attrition-rate Prediction and Stabilization
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 equations 0.75 = 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 appropriate objective. In order to avoid having to change the transition probabilities too much a suitable objective would be Maximize p1 I.e. find the largest value for the transition probability from company 1 to itself such that the recruiter achieves the long-run employee demand of 75%.
Conclusion
The above approach through a Markov analysis is a proposed model. This model may be followed and can be mapped to a much more complex data through the construction and the solving of the probability matrix through a mathematical tool. The objective of the paper was to propose a quantitative way to predict attrition rate in any industry and therefore take the necessary steps to prevent it, or plan the manpower inventory accordingly. Companies should project retirements and attrition over the next five years. List the internal and 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 just treating employees the way it is normally done. In fact, the companies with leading-edge retention programs address all the areas mentioned below. 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 prediction becomes vital.
Relevant Link
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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 Issue 1 March/April 2003, P-10. 7 “Hay Group Study Identifies Training as One of Top 7 Employee Attrition Fighters” IOMA’s report on managing training & development, April 2002 issue, P-13 8 REDUCING PANEL ATTRITION , By: Hill, Daniel H., Willis, Robert J., Journal of Human Resources, 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 is involved with e-HR initiatives of I-desk, in the capacity of a domain consultant. He holds a Degree in Mechanical Engineering and is a Post-Graduate in Personnel Management and Industrial Relations from one of the premier Business Schools in India.
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About Wipro Technologies
Wipro 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, IS outsourcing, package implementation, software application development and maintenance) and Research & Development services (hardware and software design, development and implementation) to corporations globally. Wipro’s unique value proposition is further delivered through our pioneering Offshore Outsourcing 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-toend Collaboration and Knowledge Management services to Global Corporate Enterprises. Wipro can help organizations develop KM applications such as Knowledge Portals, Expertise Management Systems, Knowledge Repositories and Dashboards. KM Assignments typically involve a Proof-of-Concept to showcase quicker results at lower risks. Wipro also provide expertise around Taxonomy Development, Knowledge Discovery using Automated Categorization Tools and Intelligent Agents. Wipro offers services for integration of KM systems with other enterprise systems such as ERP, CRM, Content & Document Management, etc. Wipro also has expertise in solutions around OpenText’s Livelink, Microsoft SharePoint, eRoom, Groove and IBM Lotus. The services also include maintenance and sustenance around these tools and other legacy systems.
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