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2. - 1 - Corporate profile Founded by IIT Madras alumnus having extensive global business experience with Fortune 100 companies in United States and India having three lines of business Prof Prakash Sai Dr. Prakash Sai is professor at the Department of Management Studies, Indian Institute of Technology Madras. He has wealth of international consulting experience in Strategy Formulation Puneet Gupta Puneet spearheads the IFMR Mezzanine Finance (Mezz Co.), is strengthening the delivery of financial services to rural households and urban poor by making investments in local financial institutions. Padma Shri Dr. Ashok Jhunjhunwala Dr. Ashok Jhunjhunwala is Professor at the Department of Electrical Engineering, Indian Institute of Technology Madras India. He holds a B.Tech degree from IIT, Kanpur, and M.S. and Ph.D degrees from the University of Maine, USA. Analytics Appropriate statistical models through which clients can measure and grow their business. Competitive Intelligence Actionable insights to clients for their business excellence Livelihood Services ranging from promotion of livelihoods, implementation services, livelihood & feasibility studies. Key Focus Areas in Advanced analytics and Predictive analytics Product geniSIGHTS (Analytics/BI), Ordo-ab-Chao (Social Media) More than 25 consulting assignments for Businesses & Govt orgs Partnership Actuate, IIT Madras, TIE and 3 strategic partnerships Dedicated corporate office at IIT Madras Research park since 2009 Aaums office, IIT Madras Research Park 3. - 2 - Competencies in Advanced analytics Build appropriate statistical models through which clients can measure and grow their business. Expertise in Digital Media Finance/Insurance Retail Entertainment Human Capital Government organizations Research & training Competitive assessment Competitive intelligence Provide actionable insights to clients for their business excellence. Expertise in Business Entry Business Expansion Market research Livelihood Perform livelihood services ranging from promotion of livelihoods, implementation services, livelihood and feasibility studies. Expertise in Government organizations Non Government organizations Corporate with livelihood focus Research 4. Objective Metrics definition Analysis & Key insights Predictive modeling Way forward 5. - 4 - Objective Client management approached Aaum to develop comprehensive metrics to be standardized for its clients. Aaum team developed standard metrics that could be rolled out across clientss customers to derive appropriate corrective actions and preventive actions performed interaction analysis for key metrics to derive a holistic understanding developed predictive models for some very useful parameters predicting employee productivity based on leave parameters derived insights based on the performance of the RO. 6. Objective Metrics definition Analysis & Key insights Predictive modeling Way forward 7. - 6 - S.No. Metric Definition Interpretation 1. a. Productivity in days Total number of working days of the employee/ total number of expected working days This metric will range from 0 to 1. The closer the value is to 1, the higher the productivity. 2. Average leave day Average of the difference between leave reporting date and leave availed date. Ideally this metric should be closer to zero. A higher deviation implies the gross indiscipline 3. Swipe Indiscipline (including OD) No. of days not swiped/ Total working days (including OD) This metric will range from 0 to 1. Value closer to 0 implies a good scenario. 4. Swipe Indiscipline (discounting OD) No. of days not swiped/ Total working days (excluding OD) This metric will range from 0 to 1. Value closer to 0 implies a good scenario. 5. Swipe OD discipline No. of OD swiped/Total number of OD This metric will range from 0 to 1. The closer the metric is to 1, the better it is. 6. OD indiscipline No. of rejected OD/Total number of OD This metric will range from 0 to 1. Value closer to 0 implies a good scenario. 7. Regularization Rejected (RR) No. of rejected regularization/Total no. of regularization requested This metric will range from 0 to 1. Value closer to 0 implies a good scenario. Metrics definition 8. - 7 - Metrics definition cont S.No. Metric Definition Interpretation 8. Leave Discipline Total no. of leave accounted/ Total absent days This metric will range from 0 to 1. The closer the metric is to 1, the better it is. 9. Leave affinity Total days of leave taken/Frequency of leave This metric defines the no. of leaves per installment. 10. Effort Variance (Actual hours worked-Ideal hours to be worked) / Ideal hours to be worked A positive value of the metric indicates over productivity. A negative value indicates under productivity. 11. Attrition No of people leaving the organization/Total count An organization would always wish attrition to be close to Zero 12 Forming No of people leaving the organization with in 20 days/Total count Forming cost A metric that qualifies these severity of an employee leaving with in 20 days Norming No of people leaving the organization with in 90 days/Total count Norming cost A metric that qualifies the situation where in the employee leaving between the period 20 days - 90 days Performing No of people leaving the organization after 90 days/Total count Performing cost A metric that qualifies the situation where in the employee leaves the organization after 90 days Attritionchildcosts 9. Objective Metrics definition Analysis & Key insights Predictive modeling Way forward We have come up with Spread metric to qualify the dispersion of the data. The metric in comparison with the central tendency (mean or median) measure throws lights on how well the data is represented at unit level versus the overall metric. 10. - 9 - Metric 1. Productivity in days Region Unit Pay grades Departments Main effects N, Spread N, Spread N, Spread Productivity at location 4, 5 as well as productivity at pay grades 7,8 and T are lower. The closeness of the spread and productivity metric of location 4 reveals that the metric best mimics the overall productivity metric. The spread metric of the printing dept, which is 2.8 times greater than the overall spread, and its low productivity implies the presence of many influential observations with a very high dispersion values (outliers) has brought the productivity level significantly down. 11. - 10 - Metric 1. Productivity in days Main effects Location Age group Experience N, Spread N, Spread Shift Spread Spread Location Productivity of freshers as well as those between age 18 -25 are much lower. Productivity at location 2 is much higher than any other location. The spread metric of Gen shift which is 2.79 times higher than the overall spread in comparison with its productivity metric which lies in sync with the overall productivity implies the presence of few outliers which tend to bias the metric. . 12. - 11 - Metric 1. Productivity in days Interactions Location-Pay grade Department-Pay grade Productivity of T grade employees in Unit 5 and Seamless department depict a relatively lower productivity. The closeness of the spread and productivity metric of T grade employees in Location 5 to the overall values shows how closely the data mimics the overall data. . N, Spread N, Spread 13. - 12 - Metric 2. ALD Main effects Region Department Age Experience Accounts, Admin department and experienced category of 1-3 yrs show maximum indiscipline in ALD 14. - 13 - Metric 2. ALD Main effects Unit Pay grade Head office, Pay grade 1, T and 8 are the most critical segments as far as leave reporting discipline is concerned. 15. - 14 - Metric 2. ALD Age-Experience Unit-Pay grade Location-Pay grade Interaction effects reveal that employees in Head Office and regional team with pay grade 4 and Corporate and Strategy with pay grade 2 are the segments where leave report indiscipline is very high. Location-Pay grade Interactions 16. - 15 - Swipe Indiscipline (including OD) Swipe Indiscipline (including OD) Swipe Indiscipline (including OD) Swipe Indiscipline (including OD) Region Departments Pay grade Metric 3. Swipe Indiscipline (+OD) Unit Main effects Location1, Printing and Seamless departments, Pay grade T exhibit high swipe indiscipline while Marketing department exhibits low swipe indiscipline. 17. - 16 - Shift Location Age Experience Metric 3. Swipe Indiscipline (+ OD) Main effects Swipe Indiscipline (including OD) Swipe Indiscipline (including OD) Swipe Indiscipline (including OD) Swipe Indiscipline (including OD) The younger age group displays higher indiscipline as compared to the older proportion. Employees with 6months-1 year experience, Shift C and Location 1 are other categories displaying high swipe indiscipline. 18. - 17 - Metric 3. Swipe Indiscipline (in OD) LocationPay grade Dept-Unit Experience-Pay grade Age-Experience Interactions Pay grade-Age Further drill down to interaction effects reveal, swipe indiscipline is particularly noticeable in Admin & Support department of Location 5, youngest age group of pay grade 7 and employees with experience 1-3 of grade T. 19. - 18 - Region Pay grade Department Unit Metric 4. Swipe Indiscipline (- OD) Main effects Head office and regional team, pay grade 4 and IT department show high swipe indiscipline. 20. - 19 - Age Shift Location Experience Main effectsMetric 4. Swipe Indiscipline (- OD) The middle age group of 25-45, employees with experience ranging from 6months to 3 years, Headoffice shift and employees belonging to Location 2 depict high swipe indiscipline. 21. - 20 - Unit-Pay grade Age-Pay grade Age-Location Dept-Pay grade Metric 4. Swipe Indiscipline (- OD) Interactions However interaction effects reveal that Head office and regional team employees of pay grade 4, IT department employees with pay grade 5, middle aged proportion with pay grade 4 and experience of 1-3 years are the critical categories in terms of high swipe indiscipline. 22. - 21 - Region Pay grade Department Metric 5. Swipe OD Discipline Head office & regional team show good swipe discipline for OD, while Location 3 and 6 employees depict low discipline. Main effects Unit 23. - 22 - Age Shift Location Experience Metric 5. Swipe OD Discipline Main effects The swipe OD discipline decreases with increase in age. Location 6, Gen shift, and 3-5 years of experience are the critical segments in terms of swipe OD discipline. 24. - 23 - Region Pay grade Department Unit Metric 6. OD indiscipline Main effects Location 4, Commercial department and pay grade 7 are the segments depicting high OD indiscipline. However marketing department and Location 2,3 show low indiscipline. 25. - 24 - Age Shift Location Experience Metric 6. OD indiscipline Main effects The younger proportion of 18-25 age group and employees with experience 1-3 years are highly undisciplined in terms of OD. Corporate shift and Location 4 exhibit the same trend. 26. - 25 - Region Pay grade Department Unit Metric 7. RR Main effects Location 6, pay grade 8 and Seamless department are the segments which show high regularization rejections. 27. - 26 - Age Shift Location Experience Metric 7. RR Main effects Employees belonging to the age group above 55, and experience with 5-10 years depict higher rejection rate. 28. - 27 - Metric 8. Leave discipline Main effects Region Pay grade Department Unit Grade T, Location 4 and Printing are the critical segments that show poor leave discipline. 29. - 28 - Metric 8. Leave discipline Main effects Age Shift Location Experience Interestingly the leave discipline improves as age and experience increases. Shift C and Location 4 show high leave indiscipline. 30. - 29 - Unit-Pay grade Experience-Age Experience-Pay grade Dept-Pay grade Metric 8. Leave discipline Interactions Interaction effects reveal that Trainees as well as the freshers maintain very low leave discipline compared to other segments. 31. - 30 - Metric 9. Leave affinity Main effects Region Pay grade Department Unit Leave affinity increases with decrease in pay grade. Seamless and Tubeline show greater leave affinity. 32. - 31 - Metric 9. Leave affinity Main effects Age Shift Location Experience Leave affinity decreases with increase in age. The frequency of leave taking is high for the freshers. Employees with 5-10 years of experience, Shift C and Location 1 exhibits the maximum leave affinity. 33. - 32 - Metric 9. Leave affinity Interactions Unit-pay grade Pay grade-Experience Age group-Experience Dept-Pay grade However interactions do not reveal any interesting patterns. 34. - 33 - Metric 10. Effort Variance Main effects Region Pay grade Department Unit Pay grade 7, Location 5 and Engineering department are the overstretching segments in terms of working hours. Whereas the segments Management, Head office & regional team and Admin & Support are under performing in terms of the same. 35. - 34 - Metric 10. Effort Variance Main effects Age Shift Location Experience The Effort Variance increases with increase in Age and when the experience is over 3 years. The critical segment here is the HeadOffice that shows a negative Effort Variance 36. - 35 - Interactions Admin & Support department of the Head office and regional team depict very low effort variance as compared to the other segments. However tying productivity and effort variance together, it was noticed that the departments of Printing and Seamless which exhibited low productivity in terms of days has actually overstretched themselves to arrive at positive effort variance implying that these departments are not actually underperforming. Metric 10. Effort Variance Unit-Dept Productivity Categorized 37. - 36 - Metric 11. Attrition Main effects Region Pay grade Department Unit Shift Location 6, Pay grade 7 and Departments Laminator and Printing show very high attrition rate. 38. - 37 - Metric 12. Attrition Child Costs June July Aug Sep Oct Nov Dec Jan June July Aug Sep Oct Nov Dec Jan Printing Seamless Attrition costs are benchmarked at a base level and compared over a period of time. This graph illustrates month wise split of the attrition child costs incurred by the two critical departments Printing and Seamless. For printing department, the child costs are very high in the month of June. Performing costs are nil for both the departments in the month of August. 39. Objective Metrics definition Analysis & Key insights Predictive modeling Way forward 40. - 39 - Predictive modeling using Random Forests Prediction is based on a sound understanding of the client business and deriving insights from various data sources to explain the underlying characteristics. We have built Random forests to develop predictive modeling. Random forests are state of the art analytical techniques that constructs several decision trees to arrive at variables with predictive intelligence 41. - 40 - Pr_Day TRUE < 0.2 0.2 0.75 0.75-0.90 0.90-0.98 >0.98 < 0.2 4 0 0 0 0 0.2 0.75 0 181 0 0 0 0.75-0.90 0 0 331 0 0 0.90-0.98 0 0 0 131 0 >0.98 0 0 0 0 4 S,No. row.names %IncMSE IncNodePurit y 1 Department_new 7.7744527 0.784334305 2 ALD 9.4599482 0.897245267 3 SCorporate 0.4160107 0.073057995 4 SGen 9.0660538 0.340773951 5 SHeadoffice 3.8128584 0.070116793 6 SShift.A 9.1486368 0.346673313 7 SShift.B 9.3715632 0.410356133 8 SShift.C 6.9114632 0.337520495 9 SWeeklyOff 0.9108635 0.136731588 10 Not.swiped.including.OD 22.0522982 1.769871655 11 Not.swiped.when.present..TWDays. OD 1.2986346 0.038448390 12 Swiped.OD..OD.All 2.2035039 0.123827886 13 Rejected.OD..Total 2.8027272 0.095028156 14 Experience 9.7536757 0.638688390 15 Status 0.0000000 0.002368345 16 Pr_Effort.Variance 9.7623909 0.546977854 17 RR.Metric 2.8510718 0.154144889 18 LD.Metric 42.9440334 3.486118889 19 LAFF.Metric 12.2612422 0.715462753 20 Age.group 8.2907674 0.297730402 Predicted values against the true classification Predicting the productivity of the employee About 20 variables where chosen to construct a random forest model on productivity parameter. Productivity is converted to a factor variable consisting of 5 levels. i.e. < 0.2, 0.2 0.75, 0.75-0.90, 0.90- 0.98, >0.98 We have constructed a new random forest model based on the high importance score value on the previously constructed random forest. Cross segment ratio = 1 Inferences Our new random forest model indeed showed up 100 % accuracy in classifying the productivity parameter correctly. The variables retained in our model are Department_new Not.swiped.including.OD,Experience LAFF.Metric LD.Metric This model can be used to predict the productivity of the employee 42. - 41 - Predictive modeling Prediction involves integrating various data sources with a good sample strength. Previous slide demonstrates a scenario of predicting productivity of the employee based on his leave patterns, department and swipe discipline. The model is specific to this particular department as the solution is optimized for this data set Poor sample strength and unavailability of dataset became a severe bottleneck in modeling some of the very useful metrics e.g. Attrition! A few more possibilities are discussed in the following slides and can be further explored with the availability of data 43. - 42 - Objective: Attrition is a plaguing problem in any company or industry. With intensive data mining, it is quite possible to build models to better understand the attrition patterns at employee level, department level, unit level and organizational level Case illustrations: Predicting the attrition at employee, unit, organizational levels Explanatory variables: Information on the below mentioned datum collected over a period of 3 -5 years. 1. Socio-demographic Age, sex, marital status, location, education, place of schooling, previous employment information. 2. Interview Short listing criterion, position, pay grade, department, region, etc offered at the time of the joining. 3. Time sheet Department, pay grade, unit, region, location, shift, DOJ, RO details, productivity, swipe patterns, etc. 4. Leave Leave patterns, regularization pattern, outdoor pattern, affinity, ALD, RO details. 5. Performance Appraisal scores, remarks, achievements, recognition, issues/concerns, negative feedback, pay grade history, bonus info, project specific info, promotions, etc. 6. Learning and Development Trainings undergone, skill sets developed, etc. 7. Exit interview Reason, issues, strengths, etc. Organizational benefits: Predicting employee tenure based on his/her characteristics. Forecasting attrition effects at project, department, unit & organization levels. Root cause analysis and arrive at corrective/preventive actions to bring down the attrition rate in critical departments Can further be delved to determine the effectiveness of other departments. e.g. Effectiveness of the training department. Modeling approach: Structured data mining approach will be adopted based on underlying characteristics of data. Logistic regression, neural networks, support vector machines, decision trees, random forests, etc would be administered to arrive at predictive models. 44. - 43 - Objective: Analyzing the past staffing, efforts at project level, unit level, employee level and developing the future requirements for existing/new projects based on appropriate forecasting techniques. Case illustrations: Forecasting project specific staff requirements Explanatory variables: Information on the below mentioned datum collected over a period of 3 -5 years. 1. Project Project detail, milestones, staffing efforts, etc. 2. Customer feedback/satisfaction scores Customer remarks, issues, suggestions, etc. 3. Time sheet Department, pay grade, unit, region, location, shift, DOJ, RO details, productivity, swipe patterns, etc. 4. Leave Leave patterns, regularization pattern, outdoor pattern, affinity, ALD, RO details. 5. Performance - Appraisal scores, remarks, achievements, recognition, issues/concerns, negative feedback, pay grade history, bonus info, project specific info, promotions, etc. Organizational benefits: Forecasting staff requirements, understanding of lean/stressed periods Equipping the departments/organization with the necessary staffing requirements well in advance. Remove inefficiencies, delay in the project delivery. Modeling approach: Structured data mining approach will be adopted based on underlying characteristics of data. Logistic regression, neural networks, support vector machines, decision trees, random forests, etc would be administered to arrive at predictive models. 45. - 44 - Objective: Training programs are very important in building role based skill sets (project specific) as well as the behavioral skills sets. Organizations spend significant resources on this front but return on their investment needs to be analyzed for building a successful training & development. Case illustrations: Improving the effectiveness of the organizations development & learning programs Explanatory variables: Information on the below mentioned datum collected over a period of 3 -5 years. Costs Training/learning cost, resource requirement, etc. Project Project detail, milestones,staffing efforts, customer score/feedback,etc. Time sheet Department, pay grade, unit, region, location, shift, DOJ, RO details, productivity, swipe patterns, etc. Leave Leave patterns, regularization pattern, outdoor pattern, affinity, ALD, RO, etc. Performance Appraisal scores, remarks, achievements, recognition, issues/concerns, negative feedback, pay grade history, bonus info, project specific info, promotions, etc. Learning and Development Trainings undergone, skill sets developed, etc. Organizational benefits: Analyzing the effectiveness of a training program, performance of the employee before/after the training in actual projects, project performance (customer satisfaction scores) Identifying the employees in need of the training program. Identifying the kind of training program that would benefit the employee and the organization. Cost-benefit analysis. Time and efforts spent on the training program really worth it? Modeling approach: Structured data mining approach will be adopted based on underlying characteristics of data. Logistic regression, neural networks, support vector machines, decision trees, random forests, etc would be administered to arrive at predictive models. 46. - 45 - Objective: Profiling of candidates to suit the specific roles and requirements of the organization with learning's from the past. Case illustrations: Streamlining the hiring process with learnings from the past Explanatory variables: Data set covering the below mentioned datum collected over a period of 3 -5 years. 1. Socio-demographic Age, sex, marital status, location, education, place of schooling, previous employment information. 2. Interview Short listing criterion, position, pay grade, department, region, etc offered at the time of the joining. 3. Time sheet Department, pay grade, unit, region, location, shift, DOJ, RO details, productivity, swipe patterns, etc. 4. Leave Leave patterns, regularization pattern, outdoor pattern, affinity, ALD, RO details. 5. Performance Appraisal scores, remarks, achievements, recognition, issues/concerns, negative feedback, pay grade history, bonus info, project specific info, promotions, etc. Organizational benefits: Identifying if an employee is most suited to his role in the organization. Identifying other possible avenues in the organization where his skills would be most suited. Modeling approach: Structured data mining approach will be adopted based on underlying characteristics of data. Logistic regression, neural networks, support vector machines, decision trees, random forests, etc would be administered to arrive at predictive models. 47. - 46 - Questions/Feedback? Contact us 01 N, 1st floor IIT Madras Research Park, Kanagam road, Chennai 600113 Tel :` +91 44 66469877, Fax:+91 44 66469877 Email: info@aaumanalytics.com Twitter: AaumAnalytics, Web: www.aaumanalytics.com Facebook: http://www.facebook.com/AaumAnalytics LinkedIn: http://www.linkedin.com/company/aaum-research-and-analytics-iit-madras Aaums office at IIT Madras Research Park About Aaum Aaum Research and Analytics founded by IIT Madras alumnus brings in extensive global business experience working with Fortune 100 companies in North America and Asia Pacific. Incubated at IIT Madras Incubator ecosystem with a focus on researching and devising the sophisticated analytical techniques to solve the pressing business needs of corporations ranging from finance, insurance, HR, Health Care, Entertainment, FMCGs, retail, Telecom.