Optimizing Cost Through Operational Analytics

1. Cost Per Hire

• Cost per hire compares sourcing channels and related costs to identify the optimal way in which to acquire fresh talent.

• The cost per hire computation also includes the geographical challenges of employment (travel, relocation etc.) and associated time costs for talent acquisition.

• Key decision support: The analysis enables HR to make the right calls around sourcing channel mix and processes around time to fill.

2. Absence Factor

• The absence factor is used to compute and assign a weighted attendance employee score that is a crucial input into both payroll, disciplinary, well- being, productivity and workforce planning and deployment decisions.

• The absence factor is used to analyse relative absence within and across teams and its linkages to factors of productivity and cost.

• Key decision support: The analysis enables HR to set absence thresholds via heat maps to highlight cases for disciplinary action or formal monitoring of utilization to drive productivity.

3. Absence Heat Map

• Measuring absence rates and patterns as possible predictors of engagement, performance/flight risk

• The absence heat map enables managers to deploy thresholds for various categories of absence depending on its impact on business and operations (Exhibits 2.4 and 2.5).

4. Labour Cost

• The salary, bonus and related expenses incurred on payment to employees during a specific period; this is split into direct and indirect costs.

Exhibit 2.6 Salary cost and spend roll-ups—role and function wise split by performance

• Key decision support: Analysis enables HR to monitor cost thresholds and highlight which are the business units/levels/roles, which are not in line either with market reality nor in line with internal parity (Exhibit 2.6).

5. Cost and Impact of Attrition

• This analysis will allow you to determine the impact of employee turnover on the business and profitability.

• Often the costs that are apparent such as hiring or on-boarding or training are easy enough to trace back and compute; it is often the more hidden costs that cause a problem.

• Key decision support: Analysis enables HR/business to get a view of all hidden costs related to attrition; providing a more tangible view of cost/benefit ratios (Exhibit 2.7).

This histogram mirrors how analytics is used to predict tenure by modelling the probability of an individual to terminate or attrite at a certain duration. The outcome of an analytics model will be a density curve, much like the histogram, showing the probability of termination for; “good fit” blue and “bad fit” brown, employees at each point of tenure (Exhibit 2.8).

Exhibit 2.7 Probability of employee attrition histogram—histogram to predict turnover/tenure

6. Turnover Rate

• Employee turnover is the rate at which employees exit the company. A high turnover rate is reflective of instability and a rapid burn through of employees.

• A healthy organization that requires a steady injection of new capabilities, fresh perspectives and passion requires a reasonable turnover rate to allow for the churn.

• Key decision support: Analysis enables HR/business to arrive at an optimum turnover/retention balance. A bulge analysis of turnover across levels/roles/locations and businesses throws up imbalances that require corrective action (Exhibit 2.9).

The histogram allows you to analyse how employee turnover plays out and impacts organization performance. This can be developed from regular HR data with a deeper analysis being culled out from the graphic. Most managers are able to understand histograms with some coaching. In Exhibit 2.9a, the horizontal “X”- axis reflects tenure (number of years) in a given role. The “Y”-axis reflects employee headcount against the tenure. There is a spike in early exits in the first nine months, then another spike later. However, this is deceptive. The spiked pat- tern is actually the sum of two simpler employee clusters “good fit” individuals in Exhibit 2.9b who left the role by being promoted or being hired away and “bad fit”

individuals in Exhibit 2.9c who left the role because of underperformance prob- lems, working hours, commute time or for disliking or not fitting into the work/role (Exhibit 2.10).

7. Performance Management

• It is the process of setting specific standards around objectives and goals to an individual or team with a view towards measuring their effectiveness over a designated period.

• A performance-driven organization is one that continuously raises the bar, i.e. standards.

• Key decision support: Analysis enables HR/business to highlight performance gaps across role hierarchies/levels/business units/locations; to enable corrective action (Exhibits 2.11, 2.12 and 2.13).

8. Headcount Utilization/Optimization

• This brings out how effectively headcount in FTEs is being utilized.

• This metric highlights the productivity standards in an organization and whether those standards are being met or exceeded in terms of units produced or sold or services rendered.

• This has a direct impact on both labour/people cost as well as revenue in terms of business per employee or team.

• It is critical to track headcount evolution on a regular basis to stay on top of people planning and optimization.

Significant disconnect seen between Function Head / Manager Performance scores and the rest of the team at the lower end of the Role Hierarchy

Exhibit 2.11 Sunburst provides a holistic view of performance across the workforce. The Sun- burst depicts both span of control as well as the performance distribution of an organization, team with an individual drill down view. The illustrated Visualisation has the senior most team member / role holder in the centre and each subsequent concentric circle depicts the next report- ing layer. The “greens” represent Excellent performers, “ambers” good performers, “reds” poor performers and “whites” not evaluated. The sunburst view can additionally be deployed across multiple parameters to bring out key insights. The Sunburst gives a quick snapshot of where the talent risks and challenges lie. The performance distribution matrix across the organization can be visualised at a glance and key insights derived with strong/weak pockets of team and indi- vidual performance highlighted or anomalies related to performance scores across hierarchy.

This may help build a case for leadership assessment and development in certain parts of the organization. Over a period of time the Sunburst would highlight quick wins for development and rationalisation

Exhibit 2.12 Performance by year of joining—highlighting historical patterns on performance

• Variance analysis in terms of actual versus budgeted headcount and cost across roles, locations, business units and functions is critical to highlight cost overruns and surplus manpower which would impact people/labour productivity standards.

• Key decision support: Analysis enables HR/business to highlight whether they have the optimum staffing model (in production, sales, services or support/ancillary areas) or do they need to restructure or redesign the organization to meet productivity standards or exceed the standards to drive profitability—“to get more with same or less”; “alter their teeth-to-tail ratios” by having more headcount deployed in customer facing roles for instance (that generate revenue) as opposed to support or production roles

which could be automated; ability to forecast capacity with headcount and skill requirements for the future (Exhibit 2.14).

9. Demographics and Diversity

• This highlights how age, tenure, education, ethnic identities and gender diversity play out in an organization to drive sustainability and growth with profitability.

• The interplay of demographic and diversity factors with other variables like performance, compensation, engagement, etc., provides critical insights for HR and business leaders.

• These factors determine both the diversity quotient and the demographic mix of the organization which in turn impacts both growth and profitability by ensuring diverse set of ideas play out enabling teams to collaborate and engage more effectively to deliver key outcomes.

• Key decision support: Analysis enables HR/business leaders to deploy the optimum demographic and diversity mix to build the right culture; enhance engagement and drive business outcomes (Exhibit 2.15).

10. Cost to Serve

• The activities that people do create the workflows in organizations. How- ever, these activities that are typically captured in job or position descrip- tions are limited in nature and neither reliable or detailed enough to cover all key aspects of any given role.

• Database that visually depicts people data quickly reflecting the organization hierarchy.

• Unique visualizations that enable managers to dive into the insight rapidly.

• Seamless cleansing and updating of the data.

• Respond to questions around people and data—with a wide range of analytics, dashboards and organograms? What is the strength of the team? Who is responsible for what range of activities? Does the organization have the right fit? Is the organization bringing in the right capabilities with the right mix, in the right domains?

• Key decision support: Analysis of process cost; time spent by each employee on each process; time and cost of customer facing and revenue

generating processes versus back-end support processes; time spent on non- value adding activities versus value adding versus time spent on decisions (Exhibit 2.16).

Let us examine a case example of predictive modelling of employee turnover in a Sales organization of a Consumer company through leveraging of data science tools.

CASE 1: Identification of Predictors of Employee Turnover in a Sales Consumer Organization

Situation and Context

Every year ~15% of employees leave XYZ company and need to be replaced with the talent pool available in the job market. The percentage of employee turnover (employees leaving, either on their own or because they got retrenched) impacts the company negatively due to the following reasons:

• Project delays, resulting in a reputation loss among consumers and partners

• An enhanced employee headcount needed to be maintained as attrition cover increasing employee costs

Exhibit 2.16 Cost to serve—role analysis and process mapping—RACI MATRIX visualization and costing of processes linked with roles

• Productivity impact due to training and acclimatization of new hires before being put on the job thereby losing valuable time

• Increased salary cost as invariably new hires came at a minimum 30% higher salary

• Impacted engagement levels in the salesforce.

Given the alarming situation, a cross-functional team was quickly put together led by Ketan from the -business planning and MI team which pooled skill sets from multiple domains such as HR, business planning and MI and sales. The team was as a result able to deploy a set of tools to analyse this phenomena and pool its resources for maximum impact.

Data science and statistical modelling tools were used to analyse the data that was collected painstakingly from multiple sources. The study was carried out methodically with a governance process in place to review status.

Analytics Objectives

• To identify key predictors of employee turnover leveraging data science tools

• To understand what factors XYZ company should focus on to curb turnover

• In other words, what changes should be made in the workplace, in order to get majority of their employees to stay engaged and productive

• To isolate the factors that correlated highly with attrition and then deepen the understanding of those factors.

Data Understanding

The following datasets were collected and analysed.

• General data. It contained demographic information and other behavioural information of the employees. It had 4410 observations and 24 variables.

• Employee survey data. It contained survey data collected from the employees with 4410 observations and four variables.

• Manager survey data. Contained survey data collected from the managers with 4410 observations three variables.

• In-time data. It contained a year worth data of the employee’s office in time in date time format with 4410 observations and 262 variables which contained in-time details for days 01/01/2015 through 12/31/2015.

• Out-time data. It contained out-time details for days 01/01/2015 through 12/31/2015.

Merging datasets—There was a need to merge general data, employee survey data and manager survey data along with time data by key variable employee ID to create master employee dataset.

Data Preparation

• In-time and out-time data converted from wide format to long format using R programming to calculate the average working hours and then eventually merge with the master employee data

• Derived a new variable called time in office based on the average working hours.

An employee was classified as working overtime if he/she worked for more than 10 avg. hours.

An employee was classified as working less time if he/she worked for less than 7 avg. hours.

An employee was classified as normal if he/she worked between 7 and 10 avg. hours.

• Missing values were treated as follows:

missing values (NA values) less than 1% in columns Num. companies worked, total working years, environmental satisfaction, job satisfaction and work–life balance were replaced with mode.

• Variable with unary data or constant values were removed.

• The variables employee count and standard hours contain a single constant value which does not help in analysis and modelling hence were removed.

• Employee ID column removed as it has no use in the modelling and analysis.

• All categorical variables converted to factors and then dummy variables before model building

• All continuous variables were scaled or normalized for the purpose of model building. (Exhibits 2.17 and 2.18).

• Attrition rate was found higher for following variables.

Exhibit 2.18 Using stacked bar chart for multivariate analysis with categorical variable

• Frequent business travel—26%

• Human Resource department—30%

• Education field—Human Resource—41%

• Job role—Research director—24%—an exception

• Single marital status—20%

• Low environment satisfaction—25%

Exhibit 2.19 Correlation plot coloured by attrition was used for multivariate analysis of continuous variable

• Low job satisfaction—23%

• Low work–life balance—31%

• Low job involvement—22% (Exhibit 2.19).

• Attrition showed positive correlation with average working hours and negative correlation with age, total working years, years at company, years with current manager and number of companies worked

• The box plot below indicates high attrition rate for employees across the following cohorts:

• Who travel frequently

• Younger in age

• With lesser work experience

• Working less number of years with the company

• With less number of years under current manager

• With less number of companies worked (Exhibit 2.20).

• Green bar below indicates higher information value and thus indicated strong association for the following variables with attrition:

• Total working years

• Average hours worked

• Years at company

• Age

• Years with current manager

• Marital status

• Time in office

• Business travel

• Number of companies worked

• Environmental satisfaction and job satisfaction (Exhibit 2.21).

Dalam dokumen People Analytics Rahul Ghatak (Halaman 39-55)

Optimizing Cost Through Operational Analytics—Some