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Predictive Workforce Analytics for Critical Times

by Becca Goren, Global Product Marketing Manager for Strategy Management and Human Capital Management , http://www.sas.comThursday, March 19, 2009

Worldwide, businesses are in critical times. The impact of the financial tornado that touched ground with fury in the U.S. in late 2008 is one that is now felt worldwide. Organizations that survived the initial hit were besieged with rampant layoffs and organizational restructuring --worsening human capital issues including the impending retirement of baby boomers, their post-internet “replacements” and the worldwide skill shortage. Managers struggle to maintain performance levels with fewer workers – increase their productivity and optimize workforce skills. But where do they start?

Leading organizations support this workforce decision-making with workforce analysis. This analysis is supported by software that provides a holistic workforce view, predictive analytics and optimization. These organizations can predict the incidence and impact of events, model and test alternate scenarios, and be confident of making wise and fair decisions within multiple, interdependent variables. With this kind of intelligence about the workforce, you can be prepared for short-term transitions and long-term success.

Capitalizing on analytics to optimize the workforce

There are several methods and types of analyses that can be applied to optimize the workforce. With predictive analytics, you can confidently anticipate the result of a strategy in advance, test various scenarios and select the best of all possible courses. You can explore and understand complex relationships among resources, behavior, systems and processes; assess the impact of changes in key performance indicators; and respond more quickly with fact-based decisions.  This technology then helps you learn from past results so you can use that knowledge to realign indicators and improve workforce decisions the next time around. Unlike generic business intelligence software that reports on what has happened, optimization software and business analytics identify the best forward-looking course of action—the best use of limited resources to achieve strategic objectives.  For example:

  • Data mining delves into huge volumes of data to detect patterns and indicators that would otherwise be hidden, finding answers to questions such as: “Why are veteran employees leaving?”  “Who is likely to leave within the next three months?”  “What impact would it have if we added a new benefit package?”
  • Operations research (OR) systems apply sophisticated mathematical programming capabilities to answer complex questions for which variables, constraints and desired outcomes can be mathematically defined.  The purpose is to identify the combinations of variables that will produce the best results, within resource limitations and other restrictions. For example: “How should we allocate merit increases to maintain the best internal and external pay equity?”
  • Forecasting capabilities enable managers to accurately plan headcount and skills for any area, even as the organization and markets undergo changes.
  • Descriptive and predictive modeling enables managers to analyze the past and look forward to spot trends in key factors related to voluntary termination, absences and other sources of risk.
  • Text miningenables you to investigate large volumes of free-form documents—such as emails, performance reviews and resumes—to discover and to use knowledge in the collection as a whole.
  • Discrete event simulationmodeling enables you to measure and assess potential employee behaviors and outcomes under various different scenarios, before you take action.

 A workforce optimization model

Whether you are developing a fully mathematical optimization, or just trying to drive more effective and efficient use of the organization’s workforce, the optimization model would be based on the following components:

  • An objective that is the goal of the optimization exercise—something measurable to be achieved. Examples include: retaining the most valued and strategic employees, minimizing turnover, and reducing the time and cost of backfilling open positions.
  • Decision variables, the available actions or choices, which can be represented numerically for mathematical formulation.  Examples include: requisitions for new hires, recruitment activities and compensation levels.
  • Constraints specifying requirements or rules, which place limits on how the objective can be pursued by limiting the permissible values of the decision variables.  Constraints can be finite limits on available resources, such as salary ranges for a given position, available workspace, productivity quotas or monetary budgets.  Constraints can also be “soft” considerations that encourage but do not compel compliance with the rule. Both types of constraints can and should consider the greater sphere, extending to suppliers, customers, partners, market conditions and regulatory requirements.

Within this framework, the purpose of optimization is to maximize or minimize, as appropriate, the performance metric in the objective by assigning values to the decision variables that satisfy the constraints.  The following are two examples of workforce optimization, each using a different set of analytics to solve workforce issues.

Reallocating existing workforce: Utilizing operations research

Case Study:  In an effort to cut costs, a leading energy company set a goal of replacing only one worker out of every two retiring.  Reallocating existing employees would allow the company to save money and reduce the number of external hires. With a growing number of retirees and high employee turnover costing millions each year, the company wanted to find a way to capitalize on the internal mobility of its employees.

Solution:  By combining workforce analytics, forecasting and optimization software, the company was able to address complex workforce issues by:

  • Analyzing the career path of individual employees
  • Modeling the probability of mobility between job positions and locations
  • Calculating associated costs and time involved
  • Building optimization models to synthesize this information into recommended actions

Results:  Given the objectives, data inputs, decision variables and constraints, managers have acted on recommended actions from the optimization model.  Employee turnover and cost per employee have been dramatically reduced.  Furthermore, in less than 3.5 seconds, the management team can calculate, simulate and determine the optimal career path for approximately 100,000 employees, representing 3,000 employee segments and 3,000 possible career paths.  Management now focuses primarily on data-driven intelligence for decision making, thanks to the ability of the optimization solution to define what actions are needed to achieve goals, based on a rational and well-supported hypothesis.

Sample workforce optimization model: Reallocation

Maximizing retention of critical workers: Utilizing data mining retention model

Case Study:  One of the oldest banks in Europe (with 5,000 employees) was concerned about the high turnover rates of its key branch employees and sought to identify those who were most likely to resign and prevent loss of these valuable and expensive assets. Personnel data was stored in many different systems - one for payroll, one for management, one for employee profiles and so on.  

Solution:  The bank consolidated employee data into an analytical human capital data mart for one complete version of its employees. Through ad-hoc, "what-if" analysis and salary simulations, managers could quickly answer questions that had previously taken days.  By applying a predictive analytical retention model based on data mining technology, bank managers now have an accurate way to identify key employees who are likely to leave and why.  As a result, it reduced costs through better employee retention activities and faster reporting, and reduced employee turnover to three to four percent.

Sample workforce optimization model: Retention

Remaining focused

As you work to manage the impact of the financial tornado, your business and your workforce are likely to undergo many changes. It is up to today’s business leaders to use human capital information in a purposeful, precise and proactive way to optimize the work force. It’s not always easy to manage short-term transitions while maintaining your focus on long-term success. The most effective leaders will keep a firm grasp on their most pressing business issues by using information technology. That will help their organization remain focused and productive.

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About the Author

Becca Goren

Global Product Marketing Manager for Strategy Management and Human Capital Management - SAS

As the Global Product Marketing Manager for Strategy Management and Human Capital Management at SAS, Becca Goren develops and drives the global positioning and marketing plan for these areas. In this role, she leads research studies, authors white papers and articles, and speaks on these topics. Becca has led marketing efforts in related areas at SAS including customer relationship management, supply chain management, financial and IT performance management across a broad array of industries. Becca studied International Business and Speech Communications at the University of North Carolina at Chapel Hill where she received her BA.  Prior to SAS, Becca worked on the Balanced Scorecard initiative at MCI and has held several senior marketing positions in software companies.

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