Many managers however have concentrated their efforts on implementing new systems, partnering with better suppliers, fine tuning marketing proposals or outsourcing particular functions. Of course these are all essential elements to achieving increased efficiencies for the operations of an organization, however there is another critical factor that many overlook when seeking to increase the performance of their company and this is probably the most vital factor to the success of any enterprise. This critical factor is the human factor; the people that work with new systems and make decisions that impact the bottom line activities for any firm.

The Importance of Employee Productivity
Many have attributed recent gains in firm productivity to the investment in and implementation of new Information Technologies (e.g. faster hardware and flexible software applications along with the power of the Internet).

This makes perfect sense, but studies have revealed that in many cases, mere investment in new technology many times does not result in the increased profitability or productivity that was initially expected. Why is this? Because technology itself does not run the operations of an enterprise. The secret to success is combining the right amount of adequately trained, and properly motivated employees with the appropriate tools that enable them to most efficiently carry out their tasks.

This last sentence introduces a powerful and far-reaching ideal to corporations around the world. The human asset of an organization is probably the most important input to a firm’s activities and therefore, the process of managing that resource should not be taken lightly. One can invest in the most sophisticated CRM system or adopt the latest ERP platform, but if the organization does not have the right mix of personnel to support and utilize these technologies, the results could be catastrophic for the firm’s bottom line.

The management of a company’s labor resource is however a complex task. Other forms of organizational inputs (e.g. machinery, information technology, materials) posses a tangible, straightforward characteristic, however the “human element” is a complex variable.

When connecting the right individuals to the right task, one must consider skill level, compensation, demographic attributes, ability to work with others, communication skills and the list goes on. Mastering this task results in increased efficiency for the entire organization. Properly managed labor resources result in more productive activities at the individual level, reduction in under or over utilized workers on the aggregate, and an overall reduction in employee attrition for the firm as a whole.

HR management with Information Technology
The world of IT has come to the aid of the Human Resource manager. Today there are a number of software vendors with corresponding systems that enable managers to view and analyze information about organizational personnel. This information corresponds to all levels of workers; from administrators to mid management to the CEO level. The information boom since the mid 1990s has involved the collection and storage of vast amounts of data that describe operational activities of organizations.

IT systems, over the years, have enabled business managers and analysts to leverage off this data in a more efficient manner. Through the use of data warehousing and reporting systems, decision makers can now more effectively retrieve meaningful data, manipulate it, analyze it and communicate it within departments and across functional areas. The key to the process is the creation of information from available data. This information then increases the knowledge of decision makers of business activities, which enhances the strategizing process.

More specific to the Human Resource area, many of these systems revolve around enhancing the ability of managers to view HR data both numerically and graphically in a flexible, accurate and timely manner. Managers can easily select variables according to level of aggregation to view such topics as turn over or attrition rates, retirement trends, or employee performance according to specific departments, functional areas and branches and subsidiaries. The result is a better-informed decision maker who, with the power of information, can better manage the “human resources” for their particular enterprise.

The systems mentioned above are essential to the world of HR in that they empower a large number of users to quickly view important information of what has happened or is happening in a firm’s operations; for example:

1) What was the turnover rate for North Eastern programmers over the past 3 quarters?

2) What was the rate of retirement for the entire organization?

3) How much has sales performance dropped off in the NY region?

4) What was the rate of customer complaints in our service department?

This is critical information regarding various operational activities of an organization. However, a limitation of this scenario comes when decision makers need to formulate strategies. When making decisions on how to better motivate employees or reduce attrition rates or hire new employees, managers often need to make assumptions as to the results of particular policy initiatives. For example:

1) If I take this action I expect to reduce attrition by a particular amount, or

2) If I implement this compensation policy, I can expect performance to increase by a particular amount or customer dissatisfaction to decrease by a particular amount.

Analytic Decision Support Technology (A Critical Value Added)
The world of IT has more recently augmented this knowledge creation process to a new level. High-end analytical technology can give decision makers the power to avoid ad-hoc or “gut feel” decision making, as it enables them to actually view the corresponding outcomes to particular strategic initiatives. This helps reduce the uncertainty of “what to expect” following the implementation of HR policies. The key to this process is availability of data that describes the process you are looking to analyze. This data issue may have been an obstacle for many organizations years ago, but as I mentioned at the beginning of this article, organizations today are collecting more data than any time in history.

The next step is for the decision maker to incorporate those variables that play a role or explain that process they are looking to analyze. Variables for an attrition analysis may include descriptive (demographic) information of employees along with variables that describe their activities within the organization, (e.g. what department they’re in; what manager they report to; the amount of their last salary increase…).

High-end, decision support analytics then process this information to determine whether there are relationships between variables that may drive the process you’re analyzing. Analytical methods not only determine whether certain variables are reliable in influencing the chosen process but identify just how much they impact a particular target measure of interest.

For example, the charts below illustrate how employee training impacts their performance, (Chart 1). The graph shows that after 12 days of training, there is no increase in performance. Chart 2 illustrates how certain compensation schemes motivate workers.

Decision support models, which incorporate a number of critical variables, ultimately give managers/decision makers, the ability to perform “what if” simulations, which produce tangible outcomes to proposed strategies. The figures below provide a simplistic view of how analytical methods facilitate “what if” analysis.

Sales Force Performance: Driving Variables: -Bonus Plan                          Semi Pay -Training                                4 Weeks per year -Client Visits                         10 per week -Phone Time                          2 Hours per day Target Variable: Sales Generated    $420,000 per qtr

Sales Force Performance: Driving Variables: -Bonus Plan                          Quarterly Pay -Training                                2 Weeks per year -Client Visits                         10 per week -Phone Time                          2 Hours per day Target Variable: Sales Generated    $300,000 per qtr

Figure 2
Managers can fine tune their policy initiatives by viewing how a change in particular “drivers” impact target variables which is Sales Generated in this case. This “what if” or sensitivity analysis can be done by changing one or all of the driving variables to see corresponding expected changes in the specified process measure.

These methodologies incorporate mathematical and statistical techniques which enable users to determine whether there are reliable relationships between variables in their business scenarios and then quantify the relationship between them, (e.g. if I increase salary by (x%), I can expect (y%) change in resulting performance). Although many of these methodologies have been in existence for some time, recent innovations in software capabilities have made their usability much more user friendly and are increasingly meant to be used by managers and decision makers.

Industries and Applications
Today, organizations across industry sectors are using this technology to help solve a number of HR related problems. Common applications include:

- Employee Attrition

- Retirement Trends

- Hiring Initiatives

- EOE Analysis

- Employee Performance (Sales, Customer Service)

- Compensation Effectiveness

Of course, some applications play more significant roles depending on the HR manager you speak to and the industry they operate. The Sales Force Performance application mentioned above and the closely related Motivational or Compensation analysis is of particular interest for firms that depend on the successes of their sales personnel. This is a widely encompassing sector which scans from IT vendors to Pharmaceuticals.

Customer Service applications play a major role for Retailers and CRM initiatives, while other applications such as Equal Opportunity Employment analysis play an important role in large Fortune 500 sized and governmental organizations. Also, the more rigid structures of governmental agencies have interest in retirement analysis in order to maintain optimal personnel across job classifications.

Finally, attrition analysis is a critical factor for industries in all sectors as organizations realize the importance of retaining valued employees as they wish to avoid the costly, complex and timely task of finding new employees.

Through the use of the analytic technology mentioned above, decision makers can reduce the uncertainty of the expected results of their policy initiatives. By doing this, they can achieve a more optimal HR infrastructure that is made up of properly placed, motivated and performing personnel, which is a key factor to overall firm productivity.

Closing Comments (Analytical Technology, A Crystal Ball?) Analytical, decision support-based technology can no doubt play a critical value added role in the strategizing process for HR managers and decision makers. However, it must be mentioned, that these systems are not a tell all provider of the answers to problems at hand.

To put their value in context, they should be viewed as tools to help provide greater insights into the everyday processes that comprise HR activities. Analytical technology can give individuals greater understanding on how certain variables impact measures and whether certain factors are important at all to the problem at hand.

They enable users to leverage off of the vast data that is more and more accessible to them. They should not be viewed as a mechanism to replace the experience and wisdom of experienced managers but rather as an aid to help optimize and improve strategies at hand. The realm of Human Resource management is a widely encompassing field, where the task of maintaining the right mix of motivated and efficiently functional employees is vast and complex. Today’s availability of data and technology can help managers achieve greater success.