Human Resources Decision Management

Some of the hardest decisions in any business are the human resource decisions. How to attract, hire, and retain the best people. How to manage pay and bonuses. How to ensure the right organization for your business. These can be among your biggest HR challenges.

Optsee^® can help you manage your organization's most difficult HR decisions more effectively and objectively. You'll be able to quantify and rank your choices based on your inputs and strategy. With Optsee^®, you can quantify even "soft" attributes so that they are considered in the decision model along with the more easily quantifiable attributes. Plus, Optsee^® gives you a consistent decision process so you're not making the same decision over and over again using a different process each time.

Let's take a look at an example of using Optsee^® to allocate merit raises among a group of technical services engineers. The results will be used to distribute annual merit raises based on a percentage of pay from a high of 5% to a low of 0% to a team of fifteen engineers that have been evaluated against six job performance attributes. At least one of the highest performers will receive a 5% raise and at least one of the lowest performers will receive no merit increase (0%).

(You can also see an example of using the Optsee prioritizer™ in a hiring decision by clicking here.)

Each engineer will be evaluated using the following 6 attributes:

Quality of Work: Competency, accuracy, neatness, thoroughness.
Productivity: Time management, volume of work accomplished, ability to meet deadlines, productivity levels.
Job Knowledge: Degree of technical knowledge, understanding of job procedures and methods, , problem solving, decision making ability.
Working Relationships: Cooperation and ability to work with supervisor, co-workers, and customers.
Supervisory Skills: Training and directing subordinates, delegation, evaluating subordinates, planning and organizing work, ability to communicate.
Growth Potential: Willingness and ability to take new and different assignments, continuing education, adding value to company outside Technical Services.

Each attribute has been entered into an Optsee^® decision model shown in Figure 1:

Figure 1 [View Larger Image]

Note that the attributes have been ranked (left column) in order of importance based on the weight assigned in the right column (higher weight = higher rank). For example, in this model, "Quality of Work" (1,800) is considered 3 times as important as "Growth Potential" (600) and "Productivity" (1,600) is considered twice as important as "Supervisory Skill" (800). Of course, you could change these weights to whatever reflects your relative attribute values, as well as add or subtract attributes.

Each attribute numerical scale assigned to it where "5" is the best outcome and "1" is the worst outcome. Each engineer will be ranked on each attribute based on this scale as follows:

Outstanding: The employee has exceeded all of the performance expectations for this attribute and has made many significant contributions to the efficiency and economy of the company
Exceeds Expectations: The employee regularly works beyond most of the performance expectations of this attribute and has made significant contributions to the efficiency and economy of the company.
Meets Expectations: The employee has met the performance expectations for this attribute and has contributed to the efficiency and economy of the company.
Needs Improvement: The employee has failed to meet one or more of the significant performance expectations for this attribute.
Unsatisfactory: The employee has failed to meet the performance expectations for this attribute.

Figure 2 displays the portfolio containing the names of each employee as well as their individual attribute values. The employees have been ranked in order of "overall attractiveness" (second column) based on their individual attribute values and the order of importance of each attribute as defined by the decision model in Figure 1. The employees range from a high attractiveness value of 88.7 to a low of 30.5.

Figure 2 [View Larger Image]

You can see the distribution of the employees relative to the top three attributes in the comparison chart below (Figure 3). In both the charts, the "best" employees are in the upper-right (green) quadrants and the "worst" employees are in the lower-left (red) quadrants. The bubble radius is proportional to "Job Knowledge". In this figure, it is clear that the employees with the lowest job knowledge, in general, are also the lowest in productivity, quality of work, and overall attractiveness.

Figure 3 [View Larger Image]

If we stopped here, we'd probably decide to give Edward Marvin and Susan Chambliss the highest salary increases, and offer no increase to Albert McNally, Robert Nichols, and John March. We could then distribute the remaining salary increase to the other employees relative to our remaining budget and the relative rank of each employee.

However, instead of using just a single fixed-weight decision model, we are going to evaluate the portfolio in multiple decision models using the Optsee Prioritizer™ to obtain a statistical ranking of the employees.

First we'll set up the prioritization as shown in Figure 4. We'll test the portfolio in 5000 models with the highest attribute weight being 2000. These settings mean that the portfolio will be evaluated in 5,000 models, and that the highest possible attribute weight is 2,000 and the lowest possible attribute weight is 1. We'll also set the "Attribute Order" to "Ranked Attribute Order" which means that the attribute order will be ranked the same in all the test models as the parent decision model (Figure 1, left column) but the attribute weights (Figure 1, right column) will be changed in each test model.

Figure 4

The results are displayed in several charts. The prioritization statistics chart displays the average rank and standard deviation for each chart (Figure 5). In this result, Benjamin Marvins is clearly the top performer (blue circle) followed by 4 others (red circle). The bottom performers are in the grey circle.

Figure 5 [View Larger Image]

Another way to look at these results are by using the Cumulative Percentage chart (Figure 6). Benjamin Marvins was ranked first in ~70% of the models (Point A), second or higher in ~84% (Point B), and third or higher in ~95%.

Figure 6 [View Larger Image]

The cumulative percentage data can also be displayed in a bar chart (Figure 7). Here, the height of each bar represents the normalized area under the curve (AUC) for each engineer, therefore, the relative height represents the strength of each individual based on the cumulative percentage outcome.

Figure 7 [View Larger Image]

The results from the prioritization are also summarized in the Summary Statistics list form (Figure 7). The cumulative percentage rankings are displayed numerically in column 6.

Figure 8 [View Larger Image]

From this analysis, it is clear that Benjamin Marvins is the top-ranked performer, and Albert McNally is the bottom-ranked performer. The engineers ranked in between could have their merit raises allocated to them based on their cumulative percentage ranking, for example. By using this system, you've captured and integrated the key merit performance criteria, and used it to rank the employees in a way that is fair and objective.

As you can see from this example, Optsee^® provides you with a number of powerful ways to visualize and analyze your HR decisions.