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Case Study: How we Determined...

Case Study: How we Determined Optimal Staffing Levels

Written by Memory Nguwi

5 minutes read

The challenge

We were recently contracted by a major player in Zimbabwe. The company wanted to know if it was paying unnecessary labour costs as a result of overstaffing. The exercise required us to go into each department and establish if the department was overstaffed in its individual roles and job families and was an opportunity to identify departments that were understaffed. In this post you will read how we cut costs and improved productivity by reducing overstaffing and eliminating understaffing.

How we intervened

Human resources practitioners have often been said to rely on subjective judgement when choosing when to hire, maintain or downsize their workforce. This leads to overstaffing or understaffing which in turn leads to unnecessary labour costs or failure to meet targets and deadlines. To avoid this, we developed a methodology that includes the input of subject matter experts (Heads of Departments – HODs) and statistical techniques in determining whether headcount adjustments should be done in each role.

Broadly speaking, our methodology is defined as follows:

Defining headcount drivers – Each role consists of a specific set of duties. If business activity related to these duties changes, the number of incumbents required in the role changes. This is especially true for operational and tactical roles. For example, the number of required by a mine depends on the number of shifts the mine has. The number of Human Resources Officers a company requires depends on its total staff complement. The number of shifts and the company’s total staff complement are the headcount drivers for the Guard and Human Resources Officer roles respectively.
Engaging HODs to provide data – We encourage the participation of each HOD in the data collection stage. This encourages buy in from these important stakeholders as it improves the chances of them implementing our recommendations. We explained to all HODs in this company the importance of providing accurate data and how it would be used. We requested the following information from each HOD:
1. Average number of employees per quarter in each role
2. Headcount drivers – to analyse how many employees are needed to do the work
3. Average tenure and average salaries of employees in each role. Retrenchment costs in Zimbabwe are linked to the number of years a person has worked in an organisation and their average salary.
4. Estimated cost of hiring an employee by role
Data analysis – Where more than one headcount driver is provided, we combine the headcount drivers into an aggregate measurement of business activity using weighted averages. We then rank the resulting aggregate business activities for the period under study: for this particular exercise, we had seventeen business quarters. If a quarter has business activity of zero percent (0%), it implies that the quarter has the least activity when compared with other quarters. If a quarter has one hundred percent (100%) business activity, it implies that the quarter has the highest recorded business activity in the period under study.
The resulting ranked business activities are labelled as relative business activity since they are a comparison of several periods. We use ordinary least squares regression (OLS regression) to establish the relationship between the relative business activity and the number of employees in each role. Where the coefficient of determination (R squared) is less than 50%, we use simple proportion calculations to determine the required number of employees in a role.

The diagram below (click to enlarge) shows an example of the relationship between relative business activity and the number of employees in the Technician position.

In the example above, a 22.5% increase in relative business activity calls for an additional technician if workload is to be maintained. An R squared equal to 70.4% implies that the relationship between business activity and the number of employees is strong. The stated OLS regression equation can be used to determine the required number of employees given the current relative business activity. The isolated data point labelled 98% represents the technicians’ current relative business activity.

Given the established relationship between relative business activity and the number of technicians, the trend line suggests that the staff numbers are in excess of what is required. A downward adjustment of two technicians would shift the data point downwards towards the trend line which represents optimal staff numbers. If the point was below the trend line, an upward adjustment towards the trend line would be required.

To establish whether a department is overstaffed or understaffed, we determine the number of employees that would be required if relative business activity was at its 25th percentile level and the 75th percentile level respectively. If a department’s relative business activity is above its 75th percentile level, we flag the department as potentially understaffed. If a department has relative business activity that is below its 25th percentile level, we flag it as potentially overstaffed.

The argument here is that there is a natural relative business activity level that is neither too high nor too low. A significant deviation that is above the 75th or below the 25th percentile relative business activity level represents significant deviation from optimal staff numbers. If relative business activity per employee is too high, there is possibly employee burn out whereas if business activity per employee is too low, it is possible that there are too many employees doing little work.

Results

We flagged 63 employees as potential overstaffing for this company. We presented results in the following format for each department in a dynamic dashboard based in Microsoft Excel.

The example above shows a recommended downward adjustment of 2 employees in the technician role from 8 incumbents to 6 incumbents. Retrenchment costs corresponding to this reduction amount to $2,800. The savings to be gained from this adjustment amount to $16,800 per year. The number of months to breakeven represent the time it would take the company to recover the retrenchment costs if the recommendation is implemented. In this example, the company would need only two months to recover the retrenchment costs. All this information was summarised in a dynamic calculator in Microsoft Excel. See the table below (click to enlarge).

Reassignment of employees

There are options when it comes to adjusting headcount. Instead of terminating employment contracts, your organisation can reassign employees in the same job family from one role to the other if one role is overstaffed and the other is understaffed. For this to be implementable, employees should have the capacity to deliver once they have been reassigned. For the company in question here, most employees in operational positions had the same basic skills and could be reassigned which minimised the number of terminations.

Conclusion

By identifying the key duties carried out by employees in each role, Human Resources Practitioners can make better decisions regarding whether they should hire, maintain or downsize their staff complement. The use of statistical techniques in establishing the link between business activity and staffing levels brings objectivity to the decision making process. Furthermore, the process of monitoring your headcount is not a one-time exercise. You should continuously monitor your staff complement as business activity fluctuates to ensure that you do not overwork your employees due to understaffing or pay unnecessary labour costs due to overstaffing.

Team Members

Memory Nguwi – Managing Consultant, Industrial Psychology Consultants
Leopold Ramutsamaya – IT Consultant, Industrial Psychology Consultants
Tapiwa Chipoyera – Research Consultant, Industrial Psychology Consultants

Check out our previous articles on Key Drivers of Retail Sales Performance and on How to Reduce Workplace Accidents Using People Analytics.

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Memory Nguwi

Memory is an Occupational Psychologist, Data Scientist, Speaker & Managing Consultant at Industrial Psychology Consultants (Pvt) Ltd., a management and human resources consulting firm helping companies to maximize returns on human capital. He has extensive experience in Human Resources Management, in both line function and consultancy services.