Thursday, July 19, 2018

Revenue Per Employee Benchmarks By Industry

Sales per Employee Ratio

In a world with countless life hacks and must-have morning routines, we’re all searching for the latest and greatest way to boost productivity. As employees, we want to do more in a shorter amount of time. When we represent our organizations, we want to generate more revenue with fewer resources. And we’re all trying to show that the tools we use actually have an effect on productivity. 
The real challenge is proving that value of our efforts. How do you calculate productivity in employees, an organization, or from office productivity software?
This article will walk you through the different formulas to measure productivity at all levels (employee, organization, and software) and give some quick tips and tricks to improve productivity. We’ll also share a case study from market research firm, Forrester, and how they calculated the value of Smartsheet, an office productivity and work management tool. 

The sales per employee ratio is an asset utilization metric that allows analysts to understand how efficiently a company uses its staff to generate revenues.  Sales per employee is a popular industry measure; oftentimes used by both the investor-analyst and company management to benchmark performance.

Calculation

Sales per Employee Ratio = Net Sales / Full Time Equivalents
Where:
  • Net Sales = Gross Sales - Returns
  • Full Time Equivalent:  commonly abbreviated as FTE, the number of full time equivalents is calculated as the annual straight time hours worked by employees divided by 2,080.  A part time employee that works 20 hours per week would work 52 x 20, or 1,040 hours per year, while a full time employee would work 52 x 40, or 2,080.  Overtime hours are usually not included in the calculation of an FTE.

Explanation

Also known as sales per person, the sales per employee ratio provides the analyst-investor with insights into how efficiently a company uses its employees to generate revenues.  The metric is oftentimes used by company management, as well as investors, to benchmark a company's performance against industry peers.  Higher ratios indicate more revenue generated per employee, which is desirable.
Sales per employee is considered a very strong indicator of performance when evaluating companies in the services sector of the economy such as financial institutions, the banking industry,  producers of software, as well as retailers.
Manufactures can displace labor by automating production with capital equipment, making benchmarking more difficult.  In the same way, companies that engage in significant outsourcing activities can have unusually high sales per employee ratios.
Companies in the early stages of their development may be less efficient in their operations than more mature companies.  Tracking this metric over time allows the analyst-investor to understand if the company is becoming more efficient as it grows.

Example

The process improvement team at Company A wanted to understand if their recommendations were resulting in an increase in the company's sales to employee ratio.  Company A had been increasing sales over the last four years, and the process improvement team was working hard to ensure employees were working efficiently.

Sales per employee

This ratio compares the dollar volume of sales against the total full time employee equivalent of people working in the business.
Use information from your business' annual profit and loss statement and your employee records for that year to input into the calculator.
For information on using this calculator see below.
Input Sales$Field required
Input Average Number of Full Time Employees  
Input Average Number of Part Time Employees  
Input Total average weekly hours worked for all part timers  
Number of owners/directors  
A red star Field required indicates a mandatory field.
reset  calculate
You will need to consider whether people are employed on a full time or part time basis when using the calculator. For example, the number of people employed in a business could amount to 3.6 including 2 owners, one full time employee and one part time.
If you have part time employees, the calculator estimates the number of full time equivalents based on a 40 hour week.
The ratio provides a useful productivity measure. It can be used to assist monitor ongoing performance. It can also assist determine the level of sales that a business needs to generate when increasing staffing levels.
It is worth considering the role of employees when using the ratio. The degree of specialisation of employees tends to increase with larger businesses. Different employees will undertake varying roles such as sales, production, administration or management. Personnel directly involved in sales will tend to have a stronger influence on the ratio than those in other roles.
Ratios should be considered over a period of time (say three years), in order to identify trends in the performance of the business. You should seek professional advice when analysing or acting upon this ratio.

How do you know if you are over or under-staffed? If sales are increasing, are you hiring too quickly or slowly to keep up? If your sales are dropping, are you cutting too many or not enough employees?
These are tough questions to answer, and every business owner needs to carefully consider quantitative and qualitative information to make the best decisions. Let me explain how this works and then give a couple of examples. 
The most common quantitative measures to determine if you have the right number of employees are revenue per employee benchmarks. Your industry has a benchmark that you can get from others in your industry or from one of the CFOwise partner's Business Dashboards. Request a Free Industry Report. For example, certain medical device manufacturers average about $250,000 per employee. Just take your total sales revenue and divide it by your total employees or full-time equivalents (FTEs). You should also consider your total salary, wages, payroll taxes, and benefit costs as a percentage of revenue relative to your industry averages and your historical performance. 
The qualitative measurements include walking throughout your business and trying to determine how busy  your employees appear, listening to your employees complain about how they need more help if you expect them to keep up with the growing demand for your products, and more.
For example, your employees appear busy, but your sales are dropping meaning your revenue per employee is dropping, too. These two are not consistent, so you investigate to find out that your employees are taking longer to do the same work. In this example, the quantitative analysis wins and you know you need to "right-size" your staff.
Here's another example. Your sales are flat, yet your employees are increasingly complaining about being overworked. You investigate this inconsistency to find that the manufacturing function you used to outsource but then brought in-house is taking three times longer than anyone expected. Your analysis leads you to conclude you actually need to hire more employees to handle the extra work. When you compare the cost for the extra employees and the savings you generate from in-sourcing, you find you are actually more profitable than before, even though revenue per employee dropped.
A careful consideration of both qualitative and quantitative measurements will bring to the most effective staffing conclusions. Don't be afraid to ask the tough questions, and never depend exclusively on just the numbers or just subjective opinions.
Original Location: 
http://www.kenkaufman.net/2011/11/how-to-use-revenue-per-employee.html

Comments

Calculating Productivity in Employees

Many external factors can affect your organization’s productivity -- the national economy, a recession, inflation, competition, etc. Although you can’t control everything, you can control and measure employee performance. Employee productivity has a huge impact on profits, and with a simple equation, you can track productivity per individual, team, or even department.
You can measure employee productivity with the labor productivity equation: total output / total input. 
Let’s say your company generated $80,000 worth of goods or services (output) utilizing 1,500 labor hours (input). To calculate your company’s labor productivity, you would divide 80,000 by 1,500, which equals 53. This means that your company generates $53 per hour of work.
You could also look at labor productivity in terms of individual employee contribution. In this case, instead of using hours as the input, you would use number of employees.
Let’s say your company generated $80,000 worth of goods or services in one week with 30 employees. You would divide 80,000 by 30, which equals 2,666 (meaning each employee produced $2,666 for your company per week).
Labor Productivity Formula

Calculating Productivity by Industry 

While the formula to calculate employee productivity appears fairly straightforward, you may want to make tweaks based on industry. How you define and measure productivity changes based on your job, so you’ll have to adjust your equation.
For example, the unit of service (UOS) will change depending on the job. The labor productivity formula doesn’t require a UOS, but defining it can be helpful to add context to the output. A salesperson may have “calls made” or “deals closed” as his or her UOS, while a housekeeper in a hotel might have “rooms cleaned per shift” as her UOS. 

Benchmarks and Targets

Productivity benchmarks and targets also change depending on the industry. Some jobs already have basic benchmarks established. For example, customer service representatives have benchmarks that establish how long a “productive” call should take. However, many companies will have to establish these benchmarks themselves.
And, based on these benchmarks, you may decide to change the target productivity. In many jobs, like customer service jobs, employee don’t have much control over their own productivity (i.e. it depends on how many calls they receive, which they can’t control). In that case, it’s unrealistic to say they should target 100% productivity, so you may lower the target.

Industry Factors to Consider

When you calculate productivity using the labor productivity method, your outputs will change based on the industry. Here are some examples:
  • Sales: To measure sales productivity, you should measure a variety of additional outputs, like the number of new accounts opened, the number of calls made, and the volume of sales in dollars.
     
  • Services: The service industry is one of the hardest industries in which to calculate productivity because of the intangible outputs involved. You could measure the number of tasks performed or the number of customers served.
     
  • Manufacturing: If you manufacture goods, you may want to use output per worker-hour required to produce a single product. In other words, you would want to calculate the product cost of one unit.


Measuring Efficiency 

While productivity measures quantity, efficiency measures quality. You could calculate a very high productivity number per employee, but that number alone doesn’t give you any insight into the quality of work (in theory, an employee could seem very productive, but actually be producing horrible outputs). 
To compare the productivity numbers against a benchmark, you can compare the current productivity with the standard amount of effort needed for the same output. Divide the standard labor hours by the actual amount of time worked and multiply by 100. The closer the final number is to 100, the more effective your employees are. 
For example, let’s say the standard labor hours for a certain project is 80 and the actual amount of time worked is 92. You would divide 80 by 92, and multiply by 100, calculating your efficiency to be 87%.
Efficiency Formula
As you compare productivity and efficiency, there are a few different ratios to consider:
  • Idle time ratio: (Production downtime / total labor hours) x 100
     
  • Activity ratio: (Expected hours needed to produce actual output / actual hours need to complete) x 100
     
  • Labor capacity: (Actual hours worked / total budgeted labor hours) x 100

Longitudinal Reporting

The biggest benefit to measuring employee efficiency is in longitudinal reporting, where you calculate efficiency over a period of time. This allows you to identify trends that may impact how you organize staff, or hire and remove employees. Measuring long-term efficiency and productivity can also help you decide who should receive a promotion or bonus. And lastly, this type of reporting can play a role in predictive modeling: if you know an employee’s efficiency rate, then you can predict how many items/tasks will be produced or completed in a certain amount of time.


Calculating Productivity in an Organization

In an organization, productivity measures how certain resources are managed to accomplish timely objectives. It can also be defined as an index that measures output (goods and services) relative to input (labor, materials, energy). If organizations want to improve productivity, then they need to increase their output or decrease their input. 
Here are three ways to express productivity in an organization.
 
1. Partial factor productivity
This formula is made up of the ratio of total output to a single input. Managers tend to use this formula most often because the data is available and easy to access. Also, partial factor productivity equations are easier to relate to specific processes because they only deal with one input.
To calculate partial factor productivity, let’s say that a company produces $15,000 worth of output and the weekly value of all inputs (labor, materials, and other costs) is $8,000. You would divide 15,000 by 8,000, calculating a partial factor productivity of 1.8.
Partial Factor Productivity

2. Multifactor productivity
Whereas the partial factor productivity formula uses one single input, the multifactor productivity formula is the ratio of total outputs to a subset of inputs. For example, an equation could measure the ratio of output to labor, materials, and capital. This method is a more comprehensive measure than partial factor productivity, but it’s also harder to calculate. 
We’ve asked Dan Keto, a productivity expert from Easy Metrics, to provide an example to illustrate one possible multifactor productivity equation.
One of our clients manages cross-docking operations for one of the nation's largest retailers. Cross-docking is where you take imported containerized ocean freight, unload it, then reload it into outbound truck freight. It is basically like taking apart a Rubik's cube and then reassembling it. The industry paradigm is to look at the production metric for the workers handling the freight in terms of cases per hour (CPH). Over a longer period of time, this is a reasonable metric. However, to manage operations daily per employee, it is not effective.
Each freight container can have from 40 cases to 20,000 cases on it depending on the type of product on the container and have as many as 100 different SKUs. The freight mix has a dramatic impact on the time it takes to process the work. Depending on the container mix, CPH can vary from 20 cases per labor hour to over 400. Using CPH, the client was unable to have any consistency in either its productivity or labor forecast requirements because they were not using other factors present in the data to more accurately calculate the labor standard. By incorporating SKUs, splits, case weight and cube into the calculations, we were able to develop a multi-factor labor standard that could accurately and consistently predict the amount of labor required for each container of freight.  
Using a linear regression model, the standard formula for this method is

HOURS = AX + BY + CZ + D 

In the case of the above example, HOURS = A*(# Cases) + B*(Splits) + C*(SKUs) + D*(Cube) + E*(Weight) + F. The coefficients A-F are the calculated weighting factors multiplied against the input to get you the end result. These coefficients can be either calculated using time in motion studies (industrial engineering model) or if you have a large enough data set, linear regression tools. Modern technology and big data can now give even small operations the ability to calculate cost effectively multi-factor productivity standards.
The end result using the above example was that the client was able to see down to each employee what the productivity level was and then proactively manage and train accordingly.  Labor costs were reduced by over 30%.

3. Total factor productivity
This formula combines the effects of all the resources used in the production of goods and services (labor, capital, material, etc.) and divides it into the output. This method can reflect simultaneous changes in outputs and inputs, however they do not show the interaction between each output and input separately (meaning they are too broad to be improve specific areas).
Once again, this equation is hard to calculate. Our productivity expert from Easy Metrics, a labor management system, shares an example to illustrate one possible calculation. 
Measuring total factor productivity is both art and science. The key thing to keep in mind when building out this productivity metric is to focus on inputs that have a reasonable correlation for cost and efficiency to the output. Engineers will often want to measure every possible input factor around a process. Using big data analysis, we have often found that the correlation of many of these input factors is below the natural variance (noise) that occurs within the process, so collecting that information is often not worth the cost of doing so.
One of our customers is a large food processor that produces packaged vegetable products.  They have roughly 200 employees per shift, working 16 equipment driven production lines and approximately 1000 different product SKUs. Their standard metric was to look at lbs per labor hour produced to measure their efficiencies, however this can be very misleading because depending on what the product is, there is a high variance between each product.
We worked with them to increase the number of factors measured to get a clear understanding of overall productivity as well as identify areas to focus on that can drive higher productivity.  These factors were:
  • Machine uptime: Measured as a percentage of shift hours
  • Missing time: Variance between worker time clock time and time on production line
  • Labor standard versus lbs/hr: Developed multivariate production standards based on pack style and commodity type/mix.  Result scored as a percentage of standard where 100% means operating at expected productivity level.
  • Product yield/loss: Qualitative factor that measured output weight versus input weight
  • Input product quality factor: Commodity quality variances create variances in productivity
  • Production run factor: Takes into account time to switch lines over to new product types. Short runs proportionately have comparatively larger set-up times per pound produce.
The end result was a comprehensive reporting dashboard with one macro result of the total productivity factor, scored as a percentage where 100% is daily goal, then each above sub-factor broken down so they could identify deficiencies. Each sub-factor is weighted in proportion to its importance. Labor standards and machine uptime were give weightings of each 30% with the other factors weighted less since those two factors were the primary driver of productivity.

Total Productivity Factor = 0.30 × Machine Uptime + .10 × Missing Time + .30 × Labor Standard + .10 × Product Yield + .10 × Input Quality Factor + .10 × Production Run Factor

The client now has clear visibility into their operations and the information at hand to address deficiencies as they arise.


Other Things to Consider 

Once you have identified the formula that works best for you organization, there are a number of other factors to consider:
  • Productivity index: Because productivity is a relative measure, it must be compared to something else for the data to be valuable. You may have a set of numbers representing your organization’s productivity, but how do you know if those are good numbers or bad numbers? To understand your own organization’s productivity, you must create a productivity index. This is the ratio of productivity measured in a certain period of time to the productivity measured in a base period. For example, if the base period’s productivity is 2 and the following period’s productivity is 2.3, the productivity index would be 2.3/2 = 1.15, meaning that your organization's productivity had increased 15%. By tracking these numbers over time, you can identify patterns and evaluate success or failure.
     
  • Value added: Value added is a common measure for goods and services (output). It is the difference between what a customer pays and what the business pays for the raw materials. The higher the value of a product or service, the more money that can be put toward wages, profits, or taxes. Value added is calculated by subtracting the total cost from the sales of output.
     
  • Utilization: Utilization measures management’s work. It looks at labor (available time) to open work and indicates how well managers have used labor resources. For example, there are 450 working minutes in an eight-hour shift. If 380 of those minutes were spent actually working, that person’s utilization would be 84%.

Calculating Productivity From Office Productivity Software 

You know that technology helps your employees and organization be more productive, but how can you measure and prove it? 
Calculating the value of office productivity software isn’t as clear cut as calculating productivity for employees or your company. There is no single formula that will generate the ROI from a new tool. 
However, there are a couple of different tactics you can implement to help you prove the value of office productivity tools. 
  • Compare metrics before and after implementing the tool: Before you start calculating value and productivity from a tool, you must track metrics before the tool has been implemented. You need to have numbers to compare against, otherwise you won’t be able to attribute any upticks to the office productivity tool. If possible, track total numbers on a yearly, quarterly, monthly, weekly, and daily basis before the tool. For example, in a subscription-based company, you should track trials, conversions, upgrades, downgrades, and cancels for each timeframe. Then, after you implement the tool, you can track the same metrics and compare numbers. While you won’t be able to 100% correlate changes to the new tool (other factors could also be contributing), you’ll have a strong anecdotal cause.
     
  • Frame value in terms of employee time: Once employees are using the tool, ask each person how much time he or she is saving. For example, if your office productivity software automates a process that used to be managed full time by an employee, you’re essentially saving that person’s annual salary. Or, if the tool saves a certain employee 5 hours a week, figure out how much you are paying that employee per hour and multiply that by 5 to calculate the total savings per week.


Case Study: How Forrester Calculated Productivity from Smartsheet

Forrester Research, an independent technology and market research company, quantitatively calculated the value of Smartsheet, a collaborative work management tool, in its study, “The Total Economic Impact of Smartsheet.” Forrester’s approach acts as a case study to help other organizations figure out how to assign a monetary value to software. 

Forrester calculated the following business benefits from using Smartsheet:
  • 25% increase in productivity of work teams, leading to $3,609,375 in savings over three years.
  • 21% time savings for project, product, and process managers, leading to a $5,890,500 savings over three years.
  • A reduced cost of $835,200 over three years to consolidate customer issues, increasing the responsiveness of sales and service engineers.
  • Improved resourcing around projects, resulting in a cost avoidance of $450,000 over three years.
  • ROI of 1,437%

How did Forrester come up with these numbers? Here is the framework and methodology they followed:
  1. Interviewed internal Smartsheet employees across departments 
  2. Interviewed organizations currently using Smartsheet
  3. Constructed a financial model populated with the cost and benefit data obtained from the interviews.
  4. Risk-adjusted the financial model based on issues and concerns highlighted in interviews. 

After extensive research and collecting hard data, Forrester plugged in their findings into a table like this:
 

Here is a formula to replicate this approach for your own organization:
 
Number of teams in your organization X weekly productivity gain for team in hours X number of weeks per year excluding vacation X average hourly rate X percentage of utilization annually = increased productivity before risk adjustment 

Then, once you get that number, you’ll need to adjust for risk (this requires that you assign a percentage value to represent risk in your organization).

Make HR Decisions Using Revenue Per Employee KPI

Revenue per employee is another popular HR indicator. It is supposed to show the managers if the company has the right number of employees, what the costs are for lost employee and the costs of turnovers. Let’s have a closer look at this HR KPI and see how one can use it.

The Formula of Revenue per Employee

By the definition the revenue per employee is calculated as:
  • Revenue per employee = (Total revenue / The number of employees) x 100
The number of employees might vary during the reporting period, so companies take an average value. Also, if company employs part-time employees, then for this formula it is necessary to use FTE (full time employee equivalent).
Revenue per employee is another popular HR indicator

Values for Benchmarking

It makes sense comparing the revenue per employee in the company with industry average to have another point of view on the company’s efficiency.
Revenue and employees numbers are published online. For example, we can use a report by Inc.com for 2013 or use Hoovers website where you can find a company by name and then check out important financial details.
An expert in data visualization, Josh Laurito, in his blog gives an excellent example of how the revenue per employee can be visualized to have some insights. He chose to look at advertising, media and software companies where major expenses are employees. According to what we can see on the resulting chart the average revenue per employee for these companies is between $230,000 and $310,000.
Apple’s revenue per employee
In previous articles where I analyzed HR KPIs we had a look at best practices suggested by Apple (check out Apple’s strategy for turnover KPI). If we have a look at Apple’s revenue per employee then we’ll see that it is ‘off the charts.’ For 2013 it is $2.13 million per FTE (full-time equivalent) employee. Nice numbers, but what does it mean for a CEO of other IT companies? Let’s discuss it below.

How/if Revenue per Employee helps to execute a strategy better

The ultimate goal of any measurement effort is to help CEO and top managers execute a company’s strategy better. How would revenue per employee help in this case?
Before we go on with strategic objective of Revenue per Employee KPI, check out the story compiled by Verne Harnish in his short, but informative analysis of Revenue per Employee. By the way, he calls this KPI to be “Nation’s Most Critical Number.”
Here some major findings:
  • The HR productivity strategy of The Container Store is to replace three good employees with one great employee. Give great employees 160 hours of training and pay them twice as much.
  • Henry Ford did a great move that increased morale and motivation. After the success of Model T he doubled the salaries of his employees.
How revenue per employee helps to execute a strategy
Does KPI tell that you are over/under staffed?
CEOs like to use Revenue per Employee to check out if the company has the right number of employees. Together with benchmarking values this KPI for sure will give some idea about the correct balance of employees. But what if companies should really act according to this KPI?
Let’s have a look at Apple’s example. With $2.13 million per FTE compared to average $0.47 million per FTE Apple looks a “little bit” understaffed. Should they immediately invest in changing the situation and hire more employees? I doubt.
In exactly the same way, CEO of a small company can be mistaken about his conclusions. Lower than average “Revenue per employee” doesn’t mean that you need to start firing people; probably the company should work on another part of the formula, the revenue.
Finally, when comparing benchmarking values one should always take into account not only the industry, but the geographical location of your company.
Does KPI track important changes in productivity?
Another way of using Revenue per Employee KPI is to compare its current value with the past values. For example, company managers can find out that the number of employees increased by 40%, but the revenue increased just by 20%. As a result the revenue per employee is going down.
What management decisions can one take using this KPI? The company might have hired unnecessary employees, so the productivity of the employees might have decreased, or the market might have changed and now has to generate the same revenue so the company needs to invest more…
By itself the “Revenue per Employee” KPI won’t give us an answer. But the comparison to historical values is still useful as it might generate some insights for top managers.
Focusing on specific business unit
The simplest way to have more business insights is to focus the Revenue per Employee not on the whole company, but on the specific business unit or a person. It is easy to do for a sales force. As for these who are in R&D, Marketing, HR, etc. it worth using the HR ROI approach that we discussed.
Turnover costs and cost to hire
Revenue per employee KPI is useful when you need to work with other HR KPIs such as time to fill, turnover rate, and  hiring costs. You will be able to see that for a high-performing position your hire costs are not just the cost to find and train a new employee. You need to take into account lost revenue costs as well.
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Revenue per Employee in the Balanced Scorecard

Let’s have a look how this KPI is used in the HR Balanced Scorecard project. As always, you can use this web-based project to build your own HR Balanced Scorecard.
KPI Name: Revenue per FTE employee
Formula:   Revenue per FTE employee = (Total revenue / The number of FTE employees) x 100
FTE stands for Full-Time Employee Equivalent.
An example of strategy objective:
  • Focus on hiring excellent employees, rather than good ones;
  • Control and improve productivity of employees;
An example of action plan:
  • Measure revenue per employee across the company and in specific business units, compare actual values with historical values, and analyze the reasons of changes.
Do you have some thoughts about using Revenue per Employee KPI? Share them in the comments below!

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