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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.
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).
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.
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.
When you calculate productivity using the labor productivity method, your outputs will change based on the industry. Here are some examples:
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%.
As you compare productivity and efficiency, there are a few different ratios to consider:
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.
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.
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
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:
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.
Once you have identified the formula that works best for you organization, there are a number of other factors to consider:
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.
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:
How did Forrester come up with these numbers? Here is the framework and methodology they followed:
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).
Increased productivity before risk adjustment X risk adjustment percentage = risk adjustment in dollars
Increased productivity before risk adjustment - risk adjustment in dollars = final increased work team productivity
After you’ve gone through and calculated productivity at the employee, organization, and software level, the next logical question is, “How do I improve the productivity?”
Here are some simple tips and tricks to improve productivity at all levels:
Empower your people to go above and beyond with a flexible platform designed to match the needs of your team — and adapt as those needs change.
The Smartsheet platform makes it easy to plan, capture, manage, and report on work from anywhere, helping your team be more effective and get more done. Report on key metrics and get real-time visibility into work as it happens with roll-up reports, dashboards, and automated workflows built to keep your team connected and informed.
When teams have clarity into the work getting done, there’s no telling how much more they can accomplish in the same amount of time. Try Smartsheet for free, today.
Notes from trying to learn about Vitamin-R version 2:1
global:
in-app:
The purpose is to break up large tasks into short time slices with specific reachable objectives. Each slice moves closer to the goal while not triggering fight-or-flight response to prevent procrastination.
now & later board is a full-features RTF scratchpad to leave anything on to be a “memory outside yoru brain” (c.f. exocortex)
It is also a good place to review before taking a break (a la priming).
It is divided into four different sections:
There are ready-made templates that get inserted at the beginning of the relevant sections and walk you through the questions to ask and the things to write down. You can restore them by clicking on the gear icon in the bottom-left of the Board.
Examples:
The FastType feature allows you to create action items, bulleted lists and checklists with a minimum of fuss by automatically translating certain characters into those formats.
Commands:
* Tools → Statistics → Toggle Statistics Window (or Cmd-1)
* The bottom-left popup menu chooses display period
It is really important to choose the right time and place for a specific kind of work and it can be tricky to work out what really works best, instead of acting like all chunks time is interchageable. Finding the right times to do the right sort of work helps get rid of internal and external “friction” in our effort.
The Statistics Window provides many tools that allow you to gain objective insights into the work patterns that actually work best for you.
Overview ChartL Allows you to evaluate both the total time worked during the preceding period and how your concentration levels evolved during that period.
Examples:
Day-of-Week Analysis: evaluate how your concentration levels evolve throughout the week. (longer = more data the better).
Commands:
log book is see how spend time for insights, keep motivated.
Things to store:
® ⇒ Settings ⇒ Preferences ⇒ Cloud Sync ⇒ Activate Dropbox Synching (remember to do this on every Mac with Vitamin-R). Normally logs are stored in ~/Library/Application Support/Vitamin-R
Uses Growl if available for notifications.
Customize through “® ⇒ Settings ⇒ Preferences ⇒ Notifications”
® ⇒ Settings ⇒ Preferences ⇒ Integration
When you are asked to specify your objective for the next time slice, you can drag one or more to dos straight from Things into the text field (window must be open so no auto-hiding)
W(Optional) hen the time slice is logged as “completed” (in the Rate Your Time Slice step) the to-do will automatically be marked as “completed” in Things.