Calculating the ROI of a Robotic Cell: A Comprehensive Guide

The integration of robotic cells in manufacturing processes has proven to be a game-changer for businesses. It has helped businesses achieve high efficiency, accuracy, and productivity while reducing labor costs and non-quality costs. That being said, the initial cost of automation is usually a cause for concern, and businesses need to assess the return on investment (ROI) before implementing a robotic cell.

More recently, there has been more and more pressure put on the reduction of manufacturing costs, forcing industrial companies to aim for a faster ROI. In some industries, the target for ROI has been reduced from three years to less than one year.

This puts a strain on the current state of automation and is further encouraging companies to turn to automation.

Calculating ROI can be a daunting task, especially for a first time, but it is essential, as it will allow the company to not only know if the investment in automation is worth the cost but it will also allow them to work through various existing automation systems to determine the best for their specific needs.

How to compute a Return on Investment

In the cobweb that is computing automation ROI, the following benefits and costs should be taken into account:

Total Benefits of Automation

The benefits of automation, regardless of the system put in place, include:

  • Increased accuracy and precision
  • Consistent cycle times
  • Reduced variability in the manufacturing process
  • Improved safety for workers
  • Reduced labor costs
  • Improved quality of the finished product

Total Cost of Automation

The total cost of automation includes both non-recurring and recurring costs:

  • Non-Recurring Costs: These are one-time costs associated with the implementation of the robotic cell:
    • Hardware equipment: robot, cell, effector, jig, additional equipment, table, cables, PLC, encoders
    • Integration services: training and integration days
  • Recurring Costs: These are ongoing costs associated with maintaining and operating the robotic cell. Such costs especially arise when launching a new reference in production:
    • Trajectory re-programming
    • Vision system re-programming
    • New jigs
    • Maintenance

Impact on Productivity

When considering the impact on the productivity of automating a manual workstation. Indeed the cycle time of the workstation should remain the same or, at least, the flow of the line should be equilibrated.

This is particularly the case for automating manual assembly workstations on moving lines. When changing such a station into a fixed robotic station, one must take into account the time it takes for the part to move from the moving line to the fixed station.

Do you want to create your own ROI calculator?

Contact us, and we’ll send you the template for your own ROI calculation.

Example – Automating a manual mastic application workstation on a moving line


💡For the purpose of this ROI calculation, we will take the use case of the automation of mastic application on a moving line, aimed at improving the efficiency of a car manufacturing assembly line.

All the nitty gritty calculations, as well as our hypotheses, will be made available on request by filling out the Typeform at the end of this article.

➡️ The customer, a car manufacturer, currently has a legacy assembly line with manual workstations on a moving line.

➡️ One of the mastic application workstations needs to be automated due to the high volume and low diversity of parts manufactured.

The project involves computing the different ROIs the customer can achieve with three different ways to automate the workstation:

  1. Automating into a fixed workstation (stop-and-go line)
  2. Automating the workstation on the moving line with traditional equipment
  3. Using GuideNOW with Inbolt to automate the moving line

➡️ The circumstances surrounding the project include a moving workpiece, manual legacy system, low mix, and very high volume of parts manufactured in France.

For this scenario and for simplification, we computed solely the initial total cost of automation, i.e., the non-recurring costs of the different automation options.

The expected benefits for the customer rely mostly on the reduction of labor costs.

1️⃣ Automating into a fixed station

The total cost of automation: approx. 70k€

Productivity : -14%

Cost of automation

On top of all the required components to automate the cell (robot, PLC, end-effector, etc.), there is an additional need for handling equipment in this scenario to transport the workpiece from the moving line to the fixed workstation. This proves expensive, takes up space, and lacks flexibility, thus driving high investment.

Impact on productivity

A fixed workstation involves a travel time of the workpiece between two stations. During this time, as no operation is performed on the car, such time is lost.

Usually moving from moving workstation to fixed requires a complete change in the entire process, leading to productivity loss and a necessary rebalancing of the line.

We anticipate a 14% decrease in productivity compared to the manual station. This means that the total number of cars going through the station per year will decrease due to the implementation, at least for a time.

2️⃣ Automating the workstation on the moving line with traditional equipment

The total cost of automation: approx. 110k€

Productivity: same as for the moving line, should the step cycle not change

Cost of automation

Automating a moving line with traditional equipment requires a heavy infrastructure which includes: encoders, sensors, etc. In such cases, a mobile jig that allows the robot to attach itself to the moving tooling carrying the workpiece is required to make sure the robot performs the process properly on the part. Such equipment is very expensive and constraining, resulting in high integration costs.

The productivity of the cell will remain the same as the manual station as there is no change in the process other than the addition of the robot.

3️⃣ Automating the Moving Line with Inbolt

The total cost of automation: approx. 55k€

Productivity: same as for the moving line, should the step cycle not change

Automating a moving line is the most expensive endeavor, but switching to Stop-and-Go will generate a loss of productivity in the cell and a necessary rebalancing of the line.

One of the most advantageous options for automating a moving line is pairing it with a real-time industrial robot guidance system like Inbolt.

Inbolt’s solution, GuideNOW, offers flexibility and is workpiece-centric, making it easy to set up thanks to a CAD model of the workpiece. This means there’s no need to constrain the environment (jigs) or use markers, which is especially useful for customers with high productivity.

Additionally, GuideNOW is hardware-independent and can work on any 3D vision camera, allowing for adaptation to different use cases through parameter optimisation.

Reach out to us for access to the template, and take the first step towards optimising your manufacturing process.

The Rise of Autonomous Industrial Robotics: From Structured to Unstructured Environments

Defining terms

From Structured to Unstructured

In robotics, we usually categorize robotic environments into three types: structured, semi-structured, and unstructured.

Structured environments are defined spaces (cells) where the robot moves along a predetermined path. These environments often use mechanical jigs and cages to ensure maximum robot accessibility and efficiency, as well as repeatability in terms of the position of the workpiece.

This environment can be referred to as a « constrained » one. However, any environmental alteration can lead to robot collisions and non-quality.

Semi-structured environments are mostly constrained, but they can accept some kind of unknown variation, like the position of the workpiece. Intelligence is programmed into the robot to help it react to these changes, but its flexibility is limited due to its perception capabilities.

Unstructured environments. Most natural environments are unstructured. They can be cluttered and filled with obstacles. Navigating these environments requires real-time perception and decision-making, the same way our eyes and brains work.

From Planned to Unplanned

Similarly, we can categorize events around us into two types:

Planned events, where the robot’s response to a situation is already programmed in advance.

Unplanned events, when something unexpected happens, like someone entering the robot’s workspace or a random workpiece appearing, the robot needs to be able to perceive it in real-time and adjust its movements accordingly.

Less Structured Environments will be at the Heart of Tomorrow’s Manufacturing Revolution

Until recently, most robots that were developed were pre-programmed industrial robots designed for repetitive tasks in structured environments. The purpose of these robots was to automate tasks that would otherwise be performed by humans.

But the future calls for robots to do more than just run automated processes in the background.

These new robots won’t replace human jobs but enhance them by taking on tasks that can be automated, freeing up time for more human-centered activities (we discussed this phenomenon in our newsletter).

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These robots will be able to operate in semi-structured and cluttered environments, and have the ability to continuously learn and adapt to unexpected situations. Some might even be able to operate in fully unstructured environments, though we aren’t quite there yet. Tesla is one of the most notable companies that use flexible assembly lines.

Photo by Spencer Lowell for WIRED

We’re seeing a paradigm shift in industrial robotics, just like we did with autonomous driving.

Autonomous Industrial Robotics is on the Rise

This shift is powered by better and more affordable sensors, better computing power, and a change in the mentality of industrials. Indeed, small and medium enterprises (SMBs) are increasingly willing to automate their manufacturing processes, which remain very little automated today. Such companies have, due to the high share of labor, very unstructured & unplanned production facilities. Making industrial robots autonomous will enable flexible and low-cost automation for such industrials. On the other hand, large OEMs are also pressured by the need to reduce production costs and are driving the adoption of autonomous industrial robotics.

This paradigm shift will have several impacts on the industry, as it will:

  • Eliminate the need for jigs and indexing systems to increase manufacturing flexibility, making it easy to launch a new product reference. This has been a hot topic for years but has really started to take off with the increased need for personalization we are seeing.
  • Reduce automation costs by decreasing infrastructure and minimizing the impact of the cell.
  • Reduce commissioning time for the cells, which will no longer require millimeter trajectory fine-tuning, thus eliminating the need to plan everything.
  • Improve safety and process quality.

Autonomous Industrial Robotics Driven by Real-time Environment Perception & Decision-making

Vision sensors allow robots to see the environment or the workpiece they are trained to work on.

2D vision sensors are electronic imaging devices that capture and process two-dimensional images.

They provide better accuracy and repeatability; higher resolution; faster, more reliable processing, and better scalability and flexibility. They also provide data in real-time, allowing for better decision-making and greater efficiency. 2D vision sensors are used to automate tasks like quality control and inspection, robotic guidance, or human-machine interaction. They are a cost-effective and reliable solution, but they have their limits.

They cannot detect depth or measure the size of objects and cannot recognize objects that are hidden from sight or occluded. Their accuracy is affected by lighting conditions, object shape, and object location. So it happens that some objects that are far away or difficult to detect won’t be detected at all. 2D vision sensors also struggle with textured surfaces, which can make them less reliable.

3D vision sensors are more robust and offer more advantages.

There are a variety of technologies that are used to apply vision sensors in industrial robotics.

Some of the more common technologies used include stereo vision, structured light, and time-of-flight cameras. Stereo vision typically uses two cameras to produce a 3D image, while structured light involves projecting a pattern onto the object being scanned and then analyzing the deformation of the pattern. Time-of-flight cameras, on the other hand, use infrared light to measure the distance to the object being scanned.

Whatever the chosen method chosen, these technologies allow for the generation of 3D point clouds, which can vary in terms of their density and accuracy, as mentioned in our article.

Inbrain, today’s fastest point cloud processing AI will power such revolution

At Inbolt, we developed the world’s fastest point cloud processing AI: Inbrain.

This state-of-the-art AI technology processes massive amounts of 3D data at an incredibly high frequency, making robot control in real-time possible thanks to real-time feedback from 3D vision sensors.

Inbrain is hardware agnostic and can process data from all types of 3D technology, enabling a robust and reactive robot guidance, even in unstructured and unplanned environments.

Such high-frequency robot control makes it possible to have robust, reactive & safe robot guidance in unstructured and unplanned environments.

GuideNOW, a real-time robot guidance system, is our first product powered by Inbrain.

Reach out for more information!

Get the brochure of the Inbolt GuideNOW System

The Inbolt GuideNOW system helps you automating complex operations easily and rapidly! Our camera mounted onto the robot tracks the workpiece in real time and guides the robot accordingly. No need for jigs, tooling, calibration systems anymore. The system makes it easy to automate a line with high product diversity.

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