Calculating Robot ROI
Google Trends data demonstrates that web searches for the phrase, “how much does a robot cost?” have doubled since 2009. From heavy duty six-axis arms to SME-friendly collaborative models, robot pricing can vary as wildly as the specifications of each machine. Determining the total cost and return on investment (ROI) of a robot isn’t straightforward, as Nigel Smith, managing director of supplier TM Robotics, explains.
Remember, the cost of deploying robotics can go far beyond the robot’s price tag. As well as installation costs, factories may need to build segregated work areas or additional backup power units before a robot can be deployed. That’s not to mention peripheral technology, such as sensors, variable robot grippers and any necessary mounting apparatus. When you factor in the robot’s engineering and maintenance costs too, budgeting isn’t always as easy as requesting a quote.
A report by the Boston Consulting Group suggested that, in order to arrive at a solid cost estimate for robots, customers should multiply the machine’s price tag by a minimum of three. Let’s say a costs £65,000, customers should therefore budget £195,000 for the investment. That said, should the robot require a more extensive equipment overhaul, such as the addition of auxiliary machinery or conveyors, a multiplication of four or five times the cost of the robot may be required.
Then, of course, there are variable costs to contemplate. These include the labour, energy, materials, ongoing maintenance and production supplies required to deploy a robot successfully. Due to the varying nature of manufacturing facilities, these costs can fluctuate dramatically depending on the industry sector and size of the operation. Plus, these costs are not always linear. Maintenance costs, for example, can change significantly during the machinery lifecycle.
Manufacturers can only calculate the ROI of an investment after establishing the robot’s total purchasing cost. Even then, manufacturers must consider several other elements, starting with robot use.
Consider the following example. A food manufacturer plans to use two to automate pick-and-place processes. The robots will run for three shifts a day, six days a week, 48 weeks of the year. The equivalent labour usually requires two operators per shift, equating to six operators to complete the same throughput over a working week.
Using the average salary of a UK production operative as an example, at £25,000 per annum, removing these roles would reduce labour costs by £150,000 a year. However, even with the deployment of a robot, human labour is not entirely eliminated. A good rule-of-thumb for labour estimations alongside a robotic system is 25 per cent of current costs, reducing the total labour budget to an impressive £37,500 per year.
Minus this figure from the total robot purchasing cost we determined earlier, and manufacturers have an estimated ROI for the first year. Choosing a reputable supplier and robot manufacturer will also ensure the longevity of the machine, allowing users to reap these ROI rewards for years to come.
With this calculation in tow, the reward of robot investment clearly outweighs any risk.
That said, there are some flaws in this method of ROI calculation. Unless a longwinded procurement and production analysis is completed, many of these figures are estimates. What’s more, this process does not consider problems that could occur, such as equipment breakdown or unplanned downtime. For a true reflection of ROI, manufacturers should conduct a thorough cost analysis based on the operations of their facility, as well as a risk assessment.
But what about the complementary benefits of robots that aren’t considered in this calculation? Robots are predictable and therefore offer peace-of-mind for delivering productivity gains to improve a factory’s bottom line. For instance, eliminating the likelihood of human error in manufacturing processes can reduce scrap material, minimise reworks and improve the consistency of products. Each of these factors represent increases to a manufacturer’s profit, separate to the overarching ROI of the robot.
Search queries for robot pricing are increasing, demonstrating a growing demand for productivity gains through robotic deployment. In fact, according to the Annual Manufacturing Report 2019, over three quarters of manufacturers are ready to invest in new technologies to boost productivity. No doubt, robots will be among these investments.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.