*rodney disgust
Since the Industrial Revolution, companies have invested in improving their production processes and technology is the result of this process and the evolution of human knowledge. Over the last two centuries there has been much progress in all segments of the industry. Today, artificial intelligence (AI) and Machine Learning are just two of the latest technologies in the industry.
But what do small and medium-sized enterprises (SMEs) that work in manufacturing need to know about these two technologies? Given the scope of both and the fact that they are still evolving, their significance can be broad, leaving industrial SMEs uncertain about the benefits they provide. And we still have to take into account the Internet of Things (IoT) and 5G, which came into operation in many cities, starting from the big centers.
Simply put, AI is the simulation of human intelligence processes by machines, especially computer systems, and focuses on building the simulation of human intelligence, considering all the limits it imposes. Machine learning, on the other hand, is a subfield of AI, which can be defined as the ability of a machine to make decisions and predictions based on deep analysis of data.
Given the events of the past two years and the long-term disruption to global supply chains, it's clear that both technologies have an important role to play in ensuring that industries continue to operate as efficiently as possible, regardless of the challenges they face, from the point of view of economics and global geopolitics. The biggest companies in this sector have made some positive inroads when it comes to AI and machine learning, but progress in smaller industries has not been as fast.
Per that many small industries are not using AI and machine learning?
A recent survey – https://www.themanufacturer.com/articles/power-artificial-intelligence-manufacturing/ showed that 92% of senior manufacturing executives already saw AI at the time as an essential tool to increase their productivity. However, the smallest companies in the segment still had a lower rate of AI and machine learning implementation in their processes. Below, we can examine the reasons:
1 – Pricing is a major setback for many industries, and with a limited budget, your business leaders need to choose carefully which investments they will make. The cost of implementing complete AI solutions varies, but is generally at least US$ 20,000 and can be as high as US$ 1,000,000. Furthermore, while many larger manufacturers are improving their AI and machine learning strategies, smaller ones, in many cases, are just beginning to implement the data capture technology that these technologies make possible. A company may have implemented some elements of this evolution, but there is a big difference between having a few basic functions implemented and fully mastering the technology.
2 – The speed of implementation and integration is another obstacle for small companies. While the rapid evolution of technology is good for the manufacturing sector as a whole, smaller companies may struggle to keep pace. Organizations frequently need to bring in experts to teach workers how to run, maintain and implement new solutions, which is often expensive and time-consuming.
3 – There is a certain level of fear and suspicion surrounding the effects these technologies will have on maintaining employment. At best, it's not easy to change work practices and train staff to use new technologies – and this is significantly compounded when the suspicion prevails that jobs may indeed be at risk. Managers are often hesitant to initiate the process because of this perception.
You benefits outweigh the inconveniences
While there are a number of challenges that small businesses face when implementing and developing AI and machine learning, adoption can help dramatically improve efficiency, throughput, and business agility in the long term. Consequently, the financial results at the end of the day.
One McKinsey research – showed that when AI is used to monitor and analyze equipment in production, it can cut machine downtime in half due to its ability to quickly and thoroughly analyze a vast array of data points and leverage past historical data to help the team identify potential problems. This data can be used to predict service requirements and ensure machines are fixed before they break down. Not only does this reduce downtime, but it also increases the machine's life expectancy. The same report expects the worldwide savings on predictive maintenance to be around US$ 500 billion.
Artificial Intelligence is specifically capable of detecting patterns and drawing conclusions precisely and at a rate that humans would not be able to match. This allows managers to choose which manufacturing processes they would like to change. The benefits this brings to the business are numerous:
Removing bottlenecks and identifying new efficiencies: When fully integrated and given time to mature, AI and machine learning can be instrumental in spotting where processes can be streamlined and identifying issues that human eyes cannot discern. This can help save money and revolutionize the way the organization operates in the long run.
More accurate root cause analysis: Getting to the source of a manufacturing issue and moving beyond a short-term approach of ad-hoc fixes is crucial. AI and machine learning allow organizations to dig deeper into their data than ever before, providing an effective way to diagnose issues at their source, without taking hundreds of man hours to achieve;
Better supply chain management: having too much stock in one place can be a huge waste, while too little can seriously affect production and planning. AI and machine learning can help companies better respond to market demand by predicting long-term production requirements, which improves inventory planning and lowers supply chain management costs.
Meet regulatory standards: as in most industries, industries need to keep constantly up to date with regulatory standards in areas such as worker safety and product quality. Artificial Intelligence and Machine Learning make this task much easier as they can be used to instantly confirm that all new processes are fully compliant with industry specifications.
Investing in future-proof technologies
Like large manufacturing organizations, small industries must be in the race to make use of new technologies, as they will be key to maintaining the ability to compete in the market. There are some hurdles to overcome, but the rewards are well worth the time and investment. Success won't happen overnight, as integrating AI and machine learning is a gradual, iterative process. With the right strategy in place and a strong determination to succeed, companies can greatly improve their current operations and profitability.
Making use of business applications that are resistant to the future is an important premise to be followed. It is not worth betting on technologies that can be left behind and not be able to receive the necessary evolution from their suppliers. Currently, future proof is related to the development of applications that have the capacity to receive constant and consistent updates.
Artificial Intelligence and Machine Learning can help improve all processes of production and business management, but their benefits may lose their effect if the business software applications used to run the business do not have the capacity to support the growth of the company. The challenges are enormous for a business, which has shown itself to be highly promising, to fall by the wayside just because the chosen systems have not evolved enough.
*rodney disgusted, CEO of Magic Software Brasil.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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