Customers are always on the lookout for new products and manufacturers have welcomed innovations in technology to cater to these demands and for increasing output. Undoubtedly artificial intelligence (AI) and machine learning (ML) are the recent technologies gracing this sector. What do the small and medium-sized enterprises (SMEs) in the manufacturing sphere need to know regarding artificial intelligence and machine learning? Based on the scope of both these technologies and the fact that they are even now developing, their meaning can be broad, leaving SMEs unsure of the benefits offered by them.
While AI is the reproduction of human intelligence procedures by machines, specifically computer systems focusing on building technology that can reproduce human intelligence, machine learning meanwhile is a subset of AI which can be defined as the machine’s ability to decide and forecast based on deep data analysis.
Regardless of the challenges faced by the manufacturers, AI and ML play a major role in ensuring that manufacturers continue their run with maximum efficiency. The role of both these technologies has become evident during the events of the previous 18 months and longstanding disruption to worldwide supply chains.
Why AI and ML are not popular with SMEs?
92 percent of senior manufacturing executives consider AI as a vital tool in enhancing their productivity as per the research’s revelation. Nevertheless, AI and ML are not so popular with SMEs and their execution rate in processes is very much lower. It’s time to examine the reasons.
- Pricing
SMEs contain a limited budget and hence they should be cautious in their choice of investment. Hence pricing is a vital hindrance for SMEs. AI solutions’ complete implementation cost differs, however, the minimal price is at least $20,000, while the maximum can be as high as $1,000,000. While many huge manufacturers are opting for high-end AI and ML strategies, SMEs in many scenarios remain in the beginning stage. i.e., they are just commencing to execute the data capture technology responsible for making AI and ML feasible.
Though businesses may have executed a few elements of AI and ML, still there is a vast difference between using a few basic functions and entirely knowing the inside outs of the technology.
- Speed
Another hindrance encountered by the SMEs is the deployment speed and incorporation. Overall, the rapid development of technology is a boon for the manufacturing industry, however, SMEs are the ones facing the brunt of the pace. They have to struggle a lot to catch up with the speed.
Employees need to have frequent training regarding the new technology or solutions in order to maintain, run, and execute the new solutions which are mostly expensive and tedious to the organization.
- Panic and Distrust
When new technologies emerge, it is quite common that distrust and panic arise too. This is because one may not be sure of the impacts these technologies may cause and the fear of losing their job is almost on everyone’s mind. It’s not a simple process to change the existing work culture and train the team in using the new technology and this scenario is aggravated when there are rumors floated that there is a chance of manpower reduction.
Managers are the ones responsible to initiate this process, however, they are skeptical regarding this due to the opposition they might encounter from the team members.
The benefits overshadow the flaws
There are many challenges encountered by the SMEs during the execution and development of AI and ML and one way to overcome this is through adoption which can significantly enhance efficiency, production, and business agility in the long run.
McKinsey’s research has revealed that using AI to observe and analyze machines in the factory decreases the machine downtime by half. This is because AI can swiftly and meticulously analyze a broad range of data points and use preceding histories to help the employees recognize the possible issues.
This data can then be used to forecast service needs and ensure machines are repaired before they break. This not only reduces downtime but also increases machine life expectancy. According to the same report, predictive maintenance will save the world around $500 billion.
AI surpasses humans in detecting patterns and concluding with accuracy. This enables department specialists to select which manufacturing processes to modify. The advantages for the business are numerous:
- Streamlining processes and identifying issues
AI and ML are masters in identifying issues and streamlining processes that escape the human eyes when they are fully incorporated and matured. This results in saving money and transforming the operation of the organization in the long run.
- Diagnosing the root cause of the problems
It is critical to identify the root cause of a manufacturing problem and move beyond ad hoc fixes. AI and ML help organizations in digging deeper into their data and offers an effective solution to diagnose the root cause of their problems which doesn’t require the need for hundreds of hours of manpower to accomplish.
- Enhancing planning
Having anything in excess or too little is always a problem. Similarly having too much stock in a particular place is useless while having too little affects production and planning. Thanks to AI and ML, businesses can respond efficiently to the market demands by forecasting long-term manufacturing needs thereby enhancing inventory planning and reducing the costs of supply chain management.
- Adhering to regulations
Manufacturers, like most industries, must stay up to date on regulations governing areas such as worker safety and product quality. AI and machine learning make this task much easier by instantly confirming that any new processes are fully compliant.
