According to a recent survey report released by a research organization, the value that artificial intelligence will bring to the manufacturing industry will reach US$2.3 billion by 2022, and is expected to reach US$16.7 billion by 2027. From automation and predictive analytics, to natural language processing (NLP) and computer vision, the results of adopting any form of artificial intelligence can be seen in the success and success of early adopters such as IBM, Intel, General Electric, Siemens and others. Business growth.
#This article will look at some of the ways manufacturing companies can benefit from implementing artificial intelligence into their processes. Additionally, various applications of artificial intelligence will be shared that will help businesses save costs and improve processes, regardless of product details.
Industry experts point out that leveraging advances in robotics, 3D printing and artificial intelligence can improve efficiency, Reducing costs and improving safety are critical. The benefits of artificial intelligence to manufacturing are twofold. On the one hand, people see the unprecedented growth and scalability it offers businesses, and on the other hand, the positive impact on employees and their productivity and satisfaction.
Forecasting inventory levels and demand has always been a challenge. While traditional methods like Excel sheets and probabilities based on last year’s demand and sales may have worked before, artificial intelligence now helps achieve a new level of accuracy. Using large amounts of historical data, trends and current events, and leveraging the right artificial intelligence tools and machine learning models to predict business needs, the highest level of accuracy can be guaranteed. This includes every part of the supply chain. For example, which products sell the fastest at certain times of the year; how quickly companies run out of certain products when demand fluctuates, and so on. Therefore, collecting historical data and enriching it with real-time data can provide an accurate picture of the demand outlook. It also increases sales and inventory turns while reducing costs and overproduction.
According to data from the World Economic Forum, one-fifth of global carbon emissions come from manufacturing. This includes waste, overproduction and, of course, carbon emissions from fossil fuels. Therefore, using technology to minimize the negative impact of production on the environment is an issue that companies should address as early as possible. Having already adopted digital technologies, the next step for many manufacturing companies is to make the data they collect more transparent. Not only will this become the benchmark for decarbonization efforts, it will also win the trust of customers. Using artificial intelligence technology to monitor emissions throughout the production process, transportation, equipment, etc., the actual carbon footprint can be understood. As a result, businesses can optimize their efficiency, predict emissions, and plan for future needs and regulations.
Artificial intelligence can help enterprises transform and optimize internal and external processes by maximizing productivity and profitability. Changes to workflow can impact costs, production quality, delivery and every aspect of the production process. One of the biggest improvements in the product lifecycle is automation. Some of the benefits it offers include reducing costs and time to market by automating complex or repetitive tasks, eliminating risks prone to human error, enabling more scalable production lines, increasing productivity, and minimizing energy consumption.
Introducing artificial intelligence into the manufacturing process has an equally important and valuable impact on employee satisfaction and mental health. According to one study, artificial intelligence improved mental health, especially among low-skilled workers by 2.342 points, and by 2.070 points among workers born before the 1980s. Reaching these numbers is not surprising when you consider the impact that artificial intelligence can have not only on the business aspects of manufacturing but also on corporate employees. It decreases over time, aiding in learning new skills and techniques while reducing the time required to onboard and generally improving the work environment. In addition, using artificial intelligence can improve employee productivity by automating repetitive tasks such as data entry and creating Excel sheets. This way, employees have more time to focus on other more important aspects of their work.
Quality assurance is often an afterthought, which results in additional unplanned costs , delays in time to market, customer dissatisfaction, and loss of company reputation. To eliminate these risks, Accedia created a solution for one of its customers in the manufacturing industry to help their employees, engineers and customers predict future failures in bearing production. The project leverages machine learning and computer vision models to identify and classify damage in uploaded images of failed bearings. Robust cloud distribution allows the benefits of predictive analytics to spread across customers’ factories around the world and detect production errors before bearings reach the end customer. It also allows for precise root cause analysis and production optimization. A McKinsey & Company report claims that AI can improve defect detection by 90% compared to manual inspection.
According to a recent study, about 90% of all robots currently in use can be found in manufacturing facilities. When people talk about robotics in manufacturing, people usually think of hardware. However, robotics relies as much on hardware as on software. Using advanced artificial intelligence and machine learning models, robots can perform tasks in production plants faster than humans while eliminating the risk of errors. All robots specialize in specific tasks and are completely independent of human supervision. This means that while robots are responsible for assembly, material handling, welding, material distribution or handling, employees can focus on more advanced and business-critical tasks.
The use of robots on the manufacturing floor is likely to attract greater sales and higher investment, and will improve quality and repeatability. It will greatly increase flexibility and speed to market. Automating manufacturing processes and outsourcing tasks to robots will allow payroll budgets to be allocated to retraining talent and supporting business growth.
Through artificial intelligence technology, especially natural language processing (NLP), the most common method of publishing reports is chatbots. Natural language processing (NLP) is a fairly new technology that understands unstructured human language and converts it into structured data that can then be analyzed. Using chatbots, manufacturing employees have access to accurate, real-time information about different production levels, machine parts, and their condition at any time, which is extremely important, especially in time-sensitive situations. Other natural language processing (NLP) and chatbot use cases can include customer support automation, delivery or update notifications, management floor inquiries, inventory and supplier checks. Artificial intelligence will provide additional benefits such as quick and easy access to databases and knowledge, improved efficiency and operations, and innovative interactive experiences for end users.
Another important use case of artificial intelligence in manufacturing is industrial network security. This can include IoT breaches, supply chain infections, phishing, intellectual property theft, and even ransomware, which can result in the loss of large amounts of money and valuable data. Unfortunately, as a lucrative industry, manufacturing is an obvious target for hackers. As a result, more than 40% of manufacturing companies suffered cyber attacks in 2020 alone.
Adopting recommended security guidelines and cybersecurity frameworks is a must for everyone. However, this is sometimes not enough to address threats and minimize risks. As a result, relying on AI-driven cybersecurity strategies is becoming the new norm. It allows detection of malicious internal reconnaissance behavior, command and control attacks (including the use of external remote access tools), SMB brute force attacks, account scans, and more. Artificial intelligence can detect all of these threats and attacks in real time and take remedial actions faster, more effectively, and more accurately. It can also collect data on all network traffic, analyze logs and events, and predict threats.
According to a recent survey report by Deloitte:
As competition in the global market becomes increasingly fierce, more and more manufacturing sectors have joined the artificial intelligence game - food, pharmaceuticals, chemicals, automobiles, electronics, etc. However, increased implementation of the AI technology stack will not be without challenges. The number one obstacle companies face in researching artificial intelligence is the need for skilled talent and a lack of trust in internal resources. So, as early adopters have shown us, the best way to accomplish this daunting task is to outsource it to a dedicated AI team.
It is now possible to see numerous applications of artificial intelligence in manufacturing and its benefits in predicting maintenance needs, optimizing manufacturing processes, managing supply chains, scaling up or quality control . Cost reduction is difficult until parameters like sales and quality are increased, then the right AI technology stack and software partner can make it happen.
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