To understand the enormity of this shift, here are 3 statistics you need to be aware of:

• AI, machine learning, and robotics will drive 70% of GDP growth over the next decade.
• By 2030, AI will contribute an estimated $15.7 trillion to the global economy, more than the current output of China and India combined.
• 62% of leaders are putting plans in place to succeed in a world filled with connected machines and smart automation – 16% are already investing and performing strongly.

It’s now clear that decision-making AI and machines will be the primary driver of economic growth over the next decade in what’s being referred to as the “Machine Economy”. This is especially true in the manufacturing sector, where machines and AI can help companies create a competitive edge by driving down costs, increasing efficiency, and opening up new markets.

To remain competitive, manufacturing companies need to start data-focused AI and machine learning initiatives immediately. The good news is that the manufacturing sector is in an especially good position to take advantage of these next-generation technologies.

Top 10 applications of AI, machine learning, IoT, and robotics in the manufacturing sector

1.Speed up production processes

By automating tasks and processes, AI and robots can speed up production, while reducing waste and increasing efficiency. In fact, many manufacturing companies are already using AI and robots to boost their productivity.

For example, auto manufacturers use robots to weld and paint cars, while food companies use robots to package and sort products.

The use of AI and robots can also help to improve the quality of products while also reducing costs. As a result, there is no doubt that these technologies will continue to play a major role in manufacturing in the years to come.

2.Reduce energy consumption

In manufacturing, companies are always looking for ways to reduce energy consumption. After all, manufacturing is a very energy-intensive process, and even a small reduction in energy use can have a significant impact on the bottom line.

One way that manufacturing companies are achieving this goal is by using AI and robots. By using these technologies, companies can more accurately control manufacturing processes and make them more efficient. As a result, AI and robots can help manufacturing companies to reduce their energy consumption and improve their bottom line.

3. Improve product quality

Artificial intelligence and robots are increasingly being used in manufacturing to help improve product quality. By analyzing data collected from sensors throughout the manufacturing process, AI-enabled systems can identify patterns that indicate when a product is likely to fail. This information can then be used to make adjustments to the manufacturing process in order to improve quality.

In addition, robots can be used to carry out quality control tasks such as inspection and testing. By automating these tasks, manufacturing companies can reduce the amount of human error and improve the overall accuracy of their quality control procedures.

4. Enhance safety

There is no doubt that manufacturing companies have to deal with a lot of health and safety concerns. From preventing injuries to protecting workers from hazardous materials, there are many dangers that need to be taken into account.

However, AI and robots can help to enhance safety in manufacturing companies in several ways.

AI can be used in manufacturing settings to keep track of employee movement and identify potential hazards. For example, if an employee is working on a machine that requires repetitive motion, AI can be used to monitor the employee’s movements and identify when they may be at risk of developing an injury. Additionally, AI can be used to monitor environmental conditions in manufacturing facilities and identify when conditions may be unsafe for employees.

Secondly, robots can be used to carry out tasks that are dangerous for humans, such as working in areas where there is a risk of exposure to hazardous materials. Robots can also be equipped with sensors that allow them to detect when something is amiss and raise the alarm, which can help to prevent accidents and injuries.

5.Prevent equipment breakdowns

In manufacturing, even a small equipment breakdown can have major consequences. Production delays, product defects, and safety issues can all result from a manufacturing equipment failure.

As manufacturing companies strive to reduce these risks, they are turning to AI and machine learning technologies. By analyzing data from IoT sensors and other sources, AI and machine learning can help to predict when manufacturing equipment is likely to break down. This information can then be used to schedule maintenance before the equipment fails, thus preventing production disruptions.

In addition, AI and machine learning can also be used to identify manufacturing process improvements that can reduce the likelihood of equipment breakdowns. As manufacturing companies adopt these technologies, they are becoming better equipped to prevent equipment failures and the costly disruptions they cause.

6. Streamline administrative tasks

Manufacturing companies are increasingly turning to artificial intelligence and machine learning to streamline administrative tasks. By automating repetitive tasks such as data entry and generating reports, manufacturing companies can free up their employees to focus on more strategic tasks.

In addition, AI and machine learning can help manufacturing companies improve their forecasting accuracy, meaning that they can better predict demand and adjust their production levels accordingly.

In addition, AI and machine learning can help manufacturing companies to improve their customer service. For example, by using chatbots, manufacturing companies can provide 24/7 customer support.

7. Generate new business opportunities

The manufacturing sector is under pressure as consumers demand more personalized products and faster turnaround times. In response, manufacturing companies are turning to AI and machine learning to help them meet these challenges.

By analyzing data from past sales, customer feedback, and market trends, AI can identify gaps in the market and suggest new product concepts that would meet customer needs. Machine learning can then be used to refine these concepts and develop prototypes. This approach can help manufacturing companies quickly bring new products to market, giving them a competitive advantage.

Additionally, AI and machine learning can be used to develop prototypes of new products faster and more cheaply than traditional methods. By leveraging AI and machine learning, manufacturing companies can become more agile and responsive to the needs of their customers.

8. Optimize inventory management

In the manufacturing industry, inventory management is a critical component of ensuring that production runs smoothly. By keeping track of the materials and finished products on hand, manufacturing companies can avoid disruptions due to shortages or delays.

However, inventory management can be a complex and time-consuming task, particularly for large companies with multiple production facilities. Fortunately, AI and robotics can help manufacturing companies optimize their inventory management processes.

By automating tasks such as tracking stock levels and placing orders, AI can help manufacturing companies to reduce the amount of time and labor required for inventory management.

In addition, robotics can be used to build, store, and move products in warehouses, reducing the need for manual labor, and making it easier and faster to restock inventory.

9. Address the skills shortage

In recent years, manufacturing companies have been struggling to find qualified workers to fill open positions. This skills shortage has been caused by a number of factors, including the retirement of experienced workers and the reluctance of young people to enter the manufacturing field.

As a result, many companies have turned to AI and robotics to help fill the gap. By automating tasks that are traditionally performed by human workers, these companies can reduce their reliance on scarce labor resources.

Robots can also be used to train new employees, and AI can be used to identify potential candidates for open positions.

10. Improve marketing efforts

Manufacturing companies are always looking for ways to improve their marketing efforts and reach new customers. In recent years, AI and machine learning have emerged as powerful tools that can be used to analyze customer data and identify patterns that can be used to improve marketing efforts.

For example, AI-powered marketing tools can be used to segment customers, personalize messages, optimize pricing, monitor customer satisfaction levels, and identify potential issues early on.

In addition, AI and machine learning can also help manufacturing companies to automate their marketing processes, saving time and money.

Data is the Lifeblood of the Machine Economy

It’s clear that the manufacturing sector has a lot to gain from these technologies. While the opportunities that these new technologies bring are incredibly exciting, none of it is possible without data.

Not only does the Machine Economy depend on data to operate, but all these smart applications, autonomous machines, and connected devices will also continue to generate exponentially increasing amounts of data.

Unfortunately, data teams across all industries continue to face daunting challenges in the process of consolidating, preparing, and delivering reliable data to stakeholders.

Data scientists still report spending around 45% of their time just on data preparation tasks. Data preparation is an extremely tedious and time-consuming and costly process that often involves:

• Data discovery to identify data sources
• Data profiling to determine data attributes and data quality
• Data ingestion into data warehouses or data lakes using complex ETL processes
• Data cleansing to deal with data errors such as missing data, wrong data, or illogical data
• Data enrichment to fill in missing information such as location or date
• Data transformation to convert data from one format to another

What you need is a smart, fast, and flexible tool that will help you quickly build the data and analytics foundation you need to make data-driven decisions, improve operational efficiency, and drive innovation in the manufacturing sector.

Your Options: Stack, Platform, or Builder

Approach #1: The Stack

Traditionally, the data preparation process has relied on a highly-complex stack of tools, a growing list of data sources and systems, and months spent hand-coding each piece together to form fragile data “pipelines”.

Approach #2: The Platform

Then came data management “platforms” that promised to reduce complexity by combining everything into a single, unified, end-to-end solution. In reality, these platforms impose strict controls and lock you into a proprietary ecosystem that won’t allow you to truly own, store, or move your own data.

It’s clear that these old approaches to data management simply cannot meet the needs of data teams in the modern economy.

Fortunately, there is a third approach.

Approach #3: The Builder

In order to overcome the data management challenges listed above, data professionals need a solution that meets all 3 of these criteria:

• Low-Code: It must be smart enough to build your entire data estate for you by automatically generating all the underlying code and documentation, from end to end.
• Agile: It must provide both technical and business users with a simple, drag-and-drop user interface for quickly ingesting, preparing, and delivering corporate data for analytics and AI/machine learning.
• Integrated: It must seamlessly overlay your data storage infrastructure, with no vendor lock-in, while integrating all the data ingestion, preparation, quality, security, and governance capabilities you need into a simple, unified, metadata-driven solution.

Meet TimeXtender, the Low-Code Data Estate Builder

TimeXtender empowers you to build a modern data estate 10x faster by eliminating manual coding and complex tool stacks.

With their low-code data estate builder, you can quickly integrate your siloed data into a data lake, model your data warehouse, and define data marts for multiple BI tools & endpoints – all within a simple, drag-and-drop user interface.

TimeXtender seamlessly overlays your data storage infrastructure, connects to any data source, and integrates all the powerful data preparation capabilities you need into a single, unified solution.

Because all code and documentation are generated automatically, you can reduce build costs by 70%, free data teams from manual, repetitive tasks, and empower BI and analytics experts to easily create their own data products – no more bottlenecks.

Contact us to learn how we can help you build a modern data estate 10x faster and become data empowered.