We are all blessed to live in very exciting times. Exponential technological progress over the last couple of decades has influenced not only our personal lives but also heavily impacted business. Trends are obviously evolving occasionally, but it is safe to say that now is the time of advanced analytics.

Without a doubt, the such progression would not be possible without the underlying power of computation and services available via Cloud vendors’ products like Azure or AWS. In the following article, I will take a closer look at the nature and challenges of the Business Transformation process. I’m going to focus on the manufacturing industry. And propose the potential best way of becoming a data-driven organization leveraging cloud integration with AI.

AI Data Discovery

AI Data Discovery supported by cloud can be a game-changer for manufacturing

Why? Simple, because it enables producers to harness the power of their data to optimize operations, reduce costs, and improve overall efficiency. The manufacturing industry is a data-rich environment where vast amounts of data are generated from production processes, supply chain operations, quality control, and customer interactions. With the help of AI Data Discovery approach, manufacturing companies can extract valuable insights from this data to make smart data-driven decisions that improve their bottom line.

Find undiscovered patterns

One of the key benefits of AI Data Discovery is that it can help manufacturing companies identify patterns and trends in view of their to data that may not be immediately apparent. This can enable them to anticipate and proactively respond to potential issues before they occur, thereby avoiding costly downtime and reducing waste.

Additionally, AI Data Discovery can help manufacturers optimize their supply chain by providing real-time visibility into inventory levels, demand forecasts, and production schedules. This can help them reduce stockouts, improve order fulfillment rates, and ultimately improve customer satisfaction.

Anticipate what has been unpredictable so far

Another area where AI Data Discovery can benefit manufacturing companies is predictive maintenance. By analyzing data from sensors and other IoT devices, manufacturers can predict when equipment is likely to fail and proactively schedule maintenance before a breakdown occurs. This can help them avoid costly repairs and downtime and ultimately improve equipment reliability and uptime.

In conclusion, AI Data Discovery can provide significant benefits to manufacturing companies looking to become more data-driven. By leveraging the power of advanced analytics and cloud integration, manufacturing companies can:

  • gain valuable insights from their data,
  • optimize their operations,
  • improve their overall efficiency and profitability.

Benefits of AI & Cloud Transformation for industrial companies

As I mentioned above manufacturing companies are turning to Data and AI to help them increase productivity, reduce costs, and make data-driven decisions. The desire for data monetization is a key driver for cloud adoption and transformation, as cloud-based solutions provide the necessary computing power and scalability to train and run AI models on vast amounts of data.

This makes it possible to discover hidden patterns and insights that would be impossible to detect otherwise. Business users can also utilize traditional BI tools for data visualization once production data becomes well organized and accessible after data preparation.

AI Data Discovery

Improved business agility and cost control

Cloud-based AI systems have become increasingly popular in the manufacturing industry due to their ability to provide greater flexibility and scalability. By leveraging the cloud, companies can quickly adapt to changing business needs and easily scale their operations up or down as required. Cloud can also reduce the burden on in-house IT teams by outsourcing infrastructure management, allowing them to focus on other strategic initiatives.

All in all – the whole game is all about increasing the profitability of the business and thanks to cloud services manufacturing companies can better control IT-related costs and ultimately – adjust the balance of required investment versus expenses needed to design, implement and maintain AI solutions.

Role of edge computing in Cloud

It is also a very common misconception that cloud computing is not applicable to the manufacturing industry. Besides security concerns (which are by the way – totally inadequate) the main challenge is latency – production lines are often working at very high speeds, controlled by on-prem PLC-based systems which transfer signals to machines in a matter of a couple of milliseconds.

By definition – if you want to control your devices from the cloud and make some informed decisions based on an AI solution that is deployed in the cloud (let’s say adjust process parameters in real-time based on predictions performed on real-time data) – you must first transfer the data from PLC to cloud, perform inference, construct instruction and transfer it back to PLC – this will all take time. Maybe not a very long time, but still significant from the manufacturing process perspective.

However, edge computing in the cloud can help solve the challenges. It enables real-time data analysis and decision-making at the edge of the network, which is essential in manufacturing where production lines need to operate without interruption, while still taking advantage of cloud services.

Shifting mindsets and a chance to boost businesses transformation

The role of AI in the cloud transformation of industrial companies goes beyond just increasing productivity and reducing costs. Cloud-based AI solutions are transforming the way manufacturers approach their businesses by enabling them to make data-driven decisions at every stage of the production process.

One key advantage of AI in the cloud transformation process is that it allows industrial companies to leverage the power of machine learning to automate routine tasks and improve operational efficiency. Manufacturing organizations can ultimately focus on the most crucial aspects of their business instead of taking care of repeatable, manual processes.

Leveraging cloud-based AI solutions for supply chain optimization

AI in the cloud can also optimize the supply chain by providing real-time visibility into inventory levels, demand forecasts, and production schedules. This enables manufacturers to adjust their production processes in real-time to meet changing demand, reduce stockouts and improve customer satisfaction.

Gartner predicts that by 2023, ML will already be a well-embedded element across 25% of all supply chain technology solutions.​

According to McKinsey, AI application in the supply chain improves logistics costs by 15%, inventory levels by 35%, and service levels by 65 %, compared with slower-acting competitors.

Manufacturing Intelligence at a glance

Visual analysis and business intelligence (BI) have already proven to be powerful tools for data analysis in the manufacturing industry, providing a semantic layer that allows companies to harness information using business terms and KPIs instead of raw data.

However, when coupled with artificial intelligence (AI), the full potential of data streams can be unleashed, enabling companies to gain a deeper understanding of their operations, identify areas for improvement, and even predict future outcomes.

Elimination of the Six Big Losses

Manufacturing intelligence powered by AI in the cloud has numerous benefits, including the elimination of the Six Big Losses related to the availability, performance and quality of industrial equipment.

Manufacturing intelligence powered by AI. The elimination of the Six Big Losses

By using machine learning algorithms, predictive maintenance systems can analyze sensor data and anticipate when maintenance will be required, reducing downtime and increasing productivity. In addition, AI can be used for predictive quality analytics, enabling operators to control end-product quality and optimize manufacturing processes in real-time to achieve the best possible results.

React before a serious problem arises

Moreover, advanced anomaly detection algorithms can identify small deviations in equipment signals that are often difficult to recognize organoleptically, giving companies sufficient time to react before problems escalate. These applications of AI in manufacturing can lead to increased efficiency, reduced waste, improved quality, and better utilization of resources.

The combination of visual analysis, BI, and AI in the cloud can revolutionize the way manufacturing companies approach visual data discovery & analysis. The power of AI allows for deeper insights, better decision-making, and optimization of production processes, leading to improved outcomes and increased profitability.

AI and advanced anomaly detection algorithms

Common challenges of AI technology

The benefits of AI in manufacturing are clear. As with any new technology, the adoption of AI in manufacturing comes with its own set of challenges that companies must overcome in order to implement Industrial Analytics solutions successfully.

While the potential benefits of AI are undeniable, companies must navigate obstacles such as data sources quality and availability, data security, and the need for skilled personnel to implement and maintain AI solutions effectively:

  • No clear goals

A lack of clearly defined business goals and ROI can lead to a lack of direction in the implementation of AI technology in manufacturing. Without a concrete definition of the use case, it can be difficult to measure the success of the solution and justify the investment. It is important for companies to carefully assess their needs and goals to ensure that the implementation of AI technology is aligned with their overall business strategy.

  • Unstructured data

Another challenge comes with data quality and availability. Manufacturing data can be highly complex and heterogeneous and may be stored in a variety of different systems and formats. This can make it difficult to extract meaningful insights from the data and may require significant data cleaning and preprocessing before analysis can take place. Thankfully – the cloud allows building complex Data Platforms to overcome those data silos obstacles, but this will be covered in another article.

  • Importance of the initial phase

Implementing AI projects requires a different delivery methodology compared to traditional IT projects. The success of AI projects heavily depends on the initial phase of data discovery and validation of potential use cases. This allows manufacturing companies to make informed decisions about the feasibility of the project, assess the potential risks, and determine if the investment will bring the expected value. By having a clear understanding of the potential business value at the beginning of the project, companies can mitigate risks and avoid wasting resources on projects with low potential for return on investment.

  • Security challenges

As AI technology becomes increasingly widespread and important in various industries, the issue of data security has become a critical challenge for businesses. AI algorithms rely heavily on large amounts of data to learn and make accurate predictions and this data often contains sensitive and confidential information about customers, employees, and the company itself. As a result, it is crucial for companies to establish robust data security protocols to protect against cyber-attacks and data breaches. Another aspect is proper design and implementation of Data Governance and making sure that the right data is available to the right people at the right time. Effort related to setting those policies correctly is often underestimated, whereas a proper Data Management toolset is a must-have for organizations who are willing to monetize their data.

  • Lack of skilled people onboard 

One of the main challenges in implementing and maintaining AI solutions in manufacturing is the need for skilled personnel. AI is a complex technology that requires specialized skills and knowledge, including importance of data discovery science, machine learning, and computer programming. It can be difficult for companies to find and hire people with these skills, and even more challenging to retain them. Additionally, AI technology is constantly evolving, requiring ongoing training and development for personnel to stay up-to-date with the latest advancements. Without a team of skilled personnel, manufacturing companies may struggle to effectively implement and maintain AI solutions, hindering their ability to reap the benefits of this technology.

  • AI is not the answer to all issues

Finally – but probably most importantly – there still exists a belief that AI is sort of a magic wand that you can apply to data and have instant results and business value. Whereas it is one of the most complex kinds of solutions companies can adopt, especially due to the fact that AI algorithms are not deterministic – you cannot be really sure of end results before you apply them. Maybe there is not enough historical data collected, maybe there is a need for installing more sensors on the production line or maybe simply there are no correlations in the data itself and the initially chosen goal must be adjusted – those are just examples of many open questions that should be answered prior to fully blown AI solution implementation.

AI Data Discovery Service

AI adoption in the manufacturing industry can be a complex process. Companies need to have a comprehensive understanding of their data and use cases to ensure that their AI initiatives will provide significant value. At our company, we offer AI Data Discovery service to help industrial sector customers overcome this challenge.

AI Data Discovery

a. Identifying potential use case

Our AI Data Discovery process begins with a thorough understanding of our client’s business objectives and challenges. We identify potential use cases that can benefit from AI and conduct a detailed feasibility study to assess the viability of each use case. We then analyze our clients’ data to identify correlations and patterns that can be used to develop effective AI models.

b. Workshops

During our workshops, we educate our clients about the potential of AI and cloud technology and help guide them through the organizational transformation process. We provide a detailed adoption roadmap that outlines the steps needed to implement AI solutions effectively. Our customers receive a final effort estimation, architecture and infrastructure high-level proposals, and a report analysis that empowers them with a solid foundation for leveraging AI.

c. The result

With our AI Data Discovery services, manufacturing companies can mitigate risks and avoid wasting resources on projects with low potential for return on investment. Our data-driven approach enables companies to dramatically increase their chances for successful business transformation and data monetization, giving them a competitive edge in the market.

Dashboard 1

AI Data Discovery - Dashboard

Dashboard 2

AI Data Discovery - Dashboard

The future of AI

The future of AI is both exciting and uncertain. On the one hand, AI has the potential to revolutionize industries and transform the way we live and work.

With advances in machine learning, natural language processing, and computer vision, AI is becoming more sophisticated and capable of performing tasks that were once thought to be exclusively human.

On the other hand, there are concerns about the impact of AI on employment, privacy, and security. As AI becomes more powerful, there is a risk that it could be used to automate jobs and displace workers or be weaponized for malicious purposes.

To ensure that the future of AI is positive and beneficial for all, it will be important to develop ethical and responsible AI systems, establish regulations and guidelines for their use, and invest in education and training to prepare people for the changing job market.

Right now you can see what our Industrial Analytics Accelerator solution based on machine learning and artificial intelligence offers.
Check out the demo.

Industrial Analytics Accelerator

Summary

The next years of AI and manufacturing evolution are promising, as AI is set to change the industry in the coming years. With advancements in machine learning and data analytics, AI can help manufacturers optimize their operations, reduce costs, and improve overall efficiency. AI-powered predictive maintenance can help prevent machine breakdowns, while AI-powered quality control can help detect defects earlier in the production process.

Additionally, AI can help manufacturers develop more personalized products and improve supply chain management. As technology continues to evolve, we can expect to see even more innovative solutions and increased adoption of AI in the manufacturing industry.

While AI is not a universal solution to all manufacturing problems, it has the potential to provide significant benefits to companies that adopt it.

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