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By Stephan Romeder, vice president Global Business Development at Magic Software Enterprises.
Is your organization prepared to reap the benefits of the modern digital industry?
Manufacturing has faced many challenges, including rising operating costs, operational inefficiencies, reduced budgets and the need to launch products faster. Digital technologies such as Artificial Intelligence (AI), cloud and analytics can allow manufacturers to differentiate themselves with increasing productivity and reducing operational risk, thus improving product quality and customer satisfaction.
However, many business processes in today's production environment are often filled with information gaps and many large organizations are paralyzed by legacy IT architectures or legacy systems, with their inability to properly manage manufacturing processes, that involve multiple data sets between the shop floor and the back office.
A checklist of preparation for the Industrial Internet of Things (IIoT) and AI can help assess your organization's ability to support the factory of the future.
1. Are the processes based on paper?
You may be surprised that something as complex as outsourcing components from multiple suppliers and factories within the group itself, including products with long lead times, is still being controlled using Excel. However, relying on paper processes will prevent the ability to synthesize information from various data sources and calculate, communicate and compare key performance indicators (KPIs) to continuously improve productivity.
2. Are there manual processes that can benefit from automation?
In many cases, manufacturers still rely on manual processes for quality control and auditing to measure regulatory compliance. Automation is essential, not only to save man hours and reduce product defects, but also to provide the flexibility needed to adjust to the rapid rate of change in regulations and new product requirements.
3. Do you have a complete real-time view of shop floor operations for pre- and post-production?
Many manufacturers suffer from many operational inefficiencies, such as delays, false starts and rework, which can be avoided by using real-time data to make better decisions. By using IoT to automatically collect operational data and artificial intelligence to analyze results, manufacturers can improve the accuracy of decisions in real time and minimize risks.
4. Are the systems integrated, from end to end, including ERP, CRM, MES etc., to increase productivity?
To ensure the factory of the future there needs to be a production environment in which shop floor and back office systems can easily communicate and offer real-time access to critical data anywhere in the supply chain on any system . A steady stream of performance and production data needs to be collected, stored, analyzed and shared efficiently, reliably and securely. When systems can communicate seamlessly, an end-to-end process can be optimized. For example, when the MES and ERP systems are fully integrated and there is an equipment problem, spare parts can be ordered and a service ticket for a qualified technician can be generated automatically.
5. Did you create new business models based on your digital transformation activities?
There are many new business processes that are created due to improved ways to collect, share and analyze production data. For example, operations can be monitored and even controlled remotely. Different processes or departments operating in silos can collaborate better to create more efficiency and value. Consumer goods companies can detect and reduce theft by tracking products across the supply chain. With the advent of IIoT and AI, there are numerous new ways to use connected operations to create differentiation and advantage, but before technology can be implemented, new business processes need to be identified and analyzed. This is an opportunity for companies to take their business to the next level, providing new revenue streams, opportunities to produce value and growth - to differentiate themselves from their competitors.
6. Are you using data completely or are you stuck in an ocean of data?
For many plant managers, the increase in data as a result of IIoT just creates more and more work. The deployment of sensors to monitor machines and products offers vast opportunities for automated collection of operational and supply chain data.
According to a recent survey, "80% or more of the production systems have legacy equipment that was never designed to communicate beyond the factory floor." Driven by the need for real-time access to critical data from multiple data sources, as well as collaboration across the supply chain, more and more production software applications are migrating to the cloud and using advanced, predictive analytics for better operational intelligence . This includes processing data from multiple data sources, which allows for the identification of unexpected failures and events, more accurate decisions in real time, reduced operational risk and a better understanding of customer behavior.
7. Are you still relying on encrypted connections between systems?
Manual system integrations can be more efficient in the use of resources in the beginning, since projects are limited in scope and, generally, internal resources can be used. But over time, these designs can multiply quickly and become more difficult as more and more systems become part of a manufacturer's information network. A more practical and resource-efficient solution may be to adopt a third-party software platform capable of providing scalability and proven unified data manipulation for all integrated systems.
The advantages of AI and IoT will only increase as the technology matures and more use cases are developed. However, before advanced technologies can be implemented, processes need to be automated and systems need to be connected. That said, to reap the benefits of the era of modern digital manufacturing, new business processes that can benefit from shared information need to be identified and analyzed. After the relevant business processes are digitized, an IT infrastructure for sharing and analyzing production data can power the factory of the future. The digital divide is happening now.
However, many business processes in today's production environment are often filled with information gaps and many large organizations are paralyzed by legacy IT architectures or legacy systems, with their inability to properly manage manufacturing processes, that involve multiple data sets between the shop floor and the back office.
A checklist of preparation for the Industrial Internet of Things (IIoT) and AI can help assess your organization's ability to support the factory of the future.
1. Are the processes based on paper?
You may be surprised that something as complex as outsourcing components from multiple suppliers and factories within the group itself, including products with long lead times, is still being controlled using Excel. However, relying on paper processes will prevent the ability to synthesize information from various data sources and calculate, communicate and compare key performance indicators (KPIs) to continuously improve productivity.
2. Are there manual processes that can benefit from automation?
In many cases, manufacturers still rely on manual processes for quality control and auditing to measure regulatory compliance. Automation is essential, not only to save man hours and reduce product defects, but also to provide the flexibility needed to adjust to the rapid rate of change in regulations and new product requirements.
3. Do you have a complete real-time view of shop floor operations for pre- and post-production?
Many manufacturers suffer from many operational inefficiencies, such as delays, false starts and rework, which can be avoided by using real-time data to make better decisions. By using IoT to automatically collect operational data and artificial intelligence to analyze results, manufacturers can improve the accuracy of decisions in real time and minimize risks.
4. Are the systems integrated, from end to end, including ERP, CRM, MES etc., to increase productivity?
To ensure the factory of the future there needs to be a production environment in which shop floor and back office systems can easily communicate and offer real-time access to critical data anywhere in the supply chain on any system . A steady stream of performance and production data needs to be collected, stored, analyzed and shared efficiently, reliably and securely. When systems can communicate seamlessly, an end-to-end process can be optimized. For example, when the MES and ERP systems are fully integrated and there is an equipment problem, spare parts can be ordered and a service ticket for a qualified technician can be generated automatically.
5. Did you create new business models based on your digital transformation activities?
There are many new business processes that are created due to improved ways to collect, share and analyze production data. For example, operations can be monitored and even controlled remotely. Different processes or departments operating in silos can collaborate better to create more efficiency and value. Consumer goods companies can detect and reduce theft by tracking products across the supply chain. With the advent of IIoT and AI, there are numerous new ways to use connected operations to create differentiation and advantage, but before technology can be implemented, new business processes need to be identified and analyzed. This is an opportunity for companies to take their business to the next level, providing new revenue streams, opportunities to produce value and growth - to differentiate themselves from their competitors.
6. Are you using data completely or are you stuck in an ocean of data?
For many plant managers, the increase in data as a result of IIoT just creates more and more work. The deployment of sensors to monitor machines and products offers vast opportunities for automated collection of operational and supply chain data.
According to a recent survey, "80% or more of the production systems have legacy equipment that was never designed to communicate beyond the factory floor." Driven by the need for real-time access to critical data from multiple data sources, as well as collaboration across the supply chain, more and more production software applications are migrating to the cloud and using advanced, predictive analytics for better operational intelligence . This includes processing data from multiple data sources, which allows for the identification of unexpected failures and events, more accurate decisions in real time, reduced operational risk and a better understanding of customer behavior.
7. Are you still relying on encrypted connections between systems?
Manual system integrations can be more efficient in the use of resources in the beginning, since projects are limited in scope and, generally, internal resources can be used. But over time, these designs can multiply quickly and become more difficult as more and more systems become part of a manufacturer's information network. A more practical and resource-efficient solution may be to adopt a third-party software platform capable of providing scalability and proven unified data manipulation for all integrated systems.
The advantages of AI and IoT will only increase as the technology matures and more use cases are developed. However, before advanced technologies can be implemented, processes need to be automated and systems need to be connected. That said, to reap the benefits of the era of modern digital manufacturing, new business processes that can benefit from shared information need to be identified and analyzed. After the relevant business processes are digitized, an IT infrastructure for sharing and analyzing production data can power the factory of the future. The digital divide is happening now.
Disclaimer: 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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