By Javier Jiménez, President of Magic Software Enterprises Americas
The Artificial Intelligence market is expected to have a compound annual growth rate (CAGR) of around 52% from 2017 to 2025, according to a recent report by Research and Markets and, thanks to recent innovations in cloud computing, storage and data analysis. Big Data, it is taking big steps and improving the efficiency of production environments, driving companies to achieve better performance, as well as being able to provide critical information to help managers make more informed and faster business decisions.
We know that machine automation produces quality products more quickly and efficiently, while providing critical information to help managers make more informed business decisions. However, there are still some obstacles: many companies are reluctant to share sensitive production and process data. There is also the challenge of integrating large amounts of data, from the shop floor to the back office, to create real-time insights. To make a smart factory work as a unified system, some companies use a middleware platform.
We can list five ways in which manufacturers can increase their level of productivity using artificial intelligence.
1. More accurate demand forecasting
With artificial intelligence and machine learning, systems can test hundreds of mathematical models of production and possibilities for results and can be more accurate in their analysis while adapting to new information, such as new product launches, supply chain disruptions or requirements of sudden changes. According to the McKinsey consultancy, with automatic learning it is possible to reduce the global inventory from 20% to 50%. Artificial intelligence can also increase efficiency in things as simple as taking a physical inventory. A task that employees take a month to complete at Walmart, for example, can be completed in 24 hours using sophisticated drones that fly through the warehouse, scan items and check for missing items.
2. Predictive maintenance
Organizations are beginning to realize that predictive maintenance solutions are worth investing in because they are a surefire way to improve operational efficiency and therefore have an almost immediate impact on the bottom line. Predictive maintenance uses sensors to track equipment conditions and continuously analyzes data, allowing organizations to intervene in equipment when it is really needed, instead of doing so only on scheduled service hours, minimizing downtime.
The machines can even be configured to assess their own condition, order their own spare parts and a field technician when needed. Taking predictive maintenance even further, Big Data-based algorithms can be used to predict future equipment failures. McKinsey identified that predictive maintenance of industrial equipment, enhanced by artificial intelligence, can generate a 10% reduction in annual maintenance costs, up to 20% reduction in downtime and a 25% reduction in inspection costs.
3. Hyper-customized manufacturing
Advances in artificial intelligence and software are enabling companies to take personalization to the next level by creating products and services that are highly relevant to individual consumers. This is important because personalization generates more revenue.
In a recent survey, 20% of consumers said they would be willing to pay up to 20% more for personalized products or services. And brands that are willing to customize products are also able to build greater trust with their customers. According to Accenture, 83% of consumers in the US and the UK are willing to have trusted retailers using their personal data to receive personalized, targeted products, recommendations and offers.
4. Optimization of manufacturing processes: by the end of this year, there are expected to be several types of machines powered by artificial intelligence engines running automatic learning algorithms capable of autonomously improving the efficiency of manufacturing processes.
Artificial intelligence systems will monitor the quantities used, cycle times, temperatures, wait times, errors and downtime to optimize production runs. The first stage of the implementation of the AI will be a “operator assistance” mode, in which it will run in the background and suggest responses to the operator. Artificial intelligence systems will use the operators' final decisions to learn how the human mind works so that they can be deployed in an “operator substitution” mode. In the future, it will allow us to transform data into intelligence in a vendor-independent environment, where all machines speak the same language, increasing the efficiency of machine-to-machine production across the shop floor.
5. Automated material acquisition analysis combined with machine learning: record and critique everything, including the early stages of quotation and establishing the supply chain. McKinsey predicts that machine learning will reduce supply chain forecasting errors by around 50% and will also reduce costs related to transport and storage and supply chain management from 5% to 10% and from 25% to 40%, respectively. Honeywell is already integrating AI and machine learning algorithms in purchasing, strategic sourcing and cost management.
Disclaimer: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies.
We know that machine automation produces quality products more quickly and efficiently, while providing critical information to help managers make more informed business decisions. However, there are still some obstacles: many companies are reluctant to share sensitive production and process data. There is also the challenge of integrating large amounts of data, from the shop floor to the back office, to create real-time insights. To make a smart factory work as a unified system, some companies use a middleware platform.
We can list five ways in which manufacturers can increase their level of productivity using artificial intelligence.
1. More accurate demand forecasting
With artificial intelligence and machine learning, systems can test hundreds of mathematical models of production and possibilities for results and can be more accurate in their analysis while adapting to new information, such as new product launches, supply chain disruptions or requirements of sudden changes. According to the McKinsey consultancy, with automatic learning it is possible to reduce the global inventory from 20% to 50%. Artificial intelligence can also increase efficiency in things as simple as taking a physical inventory. A task that employees take a month to complete at Walmart, for example, can be completed in 24 hours using sophisticated drones that fly through the warehouse, scan items and check for missing items.
2. Predictive maintenance
Organizations are beginning to realize that predictive maintenance solutions are worth investing in because they are a surefire way to improve operational efficiency and therefore have an almost immediate impact on the bottom line. Predictive maintenance uses sensors to track equipment conditions and continuously analyzes data, allowing organizations to intervene in equipment when it is really needed, instead of doing so only on scheduled service hours, minimizing downtime.
The machines can even be configured to assess their own condition, order their own spare parts and a field technician when needed. Taking predictive maintenance even further, Big Data-based algorithms can be used to predict future equipment failures. McKinsey identified that predictive maintenance of industrial equipment, enhanced by artificial intelligence, can generate a 10% reduction in annual maintenance costs, up to 20% reduction in downtime and a 25% reduction in inspection costs.
3. Hyper-customized manufacturing
Advances in artificial intelligence and software are enabling companies to take personalization to the next level by creating products and services that are highly relevant to individual consumers. This is important because personalization generates more revenue.
In a recent survey, 20% of consumers said they would be willing to pay up to 20% more for personalized products or services. And brands that are willing to customize products are also able to build greater trust with their customers. According to Accenture, 83% of consumers in the US and the UK are willing to have trusted retailers using their personal data to receive personalized, targeted products, recommendations and offers.
4. Optimization of manufacturing processes: by the end of this year, there are expected to be several types of machines powered by artificial intelligence engines running automatic learning algorithms capable of autonomously improving the efficiency of manufacturing processes.
Artificial intelligence systems will monitor the quantities used, cycle times, temperatures, wait times, errors and downtime to optimize production runs. The first stage of the implementation of the AI will be a “operator assistance” mode, in which it will run in the background and suggest responses to the operator. Artificial intelligence systems will use the operators' final decisions to learn how the human mind works so that they can be deployed in an “operator substitution” mode. In the future, it will allow us to transform data into intelligence in a vendor-independent environment, where all machines speak the same language, increasing the efficiency of machine-to-machine production across the shop floor.
5. Automated material acquisition analysis combined with machine learning: record and critique everything, including the early stages of quotation and establishing the supply chain. McKinsey predicts that machine learning will reduce supply chain forecasting errors by around 50% and will also reduce costs related to transport and storage and supply chain management from 5% to 10% and from 25% to 40%, respectively. Honeywell is already integrating AI and machine learning algorithms in purchasing, strategic sourcing and cost management.
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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