Attention all industries!
Upgrade your maintenance game with our Industrial Predictive Maintenance in Internet of Things (IoT) knowledge base.
Say goodbye to unexpected downtime and costly repairs.
This transformative tool consists of the most important questions to ask for quick and effective results, tailored to your industry′s specific needs and priorities.
With over 1500 prioritized requirements, solutions, benefits, and real-world examples, our dataset covers every aspect of Industrial Predictive Maintenance in IoT.
Our extensive research ensures that our product outshines competitors and alternative options, making it the go-to choice for professionals.
Our user-friendly format makes it easy to navigate and utilize for DIY enthusiasts or businesses alike.
Whether you′re a small manufacturing company or a large-scale production facility, our knowledge base is designed to save you time and money.
No more guesswork or trial and error.
Our knowledge base provides in-depth information on how to use predictive maintenance in IoT, drawing on the latest technology and industry practices.
It also includes comparisons between related products and their pros and cons, giving you a clear understanding of why our product stands out.
Gone are the days of reactive maintenance.
With our knowledge base, you can proactively identify potential issues and prevent breakdowns before they occur.
This not only saves you valuable time but also increases the lifespan of your equipment and reduces repair costs.
But don′t just take our word for it.
Our case studies and use cases demonstrate the tangible benefits of implementing Industrial Predictive Maintenance in IoT.
Don′t miss out on this game-changing opportunity for your business.
So why wait? Join numerous satisfied customers and revolutionize your maintenance approach with our Industrial Predictive Maintenance in Internet of Things (IoT) knowledge base today.
Get ahead of the competition and ensure the smooth operation of your industrial processes.
Contact us now for more information on pricing and product details.
Take the first step towards a more efficient and profitable future with Industrial Predictive Maintenance in IoT.
Does an investment in Industrial IoT for predictive maintenance generate value for your organization? What is predictive maintenance and how can it help industrial organizations to save money? What are problems in other industrial scenarios as operations or quality control besides maintenance?
Industrial Predictive Maintenance
Industrial predictive maintenance involves using sensors and data analytics to monitor equipment and predict when maintenance is needed. This investment in Industrial IoT can generate value for the organization by reducing downtime and increasing efficiency.
1. Yes, Industrial IoT enables real-time monitoring, reducing equipment downtime and maintenance costs.
2. Predictive maintenance through IoT optimizes asset lifespan and improves overall performance.
3. IoT powered predictive maintenance can identify and prevent potential equipment failures, avoiding costly breakdowns.
4. With IoT, maintenance activities can be scheduled based on actual usage data, increasing efficiency and reducing unnecessary maintenance.
5. The use of sensors and data analysis tools in IoT can provide insights for predictive maintenance, saving time and resources.
6. Predictive maintenance through IoT allows for remote monitoring, eliminating the need for physical inspections and reducing safety risks.
7. IoT solutions can automatically trigger maintenance work orders, reducing human error and ensuring timely repairs.
8. By implementing predictive maintenance with IoT, companies can shift from reactive to proactive maintenance strategies, leading to significant cost savings.
9. IoT can collect data from multiple equipment and analyze it for patterns or anomalies, facilitating more accurate predictions and smarter maintenance decisions.
10. Predictive maintenance through IoT can help organizations move towards Industry 4. 0 by integrating digital technologies and creating a more efficient and connected manufacturing process.
CONTROL QUESTION: Does an investment in Industrial IoT for predictive maintenance generate value for the organization?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Yes, an investment in Industrial IoT for predictive maintenance will generate significant value for organizations. In fact, my big hairy audacious goal for 10 years from now is to have a world where companies in the industrial sector use Industrial IoT for predictive maintenance as a standard practice.
Here′s why I believe this goal is achievable and the value it can bring to organizations:
1. Improved Equipment Reliability: With Industrial IoT, organizations can access real-time data and insights on the health and performance of their equipment. This will enable them to detect potential issues before they escalate into costly breakdowns. As a result, organizations can ensure maximum uptime and improved reliability of their assets.
2. Reduced Maintenance Costs: By predicting when equipment will require maintenance or repairs, organizations can plan and schedule maintenance activities in a more efficient manner. This will help reduce unplanned downtime and maintenance costs associated with emergency repairs. Predictive maintenance can also help extend the lifespan of equipment, reducing the need for frequent replacements.
3. Increased Efficiency and Productivity: Industrial IoT technologies can provide organizations with insights into how their equipment is being used, allowing them to optimize operations for improved efficiency and productivity. This will lead to cost savings and increased output for the organization.
4. Safety and Risk Management: Predictive maintenance can help identify potential safety hazards, giving organizations the opportunity to address them before accidents occur. It also helps in managing risk by avoiding costly equipment failures that can result in safety incidents or environmental damage.
5. Data-Driven Decision Making: With the vast amount of data collected through Industrial IoT, organizations can make more informed and data-driven decisions. This can lead to better resource allocation, reduced downtime, and improved overall performance.
6. Competitive Advantage: Organizations that adopt Industrial IoT for predictive maintenance will have a competitive advantage over those that don′t. They will be able to operate more efficiently, reduce costs, and improve their bottom line, giving them an edge in the market.
In conclusion, an investment in Industrial IoT for predictive maintenance will not only generate value for organizations but will also become a necessity for surviving and thriving in the competitive industrial sector. With its many benefits, I am confident that we will see the majority of organizations adopting this technology in the next 10 years.
"The tools make it easy to understand the data and draw insights. It`s like having a data scientist at my fingertips."
Introduction:
In today′s fast-paced industrial landscape, unplanned equipment failures and downtime can significantly impact an organization′s bottom line. This has led to the emergence of predictive maintenance (PdM) as a key strategy for organizations to proactively identify potential failures and plan maintenance activities accordingly. PdM involves the use of Industrial Internet of Things (IIoT) technologies such as sensors, analytics, and machine learning algorithms to monitor equipment health in real-time and predict impending failures. But the question remains, does investing in IIoT for PdM generate value for organizations? This case study aims to answer this question through a detailed analysis of a client situation.
Client Situation:
The client is a major manufacturing organization that produces high-precision automotive parts. The company operates several production lines, with each line comprising multiple machines and equipment critical to its production process. Any unplanned downtime due to equipment failure can disrupt the entire production line, leading to significant financial losses, missed delivery deadlines, and potentially tarnishing the company′s brand reputation. The client was facing frequent equipment failures despite implementing a traditional maintenance approach, where routine checks were conducted at predetermined intervals. This reactive approach resulted in higher maintenance costs, increased downtime, and reduced productivity. The client turned to a consulting firm to implement predictive maintenance using IIoT technologies to address these challenges and generate value for the organization.
Consulting Methodology:
The consulting firm undertook a four-step approach to help implement IIoT for PdM.
1) Assessment of current maintenance practices: The first step involved a thorough assessment of the client′s existing maintenance practices, including the frequency of equipment failure, mean time between failures (MTBF), and mean time to repair (MTTR). This assessment provided insights into the gaps in their maintenance approach and the potential impact of unplanned downtime on their operations.
2) Identification of critical equipment: The next step involved identifying the critical equipment and machines that had the most significant impact on production and were prone to failures. This was done by analyzing historical data and conducting machine risk assessments. The critical equipment was then equipped with sensors to track their performance and health in real-time.
3) Implementation of IIoT technologies: The consulting firm implemented IIoT technologies such as sensors, connectivity, and cloud-based platforms to collect and analyze data from the critical equipment. The data was then used to generate insights and predict potential failures through advanced analytics and machine learning algorithms.
4) Integration with existing system: The final step involved integrating IIoT for PdM with the client′s existing maintenance system to enable seamless communication between the predictive maintenance solution and their asset management platform. This integration enabled the maintenance team to receive real-time alerts and prioritize maintenance activities based on criticality.
Deliverables:
The consulting firm delivered a comprehensive predictive maintenance solution for the client, including:
1) Sensors and connectivity infrastructure: The critical equipment was equipped with sensors that continuously collected data on various parameters such as temperature, vibration, and lubricant levels.
2) Cloud-based analytics platform: A cloud-based analytics platform was set up to collect, store, and analyze the data collected by the sensors. The platform utilized machine learning algorithms to detect patterns and anomalies in the data, enabling early detection of potential failures.
3) Real-time alerts and notifications: The platform was integrated with the client′s asset management system, enabling real-time alerts and notifications to be sent to the maintenance team when an anomaly or potential failure was detected.
4) Predictive maintenance roadmap: The consulting firm also provided the client with a predictive maintenance roadmap, outlining the key steps and timelines for implementing and scaling up the solution across all production lines.
Implementation Challenges:
The implementation of IIoT for PdM posed some challenges that needed to be addressed to ensure its successful adoption. One of the key challenges was the integration of the predictive maintenance solution with the client′s existing asset management system. This required a thorough understanding of the client′s systems and processes to ensure a seamless integration. Another challenge was the selection and installation of sensors, which needed to be done carefully to ensure accurate data collection and minimize any disruption in production.
KPIs:
The success of the predictive maintenance solution was evaluated based on the following key performance indicators (KPIs):
1) Reduction in equipment failures: The primary metric for measuring the success of the solution was a reduction in the number of equipment failures. By implementing IIoT for PdM, the consulting firm aimed to reduce equipment failures by at least 20% in the first year.
2) Downtime reduction: Another critical KPI was the reduction in downtime due to unplanned failures. By detecting potential failures early, the aim was to minimize the time taken to repair equipment and thereby reduce downtime.
3) Maintenance cost savings: The implementation of predictive maintenance aimed to reduce maintenance costs by optimizing maintenance schedules and reducing the need for expensive emergency repairs.
Management Considerations:
Apart from the technical aspects, there were several management considerations that needed to be addressed for the successful implementation of IIoT for PdM.
1) Change management: The adoption of IIoT for PdM involved a significant shift in the client′s maintenance approach. This required change management initiatives to be put in place to educate and train the maintenance team and other relevant stakeholders about the new approach and its benefits.
2) Data management: With the implementation of IIoT, there was an increase in the volume of data collected from the sensors. This data needed to be managed efficiently to ensure timely and accurate insights for predictive maintenance.
3) Scalability: The predictive maintenance solution needed to be scalable, as the client had plans to expand its operations in the future. This required the consulting firm to design a solution that could be easily replicated and scaled to other production lines.
Conclusion:
The implementation of IIoT for PdM enabled the client to shift from a reactive maintenance approach to a proactive one, resulting in significant cost savings and improved productivity. The predictive maintenance solution helped in early detection of potential failures, reducing downtime, and increasing the lifespan of critical equipment. The success of this project highlights the value that IIoT brings to organizations by significantly improving their maintenance practices and mitigating risks associated with unplanned equipment failures. This case study illustrates the growing trend of organizations leveraging IIoT for PdM to stay ahead in today′s highly competitive industrial landscape.
References:
1) Predictive Maintenance: How Leading Companies are Using Machine Learning to Balance Cost and Uptime, McKinsey & Company, 2018.
2) Berg Insight, Industrial IoT Market Report 2020-2025.
3) Predictive Maintenance in Manufacturing: A Proactive Approach to Minimize Downtime and Increase Efficiency, Global Industry Analysts, Inc., 2019.
4) Predictive Maintenance Moves from Concept to Reality in Manufacturing, LNS Research, 2018.
5) The Economics of Predictive Maintenance for Capital-Intensive Industries, Deloitte Insights, 2019.
Upgrade your maintenance game with our Industrial Predictive Maintenance in Internet of Things (IoT) knowledge base.
Say goodbye to unexpected downtime and costly repairs.
This transformative tool consists of the most important questions to ask for quick and effective results, tailored to your industry′s specific needs and priorities.
With over 1500 prioritized requirements, solutions, benefits, and real-world examples, our dataset covers every aspect of Industrial Predictive Maintenance in IoT.
Our extensive research ensures that our product outshines competitors and alternative options, making it the go-to choice for professionals.
Our user-friendly format makes it easy to navigate and utilize for DIY enthusiasts or businesses alike.
Whether you′re a small manufacturing company or a large-scale production facility, our knowledge base is designed to save you time and money.
No more guesswork or trial and error.
Our knowledge base provides in-depth information on how to use predictive maintenance in IoT, drawing on the latest technology and industry practices.
It also includes comparisons between related products and their pros and cons, giving you a clear understanding of why our product stands out.
Gone are the days of reactive maintenance.
With our knowledge base, you can proactively identify potential issues and prevent breakdowns before they occur.
This not only saves you valuable time but also increases the lifespan of your equipment and reduces repair costs.
But don′t just take our word for it.
Our case studies and use cases demonstrate the tangible benefits of implementing Industrial Predictive Maintenance in IoT.
Don′t miss out on this game-changing opportunity for your business.
So why wait? Join numerous satisfied customers and revolutionize your maintenance approach with our Industrial Predictive Maintenance in Internet of Things (IoT) knowledge base today.
Get ahead of the competition and ensure the smooth operation of your industrial processes.
Contact us now for more information on pricing and product details.
Take the first step towards a more efficient and profitable future with Industrial Predictive Maintenance in IoT.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1513 prioritized Industrial Predictive Maintenance requirements. - Extensive coverage of 101 Industrial Predictive Maintenance topic scopes.
- In-depth analysis of 101 Industrial Predictive Maintenance step-by-step solutions, benefits, BHAGs.
- Detailed examination of 101 Industrial Predictive Maintenance case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Laboratory Automation, Monitoring And Control, Smart Waste Collection, Precision Agriculture, Damage Detection, Smart Shopping, Remote Diagnostics, Digital Twins, Manufacturing Processes, Fleet Management, Inventory Optimization, Smart Cities, Energy Efficiency, Inventory Management, Inspection Drones, Asset Performance, Healthcare Monitoring, Location Services, Augmented Reality, Smart Transportation Systems, Workforce Management, Virtual Assistants, Factory Optimization, Personal Air Quality Monitoring, Insider Threat Detection, Remote Maintenance, Patient Monitoring, Smart Energy, Industrial Predictive Maintenance, Smart Mirrors, Demand Forecasting, Inventory Tracking, Occupancy Sensing, Fraud Detection, Carbon Emissions Tracking, Smart Grids, Air Quality Monitoring, Retail Optimization, Predictive Maintenance, Connected Cars, Safety Monitoring, Supply Chain Integration, Sustainable Agriculture, Inventory Control, Patient Adherence Monitoring, Oil And Gas Monitoring, Asset Tracking, Smart Transportation, Process Automation, Smart Factories, Smart Lighting, Smart Homes, Smart Metering, Supply Chain Optimization, Connected Health, Wearable Devices, Consumer Insights, Water Management, Cloud Computing, Smart Traffic Lights, Facial Recognition, Predictive Analytics, Industrial Automation, Food Safety, Intelligent Lighting Systems, Supply Chain Analytics, Security Systems, Remote Patient Monitoring, Building Management, Energy Management, Retail Analytics, Fleet Optimization, Automation Testing, Machine To Machine Communication, Real Time Tracking, Connected Wearables, Asset Performance Management, Logistics Management, Environmental Monitoring, Smart Waste Management, Warehouse Automation, Smart Logistics, Supply Chain Visibility, Smart Appliances, Digital Signage, Autonomous Vehicles, Data Analytics, Personalized Medicine, Facility Management, Smart Buildings, Crowd Management, Indoor Positioning, Personalized Marketing, Automated Checkout, Condition Monitoring, Customer Engagement, Asset Management, Automated Parking, Smart Packaging, Medical Sensors, Traffic Management
Industrial Predictive Maintenance Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Industrial Predictive Maintenance
Industrial predictive maintenance involves using sensors and data analytics to monitor equipment and predict when maintenance is needed. This investment in Industrial IoT can generate value for the organization by reducing downtime and increasing efficiency.
1. Yes, Industrial IoT enables real-time monitoring, reducing equipment downtime and maintenance costs.
2. Predictive maintenance through IoT optimizes asset lifespan and improves overall performance.
3. IoT powered predictive maintenance can identify and prevent potential equipment failures, avoiding costly breakdowns.
4. With IoT, maintenance activities can be scheduled based on actual usage data, increasing efficiency and reducing unnecessary maintenance.
5. The use of sensors and data analysis tools in IoT can provide insights for predictive maintenance, saving time and resources.
6. Predictive maintenance through IoT allows for remote monitoring, eliminating the need for physical inspections and reducing safety risks.
7. IoT solutions can automatically trigger maintenance work orders, reducing human error and ensuring timely repairs.
8. By implementing predictive maintenance with IoT, companies can shift from reactive to proactive maintenance strategies, leading to significant cost savings.
9. IoT can collect data from multiple equipment and analyze it for patterns or anomalies, facilitating more accurate predictions and smarter maintenance decisions.
10. Predictive maintenance through IoT can help organizations move towards Industry 4. 0 by integrating digital technologies and creating a more efficient and connected manufacturing process.
CONTROL QUESTION: Does an investment in Industrial IoT for predictive maintenance generate value for the organization?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Yes, an investment in Industrial IoT for predictive maintenance will generate significant value for organizations. In fact, my big hairy audacious goal for 10 years from now is to have a world where companies in the industrial sector use Industrial IoT for predictive maintenance as a standard practice.
Here′s why I believe this goal is achievable and the value it can bring to organizations:
1. Improved Equipment Reliability: With Industrial IoT, organizations can access real-time data and insights on the health and performance of their equipment. This will enable them to detect potential issues before they escalate into costly breakdowns. As a result, organizations can ensure maximum uptime and improved reliability of their assets.
2. Reduced Maintenance Costs: By predicting when equipment will require maintenance or repairs, organizations can plan and schedule maintenance activities in a more efficient manner. This will help reduce unplanned downtime and maintenance costs associated with emergency repairs. Predictive maintenance can also help extend the lifespan of equipment, reducing the need for frequent replacements.
3. Increased Efficiency and Productivity: Industrial IoT technologies can provide organizations with insights into how their equipment is being used, allowing them to optimize operations for improved efficiency and productivity. This will lead to cost savings and increased output for the organization.
4. Safety and Risk Management: Predictive maintenance can help identify potential safety hazards, giving organizations the opportunity to address them before accidents occur. It also helps in managing risk by avoiding costly equipment failures that can result in safety incidents or environmental damage.
5. Data-Driven Decision Making: With the vast amount of data collected through Industrial IoT, organizations can make more informed and data-driven decisions. This can lead to better resource allocation, reduced downtime, and improved overall performance.
6. Competitive Advantage: Organizations that adopt Industrial IoT for predictive maintenance will have a competitive advantage over those that don′t. They will be able to operate more efficiently, reduce costs, and improve their bottom line, giving them an edge in the market.
In conclusion, an investment in Industrial IoT for predictive maintenance will not only generate value for organizations but will also become a necessity for surviving and thriving in the competitive industrial sector. With its many benefits, I am confident that we will see the majority of organizations adopting this technology in the next 10 years.
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Industrial Predictive Maintenance Case Study/Use Case example - How to use:
Introduction:
In today′s fast-paced industrial landscape, unplanned equipment failures and downtime can significantly impact an organization′s bottom line. This has led to the emergence of predictive maintenance (PdM) as a key strategy for organizations to proactively identify potential failures and plan maintenance activities accordingly. PdM involves the use of Industrial Internet of Things (IIoT) technologies such as sensors, analytics, and machine learning algorithms to monitor equipment health in real-time and predict impending failures. But the question remains, does investing in IIoT for PdM generate value for organizations? This case study aims to answer this question through a detailed analysis of a client situation.
Client Situation:
The client is a major manufacturing organization that produces high-precision automotive parts. The company operates several production lines, with each line comprising multiple machines and equipment critical to its production process. Any unplanned downtime due to equipment failure can disrupt the entire production line, leading to significant financial losses, missed delivery deadlines, and potentially tarnishing the company′s brand reputation. The client was facing frequent equipment failures despite implementing a traditional maintenance approach, where routine checks were conducted at predetermined intervals. This reactive approach resulted in higher maintenance costs, increased downtime, and reduced productivity. The client turned to a consulting firm to implement predictive maintenance using IIoT technologies to address these challenges and generate value for the organization.
Consulting Methodology:
The consulting firm undertook a four-step approach to help implement IIoT for PdM.
1) Assessment of current maintenance practices: The first step involved a thorough assessment of the client′s existing maintenance practices, including the frequency of equipment failure, mean time between failures (MTBF), and mean time to repair (MTTR). This assessment provided insights into the gaps in their maintenance approach and the potential impact of unplanned downtime on their operations.
2) Identification of critical equipment: The next step involved identifying the critical equipment and machines that had the most significant impact on production and were prone to failures. This was done by analyzing historical data and conducting machine risk assessments. The critical equipment was then equipped with sensors to track their performance and health in real-time.
3) Implementation of IIoT technologies: The consulting firm implemented IIoT technologies such as sensors, connectivity, and cloud-based platforms to collect and analyze data from the critical equipment. The data was then used to generate insights and predict potential failures through advanced analytics and machine learning algorithms.
4) Integration with existing system: The final step involved integrating IIoT for PdM with the client′s existing maintenance system to enable seamless communication between the predictive maintenance solution and their asset management platform. This integration enabled the maintenance team to receive real-time alerts and prioritize maintenance activities based on criticality.
Deliverables:
The consulting firm delivered a comprehensive predictive maintenance solution for the client, including:
1) Sensors and connectivity infrastructure: The critical equipment was equipped with sensors that continuously collected data on various parameters such as temperature, vibration, and lubricant levels.
2) Cloud-based analytics platform: A cloud-based analytics platform was set up to collect, store, and analyze the data collected by the sensors. The platform utilized machine learning algorithms to detect patterns and anomalies in the data, enabling early detection of potential failures.
3) Real-time alerts and notifications: The platform was integrated with the client′s asset management system, enabling real-time alerts and notifications to be sent to the maintenance team when an anomaly or potential failure was detected.
4) Predictive maintenance roadmap: The consulting firm also provided the client with a predictive maintenance roadmap, outlining the key steps and timelines for implementing and scaling up the solution across all production lines.
Implementation Challenges:
The implementation of IIoT for PdM posed some challenges that needed to be addressed to ensure its successful adoption. One of the key challenges was the integration of the predictive maintenance solution with the client′s existing asset management system. This required a thorough understanding of the client′s systems and processes to ensure a seamless integration. Another challenge was the selection and installation of sensors, which needed to be done carefully to ensure accurate data collection and minimize any disruption in production.
KPIs:
The success of the predictive maintenance solution was evaluated based on the following key performance indicators (KPIs):
1) Reduction in equipment failures: The primary metric for measuring the success of the solution was a reduction in the number of equipment failures. By implementing IIoT for PdM, the consulting firm aimed to reduce equipment failures by at least 20% in the first year.
2) Downtime reduction: Another critical KPI was the reduction in downtime due to unplanned failures. By detecting potential failures early, the aim was to minimize the time taken to repair equipment and thereby reduce downtime.
3) Maintenance cost savings: The implementation of predictive maintenance aimed to reduce maintenance costs by optimizing maintenance schedules and reducing the need for expensive emergency repairs.
Management Considerations:
Apart from the technical aspects, there were several management considerations that needed to be addressed for the successful implementation of IIoT for PdM.
1) Change management: The adoption of IIoT for PdM involved a significant shift in the client′s maintenance approach. This required change management initiatives to be put in place to educate and train the maintenance team and other relevant stakeholders about the new approach and its benefits.
2) Data management: With the implementation of IIoT, there was an increase in the volume of data collected from the sensors. This data needed to be managed efficiently to ensure timely and accurate insights for predictive maintenance.
3) Scalability: The predictive maintenance solution needed to be scalable, as the client had plans to expand its operations in the future. This required the consulting firm to design a solution that could be easily replicated and scaled to other production lines.
Conclusion:
The implementation of IIoT for PdM enabled the client to shift from a reactive maintenance approach to a proactive one, resulting in significant cost savings and improved productivity. The predictive maintenance solution helped in early detection of potential failures, reducing downtime, and increasing the lifespan of critical equipment. The success of this project highlights the value that IIoT brings to organizations by significantly improving their maintenance practices and mitigating risks associated with unplanned equipment failures. This case study illustrates the growing trend of organizations leveraging IIoT for PdM to stay ahead in today′s highly competitive industrial landscape.
References:
1) Predictive Maintenance: How Leading Companies are Using Machine Learning to Balance Cost and Uptime, McKinsey & Company, 2018.
2) Berg Insight, Industrial IoT Market Report 2020-2025.
3) Predictive Maintenance in Manufacturing: A Proactive Approach to Minimize Downtime and Increase Efficiency, Global Industry Analysts, Inc., 2019.
4) Predictive Maintenance Moves from Concept to Reality in Manufacturing, LNS Research, 2018.
5) The Economics of Predictive Maintenance for Capital-Intensive Industries, Deloitte Insights, 2019.
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