Cloud use case in manufacturing go beyond IT
According to Mckinsey's research, the cloud’s IT value amounts to only about 5 percent of its total potential value. In other words, around 95 percent of the cloud’s USD 600 billion value potential lies in business-related functions (e.g., manufacturing, supply chain) and procurement.
The value in manufacturing typically results from Industry 4.0 and Industrial Internet of Things use cases that are strongly enabled by and scaled through cloud technology.
For B2C companies (e.g., automotive OEMs), another great source of value lies in cloud-enabled applications within marketing and sales, such as incentive spend optimization or customer data analysis.
Based on Mckinsey's research on digital maintenance and reliability transformations in heavy industries, it has become apparent that companies can boost asset availability by a range of 5-15% and lower maintenance costs by 18-25%. These improvements can potentially impact a company's overall operations and efficiency positively.
Additional studies on the value of predictive mainatainence
- A Smart Manufacturing Leadership Coalition study found that companies that adopt predictive maintenance can see an average improvement in OEE of 5-10%.
- A study by the research firm Gartner found that companies with predictive maintenance programs can experience a reduction in maintenance costs of up to 20%.
- A study by the research firm IDC found that using predictive maintenance can lead to a reduction in unplanned downtime of up to 50%.
- A study by the research firm Frost & Sullivan found that companies that adopt predictive maintenance can see a reduction in maintenance costs of 10-30%.
These studies highlight the potential cost savings and increased efficiency that can be achieved by implementing factory predictive maintenance programs. The specific benefits will vary depending on each factory's particular needs and circumstances. Still, overall, predictive maintenance can play a crucial role in creating value in a smart connected factory.
What are the technologies used for predictive maintenance?
- Predictive analytics: Predictive analytics software uses machine learning algorithms to analyze large amounts of data and predict potential failures.
- Internet of Things (IoT) devices: IoT devices, such as sensors and smart devices, can be deployed on machinery and equipment to collect real-time data and send it to a central system for analysis.
- Condition monitoring software: Condition monitoring software analyzes data from sensors and other sources to monitor the performance and health of machinery and equipment.
- Augmented Reality (AR): AR technology can be used to provide real-time information and guidance to maintenance technicians, reducing downtime and improving efficiency.
- Artificial Intelligence (AI): AI can be used to analyze large amounts of data from various sources to predict potential failures and optimize maintenance schedules.
Cloud computing
: Cloud computing provides access to powerful computing resources, allowing for real-time analysis of large amounts of data and enabling predictive maintenance.
These technologies provide real-time monitoring and analysis of machinery and equipment, enabling predictive maintenance and reducing downtime. The specific technologies used will depend on the needs and circumstances of each factory. Still, overall, these technologies play a crucial role in improving the efficiency and effectiveness of predictive maintenance programs.
Data is collected from the equipment or the shopfloor with the help of sensors.
- Temperature Sensors: These sensors measure the temperature of specific objects or environments in a manufacturing setting, helping to monitor equipment and processes for potential overheating or performance issues.
- Pressure Sensors: These sensors measure the pressure of fluids or gases in a manufacturing setting, providing vital information for process control and safety.
- Level Sensors: These sensors measure the level of liquids or materials in a manufacturing setting, helping to ensure consistent and efficient operations.
- Infrared Sensors: These sensors detect infrared radiation in a manufacturing setting, allowing for the measurement of temperature and other parameters that can impact performance.
- Proximity Sensors: These sensors detect the presence or absence of objects within a specific range in a manufacturing setting, helping to monitor machinery and equipment for potential issues.
- Smoke Sensors: These sensors detect smoke in a manufacturing environment, providing early warning of potential fire hazards.
- Optical Sensors: These sensors use light to detect or measure various parameters, such as position or color, in a manufacturing setting, helping to improve accuracy and efficiency.
- MEMS Sensors: These tiny sensors use microelectromechanical systems (MEMS) technology to measure various parameters, such as acceleration or pressure, in a manufacturing setting.
- Vibration Sensors: These sensors measure the vibration of machinery and equipment, providing early warning of potential failures or wear and tear.
- Acoustic Sensors: These sensors measure sound in a manufacturing environment, providing information on machine performance and potential issues.
- Radar Sensors: These sensors use radar technology to detect objects, measure distance and velocity, and provide information on machine performance and potential issues.
Manufacturing is not an industry. It is a process or a business function just like Marketing. To understand the Manufacturing process better, it is imperative to study the manufacturing systems.
Cloud technology can bring multiple benefits to the manufacturing industry, to name a few:
- Cloud MES (#manufacturingexecutionsystem
) or PPS (production planning systems)
- Computer vision for quality and safety management systems
- Automation of manual processes,?#shopfloor
?#roboticsolutions
- Real-time monitoring and control of manufacturing equipment,?#OEE
- #Predictivemaintenance
- Enhancing collaboration and communication among teams
- Streamlining?#supplychainmanagement
- Improving?#logistics
?and?#inventorymanagement
- Industrial Data Platforms
- Data, AI/ML insights from the manufacturing process, forecasting, and modeling
Sidhartha Sharma (views are personal)
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