Digitalize Machines & Production
Visualize / TransparencyStore / Comply (ESG)Statistics / Arithmetics / ActuatorEnrich / Store / ForwardIdentify / Collect
Digitalizing industrial machines using advanced sensors is a transformative step towards creating a more efficient and predictive operational environment. These sensors collect and analyze valuable data points that can provide unprecedented insights into the machine’s performance and functionality. With the ability to visualize this data, operators gain a comprehensive view of the machine’s working parameters, facilitating quicker and more accurate decision making. Data visualization tools convert complex readings into easily comprehensible graphical formats, which can significantly enhance machine optimization efforts.
On the other hand, transparency, another major benefit of digitalization, enhances accountability and trust. By providing real-time access to machine performance data, organizations can detect potential issues early, plan maintenance proactively, and reduce unexpected downtime. This open and transparent approach enables a clear understanding of the machine’s operational health and promotes collaboration between teams. It also allows customers to have confidence in the reliability of the machine, bolstering the overall business-client relationship. Overall, digitalizing machines using sensors to visualize data and increase transparency can significantly improve operational efficiency, performance, and customer trust.
The ability to store data gathered from digitalizing machines using sensors is crucial for operational efficiency and strategic decision-making. By storing data, organizations can maintain a historical record of machine performance, enabling them to analyze trends, predict future performance, and make informed maintenance and upgrade decisions. Having this data at hand also helps establish baselines for performance, identify anomalies, and benchmark against industry standards or past performance.
Compliance, particularly with Environmental, Social, and Governance (ESG) standards, is another significant benefit of digitalization. The detailed data provided by sensors can help organizations demonstrate their adherence to these standards. For instance, environmental compliance can be shown by monitoring and reporting on emissions or energy use of the machines. Social compliance can involve ensuring safety standards are met, while governance might involve adhering to regulations about data handling and privacy. Digitalized data collection helps make this reporting process accurate, efficient, and verifiable. In essence, through the storage and analysis of sensor data, organizations can not only improve operational efficiency but also ensure they are meeting important ESG compliance requirements.
The digitalization of industrial machines equips them with the capability to gather, store, and analyze vast amounts of data. Among the many benefits this provides, the ability to perform statistics and arithmetics stands out.
Statistics refers to the collection, analysis, interpretation, presentation, and organization of the data gathered from the machines. This could involve calculating averages, determining ranges, or identifying outliers. These statistical analyses enable organizations to gain a detailed understanding of machine performance and efficiency, as well as track changes over time and under different operating conditions.
Arithmetic operations on the collected data, such as addition, subtraction, multiplication, and division, help convert raw sensor readings into meaningful information. For example, the rate of fuel consumption, cost of operation, or productivity can be calculated using simple arithmetic. These calculations can aid in decision-making, planning, and strategy formulation.
Lastly, an actuator is a device that translates the data and information into physical action. In a digitalized machine, based on the data collected by the sensors, decisions can be made either by human operators or automated systems. These decisions are then implemented via actuators. For example, if a sensor detects an overheating component, an actuator could trigger a cooling system. This shows that the digitalization of machines not only aids in understanding and improving performance but can directly influence and control the machine’s operation. This further enhances efficiency, safety, and reliability.
The process of digitalizing machines can significantly enrich the information we have about their operations. When sensors collect data, they’re gathering raw, unprocessed information. Enriching this data means adding context or combining it with other data to make it more valuable. For instance, a simple temperature reading becomes more meaningful when it’s enriched with additional information like the machine’s operational state, time of the reading, or ambient conditions.
The ability to store this enriched data is a fundamental advantage of digitalization. Stored data forms a historical record that can be used for trend analysis, predictive maintenance, and strategic decision-making. It allows organizations to revisit past machine states and understand how different conditions affect performance.
Finally, forwarding refers to the capacity to send this data from one place to another. In the context of digitalized machines, data might be forwarded from the machine (or ‘edge’) to a central database, or it might be sent to other systems or applications for further analysis or action. This could include alerting system operators to potential issues, triggering automatic responses, or informing business-level decisions. Together, enriching, storing, and forwarding data enhances the depth of insight into machine operations and provides a robust foundation for data-driven decision-making.
The process of identifying and collecting data is integral to the digitalization of machines and production processes. Identification refers to the ability to distinguish between different types of data and understand their significance. This might involve recognizing a particular pattern of sensor readings as a sign of machine wear, or identifying a sudden change in data as an indication of a malfunction. This capacity for identification can help prevent production delays, increase efficiency, and extend the lifespan of machinery by enabling timely interventions and maintenance.
Meanwhile, data collection involves gathering the information necessary for these identification tasks. With digitalization, sensors can constantly collect data on a machine’s performance, including temperature, speed, vibration, and much more. This real-time data collection enables operators to maintain a constant overview of machine health and performance. It can also contribute to the creation of a ‘digital twin’ – a virtual replica of the machine – that can be used for testing and optimization.
The ability to identify key data points and collect comprehensive information about a machine’s performance can significantly enhance the efficiency, reliability, and longevity of industrial machines and production processes.
Scenario / Challenges
Send home defined critical/none-compliant sequences
Consume client provided additional surrounding thing data
Avoid unnecessary overload of internal PLC processing units
Make machine and sensor data available to client
Consume client provided data stream within machine installation
Benefits
Separated solution for processing and analytics
Push home enriched operational data from all installations
Edge solution to be jointly used by client and service excellence
European Union ESG law compliance
Machines – Quality Measure and Overall Efficiency
Operation readiness in the context of machine and production digitalization refers to the real-time monitoring and understanding of a machine’s current working status and its readiness to perform its intended functions. This aspect is crucial in ensuring smooth and continuous production processes.
In the era of digitalization, sensors continuously collect and communicate data related to the machine’s operational status. Whether the machine is in an on state, off state, or if it’s displaying an error message, an alert for potential issues or is in a maintenance mode – each status is critically important to understand and manage.
The significant benefit of having this operational readiness information is that it empowers organizations to proactively manage their production processes. It helps to optimize the use of resources, reduces unplanned downtime, and enhances overall productivity. In the long run, it can lead to cost savings, better product quality and improved operational efficiency.
The environmental status is a critical aspect of machine and production digitalization that focuses on the conditions surrounding the machinery. It’s not just the machine’s internal operations that matter for optimal performance, but also the external factors like the environment in which it operates.
Sensors gather data related to various environmental aspects such as temperature, humidity, shock levels, atmosphere conditions and energy consumption. This data helps in understanding how these external factors might be impacting the machinery’s performance.
The ability to monitor the environmental status real-time offers several benefits. It can help in identifying conditions that might be causing undue stress on the machinery, reducing its efficiency, or accelerating its wear and tear. Proactive adjustments can be made to mitigate these external impacts, thereby prolonging the machine’s life, enhancing its performance, and contributing to energy conservation. Also, maintaining optimal environmental conditions can improve product quality and ensure adherence to various environmental and safety regulations, further emphasizing the importance of monitoring environmental status.
Manufacturing excellence in the context of machine and production digitalization is about achieving superior performance in the manufacturing process. This is accomplished by continuously monitoring the status of the production line and making necessary adjustments to maximize efficiency.
Sensors installed in the machinery provide real-time information on various states such as whether the machine is running, stopped, in transition, has its operation stopped or is waiting. This data can offer valuable insights into the production workflow, indicating areas of efficiency as well as bottlenecks or potential issues that might be hindering performance.
The ability to monitor these statuses and act upon them contributes significantly to achieving manufacturing excellence. It enables predictive maintenance, minimizing unplanned downtime and maximizing machine utilization. It also helps in identifying areas for process improvements, which can lead to reduced waste, increased throughput and ultimately higher productivity and product quality. Therefore, these insights play a key role in driving competitiveness, profitability, and sustainability in the manufacturing sector.
Scenario / Challenges
Combine machine PLC data with additional outside EHS (Environment, health, and safety) information
Additionally identify manufacturing excellence scenario
Collect, store and forward operational audit logs
Transparency for all involved parties (customer, vendors, workers)
Avoid unnecessary overload of internal PLC processing units
Make machine and sensor data available to client
Consume client provided data stream within machine installation
Benefits
Separated solution for processing and analytics
Increase service excellence with broader information data points
Participate in full digitalisation environments at clients
Push home enriched operational data from all installations
Edge solution to be jointly used by client and service excellence
European Union ESG law compliance
Kiwee Solutions GmbH
Über der Höhe 16
76689 Karlsdorf-Neuthard
Phone: +49 175 4496450
Contact: info@kiwee.io