In the world of scientific innovations, machine data is digital information created by the activities of computers, mobile phones, embedded systems, and other networked devices. Today, machine data is frequently associated with the Internet of Things, machine data analytics, and big data management technologies. The typical examples of machine data are application, server and business process logs, call detail records and sensor data but there’s more.
With edge computing technologies, machine data can also be caught in sources like control systems, operational systems, routers for industrial networks, or assets (sensors and devices).
Data-driven manufacturing implies that decisions controlling the manufacturing process should be based on facts, and not assumptions. Emerging technology is enabling both people and equipment to collect and process the facts they need to achieve better results.
How can companies obtain insights on business activities and operations via machine data analytics?
Many large industrial manufacturers are already analyzing machine data on the performance of field equipment in near-real-time, together with historical performance data, to better understand service problems and to try to predict equipment maintenance issues before machines break down.
Typical examples of applications based on data include setups for monitoring oil and gas pipelines, natural disaster warning systems based on feeds from marine sensors, forecasting systems that take data from satellites and weather stations to help predict the weather in small geographic areas, etc.
Manufacturers take active measures to find new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers.
Considering that new products are proliferating in manufacturing today and delivery windows are tightening, companies take advantage of machine learning to improve the end-to-end performance of their operations and find a performance-based solution to the ever-emerging challenges.
Machine data challenge to date
The good news is that the newer equipment is now increasingly internet-enabled, and some older machines can be adapted for connectivity. Additionally, there are new software technologies that enable you to reach data you did not capture before.
The majority of big team players of the sector work hard to standardize platforms for machine-to-machine communication. The leading businesses do their best to take advantage of this new connectivity to incorporate the machine data into relevant workflows.
It’s true that many manufacturers today still feel the gap between what goes on in their factories and the core business processes supported by their ERP systems. It creates significant lag times for management to access, analyze and act on data from the manufacturing and development processes. Not having this data in real-time could create problems when it comes to planning, inventory control, the supply chain or meeting customer expectations.
Before you can analyze your machine data, you need to unlock it. By using a data collector tool, you can unlock even the data you thought you couldn't reach or didn't know existed. The Datumize Data Collector can unleash and empower data locked in control and operating systems to generate new insights about operational efficiency.
The thing is, having real-time insights will lead to better-predicted forecasts and it will reveal bottlenecks, this all will eventually lead to a major cost reduction and a smoother manufacturing process.
5 Major benefits of using Machine Data
Create real-time insights
By unleashing and empowering dark data, you create better real-time insights into manufacturing activities. Having real-time insights will lead to many advantages during the manufacturing process. For instance, predict better demand forecasts, reduce costs, reveal bottlenecks, which will all be explained in-depth.
Capturing data with a tool like Datumize Data Collector can unleash data from sensors and other devices, without critical system modification or upgrade of hardware equipment. This is critical to capture real-time data insights. It will transform industrial data into manufacturing process intelligence.
Reveal bottlenecks
During the manufacturing stage, you can find a lot of different bottlenecks. Analyzing machine data will help you identify and eliminate these bottlenecks. Ultimately, it could lead up to a reduction of supply chain forecasting errors by 50% and a reduction of lost sales by 65% with better product availability.
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Smooth out the manufacturing process
Applying advanced machine data analytics to manufacturers’ data can produce insights to optimize the productivity of individual assets as well as the total manufacturing operation. Deployed in conjunction with each other, machine data collector tools enable operators to maximize their overall efficiency and profitability.
Besides that, it also helps by boosting asset management, supply chain management, and inventory management. By combining emerging technologies including IoT, AI, and machine learning, manufacturers can improve asset tracking accuracy, supply chain visibility, and inventory optimization.
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Production optimization
Having better analytics could even give you insight into business processes that can be automated. Two big effects on processes are manual steps and human error. By automating previously manual processes, you can save a lot of time and avoid errors, which would also reduce costs.
To automate your processes though, you need data to ensure that you are implementing automation that will enhance the processes, rather than make things more complex or difficult on the shop floor.
Reduce costs
What machine data analytics will eventually lead to is a reduction in costs, in many ways. Machine learning is predicted to reduce costs related to transporting, warehousing, and supply chain administration by 5 to 10% and 25 to 40%, respectively. This is because more accurate data will provide a more accurate forecast about inventory management or sales forecasts.
Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios.
Conclusion
Overall, machine learning opens up numerous possibilities in the manufacturing industry as the technology helps assembly plants build a connected series of IoT devices that work in harmony to enhance work processes. From quality control to asset management, supply chain solutions and lower spending - there are so many manners in which machine data is changing the future of manufacturing.
In the end, it will all lead to either improved efficiency during the manufacturing process or a cost reduction, if not both. However, to get to those results, you will first need to capture all the right data and then analyze to provide your decision-making team with the best insights.
