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 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.
How the companies can 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. Other typical examples of applications 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 weather in small geographic areas, etc.
Manufacturers take active measures to finding 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.
6 ways machine data analytics is revolutionizing manufacturing:
Boosting of 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.
Improved predictive maintenance is expected to increase 38% in the years to come. Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%.
Reduction of supply chain forecasting errors by 50% and reduction of lost sales by 65% with better product availability. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively.
Improving of demand forecast accuracy to reduce energy costs and negative price variances that results in price elasticity and price sensitivity. Companies are integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management.
Optimization of shop floor operations and providing of insights into machine-level loads and production schedule performance. As a result, a business gets better decisions managing of each production run.
Improvement of the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios and costs reduction by 50%. 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.
For example, when using machine learning, a manufacturer succeeds in test and calibration time reduction via accurate prediction of calibration and test results. The methodology focuses on using a series of machine learning models that would predict test outcomes over time. The process workflow below helps isolate the bottlenecks, streamlining test and calibration time in the process.
The era of connected series of IoT devices and machine data analytics is already here
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 which machine data is changing the future of manufacturing.
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.
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.
Taking advantage of the new connectivity patterns
The good news is that the newer equipment is now increasingly internet-enabled, and some older machines can be adapted for connectivity. 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.
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, these tools enable operators to maximize their overall efficiency and profitability.