The manufacturing industry has always been defined by inventions that enhanced manufacturing processes. In the 17th century, industrialization was the buzzword and its application increased productivity levels to astronomical heights. By the 20th century, industrialization had run its course and the concept known as lean manufacturing was introduced to enterprises and this paradigm shift once again enhanced productivity levels for manufacturers. Today, data analytics and real-time monitoring are driving growth in different ways.
The importance of real-time monitoring in smart manufacturing or industry 4.0 cannot be overstated. Statistics show that 81% of manufacturing facilities rely on data analytics to improve productivity. Although this statistic highlights the importance of data analytics and real-time monitoring, it still leaves the question of ‘how it enables the lights out factory’ unanswered. Thus, we have outlined five important ways data analytics improves manufacturing using case studies.
Real-time monitoring has helped many businesses tackle age-old problems such as dealing with downtime, increasing equipment efficiency, and logistics management. This is due to its ability to provide business insights and actionable intelligence concerning manufacturing operations. To better understand its value proposition to manufacturers, here are some examples.
Discrete Event Simulation and Scheduling at Lockheed Martin
In 2006, Lockheed Martin was awarded the contract to assemble the fifth generation F-35 Joint Strike Fighter jets by the US Air force. By 2011, the project had run into problems related to scheduling and costly refits which saw its deadline extended and its budget overrun.
To eliminate these challenges, Lockheed Martin turned to real-time simulations and monitoring to improve its scheduling timelines and inventory challenges. A discrete event simulation model was built using Simio and integrated into Lockheed Martin’s Enterprise Production Model. This simulation model was used to monitor the entire production process in real-time to create feasible and efficient production plans. The result was improved process efficiency which helped Lockheed Martin meet production targets for the fighter jet.
Reducing Downtime and Material Waste at Carolina Precision Manufacturing
The task of expanding a business’s services to include new offers always comes at a cost and Carolina Precision Manufacturing’s case was no different. The enterprises foray into parts manufacturing was a difficult one as it encountered challenges with understanding machine data and optimizing production cycles.
To mitigate these challenges, Carolina Precision Manufacturing turned to data analytics. With MachineMetrics, the manufacturing enterprise developed a real-time monitoring model that helped it manage shop floor productivity and receive business insight into optimizing its overall equipment efficiency levels. In the end, the manufacturing enterprise reduced downtime and eliminated material waste. Other benefits included:
Improving Track and Traceability Accuracy at a Medical Device Manufacturing Facility
An international medical device manufacturer made use of paper and manual tracking procedures to facilitate the development of its devices. This process was bogged down with traceability issues when it came to integrating regulations required by the Food and Drug Agency for third-tier medical devices. Other challenges it encountered included difficulties with managing maintenance plans and product quality.
To eliminate these challenges, the manufacturer turned to a manufacturing enterprise system (MES) and the real-time monitoring it offers. With a real-time monitoring process in place, the enterprise was able to smartly track and trace instances in the production cycle where FDA regulations apply. Thus, the manufacturer was able to integrate specified compliance requirements in real-time as their medical devices were being manufactured. With real-time monitoring, the manufacturer was able to eliminate the cost it took three quality management staff to review manufactured devices.
Reducing Operation Cost at San Mateo Community College
The San Mateo Community College District is home to approximately 40,000 students spread across 90 vocational and technical programs. These programs include discrete event manufacturing activities across healthcare and engineering niches which were known to consume large amounts of energy.
To reduce its operational cost, the college turned to data analytics and monitoring the energy consumption rate of its departments in real-time. With the aid of Schneider Electric, the college was able to achieve real-time energy monitoring across its manufacturing departments which helped with drastically reducing consumption rates. The information captured by its real-time monitoring system also helped with apportioning energy to specific manufacturing equipment and processes.
Enhancing Overall Equipment Efficiency at National Oilwell Varco (NOV CAPS)
At Nov Caps, the need to improve operational efficiency through the optimization of its machines led the enterprise to explore MachineMetrics platform features and its ability to capture and evaluate data to receive actionable insight.
NOV CAP successfully connected 60 CNC machines across two facilities to the MachineMetrics platform. MachineMetrics captured all the data produced by the CNC machines and organized the data sets into relevant categories. These categories made it possible to evaluate machine data for daily improvements. Within three months, access to visualized data captured on the MachineMetrics platforms enabled NOV CAP to solve systematic problems and increase the rate at which it used its CNC machines by 20%.
The benefits of capturing machine data in real-time include increased operational efficiency, improved accountability, and reduction in operational cost. To reap these benefits, data from manufacturing equipment must first be captured and processed. The insight from machine data helps manufacturers to improve productivity by ensuring machines on the shop floor function at their maximum capacity. You can learn more about the MachineMetrics platform and how it delivers real-time monitoring here.
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