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GEM Precare Case Study: Semiconductor Manufacturing
Key Benefits
- 24% improvement in availability
- 10% improvement in annual performance
- 5% increase in energy cost savings
- 7% increase in revenue
About the client
The client is a tier 1 semi-product and factory owner having multiple factories worldwide and a market cap of over $3B. The client specializes in the manufacturing of power, discrete semiconductor, and passive electronic components and owns multiple machines for this purpose. The client was using Applied Materials manufacturing execution systems, Nation Instruments’ OptimalPlus+, and machine OEM (Original Equipment Manufacturer) for their standard manufacturing requirements, such as collection, cleaning, and aggregation of data from multiple manufacturing locations, its analysis, and mining for coherent information.
Problem statement
While this investment in continuous improvement using high-quality legacy machines sets the client apart from its competitors, it also required heavy manual work. Digital OEE (overall equipment effectiveness) and Predictive Maintenance is one of the best measurements you can use to optimize production operations, lacking this was preventing the client from improving its production operations. Lacking digital, and having partial human data collection, the client was unable to identify losses, track and benchmark progress, and optimize the productivity of manufacturing equipment. Additionally, the client was facing issues like human erroneous data, and limitations of its own solution.
The solution
The client learned about the GEM Precare SaaS Predictive Analytics Industrial IoT Platform, which enables manufacturers to make an almost seamless transition to Industry 4.0 while maintaining their legacy machine investments, allowing them to take advantage of the potential of real-time access to machine data to improve efficiency, productivity, and quality.
The solution deployment started in 2019 with the following products:
- Precare Cloud (On-Premise Edition): To perform machine data aggregation and storage, training and validation of machine learning models for machine vision and predictive maintenance, computation of KPIs, and display of data on dashboards
- Precare Edge: To perform complex edge processing of machine digital twin data in real-time for latency-sensitive tasks, such as status and alarm notifications, as well as machine vision
- OEE Availability, Performance, and Quality Packages: To gain important insights on how to efficiently improve your manufacturing process
GEM started the deployment of the solution using its machine onboarding and OEE frameworks. The company started the deployment on 1 floor with a few machines, and gradually expanded to more machines across the floor, then to a second floor totaling over 80 machines. The customer and GEM are now engaging in discussion regarding further floors and factories in the Philippines, Taiwan, and more.
Key Results
With the deployment of the OEE availability, performance, and quality package solution, the client observed tangible and immediate benefits. The client observed more than a 24% improvement in availability, 10% improvement in yearly performance improvements, a 5% increase in energy cost savings, and an overall more than 7% increase in revenues.
Additionally, the following benefits were also observed:
- Maximum returns on investment in machinery
- Minimum production losses and greater competitiveness
- Rapid improvement in machine performance
- Minimized repetition and defective products eventually add considerably to the cost savings
- Quantification of production efficiency which provides precise insight into the operations process
- Reduced machinery and repair costs
- Improved efficiency of manufacturing plants
Conclusion
Today, the GEM Precare SaaS Predictive Analytics Industrial IOT Platform Is stronger than ever. GEM real-time & Predictive analytics are top of the lines in the Semiconductor, Electronic Manufacturing, and Automotive. GEM enables any customer user, from executive to machine operator, to get critical operational analytics insight, allowing them to take predictive actions. GEM Agents are able to take full advantage of the hardware they run on for complex event processing at the edge rather than in the cloud. It allows real-time remote monitoring and visualisation via the cloud of critical parameters and signals with notifications for alarms and other events. This significantly helps manufacturing organisations reduce latency for time-critical monitoring and control applications.
SELF SERVICE DATA STUDIO CASE STUDY This case study showcases GEMBO, a leader in SaaS Industrial IoT Platforms, enhancing a global manufacturer's operations by deploying its Precare Data Studio BI product across 70 machines. The solution, focused on self-service data management and analytics, resulted in significant manpower savings, improved self-service efficiency, and automated analytics distribution. Unique to GEMBO Precare is its ability to deploy independent sensors, connecting any machine to its cloud, thereby offering predictive maintenance and operational insights without substantial capital expenditure, distinguishing it from other market solutions.
GEMBO ( www.gembo.co ) is a leading innovator in the world of SaaS Industrial IoT Platforms, with a proven track record of delivering cutting-edge solutions that help customers achieve their business goals. The company's platform uses a combination of IoT, machine learning, and AI to provide customers with real-time insights into their operations, enabling them to make better decisions and optimize their performance. GEMBO is committed to continuous innovation, and the company is constantly looking for new ways to use technology to help its customers succeed. Customer Intro The customer is a multi $B Tier one manufacturer, with multiple factories around the globe. Manufacturing lines vary between electronic manufacturing to semiconductor, serving a variety of markets including Automotive, Networking, Industrial, Audio and Gaming. GEM has deployed Precare Cloud , Precare Edge , OEE Availability and predictive analytics Package . Its footprint grew from a few machines to over 90 machines in a few of their factories. Problem The customer has been utilizing OEE Availability analytics to identify machines with low availability and assigning them to responsible technicians. The customer's management team has been overwhelmed with emails from technicians providing status updates on the machines after they have taken corrective actions. The customer intends to streamline the process of monitoring machine status from the receipt of OEE Availability status to the resolution of availability issues, enabling the machines to operate efficiently based on established availability KPIs. The customer has provided the following workflow to illustrate their requirements.
GEMBO ( www.gembo.co ) is a leading innovator in the world of SaaS Industrial IoT Platforms, with a proven track record of delivering cutting-edge solutions that help customers achieve their business goals. The company's platform uses a combination of IoT, machine learning, and AI to provide customers with real-time insights into their operations, enabling them to make better decisions and optimize their performance. GEMBO is committed to continuous innovation, and the company is constantly looking for new ways to use technology to help its customers succeed. Customer Intro The customer is a Tier 1 multi-billion dollar global manufacturing leader in the power, discrete semiconductor, and passive electronic components (PES) space. GEMBO has deployed a suite of innovative, cloud-based solutions to the customer, including Precare Cloud, Precare Edge, OEE Availability, Performance, and Quality packages. These solutions have been deployed to 70 machines across two factory floors, and the customer intends to scale the deployment across their entire Asia-Pacific footprint. Problem The customer's equipment engineering team has been managing the productivity of their machines through manual Excel reports. These reports are prepared by machine operators, checked and consolidated by supervisors, and then reported to management by factory floor managers. The customer needs to minimize or eliminate these manual reports to improve the accuracy of the reports and save on manpower costs. The following are examples of manual reports that the customer needs to eliminate: 1. Calculation of the share of subcategories of Performance and Quality 2. Calculation of the percentage of Planned Downtime to Total Run Time 3. Graphical presentation of OEE Availability with Time Frame These reports are time-consuming and error-prone. They also require a significant amount of manpower to create and maintain. By eliminating these manual reports, the customer can improve the accuracy of their productivity data and save on manpower costs. Solution After a technical discussion with the customer, GEM integrated the manual Excel reports into the OEE analytics framework. The team then implemented the following process: Define the customer requirements Gather the excel reports Create an output image showing changes in the analytics Define data to be collected Collect data from the customers Collect the data from customer Validate the completeness of the data based on the requirements Collect missing data Confirm with customer if all data are accurate and structured properly based on their requirements Prepare the design Overview Customer Requirements Use Case System Diagram Code Diagram Review and approval of the design Testing on Developer Environment Deployment to Production Customer acceptance Results The migration of previously manually prepared OEE reports to a digital format using OEE Analytics resulted in the following benefits: 100% savings on manpower costs, as the reports are now generated automatically and do not require manual input. 100% accuracy, as the data is collected directly from the machines and is not subject to human error. 100% time savings for management, as they no longer need to spend time reviewing and validating the reports. Improved decision-making, as the Operations Group can now access real-time data and insights to make better decisions. Increased efficiency, as the Operations Group can now focus on core tasks and not on data entry and reporting. Conclusions GEMBO Precare has a set of powerful data acquisition, analytics, predictive and OEE tools for manufacturing equipment. GEMBO Precare compiles critical KPIs that can be used to easily trace back which machine or machine subsystem is responsible for a low KPI score. GEMBO Precare is also able to make predictions at equal or better than humanly possible for machine maintenance to be scheduled before a failure occurs. But most importantly, unlike other market solutions, GEMBO Precare is able to deploy its own sensors independently from any machine controller and fully connect any machine data island to the GEMBO Precare Cloud at a fraction of the cost of a new machine; hence, saving manufacturers from having to make large and risky CAPEX and OPEX investments for new machines. Contact Us Visit us at www.gembo.co or contact us via email at sales@gembo.co
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INTRODUCTION The GEMBO PRECARE platform enables manufacturers to seamlessly make the transition to Industry 4.0 literally overnight, while keeping their investments in legacy machines; thus, being able to tap into the potential of real-time access to machine data in order to improve efficiency, productivity and quality. Real-time access to machine data can be through any combination of sensors, serial and parallel port interfaces, Ethernet and sensors, no matter where the data is originating from, i.e., directly from the machine, or indirectly from a PLC or a data store on a network. Complex event process control at the edge of the cloud or at the machine itself requires hardware platforms with adequate processing power. GEMBO Precare supports edge processing through the use of powerful software agents. These agents take full advantage of the capabilities of the underlying hardware. In this white paper we show how the GEMBO Precare Industry 4.0 IoT platform takes advantage of Intel® x86 processor and Intel® Altera® Cyclone V SoC FPGA hardware capabilities to implement process monitoring and control at the edge in an industrial flow control application. GEMBO PRECARE MOTOR CONTROL AGENT Motors used in machines and production processes are very common and the ability to monitor them in real-time is understandably of crucial importance. GEMBO has developed an end-to-end motor control and monitoring solution in collaboration with Intel® and B&R Automation®, which takes advantage of the versatile and powerful GEMBO Agents for CEP at the edge and the GEMBO Precare cloud platform for monitoring and predictive maintenance. The process diagram shown below depicts a flow control application example. Control of a pump controls the flow rate in a pipe, and a safety valve prevents overpressure inside the pipe.
The Customer Semiconductor manufacturers are constantly under pressure to squeeze the most out of their capital and operational expenses, while at the same time facing tremendous pressure in a global market from their customers to deliver the highest quality within the shortest lead time. Whereas COOs are held responsible for CapEx, OpEx and overall performance across all manufacturing sites and business units, VPs at each business unit are responsible for CapEx, OpEx and overall performance of their own business unit, while factory floor directors are responsible for manufacturing operations at their plants to run as efficiently and productive as possible. In concrete terms this means minimizing unplanned machine downtime and maximizing machine throughput, while keeping manufacturing quality high; or in industry parlance, maximizing OEE (Overall Equipment Effectiveness). The Challenge Maximizing OEE in semiconductor manufacturing is a non-trivial task. The manufacturing workflow involves multiple stages; each handled by machines specialized for the particular operations involved in a stage. It is not uncommon to find machines from different OEMs handling the same or different tasks on the manufacturing floor, where some machines may be controlled by older generation Operating Systems (some with none), PLCs (Programmable Logic Controllers) that don’t have the ability to provide machine status, performance, quality and availability data in real-time to spot trends to predict when maintenance needs to be scheduled and optimize equipment effectiveness. This situation includes having no access at all to data for analysis and having access, but having to import the data in batch mode, missing crucial data. In addition, where the data is available, it is often in formats that differ from machine to machine, and more often than not, in formats incompatible with prevalent IIoT formats, such as JSON, XML, etc. To top it all off, a plethora of different communication protocols, ranging from SECS/GEMBO , SCADA and RS232/485 to various flavors of Industrial Ethernet compound the problem of access to data for analytics even further. As a consequence all stakeholders, from factory floor to corporate office, don’t have access to crucial data to help them make well-informed decisions towards OEE optimization in order to meet the end goal of growing bottom line revenue and market share. The Solution Industry 4.0 augments Industry 3.0 process control automation systems with big data and big data analytics. At one end of the Industry 4.0 spectrum this means real-time machine data access, while at the other end of the spectrum this means bridging the chasm between operational technology systems on the factory floor and information technology systems in the back-office. Industry 4.0 creates a digital twin or cyber physical system (CPS) of each machine on the factory floor. These digital twins allow all stakeholders to take advantage of the full Industry 4.0 spectrum of big data analytics opportunities. A 2018 PwC survey of the electronics manufacturing industry estimates that 45% of manufacturers are already deploying or are in process of deploying Industry 4.0 systems, while another 32% is planning to deploy Industry 4.0 systems within the next 5 years. The GEMBO PRECARE platform is able to offer semiconductor manufacturers a complete Industry 4.0 upgrade which is tailored to their specific needs, with all the benefits of big data analytics to optimize OEE, but without the cost of overhauling their existing machinery. The highlights of this solution are summarized below.