Computer integrated manufacturing is about a more innovative approach to production, supply chain, inventory and warehouse management, etc.
Manufacturing giants, such as Siemens, Autodesk, Dassault Systèmes, etc., are the companies that already use computer integrated manufacturing. If you are also interested in this topic, keep on reading.
What are the hottest technologies that will power computer integrated manufacturing in 2022? Do you need to embrace them? How to adopt them successfully? Let’s discover together.
What is computer integrated manufacturing?
Computer-integrated manufacturing (CIM) system integrates manufacturing activities with the help of technology. The vital components of automation production systems and computer integrated manufacturing are data collection with the help of sensors, data storage, and data processing.
Let’s dwell on the advantages of computer integrated manufacturing.
Benefits of computer integrated manufacturing
- Time-efficiency: a fully automated system can operate much faster and work 24/7 if required;
- Accuracy: adoption of a computer integrated manufacturing (CIM) system allows to achieve a higher accuracy level. Implementing technology into your manufacturing process allows you to achieve near-perfect precision levels in production as well as quality control;
- Repeatability: automated computer integrated manufacturing systems are highly efficient when it comes to monotonous tasks. Such a system speeds up production time and cuts down the production cost on many levels. For instance, there is no need to train or retrain staff;
- Reduced costs: with the help of computer integrated manufacturing technology, you can reduce labor costs and lower the amount of waste (as there is less of a chance for mistake or deviation from the standard, so the overall quality of the product gets better);
- Post-pandemic value: social distancing is here to stay, so reducing the number of staff members will help you maintain sanitation norms and your employees stay healthy.
Сomputer integrated manufacturing use cases and technologies
The expertise required to develop a computer integrated manufacturing (CIM) system is very versatile. However, especially important are the ones related to data collection, processing, and storage - namely IoT, big data, and cloud. These technologies are integral to more complex ones, such as robotics, computer vision, and others.
The merge of IoT and robotics allows manufacturers to automate production lines or simple repetitive processes, closely monitor complex procedures, analyze performance, and understand the ground for optimization.
With the help of industrial IoT, 3D printing, and robotics itself, you can boost the quality and consistency of the production process, as well as the final product. Also, IoT enables real-life condition monitoring of your machines and their predictive maintenance. So, you will be able to decrease machine downtime.
What’s more, robotic solutions ensure greater productivity that maximizes throughput and minimizes human error.
However, robotics expertise is a scarce one. The implementation of robotic systems is pricey both in terms of kicking off such a project and maintaining it.
The application of Computer vision (CV) in the manufacturing industry is almost limitless. So, let’s view some of the key examples of how Computer vision and ML can optimize the processes, boost productivity, and grow revenue.
CV systems are widely used for tool and detail positioning on the production lines. The system identifies the location of an object and sends these coordinates to the robot.
In this case, CV can analyze new images and compare them to an already existing dataset to find anomalies and prevent potentially dangerous situations on production lines and manufacturing sites.
Typically, a large number of items need to be inspected on a production line. Computer vision can help you automate this process. A complex CV solution can scan the item from several angles and match it to the acceptance criteria. Also, it can save the accompanying metadata. When there is a certain number of faulty items, the system can inform the manager or even halt the production for further inspection to be performed.
Learn more about computer vision in manufacturing
Big data and Artificial Intelligence (AI) allow you to identify patterns in the great piles of data, so you can foresee when a specific machine might fail. Thus, it gets easier to solve the issue and cut operational maintenance costs even by half. Also, big data and AI can help you with predictive maintenance.
All these activities require robust data-related expertise. However, skilled data experts are not easy to come by. Many companies choose to hire big data developers overseas to solve this problem.
Why exactly do you need such professionals by your side? What are the key solutions that can help you experience the benefits of computer integrated manufacturing? Let’s take a closer look.
Learn more about how to succeed with big data in manufacturing
How to adopt computer integrated manufacturing effectively?
There are three important steps you should go through to adopt CIM computer integrated manufacturing:
1. Establish clear business KPIs as well as calculate ROI
It is critical to set clear KPI’s and evaluate ROI. If you need to validate the profitability of your business, you can undertake a Discovery Phase. Based on calculations for different scenarios, you can understand the advantages of implementing computer integrated manufacturing. The Product Discovery phase provides all the deliverables required to kick off the implementation phase efficiently while addressing risks and optimizing costs.
2. Analyzing your manufacturing problems
It is critical to get more visibility into your manufacturing issues and requirements. You need to analyze your final product's quality and how it can be enhanced. Then, you have to consider all the advantages and disadvantages of computer integrated manufacturing. Also, you should understand how the quality improvement process can be boosted by computer integrated manufacturing technology.
3. Ensuring an efficient CIM engineering process
You have to keep in mind that the success of any computer integrated manufacturing (CIM) project heavily depends on the following aspects:
- finding top-notch specialists that will assist you on this path;
- choosing the suitable sources of data;
- allocating IoT sensors that collect data from different devices;
- developing an ecosystem of platforms that collect data from different sources;
- cleaning, aggregating, and preprocessing the data;
- applying machine learning/AI or data science models;
- visualizing the insights.
Companies that use computer integrated manufacturing: case studies
1. Fluke Corporation
Our client is the global leader in the manufacture, distribution, and service of electronic test tools and software. The client requested the development of multiple solutions in the field of enterprise asset maintenance. They needed the solution with high standards of security, reliability, scalability, and extensibility.
Our team has worked on Fluke Mobile — an application that allows users to manage orders and inventory tasks from mobile devices and comprises several products: Inventory Manager, Work Order Manager, and FM Alarms.
Implementation of Fluke Mobile solutions allows enterprises to reduce unplanned downtime of assets by as much as 60%.
Work Order Manager (Work Order Management System) improves the mobility of maintenance team members making CMMS functions accessible from anywhere and providing technicians with the information they need. The competitive advantage of the system is the ability to run offline outside of network coverage. It integrates with several CMMS systems: IBM Maximo, SAP ERP, EMaint.
FM Alarms (Industrial Alarm Notification and Processing System) can detect abnormal behavior of equipment and notify responsible team members via mobile devices. It identifies affected assets and provides the user with various information needed for reactive maintenance scenarios as well as equipment control functions from mobile.
Inventory Manager (Inventory Management System) offers simple and efficient use cases for Inventory Management on CMMS.
Also, we contributed to the development of the Fluke data platform — an IoT software platform supporting a suite of apps. The solution provides insights into asset conditions. In case of an issue, the indicator appears on the screen in real-time that allows taking appropriate actions immediately.
2. German-based, Fortune 100 engineering and technology company (under NDA)
This client introduced a platform to improve the logistics between 400+ warehouses. The platform turned out to be inefficient and hard to scale. So, they have partnered with N-iX to upgrade and extend their system.
This project consists of three core aspects:
Adopting microservices architecture enabled adding new Artificial Intelligence-related services. For instance, we implemented solutions for anomaly detection, route recommendations, delivery prediction, and object detection in logistics.
Also, we have been working on optical character recognition (OCR) of labels on boxes, NLP used for verification of documents, data mining, and sensor data processing.
Computer Vision solution
This client had computer vision algorithms created by another provider. They were insufficient and not suitable for production. So, we found a top-notch CV specialist to run the computer vision workstream. After our experts carefully examined the existing algorithms, they decided to redevelop them entirely. We made the architectural changes of the solution and introduced Continuous Delivery for ML, that enables the implementation of continuously repeatable cycles of training, testing, deploying, monitoring, and operating the machine earning models. That is especially critical given the global scale at which our client is operating.
Multiplatform computer vision mobile application
Our team also designed the architecture of the multiplatform computer vision mobile application and is responsible for its development (end-to-end). The application covers object detection, package damage detection, OCR, and NLP for document processing.
Why implement computer integrated manufacturing with N-iX?
- N-iX boasts an internal pool of 2,200+ experts;
- Our professionals have long-standing expertise in the most relevant tech stack to implement computer integrated manufacturing, including business intelligence, data science, AI/machine learning, computer vision, and others;
- N-iX is compliant with international Infosecurity regulations norms, such as ISO 27001:2013, PCI DSS, ISO 9001:2015, GDPR, so your sensitive data will always be safe;
- N-iX offers top-notch DevOps services, such as cloud adoption (infrastructure set up, migration, optimization), building and streamlining CI/CD processes, security issues detection/prevention (DDOS & intrusion), firewall-as-a-service, and more.
- For over two decades, N-iX has worked with American companies - from our first client Novell, a large tech company we partnered with back in 2003, to dozens of established US industry leaders.