2023 calls for digitalization and digitization in manufacturing to help business owners address upcoming challenges, boost their growth, and remain flexible in the face of the changing market. The experience of the past few years shows that the enterprises that made a stronger emphasis on digitalization during the pandemic have displayed greater resilience in 2021-2022. That is why there has been an increased interest in digitization and the faster adoption of new technologies among manufacturers.
According to Deloitte projections, manufacturing GDP is expected to grow by 2.5% in 2023. But despite the rise in demand and production, the near future may not be as positive due to inflation, economic instability, and lack of qualified personnel, which are the issues that the industry is still facing. Digitization of manufacturing will be the deciding factor between the winners and outsiders in 2023. With all the uncertainty and instability, manufacturers should focus on the five core solutions analyzed in this article when forming their digital strategies.
Step 1. Get visibility into the production process with real-time analytics
Real-time analytics is the aspect of digitization in manufacturing industry that provides improved visibility into the manufacturing process by allowing you to monitor and analyze the production operations in real time. It allows you to identify potential issues as they arise, making it easier to take corrective action to prevent downtime and delays. Real-time analytics can provide insights into production efficiency and help companies understand customer demand and track supply chain performance.
There are a few different ways to implement real-time analytics and improved visibility into the manufacturing process.
- Implement an Enterprise Resource Planning (ERP) System: An ERP system is a comprehensive suite of applications designed to automate and streamline the manufacturing process. It provides visibility into the entire production process and helps to analyze data in real time.
- Use a Manufacturing Execution System (MES): An MES is a software system that is used to monitor, record, analyze, and optimize the production process. It can provide real-time visibility into manufacturing operations as well as detailed analytics on the process fostering further digitization in manufacturing.
- Implement IoT Solutions: IoT solutions can help you gain real-time insights into the manufacturing process. IoT devices can be used to monitor machines, track production, and collect data that is then used to generate analytics.
- Leverage Predictive Analytics and Machine Learning: Predictive analytics and machine learning can be used to analyze data in real time and provide insights into the manufacturing process. This can help identify problems before they occur, allowing for more efficient production.
N-iX case study: Real-time analytics for in-flight internet giant Gogo
Gogo has been a leader in in-flight broadband Internet for two decades, employing more than 1,000 staff. Their advanced technology, first-rate customer service, and worldwide coverage make flying more efficient for airlines, and more enjoyable for passengers.
We helped Gogo implement a real-time analytics solution that allowed the client to determine the root causes of antenna performance problems, predict equipment malfunctions, and reduce the number of cases where no fault is found. We helped the company decrease the no-fault-found rate by 75% through real-time analytics, helping in the identification of the problem areas and lowering the expenses incurred due to penalties to the airliners for inadequate in-flight Internet performance. Also, N-iX provided Gogo C-level management with comprehensive reports that give insights into the performance of the client's technologies and facilitate service improvements.
Step 2. Avoid asset and process breakdowns with predictive analytics
Predictive analytics, in conjunction with real-time analytics, plays an increasingly important role in the digitization of manufacturing as it helps companies optimize their operations and achieve greater efficiency. Predictive analytics uses data from current and past processes to identify trends and predict future outcomes. Data Analytics can help manufacturers anticipate demand, ensure quality, reduce costs, and find new opportunities for growth.
Predictive analytics can also help identify areas of improvement in the production process, enabling manufacturers to increase output and reduce waste. Moreover, predictive analytics can be used to identify potential opportunities for automation, allowing manufacturers to reduce labor costs while improving accuracy and speed. By leveraging predictive analytics in the digitalization of manufacturing process, you can understand the business better and stay ahead of the competition.
N-iX case study: Predictive analytics for Gogo
N-iX also successfully implemented antenna health monitoring and developed models to predict satellite antenna malfunctions. We have implemented predictive analytics solutions that allow Gogo to predict potential issues and respond accordingly.
- Implemented predictive analytics to anticipate any potential antenna failures with a 90% accuracy up to 30 days in advance,
- Identified the reasons behind antenna malfunctioning, thus helping Gogo to prevent several common causes of failure.
Step 3. Improve inventory management through digitalization
In 2021, the inventory management software market surpassed $1.5B and is expected to reach up to $5B by 2026. North America is currently responsible for 40% of the market, although the Asia-Pacific region will remain a close second attempting to gain 25% of the global market. This raises the question of why inventory management is such a crucial part of digitalization and digitization manufacturing industry undergoes.
Digitalization in manufacturing helps you optimize the amounts and costs of used inventory, reducing waste and improving the accuracy of orders. It involves the management of the flow of materials and products within the production process. The goal of inventory management is to ensure that the right amounts of materials and products are available at the right time to meet production demands. Establishing a proper manufacturing execution system is one of the aspects of digitalization that can also strengthen your inventory management efforts.
N-iX case study: Inventory management through manufacturing digitalization
One of our clients, a Fortune 100 engineering and technology company, needed to streamline inventory management for more warehouses, but the existing logistics solution was difficult to scale.
N-iX, as a digital transformation partner, worked with the client to modify their logistics platform, integrating microservices architecture, DevOps principles, as well as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision. This improved platform has enabled the client to swiftly expand to extra warehouses, increasing overall performance and responsiveness, and leading to further digitalization in manufacturing.
- Streamlined the inventory management for over 400 warehouses across the globe;
- Implemented near-real-time package tracking to maximize the delivery management efficiency and prediction of warehouse load.
Step 4. Cut down manufacturing errors with quality testing
By leveraging digitalization, manufacturers can detect and address any issues in product quality quickly and accurately to prevent further damage. Automated quality assurance, as a part of digitization manufacturing efforts, can be used in various ways, such as inspecting, testing, and analyzing products to ensure they meet the necessary standards. Such a system typically consists of several components, including sensors, actuators, and controllers. Sensors are used to detect any abnormalities in the production process and alert the controller to take corrective action. The actuators carry out corrective measures, such as stopping the production line or making adjustments to the process. Finally, the controller makes decisions based on the data collected by the sensors and actuators. These components cannot exist on their own when talking about the digitalization of manufacturing – they have to be integrated into your process, which cannot be achieved without digitization and digitalization.
By using automated Quality assurance (QA) as a part of digitalization in manufacturing industry, you can reduce the chances of defects and improve the overall quality of your products. Additionally, it can help to reduce costs by allowing the manufacturer to quickly detect and fix any issues before they become costly problems. Of course, there has to be a systematic approach for your quality control effort to maximize its efficiency.
N-iX case study: Computer vision solution and improved quality for a Global Fortune 100 manufacturer
Our client had the need to revamp the existing platform but lacked the necessary technical know-how to fix the issues and make it more useful and scalable. Here we have a digitalization in manufacturing industry case study where we have deployed a Computer vision solution to expand the impact of digitalization on manufacturing process. All of that was backed up by the N-iX DevOps expertise to set up an effective CI/CD pipeline. Our approach allowed us to achieve the following results:
- Reduced the number of defective packages through a damage detection system;
- Improved planning efficiency and reduced operational overheads as well as warehouse downtimes.
Step 5. Optimize the supply chain
By optimizing the supply chain through digital transformation, manufacturers can reduce inventory costs and improve turnaround time, while increasing the efficiency of manufacturing facilities and reducing the amounts of wasted materials.
The benefits of digitalization manufacturing supply chains go through also include supply chain optimization. It can help manufacturers gain visibility into their supply chain, allowing them to identify potential risks, mitigate disruptions, and manage supplier relationships. As you can see, all the aspects of manufacturing and digitization are connected in more than one way.
N-iX case study: digitalization for Logistics and Supply Chain company
Our client is a Fortune 500 industrial supply company that provides over 1.6 million quality in-stock products in areas such as safety, material handling, and metalworking. Additionally, they offer inventory management and technical support to over 3 million customers located in North America. The client had to convert its existing on-site data system and migrate it to the cloud in order to increase scalability, enhance reliability, and minimize expenses on logistics and the supply chain.
The N-iX team built an AWS-based Big Data platform from scratch to migrate from an on-premise Hadoop Hortonworks cluster to AWS and process additional data in AWS, achieving the following results:
- Reduced infrastructure costs through cloud migration.
- Increased the efficiency of data management with a unified data platform for convenient data storage.
- Implemented predictive analytics capabilities. For instance, the finance department will be able to predict expenses related to inventory stockouts.
- Reduced overheads on software development by replacing expensive on-site vendors.
Achieve digitalization and digitization in manufacturing with N-iX
The most important aspect of the successful digitalization of the manufacturing process is the technical expertise your technology partner can offer. Partnership with a reliable manufacturing software development company can help your enterprise by creating custom solutions tailored to your specific needs.
N-iX helps clients achieve digitization in manufacturing by providing a comprehensive range of services, from developing custom software solutions to implementing the latest technologies and tools like Computer Vision, Big Data, AI and Machine Learning, and more. Our team has extensive experience in engineering, Robotic Process Automation, and industrial solutions. Our experts can create digital twins of physical objects to simulate production processes, develop predictive maintenance solutions, and automate workflow with machine learning algorithms. We also offer innovative solutions such as data visualization, deep learning, IoT and Embedded software.