Manufacturing or industrial processing, energy generation, and natural resources processing industries are the main industries where the Industry 4.0 approach can be widely and naturally applied.
This is because there are usually multiple machines being used, and collecting and using the operations data from those machines enables new options for better and customized products, along with integrating supply chains globally. This leads to smart factories where cyber-physical systems (CPSs; machines and devices with digital connectivity) are interconnected, so that machine-to-machine (M2M) communication can be enabled and each machine or device can talk with each other (usually through a message broker or unified namespace) by sharing operational data.
This helps in tracking the product and inventory across the shop floor and warehouses in real time by RFID and other sensor technologies to determine the accurate quantity and condition of the product. The intelligence derived from the data can help utilize resources in a more optimized way, for example, collecting and analyzing the energy consumption, production, and machine operating parameters data helps to optimize performance. The trend is transforming the whole shop floor manufacturing process toward robotic and automated processes for more interconnected and efficient operations.
In Industry 4.0, another important aspect is to digitally enable the whole supply chain process instead of the traditional manual supply chain process. Advanced market analysis, prediction of market demand, and forecasting are the major aspects to run a successful manufacturing industry. An integrated planning and forecasting engine can provide the demand forecast of the product for a time period, which considers the situation data such as weather, regional, and so on. This data can help the planner perform advance planning.
Earlier, the objective was about automation and mass production, but today manufacturers are facing demands to provide customized products for unique and different customer requirements. Like many other consumer industries, the manufacturing industry is also gradually becoming a personalized industry that can cater to specific customer needs. To address the situation, lot size of one–based production is introduced where each product unit can be customized with specific requirements from customers and can be identified uniquely during the manufacturing process as well as while identifying the defect. Additive manufacturing, also known as 3D printing, and dynamic routing setup with variant-based product definition are used to manufacture products based on customers’ unique requirements to manufacture the unit quantity of the product per customer specifications, which helps in bringing the product to market faster and personalizing the product for individual customers.
Increasing productivity with mass customization is also the focus for smart manufacturing process. For increased productivity, both the automated production process and the right data analysis with real-time information are required to enable quick decisions and proactive actions where needed to avoid downtimes and losses. Digital collaboration with all supply chain components, smart services, proactive maintenance, prescriptive data analysis, real-time M2M integration, and proper skilled resources all together can increase the productivity and support smart production process. In the traditional process, on the manufacturing shop floor, operators pick up the material based on the system’s instruction and scan the barcode through handheld devices. The correct pickup is confirmed to the materials management system or MES through the handheld devices. In the smart collaboration process, the trend is system-driven automation management. The forklift will get the information from the warehouse management system (WMS), and it will guide the operator to move to the correct location of warehouse and lift the correct material. The forklift will also perform barcode scanning and inventory validation in an automated way along with the correct material pick-up confirmation.
In a competitive market, the quality of the product not only increases the demand but also helps to retain and enhance the brand value of the manufacturer. It’s important to track the product to its manufacturing components and parameters when a defect is raised for the product after sale, or it’s returned. Industry 4.0 processes enable tracking the product’s manufacturing and operational data throughout the lifecycle of the product, through genealogy tracking or as-built reports. This is enabled by capturing the component information used for the assembly while manufacturing the product along with the relevant process parameters digitally in the MES for the product serial number or lot number.
In manufacturing quality checking operations, all the checkpoints of the product should be captured through an automated data collection mechanism or image or video feed analysis of the products, and defects can be automatically determined via ML models using the Visual Inspection process, where based on the image of the product taken during the manufacturing process, structural and visual defects can be automatically detected by ML models that are pretrained with a set of product images of both good and defective qualities. Following this approach, the nature of the work will change, and manual interventions will be minimal.
For example, in an automobile manufacturing process, the quality check of assembled components is usually done manually. It’s a traditional process, but it’s time-consuming, and the quality check of assembly depends on the understanding of the inspection operator. A smarter and more efficient process may be to use sensors to capture the quality data (e.g., torque applied) and cameras to capture images from multiple angles of the assembled product for structural and painting defects. Then, the system will use advanced image analytics and, based on the ML algorithm, will determine the percentage of accuracy or defect, thereby making the inspection process faster and more accurate.
Sustainability is another dimension that is now important for both individuals and organizations globally to prevent climate changes and enable sustainable living. It’s mandatory for manufacturing organizations to track and report the sustainability metrics based on various parameters such as emission, waste, natural resource usage, carbon footprint generated, employee and community well-being, and so on. Collecting data for these parameters is often very tedious and needs end-to-end digitalization to effectively collect and analyze the data. It’s not only needed for compliance reporting to authorities, but today many consumers want to know the sustainability factors for a product they are buying and may often choose the one that is most sustainable to nature and society. The concept of sustainable purchasing refers to both the businesses and consumers where the buyer is conscious about the product they are buying and how much it affects the environment and its sustainability factors. Therefore, manufacturers need to enable digitalization across the value chain to collect the data and effectively report it to enhance brand value.
Preventive maintenance of manufacturing equipment is important to keep the machines as well as manufacturing plant up and running for each production shift. Through preventive maintenance, manufacturers can get maximum productivity and usually prevent the equipment from going down unexpectedly. In the traditional approach, the maintenance activity is performed mostly after detection of a machine breakdown or at the time of parts replacement for a machine and some prescheduled maintenance timelines. In this approach, the maintenance person goes to the shop floor and fixes the issue when a breakdown is reported or at prescheduled intervals only.
In the Industry 4.0 approach, predictive maintenance helps to predict the breakdown of a machine before it actually goes down, helps to maintain the machine in advance to keep production up and running almost every time, and optimizes the maintenance operations cost and time needed. Prediction of asset failure is performed based on the analysis of machine operations data collected through sensors, which are attached to the machines and continuously collect data from the machines, along with the historical data set for machine performance and failure events used to train the ML models. A digital twin also helps in monitoring the real-time status of the asset and its operational parameters.
In a smart factory setup, a lot of sensors are connected with machines to collect health and operations data, and machines are interconnected to perform the activities without manual intervention. The centralized system can control the activities that need to be performed by the machines with intelligence based on the data collected from machine sensors. In this situation, cybersecurity and physical security among all the machines and devices is the priority. In Industry 4.0, the digital collaborative supply chain also gets high priority where information is shared with vendors, customs, dealers and so on. Therefore, manufacturers need to ensure a stricter security policy to protect the connected factory and shared information from cyber theft. This means security at the physical connectivity and network layer by encryption and network firewalls, data level to restrict specific data access to specific roles and users, and application level to restrict specific applications or functionality to specific roles and users.
Editor’s note: This post has been adapted from a section of the book Industry 4.0 with SAP by Dipankar Saha, Chandan Jash, Soumya Das, and Anupkumar Ketkale.
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