What Is Process Manufacturing?
Process manufacturing creates homogeneous products through precise, sequential steps involving physical mixing, heating, or other chemical processes. The resulting products change the physical properties of the ingredients, such as volume, density, and mass. Goods are produced continuously or in batches and, once combined, the ingredients cannot be separated.
Process manufacturing relies on operational technologies such as edge AI, near-real-time data analytics driven by powerful sensors, and enterprise resource planning (ERP) tools. These technologies help manage raw materials testing, ensure product quality, and optimize yield.
Process vs. Discrete Manufacturing
Manufacturing can be grouped into two distinct categories: process and discrete. These are the key differences:
- Process manufacturing synthesizes finished products by refining combined ingredients in a continuous flow, often by following a recipe or formula. Examples include beverages, gasoline, chemicals, and pharmaceuticals.
- Discrete manufacturing creates finished, tangible products by assembling them from individual parts. Examples include automobiles, appliances, and consumer electronics.
Types of Process Manufacturing
Process manufacturing typically has two main production types: batch and continuous flow. In batch production, materials move through a production line at specific times or in controlled volumes, whereas in continuous-flow manufacturing, materials move constantly through the production line.
Process Control Systems and Quality Assurance (QA)
Process control systems are designed to maintain operational consistency and batch repeatability within preset limits on ingredients, temperature, pressure, flow speed, and other critical factors. Sensor data is automatically analyzed, with human intervention needed only when the process diverges from accepted ranges. AI-assisted industrial controls and analytics can also supplement skilled labor at various stages in process manufacturing.
By linking process control to QA, advanced systems connect sensors and controllers in an integrated, rapid feedback loop, which triggers instantaneous corrections to minimize or eliminate defects and inconsistencies. Some product defects can be detected inline, such as in paper mills, where the product’s weight, moisture, tensile strength, thickness, porosity, and color can be monitored in near-real time during production. Other product types may require extracting production samples for laboratory testing.
Benefits of Smart Process Manufacturing
Modern process manufacturing facilities are becoming more intelligent and automated in a wave of transformation known as Industry 4.0. By leveraging AI-enabled production lines and IIoT technologies, manufacturers can drive greater efficiency, quality, and scalability to meet dynamic demands.
Information technology (IT) and operational technology (OT) convergence is a key innovation enabling industry 4.0. By connecting systems on the factory floor to IT networks, process manufacturers can connect data to operational systems in near-real time, integrate disparate functions, and get system data that supports automated adjustments to help reduce yield loss from defective products.
Integrating ERP software—centralized orchestration platforms that allow manufacturers to understand and act on data—can further streamline operations by synchronizing supply chain integration, order management, line resupply, and administrative tasks across all departments.
Challenges of Enabling Process Manufacturing
The process industry faces a complex web of interdependent challenges in today’s fast-paced markets. At the core, manufacturers need to maintain efficient production while reducing costs, waste, and yield loss, which is a significant achievement when dealing with intricate equipment and workflows.
Factors such as equipment downtime, production bottlenecks, and inefficient operations can hinder productivity and increase costs. Add to this the pressure of complying with regulations, such as those established by the US Food and Drug Administration (FDA) and Environmental Protection Agency (EPA), and the stakes get even higher.
Many manufacturers are now moving toward open, integrated Industry 4.0 systems that can leverage data across their entire operation, from supply chain to quality control. This empowers manufacturers to have the right data at the right time, enabling fast, actionable insights.
Process Manufacturing Use Cases
Several industries rely heavily on process manufacturing to provide essential goods, either as ingredients in other manufacturing processes or as an offering to end customers. The application of Industry 4.0 technologies provides advantages to each use case in terms of process optimization and quality control.
Healthcare and Life Sciences
Process manufacturers in pharma, nutraceuticals, and biotech can deploy sensors to collect comprehensive manufacturing data, from facility conditions to supply chains to product quality. AI analysis of this data reveals otherwise imperceptible patterns, enabling greater visibility into processes to inform decision-making.
Food and Beverage
AI, machine learning, and edge computing are revolutionizing food and beverage process manufacturing by enabling near-real-time data analysis throughout the production line. These technologies help manufacturers optimize operations through improved forecasting, quality control, and automated remediation of system issues.
Cosmetics
The cosmetics manufacturing industry has embraced smart technology through automation, robotics, and AI-driven solutions that help streamline production and minimize human error. Smart factories facilitate communication between machines, plants, and product lines, while data analytics help optimize operational processes and enhance product quality.
Oil and Gas
Petroleum products are manufactured through complex, multifacility processes. For example, crude oil is refined by heating and separating it into gasoline, paraffin, diesel fuel, and other products. Gasoline is transported to a blending terminal, where it is combined with ethanol, detergents, and other additives to meet seasonal requirements and local regulations.
Throughout the process, robotic process automation (RPA) can perform repetitive tasks, saving hands-on time and increasing capabilities in the field, back office, and shipping channels. Digital twin technologies can help improve safety, reduce downtime, and optimize operations through virtual simulation. And IIoT can help process manufacturers gain visibility across the value chain and break down silos between operational functions.
The Future of Process Manufacturing
Process manufacturing plants are becoming smarter by connecting factory floor equipment with business systems through IT/OT convergence. Industry 4.0 and IIoT technologies, including machine vision, augmented reality (AR), and digital twin technology, will help improve automation, supply line operations, and quality control. ERP software can centralize data across financial, supply, order, and customer management systems for greater visibility across the manufacturing value chain.
The good news is that transitioning to an open IIoT system does not have to happen all at once. Manufacturers can gradually adopt new technologies using standard hardware and software. Over time, multiple single-purpose devices and systems can be consolidated in a scalable, shared platform that improves operational efficiency, enhances security, reduces costs, and enables innovation.
