What Is Process Manufacturing?
Process manufacturing is a production method in which ingredients or raw materials are combined by following a formula or recipe—often requiring heat, time, and/or pressure—to create goods.
In process manufacturing, ingredient collection or formulation is typically the first of many sequential steps. Developers choose raw materials and additives, test all the proportions, and derive a formula that must be followed precisely and consistently.
Operating under strict quality control protocols, the ingredients are combined under the correct conditions to create a predetermined amount of product. Once the product is created, it cannot usually be broken down again into its separate ingredients.
Process manufacturing relies on operational technologies such as real-time data analytics driven by powerful sensors as well as the information technologies that comprise enterprise resource planning (ERP) and other management systems. These systems manage and monitor ongoing tests to ensure the safety and integrity of the raw materials and manufacturing processes and to optimize quality and yield of the end product.
Process vs. Discrete Manufacturing
Manufacturing processes are grouped into two broad categories: process and discrete.
Process manufacturing produces bulk goods and commodities. In process manufacturing, raw ingredients are collected, combined, and refined according to a precise, consistent formula to create a predetermined amount of the end product.
This product category includes petroleum products, chemicals, plastics, metals, and many types of food, beverages, and medicines. Commodities produced by process manufacturing cannot usually be broken down again into component elements, although some such products can be recycled or repurposed.
Discrete manufacturing produces individual units that are typically assembled from parts and subsystems. Examples of products that result from discrete manufacturing include vehicles, computers, furniture, appliances, and clothing.
The line between process and discrete manufacturing can sometimes be blurred. Many products that are produced via process manufacturing are subsequently integrated with products of discrete manufacturing. Beverages are produced by process manufacturing, for example, and then they are often bottled and packed in separate facilities. The bottles, cartons, and cases are discrete units, however, and most bottling plants are built around an assembly line, which is a feature of discrete manufacturing.
Process Manufacturing in Vertical Markets
Five leading industries invest heavily in process manufacturing and new technologies that support production quality and efficiency:
- Oil and gas
- Food and beverages
Process manufacturing often entails a complex, multistep procedure that may require more than one manufacturing line or even an entirely separate facility.
For example, crude oil is refined by heating and separating it into gasoline, paraffin, diesel fuel, and other products. Those processes occur at a refinery.
These interim products may undergo additional processes. In the US, gasoline from the refinery is transported to a blending terminal, where it is combined with ethanol—an alternative fuel made from plants—as well as detergents and other additives.
The formula for gasoline varies by region and season. The different variations accommodate emissions-control regulations in individual states, manage the volatility of the gasoline for winter and summer conditions, and adjust octane levels to suit different classes of vehicles.
Because of the nature of many process manufacturing products, seeking ways to produce goods more sustainably is a critical concern for businesses. IIoT technologies are offering new paths to sustainable production through capturing and analyzing more data at the edge.
For example, ExxonMobil has invested in open systems in recent years. This new process manufacturing strategy enables the energy giant to replace outdated industrial control systems with flexible platforms that can be updated and upgraded to take advantage of new, more powerful technologies and meet the company’s changing needs.
As part of its development journey, ExxonMobil joined the Open Process Automation Forum to encourage collaboration and innovation in open standards‒based process control systems. ExxonMobil worked on these initiatives with companies that face similar challenges in a variety of industries. Intel is a platinum member of the Open Group, which hosts the forum.
Benefits of IoT in Process Manufacturing
Process manufacturers deploy advanced technologies, including robotics, smart controllers, and real-time data analytics, to optimize product quality, consistency, and yield in a secure environment.
New open platforms enable production and test data to be collected, analyzed, and fed back to the manufacturing controllers in a continuous loop that adjusts processes in real time. This convergence of information technology (IT) and operational technology (OT) is a powerful example of an IIoT approach, also called Industry 4.0.
In an IIoT framework, process manufacturers can connect data to operational systems in real time and synchronize supply chain, order management, and delivery functions with the manufacturing systems.
Working closely with supply chain partners, process manufacturers can integrate and streamline disparate functions that were typically treated as distinct silos. The resulting improvements in efficiency and quality control help manufacturers respond to changing market conditions and accelerate the introduction of new products.
IIoT and intelligent edge technologies also enable solutions like predictive maintenance, which can detect problems with a machine before a serious malfunction occurs. Downtime in process manufacturing can be very costly. A single moment of downtime may result in an entire batch of product going to waste.
COVID-19 Pandemic Highlights the Need for Agility
The COVID-19 pandemic drew the world’s attention to the need for agility and resilience in manufacturing during a time of rapid, unanticipated shifts in demand coupled with massive supply chain disruptions.
Some industries adapted quickly to volatile market conditions. Certain pharmaceutical companies, for example, achieved record-breaking sales of innovative vaccines and other new products, even as they maintained their ongoing commitments.
Other industries did not fare nearly as well, as store shelves emptied of paper towels, cleaning supplies, and other products of process manufacturing that were not replenished in a timely way.
One result of that market pressure has been an increased focus on digitalization of process manufacturing. The key objectives of this digital transformation are to improve yield and flexibility by connecting processes and equipment within the factory or refinery and to integrate supply chain and distribution partner activities with manufacturing. Companies in traditional, stable vertical segments are investing more seriously in strategic planning and implementation of IIoT as they begin to meld digital information with physical products throughout all aspects of process manufacturing.
The pandemic has reinforced the message that digital solutions have the most impact when they extend beyond the walls of an organization and encompass more of its end-to-end value chain.”1
Open IIoT Supports QA Feedback Loop
Until recently, most process manufacturing systems have been delivered as a proprietary integrated solution from a single vendor. The manufacturer is typically locked into that vendor’s equipment and software and is dependent on that vendor for costly upgrades and maintenance contracts, often for 10 years at a time or longer. In many cases, the manufacturer’s supply chain partners were unable to interact fully with those systems, leading to expensive inventory overstocks or even more problematic shortages.
Many of these legacy systems are now being replaced or supplemented by scalable solutions built on off-the-shelf hardware and software. This open approach to ERP enables manufacturers to apply the latest technologies in artificial intelligence (AI) and real-time data collection to accelerate, control, and validate production processes and inspect the resulting products.
Transitioning to an open IIoT system is not necessarily an all-or-nothing move. For many manufacturers, a gradual approach to digitalization can confer immediate benefits even if manufacturing equipment and processes are not replaced or modified all at once.
Manufacturers often begin the digitalization process by replacing a siloed industrial process control system with an open solution that can be integrated with ERP at the front end and quality assurance at the back. Process controls are designed to maintain operational consistency within preset limits on quantities and proportions of ingredients, temperature and pressure applied to the materials, speed of flow, and other critical factors.
These conditions are measured and monitored by sensors. Typically, sensor data is analyzed automatically by the system so that human intervention is required only when some aspect of the manufacturing process diverges from the accepted range of measurements. AI-assisted industrial controls and analytics reduce the need for skilled labor in process manufacturing.
“Industry 4.0 estimated to create potential value of overall USD 3.7 trillion in 2025 and drive the next industrial revolution for discrete manufacturing. Yet only about 30 percent of companies are capturing value from Industry 4.0 solutions at scale today.”2
Linking process control to quality assurance, the most advanced systems connect sensors and controllers in an integrated, rapid feedback loop, so unexpected changes in the manufacturing process trigger defect detection.
Likewise, defect detection spurs instantaneous correction to the manufacturing process, ensuring product quality and consistency.
In process manufacturing, some product defects can be detected inline. For example, in paper mills, the weight, moisture, tensile strength, thickness, porosity, and color of the product can be monitored in real time during production.
For other product types, quality assurance may entail extracting production samples for laboratory testing. While these tests are seldom accomplished in real time, frequent, automated testing at regular intervals can be incorporated into the feedback loop. If samples begin to diverge even slightly from expected values, the QA system will signal the appropriate process controllers, which will correct the problem automatically. These preventive measures help to avoid costly slowdowns or shutdowns of the manufacturing process.
Intel® Technology in Process Manufacturing
The interrelated nature of process manufacturing environments means a single machine failure can have immediate, catastrophic consequences.
Intel® solutions focus on enabling:
- Process optimization (batch or continuous flow)
- In-line quality control
- Batch repeatability
- Maximum uptime
- Modeling and simulations
- Virtual metrology
- Safety and compliance monitoring
Intel and its global network of partners are helping businesses pave a path to open, software-defined infrastructures that can accommodate an integrated technology stack. Intel-based solutions are scalable with advanced security measures that can help protect proprietary data even while it’s in use.
Many Intel® products and technologies support process manufacturing. Among them:
- Intel® Edge Controls for Industrial, a reference software platform that combines elements of real-time computing with workload consolidation, industrial connectivity, security and functional safety, as well as software and infrastructure management.
- Intel® Edge Insights for Industrial, a free, open platform for machine vision and time series data.
- Industrial PCs built on Intel® architecture are designed for high performance and compatibility even in harsh operating environments.