How manufacturing process control and optimization yields big gains

Manufacturing process control is a crucial element of all manufacturing activities. It is achieved by deploying software and systems to monitor and regulate different stages of production.

Such manufacturing process controls comprise both infrastructure and smart devices, for instance:

  • Networks to gather data and connect equipment
  • Data processing equipment
  • Algorithms to correlate variables in the manufacturing processes to various product attributes
  • Sensors, actuators, and probes

Why is manufacturing process control and optimization necessary?

Manufacturing is the process of creating a product from a range of materials and products. This endeavor includes various interrelated activities at different stages of the process, including production, integration, and assembly of the product’s components. It can be a highly complex operation, depending on the industry or product.

Modern manufacturing is an amalgamation of product design, material selection, process planning, quality control, quality assurance, documentation, and product management. Technological advances in the last few decades have made manufacturing increasingly complex, requiring technical sophistication, automation, and finesse in managing operations. Added to this is the high levels of process safety and adherence to safety norms needed with shorter production cycles. Without proper process optimization, a company may lag behind on its expansion plans and aspirations.

Globalization has been the major factor in increasing competition among industries, and the manufacturing sector is no exception. Companies must utilize existing and emerging tech to gain a competitive edge. Manufacturing process control and optimization holds the promise of a bigger market share by making better quality products possible.

Manufacturing process control and optimization can facilitate a more efficient use of assets, resources, and revenue by decreasing production costs and material consumption. Running manufacturing operations under optimized conditions brings savings, increases productivity, and boosts the quality of the manufactured products. It also makes the enterprise more sustainable by optimizing energy consumption and reducing environmental impact.

Where is manufacturing process control applicable?

Manufacturing process control is particularly vital in the following stages of the manufacturing process:

  • Measuring temperature profiles in sensitive or harsh processing environments
  • Measuring critical physical attributes at high temperatures and high line speeds
  • Monitoring combustion processes and the chemical composition of the mix

This further impacts quality control and quality assurance for the manufacturing product.

From a production line perspective, manufacturing process control and optimization can eliminate the following problems that cause reduced productivity and quality issues:

  • Production line problems
  • Isolated workstations overloaded beyond their capacity
  • Excessive work in progress
  • Unused assets and equipment taking up floor space
  • Human errors, resulting in rework or delays
  • Inconsistent, unsatisfactory productivity
  • Production management issues
  • Workflow bottlenecks

Becoming proactive with manufacturing process control

Manufacturing process control is all about preemptive action and contingency planning with automation.

Contingency planning

For instance, manufacturers can design an automated control chart that provides alerts, along with corresponding action plans, in case of problems.

Quality control

Anomaly detection and automated defect classification is a common and important feature of manufacturing process control to maintain product quality and integrity.

Why predictive maintenance matters

Predictive maintenance is the use of data analytics to predict and prevent machine failure. It presents a novel way of restructuring maintenance activities at an industry-wide scale. This is especially important in the manufacturing industry because a lot of money and resources are dependent upon the optimal functioning of investment-heavy equipment. These assets can require periodic maintenance, resulting in downtime. Inadequate maintenance strategies can cause a significant drop in a plant’s manufacturing capacity, which could lead to the loss of both revenue and competitive edge.

Conversely speaking, the payoffs of investing in manufacturing process control systems are also quite significant: Predicting failures via advanced analytics can reportedly increase equipment uptime.

On average, predictive maintenance can:

  • Increase productivity
  • Reduce breakdowns
  • Lower the cost of equipment maintenance

In general, maintenance strategies formulated on an industrial scale need to factor in available and working assets, their operating costs, and levels of technical sophistication. Depending on the combination, an organization can choose from four kinds of responses. The first three are more conventional, while the fourth, predictive maintenance, is a new addition, driven by technology and the Internet of Things.

1. Reactive maintenance

If it breaks, you have to fix or replace it. This term and action are typically applicable to easily replaceable items of low value such as a lightbulb or remote.

2. Preventative maintenance

This term refers to pre-emptive action taken to prevent failure by maintaining equipment and machines at pre-scheduled downtime and fixed time intervals. This option usually works when the cost of maintenance is not very high, and when it can be scheduled outside of production hours, with minimal losses.

3. Condition-based maintenance

This form of maintenance activity is heavily dependent on the usage levels of a particular machine or piece of equipment.

4. Predictive maintenance

Predictive maintenance is a maintenance approach that derives its strategy and frequency by analyzing a body of operation process data. It employs highly advanced methods of analysis to predict the possibility and timeline of equipment failures well in advance, making it possible to take preemptive action as well as estimate the remaining life or runtime of equipment and machinery. In conjunction with data collected by systems like Smart Factory or Industry 4.0, it builds upon itself to be more and more accurate with time.

Predictive maintenance is valuable in instances when maintenance activities are multi-level or complex, implementing them can be expensive, or when downtime can result in significant losses.

Making the shift from reactive to proactive predictive maintenance: challenges and solutions

Setting up a digital factory, process, or machine is not a simple or straightforward proposition. Smart monitoring systems must integrate into existing or legacy systems, and production has to go on without missing a beat. Similarly, predictive maintenance needs to kick in before any repairs or downtime are needed to be cost-effective.

For a smooth evolution and transition from reactive to proactive maintenance, manufacturing industry experts must concentrate their efforts on offsetting a range of challenges.

Geography and complexity

The challenge of operational complexity or geographical distances can be dealt with by establishing a connected manufacturing unit ecosystem, which brings in an element of real-time situational awareness to the entire operation. Plus, it makes monitoring easier for those overseeing the operation. The smart systems should be set up to form viable links between the “intake” devices like sensors, the actuators, the data collection infrastructure, and the human staff.

Accessibility of data

Data analytics should bridge every aspect of the manufacturing business, from the supply chain to the end-user. The data structure should be designed such that a real-time snapshot of where everything is becomes readily available via reports. This gives enterprises the edge on making agile decisions based on real-time situations.

Anomaly detection

Anomaly detection forms a major part of predictive maintenance and quality control optimization. For it to work, the system needs a great deal of detailed log data regarding process failures. Anomaly detection is based on accumulated sensor data with a minimum frequency of measurements for each time unit and sensor in the infrastructure.

Advanced analytics can then be applied to this data for the system to set parameters for what counts as “normal.” Any departures from the norm or aberrations in the system then raise red flags and send system alerts to the operators. The latter then have to ascertain whether an actual equipment failure has indeed occurred, and then make decisions regarding corrective measures.

Advantages of predictive maintenance In achieving manufacturing control and optimization

There are many advantages of predictive maintenance:

  • Reduce cost of equipment downtime by anticipating failures before they happen and applying preventative maintenance
  • Automate equipment monitoring and system alerts to reduce failures and response times
  • Contribute to supply chain agility by responding to volatile conditions in real-time
  • Reduce product defects and establish sharper quality control
  • Understand every aspect of the manufacturing process, from simple to complex, allowing a 360-degree view of operations at all times

Digital drivers of manufacturing process control and optimization

Robotic process automation

Robotic process automation takes over dull, labor-intensive, mindless, and mechanical operational tasks. Robotics process automation frees up human potential and refocuses it on more high-value areas to better drive business growth. Many human work hours are wasted in tedious, repetitive tasks that automation should take over.

In recent times, there has been a boom in intelligent automation companies and start-ups offering a suite of intelligent automation solutions and products that promise to streamline even the most complex workflows.

Adopting robotic process automation in the manufacturing sector is believed to be a move that will bring big payoffs for companies over the short- as well as long term.

Artificial intelligence

The biggest benefit of artificial intelligence in manufacturing process control and optimization is its ability to constantly grow and build upon itself to become smarter, more intuitive, and more proactive. It takes the data generated by connected processes (such as digital process automation and robotics process automation) to become stronger and more precise in detecting anomalies and predicting failures.

Real-time process optimization

Real-time optimization is defined as a process that ensures performance is constantly being improved in real time. In comparison with traditional process controllers, real-time process controllers differ in the sense of scale and design. Real-time optimization processes are mostly built upon model-based optimization systems. They are usually larger in scale compared to traditional process controllers.

Real-time optimization relies on system feedback and data analysis insights. With real-time optimization, error detection is automatic, and the system can modify and eliminate random and non-random errors.

Besides that, real-time optimization makes computed data available in real time and can send it to various locations. It can be programmed to assess performance details for any specific time frame and for any task or application within the process. It is a highly cost-effective solution to enhance system performance and achieve process optimization.

Don’t confuse real-time process optimization with advanced process control (APC). They are not interchangeable but are complementary functions. Advanced process control is a technique designed to yield control strategies that effectively minimize the difference between process setpoints and actual values. An example would be minimizing overshoot during a process change or shrinking the time needed for a system to return to a steady state following a process upset.

Real-time process optimization is used to define the target process parameters for APC to stay aligned with business goals and targets. Real-time process optimization can be applied to basic on or off control through complex techniques of predictive control and dynamic matrix control.

Automation and process optimization systems can optimize several manufacturing processes—ranging from simple to highly complex. A clear and well-defined transition strategy is quite valuable when shortlisting processes that will yield maximum benefit and build operational efficiency over time.

Manufacturing Process Control diagram