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In the industrial manufacturing sphere, continuous improvement is not only a buzzword used in management meetings but a necessity to ensure optimal resource consumption, higher production rates, increased product quality, and yes also larger profit margins.
Process optimization in a manufacturing environment encompasses various aspects of the manufacturing process, including identifying bottlenecks, adjusting process variables, increasing equipment availability and capacity, as well as increasing safety and cost efficiency.
Manufacturing process optimization initiatives are commonly impeded by a “good enough” mentality.
“The process is running fine. Why change something?” is an all too common sentence.
To better understand the necessity of optimizing manufacturing processes, we first need to understand what it entails.
Mărgineanu et al., in the 5th International Conference on Computing and Solutions in Manufacturing Engineering (Conf. Ser.: Mater. Sci. Eng. 1009) define process optimization as:
“Production process optimization is the technique of adjusting a process to perfect a specified set of parameters without violating certain constraints. The most common objectives are to minimize costs and maximize efficiency”
In the most basic of definitions, this means that process optimization is a blanket statement that engulfs all initiatives to increase the efficiency of a process.
Think of a car, if you were to drive your stock 2021 Toyota Camry across the country, you would get an average fuel consumption of 32 mpg.
Now if you were to accelerate gradually, maintain a steady speed by implementing adaptive cruise control (ACC), and minimize your air-conditioner (AC) usage, you could get 39 mpg.
By implementing optimization initiatives, you would simultaneously use less fuel while increasing the distance you can travel by 100 miles.
In the same sense, optimizing manufacturing processes allows companies to:
- Better utilize raw materials and resources such as energy and water.
- Manufacture higher quality products, safer and at a lower cost.
- Improve the time it takes to manufacture quality products.
In the below sections, we will look at why the optimization of manufacturing processes is a necessity, explore three common approaches to optimizing manufacturing processes, discuss optimization initiatives, and investigate how Basetwo can help you optimize your manufacturing processes through its AI platform.
3 common approaches to optimizing manufacturing processes
Manufacturing process optimization is a systematic and iterative approach.
It utilizes various factors including, but not limited to, models based on real-time data, industrial machine layout, standard operating procedures (SOPs), and labor usage.
As with the Toyota Camry, these steps are aimed at enhancing resource consumption and increasing overall efficiency.
A common approach to optimizing a manufacturing production process is:
- Analyzing the current state of the existing process.
- Identifying and eliminating operational bottlenecks in the existing process.
- Optimizing the process by varying essential process variables.
1. Analyzing the current state of the existing process
The old saying, “You don't know what you don't know” rings true in this approach. To be able to optimize a system you need to first understand it. A common approach to analyzing your current processes is:
Establish boundaries:
To understand the objectives of the optimization efforts, clearly define the constraints within the process you aim to optimize.
These boundaries may be physical, such as optimization focused on a specific production line, equipment, or section.
It may also be aimed at process outcomes, such as increasing the yield of a chemical reaction.
Data collection:
Collect all available data related to the current process, including process documentation, financial data, and log sheets.
Ensure the data you are collecting is the most accurate and recent.
Process mapping:
Create a visual representation of the process. Commonly a mass flow diagram or a process flow diagram is used.
Benchmark and identify key performance indicators (KPIs):
Benchmark the existing process by comparing it to industry best practices. This involves understanding what equipment and processes are capable of.
As in the case of your Toyota Camry, the fuel efficiency can only increase to a certain mpg.
After benchmarking, identify or define relevant KPIs.
These KPIs are used to measure the performance of the process. Common KPIs include cycle time, throughput, yield, error rates, and cost per batch.
2. Identifying and eliminating operational bottlenecks in the existing process
After understanding the system, the next step is to identify and eliminate any operational bottlenecks.
Operational bottlenecks are points in a manufacturing process that slow down or impede production.
These can arise from any points in the system that are linked to day-to-day manufacturing operations.
In the case of your Toyota Camry, the time it takes to refuel could be an operational bottleneck, directly affecting and reducing travel efficiency over longer distances.
The two most common types of operational bottlenecks faced in manufacturing processes are short-term and long-term bottlenecks:
Short-term bottlenecks:
These bottlenecks arise from temporary disruptions in the manufacturing process, such as unexpected machinery malfunctions or the absence of employees.
These operational bottlenecks can be easily identified as well as mitigated relatively quickly through proactive action.
An example of a short-term bottleneck is an unexpected machinery malfunction, which can be addressed proactively through predictive maintenance software, such as Basetwo.
Long-term operational bottlenecks:
These bottlenecks are usually harder to identify and are embedded in the manufacturing process, significantly hindering production.
Long-term operational bottlenecks might include chronic manufacturing issues such as persistent equipment underperformance, inefficient production line layouts, or a consistent lack of critical skills among employees.
The Toyota Camry’s engine size could be an example of a long-term bottleneck if it limits the maximum speed or fuel efficiency, necessitating an engine upgrade or modification.
Various strategies exist to address operational bottlenecks including workflow optimization, comprehensive employee training, and implementing software solutions for simulation and model-driven optimization.
To identify operational bottlenecks in a process, some common approaches are:
Root cause analysis:
- Identifying the root cause of any problem or limitation is a crucial step in determining and eliminating operational bottlenecks.
- Techniques like creating a Fishbone Diagram, which uses a visual representation to trace the origins of a defect or problem back to its root cause across various categories, can be especially effective.
Mapping production bottlenecks:
- Creating visual representations, or value stream maps, of the manufacturing process can assist in identifying areas in the process where the workflow can be optimized or where additional resources are needed.
3. Refining process controls & operating procedures
Refining operating procedures and implementing the correct process controls involves adjusting process variables and identifying the correct setpoints.
Varying and adjusting process variables sounds quite intense and risky, but the idea behind this is not to implement drastic changes in the configuration of your systems and wait for the inevitable fallout.
Instead, process optimization initiatives can be implemented safely and efficiently through strategic and incremental adjustments to process variables.
This approach focuses on enhancing the process, ensuring that changes lead to valuable outcomes without compromising product quality.
Consider the analogy of your Toyota Camry, with the aim being to optimize fuel consumption.
By varying conditions like speed, acceleration, and AC usage, you can identify the correct parameters of these variables that enable you to drive for longer.
Similarly, in manufacturing, fine-tuning process variables, through incremental changes, can yield substantial improvements in efficiency and product quality, with minimal risk.
Manufacturing process optimization initiatives, technology-driven or not, are crucial in modern manufacturing environments.
They allow for continuous improvement in the face of operational and market demands.
Common manufacturing process optimization initiatives that utilize technology are discussed in the following section.
How to optimize processes more efficiently using technology
The following manufacturing process optimization initiatives are implemented in conjunction with technological initiatives.
1. Real-time analytics and alerting
Data analytics encompass the collection of real-time data through sensors and other devices allowing immediate insight into a manufacturing process and aiding in informed decision-making.
Through analyzing the collected data, improvement areas are better identified and optimization efforts are guided.
Real-time analytics and alerting allow for prompt intervention in case of deviations, optimizing resource allocation, and minimizing downtime.
2. Predictive maintenance
Through technology-driven predictive maintenance, equipment health is monitored in real-time using sensors and data analytics, anticipating maintenance needs and preventing unexpected breakdowns.
Predictive maintenance can be seen as collecting data on the engine of your Toyota Camry and automatically scheduling your maintenance appointments at the best time given your car’s current health. By comparison, with traditional maintenance, you might schedule your car’s maintenance every 10,000 km.
In the same sense, predictive maintenance can alert manufacturing teams to impending issues in machinery, reducing unplanned downtime and ensuring uninterrupted production.
3. Simulation and model-driven optimization
Simulation and modeling software enables virtual experimentation and process optimization in the manufacturing industry.
As expanded upon in the section, refining process controls & operating procedures, adjusting process variables can help manufacturers identify the optimal operating conditions of their processes.
Models allow the adjustment of process variables and the measurement of their effect on the manufacturing process, without making any changes to the physical process.
The ability to investigate the effects of adjusting process variables is accredited to models being created by incorporating real-time data and historical trends.
Engineers can implement what-if analysis, through simulations, based on different scenarios to identify the most efficient configurations and parameters.
An example of this would be to optimize the fuel consumption of your Toyota Camry, you create a model of your car by utilizing data you have collected.
After creating your model, you run various simulations varying process variables such as acceleration, speed, and AC usage until you identify the optimized operating conditions for maximum fuel efficiency.
After identifying these optimized operating conditions, you refine and implement your process control, such as setting your adaptive cruise control, to ensure maximum fuel efficiency.
How Basetwo can help you optimize your manufacturing process
Basetwo is an AI-driven process optimization tool that leverages a low code, drag-and-drop interface. Basetwo seamlessly integrates with real-time data sources and virtually simulates existing processes to optimize production performance.
Basetwo independently handles the entirety of the modeling value chain, from data ingestion and processing to simulation, optimization, and version control.
Basetwo offers valuable insights that can lead to reductions of up to 40% in cycle time and material usage, enhancing operational efficiency and cost savings in manufacturing and process development.
The Basetwo platform provides various features aimed at manufacturing process optimization, including:
- Easy data ingestion and cleaning from commonly used sources like OSI-PI.
- User-friendly drag-and-drop interfaces for creating data-driven, mechanistic, or hybrid models.
- Rigorous testing and validating of models for reliable deployment.
- Built-in optimization and process control designed to streamline manufacturing processes.
- Empowering teams to find optimal control points.
The Basetwo platform also has ready-to-use templates for quick and easy modeling, advanced data integration from various industrial or cloud-based sources, and hybrid modeling capabilities allowing a user to combine multiple technologies, such as data analytics, artificial intelligence, and process automation.
Are you interested in learning how Basetwo can help you optimize your manufacturing processes?
