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Posted by Sadia Waseem
Process optimization in manufacturing is quite critical in today’s competitive environment. Manufacturing optimization involves streamlining the process to improve product quality, reduce waste, and enhance efficiency. You cannot work towards process improvement simply if your manufacturing process is not optimized, as Sophie Miles famously said:
“Process improvement is constantly optimizing the process.”
However, there is no straight “one-shot” way to achieve process optimization in one go, although some approaches may work better than others. With the integration of AI technology in manufacturing processes, it has become relatively easier to collect process-related data and analyze the process, which is quite essential for optimization. In this article, we will look into best practices and tools to optimize your process.
Process Optimization in manufacturing is a systematic approach to analyze the process and reduce waste and cost to improve the efficiency and quality of the process. The goal is to make the system as functional and practical as possible. This can be done by automating all the repetitive tasks, minimizing material movement, reducing unnecessary processing steps, standardizing the processes, and many more.
When it comes to process optimization, it’s tempting to try to optimize the entire process right from the beginning. This creates a cluster image of the process, making it challenging to implement the process optimization techniques. By breaking down the process, you can see the areas for improvement and bottlenecks that slow down your entire production line. By working on these areas and implementing targeted strategies, you can optimize each process step.
Optimizing your manufacturing process help to improve different parameters of your process. The benefits associated with process optimization are:
Some of the methodologies that you can implement to optimize your process are:
Lean Manufacturing: Lean Manufacturing focuses on reducing waste from the process to maximize the value. The waste defined in lean manufacturing is unnecessary transportation, inventory, motion, over-processing, over-production, defects, and waiting. Some of the significant lean tools that could help you in process optimization are:
Six Sigma: Six Sigma is a data-driven approach that improves the quality of the process by reducing defects and minimizing variability. It follows the DMAIC approach, which is:
Computer-Aided Manufacturing (CAM): CAM refers to the use of software and digital technology to automate the process. It increases the precision of the product and simplifies the manufacturing process, resulting in reduced lead time and improved quality.
Data Analytics: Data analytics allows you to monitor performance, identify trends, and make data-driven decisions. Real-time data from sensors, AI based technology and IoT devices enable continuous optimization.
These methodologies may overlap and can be combined to optimize the process. The choice depends on the nature of the manufacturing process, specific needs, and available resources for process optimization.
As the Industry 4.0 revolution transforms manufacturing, organizations increasingly shift towards automated technologies and data-driven approaches to optimize their processes. One way to improve the quality of your products and make the data collection process seamless is through AI and computer vision, such as Retrocausal’s AI Copilots: Kaizen Copilot and Assembly Copilot. Both products use generative AI technology and computer vision to streamline data collection and automate analysis, leading to process and quality improvement.
Automated Time and Motion Study: Time and motion study is a well-established technique to optimize your process and increase efficiency. A study published in the Journal of Physics analyzed the application of time and motion study to increase productivity and efficiency. After implementing time and motion study corrections, the researchers observed a 37.95% increase in efficiency and a 218.03% increase in productivity. However, the traditional way of conducting time and motion studies possesses significant challenges. Industrial engineers spend countless hours collecting data for time study rather than implementing improvement projects. Moreover, there are concerns about data reliability in manual data collection due to factors such as observer fatigue, timing variations, and the Hawthorne effect.
Modern technology is mitigating these challenges using AI assistants such as Kaizen Copilot. These advanced tools can automatically collect and analyze motion data, ensuring consistent, objective measurements while allowing engineers to focus on process improvements.
Figure 4: An example of Time and Study Analysis in Kaizen Copilot
AI-Powered Ergonomic Assessment: Worker’s safety and well-being are crucial to optimize the manufacturing process. A study in the Applied Ergonomics journal highlighted those ergonomic improvements in assembly line tasks decreased back pain complaints from 60.3% to 31.3%. Ergonomics interventions can be implemented through tools such as REBA assessment, RULA assessment, NIOSH lifting equation, or Snook tables. However, these ergonomics assessment tools can often require time-consuming manual measurement and calculations with limited ability to analyze dynamic movements accurately. These limitations can be overcome through using AI technology and computer vision.
Advanced technologies can automatically analyze workers’ movements and postures through video, with immediate feedback on ergonomic risks. For example, AI tools like Kaizen Copilot automate the assessment process, enabling you to focus on implementation and ergonomic intervention rather than conducting manual measurements and complex calculations.
AI-Based Line Balancing: In manufacturing, line balancing is dividing the workload in an assembly line to maximize the use of available resources. Research indicates that line balancing minimizes the cycle time, idle time, and overall length of the process. Line balancing is the first step in optimizing the assembly line to obtain maximum efficiency. The manual line balancing requires industrial engineers to spend hours on the assembly line collecting data, followed by calculating workloads for each station. This approach is time-consuming and often makes it difficult to experiment with different configurations or quickly adapt to product changes.
Digital technologies are changing the traditional approach to line balancing. These systems use video analysis and data processing to suggest optimal task distribution. Digital technologies like Kaizen Copilot allow you to conduct line balancing using AI technology and computer vision while seamlessly collecting data and automatically balancing workload across all the stations.
Advanced Quality Analysis: Failure Mode and Effect Analysis (FMEA) is a widely used method to improve product quality and optimize the process. According to a study in the International Journal for Quality Research, applying FMEA to the shell molding process reduced rejection rates from 7.13% to 3.14%.
Traditionally, conducting FMEA manually requires engineers to invest significant time in documenting process steps, identifying potential failures, and calculating risk priority numbers. This approach is not only time-intensive but also prone to errors, making it difficult to adapt to rapid process changes.
AI systems offer a streamlined alternative by automating much of this complex process. These tools can analyze process data, identify potential failure modes, and recommend preventive measures. For instance, tools like Kaizen Copilot enable quality teams to focus on implementing improvements by automating the data collection and analysis phases of PFMEA.
Figure 7: An example of PFMEA analysis in Kaizen Copilot
With the rise of Industry 4.0 in manufacturing, AI assistants are revolutionizing process optimization. These technologies streamline operations by automating time-consuming tasks such as data collection for time and motion studies, performing real-time ergonomic assessments, optimizing line balancing, and improving quality analysis. Tools like Kaizen Copilot exemplify how AI enables engineers to shift their focus from tedious data gathering to implementing meaningful improvements, all while ensuring more consistent and reliable results. This shift from manual processes to AI-driven solutions marks a transformative step forward in manufacturing optimization.
While process optimization is critical for improving efficiency, quality, and productivity, it’s important to address several key challenges to ensure successful implementation:
Resistance to Change: Workers may resist new methods due to concerns about potential productivity loss or discomfort with unfamiliar systems. Overcoming this resistance requires a well-executed change management plan, clear communication, and comprehensive training.
Data Management: Successful process optimization depends on accurate, comprehensive data. Inaccurate data can distort the process analysis and hinder improvement efforts. Organizations must focus on streamlining data collection, analysis, and utilization to make informed decisions.
Complex Process: Manufacturing workflows can be intricate, with many interconnected steps. Optimizing one area might unintentionally create bottlenecks or inefficiencies elsewhere. To address this, a holistic approach, breaking the process into manageable segments, can help identify and resolve potential issues.
Balancing Conflicting Objectives: Companies often face competing goals, such as maintaining product quality while reducing costs. Striking a balance between these objectives can complicate optimization efforts, requiring careful consideration of trade-offs to achieve the best overall outcome.
Process optimization is essential for organizations seeking to improve productivity, quality, and competitiveness. However, effective change management and training are required to overcome challenges like resource limitations and resistance to change. While traditional optimization methods have proven effective, the integration of AI technologies is changing how manufacturers approach data collection and analysis. These advanced tools streamline data collection and simplify implementation. The key lies in combining established optimization principles with AI-powered solutions to drive ongoing improvements in manufacturing processes.
Brandin and his team help our customers with setting up the Pathfinder platform and getting up-to-speed on all the features and functionalities, in addition to assisting them with standardizing their processes where needed.
Brandin is a seasoned manufacturing leader, who has spent more than a decade designing and setting up assembly lines in both the US and China, optimizing them, and working with human operators to maximize productivity and quality.
Jesse works closely with our marquis customers to understand their needs, and figures out the best solution that immediately adds dollars to their bottom line.
Jesse has two decades of enterprise sales experience. He has held sales leadership roles at pioneering technology startups.
Andrey takes inventions and converts them into innovations. He leads our product engineering team to turn our scientific prototypes into robust customer-facing products.
Andrey co-founded and successfully exited a startup to immigrate to the US. He spent his next eight years building the core data science infrastructure for Amazon’s supply chain planning team, and the CV/ML infrastructure for Microsoft’s HoloLens organization.
Zeeshan focuses on delighting our customers by understanding their needs and defining product and strategy to meet and exceed these needs. He works closely with our sales leadership to grow our customer base.
Zeeshan was a scientific leader at Microsoft’s HoloLens division, where he invented AI technologies to assist frontline workers across industries. Earlier, he shipped IoT platforms and visual defect detection software for manufacturers at Siemens and MVTec GmbH.
Huy takes our product roadmap and figures out what’s achievable with today’s AI advances, and what we need to invent to solve our customers’ pain points.
Huy led teams of scientists and engineers at NEC and invented technologies for driver assistance systems that are part of 500,000+ Honda cars. He is an inventor on more than 20 US patents, and his research papers are cited by every lab from MIT, Stanford, and CMU to Apple, Google, and Baidu.
