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Posted by Sadia Waseem
Whether it is a small manufacturer or a large conglomerate, their goal is the same: to attain maximum efficiency and output from their resources, which is why we often hear the term “Time Study” in manufacturing. Developed by Frederick W. Taylor in the early 20th century, manufacturing time study is the best way to determine the standard time it takes a qualified worker to complete a specific task, so it is easier to identify bottlenecks, and allocate resources accordingly.
However, conducting an effective time study is no more a single stopwatch exercise. While the fundamental remains relevant, the methodology for conducting time study has evolved considerably. Today, manufacturing processes are far more complex than what they were in the 20th century, which is why manufacturers are employing cutting-edge artificial intelligence (AI) technology and computer vision that make time studies more accurate and easier to conduct.
In this article, we will explore best practices that can level up your time studies. We will also look at how AI can not only streamline the data collection process but provide real-time insights.
Manufacturing time study is one of the most significant tools in industrial engineering and operations management that provides insights into a process. It is a structured way to evaluate and measure time required to perform each step during a production process.
In manufacturing time study, complex operations are broken down into activities, and then observed and recorded individually. Apart from the time taken for each step, industrial engineers also observe data related to the process such as worker movements, delays, bottleneck steps.
The primary purpose of conducting a manufacturing time study is to:
Manufacturing time studies include some key concepts and terms which are essential for you to understand. Some of the important ones are:
If you want to time study manually, then here are some simple steps that you should follow:
Manufacturing time studies are usually conducted with a stopwatch and paper like we just described. Sometimes time study boards are used in place of paper. A time study board consists of predefined columns to note down the time for each activity. Many organizations still follow this practice, but you can see the issues associated with doing it manually; it is time consuming and prone to error.
Moreover, the presence of a person observing time during a process can make workers cautious, known as the “Hawthorne effect”. A worker can deviate from their natural work procedures, which may skew the results. Also, manual observations can disturb natural production flow and impact productivity.
The AI Revolution:
The probability of error is higher in complex processes that we see today, which has paved way for AI-powered solutions that make it easier to conduct time studies. These solutions don’t disrupt operations and they are more accurate than manual measurements.
AI-powered solutions like Kaizen Copilot uses computer vision and different machine learning models to track worker movements and time for each activity without the need for a human observer. It seamlessly integrates into your existing work manufacturing environment and captures real-time data to provide valuable insights into the process.
Kaizen copilot is packed with multiple features that can help you observe, analyze, and reduce waste on your manufacturing floor. From time and motion study to line balancing, and various ergonomics assessment tools, Kaizen Copilot provides the ultimate solution to boost productivity. It performs your redundant and non-productive tasks leaving you with ample time for critical thinking and productive work.
With Kaizen Copilot, you don’t need any stopwatches or time study boards; you can perform manufacturing time and motion study of any process with just one click. All you need is a video whole process within a single frame that you can upload to the software and Kaizen Copilot will take care of the rest.
The AI technology and computer vision in Kaizen Copilot divides the whole process into a number of steps as defined by auser. The time and motion study results are based on these steps:
Actual Cycle Time: Actual cycle time is the total time needed to complete a process.
Value-Added Time: Value-added time is the time from the total cycle, which contributes to the transformation of material into a product or services for which customers are willing to pay for.
For example: During the assembling of doors into a car, value-added time can be:
Non Value-Added Time: Non value-added time is the time during a process that do not transform the product or add value according to a customer’s perspective.
For example: In the same process of assembling doors into a car, non value-added time can be:
Necessary Non Value-Added Time: Necessary non value-added time does not add value to the product. However, these activities are necessary for completing a product and for business operations.
For example: In the same example, necessary non value-added activities can be:
Step Time per Cycle: It is the graphical representation of time for each step. The time of each step can be viewed separately to evaluate the longest step and optimize this activity. The steps are further divided into value-added, non-value-added, and necessary non-value added for analysis purposes.
Kaizen Copilot not only conducts manufacturing time studies but also analyzes them and gives you recommendations to implement in your manufacturing floor to increase the overall efficiency and decrease waste.
Manufacturing processes have evolved considerably and so it was only a matter of time that time studies got an upgrade. AI and computer vision helps in accurate classification of time into value-added, non-value-added and necessary non-value-added activities and makes it easier to explore areas for improvement with accurate precision. Kaizen Copilot has been designed keeping in mind the challenges associated with traditional methods. It not only helps in conducting more accurate and insightful time studies, but it takes less time as well. Ready to Experience the power of Kaizen Copilot yourself? Book your free demo today!
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.
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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.
