The use of time motion studies to increase efficiency was important because:

Time and Motion Insights

• Using sensors, analyzers, cameras and other technology to collect information improves accuracy and transparency as opposed to collecting data manually, which can lead to slower responses, more errors and can contain human biases.
• Having accurate estimation of standard time and motion is important and can be achieved better by training employees to be experts in this field. Implementing augmented reality can be a great tool in training to give employees experience before working on a live plant.
• Deploying a computerized maintenance management system (CMMS) can also help eliminate some human errors and give more accurate readings. With having a CMMS set in place, employees would already have access to a detailed background of every task to go off of for their reports, so they wouldn’t have to start from scratch.

Time and motion study holds prime importance whenever a repetitive task or work is performed by a human. Originally developed by Frederick Winslow Taylor and Frank Gilbreth almost 100 years ago as “One Best Method,” the study still holds significant relevance in an industrial setting to improve the productivity of workers’ actions in a given time period.

The purpose of time and motion study is to carefully analyze tasks and break them down into their components for simplicity of analysis, and come up with standard methods and time to perform each task. Standard time is essentially an average time for a group of qualified and well-trained workers, performing specific work or tasks at their normal pace. In time and motion study, standard time serves as a critical metric to evaluate production efficiency and identify areas of improvement at the bottom line of any facility.

The purpose of this article is to identify different ways technology can be used to improve time and motion studies in any plant or production facility.

In time and motion study, standard time serves as a critical metric to evaluate production efficiency and identify areas of improvement at the bottom line of any facility. Courtesy Limble

1. Advanced data-collection technologies with time and motion

Since time and motion studies are pivoted around standardizing the work, collecting data accurately and efficiently is of prime importance – so that enough evidence can be collected for later analysis.

Earlier, the collection of data was reliant on expert observers who would manually observe the workers performing similar tasks. They would use simple devices such as stopwatches or counters to quantify the number of tasks and time taken to complete each task along with their efficiencies. Nowadays, advanced computer vision technologies such as sensors, analyzers and cameras can be used to collect information about the quality of the product with extreme accuracy and transparency.

Some organizations use drone technologies to collect data from places where it is difficult for a human observer to reach and collect data. For example, a drone can effectively be used to collect data from a construction site of skyscrapers where a worker might be working on the top floor wearing a harness. The use of technology-based data collection enables faster responses, reduces errors and eradicates biases of human assessment.

2. Use of augmented reality and digital twins

To enable accurate estimation of standard time and motion, the worker performing the work and the observer recording the work must be trained professionals. This means training and competency building of workers holds great importance. Nowadays, industries are adopting advanced technologies – such as augmented reality – to train workers without exposing them to the real-time danger of working on live plants. Such technology can also be used to envision the final product and increase the situational awareness on part of the observer as well as the worker.

Similarly, the use of digital twins is also gaining traction in modern industry settings. Digital twin essentially involves modeling the plant and equipment through software algorithms based on the data collected from Internet of Things (IoT) sensors and other monitoring equipment.

There has been development going on as part of the Industry 4.0 transformation where data-driven algorithms based on machine learning and artificial intelligence along with digital twin technology may be used to simulate the time and motion parameters that can provide a reflection of the actual scenario in the physical plant.

3. Deployment of computerized maintenance management systems (CMMS)

The deployment of a CMMS is another way to improve the structure of the work processes and bring time efficiencies through automation of work order management. Increasing the structure of work leads to the standardization of workflows, and enables an accurate calculation of key metrics, such as standard time. Moreover, CMMS also acts as a single source of truth about plant equipment and helps in different steps involved throughout the entire time and motion study.

For example, one of the key steps of the study is to perform a time-sampling of work and an analysis of individual tasks of the work. With the CMMS deployed, the detailed process of every task would already be established, which the observer can use as a baseline standard. This would save time on part of the observer to develop processes and bring cost efficiencies.

Moreover, CMMS would also provide enough data points for a variety of equipment necessary for accurate time and labor sampling. Without the CMMS, a lot of this information would be reliant on the observer’s expertise and knowledge, which can introduce a range of human errors through different stages of the study.

Final words on time and motion

In short, a time and motion study can be a complicated exercise that requires a series of steps – such as setting up goals and boundaries, breaking down work into components, collecting information about the workforce and equipment, and sampling information. The article presented how the use of modern technology – such as IoT sensors and advanced imaging systems, together with the deployment of relevant software – can enable faster and more accurate processing of time and motion studies.

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