• Written by Sonali Subhadarsini, Product Manager

This week, the shift supervisor at a manufacturing plant has to decide whether to adjust the production schedule to meet the changing demand for their products, and they currently base this decision on their experience and manual analysis of the maintenance log, which can be a time-consuming and error-prone process. The introduction of AI-powered tools like AsscherAi aims to change this by providing real-time insights into the production data, potentially saving time for the shift supervisor and other production staff. However, the question remains whether this saved time is actually redeployed to improve productivity.

The difference between time saved and time redeployed

The distinction between time saved and time redeployed is crucial in understanding the impact of AI productivity tools on production. While AsscherAi can automate certain tasks, such as data analysis and reporting, the time saved by these automations may not necessarily be redeployed to improve productivity. For instance, the category manager may spend less time comparing supplier performance, but this does not mean they will use the saved time to optimize the supply chain. The time redeployed may be absorbed by other tasks or activities that do not directly contribute to productivity gains.

A closer look at the production workflow reveals that the time saved by AI-powered tools can be significant, but the benefits may be limited if the saved time is not redeployed effectively. The production team may need to adjust their workflow and processes to take full advantage of the time saved, which can be a challenging task, especially in complex production environments.

Why self-reported time savings overstate the effect

Self-reported time savings are often used to measure the effectiveness of AI productivity tools, but these estimates can be unreliable and overstate the actual impact. Production staff may overestimate the time saved or report time savings that are not actually realized. Moreover, self-reported time savings do not account for the time spent on tasks that are not directly related to production, such as training or maintenance. A more accurate assessment of time savings requires a detailed analysis of the production workflow and the tasks performed by the production staff.

The shift supervisor, for example, may report that they save several hours per week using AsscherAi, but this may not translate to actual productivity gains if the saved time is not redeployed effectively. A more nuanced understanding of the production workflow and the tasks performed by the production staff is necessary to accurately assess the impact of AI productivity tools.

The production data this depends on

The effectiveness of AI productivity tools in production depends on the quality and availability of production data, including output against plan, cycle times, scrap and quality records, and maintenance history. This data is used to train the AI models and provide insights into the production workflow. However, the accuracy and completeness of this data can vary significantly depending on the production environment and the systems used to collect and store the data. For instance, the maintenance history may be incomplete or inaccurate, which can affect the ability of the AI models to provide reliable insights.

The category manager, for example, may use AsscherAi to analyze the supplier performance data, but the accuracy of this analysis depends on the quality of the data collected from the suppliers. If the data is incomplete or inaccurate, the analysis may not provide reliable insights, which can affect the ability of the category manager to make informed decisions.

For more information on production data and how it can be used to improve productivity, visit our production page.

What it changes about: unplanned downtime and stock cover against the plan

The introduction of AI productivity tools can change the way production staff approach unplanned downtime and stock cover against the plan. By providing real-time insights into the production workflow, AsscherAi can help the shift supervisor and other production staff to identify potential issues before they occur, reducing the likelihood of unplanned downtime and improving stock cover against the plan. However, this requires a significant change in the way production staff work and interact with the AI-powered tools.

The production team may need to adjust their workflow and processes to take full advantage of the insights provided by AsscherAi, which can be a challenging task, especially in complex production environments. Moreover, the production staff may need to develop new skills and expertise to effectively use the AI-powered tools and interpret the insights provided.

Measured with: OEE, unplanned downtime hours, scrap rate

The impact of AI productivity tools on production can be measured using a range of metrics, including Overall Equipment Effectiveness (OEE), unplanned downtime hours, and scrap rate. These metrics provide a comprehensive view of the production workflow and can help to identify areas for improvement. However, the choice of metrics depends on the specific production environment and the goals of the production team.

For instance, the shift supervisor may use OEE to measure the effectiveness of the production equipment, while the category manager may use scrap rate to measure the quality of the products. By using a range of metrics, the production team can gain a more complete understanding of the production workflow and identify areas for improvement.

If you would like to discuss how AsscherAi can help you to improve productivity in your production environment, please contact us.

What this does not do

While AI productivity tools like AsscherAi can provide significant benefits in production, they are not a panacea for all production issues. They do not replace the need for skilled production staff or the importance of effective workflow and process management. Moreover, they may not be suitable for all production environments, especially those with complex or highly variable workflows.

In some cases, the introduction of AI productivity tools may even create new challenges, such as the need for significant changes to the production workflow or the development of new skills and expertise. Therefore, it is essential to carefully evaluate the potential benefits and challenges of AI productivity tools in production before introducing them into the production environment.