• Written by Sonali Subhadarsini, Product Manager

The dashboard for the production planning team at a large automotive manufacturer sits idle, its screensaver kicking in after a few minutes of inactivity, a testament to the fact that the team rarely needs to refer to it, as the information they need is often locked away in reports that are only updated weekly, or worse, monthly, which is why they have started to explore the potential of AI for operations in automotive to get answers to their questions in real time.

The operations questions that currently wait on a report

The operations team in an automotive manufacturing plant is constantly asking questions about the status of production, the performance of suppliers, and the impact of engineering changes on the production schedule, but these questions often have to wait for a report to be generated, which can take days or even weeks, and by the time the report is available, the situation on the ground has already changed, making the report obsolete, which is why the team is looking for a way to get answers to their questions in a more timely manner, such as through the use of AI for operations in automotive, which can provide real-time answers to questions about their own data.

For example, a shift supervisor might want to know which production lines are currently running behind schedule, or a category manager might want to know which suppliers are not meeting their delivery targets, but these questions are not easily answerable with the current reporting system, which is why the team is looking for a more flexible and responsive solution, such as AI for operations, which can provide answers to these types of questions in real time.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that the operations team needs to answer their questions is scattered across multiple systems, including process and exception records, project status updates, budget consumption reports, and supplier performance metrics, which makes it difficult to get a complete picture of what is happening on the production floor, and this is where AI for operations in automotive can help, by providing a single platform that can access and analyze all of this data, and provide answers to questions in real time.

The data includes process records, such as production schedules and quality control checks, exception records, such as equipment failures and supply chain disruptions, project status updates, such as progress against milestones and deadlines, budget consumption reports, such as spending against budget and forecasts, and supplier performance metrics, such as on-time delivery and quality ratings, all of which are critical to understanding what is happening on the production floor, and making informed decisions about how to optimize production.

Sector constraints: supplier escalations and engineering change propagation

The automotive manufacturing sector is subject to a number of constraints that can impact production, including supplier escalations and engineering change propagation, which can cause delays and disruptions to the production schedule, and these constraints are often difficult to anticipate and mitigate, which is why the operations team needs to be able to respond quickly and effectively to changes in the production environment, and this is where AI for operations in automotive can help, by providing real-time visibility into the production process, and enabling the team to make informed decisions about how to optimize production.

What improvement looks like: on-time delivery, exception rate, expediting cost

Improvement in the automotive manufacturing sector can be measured in a number of ways, including on-time delivery, exception rate, and expediting cost, all of which are critical metrics that can help the operations team understand how well they are performing, and where they need to improve, and by using AI for operations in automotive, the team can get real-time answers to questions about these metrics, and make informed decisions about how to optimize production, which can help to improve on-time delivery, reduce the exception rate, and lower expediting costs.

For example, a category manager might want to know which suppliers are consistently delivering on time, or a shift supervisor might want to know which production lines are experiencing the most exceptions, and by using AI for operations in automotive, they can get answers to these questions in real time, and make informed decisions about how to optimize production, which can help to improve performance and reduce costs, and if they need more information or support, they can contact us to learn more about how AI for operations in automotive can help them achieve their goals.

What this does not do

While AI for operations in automotive can provide real-time answers to questions about production, it is not a replacement for human judgment and decision-making, and it is not a solution for all of the challenges facing the automotive manufacturing sector, but rather a tool that can help the operations team make more informed decisions, and optimize production, and it is not a magic bullet that can solve all problems, but rather a powerful tool that can help the team achieve their goals, and improve performance, and it is up to the team to use this tool effectively, and to make the most of the insights and answers it provides.