• Written by Sonali Subhadarsini, Product Manager

A shift supervisor on the production line checks the maintenance log to see if any equipment is due for servicing, a routine task that is critical to ensuring the smooth operation of the factory, but one that can be fraught with complexity when it comes to managing the supply chain and propagating engineering changes, which is where AI process automation in automotive can help, by automating the steps around a decision rather than the decision itself, under the constraints of tiered supply with tight takt and long programmes.

What makes automotive harder

The automotive industry presents a unique set of challenges when it comes to process automation, particularly when it comes to supplier escalations, engineering change propagation, and warranty root cause analysis, where the complexity of the supply chain and the need for precise tracking and tracing of components can make it difficult to identify and rectify issues, and where the consequences of failure can be severe, in terms of both cost and reputation, which is why it is so important to get the process right, and to use tools like AsscherAi to support the decision-making process, by providing real-time answers to questions about the organisation's own data.

For example, when a supplier fails to deliver a critical component on time, the shift supervisor may need to escalate the issue to the procurement team, who will then need to negotiate with the supplier to resolve the issue, a process that can be time-consuming and laborious, and one that may require the involvement of multiple stakeholders, including the engineering team, who will need to assess the impact of the delay on the production schedule, and the logistics team, who will need to arrange for the transportation of the component to the factory, which can be a complex and challenging process, especially when dealing with a tiered supply chain, where multiple suppliers are involved, and where the failure of one supplier can have a ripple effect throughout the entire chain.

Mapping the current process before automating any of it

Before automating any part of the process, it is essential to map out the current process in detail, to identify the key steps and stakeholders involved, and to understand how the process currently works, which can be a time-consuming and laborious task, but one that is critical to ensuring that the automation process is successful, and that the new process is efficient and effective, and one that takes into account the specific needs and requirements of the organisation, and the constraints of the tiered supply chain, with its tight takt and long programmes, which can make it difficult to implement changes to the process, and to ensure that all stakeholders are aligned and working towards the same goals.

This involves working closely with the shift supervisor, the procurement team, the engineering team, and other stakeholders to understand their roles and responsibilities, and to identify areas where the process can be improved, and where automation can add value, such as by reducing the time and effort required to complete tasks, or by improving the accuracy and consistency of the data, and by providing real-time visibility into the process, which can help to identify issues and exceptions, and to enable faster and more effective decision-making, and to learn more about how AsscherAi can support this process.

The handoffs that create most of the delay

The handoffs between different teams and stakeholders are often where the process breaks down, and where delays occur, as information is passed from one person to another, and from one system to another, which can lead to errors and misunderstandings, and to a lack of visibility and control, particularly in a tiered supply chain, where multiple suppliers and stakeholders are involved, and where the process can be complex and difficult to manage, which is why it is so important to identify and address these handoffs, and to find ways to streamline and automate the process, such as by using AI process automation to reduce the need for manual intervention, and to improve the flow of information between different teams and stakeholders.

For example, when the shift supervisor identifies an issue with a component, they may need to pass this information to the quality control team, who will then need to investigate and resolve the issue, a process that can be time-consuming and laborious, and one that may require the involvement of multiple stakeholders, including the supplier, who will need to be notified and involved in the resolution of the issue, which can be a complex and challenging process, especially when dealing with a tiered supply chain, where multiple suppliers are involved, and where the failure of one supplier can have a ripple effect throughout the entire chain, and where the support team can provide guidance and assistance to help resolve the issue.

Exception handling as the majority of the real work

Exception handling is often the majority of the real work in process automation, as it is the exceptions and anomalies that require the most time and effort to resolve, and that can have the greatest impact on the process, particularly in a tiered supply chain, where multiple suppliers and stakeholders are involved, and where the process can be complex and difficult to manage, which is why it is so important to have a robust and effective exception handling process in place, one that can identify and address issues quickly and effectively, and that can provide real-time visibility and control, and that can enable faster and more effective decision-making.

This involves identifying and addressing the root causes of exceptions, and finding ways to prevent them from occurring in the future, such as by improving the design of the process, or by providing additional training and support to stakeholders, and by using AI process automation to reduce the need for manual intervention, and to improve the flow of information between different teams and stakeholders, and to provide real-time answers to questions about the organisation's own data, which can help to identify issues and exceptions, and to enable faster and more effective decision-making.

For example, when a supplier fails to deliver a critical component on time, the exception handling process may involve notifying the shift supervisor, who will then need to escalate the issue to the procurement team, who will then need to negotiate with the supplier to resolve the issue, a process that can be time-consuming and laborious, and one that may require the involvement of multiple stakeholders, including the engineering team, who will need to assess the impact of the delay on the production schedule, and the logistics team, who will need to arrange for the transportation of the component to the factory, which can be a complex and challenging process, especially when dealing with a tiered supply chain, where multiple suppliers are involved, and where the failure of one supplier can have a ripple effect throughout the entire chain.

What this does not do, or when it is the wrong choice

AI process automation in automotive is not a replacement for human judgment and decision-making, but rather a tool to support and augment the decision-making process, by providing real-time answers to questions about the organisation's own data, and by automating the steps around a decision rather than the decision itself, which is why it is so important to understand the limitations and constraints of the technology, and to use it in a way that is tailored to the specific needs and requirements of the organisation, and that takes into account the complexity and nuance of the process, and the need for human oversight and intervention.

For example, when dealing with complex and high-risk decisions, such as those related to product safety or regulatory compliance, it may be necessary to involve human stakeholders and decision-makers, rather than relying solely on automation, and to use the automation technology as a tool to support and inform the decision-making process, rather than as a replacement for human judgment and decision-making, and to

• identify the key steps and stakeholders involved in the process
• understand the complexity and nuance of the process