• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the shift supervisor checks the maintenance log to see what tasks are pending, while the category manager compares two suppliers to determine which one to use for the upcoming project, and the operations manager tries to make sense of the project status reports to identify potential bottlenecks. As they work, they may be thinking about how to automate some of these processes to make their jobs easier, but they often overlook a crucial step: mapping the current process before automating any of it.

Mapping the current process

Most people get wrong the idea that they can automate a process without fully understanding how it currently works. They are wrong because automation is not just about replacing human labour with machines, but about streamlining the entire workflow. To do this, you need to have a clear picture of the existing process, including all the steps, handoffs, and decision points. This is where AI process automation for operations comes in, helping to identify areas where automation can have the most impact.

The mapping process involves observing and documenting how tasks are currently performed, including the systems and tools used, the data collected, and the decisions made. This can be a time-consuming process, but it is essential to ensure that automation is targeted at the right areas. By doing so, organisations can avoid automating inefficient processes, which would only serve to amplify existing problems.

The handoffs that create most of the delay

One of the main reasons processes stall is due to handoffs between different teams or departments. When the shift supervisor completes a task, it often needs to be reviewed or approved by someone else, such as the operations manager, before it can move forward. These handoffs can create significant delays, especially if the teams are not well-coordinated or if there are misunderstandings about what needs to be done. AI process automation for operations can help streamline these handoffs by automating the transfer of information and tasks between teams.

For example, when the category manager selects a supplier, the system can automatically notify the purchasing department to place an order, eliminating the need for manual intervention. By reducing the number of handoffs and automating the flow of information, organisations can significantly speed up their processes and improve productivity.

The operations data this depends on

AI process automation for operations relies on access to a wide range of operations data, including process and exception records, project status, budget consumption, and supplier performance. This data is used to identify areas where automation can have the most impact and to monitor the effectiveness of automated processes. The operations manager can use this data to identify trends and patterns, such as which projects are consistently running behind schedule or which suppliers are frequently late with deliveries.

By analysing this data, organisations can identify opportunities to improve their processes and make more informed decisions. For instance, if the data shows that a particular project is consistently consuming more budget than expected, the operations manager can investigate the cause and take corrective action. This might involve adjusting the project plan, reallocating resources, or renegotiating with suppliers. To learn more about how to use data to improve operations, visit our operations page.

What it changes about anomalies and project budget

AI process automation for operations can help identify anomalies that may not be visible when looking at individual processes or data points in isolation. By combining data from different sources and analysing it in real-time, the system can detect patterns and exceptions that may indicate a problem. For example, if a project is consuming budget faster than progress, the system can alert the operations manager to take a closer look.

This can help organisations avoid costly surprises and take corrective action earlier. The operations manager can use this information to adjust the project plan, allocate additional resources, or take other steps to get the project back on track. By automating the detection of anomalies and exceptions, organisations can reduce the risk of project failures and improve overall performance.

Measured with on-time delivery, exception rate, and expediting cost

The effectiveness of AI process automation for operations can be measured in several ways, including on-time delivery, exception rate, and expediting cost. By tracking these metrics, organisations can see how automation is impacting their processes and make adjustments as needed. For instance, if on-time delivery rates are improving, but exception rates are increasing, the operations manager may need to investigate the cause and make changes to the automated process.

By monitoring these metrics and making data-driven decisions, organisations can continuously improve their processes and achieve better outcomes. If you have questions about how to implement AI process automation for operations or would like to learn more about how it can benefit your organisation, please contact us.

What this does not do

While AI process automation for operations can bring many benefits, it is not a silver bullet that can solve all process-related problems. It is not a replacement for human judgement and decision-making, but rather a tool to support and augment these capabilities. Organisations should not expect automation to solve deeply ingrained process issues or to make up for inadequate training or resources.

In some cases, automation may even be the wrong choice, such as when processes are highly variable or require a high degree of human creativity and flexibility. In these situations, organisations may need to focus on other improvement strategies, such as process redesign or employee training and development. By understanding the limitations of AI process automation for operations, organisations can use it more effectively and achieve better outcomes.