• Written by Sonali Subhadarsini, Product Manager

This week, a category manager at a manufacturing company needs to decide whether to increase the inventory of a critical component, and by how much, based on the current lead time and demand signal, which can be informed by using AI process automation for supply chain to query the organisation's own data in real time. The category manager currently relies on a combination of experience, manual checks of the inventory management system, and ad-hoc phone calls to the suppliers, which can be time-consuming and prone to errors. However, by automating the steps around this decision, the category manager can make more informed choices, and the organisation can benefit from improved supply chain efficiency.

Mapping the current process

Before automating any part of the supply chain, it is essential to map out the current process, including all the steps involved in making a decision, such as the one described above. This involves identifying the roles and responsibilities of each team member, including the category manager, the shift supervisor, and the logistics coordinator, as well as the systems they use, such as the inventory management system, the enterprise resource planning system, and the transportation management system. By understanding the current process, organisations can identify areas where automation can add the most value, and where it may not be necessary, which can be further explored by visiting our operations page.

The mapping process typically starts with a high-level overview of the supply chain, from procurement to delivery, and then drills down into the specific steps involved in each stage. This can be a time-consuming process, but it is crucial for identifying the handoffs and bottlenecks that create delays and inefficiencies in the supply chain. For example, the category manager may need to check the inventory levels, lead times, and demand signal before making a decision, which can involve multiple systems and stakeholders.

The handoffs that create most of the delay

One of the main causes of delay in the supply chain is the handoff between different teams and systems. For example, when the category manager needs to check the inventory levels, they may need to send a request to the logistics coordinator, who then needs to check the inventory management system and respond to the category manager. This handoff can create a delay of several hours or even days, depending on the complexity of the request and the responsiveness of the teams involved. By automating the steps around the decision, organisations can reduce the number of handoffs and minimize the delays associated with them.

In many cases, the handoffs are not just between teams, but also between systems. For example, the inventory management system may not be integrated with the enterprise resource planning system, requiring manual data entry or complex workarounds. By automating the integration between these systems, organisations can reduce the errors and delays associated with manual data entry and improve the overall efficiency of the supply chain.

The supply chain data this depends on

The automation of the supply chain depends on access to high-quality data, including purchase and delivery history, lead times, inventory by location, and demand signal. This data is typically scattered across multiple systems, including the inventory management system, the enterprise resource planning system, and the transportation management system. By integrating this data into a single platform, organisations can provide a unified view of the supply chain and enable automated decision-making. For instance, the category manager can use this data to determine the optimal inventory levels and lead times, reducing the risk of stockouts or overstocking.

The data required for automation includes not just the current state of the supply chain, but also historical data and predictive analytics. For example, the organisation may need to analyze the lead time variance over time to identify trends and patterns, and adjust the inventory levels accordingly. By using AI process automation for supply chain to query the organisation's own data in real time, organisations can make more informed decisions and improve the overall efficiency of the supply chain.

What it changes about lead time variance and inventory

One of the key benefits of automating the supply chain is that it can help organisations to better manage lead time variance and inventory. By analyzing the historical data and predictive analytics, organisations can identify areas where the lead time is likely to vary, and adjust the inventory levels accordingly. This can help to reduce the risk of stockouts or overstocking, and improve the overall efficiency of the supply chain. For example, if the lead time for a particular component is typically 10 days, but can vary by up to 5 days, the organisation can adjust the inventory levels to account for this variability.

In addition to managing lead time variance, automation can also help organisations to better manage inventory. By analyzing the demand signal and inventory levels, organisations can identify areas where inventory is being held against the wrong risk, and adjust the inventory levels accordingly. This can help to reduce waste and improve the overall efficiency of the supply chain. For instance, if the demand for a particular product is decreasing, the organisation can reduce the inventory levels to avoid overstocking.

Measured with lead time variance, fill rate, and inventory turns

The success of automation in the supply chain can be measured using a range of key performance indicators (KPIs), including lead time variance, fill rate, and inventory turns. Lead time variance measures the variability in the time it takes for products to be delivered, while fill rate measures the percentage of orders that are filled on time. Inventory turns measure the number of times that inventory is sold and replaced within a given period. By tracking these KPIs, organisations can identify areas where automation is having a positive impact, and make adjustments as needed to optimize the supply chain.

For example, if the lead time variance is decreasing, it may indicate that the automation is helping to improve the predictability of the supply chain. Similarly, if the fill rate is increasing, it may indicate that the automation is helping to improve the ability of the organisation to meet customer demand. By using these KPIs to measure the success of automation, organisations can make data-driven decisions and drive continuous improvement in the supply chain.

What this does not do

While automation can bring many benefits to the supply chain, it is not a silver bullet. There are many cases where automation may not be the right choice, such as when the process is highly complex or requires a high degree of human judgment. In these cases, organisations may need to rely on manual processes or a combination of manual and automated processes. Additionally, automation may not be suitable for small or medium-sized organisations, where the cost of implementation may outweigh the benefits. If you are considering automation for your supply chain, we recommend that you contact us to discuss your specific needs and determine whether automation is right for you.