• Written by Azhar Aliyar, Director

A supply chain manager at a manufacturing company is deciding this week whether to hold more inventory of a critical component, and is currently basing it on a rough estimate of lead times and a general sense of demand. However, this decision could be better informed by a more detailed analysis of the company's own data, using a system like AsscherAi to answer questions about the supply chain in real time.

Field-level meaning and why column names are not documentation

The first requirement for using AsscherAi to inform supply chain decisions is that the data must have clear and consistent field-level meaning. This means that each column in the purchase order table, for example, must have a well-defined meaning that is understood by all users. Column names like "status" or "date" are not sufficient documentation, as they do not provide enough context to ensure that the data is being used correctly.

A shift supervisor checking the maintenance log, for instance, needs to know exactly what the "status" column represents, and how it relates to the overall supply chain. Without this clarity, the data cannot be trusted to inform decisions, and the use of AsscherAi will be limited.

The systems that get left out of scope and the partial answers that follow

Many organisations have multiple systems that contain relevant supply chain data, but some of these systems are often left out of scope when implementing a data analysis platform like AsscherAi. This can lead to partial answers, as the system is only considering a subset of the available data. For example, if the enterprise resource planning system is not integrated with AsscherAi, then the analysis will be missing critical information about inventory levels and supplier performance.

This can result in decisions being made based on incomplete information, which can have serious consequences for the supply chain. By ensuring that all relevant systems are included in the scope of the project, organisations can get a more complete picture of their supply chain and make better-informed decisions.

The supply chain data this depends on: purchase and delivery history, lead times, inventory by location, demand signal

The use of AsscherAi to inform supply chain decisions depends on having access to a range of data, including purchase and delivery history, lead times, inventory by location, and demand signal. This data must be accurate, up-to-date, and consistent, in order to provide a reliable basis for decision-making. The purchase order table, for example, must contain detailed information about each purchase, including the date, quantity, and supplier.

The inventory management system must also be able to provide real-time information about inventory levels by location, in order to inform decisions about where to hold stock and how much to order. By analysing this data using AsscherAi, organisations can gain a better understanding of their supply chain and make more informed decisions about inventory management and supplier selection.

What it changes about: lead time variance hidden behind a stable average and inventory held against the wrong risk

One of the key benefits of using AsscherAi to inform supply chain decisions is that it can help to identify lead time variance that may be hidden behind a stable average. This can occur when the average lead time appears to be stable, but there is actually a significant amount of variability in the data. By analysing the data in more detail, organisations can identify the causes of this variability and take steps to mitigate its impact on the supply chain.

AsscherAi can also help organisations to identify situations where inventory is being held against the wrong risk. For example, if the demand signal is indicating a high level of variability in demand, but the inventory is being held against a stable average demand, then the organisation may be holding too much or too little inventory. By using AsscherAi to analyse the data and identify these situations, organisations can make more informed decisions about inventory management and reduce the risk of stockouts or overstocking.

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

The effectiveness of AsscherAi in informing supply chain decisions can be measured using a range of metrics, including lead time variance, fill rate, and inventory turns. Lead time variance measures the amount of variability in the time it takes for orders to be fulfilled, while fill rate measures the percentage of orders that are fulfilled on time. Inventory turns measures the number of times that inventory is sold and replaced within a given period.

By tracking these metrics and using AsscherAi to analyse the data, organisations can gain a better understanding of their supply chain performance and identify areas for improvement. For more information on how to use these metrics to measure supply chain performance, see our operations page.

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

While AsscherAi can be a powerful tool for informing supply chain decisions, it is not a replacement for human judgement and expertise. There will be situations where the data is incomplete or inaccurate, and where human intervention is required to make a decision. In these situations, AsscherAi can provide a useful input to the decision-making process, but it should not be relied upon as the sole basis for a decision.

If you are considering using AsscherAi to inform your supply chain decisions, but are unsure about how to get started, please contact us for more information. We can help you to determine whether AsscherAi is the right choice for your organisation, and provide guidance on how to implement it effectively.