• Written by Azhar Aliyar, Director

The purchase order table in the enterprise resource planning system is where the category manager at a large organisation checks the status of orders, and it is here that inference can be applied to answer questions about the data in real time. Inference is the process of running a trained model to get an answer, and it is a crucial component of the AsscherAi platform. The model is trained on the organisation's own data, and the inference process involves using this trained model to generate answers to specific questions. This process is often confused with the training of the model itself, but it is a distinct step that allows the organisation to extract insights from their data.

Definition and distinction

Inference is a specific process that involves using a trained model to make predictions or answer questions about new, unseen data. It is not the same as the training process, which involves teaching the model to recognise patterns in the data. While the training process is a one-time effort, inference is an ongoing process that allows the organisation to generate answers to new questions in real time. The distinction between training and inference is important, as it allows organisations to understand the costs and benefits of implementing a system like AsscherAi.

The training process involves feeding the model a large dataset, which it uses to learn patterns and relationships. Once the model is trained, it can be used for inference, which involves providing the model with new data and generating answers based on the patterns it has learned. This process is often automated, allowing organisations to generate answers quickly and efficiently.

Applications in a large organisation

In a large organisation, inference can be applied in a variety of ways to generate insights from data. For example, a shift supervisor can use AsscherAi to check the maintenance log and generate answers to questions about equipment downtime or maintenance schedules. The category manager can use the system to compare suppliers and generate answers to questions about pricing or delivery times. Inference can also be used to generate reports or dashboards that provide insights into organisational performance.

One of the key benefits of inference is that it allows organisations to generate answers to questions in real time, without requiring manual analysis or reporting. This can be particularly useful in situations where data is changing rapidly, such as in a manufacturing or logistics environment. By using inference to generate answers, organisations can respond quickly to changes in the data and make informed decisions.

What inference does

Inference is a powerful tool for generating insights from data, and it has a number of key benefits. It allows organisations to generate answers to questions in real time, without requiring manual analysis or reporting. It also allows organisations to automate the process of generating reports or dashboards, which can save time and reduce the risk of errors. In addition, inference can be used to generate predictions or forecasts, which can help organisations to plan and make informed decisions.

For example, a category manager can use AsscherAi to generate answers to questions about supplier performance, such as which suppliers are delivering on time or which suppliers are offering the best prices. The system can also be used to generate predictions about future demand or supply chain disruptions, which can help the organisation to plan and prepare. To learn more about how AsscherAi can be used in your organisation, visit our website at www.asscherai.com.

What inference does not do

While inference is a powerful tool for generating insights from data, it is not a replacement for human analysis or decision-making. It is also not a guarantee of accuracy, as the quality of the answers generated by the system depends on the quality of the data and the model. In addition, inference is not a one-time process, but rather an ongoing process that requires ongoing maintenance and updates to ensure that the model remains accurate and relevant.

Some people believe that inference can be used to replace human decision-making, but this is not the case. Inference is a tool that can be used to support decision-making, but it is not a substitute for human judgment and expertise. To get in touch with our team and discuss how AsscherAi can be used to support decision-making in your organisation, visit our contact us page.

When inference is the wrong choice

In some cases, inference may not be the best choice for generating insights from data. For example, if the data is highly complex or nuanced, human analysis may be required to generate accurate and relevant insights. In addition, if the organisation is dealing with a highly uncertain or rapidly changing environment, inference may not be able to keep up with the pace of change.

In these situations, other approaches may be more suitable, such as manual analysis or reporting. It is also important to consider the costs and benefits of implementing an inference system, as well as the potential risks and limitations. By carefully evaluating these factors, organisations can determine whether inference is the right choice for their specific needs and goals.