AI APPLICATIONS

The application integrates with MES systems to monitor and predict in real time the likelihood that a finished product (machine or plant) will have defects once it reaches the market or will fail the final quality control check.

• ⟶ It enables the early identification and correction of process anomalies that cause defects.

Enables you to transform complex historical data into concrete, actionable forecasts for the current production plan.

The app uses ML algorithms to analyze historical data on:

• • OPERATIONAL data: machinery used, actual cycle times, OEE.
• • QUALITY data: historical data on the scrap rate by product/machine/operator.
• • Historical PLANNING data: production orders, models, planned and actual quantities.

Output 1 - Detailed factory productivity and scrap forecast

The system generates a quantifiable forecast for the current production period.

Output 2 – Order-by-order validation (On-Time delivery)

The AI assesses the feasibility of every single order in the plan.

• • Risk traffic light: indicates, order by order, whether the time/delivery target is achievable (green), at risk (yellow) or unachievable (red) based on historical performance.
• • Optimized cycle time: suggests a realistic cycle time for each stage of the process.

• ⟶ opportunity to adjust the sequence or resources before the order enters production, ensuring the customer’s promise is met.

Production line operators often record faults using free-text descriptions, making the categorization process slow, error-prone and inconsistent.

With Esi AI Category, it is possible to transform unstructured descriptive input into categorized, accurate data that can be used in real time.

Esisoftware’s Artificial Intelligence application automatically suggests and selects the most appropriate anomaly category based on a simple text description entered by the operator. This results in a significant increase in efficiency and a substantial reduction in errors.

This application intelligently optimizes recipes and production processes.

The aim is to maximize efficiency, reduce waste and ensure quality through historical learning.

The application analyses historical data to provide recommendations based on evidence, not intuition.

The similarity engine quickly identifies ‘twins’ between past and current production orders.

Thanks to continuous learning, every new completed work order refines the system’s accuracy.

The application integrates with the MES to work in synergy with existing production management systems.

Using machine learning algorithms, the application analyses historical data on:

• • Sales volumes across different customers
• • Customers/items accounting for a higher percentage of sales
• • Item turnover rates
• • Seasonality of items and internal patterns
• • Total quantities produced
• • Total quantities shipped

In this way, the software is able to optimize the map showing warehouse locations and can calculate the distance the operator will need to travel to transfer the UDCs from the suggested storage area to the loading bay. Furthermore, the instructions provided will be designed to optimize the total route.

The application is designed to optimize production recipes. It has been developed by Esisoftware for the process manufacturing sector or for assembly/mixing lines that use complex formulations. The aim is to maximize the quality of the final product and/or minimize the cost and usage of raw materials, whilst maintaining the required specifications.

When a new recipe is introduced that uses components that are already known and have been trained, the AI starts from a high level of knowledge, drastically reducing the calculation and optimization times for the new formula.

This app drives production towards autonomous and standardized formulation, where the AI acts as a digital chemist that adjusts the proportions of the components to be used in production with maximum efficiency.