Digital Twins, AI, and ... Industry 5.0

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Industry 5.0 emphasizes human-centricity, sustainability, and resilience in industrial processes. Simplifying, one may say that it is Industry 4.0 + human + AI. One of the components used in industrial or FMCG sector are Digital Twins (DTs). Well, the idea is to show you, how it may be coupled with Artificial Intelligence (AI) and business process mapping, which are critical in achieving, for example, ROI goals or KPIs that may be in the responsibility of a digital transformation manager.

What are Digital Twins (DTs)?

A Digital Twin (DT) is a digital replica of a physical asset, system, or process. It consists of three main components:

1. The actual equipment or process in the real world, a physical entry.
2. A digital replica created using CAD, simulation, and real-time data.
3. Continuous, bidirectional data exchange between the physical entity and its virtual model, enabled by IoT sensors and cloud computing, which gives some data about it.

The best way is to image a movie, where human is connected to a virtual world (can’t use that name due to copyrights). His physical layer was transferred to a digital twin in the M..x world. Fighting for good, same as DTs may for your industry.

DT technology originated with NASA's use of virtual models to troubleshoot and optimize the performance of space equipment during the Apollo missions. This time saving or cost distribution saves a huge amount of resources. This standard is starting to naturally appear in other industries.  Over the years, DTs have evolved to incorporate advanced modeling techniques, real-time data integration, and sophisticated analytics.

Digital Twins in Industry 5.0

Implementing DTs requires detailed mapping of all fabric equipment:

• IoT sensors gather real-time data on equipment performance, operational parameters, and environmental conditions.
• Accurate virtual models are developed using CAD and simulation tools to replicate the physical characteristics and behaviors of the equipment.
• Seamless integration between physical and virtual models is achieved through robust data flow mechanisms, leveraging cloud computing and edge technologies.

AI enhances the analysis of data generated by DTs through:

• ML algorithms identify patterns and anomalies in the data, enabling predictive maintenance and performance optimization.
• AI predicts equipment maintenance needs, reducing downtime and extending asset lifespan.
• AI-driven analytics assess the impact of operational improvements and cost savings, providing accurate ROI calculations.

Importantly, AI has the advantage that it can be integrated above the main system, for example used for predictive maintenance. It can be fed with data from other systems, e.g. logistics, sales, HR, and then trained to examine the influences of these sets on each other. In the human centric design approach, this is an important parameter that may, for example, consider HR planning not only in terms of proper machine utilization and planning of breaks, but also against the background of other factors - for example, the need to increase stock levels due to a promotional campaign, for example, of our partner B2B (generated so-called peak).

Business Process Mapping

DTs bridge the gap between technical and business factors by:

• Integrating DT data with business process mapping tools to identify bottlenecks, streamline workflows, and enhance overall efficiency.
• Ensuring technological advancements align with employees' needs and improve their work experience.
• Providing comprehensive insights into technical and business aspects, facilitating informed and strategic decisions.

One of the cases..

The manufacturing facility faced significant challenges in managing peak production demands during November, driven by a surge in e-commerce sales. To address these issues, the facility sought to implement DTs for real-time monitoring, predictive maintenance, and strategic workforce and inventory management. The project leveraged advanced AI algorithms and microservices architecture to ensure seamless integration with existing systems.

Data Integration and Sensor Deployment

The first phase involved deploying IoT sensors across the production line to capture real-time data on equipment performance, environmental conditions, and operational parameters. The data collected included machine temperature, vibration, output rates, and downtime incidents. This data was then fed into the DT system, creating an accurate and dynamic virtual model of the physical production environment.

Gen AI Microservices

Gen AI components were developed as microservices, designed to perform specific tasks such as predictive maintenance, demand forecasting, and workforce optimization. These microservices were connected to the manufacturer's legacy systems via APIs, enabling real-time data exchange and decision-making.

1. Utilized machine learning algorithms to analyze sensor data and predict potential equipment failures. Maintenance schedules were optimized to ensure all critical maintenance was performed during the summer months, minimizing disruptions during the peak production period in November.
2. Integrated with the e-commerce platform to analyze order data and forecast demand. This microservice adjusted production rates dynamically to align with market demands, ensuring high efficiency during peak periods.
3. Incorporated HR data to manage workforce availability. The system accounted for employee leave schedules, ensuring sufficient staff availability for maintenance tasks during the summer and peak production support in November.
4. Linked with the warehouse management system to optimize inventory levels. Simulations ensured the warehouse was adequately stocked to meet increased production demands during peak periods, reducing inventory costs and preventing shortages.

Results

By predicting equipment failures and scheduling maintenance during low-demand periods, the facility reduced unplanned downtime by 15%. The dynamic adjustment of production rates in response to real-time demand forecasts resulted in a 20% increase in production efficiency during peak periods. The predictive maintenance microservice effectively identified potential equipment issues before they became critical, enabling the facility to schedule necessary repairs and maintenance during the summer months. This proactive approach ensured that all machinery was fully operational during the peak production period, minimizing disruptions and maintaining high output levels. The integration of HR data allowed the workforce optimization microservice to create efficient leave schedules and allocate staff effectively. This resulted in a 15% reduction in overtime costs and improved employee satisfaction by ensuring balanced workloads and adequate staffing during critical periods. The inventory management microservice ensured that the warehouse was well-prepared for the peak season. By simulating various inventory scenarios and aligning them with production schedules, the facility maintained optimal stock levels, reducing inventory holding costs by 15% and preventing last-minute shortages.

A critical challenge lies in data integration, which involves combining data from diverse sources such as IoT sensors, CAD models, and historical archives into a unified, cohesive model. This process demands rigorous attention to data accuracy and completeness, as inaccuracies can severely impact the functionality and reliability of DT solutions. IoT sensors generate real-time data on equipment performance and environmental conditions, CAD models provide detailed structural and design information, and historical archives offer contextual and operational insights. The integration of these heterogeneous data streams into a single model is complex but essential for an accurate digital representation of physical entities.

But at the end, AI may be used to facilitate those results, based in trained modes, which helps digital transformation managers to plan those projects correctly or, with a larger “birds-eye” view.

Read more:

Case studies - Fabrity Software House

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Greg Sperczyński

Digital Transformation Manager

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