Across industries, digital-era technologies like artificial intelligence (AI) are proving to be effective tools for process optimization in manufacturing. Their relevance is increased during times of adversity, when manufacturers are constantly challenged by a range of ongoing external pressures. 

DataProphet’s Monetizer PRESCRIBE installation at Condals Group Foundry illustrates the impact of AI on a manufacturing process at a leading manufacturer of iron and steel castings in Spain and Slovakia. Read on to discover how the use of contextualized prescriptive analytics has helped Condals Group Foundry overcome external pressures and improve its manufacturing processes.

Understanding Condals’ Challenges and Manufacturing Optimization Goals

Condals Group has more than 50 years of experience in the foundry space and is known for its high-quality products, advanced manufacturing techniques, and efficient logistics services. Between its Spanish and Slovakian locations, the foundry’s three molding lines produce more than 43,000 tons of iron castings each year. 

As highlighted in our case study, Condals Group CIO David de la Cruz stated that the primary objective of the foundry was to reduce scrap. However, a number of challenges stood between the company and its goal. These included: 

  1. Equipment Failures and Unplanned Downtime

The first key challenge was the frequency of equipment failure and unplanned downtime. The result of this was significant opportunity cost for the foundry’s productivity, as well as high costs due to scrap disposal and rework impacting the company’s bottom line.  

  1. Inefficient Scrap Management 

This issue was exacerbated by the foundry’s limited visibility into the amount and type of scrap being generated, which made it challenging to manage and reduce the amount of waste produced. 

  1. Mass and Complexity of Foundry Data 

Like many advanced foundries, Condals faced challenges managing, analyzing, and interpreting the large volume of data generated by their manufacturing equipment sensors, and other sources. This made it increasingly difficult for them to identify the root cause of scrap in their production processes and glean the insights needed to optimize for this efficiently. 

Ultimately, these challenges were due to the lack of accurate and timely information about the production process, which made it hard to detect and correct process anomalies in a dynamic manufacturing environment. 

To address these challenges, Condals set a goal to become more data-driven. Let’s uncover how the foundry was able to leverage its own production data to optimize its manufacturing processes.  

Optimizing Manufacturing Production Processes With Monitizer Prescribe 

To jumpstart and drive their digital transformation journey, Condals implemented DataProphet’s Monitizer Prescribe. The AI-driven solution helped solve many of the challenges the foundry faced by:

  1. Analyzing Large Amounts of Data

Monitizer Prescribe is now able to analyze large amounts of data generated during the production process. In Condals’ case, the casting process comprised more than 700 parameters. Embedding its software traceability technique, DataProphet worked with Condals’ plant personnel to track parameters such as the melting line, the molding line, and sand preparation. This digital-era approach rendered the manufacturing process leverageable for prescriptive  AI. 

  1. Identifying Patterns and Anomalies 

Monitizer Prescribe then harnessed deep learning applied as prescriptive analytics to identify patterns and anomalies in the data and visualize a unified view of the plant state. This helped the Condals perceive the factors impacting their production process outcomes in a more fine-grained manner and identify areas where improvements could be made.

  1. Providing Preemptive Insights to Optimize Production

Based on the analysis of the data, Monitizer Prescribe generated its prescriptions ahead of real-time. The recommendations provided Condals process engineers and operators with the insights needed to move the plant towards its most optimal operating region, resulting in improved efficiency and reduced production losses.

Condals Factory Optimization Results 

The implementation of Monitizer Prescribe at Condals solved many of the foundry’s key problems, particularly those relating to the lack of accurate and timely information about the production process. By analyzing large volumes of production data, identifying bottlenecks, and providing actionable steps ahead of real-time, Condals were able to realize the following efficiencies:

  1. Greater traceability and productivity

By tracing the 700+ parameters present in the iron casting production process, Condals operators accessed a unified view. With easily applied and highly accurate tracking, analysis, and data interpretation, they made better decisions and ensured more efficient production. Let’s look at the actual results.

  1. Scrap rate reduction

According to Condals CIO David de la Cruz, following the AI prescriptions saw the foundry achieve a 39% decrease in one of the test patterns, as well as a 45% decrease in another. Further, 0% external scrap seems like a possibility going forward given the ongoing drop in scrap. 

  1. Improved financial performance

By increasing productivity and reducing costs related to scrap disposal and rework through the adoption of AI-driven technologies, Condals were able to improve their return on assets and bottom line.

Solve Industrial Data Challenges With Digital-Era Process Optimization

This use case illustrates the specific benefits of digital-era solutions expertly applied in partnership with functional manufacturing experts. It shows that artificial intelligence (AI) has the potential to solve some of manufacturing’s key challenges, especially surrounding the mass and complexity of industrial data. 

With Monitizer Prescribe, Condals Group Foundry was able to become more data-driven, reduce its scrap rate, and ensure a more operationally resilient future for the foundry.

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