24 Apr 2023 | Nicol Ritchie| • Foundries
Digital manufacturing is advancing globally with a focus on sustainability for long-term value. This use case shows how smart factory initiatives can help metal-forming OEMs reduce energy and material needs. An automotive foundry improved its environmental impact and digitally upskilled its employees with this approach.
Advanced manufacturing sectors are leading the way in digitally enabled sustainability. Utilizing IIoT, advanced analytics, and AI across their value chain, they achieve significant energy savings, emissions reductions of up to 62%, and slash production waste.
The good news for heavy industrials? A collaborative, cross-functional solutions approach that integrates different domain expertise yields measurable impact. In foundries, it can lead to environmental and economic efficiencies and improve digital skills quickly.
Consider the results of an automotive OEM foundry that used prescriptive AI to reach peak production performance efficiency.
With the guidance of DataProphet’s solution, the automotive foundry reduced its scrap rate by 50% within the first month.
The foundry sustained an external scrap rate of less than 0.1% within three months of using prescriptive AI optimization. Engine block defects dropped to 0.5% for up to three months. In the long term, the plant achieved an average defect reduction of 40%.
Regarding return on assets and bottom line, the foundry saves around $100k monthly, receiving a 20x ROI over a 2-year period.
The environmental impact was also noteworthy. Optimizing process control parameters to reduce waste and lower energy usage resulted in zero external defects. This saved the client 135 kg of carbon dioxide emissions for each defective block not shipped.
Data-driven manufacturing process optimization is a successful digitalization strategy, proven by the automotive OEM results. Metal-forming operations can be empowered for digital success without high capital investment.
However, integrating human input is also essential for navigating digital initiatives with clarity and confidence. Industry leaders should, therefore, recognize the need to align technological capacity and digital skills development for successful factory transformation. (See Figure 1)
The automotive foundry mentioned earlier produces 129,000 high-quality cast iron engine blocks annually, equivalent to over 46,000 tonnes.
To become a world-leading foundry, the CEO of the operation directed its focus toward digital technologies. The foundry aimed to leverage its functional manufacturing experience with machine learning expertise. In this case, it opted for the guidance of AI-driven manufacturing optimization specialists.
The project aimed to upgrade technical capabilities to optimize processes and maintain optimal efficiency and quality.
The collaboration unearthed four primary challenges for this foundry, entering the digital era:
Cross-functional teams collaborated to address the challenge of reducing scrap production and cutting CO2 emissions using prescriptive AI.
Achieving this meant modernizing the plant’s digital infrastructure and data workflows.
The first order of business was to improve data acquisition and sharing capabilities. The two companies integrated an IIoT system that covered all stages of production for the targeted engine block products.
Additionally, DataProphet’s team created a unified view of the process data that traced quality outcomes. They implemented data extraction, transformation, and loading (ETL) across the plant.
Following this, teams gathered historical production data from stakeholders across multiple departments. These included handwritten forms, excel files, databases, and CSV files.
Lastly, a comprehensive display showed data from the previous 15 months of production, containing 173,000 records and 400 distinct process variables. See Figure 2 for an example of an AI-ready IIoT system that facilitates an AI workflow.
The production process performance for two engine block products was optimized. An AI-powered industrial optimization solution, DataProphet PRESCRIBE, generated prescriptions after unifying the data using DataProphet CONNECT. The system delivered these prescriptions to plant engineers for approval.
The factory’s engineers modified their control plans after verifying the relevance and safety of the suggested course corrections. Operators could then apply the insights of advanced supervised and unsupervised machine learning methods.
The PRESCRIBE system enhances productivity by providing personalized displays to operators, engineers, and plant managers, which show essential control parameters. It generates reports to prioritize the critical parameters for improving output, factoring in upstream and downstream changes. Figure 3 displays a prescriptive AI solution engine.
For this foundry, PRESCRIBE successfully discovered the best-of-best operating regime for the two products, understanding interactions between approximately 1,000 parameters plant-wide.
With the PRESCRIBE system, the client optimized energy savings, minimized scrap, and reduced waste. The foundry was acknowledged as one of the world’s most efficient foundries in 2019. It is now working with DataProphet to expedite its digital roadmap and remain ahead of the digital adoption curve.
As the foundry’s CEO concludes:
What does the future hold?
The two companies are applying the prescriptive AI solution to accelerate the automotive foundry’s ESG strategy.
24 Apr 2023 Nicol Ritchie
Digital manufacturing is advancing globally with a focus on sustainability for long-term value. This use ...