Summary
Key results
Reduced production waste and operating costs
Higher process efficiency and stability in real time
Sustainability goals hindered by limited process visibility
To maintain progress against its sustainability objectives, Angst+Pfister needed real-time visibility into manufacturing processes. Previously, reports were analyzed only after production cycles ended, causing delayed responses, raw-material losses, and higher operating costs. Manual data collection consumed resources and increased error risk, undermining accurate efficiency measurement.
Having reduced CO₂ emissions by 16% and waste by 19% in 2023 versus the prior year, management set the next step toward optimization and digitalization. The organization asked Sii Poland to implement a real-time, data-driven analytics solution that would prevent and reduce inefficiencies—rather than merely report them after the fact.
Real-time analytics and automated process optimization
Sii Poland designed and implemented an AI-driven solution covering the full data pipeline – from integrating production sources to predictive models and results visualization. The project was delivered in close collaboration with Angst+Pfister’s operations teams to ensure alignment with real-world shop-floor conditions and ESG targets.
Scope included:
- data integration from production lines and MES/ERP systems into an AWS cloud environment to stream process information in real time.
- analytical models that detect anomalies, recommend optimal production parameters tailored to shop-floor conditions, and identify potential material losses.
- operational dashboards and alerts that present insights to manufacturing engineers in an intuitive format (forecast dashboards with risk signaling).
- automated responses to inefficiencies, including notifications on declining process stability across multiple metrics.
- automated historical analysis, comparing identical products across time periods and performing deep dives on selected batches—covering shift efficiency, process parameters, and cycle statistics.
- iterative AI model improvement with the client’s technology team to increase forecast accuracy and prepare for scaling across additional plants.
The new solution enables continuous monitoring of process parameters, optimization of production settings, and immediate reaction to inefficiencies.
Less waste, lower costs, greater predictability
Sii Poland’s AI solution helped Angst+Pfister move from reactive monitoring to active production management. The company now addresses inefficiencies during the process itself – not only in post-mortem reviews – delivering tangible material savings and operational stability.
With precise real-time data, automated production analysis supports optimization decisions, reducing both downtime risk and manufacturing costs. The initiative also strengthened ESG performance: cutting material waste and lowering environmental impact are now measurable elements of the company’s strategy. As a result, the organization gained not only higher operational efficiency but also a competitive edge grounded in responsible, modern manufacturing.