Cutting Scrap in Real Time
In discrete manufacturing, each 1% yield improvement can generate significant cost savings—often exceeding $500,000 annually in mid-sized plants (Gartner, 2021). Plants using Conversational AI for yield analysis report up to 40% fewer recurring scrap incidents within three months of deployment (Siemens Digital Industries Software, 2022).
Three Barriers to Faster Yield Gains
Slow feedback loops
Traditional yield and scrap reporting relies on weekly or monthly data aggregation. By the time engineers receive a report, process conditions have shifted, allowing defects to continue unaddressed. This reactive cycle not only prolongs losses but also erodes customer trust when issues persist over multiple orders (Jeston, 2022).
Isolated data sources
Critical scrap and yield data live in different systems: MES captures production counts and NCs, ERP tracks material costs, and SPC systems record process variation. Without a unifying query layer, connecting these datasets requires manual exports and offline analysis—a process that is slow, error-prone, and unsustainable in high-velocity manufacturing (Marr, 2018).
Overreliance on specialists
In many plants, only quality engineers or process analysts can conduct deep scrap investigations. While their expertise
is essential, acting as a bottleneck slows the organization’s ability to respond to emerging issues. In high-mix, high-volume environments, the backlog can leave quality issues unresolved for weeks (Deloitte, 2020).
Ask This → Get That: Conversational AI in Action
- Material-driven scrap in a casting plant
Ask: “Which material lots caused the most scrap last month and what were the defect types?”
Get: A ranked table of material lot IDs, suppliers, and defect categories, enabling purchasing to flag problematic suppliers and quality to launch targeted inspections.
- Operator variance in a bottling facility
Ask: “Compare scrap rates by operator for the last two weeks and highlight any shifts above target.”
Get: A chart mapping operator IDs to scrap percentages, annotated where rates exceed control limits. The plant manager can initiate refresher training or investigate process drift.
- Tooling-related defects in an electronics line
Ask: “List NCs caused by soldering station misalignment in the last 30 days and their impact on yield.”
Get: A breakdown by SKU of total units scrapped, defect counts, and material costs, enabling maintenance to fix alignment while quality monitors for recurrence.
How the Solution Benefits
Packaging Facility – Defect Reduction
Problem: A mid-sized packaging plant struggled with recurring print alignment defects, leading to waste, rework, and customer complaints. Monthly quality reports flagged the problem but lacked actionable detail.
Approach: Integrated MES, ERP, and SPC data into Conversational AI Manufacturing Data Intelligence™. Supervisors queried, “Which jobs failed print alignment checks this week and what were the machine settings?” and received immediate results (Siemens Digital Industries Software, 2022).
Result: Adjustments were made the same day issues appeared, cutting repeat defects by 40% in three months. Annual material waste dropped by $120,000, and customer complaints related to print quality decreased significantly.
Mini FAQ
Related Knowledge Topics
- Siemens Opcenter + Conversational AI – Instant Answers from SFCs, NCs & Traceability (Cluster 1)
- On-the-Fly BI™ for Manufacturing Data Intelligence (Cluster 2)
- Changeovers & Cycle Time – Find the Hidden Minutes with Chat-Driven Queries (Cluster 4)
- Closed-Loop Manufacturing – Continuous Improvement at Scale (Cluster 7)
External Resources
References
- Deloitte. (2020). Analytics advantage: How to go from insight to impact. Deloitte Insights. https://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-insight-to-impact.html
Outlines the operational impact of faster analytics cycles. A trusted source for business process improvement backed by extensive industry experience. Validates claims about shortening time-to-action.
- Gartner. (2021). Manufacturing trends shaping the next decade. Gartner. https://www.gartner.com/en/insights/manufacturing
Examines digital transformation strategies in manufacturing. Supports the cost savings estimate for small yield improvements. Provides credibility through Gartner’s research authority.
- ISO. (2018). ISO 9001:2015 – Quality management systems – Requirements. International Organization for Standardization. https://www.iso.org/standard/62085.html
Defines traceability and documentation standards critical to quality management. Relevant to automated compliance and scrap reduction use cases.
- Jeston, J. (2022). Business process management. Routledge. https://doi.org/10.4324/9781003245710
Explores the link between process delays and operational inefficiency. Supports arguments about reactive quality cycles.
- Lewis, M., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv. https://doi.org/10.48550/arXiv.2005.11401
Technical basis for AI systems that integrate multiple data sources in natural-language queries. Relevant to the multi-system integration claims.
- Marr, B. (2018). Data strategy: How to profit from a world of big data, analytics and the internet of things. Kogan Page. https://www.koganpage.com/product/data-strategy-9780749482470
Explains how democratizing access to analytics enables faster operational decisions. Directly applicable to yield and scrap troubleshooting.
- Siemens Digital Industries Software. (2022). Siemens Opcenter Execution: MES for manufacturing excellence. Siemens AG. https://www.plm.automation.siemens.com/global/en/products/manufacturing-operations-center/opcenter-execution/
Authoritative product documentation on Siemens Opcenter MES. Supports case study details and integration claims.