Breaking the BI Bottleneck

Traditional BI dashboards are valuable for monitoring KPIs, but they are slow to adapt to unplanned questions from the shop floor. Each new question can trigger a request to IT or a BI developer, often resulting in a days- or weeks-long delay. On-the-Fly BI™ addresses this gap by enabling authorized manufacturing personnel to query live MES, ERP, and quality systems directly in natural language, receiving structured, contextual answers in seconds (Deloitte, 2020; Marr, 2018).

This approach leverages Conversational AI with Retrieval-Augmented Generation (RAG) to securely translate plain-English requests into optimized queries that span multiple data sources, returning answers with operational context—stations, products, shifts, and quality codes—that frontline leaders can immediately act on (Lewis et al., 2020). By bypassing the BI backlog, On-the-Fly BI™ allows teams to diagnose root causes, compare performance across lines, and prepare compliance documents without waiting for a new dashboard or custom report.

In this article, we outline the three main limitations of dashboard-dependent analytics, show practical “Ask This → Get That” use cases, and present an example case study demonstrating measurable productivity gains. We close with an FAQ and a call to download EX12 for a practical playbook on operationalizing Conversational AI across manufacturing networks.

Manufacturers report that over 60% of BI requests take more than a week to fulfill, with 23% taking over a month (Gartner, 2021). During that time, operational opportunities can be lost, and the same problems may recur. On-the-Fly BI™ reduces that cycle to minutes by eliminating the request queue and enabling secure, direct data access for the people closest to the work (Deloitte, 2020).

Three Reasons Dashboard-Only Analytics Cannot Keep Up 

They answer yesterday’s questions
Dashboards are built to monitor expected KPIs, not to answer unplanned diagnostic questions during production (Marr, 2018). By the time a new dashboard view is built, the process conditions have changed, and the frontline team has moved on.

They centralize analysis in the hands of a few
In many plants, only BI developers or SQL experts can build new reports. This creates bottlenecks, with request queues stretching for days or weeks. The result is a disconnect between the pace of production and the pace of data-driven action (Jeston, 2022).

They struggle with multi-system complexity
Root cause often requires stitching together data from MES, ERP, quality, and maintenance systems. Traditional BI workflows depend on data warehousing or ETL processes that add latency. On-the-Fly BI™ connects to each live system and queries them in real time, preserving context like routing steps, operator IDs, and non-conformance codes (Siemens Digital Industries Software, 2022).

Ask This → Get That: Live Opcenter Examples

Quality Issue Investigation
Ask: “Which machines triggered NCs this morning, and what were the causes?”
Get: A list of machines, linked NC records, associated SFCs, and root-cause codes with timestamps.

Cross-Line Performance Comparison
Ask: “Compare scrap rates for Product X across Lines 2, 4, and 5 for the last 30 days.”
Get: A ranked chart showing percentage scrap per line, annotated with top causes, linked to operator shifts.

Audit Preparation
Ask: “Generate ISO 9001 traceability for Batch #2024Q2A, including operator sign-offs and material lots.”
Get: A complete compliance-ready packet, with drill-down to each operation’s logs and inspection records (ISO, 2018).

How the Solution Benefits

Electronics Assembly Plant – From Weeks to Minutes for Ad-Hoc Analysis
Problem: Engineers waited up to two weeks for BI developers to produce cross-system analysis on scrap trends, delaying root-cause fixes.
Approach: Implemented On-the-Fly BI™ with connectors to MES, ERP, and quality systems. Supervisors asked direct questions like, “Which work orders exceeded scrap limits last week, and what were the top three defect codes by station?” The system returned actionable tables and visualizations in under 60 seconds (Siemens Digital Industries Software, 2022).
Result: Average time-to-answer dropped from 10 business days to under 2 minutes. Scrap-related downtime fell 18% in the first quarter after implementation, with estimated savings of $450,000.

Mini FAQ

Related Knowledge Topics

• Siemens Opcenter + Conversational AI – Instant Answers from SFCs, NCs & Traceability (Cluster 1)
• Yield & Scrap – Ask-and-Act Troubleshooting for Faster Quality Wins (Cluster 3)
  
• Changeovers & Cycle Time – Find the Hidden Minutes with Chat-Driven Queries (Cluster 4)
• Audit Readiness & Digital Traceability (Cluster 5)
  

External Resources

• Siemens Opcenter Execution – Official product overview of Siemens’ MES platform.
  https://www.plm.automation.siemens.com/global/en/products/manufacturing-operations-center/opcenter-execution/
  
  
• Analytics Advantage – Deloitte Insights – How real-time analytics accelerates decision-making.
  https://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-insight-to-impact.html
  
  
• Gartner Analytics & BI Platforms Report (2023) – Analysis of BI adoption trends and pain points.
  https://www.gartner.com/en/documents/4000229
  
  
• ISO 9001:2015 – Quality Management Systems – Traceability and documentation requirements.
  https://www.iso.org/standard/62085.html
  
  
• Understanding Retrieval-Augmented Generation – Scholarly article on RAG architecture for enterprise data access.
  https://doi.org/10.48550/arXiv.2005.11401
   

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
  Summarizes how real-time analytics accelerates operational decisions. Offers credibility from a leading consulting firm experienced in manufacturing analytics. Supports claims about time-to-insight improvements.
  
  
• Gartner. (2021). Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner. https://www.gartner.com/en/documents/4004224
  Analyzes industry trends in BI adoption, including challenges of dashboard backlogs. Credible industry report used by technology buyers. Validates statistics on BI request fulfillment times.
  
  
• ISO. (2018). ISO 9001:2015 – Quality management systems – Requirements. International Organization for Standardization. https://www.iso.org/standard/62085.html
  Details global standards for quality management, including traceability. Widely adopted in manufacturing for audit readiness. Supports the audit preparation use case.
  
  
• Jeston, J. (2022). Business process management. Routledge. https://doi.org/10.4324/9781003245710
  Explains how process inefficiencies impact organizational performance. Offers theoretical grounding for removing bottlenecks in analytics workflows.
  
  
• 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
  Provides technical insight into RAG, the underlying architecture enabling multi-source, natural-language queries. Peer-reviewed and highly cited in AI research.
  
  
• 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
  Guides organizations in democratizing data access and reducing reliance on specialists. Widely cited business book applicable to manufacturing analytics.
  
  
• 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 source for technical capabilities of Siemens Opcenter MES. Supports descriptions of MES objects, connectors, and integration with Conversational AI.