Conference Agenda

Optical sorting is widely used in industrial quality control, yet conventional rule-based vision systems often struggle when quality cues are subtle, heterogeneous, or hard to formalize. We present an industry data science case study on deploying deep learning for real-time optical sorting of hazelnuts, driven by the practical need to grade product quality from fine-grained appearance characteristics under strict throughput and latency constraints.

The talk traces the path from an early prototype to an industrialized system that has been transferred into a market-ready product and is operated in practice. We summarize the end-to-end solution: multi-camera image acquisition, a supervised learning pipeline built on a representative labeled dataset, domain-specific preprocessing and targeted data augmentation, and a neural image classifier designed for on-premise inference.

We emphasize industrial aspects that proved central for making the system operational: formalizing expert grading into maintainable classes, managing imbalance and borderline cases during data preparation, data labeling and training, and setting decision thresholds based on acceptance criteria. We then cover deployment realities for industrial environments, e.g. latency, throughput, robustness, and the interface between the ML component and machine control.

Finally, we describe how the solution was productized and extended beyond hazelnuts to additional crops, enabling new application scenarios and market opportunities for the customer. We conclude with practical considerations for lifecycle management and periodic re-calibration.

While forecasting remains a cornerstone of strategic decision-making, its industrial application involves challenges that extend beyond model accuracy. In the context of supply chain management, a forecast must not only be precise but also interpretable and actionable within specific operational constraints.

This talk provides insights into how practitioners bridge the gap between theoretical models and business requirements, focusing on the following key areas:

• Operational Integration & Evaluation: Moving from pure demand forecasting to integrated planning. We will discuss the necessity of using business-centric metrics to evaluate model performance in a way that aligns with supply chain realities.

• Foundation Models for Time Series: An assessment of current trends in global models and their potential to complement or redefine classical forecasting methods in an industrial setting.

• Contextualization via Generative AI: A practical look at using Large Language Models (LLMs) and advanced search capabilities to identify external drivers and "weak signals" (e.g., geopolitical events or market shifts). We explore how to translate unstructured information into structured context for analytical models.

• Agentic Workflows: The emerging role of autonomous agents in designing and modeling forecasting pipelines, and the potential of conversational interfaces to transform human-machine interaction.

The presentation demonstrates that the value of Generative AI in forecasting lies not only in potential accuracy gains but also in its capacity to handle unstructured context and significantly improve interactability with the forecasts.

By highlighting these real-world requirements and current technical frontiers, the talk seeks to provide practical impulses and identify open questions for further academic research in the field of applied AI and time series analysis.