Production Flow & Bottleneck Intelligence

Target Audience: VP of Operations, Plant Manager, CI Lead
Core Value: Predictive Bottleneck Identification & Throughput Maximization

The Problem: The "Hidden Pileup"

Management suffered from throughput blindness—the inability to see queue build-ups or starvation risks until production stopped. This led to reactive resource misallocation and inconsistent lead times.

The Solution: Live Aggregated State Analysis

I architected the Production Flow & Bottleneck Intelligence Dashboard as a meta-layer of control. It uses Aggregated State Analysis across all 12 stations to calculate queue deltas and dynamically generates human-readable operational recommendations to balance flow.

The Commercial ROI

1. Predictive Management: Enabled management to see queue drying up hours in advance, allowing for proactive labor reallocation.

2. Throughput Maximization: Identified the true constraint of the factory, increasing the output of the entire system.

3. Executive Clarity: Answers the CEO's question: "How is the shop running today?" in a single, verified glance.

Technical Architecture

• Algorithmic Status Determination: Implemented a Queue Logic engine that calculates the delta between completed and required parts across adjacent stations (e.g., workload.cutting.completed - workload.edgeBanding.required).

• Parallel Querying: Executes massive parallel Promise.all queries across cncPrograms, parts, and cabinets collections to build a complete WIP picture.

• Dynamic Recommendation Engine: Utilized useMemo hooks to cache workload calculations and dynamically generate human-readable advice strings based on predefined critical queue thresholds (e.g., “>50 units”), transforming raw data into actionable commands.