FAQs: The Engineering Protocols
Diagnostic Library: The Frameworks & Failure Modes.
A technical knowledge base defining the engineering root causes behind stalled AI initiatives, inventory bleed, and signal latency.
The Philosophy of Friction
We diagnose business failure as a physics problem. When capital bleeds, it is usually due to Signal Latency, Entropy, or Granularity Mismatch between your Commercial Strategy and your Operational Execution.
Below are the proprietary frameworks we use to audit these systems, followed by the 15 specific failure modes we resolve.
Category 0: Core Frameworks (The Methodology)
Definition: A diagnostic filter used to identify “Innovation Theater” before capital is deployed.
- Applicability (The Filter): We determine the physics of the problem. Is it a “Plier” problem (GenAI/Creative) or a “Foundation” problem (Predictive/Optimization)? We look for “Logic Leaks” in the P&L that are mathematically solvable.
- Readiness (The Clinical Evaluation): A rigorous audit of Data Adequacy. We distinguish between “Big Data” (Volume) and “Rich Data” (Signal) to ensure we are not building on sand.
- Transformation (The Map): Identifying the specific Structural Shifts required in operations (e.g., changing Logistics logic from FIFO to FEFO), moving beyond vague goals like “efficiency.”
Definition: The architectural standard for building reliable, decision-first AI systems.
- Observe (The Physics): Identify the raw signal constraints (Latency, Decay, Entropy).
- Learn (The Model): Select the algorithm that matches the physics (e.g., Survival Analysis for decay, not Regression).
- Create (The Controller): Build the “Gearbox” that turns the prediction into a decision (e.g., an Inventory Routing Engine).
- Deploy (The Diagnostic): Implement feedback loops to measure system drift and P&L impact in real-time.
Category 1: The Money Bleed (Inventory & Margin)
The Diagnostic: Why is cash trapped in the warehouse despite record sales projections?
The Root Cause: Uncoupled Feedback Loops. Marketing forecasts based on “Potential,” while Operations buys based on “Hope.” There is no mathematical “brake” applied to marketing optimism.
The Protocol: Implement a Diagnostic Coupler. A shared dashboard that forces Marketing to sign off on the inventory cost of their forecast variance. We typically see a 15-20% reduction in working capital.
The Diagnostic: Why do our best marketing campaigns result in lost revenue?
The Root Cause: Synchronization Latency. The demand signal (Ad Spend) is generated faster than the supply response (Replenishment) can react. The gears are grinding.
The Protocol: Demand-Supply Phase Locking. We link the Marketing Campaign ID directly to the Replenishment Algorithm, triggering inventory movement before the ad spend spikes.
The Diagnostic: Why are we selling high volume but bleeding gross margin?
The Root Cause: Aggregation Error. You are forecasting at the “Category Level” (Men’s Shirts) but customers buy at the “Attribute Level” (Blue/Medium). You bought the wrong mix.
The Protocol: Attribute-Based Forecasting. We shift the model to predict demand based on features (Color, Fabric, Fit) rather than SKU IDs, ensuring the right mix lands on the shelf.
The Diagnostic: Why are “Claims” (Spoilage) eating 20% of our yield?
The Root Cause: Entropy. Treating biological inventory like static widgets. Standard FIFO (First-In-First-Out) logic fails because it ignores biological decay.
The Protocol: Survival Analysis. We implement FEFO (First-Expired-First-Out) logic based on “Time-to-Failure” models, routing fragile stock to local markets and robust stock to export.
The Diagnostic: Why are E-commerce returns destroying our net profit?
The Root Cause: Open-Loop Data. Returns are treated as a “Logistics Task” (Restock it), not a “Data Signal” (Why did it come back?).
The Protocol: The Feedback Loop. We integrate Return Codes directly into the Demand Planning engine to penalize products with high return rates, preventing the system from re-ordering “bad” revenue.
Category 2: The Time Sink (Process & Latency)
The Diagnostic: Why does our S&OP cycle take 7 days to produce a plan?
The Root Cause: Data Staging Latency. Planners spend 80% of their time cleaning/aggregating CSVs from different departments and only 20% planning.
The Protocol: Automated Data Pipelines. We replace Excel macros with SQL/Python pipelines that auto-ingest data, reducing the cycle time from days to hours.
The Diagnostic: Why has our AI team spent 12 months with zero production deployments?
The Root Cause: Environment Mismatch. The model was built for the Lab (Cloud/Python) but the Operations run on the Edge (Legacy/PLC).
The Protocol: Constraint-First Architecture. We define the deployment environment (latency, memory, connectivity) before writing a single line of model code.
The Diagnostic: Why do Finance and Ops never agree on the numbers?
The Root Cause: Unit Mismatch. Finance plans in “Monthly Dollars.” Ops executes in “Daily Units.” The translation layer is broken.
The Protocol: The Sync Engine. A logic layer that mathematically decomposes financial targets into operational constraints, ensuring both teams look at the same “Truth.”
The Diagnostic: Why is the factory manager turning off our “Smart Cameras”?
The Root Cause: Inference Latency. Cloud-based models are too slow for high-speed lines. A 500ms lag causes a bottleneck.
The Protocol: Edge Inference. We deploy lightweight algorithms (like T-Digest) directly on near-ECU hardware to achieve <5ms response times.
The Diagnostic: Why are senior managers acting as data entry clerks?
The Root Cause: Heuristic Decision Making. Humans are manually deciding “Store A needs 5 units” because the system lacks a trustable optimization engine.
The Protocol: Probabilistic Allocation. We build an engine that auto-allocates inventory based on “Probability of Sale,” leaving humans to manage only the exceptions.
Category 3: The Risk (Compliance & Brand)
The Diagnostic: Why did we miss the forecast drop until it was too late?
The Root Cause: Lagging Indicators. Forecasting based on “Delivery Date” (History) rather than “Order Date” (Intent).
The Protocol: Signal Correction. We shift the entire data architecture to ingest the leading signal (Order Date), removing the 5-10 day logistical blind spot.
The Diagnostic: Why are rare defects escaping despite our QA checks?
The Root Cause: Supervised Learning Failure. You cannot train a model to find defects you have never seen (Small Data).
The Protocol: Anomaly Detection (Unsupervised). We teach the model the “Normal Signature” (ECG) of the product. Anything that deviates is flagged, catching unknown defects.
The Diagnostic: Why are warranty costs spiking unexpectedly?
The Root Cause: Data Silos. Manufacturing data and After-Sales Service data are not linked. Engineering doesn’t know a part is failing until 6 months later.
The Protocol: The Closed Loop. We link Field Service Failure Codes back to Manufacturing Batch IDs, enabling predictive recalls before the warranty fund drains.
The Diagnostic: Why are Marketing and Ops fighting?
The Root Cause: Incentive Misalignment. Marketing is paid on Revenue (Top Line). Ops is paid on Efficiency (Bottom Line). They are incentivized to work against each other.
The Protocol: Shared P&L Metrics. We re-architect KPIs so that Marketing is penalized for “Unsold Inventory” and Ops is rewarded for “Service Level,” forcing collaboration.
The Diagnostic: Why are we canceling online orders while stores have stock?
The Root Cause: State Management Failure. The E-commerce system and the Retail POS system do not share a real-time view of inventory.
The Protocol: Unified Inventory Ledger. A single “Source of Truth” database that reserves stock in real-time across all channels, preventing ghost inventory.