From the team

Supply chain thinking from the Automcore team.

MAPE and bias metrics, safety stock formulas, POS velocity signals, BOM demand propagation — written for the planner running the actual spreadsheet, not the analyst writing about it.

Abstract visualization of forecast accuracy metrics — MAPE and bias charts
Methodology

Demand Forecast Accuracy Metrics: MAPE, Bias, and What Actually Matters to Planners

Most demand planners track MAPE but ignore bias — which means their forecast is accurate on average but consistently wrong in one direction. Here’s how to read the metrics that actually predict stockout and overstock outcomes.
Retail point-of-sale activity with data signal visualization
Data

POS Velocity Signals: What Your Transaction Data Is Already Telling You

Hourly sell-through velocity contains demand signals that weekly reports obscure. Here’s how to extract them.
Weather radar visualization representing weather signal impact on supply chain demand
Signals

How Weather Patterns Drive Demand Shifts That ERPs Never See

A three-day cold front in the Pacific Northwest can shift jacket demand by 40% in 72 hours. Your ERP won’t react in time.
Supply chain timeline visualization showing lead time variance across suppliers
Operations

Supplier Lead Time Variability: The Hidden Input Your Forecast Is Missing

A supplier’s stated lead time and actual delivery window are rarely the same. Measuring the gap is step one.
Split visual contrasting spreadsheet grids with demand forecast dashboard visualizations
Planning

Inventory Planning in Excel vs. AI Forecasting: An Honest Comparison

Excel works until it doesn’t. Here’s the specific thresholds at which a spreadsheet-based demand plan stops being adequate.
Mid-market retail warehouse aisle representing the planning gap in retail operations
Retail

The Mid-Market Retail Forecasting Gap: Why $50M–$200M Brands Get Left Behind

Enterprise forecasting platforms require data science teams. Simple reorder tools are too blunt. The gap is real and expensive.
Abstract mathematical probability distribution visualization representing safety stock calculations
Methodology

The Safety Stock Formula Revisited: Why Z-Scores Alone Aren’t Enough

The classic safety stock formula assumes normally distributed demand and constant lead times. Neither is true for most SKUs.
Dual signal visualization showing real-time demand sensing versus longer-term forecast projection
Strategy

Demand Sensing vs. Demand Forecasting: When You Need Each

Sensing is short-horizon (1–3 weeks). Forecasting is medium-horizon (4–12 weeks). They solve different problems and need different data.
Abstract BOM tree visualization representing component demand propagation in manufacturing
Manufacturing

BOM Demand Propagation: How Finished-Goods Forecasts Should Drive Component Purchasing

Most manufacturers forecast at the finished-goods level and hope the BOM math works out. It rarely does without explicit propagation logic.
Cyclical wave pattern visualization representing seasonal demand detection in forecasting models
Methodology

Seasonal Demand Pattern Detection: Reading the Cycle Before It Peaks

Seasonality isn’t just “Q4 is big.” At SKU level, patterns are complex, overlapping, and shift year-over-year. Here’s how to detect them early.
Abstract visualization of supply chain and financial planning data streams merging
Finance

Connecting FP&A to Supply Chain: Why Finance and Demand Planning Need the Same Forecast

When Finance uses revenue assumptions and Supply Chain uses sell-through velocity, the company is working from two different views of the future.
Warehouse shelving with overstocked boxes suggesting dead stock accumulation problem
Retail

Reduce Dead Stock: A Retail Planner’s Playbook for 2026

Dead stock isn’t just a margin problem — it’s a forecasting failure that compounds over buying cycles. Here’s how to break the cycle.