Supply Chain and Operations Analytics Cheat Sheet

Updated 2026-05-23

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Supply chain and operations analytics is the discipline of applying quantitative methods, KPIs, and data-driven models to plan, monitor, and optimize the flow of goods, information, and money across the entire value chain — from raw-material procurement through last-mile delivery. It sits at the intersection of operations research, business intelligence, and data science, and it underpins decisions in inventory, logistics, supplier management, and integrated planning. A practitioner's key insight is that every metric in this domain interacts with the service-cost-cash triangle: improving one dimension almost always creates trade-offs in the other two, so analytics must quantify those trade-offs rather than optimize a single KPI in isolation.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 114 indexed concepts, 106 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Inventory Efficiency MetricsTable 2: Replenishment and Stock Optimization CalculationsTable 3: Demand Forecasting ModelsTable 4: Forecast Accuracy MetricsTable 5: Inventory Classification MethodsTable 6: Service Level and Order Fulfillment KPIsTable 7: Supplier Scorecard and Vendor Performance MetricsTable 8: Lead Time AnalysisTable 9: Safety Stock and Inventory Optimization StrategiesTable 10: S&OP (Sales & Operations Planning) MetricsTable 11: Transportation and Freight Cost AnalyticsTable 12: Warehouse Productivity MetricsTable 13: Bullwhip Effect Detection and MitigationTable 14: Supply Chain Control Tower and Visibility AnalyticsTable 15: Sustainability and ESG Supply Chain Metrics

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Table 1: Inventory Efficiency Metrics

The most fundamental inventory KPIs tell you how fast stock moves, how long it sits, and how efficiently capital is deployed — they form the baseline for every inventory optimization effort.

Metric	Example	Description
Inventory Turnover Ratio	$\text{Turnover} = \frac{\text{COGS}}{\text{Avg Inventory}}$ e.g., $\frac{2{,}000{,}000}{400{,}000} = 5$ turns/yr	• Measures how many times stock is sold and replaced per period • higher turns = faster-moving inventory but risks stockouts if pushed too high
Days on Hand (DOH)	$\text{DOH} = \frac{\text{Avg Inventory}}{\text{COGS}/365}$ e.g., $\frac{400{,}000}{5{,}479} \approx 73$ days	• Also called Days Sales of Inventory (DSI) • measures how many days inventory lasts before sell-through • target 30–60 days in most ecommerce contexts
Weeks on Hand (WOH)	$\text{WOH} = \frac{\text{Avg Inventory}}{\text{COGS}/52}$	• Same concept as DOH expressed in weeks • high WOH signals slow movement, low WOH signals lean stock
Stock-to-Sales Ratio	$\text{S:S} = \frac{$\text{Inventory Value}}{$\text{Sales Value}}$	• Broad indicator of stocking efficiency • used to adjust purchasing to maintain target margins.
Sell-Through Rate	$\text{STR} = \frac{\text{Units Sold}}{\text{Units Received}} \times 100$ e.g., $\frac{800}{1{,}000} = 80%$	• Compares units sold to units received • low rates indicate slow-moving SKUs needing markdown or rebalancing
GMROI (Gross Margin Return on Inventory Investment)	$\text{GMROI} = \frac{\text{Gross Margin}}{\text{Avg Inventory Cost}}$ e.g., $\frac{500{,}000}{400{,}000} = 1.25$	• Measures profit generated per dollar of inventory • GMROI > 1 means profitable inventory investment.
Inventory Carrying Cost	$\text{ICC%} = \frac{\text{Service + Risk + Capital + Storage costs}}{\text{Total Inventory Value}} \times 100$	• Typically 20–30% of inventory value per year • captures all costs of holding stock including capital, storage, insurance, and obsolescence
Inventory Shrinkage	`Shrinkage = Book value − Physical count value` e.g., $$50{,}000 - $47{,}000 = $3{,}000$	• Gap between records and physical stock • caused by theft, damage, miscounts, or fraud • high rates signal control failures
Dead Stock Rate	$\text{Dead Stock%} = \frac{\text{Unsellable Units}}{\text{Total Units}} \times 100$	• Percentage of inventory that cannot be sold • companies with >25–30% dead stock are considered non-competitive
Inventory Accuracy	$\text{Accuracy} = \frac{\text{Items matching records}}{\text{Items counted}} \times 100$	• Measures alignment between system records and physical count • world-class targets are ≥99%.

Table 2: Replenishment and Stock Optimization Calculations

These formulas determine when and how much to order, balancing the cost of ordering too often against the cost of carrying too much stock. Getting these right is the difference between efficient supply chains and chronic overstock or stockouts.

Metric	Example	Description
Economic Order Quantity (EOQ)	$\text{EOQ} = \sqrt{\frac{2DS}{H}}$ $D=1{,}000, S=$2, H=$5 \Rightarrow \text{EOQ} \approx 28$	• Wilson formula minimizing total ordering + holding cost • $D$ = annual demand, $S$ = order cost, $H$ = holding cost per unit/year

The rest of this section is part of the full cheat sheet

Table 3: Demand Forecasting Models

Forecasting models are the engine of supply chain planning — they translate historical patterns and external signals into forward-looking demand estimates that drive every downstream decision from procurement to production scheduling.

Method	Example	Description
Simple Moving Average (SMA)	$\hat{d}t = \frac{d{t-1}+d_{t-2}+d_{t-3}}{3}$ e.g., $(100+110+90)/3 = 100$	• Averages the last $n$ periods with equal weights • best for stable, non-seasonal demand • responds slowly to trend changes

The rest of this section is part of the full cheat sheet

Table 4: Forecast Accuracy Metrics

Measuring forecast quality is distinct from building the forecast itself — these metrics tell planners how much to trust their models and reveal systematic biases that erode inventory and service performance.

Metric	Example	Description
MAPE (Mean Absolute Percentage Error)	$\text{MAPE} = \frac{1}{n}\sum\frac{\lvert A_i - F_i \rvert}{A_i} \times 100$ e.g., 15% MAPE	• Most widely used supply chain accuracy metric • <10% = excellent, 10–20% = acceptable for stable items • undefined when actual = 0.

The rest of this section is part of the full cheat sheet

Table 1: Inventory Efficiency Metrics

The most fundamental inventory KPIs tell you how fast stock moves, how long it sits, and how efficiently capital is deployed — they form the baseline for every inventory optimization effort.

Metric	Example	Description
Inventory Turnover Ratio	$\text{Turnover} = \frac{\text{COGS}}{\text{Avg Inventory}}$ e.g., $\frac{2{,}000{,}000}{400{,}000} = 5$ turns/yr	• Measures how many times stock is sold and replaced per period • higher turns = faster-moving inventory but risks stockouts if pushed too high
Days on Hand (DOH)	$\text{DOH} = \frac{\text{Avg Inventory}}{\text{COGS}/365}$ e.g., $\frac{400{,}000}{5{,}479} \approx 73$ days	• Also called Days Sales of Inventory (DSI) • measures how many days inventory lasts before sell-through • target 30–60 days in most ecommerce contexts
Weeks on Hand (WOH)	$\text{WOH} = \frac{\text{Avg Inventory}}{\text{COGS}/52}$	• Same concept as DOH expressed in weeks • high WOH signals slow movement, low WOH signals lean stock
Stock-to-Sales Ratio	$\text{S:S} = \frac{$\text{Inventory Value}}{$\text{Sales Value}}$	• Broad indicator of stocking efficiency • used to adjust purchasing to maintain target margins.
Sell-Through Rate	$\text{STR} = \frac{\text{Units Sold}}{\text{Units Received}} \times 100$ e.g., $\frac{800}{1{,}000} = 80%$	• Compares units sold to units received • low rates indicate slow-moving SKUs needing markdown or rebalancing
GMROI (Gross Margin Return on Inventory Investment)	$\text{GMROI} = \frac{\text{Gross Margin}}{\text{Avg Inventory Cost}}$ e.g., $\frac{500{,}000}{400{,}000} = 1.25$	• Measures profit generated per dollar of inventory • GMROI > 1 means profitable inventory investment.
Inventory Carrying Cost	$\text{ICC%} = \frac{\text{Service + Risk + Capital + Storage costs}}{\text{Total Inventory Value}} \times 100$	• Typically 20–30% of inventory value per year • captures all costs of holding stock including capital, storage, insurance, and obsolescence
Inventory Shrinkage	`Shrinkage = Book value − Physical count value` e.g., $$50{,}000 - $47{,}000 = $3{,}000$	• Gap between records and physical stock • caused by theft, damage, miscounts, or fraud • high rates signal control failures
Dead Stock Rate	$\text{Dead Stock%} = \frac{\text{Unsellable Units}}{\text{Total Units}} \times 100$	• Percentage of inventory that cannot be sold • companies with >25–30% dead stock are considered non-competitive
Inventory Accuracy	$\text{Accuracy} = \frac{\text{Items matching records}}{\text{Items counted}} \times 100$	• Measures alignment between system records and physical count • world-class targets are ≥99%.

Table 2: Replenishment and Stock Optimization Calculations

Metric	Example	Description
Economic Order Quantity (EOQ)	$\text{EOQ} = \sqrt{\frac{2DS}{H}}$ $D=1{,}000, S=$2, H=$5 \Rightarrow \text{EOQ} \approx 28$	• Wilson formula minimizing total ordering + holding cost • $D$ = annual demand, $S$ = order cost, $H$ = holding cost per unit/year

Table 3: Demand Forecasting Models

Method	Example	Description
Simple Moving Average (SMA)	$\hat{d}t = \frac{d{t-1}+d_{t-2}+d_{t-3}}{3}$ e.g., $(100+110+90)/3 = 100$	• Averages the last $n$ periods with equal weights • best for stable, non-seasonal demand • responds slowly to trend changes

Table 4: Forecast Accuracy Metrics

Metric	Example	Description
MAPE (Mean Absolute Percentage Error)	$\text{MAPE} = \frac{1}{n}\sum\frac{\lvert A_i - F_i \rvert}{A_i} \times 100$ e.g., 15% MAPE	• Most widely used supply chain accuracy metric • <10% = excellent, 10–20% = acceptable for stable items • undefined when actual = 0.