Metrics and Formulas That Govern Flow

Inventory turnover and days of inventory

• Inventory Turnover = Cost of Goods Sold (COGS) / Average Inventory (cost basis)

• Days of Inventory (DIO) = 365 / Turnover

Many ecommerce brands target higher turnover (e.g., 6–12 turns) to balance agility and stock availability. Actual goals depend on margins, lead times, and demand volatility.

Reorder point and safety stock (variable demand and lead time)

For normally distributed demand and lead time:

• Mean demand per day = d

• Standard deviation of demand per day = σd

• Mean lead time in days = L

• Standard deviation of lead time in days = σL

Demand during lead time:

• Mean = d × L

• Standard deviation = sqrt(L × σd² + d² × σL²)

Safety Stock (SS) = z × sqrt(L × σd² + d² × σL²)

• z is the z-score for your target cycle service level:

  • 84% → 1.00

  • 90% → 1.28

  • 95% → 1.65

  • 97.5% → 1.96

  • 99% → 2.33

Reorder Point (ROP) = (d × L) + SS

Simpler case (variable demand, constant lead time L):

• SS = z × σd × sqrt(L)

• ROP = (d × L) + SS

Tips:

• Set different service levels by SKU class (e.g., 98% for A/high-margin, 95% for B, 90% for C).

• Recompute SS and ROP when demand or lead time variability changes.

Little’s Law and flow time

Little’s Law: Inventory (WIP) = Throughput × Flow Time

In ecommerce terms:

• Average On-hand Units = Average Daily Shipments × Average Days in Process

• Shortening flow time (e.g., faster receiving or picking) reduces average inventory at the same throughput.

Use Little’s Law to sanity check your DIO against observed order volumes and process times.

Fill rate, service level, backorder rate

• Cycle service level: Probability of not stocking out in a replenishment cycle.

• Fill rate: Percentage of demand fulfilled immediately from stock (line or unit fill).

• Backorder rate: Percentage of demand not filled on time.

Aim for high fill rate on top sellers while controlling working capital via tiered service levels.

Cash-to-cash cycle and working capital

Cash-to-Cash (C2C) = DIO + DSO − DPO

• DIO: Days you hold inventory before selling.

• DSO: Days to collect receivables (often low in DTC).

• DPO: Days you take to pay suppliers.

Improving flow reduces DIO and shortens C2C, freeing cash. Negotiating better DPO can also improve C2C but should not mask slow flow.

Choosing the Right Flow Model

Push vs pull vs hybrid

• Push: Build/position inventory based on forecast. Best for stable demand and long procurement lead times.

• Pull: Replenish only on actual consumption signals (Kanban). Best for predictable, high-frequency items.

• Hybrid: Push to decoupling points (e.g., components) and pull to finish (e.g., kits/assemblies).

Decision triggers:

• High demand variability → Lean toward pull or hybrid.

• Long supplier lead time → Push upstream buffers, pull downstream.

• High margin/critical SKUs → Higher service level and strategic buffers.

JIT and Kanban fundamentals

• JIT focuses on reducing waste and shortening flow times.

• Kanban (two-bin, cards, or digital signals) triggers replenishment at consumption.

• CONWIP limits total WIP across stages to stabilize flow.

Make-to-stock, assemble-to-order, and make-to-order

• Make-to-Stock (MTS): Prebuild finished goods; use for steady, fast movers.

• Assemble-to-Order (ATO): Stock components, assemble on demand; balances variety and speed.

• Make-to-Order (MTO): Produce after order; use for customizable or slow movers.

Decoupling points and strategic buffers

• Place buffers where variability or lead times spike (e.g., overseas transit, QC).

• Use time-based buffers for lead-time risk and quantity buffers for demand spikes.

• Review buffers monthly and right-size with updated variability.

FIFO, FEFO, and lot-controlled flows

• FIFO: First in, first out; general best practice.

• FEFO: First expiry, first out; essential for perishables and regulated goods.

• Lot/serial control: Required for traceability; design pick logic to respect constraints without slowing flow.

Finding and Fixing Bottlenecks

Value stream mapping step-by-step

1. Define scope (e.g., PO to ship for top 20 SKUs).

2. Map each step with process time (PT), wait time (WT), and inventories between steps.

3. Quantify lead time by step and identify VA (value-added) vs NVA (non-value-added) time.

4. Highlight constraints (queues >24 hours, rework loops, high variability).

5. Design a future-state map with pull signals, balanced workloads, and reduced WIP.

6. Pilot changes and measure before/after lead time and throughput.

For deeper walkthroughs and templates, see our inventory management blog.

Lead time decomposition and variability

Break total lead time into components:

• Supplier production + supplier wait

• International transit + customs

• Domestic transport

• Receiving + QC

• Putaway

• Pick/pack

• Carrier transit

Measure average and standard deviation for each. Target the largest contributors to variability first; they drive safety stock the most.

Slotting and pick-path optimization

• Slot A-movers in golden zones near pack stations.

• Store items frequently ordered together nearby.

• Use one-way aisles and U-shaped paths to minimize travel.

• Re-slot quarterly as velocity shifts.

Exception management: backorders and substitutions

• Define rules for partial shipments, backorder windows, and customer communication.

• Offer substitutions for equivalent SKUs with clear consent.

• Protect preorders with dedicated allocation and dates tied to realistic lead times.

Kitting, bundling, and component availability

• Maintain a simple BOM for kits; track components and alternates.

• Decide on pre-kitting vs on-demand assembly by order profile.

• Prevent false stockouts by synchronizing kit and component availability in your OMS/WMS.

Optimization Playbook

ABC-XYZ segmentation and policies

• ABC by value/velocity (e.g., A = top 70–80% of revenue, B = next 15–20%, C = remainder).

• XYZ by variability (X = stable, Y = seasonal/trending, Z = intermittent).

Policy examples:

• AX: High service level (97–99%), Kanban or min/max, frequent replenishment.

• AY: Seasonal profiles, higher SS pre-peak, promotions-aware planning.

• AZ: Conservative stock with longer review cycles; consider MTO or dropship.

• CX: Lower service level, larger order multiples; avoid overstock.

• CZ: Consider discontinue, bundle, or MTO.

Forecasting improvements and demand sensing

• Layer methods: baseline time series, event overlays (promotions), and causal inputs (price, ads, weather if relevant).

• Separate trend, seasonality, and noise; re-fit models as data grows.

• Use short-lag signals (site views, add-to-cart, preorders) to adjust near-term plans.

If you operate on Shopify, a focused tool can help align forecasts with catalog and promotions. Explore the Verve AI Shopify inventory forecasting.

Replenishment rules: min-max, EOQ, order cycles

• Min-Max: Reorder to max when on-hand drops to min; set via ROP and order-up-to formulas.

• EOQ (Economic Order Quantity): EOQ = sqrt(2 × D × S / H)

  • D = Annual demand

  • S = Order/setup cost

  • H = Annual holding cost per unit

• Practical considerations: MOQs, case packs, tiered freight, and receiving capacity.

Cycle counting and data hygiene

• Replace annual wall-to-wall counts with daily/weekly cycle counts, weighted to A/AX SKUs.

• Enforce scan discipline for receipts, moves, and picks.

• Clean masters: units of measure, pack sizes, lead times, and supplier item numbers.

Automation and IoT triggers for flow

• Use barcode/RFID to cut manual entry and drive real-time status changes.

• Automate replenishment signals via webhooks when bins hit min.

• Sensor-based monitoring (e.g., dock doors, temperature) for quality-critical items.

Multi-Echelon and Omnichannel Strategies

Network design, allocation, and split shipments

• Place stock closer to demand centers to reduce last-mile cost and delivery time.

• Set split-ship rules to balance speed with shipping cost; avoid unnecessary splits.

• Allocate new receipts to nodes based on predictive demand and current imbalances.

Flow-through, cross-docking, and prepack

• Flow-through: Receive, sort, and ship with minimal storage dwell time.

• Cross-docking: Pre-labeled cartons or pallets move directly to outbound.

• Prepack: Vendor packs shelf-ready or order-ready units to cut touches.

Dropship and 3PL collaboration models

• Align SLAs, inventory feeds, and EDI/API order flows to maintain visibility.

• Share forecasts and promo calendars to reduce surprise spikes.

• Standardize packaging and labeling to protect unboxing experience.

Ship-from-store and BOPIS considerations

• Accurate, near-real-time store inventory and reservations prevent cancellations.

• Set rules to prevent over-promising during peak store traffic.

• Train store staff on pick/pack standards to avoid damages and delays.

Returns consolidation and refurbishment

• Route returns to the optimal node for grading and restock.

• Refurbish and re-box where margin allows; otherwise, consolidate for liquidation.

• Track rework lead time; long loops create hidden stockouts.

Risk, Resilience, and Seasonality

Safety stock under uncertainty

• Use variability-aware SS (z × σ during lead time) with service levels by ABC-XYZ class.

• Time-phase SS for peak seasons and supplier holidays.

• Review SS monthly and after major supplier or transit changes.

Supplier diversification and dual-sourcing

• Dual-source critical items; balance cost with resilience.

• Hold strategic inventory for single-sourced components with long lead times.

• Qualify alternates and pre-approve substitutions for fast response.

Scenario planning and simulation

• Run what-if scenarios for demand surges, lead time extensions, and carrier caps.

• Simulate service levels and cash impact under different SS and allocation policies.

• Feed outcomes into S&OP to set seasonal targets and contingency plans.

Peak-season playbook

• Lock forecasts and POs early; confirm capacity with suppliers and 3PLs.

• Freeze SKU changes close to peak; simplify assortments where possible.

• Expand pick labor pools and extend shifts; pre-pick fast bundles.

Sustainability and waste reduction

• FEFO to reduce expiry-related waste.

• Optimize packaging to lower damage and freight emissions.

• Disposition hierarchy: resell, refurbish, donate, recycle, dispose—as last resort.

Implementation Roadmap (90 Days)

Baseline audit and KPI targets

• Collect 12–18 months of orders, inventory, lead times, and returns.

• Baseline KPIs: turnover, DIO, fill rate, backorder rate, pick accuracy, dock-to-stock, cash-to-cash.

• Identify top 20 SKUs by revenue and volatility; start your pilot there.

Quick wins (30 days), stabilization (60), scale (90)

• 0–30 days (Quick wins):

  • Implement cycle counts on A/AX SKUs.

  • Re-slot top movers and fix pick-path bottlenecks.

  • Establish ROP with initial SS for top sellers.

  • Standardize receiving with ASNs and dock appointments.

• 31–60 days (Stabilize):

  • Roll out ABC-XYZ and service-level policies.

  • Add min/max or Kanban to forward pick locations.

  • Implement exception workflows for backorders/substitutions.

  • Pilot cross-dock or flow-through for replenishment SKUs.

• 61–90 days (Scale):

  • Extend policies network-wide and across channels.

  • Introduce network allocation rules and split-ship controls.

  • Launch weekly S&OE and monthly S&OP cadence with KPI dashboards.

  • Run peak-season simulation and finalize buffer placements.

Roles, RACI, and change management

• Define owners: Supply Planning, Warehouse Ops, Logistics, Customer Service, Finance.

• RACI for each initiative (e.g., planning owns SS/ROP, ops accountable for slotting).

• Train on SOPs; reinforce with daily standups and visible metrics.

Tech stack and integrations (WMS, OMS, ERP, 3PL)

• Core systems: ERP/Accounting for item masters and COGS; OMS for orders and allocation; WMS for inventory movements; 3PL systems if outsourced.

• Integrations: Bi-directional inventory, order status, ASN/EDI, and carrier tracking.

• If you run WooCommerce, consider a purpose-built add-on to align forecasts with catalog and orders. See WooCommerce inventory forecasting: Verve AI WooCommerce inventory forecasting plugin.

Governance and rev.iew cadence

• Daily S&OE: exceptions, backorders, late POs, and carrier delays.

• Weekly: forecast accuracy, SS/ROP recalibration, slotting changes.

• Monthly: S&OP review of demand, capacity, and working capital impacts.

Case Study and Tools

Before/after metrics and cash impact

Example scenario (mid-market DTC brand, 2 nodes, 3,000 SKUs):

• Baseline: Turnover 5.0x; DIO 73 days; fill rate 91%; backorder rate 9%; cash tied in inventory $3.5M.

• After 90 days: Turnover 8.0x; DIO 46 days; fill rate 96%; backorder rate 3%; average on-hand reduced by ~$1.3M.

• Key moves: ABC-XYZ with tiered service levels, SS/ROP on top 20% SKUs, re-slotting A-movers, cross-docking replenishment, and lead-time variance reduction from two suppliers.

These results illustrate what disciplined flow optimization can achieve; actual outcomes depend on product mix and supplier performance.

Sample dashboard and KPI definitions

• Turnover, DIO, and C2C (rolling 3, 6, 12 months)

• Fill rate (unit and line), backorder rate, and split-ship rate

• Forecast accuracy (MAPE) by SKU class, bias (%)

• Lead time mean/variance by supplier and lane

• Dock-to-stock time, pick accuracy, order cycle time

• Returns rate, restock lead time, refurbish yield

Reorder point and safety stock calculator

Set up a simple worksheet with:

• Inputs per SKU: mean daily demand (d), σd, mean lead time (L), σL, target service level (z), holding cost, order cost.

• Calculations: SS = z × sqrt(L × σd² + d² × σL²), ROP = (d × L) + SS, EOQ = sqrt(2 × D × S / H).

• Outputs: recommended min (ROP), order-up-to (ROP + EOQ or target days cover), review frequency.

You can accelerate this with inventory planning tools.

Value stream map and SOP templates

• VSM template: boxes for each step with PT/WT, inventory triangles, push/pull arrows, data box (uptime, batch size, FTT).

• SOPs: receiving (ASN, QC), putaway (slotting rules), replenishment (min/max/Kanban), picking (method by order profile), exceptions (backorders, substitutions), and returns (grading, disposition).

Soft CTA: If stockouts and slow turns are holding you back, start with a 2-hour flow audit on your top 20 SKUs. Map lead times, compute SS/ROP with the formulas above, and pilot one change per week for 90 days.

FAQs

What’s the difference between inventory flow and inventory turnover?

Inventory flow is about movement and timing—the speed and variability of goods through your processes. Inventory turnover is a KPI showing how many times you sell through average inventory in a period. Improving flow often increases turnover, but flow also covers process design, bottlenecks, and lead times.

How do I calculate reorder point with variable demand and lead time?

Use: ROP = (d × L) + SS, where d is mean daily demand, L is mean lead time (days), and SS is safety stock. For normal demand and lead-time variation: SS = z × sqrt(L × σd² + d² × σL²). Choose z based on your target cycle service level (e.g., 1.65 for ~95%).

What is a good inventory turnover for ecommerce brands?

Targets vary by category, margin, and lead time. Many ecommerce brands aim for mid-to-high single-digit turns (e.g., 6–12) for core SKUs. Focus on improving DIO and fill rate together, not on a single benchmark.

How can I improve inventory flow without raising inventory levels?

• Reduce lead-time variability with supplier SLAs and ASNs.

• Re-slot A-movers and optimize pick paths.

• Use ABC-XYZ with tiered service levels to right-size safety stock.

• Cross-dock replenishment SKUs to cut dwell time.

• Tighten exception handling for backorders and substitutions.

Should I use push, pull, or a hybrid flow model?

Match model to variability and lead time. Use push upstream where procurement is long and predictable buffers help. Use pull downstream to respond to real demand. Many brands run hybrid: push components, pull finishes (ATO) on fast movers.

How do returns and reverse logistics affect inventory flow?

Returns create variability and hidden stockouts if not processed quickly. Triage fast, restock what’s sellable, and route refurbish/liquidation appropriately. Track restock lead time; long loops inflate safety stock and reduce effective availability.

How should I manage inventory flow for bundles and kits?

Maintain BOMs, track component availability, and decide pre-kitting vs on-demand by order profile. Use allocation logic that checks components to avoid false availability. For virtual bundles, sync kit and component ATP in your OMS/WMS.

Which systems need to integrate to improve inventory flow?

Core integrations include OMS↔WMS (orders, picks, and stock movements), ERP (item masters, COGS), supplier EDI/ASNs, and carriers (labels, tracking). If you run Shopify, consider a Shopify inventory forecasting solution to align demand planning with your catalog and promotions: