How to Choose the Right Stock Control System for Multi-Warehouse Operations

Five warehouses, three systems, zero agreement on inventory—sound familiar? If one site is out while another is overstocked, the issue isn’t your people; it’s the lack of a centralized stock control system that provides a real-time, multi-warehouse view of truth. When teams are reconciling spreadsheets instead of fulfilling orders, margins quietly evaporate.

This master guide is a buyer’s playbook—practical, neutral, and built for decision-makers. You’ll get a 7-step evaluation framework, a weighted scoring matrix, and straightforward ROI math you can validate in a 30–60-day pilot. We’ll focus on what actually moves the needle: cross-location visibility and latency, smart allocation and inter-warehouse transfers with audit trails, barcode/RFID compliance on the floor, and dependable integrations with ERP, POS, and e-commerce. For a deeper software view, see our explainer on multi-site warehouse inventory control software and how it operates at the warehouse layer.

By the end, you’ll know exactly what to prioritize, how to test it before you buy, and how to justify the investment with numbers—not hope.

What is a stock control system (vs WMS/ERP) for multi-warehouse ops?

A stock control system is the system of record for inventory. It maintains a single, real-time truth of what you own, where it sits (site/bin), in what state (available, reserved, damaged, quarantine), and how it moves—including receipts, issues, adjustments, and inter-warehouse transfers. For multi-warehouse retail, it also enforces per-location policies (ROP/safety stock/service levels), traceability (lot/serial, FIFO/FEFO), ATP by location, and auditable histories.

What it includes (at minimum)

• Item & location model: SKU master, units, attributes; site → zone → bin hierarchy

• Inventory states & reservations: on-hand, available, allocated, backordered, returns

• Rules & policies: ROP/safety stock per site, substitution, aging, cycle-count cadence

• Transactions: receipts/ASNs, picks/shipments, adjustments, transfers with SLA

• Interfaces: low-latency APIs/feeds to ERP, POS, e-commerce, WMS, shipping

How it differs from WMS and ERP

 

The hand-off model (who owns what)

• Stock control: owns on-hand/ATP by site, reservations, transfers, policy logic

• WMS: owns tasks & confirmations (what got picked/packed/shipped/received)

• ERP: owns orders, invoices, costing; consumes authoritative inventory from stock control

Micro-FAQ

• Do I need both stock control and a WMS?
  Usually yes: stock control for truth & rules, WMS for execution.

• Where should the system-of-record live?
  For multi-warehouse speed and accuracy, keep inventory truth in stock control, with WMS and ERP publishing/consuming events.

Multi-location challenges your system must solve

Running inventory across sites isn’t just “more of the same.” A multi-warehouse environment adds latency, policy differences, and handoffs that break under pressure. Your stock control system must neutralize the following—by design.

1) Real-time visibility (by site/bin)

Symptom: teams argue over “the real number.”
Must do: sub-minute updates for receipts, picks, adjustments; show on-hand, available, and ATP by location.
Acceptance target: P95 sync latency ≤ 60s; site/bin accuracy ≥ 99.5%.

2) Allocation & inter-warehouse transfers

Symptom: one DC stockouts while another sits on excess.
Must do: rules to reserve, reallocate, and auto-create transfer orders with audit trails.
Acceptance target: transfer request→ship ≤ 24h; ship→receive ≤ 72h; transfer variance ≤ 0.5%.

3) Per-location policies

Symptom: generic reorder points cause over/under-stocks.
Must do: ROP, safety stock, lead times, service levels per site; seasonality and regional demand.
Acceptance target: stockout rate reduction 15–30% after go-live window.

4) Omnichannel order orchestration

Symptom: the “closest” site ships late; split shipments explode costs.
Must do: routing rules (cost/time/availability), hold/reservations, backorders & partials, ATP per site.
Acceptance target: on-time, in-full (OTIF) uplift 5–10 pts; split-ship rate down 10–20%.

5) Traceability & compliance

Symptom: recalls or expiries become manual hunts.
Must do: lot/serial tracking, FIFO/FEFO, aging rules, quarantine states, full audit trail.
Acceptance target: 100% trace back to receipt in ≤ 2 min per SKU/lot.

6) Counting & variance control

Symptom: counts fix numbers for a day, then drift returns.
Must do: cycle counts by ABC class, spot checks, variance workflows (investigate → adjust → learn).
Acceptance target: cycle-count compliance ≥ 95%; variance down 30%+ on pilot SKUs.

7) Returns & reverse logistics

Symptom: returns vanish or re-enter with the wrong status.
Must do: RMA reasons, inspections, dispositions (restock/scrap/refurb), value recovery tracking.
Acceptance target: disposition posted within 24h; mis-restocks ≤ 0.2%.

8) Integration resilience

Symptom: silent integration failures corrupt inventory.
Must do: explicit events (item master, on-hand deltas, receipts/ASNs, picks/shipments, transfers, returns), error queues, retries, daily reconciliations, clear system-of-record per object.
Acceptance target: failed events auto-recovered ≥ 99%; daily snapshot deltas ≤ 0.1%.

9) Mobile execution & scanning

Symptom: staff bypass handhelds; data lags the floor.
Must do: fast, offline-tolerant scanning for receive/put/transfer/pick; operator prompts, label/lot capture.
Acceptance target: scan compliance ≥ 95% of movements; pick errors ≤ 0.3%.

10) Governance & access control

Symptom: anyone can change anything; no one knows who did.
Must do: role-based access, maker-checker on sensitive changes, immutable logs.
Acceptance target: 100% changes attributed; critical updates dual-approved.

How to evaluate stock control systems: a 7-step framework

This is the decision engine of the guide. Work through each step in order; capture evidence; score candidates; short-list to 2–3 for a pilot.

Step 1) Map processes & data model (truth first)

Objective: Make the system fit your operation, not the other way around.
Do this:

• Diagram receive → put-away → allocate → pick/pack/ship → transfers → returns (by site).

• List inventory states (available, reserved, damaged, quarantine) and units (EA, case, pallet).

• Define location hierarchy (site → zone → aisle → bin) and ownership.
  Ask: Can the system mirror our hierarchy and states without custom code?
  Accept: No “workarounds” for core objects; supports your states & bin granularity out of the box.
  Red flags: “We flatten locations,” “We don’t track reservations,” “Adjustments only at site level.”
  Artifact: 1-page process map + data dictionary (SKUs, states, bins, UOM).

Step 2) Prioritize integrations (ERP / POS / e-commerce / shipping)

Objective: Keep inventory truth synchronized across systems.
Do this:

• Require these events: item master, on-hand deltas (site/bin), receipts/ASNs, picks/shipments, transfers (req/ship/recv), returns/RMA.

• Define system of record per object (e.g., stock control for on-hand; ERP for financials).

• Check retry policies, error queues, and daily reconciliations.
  Ask: What’s P95 event latency? How are failed messages retried and audited?
  Accept: P95 ≤ 60s; error queue + auto-retry; daily snapshot diff ≤ 0.1%.
  Red flags: “Batch once nightly,” “No error queue,” “Manual CSVs for transfers.”
  Artifact: Interface spec (events, payloads, SOR, retry rules).

Step 3) Validate multi-warehouse depth (allocation, transfers, ATP)

Objective: Balance stock and promise accurately across locations.
Do this:

• Test rules: ATP by location, reservations/holds, backorders/partials, substitution.

• Create a transfer: request → ship → receive with audit trail and variance handling.

• Simulate regional surge; verify auto-replenishment and reallocation.
  Ask: Can we codify per-site policies (ROP, safety stock, lead time)?
  Accept: Transfer SLA meets targets (24h request→ship; 72h ship→receive), variance ≤ 0.5%.
  Red flags: Allocation only at “global” level; no transfer workflow; no audit trail.
  Artifact: Allocation/transfer test script + results.

Step 4) Prove traceability & audits (lot/serial, FIFO/FEFO, cycle counts)

Objective: Track where every unit came from and where it went.
Do this:

• Receive lot/serial items, enforce FIFO/FEFO, quarantine & release.

• Run a cycle count by ABC class; drive a variance investigation → resolution.
  Ask: Can we trace any sale back to receipt within 2 minutes?
  Accept: 100% trace; cycle-count compliance ≥ 95%; variance reduced ≥ 30% on pilot SKUs.
  Red flags: Lots tracked at document (not unit) level; no FEFO; counts overwrite history.
  Artifact: Traceability report + cycle-count SOP.

Step 5) Score usability & mobile scanning (make it stick on the floor)

Objective: High scan compliance = high data quality.
Do this:

• Handheld flows for receive/put/transfer/pick; test label printing, prompts, offline tolerance.

• Observe 3–5 users; time each path; count taps & errors.
  Ask: Can a new operator complete a guided pick with zero training?
  Accept: Scan compliance ≥ 95% of movements; pick errors ≤ 0.3%; offline cache/resync available.
  Red flags: Desktop-first UI; no offline; label/lot capture bolted on.
  Artifact: Usability scorecard (time/taps/errors per task).

Step 6) Assess scalability & support (operate at size)

Objective: Ensure it won’t crack under growth or outages.
Do this:

• Review uptime SLA, RPO/RTO, peak throughput, sandbox load test plan.

• des écarts role-based access, maker-checker, immutable logs.

• Validate support: hours, first-response time, escalation path, named CSM.
  Ask: What happened in your last major incident? How was inventory protected?
  Accept: 99.9%+ uptime; auditable changes; support SLA ≤ 1h critical, 4h high.
  Red flags: “Best effort” support; shared logins; no audit export.
  Artifact: Ops & security checklist + signed SLA summary.

Step 7) Model TCO/ROI and run a 30–60-day pilot (prove it)

Objective: Buy on numbers, not hope.
Do this:

• Build TCO (licenses + implementation + hardware + training + integration + ongoing).

• Project benefits: stockout reduction, labor savings, carrying-cost reduction, shrink control.

• Execute a pilot (2 sites, ~500 SKUs, 5 scanners).

Pilot acceptance:

• Sync latency P95 ≤ 60s; delta mismatches ≤ 0.2%

• Scan compliance ≥ 95%; transfer SLA hit ≥ 90%

• Variance reduction ≥ 30%; stockout rate trending down

ROI formulas:

• Inventory Accuracy % = 1 − (|adjustments| ÷ total recorded units)

• Stockout Rate = stockout lines ÷ total lines

• Carrying Cost % = (capital + storage + insurance + obsolescence) ÷ avg inventory value

• ROI = (Annual Benefit − Annual Cost) ÷ Annual Cost

• Payback (months) = Implementation Cost ÷ Monthly Benefit
  Red flags: Vendor resists pilot metrics; “PO first, then we’ll configure.”

Scoring & short-list (use this after Steps 1–7)

• Weights: Integrations 25 | Multi-warehouse 25 | Usability/Mobile 15 | Scalability/Support 15 | TCO/ROI 20

• Method: Rate each vendor 1–5 per criterion → multiply by weight → sum /100.

• Deal-breakers (Yes/No): lot/serial, FEFO, transfer audit trail, error queue. Any “No” = exclude.

RFP prompts (copy/paste into your questionnaire)

• “Describe your event model (items, on-hand, receipts, picks, transfers, returns) and P95 latency.”

• “Show a transfer from request to receive with variance handling and audit trail.”

• “Provide offline scanning demo and resync behavior after 30 minutes offline.”

• “Share last 12-month uptime and major incident report; include recovery steps.”

• “Commit to pilot acceptance criteria above and provide the test plan.”

Non-negotiable features for multi-warehouse control (and why they matter)

Principle: a feature is “must-have” only if it prevents stock drift, speeds fulfillment, or proves ROI in your pilot. Use the checklists and acceptance targets to keep demos honest.

1) Centralized, real-time inventory (site/bin)

Why it matters: eliminates “which number is true?” debates; powers accurate promises and transfers.
Verify: live update of on-hand/available after a scan in another site; view by site→zone→bin.
Accept: P95 sync latency ≤ 60s; accuracy ≥ 99.5% at site/bin.

2) ATP by location + reservations/allocations

Why it matters: promises you can keep per region/channel; reduces split shipments.
Verify: create an order; system shows ATP per site, holds stock, honors backorders/partials.
Accept: OTIF +5–10 pts; split-ship rate down 10–20% in pilot lanes.

3) Inter-warehouse transfer workflow with audit trail

Why it matters: rebalances stock fast without spreadsheet chaos.
Verify: request → pick/ship → receive, with variance capture and user/time stamps.
Accept: request→ship ≤ 24h; ship→receive ≤ 72h; transfer variance ≤ 0.5%.

4) Per-location policies (ROP, safety stock, lead times, service levels)

Why it matters: one global ROP guarantees over/under-stocks.
Verify: set different ROP/SS by site; simulate seasonality; see auto-replenishment suggestions.
Accept: stockout rate trending −15–30% post-go-live window.

5) Traceability: lot/serial + FIFO/FEFO + quarantine

Why it matters: compliance, recalls, expiry control.
Verify: receive lots, enforce FIFO/FEFO on pick, quarantine/release flow; trace sale → receipt.
Accept: 100% trace in ≤ 2 min per SKU/lot.

6) Cycle counts & variance management (ABC)

Why it matters: sustained accuracy without stopping operations.
Verify: schedule ABC counts, perform spot check, open variance case → resolution → learning.
Accept: cycle-count compliance ≥ 95%; variance −30% on pilot SKUs.

7) Barcode/RFID + mobile scanning (offline-tolerant)

Why it matters: high scan compliance = high data quality.
Verify: guided flows for receive/put/transfer/pick, label/lot capture, offline cache & resync.
Accept: scan compliance ≥ 95% of movements; pick errors ≤ 0.3%.

8) Returns & reverse logistics (RMA → disposition)

Why it matters: bad returns logic silently corrupts inventory.
Verify: log RMA reason, inspect, set disposition (restock/scrap/refurb), value recovery report.
Accept: disposition posted ≤ 24h; mis-restocks ≤ 0.2%.

9) Location & bin hierarchy with ownership and controls

Why it matters: bin-level accuracy drives fast picks and clean audits.
Verify: create/lock bins, move stock with scan validation, permissions by role/site.
Accept: 100% change attribution; unauthorized moves blocked.

10) Integration-ready event model (preview for next section)

Why it matters: if data can’t flow, truth decays.
Verify: events exist for item master, on-hand deltas, receipts/ASNs, picks/shipments, transfers, returns; error queue + retries + daily reconciliation.
Accept: failed events auto-recovered ≥ 99%; daily snapshot delta ≤ 0.1%.

11) Analytics & alerts (per location)

Why it matters: turns data into action before service levels slip.
Verify: dashboards for fill rate, stockouts, aging, transfer SLAs; threshold-based alerts.
Accept: alert-to-action within 15 min for critical thresholds.

12) Security, roles, and immutable audit logs

Why it matters: prevents silent changes that create drift.
Verify: role-based access, maker-checker for sensitive updates, exportable logs.
Accept: dual-approval on critical changes; 100% actions attributable.

Compare these key features of multi-site inventory control against your current stack to spot the biggest gaps.

Integration deep-dive: ERP / POS / e-commerce events & reconciliation

keep a single, trustworthy inventory truth while orders, receipts, transfers, and returns fly across systems. The stock control system is the system of record (SOR) for inventory; everything else must publish/consume events without corrupting counts. If you’re evaluating event latency and error handling, here’s how RFID inventory tracking in warehouses works in real time.

What “good” looks like (objectives)

• Low-latency sync: updates visible across sites/channels in ≤ 60s P95

• Deterministic truth: every movement traceable; no “mystery deltas”

• Resilient pipes: retries, dead-letter queues, replay — no silent failures

• Daily reconciliation: automated snapshot compare; exceptions resolved same day

Core event model (must-have topics)

Publish/consume these atomic events. Each must carry idempotency keys and timestamps.

1. Item master — SKU, UOM, lot/serial flags, attributes

2. On-hand delta (site/bin) — quantity change, reason code, reference (doc/id)

3. Receipt / ASN — expected vs received, variance, lot/serial, expiry (if any)

4. Pick / Shipment — decrements, carrier/tracking, backorder/partial flags

5. Transfer — request, ship, receive, variance, who/when per step

6. Adjustment — cycle-count, damage, quarantine, write-off; approver

7. Return / RMA — reason, inspection, disposition (restock/scrap/refurb)

8. Reservation / Allocation — create/extend/release holds (ATP by location)

Minimum payload fields (all events):
event_id (UUID), event_type, occurred_at (UTC), site_id, bin_id, sku, uom, qty, lot/serial (nullable), reference_doc, actor (system/user), idempotency_key.

System-of-record map (who owns what)

Rule of thumb: Stock control publishes inventory truth; WMS publishes execution; ERP publishes commercial docs.

Latency, ordering, and scale

• Latency targets: P95 ≤ 60s; P99 ≤ 120s end-to-end

• Ordering: per-SKU/per-site sequence guarantees (message sequence or vector clock)

• Idempotency: dedupe by idempotency_key (replays must not double-count)

• Throughput: size for peak (e.g., sale events); queue depth alarms at 70% capacity

Failure handling (no silent errors)

• Error queue + auto-retry: exponential backoff, jitter, max 10 attempts

• Dead-letter queue: human triage in < 15 min; explain root cause per event

• Alerting: pager/email when retries exhausted or backlog > threshold

• Replay: reprocess by event_id range or time window without data loss

Reconciliation (prove numbers daily)

• Daily snapshots: Stock control publishes absolute on-hand by site/bin/SKU at T+0 UTC

• Compare: Downstream systems reconcile; mismatches > 0.1% flagged

• Drill-down: auto-generate variance report (offending events & timestamps)

• SLA: close recon exceptions same business day

Common failure modes & how to test them

• Partial shipments / backorders: orders split across sites — ensure reservations follow reality and release correctly.

• Transfer stuck mid-flow: ship event arrived; receive missing — variance and aged-in-transit report must surface it.

• Offline scanning: handhelds cache moves → resync; verify no double decrements on retry.

• Clock drift: enforce UTC and server time sync; reject events older/newer than window (e.g., ±10 min).

• Bulk adjustments: cycle-count posts large deltas — require maker-checker approval and audit comment.

Security & governance

• Auth: token-based, short-lived; per-integration keys

• RBAC: restrict who can post adjustments et transfer state changes

• Audit: immutable logs: who/what/when/where; exportable to SIEM

• PII: keep order/customer data minimal in inventory events

Pilot acceptance checklist (integration)

• P95 event latency ≤ 60s across order→reservation→pick/ship→on-hand

• Zero silent failures (all errors land in queue with alerts)

• Daily snapshot delta ≤ 0.1% per site/bin/SKU

• Replay 1,000 events without duplication (idempotency proven)

• Transfer lifecycle fully auditable (req→ship→receive) with variance capture

• 1
  Push or pull? Prefer push/webhooks or streaming events; allow fallback pulls for recovery.
• 2
  Deltas or snapshots? Use deltas for speed; run daily snapshots for reconciliation.
• 3
  Where to compute ATP? In Stock Control (SOR), time-phased with expected receipts.

Manual vs. automated stock control: when automation pays for itself

“Manual” means spreadsheets, ad-hoc counts, and tribal knowledge. “Automated” starts with a stock control system that enforces rules, barcode/RFID scanning, and event-based integrations—optionally adding automation hardware later (covered in the next section). The question isn’t if to automate; it’s when it returns cash.

Quick comparison (pilot-ready)

Pilot acceptance targets: accuracy ≥ 99.0%; pick errors ≤ 0.3%; P95 latency ≤ 60s; cycle-count compliance ≥ 95%; transfer variance ≤ 0.5%.

When “manual” is still acceptable (for now)

Use as a temporary state if all apply:

• One site or ≤2 sites; ≤1,000 SKUs; ≤200 order lines/day

• No regulated traceability (lots/serials rarely required)

• Variance < 0.5% of units/month; returns ratio < 5%
  Even then, minimum: barcode labels + handheld scanning for receipts and picks.

Clear upgrade triggers (move to automated)

If any one of these is true, automation pays:

• ≥3 warehouses or >2,000 SKUs

• Order lines/day >500 or multi-channel (POS + e-com/marketplaces)

• Required lot/serial traceability or FEFO

• Stockout rate >4% of order lines or split-ship rate >20%

• Variance adjustments >0.5% of units/month

• Transfer SLA misses >10% or no audit trail

• Teams spend >8 hrs/week reconciling spreadsheets

ROI snapshot (simple math you can run)

• Stockout reduction: (baseline stockout lines − pilot stockout lines) × avg margin/line

• Labor savings: (baseline hours − pilot hours) × fully-loaded rate

• Carrying cost reduction: Δ avg inventory value × carrying cost %

• Shrink reduction: Δ write-offs × cost
  ROI = (Annual Benefit − Annual Cost) ÷ Annual Cost
  Payback (months) = Implementation Cost ÷ Monthly Benefit

Typical pilot outcomes: accuracy +3–8 pts, stockouts −15–30%, carrying cost −5–15%, pick time −10–25%.

Pilot test plan: manual vs. automated (A/B on your floor)

Scope 2 sites / ~500 SKUs / 4–5 users for 30–60 days:

1. Receive & put-away with labels + scans → measure latency & errors

2. Pick/pack/ship with guided flows → measure pick time & mis-picks

3. Transferts (req→ship→receive) → measure SLA & variance

4. Cycle counts (ABC) → measure compliance & variance reduction

5. Returns → measure time to disposition & mis-restocks

Pass if: P95 latency ≤ 60s; scan compliance ≥ 95%; transfer SLA hit ≥ 90%; variance −30%+ on pilot SKUs.

Avoid these pitfalls when automating

• Automating bad process: standardize bin/location & labels before go-live.

• Skipping mobile UX: if handhelds are slow, staff will bypass them.

• Ignoring reconciliation: require daily snapshot compare; no silent deltas.

• Under-weighting integrations: events for items/on-hand/receipts/picks/transfers/returns or don’t proceed.

Manual is fine for small, single-site ops. The moment you’re multi-warehouse—or you need traceability, SLAs, or channel orchestration—automation returns cash. Use the pilot to prove it in your numbers. Not sure whether to use RFID or barcodes for floor execution? Start with this side-by-side view.

Do you need ASRS/AMR now, or later? (decision tree)

• ASRS = automated storage & retrieval (vertical lifts, buffers, carousels) to densify space and accelerate picking.

• AMR = autonomous mobile robots that move totes/cases to reduce travel and labor.

Default stance: start with stock control + barcode/RFID + solid integrations. Consider hardware only if the triggers below fire after your software pilot.

Decision tree

• Run the software pilot (Section 7).

  • If pilot hits targets (accuracy ≥99.0%, pick errors ≤0.3%, P95 ≤60s) et fulfillment SLAs are met → Stay software-only (for now).

  • If pilot misses throughput/space/labor targets despite good data quality → go to step 2.

• Check hard triggers (any ONE is enough):

  • Throughput: sustained pick lines/site/day > 3k–5k or aisle congestion stalling flow

  • Space: storage utilization > 85% with SKU growth forecasted; expansion is costly/unavailable

  • Labor: persistent shortage/overtime; travel time dominates pick labor even after slotting

  • Quality: mis-picks > 0.3% or damage persists despite scanning & training

  • Cycle time: order-to-ship SLA misses > 5% after software pilot

  • Traceability/security: high-value/regulated SKUs require controlled access & airtight trace

  → If none fire: Later (reassess in 6–12 months).
  → If any fire: proceed to step 3.

• Apply economic gates (both preferred, at least one required):

  • Payback ≤ 24–30 months, or

  • Labor offset ≥ 1.5 FTE per shift (sustained), or

  • Space deferral value (avoids a $X expansion) ≥ 30–50% of project CAPEX

  → If gates pass: Evaluate hardware now (pilot).
  → If gates fail: Later (optimize software/slotting first).

Before committing to hardware, scan the top warehouse technologies shaping distribution in 2025.

What to prove in a hardware pilot (30–60 days, one zone/site)

Throughput & accuracy

• Pick lines/hour +20–40% uplift vs. baseline

• Mis-picks ≤ 0.2%; damage ↓ vs. baseline

Space & flow

• Floor space reclaimed 50–80% for vertical systems (where applicable)

• Aisle congestion eliminated in test area; travel time per pick −30–50%

Reliability & ops

• Unplanned downtime < 1%; mean time to recover < 15 min

• Training time to proficiency ≤ 2 hours for operators

Integration stability

• All moves emit events to stock control (receive/pick/put/transfer)

• No silent failures: error queue + auto-retry + daily reconciliation passes

• Safety interlocks & emergency stops documented and tested

Go/No-go: pass ≥ 4/5 categories above → proceed to business case.

A Track  vs. Track B (what to do next)

Track A — Stay software-only (for now)
Focus next 6 months on:

• Slotting optimization (A-items near pack; velocity-based binning)

• Standardize labels & bin hierarchy; enforce scan compliance ≥95%

• ABC cycle-count program; variance −30%+ on pilot SKUs

• Route & batch picks to cut travel; tune ROP/safety stock per site

• Remove floor bottlenecks (one-way aisles, staging rules)

Track B — Evaluate hardware now
Run a structured evaluation:

• 30-day time-motion + congestion study; SKU size/velocity profiling

• Layout mock-up (footprint, clear heights, fire/safety)

• CAPEX/OPEX model (energy, maintenance, spares, service SLAs)

• Modularity & growth plan (add bays/modules without rework)

• Integration spec with stock control events (idempotency, replay)

• Risk plan: failover to manual, power outage procedures, weekly tests

How to calculate ROI (formulas, targets, worked example)

Goal: buy on numbers, not hope. Use these inputs during the 30–60-day pilot, then annualize.

Collect these inputs (pilot → annual)

Volumes & value

• Orders or order lines/year

• Avg margin per fulfilled line (not revenue)

• Avg inventory value (pre-pilot)

Baseline vs pilot metrics

• Stockout rate (stockout lines ÷ total lines)

• Pick time/line (or labor hours per 1k lines)

• Carrying cost % (capital + storage + insurance + obsolescence)

• Write-offs/shrink ($/yr)

• Variance rate et cycle-count compliance

• Transfer SLA hit rate and variance

Costs

• One-time: implementation, integration, labeling, training, data migration

• Recurring: licenses/subscription, support, hosting, handhelds/labels

Tip: keep baselines frozen before pilot; don’t double-count wins (e.g., don’t count the same hour in both “labor” and “shrink”).

Use these hidden inventory cost levers to quantify carrying cost, shrink, and process waste in your model.

Formulas

Operational KPIs — Formulas

Benefits (annualized)

Economics

Target bands (typical pilot outcomes)

• Accuracy +3–8 pts

• Stockouts −15–30%

• Carrying cost −5–15%

• Pick time −10–25%

Worked example (software + scanning pilot, 2 sites)

Assumptions

• Lines/year: 200,000

• Margin/line: $18

• Stockout rate: 6.0% → 4.2% (−1.8 pts)

• Pick time/line: 1.20 min → 0.96 min (−20%)

• Avg inventory value: $3.50M → $3.22M (−8%)

• Carrying cost %: 20%

• Write-offs: $90k → $72k (−20%)

• Labor rate (fully loaded): $28/hr

• Annual recurring cost: $90k

• Implementation (one-time): $85k

Calculations

• Stockout Reduction:

  • Baseline lines short = 0.06 × 200,000 = 12,000

  • Pilot lines short = 0.042 × 200,000 = 8,400

  • Saved lines = 3,600 × $18 = $64,800

• Labor Savings:

  • Time saved/line = 0.24 min = 0.004 hr

  • Hours saved = 200,000 × 0.004 = 800 hr × $28 = $22,400

• Carrying Cost Savings:

  • Δ Inventory = $3.50M − $3.22M = $280,000

  • Savings = 20% × 280,000 = $56,000

• Shrink Reduction: $18,000

Annual Benefit = 64,800 + 22,400 + 56,000 + 18,000 = $161,200
ROI % = (161,200 − 90,000) ÷ 90,000 = 79.1%
Payback (months) = 85,000 ÷ (161,200/12) ≈ 6.3 months

Interpretation: even without counting optional savings (e.g., fewer split shipments), this pilot supports go-live with strong ROI and sub-year payback.

Break-even “what needs to move?” (fast sensitivity)

• To cover $90k annual cost from stockouts alone:
  Break-even saved lines = 90,000 ÷ $18 = 5,000 lines/yr
  → On 200k lines, that’s 2.5 pts absolute stockout reduction.

• Or via labor only:
  90,000 ÷ $28 ≈ 3,214 hours/yr
  → At 200k lines, that’s 0.016 hr (0.96 min) saved per line.

Use whichever lever is most realistic in your operation—or a mix.

CFO checklist (what to include in costs)

• Licenses/subscription, support, hosting

• Implementation & integration (internal + external)

• Handhelds/labels/printers; device MDM

• Training/time-to-proficiency; change management

• Data migration & cleanup

• Contingency 10–15% for unknowns

Pitfalls to avoid

• Counting revenue, not margin in the stockout calc

• Double-counting the same hour across categories

• Skipping seasonality normalization when annualizing pilot gains

• Ignoring recurring internal support cost (admin time)

• Using list prices for devices instead of total lifecycle cost

 

Bottom line: measure four levers (stockouts, labor, carrying cost, shrink), annualize, and compare to real costs. If ROI is strong and payback is sub-year, you have numbers-backed confidence to proceed. To reduce revenue leakage, pair this with our stockout reduction playbook.

Common mistakes when choosing a stock control system (and how to avoid them)

Principle: if a choice increases inventory drift, slows fulfillment, or can’t be proven in a pilot—don’t ship it.

1) Starting with features, not your process & data model

• Why it hurts: you bend ops around software; drift returns in weeks.

• Fix check: map receive→put→pick/ship→transfers→returns; define states (available/reserved/damaged/quarantine) and site→zone→bin.

• RFP / proof: “Show our process & states modeled with no custom code.”

• Red flag: “We flatten locations; reservations aren’t tracked.”

2) Treating multi-warehouse like single-site

• Why it hurts: overstock here, stockouts there; chaos in rebalancing.

• Fix check: require ATP by location, allocation rules, and transfer workflow + audit trail.

• Proof: create order, hold stock, auto-create transfer; show variance capture.

• Red flag: “Allocation is global only.”

3) Underweighting integrations (batch sync, no error handling)

• Why it hurts: silent failures corrupt counts.

• Fix check: events for item/on-hand/receipts/picks/transfers/returns; P95 ≤ 60s; error queue + retries + daily snapshots.

• Proof: break the pipe in a demo; watch retries and reconciliation.

• Red flag: “We import CSV nightly.”

4) Skipping a real pilot (accepting a pretty demo)

• Why it hurts: you buy hope, not outcomes.

• Fix check: 30–60 day pilot across 2 sites, ~500 SKUs, with pass/fail KPIs.

• Proof targets: accuracy ≥ 99.0%; pick errors ≤ 0.3%; scan compliance ≥ 95%; transfer SLA hit ≥ 90%.

• Red flag: “PO first, then we configure.”

5) No clear system-of-record (SOR)

• Why it hurts: dueling truths across ERP/WMS/stock control.

• Fix check: stock control = inventory truth & ATP; WMS = execution; ERP = commercial/financial.

• Proof: ask for SOR matrix signed off by vendor.

• Red flag: “ERP is the inventory SOR—but updates are batched.”

6) Ignoring traceability/expiry early

• Why it hurts: recalls and FEFO fail when you need them most.

• Fix check: lot/serial at unit level; FIFO/FEFO; quarantine → release; 2-min trace.

• Proof: trace any sale back to receipt live.

• Red flag: lots tracked only on documents.

7) Neglecting mobile UX & scanning

• Why it hurts: staff bypass handhelds; data lags reality.

• Fix check: guided receive/put/transfer/pick; label/lot capture; offline cache & resync.

• Proof: operate offline 30 mins, resync without double-decrements.

• Red flag: desktop-first flows; no offline.

8) Underestimating change management

• Why it hurts: great software, poor adoption.

• Fix check: SOPs for labels/bins/counts; time-to-proficiency ≤ 2 hours for handheld tasks.

• Proof: 3 new users complete guided pick with zero training.

• Red flag: “We’ll train later; UI is ‘intuitive’.”

9) Weak governance & audit

• Why it hurts: anyone can “fix” numbers; no accountability.

• Fix check: RBAC, maker-checker on adjustments, immutable logs.

• Proof: export audit of last 24h changes; show dual-approval.

• Red flag: shared logins; no log export.

10) Buying hardware (ASRS/AMR) too early

• Why it hurts: capex without data discipline.

• Fix check: do software + scanning first; apply Section 8 triggers and payback gates.

• Proof: show throughput still constrained after pilot before green-lighting hardware.

• Red flag: hardware pitched to mask process issues.

11) Fuzzy ROI/TCO math

• Why it hurts: surprises kill projects in month 3.

• Fix check: use margin/line (not revenue), include all recurring costs, add 10–15% contingency.

• Proof: compute ROI & payback months from pilot deltas (Section 9).

• Red flag: savings promised without formulas.

12) Accepting vendor lock-in

• Why it hurts: can’t leave; can’t integrate; innovation stalls.

• Fix check: open APIs, event exports, data ownership, no proprietary dead-ends.

• Proof: export all inventory objects; replay events into a sandbox.

• Red flag: “Data export is a paid PS engagement.”

13) Forgetting reverse logistics

• Why it hurts: returns corrupt counts; value leaks.

• Fix check: RMA reasons, inspection, disposition (restock/scrap/refurb), mis-restocks ≤ 0.2%.

• Proof: process 3 return scenarios live.

14) No cycle-count program & variance workflow

• Why it hurts: accuracy decays post go-live.

• Fix check: ABC cadence, spot checks, investigate→adjust→learn loop.

• Proof: run a cycle count and variance case end-to-end.

15) Ignoring resilience (offline, outages, clocks)

• Why it hurts: rare failures skew inventory for days.

• Fix check: offline scanning, UTC across systems, event replay without duplication.

• Proof: kill a service in demo; replay events; verify no double-counts.

If your records drift, review the costs, causes, and solutions for inventory discrepancies.

5-minute demo gauntlet (use verbatim)

1. Create order → reserve by site → show ATP change in ≤ 60s elsewhere.

2. Raise transfer (req→ship→receive) and capture a variance.

3. Receive a lot item; pick via FEFO; trace sale → receipt in ≤ 2 min.

4. Go offline on a handheld; do a pick; come back online; show no double-decrement.

5. Break an integration call; show error queue, retry, and daily reconciliation.

How inventory records become inaccurate (and what to change).

30–60 day pilot plan: what to prove before you commit

Purpose: validate that the solution delivers accurate, real-time multi-warehouse control on your floor with measurable ROI—before a full rollout. Set up non-disruptive cycle counting during the pilot to track variance reduction by SKU class.

Scope (keep it small, real, and representative)

• Warehouses: 2 sites (e.g., East DC + West DC)

• SKUs: ~500 (mix of A/B/C; include at least 50 lot/serial and 30 expirable)

• Users: 4–6 floor operators + 1 supervisor per site

• Flows covered: receive/put-away, picks (single, multi, batch), inter-warehouse transfers, cycle counts, returns/RMA

• Systems in play: stock control (SOR), WMS (if separate), ERP, POS/e-com, shipping

Success criteria (must-pass KPIs)

• Sync latency: P95 ≤ 60s end-to-end (order/reservation/ship/on-hand)

• Inventory integrity: daily snapshot delta ≤ 0.1% per site/bin/SKU

• Scan compliance: ≥ 95% of movements scanned

• Accuracy / variance: inventory accuracy ≥ 99.0%; variance −30%+ on pilot SKUs

• Transfers: request→ship ≤ 24h; ship→receive ≤ 72h; variance ≤ 0.5%; SLA hit ≥ 90%

• Pick quality: pick errors ≤ 0.3%; pick time/line −10–25% vs baseline

• Resilience: zero silent failures; error queue + auto-retry + replay proven

Go/No-Go rule: pass ≥ 6 of 7 KPIs above (including latency et inventory integrity).

Roles & cadence (light but disciplined)

• Pilot Lead (you): scope, KPIs, decisions, daily unblocker

• Floor Champion (per site): training, compliance, feedback loop

• IT Integration Owner: events, queues, monitoring, replay drills

• Finance Analyst: ROI model, payback calculation

• Vendor SE (if used): config support, logs, fixes

Rituals:

• Daily 15-min standup (ops + IT + vendor)

• Weekly 30-min steering (Pilot Lead + finance + leadership)

• Slack/Teams channel for logs, exceptions, screenshots

Week-by-week plan (30–60 days)

Week 0 — Prep (no floor moves yet)

• Freeze baseline metrics (stockouts, pick time, variance, carrying cost %)

• Lock bin/location hierarchy, label standards, and ROP/safety stock per site

• Configure integrations (events + error queues + daily snapshots)

• Load item master; enable handhelds; smoke test API/webhooks

Week 1 — Shadow mode (no inventory impact)

• Mirror actual flows with test SKUs; verify P95 ≤60s and event ordering

• Dry-run: ASN receipt (with lot/expiry), FEFO pick, transfer, return (all events logged)

• Drill offline scanning + replay; confirm no double-counts

Weeks 2–3 — Limited live scope

• Go live on subset of SKUs/aisles; enforce scanning; start ABC cycle counts

• Execute three transfer scenarios (balancing, urgent, cross-regional)

• Start daily reconciliation; triage any deltas same day

Weeks 4–6 — Scale & stress (extend to 60 days if needed)

• Expand to full pilot SKU set; introduce batch/wave picks

• Run peak-hour stress (promo or simulated surge)

• Full returns workflow with disposition reporting

• Final KPI snapshot; build ROI model and payback

Test scripts (copy/paste for your runbook)

A. Receiving & put-away

1. Post ASN with 3 SKUs (one lot/expiry) → scan receive → directed put to bin

2. Verify on-hand and ATP by location updated ≤60s; audit trail complete

B. Picking & shipping

1. Single-line pick, then multi-line, then batch/wave

2. Capture mis-picks; confirm label/lot capture; measure pick time/line

C. Inter-warehouse transfers

1. Raise transfer from West→East (balancing)

2. Request→pick/ship→receive; capture variance; SLA timers; audit stamps

D. Cycle counts & variance

1. Schedule A/B/C counts; perform one spot check

2. Open a variance case; investigate → adjust → record root cause

E. Returns & disposition

1. Create RMA (3 reasons); inspect; set disposition (restock/scrap/refurb)

2. Ensure mis-restocks ≤0.2% and on-hand reconciles

F. Failure modes (resilience)

• Kill an integration call → see error queue, auto-retry, alert, and success

• Go offline 30 min with handheld → do a pick → resync without duplicate decrement

• Send out-of-order events → confirm idempotency and correct final state

Data to capture (auto + manual spot checks)

• Auto dashboards: latency P95/P99, scan compliance, pick errors, transfer SLA, on-hand deltas, error queue counts

• Manual samples (daily): 10 random SKUs per site — bin check vs system; 2 random lots — trace sale→receipt time

• Exception log: all variances, retries, replays, and their resolution time

Reporting templates (quick structure)

Daily Pilot Log (one sheet tab/site)

• Date • Orders • Lines • P95 latency • Snapshot delta % • Scan compliance % • Pick error % • Transfer SLA % • Variances (#/$) • Exceptions (IDs) • Notes

Weekly Steering Digest (1-pager)

• KPI trend mini-charts • Top 3 risks • Top 3 wins • Actions/owners/dates

Final Pilot Report

• KPI table (baseline vs pilot) • Screenshots/logs • ROI & payback • Gaps & mitigations • Go/No-Go recommendation

Acceptance table (paste into your doc)