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.

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.

• Check 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.

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.

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 and 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. Transfers (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.

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) and 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).

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 and 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”).

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.

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.

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.

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.

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 and 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)