Operational analytics that doesn't depend on a BI ticket queue
Ops sits on more data than any other team and gets the slowest analytics support. Connect your ERP, WMS, and shipping data — ask AnalityQa AI in plain English and get the answer before the next standup.
Where the day goes wrong
Data lives in 5 systems and the BI team has a 6-week backlog
ERP, WMS, TMS, shipping carrier data, on-time-delivery feeds. Each is a separate export. Joining them needs the BI team, who has 30 other requests ahead of yours. Operational decisions wait.
Warehouse and fleet KPIs are scattered across reports nobody reconciles
Picks per hour, dock-to-stock time, on-time-in-full rate, average fleet utilisation. Each warehouse manager builds their own version. Comparing across sites requires manually normalising — which never happens consistently.
Demand forecasting is either non-existent or owned by one person in Excel
The inventory team knows demand swings — but the forecast is a manually maintained spreadsheet that breaks every time a SKU is added or a supplier changes lead time. Ops decisions get made on last week's actuals, not a model.
Quality and incident analysis is reactive instead of pattern-driven
When a customer complains, you investigate. But the patterns across complaints — which carrier, which warehouse, which product family — never get aggregated, so the same issue recurs because nobody has time to find the structural cause.
What you actually ask AnalityQa
Plain English in. Charts, tables, and live dashboards out.
What's our on-time-in-full rate by warehouse for the past 90 days, and which SKUs are dragging the number down?
→ OTIF by warehouse with a ranked list of SKUs causing the misses.
Forecast demand for our top 50 SKUs over the next 60 days, accounting for seasonality.
→ Forecast table with confidence bands and trigger thresholds for reorder.
Why did warehouse 3 throughput drop 14% last week?
→ Investigation report — staffing, peak-time mix, equipment downtime, SKU-mix shift.
Compare carrier performance for ground vs expedited shipments by region for Q1.
→ Side-by-side performance dashboard: cost, transit time, claim rate by carrier.
Build me a daily ops dashboard: inbound, outbound, OTIF, returns, and SLA breaches by site.
→ Live dashboard URL — refreshes hourly, shareable with site leads without a license.
Which products have inventory turnover under 3x — that's tying up cash we shouldn't have tied up?
→ Ranked SKU table with carrying cost estimate and recommended action.
How AnalityQa fits your workflow
Five capabilities — every persona uses all of them, in their own way.
Chat with your data
Ask operational questions in plain English. AnalityQa AI translates them into queries against your ERP, WMS, TMS, or shipping data — no SQL skill required, no BI ticket needed.
Auto data prep
Operational data is messy: inconsistent SKU formats, rotated date columns, duplicate shipment records when carriers retransmit. AnalityQa AI's data prep step flags and proposes fixes before the analysis runs.
Live shareable dashboards
Daily site-level dashboards (inbound, outbound, OTIF, exceptions) refreshed hourly. Share each warehouse's dashboard with that warehouse's manager. Site-level URLs, no per-seat license.
Investigation mode
When throughput drops or an SLA misses, ask why. AnalityQa AI runs a structured investigation across staffing, mix, equipment, and incident data — returns a ranked list of contributors with quantified impact.
Data-aware analyst agents
Ops-aware analyst agents proactively monitor your KPI thresholds (e.g. inventory turnover, OTIF, dwell time) and flag breaches before the daily standup. The team walks in with the issues already identified instead of having to dig for them.
What changes
Use cases relevant to your role
Operations
Supply Chain Analytics Without the Spreadsheet Chaos
Operations
Warehouse Efficiency: From WMS Data to Actionable Metrics
Operations
Fleet Analytics: Stop Guessing Why Delivery Costs Are Rising
Operations
Quality Metrics: Find the 20 % of Defects Causing 80 % of the Cost
E-commerce
Stop Guessing on Inventory: Know What to Reorder and When
Operations
Your operations KPI dashboard, built by asking questions
Frequently asked questions
Can AnalityQa AI connect to our ERP and WMS?+
If they expose a Postgres-compatible endpoint or an export, yes. Most modern ERPs (NetSuite, Oracle Cloud, SAP) and WMS systems do. CSV exports work alongside live connections — many ops teams start with weekly CSV pulls and graduate to a direct database connection.
How does it handle SKU-level data with thousands of items?+
AnalityQa AI runs against your data warehouse or database directly, so SKU counts in the tens of thousands are not a problem. The bottleneck is usually the underlying source's query performance, not AnalityQa AI's. For very large catalogues, materialised views speed up the per-SKU dashboards.
Can each warehouse have its own dashboard with role-restricted access?+
Yes. Create a dashboard scoped to one warehouse's data and share the public URL with that site's manager. They see only their site's metrics. The HQ-level dashboard is a separate URL with the cross-site comparison.
Does it handle the inevitable data-quality issues — missing scans, retransmitted shipments, mis-mapped SKUs?+
Yes — that's what the data prep layer is for. Each upload or refresh runs through a quality scan: duplicates, missing values, outliers, mismatched IDs. AnalityQa AI proposes fixes (or flags for human review) before the metrics get computed.
Can the demand forecast feed into our reorder triggers?+
The forecast is data — you can export it to CSV or query it from another tool. Many teams pin a dashboard with current stock vs forecast vs reorder threshold and review it weekly with procurement.
How do we handle unit conversions across warehouses with different conventions (cases vs eaches vs pallets)?+
Tell the chat the conversion rules once and AnalityQa AI applies them consistently. "Warehouse 2 reports in cases of 12; convert to eaches for the cross-site dashboard." The rules become part of the saved analysis context.