Most supply chain problems have already happened by the time your team sees them. Datanoetic surfaces them earlier, closes them faster, and stops them recurring.

A live map of where value is being won and lost in your operation. An AI layer that identifies the cause in seconds and recommends the fix. And a process engine that turns recurring fixes into permanent change so the same problem stops happening.

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The status quo answers the question. It rarely answers yours.

Picks / hour Zone C
612 / hr target 680 / hr
Zone exceptions 4
Zone SKU Accuracy Status
Zone C SKU-4471 88.6%
Aisle 7 SKU-1180 94.2%
Zone A SKU-9302 99.1%
Dock 2 SKU-2264 96.5%
Dashboards show what, not why

KPIs flash red. Teams then spend inordinate time to identify the real root cause — manually or in disparate systems, after the fact.

Aisha
Aisha Supply chain analyst

Why is pick accuracy down in Zone C this shift?

Generic assistant General industry knowledge

Pick accuracy can dip for a few common reasons — staffing changes, equipment issues, or process variability. Consider reviewing your standard operating procedures and scheduling refresher training for the team on the affected shift.

Generic AI solutions do not know the DNA of your operations

Generic AI tools answer in averages. Your supply chain process is not an average.

Aisha
Aisha Supply chain analyst

Which aisle is it — and what changed at shift start?

Generic assistant General industry knowledge

I can't see your aisle-level data or shift logs, so I can't pinpoint it. In most operations this traces to staffing, scanner calibration, or slotting changes — I'd start by reviewing those.

Point tools don't see the value stream

WMS says on-time dispatch. TMS says on-time carrier. Neither sees the dock-to-truck dwell eating ~3% margin.

Picks / hour Zone C
612 / hr target 680 / hr
Zone exceptions 4
Zone SKU Accuracy Status
Zone C SKU-4471 88.6%
Aisle 7 SKU-1180 94.2%
Zone A SKU-9302 99.1%
Dock 2 SKU-2264 96.5%
Dashboards show what, not why

KPIs flash red. Teams then spend inordinate time to identify the real root cause — manually or in disparate systems, after the fact.

Aisha
Aisha Supply chain analyst

Why is pick accuracy down in Zone C this shift?

Generic assistant General industry knowledge

Pick accuracy can dip for a few common reasons — staffing changes, equipment issues, or process variability. Consider reviewing your standard operating procedures and scheduling refresher training for the team on the affected shift.

Generic AI solutions do not know the DNA of your operations

Generic AI tools answer in averages. Your supply chain process is not an average.

Aisha
Aisha Supply chain analyst

Which aisle is it — and what changed at shift start?

Generic assistant General industry knowledge

I can't see your aisle-level data or shift logs, so I can't pinpoint it. In most operations this traces to staffing, scanner calibration, or slotting changes — I'd start by reviewing those.

Point tools don't see the value stream

WMS says on-time dispatch. TMS says on-time carrier. Neither sees the dock-to-truck dwell eating ~3% margin.

Datanoetic reasons across all aspects of your specific supply chain process.

System A has live order schedule. System B the fulfilment plan and System C the device management. Neither shows that your end-to-end process has missed the mark. Datanoetic sees and reasons across all three to pinpoint the recurring misstep within SLA thresholds and suggests the fix.

Aisha
Aisha Supply chain analyst

Why is pick accuracy down in Zone C this shift?

Warehouse-1 shift_roster.csv device_events
  1. Loaded scope · warehouse-1 → Pick → Zone C
  2. Joined shift_roster with active operators
  3. Matched device_events for Zone C scanners
  4. Computed pick error rate per SKU cluster
  5. Ranked drivers by KPI deviation weight
DNAI

Root cause: Aisle 7 has a temp operator working without scan-confirm since 13:08. Pick errors concentrate on SKUs 4421–4438.

Warehouse-1 / Zone C

Ask DNAI anything. Use @ to mention VSMs, processes, organisational units and employees…

ChatEdit
Approve a recommended action

This is your operation — live. Every process step, every KPI relevant to your business — mapped in real time.

A live, continuously updated map of every step in your operation — so every alert and recommendation is grounded in the specifics of your process.

Value-stream maps / EMEA Network
VSM Analytics 87% 2
Main workflow 1 United Kingdom
Edit Preview Live
EU Office
1 0 0 %
Northbridge Foods
3rd party
ID: 1
97.8%
Northbridge Foods
Northbridge Foods
Vendor — Ambient FMCG • Manchester, UK
: 3 Production & Dispatch
5
KPI parameters 11
Time
Quality
Risk
Sustainability
Caledonian Dairy
3rd party
ID: 2
78.4%
Caledonian Dairy
Caledonian Dairy
Vendor — Chilled FMCG • Glasgow, UK
: 5 Cold-chain Dispatch
4
KPI parameters 9
Time
Quality
Risk
1
Continental Freight Lines
3rd party
ID: 3
89.1%
Continental Freight Lines
Continental Freight Lines
Carrier — Inbound RoRo • Dover, Kent, UK
: 1 RoRo Vessel Operations
4
KPI parameters 8
Time
Cost
Sustainability
Pre-Shipment Inspection
Embedded node
ID: 4
96.3%
Northbridge Foods QA / Pre-Shipment
ID: 12 Pre-Shipment Inspection
Metadata 5
Sarah Mitchell Sarah Mitchell
Viktor Schultz Viktor Schultz Mei Tanaka Mei Tanaka
Bartec Honeybee BHT-7 NBF Cereal 500g (NBF-22L)
+1
Vendor-managed GFSI-audited
KPI parameters 6
Quality
Risk
Vendor Booking Confirmation
Local step
ID: 5
97.1%
Vendor Booking Confirmation
Metadata 6
James O'Sullivan James O'Sullivan
Rachel Patel Rachel Patel Daniel Cooper Daniel Cooper
+1
Albion ASN Portal Slot Booking Calendar
EDI handover Customer SLA-A
KPI parameters 5
Time
Quality
Container Deconsolidation
Local step
ID: 6
95.4%
Container Deconsolidation
Metadata 7
Priya Bhatt Priya Bhatt
Kemal Yıldız Kemal Yıldız Jakub Nowak Jakub Nowak
+4
Linde H35 FL-127 Combilift C5000
+2
Container-load EDI handover
+1
KPI parameters 7
Time
Quality
Cost
UK Customs Clearance
Local step
ID: 7
88.9%
UK Customs Clearance
Metadata 5
Aisha Khan Aisha Khan
Charles Beresford Charles Beresford Fatima Yusuf Fatima Yusuf
HMRC CDS Portal ATA Carnet Bundle
HMRC CDS Customs hold-eligible
KPI parameters 6
Time
Risk
1
Daventry
Organisational unit
ID: 8
96.7%
DNAI DNAI 4 insights
Daventry
Daventry
National DC • Daventry, Northamptonshire, UK
: 2 Inbound & Receiving
7
KPI parameters 14
Time
Quality
Cost
Customer Satisfaction
Inbound Quality Hold Resolution
Local step
ID: 9
76.2%
DNAI DNAI 8 insights
Inbound Quality Hold Resolution
Metadata 8
Liam Walsh Liam Walsh
Margaret Boyle Margaret Boyle Olu Adebayo Olu Adebayo
+3
Zebra MC9300 #284 CD Yoghurt 4×125g (pulled lot)
+1
BBE-sensitive Chilled chain 2–8°C
+1
KPI parameters 9
Time
Quality
Risk
1
Network Allocation Planning
Local step
ID: 10
92.4%
Network Allocation Planning
Metadata 6
Tomasz Kowalski Tomasz Kowalski
Esther Nwosu Esther Nwosu Alistair MacGregor Alistair MacGregor
+2
Manhattan SCALE WMS S&OP Weekly Plan
System-driven S&OP weekly
KPI parameters 8
Time
Quality
Cost
Cross-Dock Eligibility Match
Local step
ID: 11
90.6%
Cross-Dock Eligibility Match
Metadata 5
Olu Adebayo Olu Adebayo
Patryk Kowalski Patryk Kowalski Nadia Hassan Nadia Hassan
+2
Crown ESR-127 Conveyor Line CL-A2
+1
Cross-dock eligible High-rotation SKU
KPI parameters 5
Time
Cost
Magna Park
Organisational unit
ID: 12
95.9%
Magna Park
Magna Park
Regional WH • Lutterworth, Leicestershire, UK
: 4 Putaway & Storage
5
KPI parameters 12
Time
Quality
Cost
Customer Satisfaction
Putaway & Replenishment UK
Workflow
ID: 13
95.2%
: 7 Putaway & Replenishment UK
6
Magna Park Regional WH
KPI parameters 9
Time
Quality
Cost
Trafford Park RDC
Organisational unit
ID: 15
91.9%
DNAI DNAI 5 insights
Trafford Park RDC
Trafford Park RDC
Regional DC • Manchester (Trafford Park), UK
: 6 Picking
5
KPI parameters 13
Time
Quality
Cost
Customer Satisfaction
National Linehaul Coordination
Local step
ID: 16
95.8%
National Linehaul Coordination
Metadata 4
David McAllister David McAllister
Clara Martín Clara Martín Ruairi Gallagher Ruairi Gallagher
Trunker Fleet — 32× DAF XF Linehaul Schedule v4
Trunking Driver-hours regulated
KPI parameters 7
Time
Cost
Sustainability
Avonmouth
Organisational unit
ID: 17
96.2%
Avonmouth
Avonmouth
Coastal DC • Bristol (Avonmouth), UK
: 2 Order Management & Wave Planning
6
KPI parameters 11
Time
Quality
Cost
Customer Satisfaction
Retail Delivery Window Slotting
Local step
ID: 18
90.4%
Retail Delivery Window Slotting
Metadata 6
Fiona Henderson Fiona Henderson
Anand Subramanian Anand Subramanian Lucy Chen Lucy Chen
+2
Customer Window Calendar Carrier Cut-off Matrix
Customer SLA-A Carrier cut-off-sensitive
KPI parameters 5
Time
Customer Satisfaction
Outbound Distribution UK
Workflow
ID: 19
93.5%
DNAI DNAI 7 insights
: 8 Outbound Distribution UK
6
Trafford Park RDC
KPI parameters 16
Time
Quality
Cost
Customer Satisfaction
Last-Mile Carrier Handoff
Local step
ID: 20
91.7%
Last-Mile Carrier Handoff
Metadata 5
Wayne Robinson Wayne Robinson
Henry Ojo Henry Ojo Penny Forbes Penny Forbes
Honeywell Voice VC-12 Yard Trailer YT-49
+1
Last-mile EDI handover
KPI parameters 8
Time
Cost
Customer Satisfaction
POD Reconciliation
Local step
ID: 21
97.4%
POD Reconciliation
Metadata 4
Isabella Rinaldi Isabella Rinaldi
Theo Brennan Theo Brennan
POD Reconciliation Dashboard Carrier API Bridge v2
Carrier-API Customer SLA-A
KPI parameters 4
Quality
Customer Satisfaction
Brightline Parcel UK
3rd party
ID: 22
95.3%
Brightline Parcel UK
Brightline Parcel UK
Carrier — Last-mile • National (HQ: Birmingham), UK
: 5 Last-mile Delivery
5
KPI parameters 9
Time
Cost
Customer Satisfaction
Highstreet Grocers
3rd party
ID: 29
97.8%
Highstreet Grocers
Highstreet Grocers
Retail Customer • London (HQ), UK — national stores
: 1 Goods Receiving
5
KPI parameters 4
Time
Customer Satisfaction
Out for Pickup
Embedded node
ID: 30
94.1%
Brightline Parcel UK / Returns Pickup
ID: 5 Out for Pickup
Metadata 4
B Bethany Clarke
No employees
ASN Handover Doc Returns Label Gen
Returns trigger RMA-driven
KPI parameters 3
Quality
Customer Satisfaction
Returns Intake from Carrier
Local step
ID: 23
89.6%
Returns Intake from Carrier
Metadata 5
Grace Adeyemi Grace Adeyemi
Marcus Lindqvist Marcus Lindqvist Roxana Petrescu Roxana Petrescu
+1
Returns Tote (RT-amber) Zebra MC9300 #312
Reverse-flow RMA-required
KPI parameters 5
Time
Quality
Reverse Logistics Grading
Local step
ID: 24
90.1%
Reverse Logistics Grading
Metadata 6
Vivienne Marshall Vivienne Marshall
O Oisín O'Reilly Ayşe Demir Ayşe Demir
+2
Grading Bench GB-04 Resalable Tote (RT-green)
+1
Resalable / VR split BBE-sensitive
KPI parameters 5
Quality
Cost
Wakefield Returns Hub
Organisational unit
ID: 25
90.8%
Wakefield Returns Hub
Wakefield Returns Hub
Returns Processing Hub • Wakefield, West Yorkshire, UK
: 3 Returns / Reverse Logistics
5
KPI parameters 9
Time
Quality
Cost
Returns & Disposition UK
Workflow
ID: 26
89.3%
: 9 Returns & Disposition UK
5
Wakefield Returns Hub
KPI parameters 8
Time
Quality
Cost
Disposition Routing
Local step
ID: 27
94.7%
Disposition Routing
Metadata 3
Hugo Lefèvre Hugo Lefèvre
Kasia Wójcik Kasia Wójcik
Disposition Rules v3
Disposition Customer Returns
KPI parameters 4
Cost
Sustainability
EU Manifest Generation
Embedded node
ID: 28
96.7%
EU Cross-Border Operations / Outbound Shipping
ID: 14 EU Manifest Generation
Metadata 5
H Hans Müller
Catarina Silva Catarina Silva Pieter de Vries Pieter de Vries
EU Customs Bundle Linehaul Trailer LH-EU-72
EU outbound AEO-secured
KPI parameters 4
Time
Quality

Reasons over your VSM, KPI thresholds, and knowledge graph. Recommend-and-confirm — never autonomous.

Not a generic AI assistant. Every answer DNAI™ gives is specific to your operation — your people, your process steps, your data.

Pick · Zone C 14:02
96.1% acc. ▼ vs 98.7%
Temp operator · A-7
Scan-confirm off
SKU 4421–4438
Alerts you trust

Breaches caught against your thresholds and explained with ranked, data-cited drivers — not a wall of red.

A
Aisha Supply chain analyst

Why is pick accuracy down in Zone C this shift?

DNAI

Temp operator on Aisle 7 working without scan-confirm since 13:08.

Ask your operation

Ask in plain language. Every answer runs on your live graph and cites the exact data behind it.

Pick · Zone C Re-enable scan-confirm
Approve a recommended action
Edit the VSM with intent

Recommends changes to your VSM and acts only once a supervisor confirms. Nothing moves without sign-off.

One orchestrated workflow across your whole value stream.

DNOVA™ reads the patterns across weeks of operational data and turns the most recurring issues into recommended automated process changes.

Demand Forecast
Embedded node
ID: 0
ID: 0 Demand Forecast
Metadata 0
No employees
No attributes
No tags
KPI parameters 0
Inbound Receiving
Local step
ID: 0
Inbound Receiving
Metadata 0
No employees
No attributes
No tags
KPI parameters 0
Quality Check
Local step
ID: 0
Quality Check
Metadata 0
No employees
No attributes
No tags
KPI parameters 0
Allocation Planning
DNOVA™
ID: 07
98%
: 1 Allocation Planning
5
Value Stream Office
Health
Status Live
Throughput 1.4k/d
Last 24 runs 98%
Latency 234ms
Autonomy 96%
Replenishment
Workflow
ID: 0
: 1 Replenishment
0
KPI parameters 0
Dispatch Scheduling
Workflow
ID: 0
: 1 Dispatch Scheduling
0
KPI parameters 0
Order Fulfilment
Local step
ID: 0
Order Fulfilment
Metadata 0
No employees
No attributes
No tags
KPI parameters 0
KPI Guard
Batch pass · L-2847 · Line 2 87.2% ▼ vs 98.5%
Batch pass rate %
AggregatedBy node
100959085
1 Jan8 Jan15 Jan22 Jan29 Jan
Ranked driversWeight
01 E-06 calibration driftCMMS · cal_events
02 Batch size varianceERP · prod_sched
03 Incoming material lot · L-2840LIMS · batch_qc
20–35%
Batch release cycle
Modelled projection
15–25%
Batch rejections
Modelled projection
40–60%
Serialisation errors
Modelled projection

Ranges are Datanoetic-modelled projections based on the precision lift from node-level root cause vs. dashboard alerting. Calibrated to your value stream during the first 30 days.

Explore Pharma & Medtech
I've run these operations. The gap was never the data — it was connecting data to process to a decision you could defend in the room. That's exactly what this closes.
Chief Supply Chain OfficerLarge Pharmacy chain
Google Cloud

Cloud & data partner

Infrastructure · Security · Vertex AI

Built with Google Cloud. Your data stays in your tenant.

SOC 2 · in progress

Partner programme

Pilot · Q3 2026

Pilot partners onboarding now. 3PL operators across EMEA.

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See it on your operation. 30 minutes. Real data. No commitment.

We'll walk your team through a mapped scenario that mirrors your operation — and one KPI you wish you could explain in real time.

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