When an SLA breach reaches your client, it's already too late.

Datanoetic gives you the warning, the fix, and — through DNOVA™ — the permanent process change that stops it recurring. Built for the specific pressures of 3PL and warehousing: multiple clients, continuous throughput targets, and the expectation that problems are caught internally — not reported by the customer.

See a demo for 3PL & Contract Logistics

The gap that costs 3PLs the most.

From "something is wrong" to "here is the cause" — node by node, in minutes, not hours.

In a 3PL environment, the margin between a clean shift and an SLA breach is measured in minutes, not hours. The data exists — WMS dashboards, TMS feeds, IoT sensors — but it is siloed, measured at facility level, and the path to root cause runs through manual investigation and time you do not have.

Datanoetic closes that gap by mapping your operation node by node, attaching KPIs to each step that owns them, and running an AI layer that reasons from your specific data — your operators, your devices, your clients' SLA thresholds — not industry averages.

/ Recommend-and-confirm. Every DNAI™ action awaits supervisor approval — explicitly not autonomous.

Where 3PL operations lose value. And how Datanoetic closes each gap.

Zone-level pick accuracy variance is one of the highest-cost, highest-visibility problems in 3PL warehousing. When it degrades, the investigation — who was on shift, which zone, which SKU cluster, which device — takes hours and relies on people who may not be available.

How Datanoetic closes it

DNAI™ KPI Guard monitors every published VSM node continuously. When pick accuracy in Zone C drops below threshold, an Insight Card is generated on the specific node — not a generic facility alert — naming the operator, device, and SKU cluster ranked by contribution, with a recommend-and-confirm action awaiting supervisor approval.

Most 3PLs run three to five systems that each know part of the story. The WMS knows pick rates. The TMS knows despatch timing. The IoT sensor knows device states. None of them know how those things relate to each other — or to the specific process step where value is being won or lost.

How Datanoetic closes it

Datapro-V™ maps your end-to-end operation as a live VSM — every step connected, every KPI anchored to the step that owns it, every entity assigned to the steps they participate in. Read-only connectors to WMS, TMS, IoT, and T&A feed a single tenant-isolated data layer. Nothing is replaced; everything is connected.

General-purpose AI assistants can describe supply chain best practices. They cannot tell you that the pick error rate on SKUs 4421–4438 in Zone C is elevated because a temporary operator is working without scan-confirm enabled, and that the supervisor needs to approve a specific fix in the next 15 minutes.

How Datanoetic closes it

DNAI™ reasons from your Knowledge Graph — every operator, every device, every SKU cluster, every client SLA — not from general training data. KPI Guard generates hypotheses grounded in your process graph, not industry templates. DNAI™ Chat answers operational questions in plain language, citing the specific records it used.

KPI Guard
Pick accuracy · Zone C 96.1% ▼ vs 98.7%
Pick accuracy %
AggregatedBy node
99989796
1 Jan8 Jan15 Jan22 Jan29 Jan
Analyst

Why is pick accuracy down in Zone C this shift?

A temporary operator on Aisle 7 has been working without scan-confirm since 13:08; the errors concentrate on SKUs 4421–4438.

Ranked driversWeight
01 Temp operator · Aisle 7shift_roster.csv
02 Scan-confirm disableddevice_events
03 SKU cluster 4421–4438sku_taxonomy
Value-stream maps / 3PL Network
VSM Analytics 87% 2
Main workflow
Edit Preview Live
EU Office
1 0 0 %
Aurora Apparel
3rd party
ID: 1
97.6%
Aurora Apparel
Aurora Apparel
Client · Apparel brand • Leeds, UK
: 1 Production & Dispatch
3
KPI parameters 4
Time
Quality
Cost
Customer Satisfaction
WH Daventry
Organisational unit
ID: 2
95.4%
DNAI DNAI New Insight
WH Daventry
WH Daventry
Warehouse • Daventry, UK
: 2 Fulfilment
6
KPI parameters 4
Time
Quality
Risk
Cost
Brightline Logistics
3rd party
ID: 3
97.8%
Brightline Logistics
Brightline Logistics
Carrier · Outbound • Crick, UK
: 1 Linehaul
3
KPI parameters 3
Time
Quality
Cost
Aurora RDC · Leeds
Organisational unit
ID: 4
98.2%
Aurora RDC · Leeds
Aurora RDC · Leeds
Client RDC • Leeds, UK
: 1 Goods-in
3
KPI parameters 4
Time
Quality
Cost
Customer Satisfaction
Value-stream maps / 3PL Network
VSM Analytics 87% 2
Main workflow
Edit Preview Live
EU Office
1 0 0 %
Aurora Apparel
3rd party
ID: 1
97.6%
Aurora Apparel
Aurora Apparel
Client · Apparel brand • Leeds, UK
: 1 Production & Dispatch
3
KPI parameters 4
Time
Quality
Cost
Customer Satisfaction
WH Daventry
Organisational unit
ID: 2
95.4%
DNAI DNAI New Insight
WH Daventry
WH Daventry
Warehouse • Daventry, UK
: 2 Fulfilment
6
KPI parameters 4
Time
Quality
Risk
Cost
Brightline Logistics
3rd party
ID: 3
97.8%
Brightline Logistics
Brightline Logistics
Carrier · Outbound • Crick, UK
: 1 Linehaul
3
KPI parameters 3
Time
Quality
Cost
Aurora RDC · Leeds
Organisational unit
ID: 4
98.2%
Aurora RDC · Leeds
Aurora RDC · Leeds
Client RDC • Leeds, UK
: 1 Goods-in
3
KPI parameters 4
Time
Quality
Cost
Customer Satisfaction

The improvement, KPI by KPI.

DNAI™ resolves today's incident; DNOVA™ removes the cause for good. Hover any metric to see the DNOVA™ process logic behind it — and the other KPIs it configures on your VSM.

Quality 98.5–99.2%

Pick accuracy

DNAI™ resolves the incident in under a minute; DNOVA™ eliminates the chronic cause.

Pick accuracy

DNAI™ acts on this shift’s incident — recommend-and-confirm, supervisor-approved. DNOVA™ acts on the chronic pattern behind it — design-and-deploy, process-owner-approved.

How DNOVA™ holds it

After six weeks of DNAI™ data, DNOVA™ identifies the chronic quality pattern: 73% of Zone C pick errors occur during temp-operator shifts on look-alike SKU clusters. It proposes three permanent changes — a mandatory scan-confirm rule for all 44xx SKUs codified in the WMS, an automated shift-start briefing for temp operators assigned to Zone C, and a packaging-differentiation flag surfaced to pickers on the device screen. The process owner approves, the rules deploy, and the chronic issue is eliminated. The raised quality baseline feeds back into Datapro-V™.

Other metrics we track in Quality
  • Defect Rate
  • First-Pass Yield (FPY)
  • Rework Rate
  • Complaint Frequency
  • Quality Index
  • Process Capability (Cp/Cpk)

Configured to your thresholds and baselines — not industry averages. Formulas are defined and maintained by Datanoetic.
Time 5–15%

Throughput per node

Bottleneck identification plus DNOVA™-driven reallocation.

Throughput per node

DNAI™ acts on this shift’s incident — recommend-and-confirm, supervisor-approved. DNOVA™ acts on the chronic pattern behind it — design-and-deploy, process-owner-approved.

How DNOVA™ holds it

Where DNAI™ clears a single-shift bottleneck, DNOVA™ reads across the incident history and finds the node where throughput repeatedly stalls under the same conditions. It generates a process-redesign proposal mapped to that VSM node — resequencing and labour-reallocation rules — and, once the process owner signs off, codifies them as automated rules. Loop closure raises the throughput baseline Datapro-V™ measures against in the next cycle.

Other metrics we track in Time
  • Cycle Time
  • Lead Time
  • Throughput Time
  • Wait/Idle Time
  • On-Time Delivery %
  • Time Variance %

Configured to your thresholds and baselines — not industry averages. Formulas are defined and maintained by Datanoetic.
Time <5min

Root-cause resolution time

From 2–4 hours of manual investigation to a cited DNAI™ root cause.

Root-cause resolution time

DNAI™ acts on this shift’s incident — recommend-and-confirm, supervisor-approved. DNOVA™ acts on the chronic pattern behind it — design-and-deploy, process-owner-approved.

How DNOVA™ holds it

DNAI™ already cuts investigation from hours to minutes on each incident. DNOVA™ removes the investigation itself for recurring causes: by recognising the chronic pattern behind your top repeat incidents, it codifies the diagnosis-and-fix into a standing process rule — so the next occurrence is prevented rather than re-investigated.

Other metrics we track in Time
  • Cycle Time
  • Lead Time
  • Throughput Time
  • Wait/Idle Time
  • On-Time Delivery %
  • Time Variance %

Configured to your thresholds and baselines — not industry averages. Formulas are defined and maintained by Datanoetic.
Cost 3–8%

Cost per unit shipped

Labour efficiency and dock utilisation.

Cost per unit shipped

DNAI™ acts on this shift’s incident — recommend-and-confirm, supervisor-approved. DNOVA™ acts on the chronic pattern behind it — design-and-deploy, process-owner-approved.

How DNOVA™ holds it

DNOVA™ surfaces the chronic cost drivers behind the per-unit number — recurring labour-idle windows and dock under-utilisation patterns that repeat across shifts. It proposes structural changes (shift-pattern and dock-scheduling rules) mapped to the VSM; once the process owner approves, they deploy as automated rules and the cost baseline is lowered for good, not recovered shift by shift.

Other metrics we track in Cost
  • Total Process Cost
  • Cost per Unit
  • Cost Variance %
  • ROI
  • Cost Savings
  • Overhead Cost Ratio

Configured to your thresholds and baselines — not industry averages. Formulas are defined and maintained by Datanoetic.
Risk 10–20%

SLA breach rate

Alerted before the breach window opens; DNOVA™ reduces repeat causes.

SLA breach rate

DNAI™ acts on this shift’s incident — recommend-and-confirm, supervisor-approved. DNOVA™ acts on the chronic pattern behind it — design-and-deploy, process-owner-approved.

How DNOVA™ holds it

DNAI™ alerts before a single breach window opens. DNOVA™ attacks the recurrence: it converts the repeat DNAI™ incidents behind your top breach causes into permanent, pre-emptive process rules — so the conditions that lead to a breach are caught and corrected automatically, reducing repeat causes cycle over cycle.

Other metrics we track in Risk
  • Risk Event Frequency
  • Severity × Impact Score
  • Mitigation Success %
  • Compliance Violation Count
  • Financial Risk Exposure
  • Risk-Adjusted Return

Configured to your thresholds and baselines — not industry averages. Formulas are defined and maintained by Datanoetic.
Risk Eliminated

Recurring incident rate

DNOVA™ converts repeat incidents into permanent process rules — gone after cycle 1–2.

Recurring incident rate

DNAI™ acts on this shift’s incident — recommend-and-confirm, supervisor-approved. DNOVA™ acts on the chronic pattern behind it — design-and-deploy, process-owner-approved.

How DNOVA™ holds it

This is the metric DNOVA™ exists to move. It recognises the top five recurring causes across the DNAI™ incident history, proposes a permanent process change or automation rule for each, and — with process-owner sign-off — deploys them. After one to two DNOVA™ cycles, those recurring incident classes are eliminated rather than repeatedly resolved.

Other metrics we track in Risk
  • Risk Event Frequency
  • Severity × Impact Score
  • Mitigation Success %
  • Compliance Violation Count
  • Financial Risk Exposure
  • Risk-Adjusted Return

Configured to your thresholds and baselines — not industry averages. Formulas are defined and maintained by Datanoetic.

Indicative improvements are Datanoetic-modelled projections, calibrated to your VSM during the first 30 days. Results depend on operational baseline, data connectivity, and deployment scope.

How the workflow changes.

Before

The ops manager gets a ping that pick accuracy is down. The next 90 minutes: pull WMS data, interview shift leads, cross-reference device logs, write an incident report — while the clock runs on the SLA window.

After

DNAI™ KPI Guard surfaces the root cause in 40 seconds — temp operator, scan-confirm disabled, affected SKU cluster — citing the exact records. The supervisor confirms the recommended action. Incident closed before the SLA-breach window opens.

The same incident cannot recur.

After six weeks of DNAI™ data, DNOVA™ identifies the pattern: 73% of Zone C pick errors occur during temp-operator shifts on look-alike SKU clusters. It proposes three changes — a mandatory scan-confirm rule for all 44xx SKUs, an automated shift-start briefing for temp operators in Zone C, and a packaging differentiation flag in the Knowledge Graph. Process owner approves. Rules deploy. Chronic issue eliminated. The improved baseline feeds back into Datapro-V™.

Live in your warehouse in 12–16 weeks.

Implementation is managed by Datanoetic — not a self-install. We scope your VSM, connect your data, configure your KPIs, and deliver your first real DNAI™-explained incident in week four. 30 days to first live alert.

  1. Scope & VSM mapping
  2. Data connect WMS TMS IoT T&A
  3. KPI thresholds
  4. First value — live alert
See the full implementation journey

30 minutes. One scenario. Your 3PL data.

We'll walk your team through the Zone C scenario on a sample VSM that mirrors a 3PL warehouse like yours — and one KPI you wish you could explain in real time.

See a demo for 3PL & Contract Logistics