Services

Nine services, mapped to the manufacturing value chain.

Manufacturers and distributors don't buy "data and AI" — they buy fewer line stops, higher OEE, lower warranty cost, visibility past the dealer and distributor, and programme margins that hold from quote to delivery. Below, each SCIKIQ service line is mapped to the domains where it does that work — for automotive OEMs and suppliers, other manufacturers and trading houses — with the use cases, the KPIs it moves and the accelerators behind it.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the manufacturing value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team a manufacturer or distributor gets in that domain. Select any domain to see use cases, the KPIs we help move and the working accelerators.

9
Service lines
8
Value-chain domains
14
Lead roles across the map
35
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance, Quality & Compliance DataGovernance
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
Supply Chain & ProcurementSupply chain
Plant Operations, Quality & ReliabilityPlant & quality
Commercial, Dealer & DistributionCommercial
Aftermarket, Service & WarrantyAftermarket
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 5 6 6 7 6 6 7 6

The mapping shows where each service line typically leads or supports; every engagement is scoped to the business. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

Product engineering & R&D

Electrification, software content and variants shorten development cycles while test results, formulations and know-how sit in LIMS, PLM, drives and people's heads.

Data
PLM, BOMs, LIMS & documents
Data product
Engineering knowledge data product
AI & agents
R&D assistant & BOM-risk agents
Outcome
Faster, better-informed engineering

Manufacturing use cases

  • R&D knowledge assistant that cites every source
  • BOM risk: obsolete parts against the programmes behind them
  • New-product gate tracking with NRE and first-article readiness
  • Specification and test-report extraction with GenAI and vision

KPIs we help you move

Time to answer an engineering questionNew-product gate slippageEngineering change cycle timeParts at end-of-life risk

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working R&D Knowledge Assistant Agentic Decision Platform
Domain 2 of 8

Supply chain & procurement

Shortages, lead-time drift and supplier-quality gaps reach the line before the plan; trading houses juggle steel, parts and logistics margins across thousands of SKUs.

Data
SAP MM, suppliers, PPAP & inventory
Data product
Supplier & material golden record
AI & agents
Supply-resilience & P2P agents
Outcome
Fewer line stops, leaner inventory

Manufacturing use cases

  • On-time delivery, lead-time drift and concentration signals
  • Procure-to-pay three-way match with exceptions explained
  • Demand sensing and inventory optimisation by SKU
  • Supplier-quality and PPAP document review

KPIs we help you move

Supplier on-time deliveryDays of supplyLine stops from shortagesPurchase-price variance

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Agentic Decision Platform SAP + DMS Data Model
Domain 3 of 8

Production & plant operations

OEE, scrap and changeover losses are measured late and MES, historians and ERP disagree, while line capacity and the engineering bench are oversubscribed unseen.

Data
MES, IIoT, SAP PP & shift data
Data product
Plant performance data product
AI & agents
OEE, yield & capacity agents
Outcome
More output from the same lines

Manufacturing use cases

  • OEE and loss-tree analytics by line, shift and product
  • Capacity and bench planning across lines and disciplines
  • Yield drivers found across process data
  • Energy intensity per unit produced

KPIs we help you move

Overall equipment effectivenessScrap and rework rateSchedule adherenceEnergy per unit

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Control Tower Enterprise Cockpit 360
Domain 4 of 8

Quality, warranty & compliance

A quality escape becomes a warranty bill, a recall or a lapsed certificate; corrective actions age and warranty claims are read one by one.

Data
QMS, CAPA, warranty & certificates
Data product
Traceable quality data product
AI & agents
CAPA, warranty & certificate agents
Outcome
Fewer escapes, lower warranty cost

Manufacturing use cases

  • CAPA ageing and first-pass-yield tracking with owners
  • Warranty-claim text classified to failure modes and suppliers
  • Certificates tracked as dated revenue dependencies
  • Lot and serial traceability for containment

KPIs we help you move

First-pass yieldWarranty cost per unitCAPA closure timeCustomer PPM defects

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Agentic Decision Platform Data Governance Hub
Domain 5 of 8

Maintenance & asset reliability

Unplanned downtime drives the schedule, maintenance history is free text, spares are stocked for the worst case and field assets are inspected on paper.

Data
CMMS, sensors, failures & field surveys
Data product
Asset health data product
AI & agents
Predictive & work-order agents
Outcome
Less downtime, right-sized spares

Manufacturing use cases

  • Predictive maintenance from condition and failure history
  • Work-order text classified to failure modes
  • Spare-parts optimisation by criticality
  • Offline field surveys with GPS, readings and photos

KPIs we help you move

Unplanned downtimeMean time between failuresPlanned maintenance shareSpares inventory value

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Field Data Collector Control Tower
Domain 6 of 8

Sales, dealers & distribution

OEMs see dealer stock late, FMCG makers lose sight of goods at the distributor, and trading houses quote on margins they can't see by SKU.

Data
SAP SD, DMS, prices & quotes
Data product
Primary + secondary sales data product
AI & agents
Distributor, quote & leakage agents
Outcome
Better sell-through and price realisation

Manufacturing use cases

  • Secondary-sales visibility and distributor health scoring
  • Quote and win-rate analytics
  • Pricing, scheme and rebate leakage detection
  • Market early-warning from news and competitors

KPIs we help you move

Secondary sales growthPrice realisationQuote turnaroundDistributor stock days

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working SAP + DMS Data Model Market Early-Warning Revenue Assurance
Domain 7 of 8

Aftermarket, service & parts

Aftermarket parts, lubricants and service carry the margin, yet demand is forecast by gut and product data differs on every channel.

Data
Parts catalogue, installed base & service
Data product
Product & installed-base data product
AI & agents
Parts, service & content agents
Outcome
Higher fill rates, consistent content

Manufacturing use cases

  • Parts demand from installed base and service history
  • Product information enriched and syndicated to channels
  • Service-ticket and feedback classification
  • Contract and renewal signals

KPIs we help you move

Parts fill rateAftermarket revenue shareService first-time-fix rateProduct content completeness

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Product Information Management Enterprise Cockpit 360
Domain 8 of 8

Finance, cost & compliance

Programmes quoted at one margin deliver another, working capital hides in stock, and CBAM, ESG and export-control rules add numbers to defend.

Data
SAP FI/CO, CO-PA, costing & emissions
Data product
Governed cost & margin data product
AI & agents
Margin, variance & commentary agents
Outcome
Margin protected, close on time

Manufacturing use cases

  • Quoted-versus-delivered margin by programme
  • Working-capital and days-of-supply analytics
  • Plan-versus-actual variance with AI commentary
  • Emissions and export-control eligibility reporting

KPIs we help you move

Programme margin varianceCash conversion cycleDays to closeEmissions per unit

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working COMPASS CLARION NARRATOR
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

CEO / COO
Chief Executive & Chief Operating Officer, plant heads
CPO
Chief Procurement Officer & supply chain
Quality
Head of quality & warranty
CFO
Chief Financial Officer
CIO / CDO
Chief Information & Chief Data Officer
Sales & distribution
Sales, dealer network, distribution & trading heads
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForCEOCIO / CDOCFO
On the value chainEngineering & R&DSupply chainProduction & plantQuality & warrantyMaintenanceSales & distributionAftermarket & partsFinance & compliance

The client problem

Pilots multiply across plants, divisions and acquired businesses, but few reach production. There is no shared, evidence-based view of where the company stands on data and AI, which use cases pay back, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap of use cases across R&D, supply, plant, sales and service, with a business case for each
  • An AI operating model that works across plants, divisions and distributors

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • Programme-margin and value-creation analytics
  • AI operating model & centre of excellence design

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Enterprise Cockpit 360 Value calculator
Advise · Service line

Data Governance, Quality & Compliance Data

ForCDOQuality headCompliance
On the value chainEngineering & R&DSupply chainQuality & warrantyAftermarket & partsFinance & compliance

The client problem

Material, customer and supplier masters differ by plant and system. Certificates, product-safety evidence, CBAM and ESG figures and export-control decisions all need data someone can defend, and AI adds a new layer to govern.

Outcomes

  • Golden master data for materials, customers, suppliers and distributors
  • Lineage and data-quality controls behind certificates, emissions and compliance reports
  • AI and agent governance: policy, approvals, evaluation and a kill switch

What we do

  • Master-data management & golden records
  • Quality-system and certificate data (IATF 16949, ISO 9001, AS9100)
  • CBAM, ESG and product-compliance reporting lineage
  • Trade & export-control data
  • AI & agent governance

Typical engagements

  • AssessmentMaster-data and compliance-data gap review
  • Pilot · 30-45 daysGolden record and DQ rules for one domain
  • BuildMDM, catalogue and controls across plants
  • Managed runOngoing DQ monitoring and stewardship

Delivered with

Data Governance Hub SCIKIQ Data Fabric Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIOCDO
On the value chainEngineering & R&DSupply chainProduction & plantQuality & warrantyMaintenanceSales & distributionAftermarket & partsFinance & compliance

The client problem

Data sits across SAP, MES, PLM, QMS, historians, dealer and distributor systems and spreadsheets from every acquisition. Every new report or model starts with another extract, and nothing reconciles to the ledger.

Outcomes

  • One governed, cloud-agnostic data platform on Azure, AWS or hybrid
  • An enterprise data model and ontology that reconcile plants, divisions and channels
  • Batch, streaming and IIoT pipelines with lineage from source

What we do

  • SAP, MES, PLM, QMS and DMS integration
  • Enterprise data model, ontology & knowledge graph
  • IIoT & machine-data pipelines
  • Lakehouse / warehouse on Azure or AWS
  • Infrastructure as Code & legacy modernisation

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and curate priority sources end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

SCIKIQ Data Fabric SAP + DMS Data Model 268 data sources
Build · Service line

AI & Agentic Engineering

ForCIOCOOHead of R&D
On the value chainEngineering & R&DSupply chainProduction & plantQuality & warrantyMaintenanceSales & distributionAftermarket & parts

The client problem

GenAI and agent pilots stall at the controls review: no autonomy limits, no audit trail, no named owner for the exceptions, and answers nobody can trace to a source.

Outcomes

  • Supervised agent squads that scan, quantify and route, with autonomy set per agent
  • GenAI that answers from engineering, quality and service data and cites every source
  • People review only the exceptions, with the money impact and evidence in front of them

What we do

  • Agent design: roles, squads & autonomy levels
  • R&D and engineering knowledge assistants
  • Document & vision AI: specs, test reports, warranty claims
  • Grounded chat over enterprise data
  • Evaluation & guardrails: policy, limits, kill switch

Typical engagements

  • AssessmentAgent opportunity and controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Agentic Decision Platform R&D Knowledge Assistant Run an agent live Governance & controls
03

Transform

Domain practices that change how a manufacturing or distribution function works, end to end, with our accelerators as the starting point.

Transform · Service line

Supply Chain & Procurement

ForCPOSupply chain headTrading desk
On the value chainSupply chainProduction & plantQuality & warrantySales & distributionAftermarket & partsFinance & compliance

The client problem

Shortages, lead-time drift, end-of-life parts and supplier-quality gaps reach the line before they reach the plan, and trading margins are managed without SKU-level visibility.

Outcomes

  • Supply risks found early, priced in money and routed to a named buyer
  • Procure-to-pay exceptions explained and cleared faster
  • Inventory and working capital right-sized by SKU and location

What we do

  • Supply-resilience & component-lifecycle analytics
  • Supplier quality & PPAP evidence review
  • Procure-to-pay three-way match
  • Demand sensing & inventory optimisation
  • Trading margin by SKU, customer and route

Typical engagements

  • AssessmentSupply-risk and procurement diagnostic
  • Pilot · 30-45 daysOne category, plant or trading desk end to end
  • BuildSupply-chain data products and agent squads
  • Managed runAgent and model operations under SLA

Delivered with

Agentic Decision Platform SAP + DMS Data Model CLARION
Transform · Service line

Plant Operations, Quality & Reliability

ForCOOPlant headsQuality head
On the value chainEngineering & R&DProduction & plantQuality & warrantyMaintenance

The client problem

OEE, scrap and downtime are measured late; corrective actions age, certificates lapse unnoticed and maintenance history is free text nobody reads.

Outcomes

  • OEE and yield losses visible by line, shift and product
  • Quality cases opened, contained and closed with a full audit trail
  • Maintenance planned from condition, not the calendar

What we do

  • OEE, loss-tree & first-pass-yield analytics
  • CAPA, non-conformance & certificate tracking
  • Predictive maintenance & spare-parts optimisation
  • Capacity & engineering-bench planning
  • Field data capture & inspections

Typical engagements

  • AssessmentPlant data and loss-tree review
  • Pilot · 30-45 daysOne line or plant end to end
  • BuildPlant data products, control tower and squads
  • Managed runBot, model and agent operations under SLA

Delivered with

Control Tower Field Data Collector RPA Automation
Transform · Service line

Commercial, Dealer & Distribution

ForSales headDealer networkDistribution head
On the value chainSales & distributionAftermarket & partsFinance & compliance

The client problem

Primary sales look healthy while dealer stock and distributor sell-out tell a different story. Quotes are slow, schemes leak, and market signals arrive after competitors have moved.

Outcomes

  • Primary and secondary sales on one model, by dealer, distributor and SKU
  • Faster quotes and better win rates from evidence, not instinct
  • Pricing, scheme and rebate leakage found and recovered

What we do

  • Dealer & distributor 360 and health scoring
  • Secondary-sales visibility from the DMS
  • Quote, RFQ & win-rate analytics
  • Pricing, scheme & rebate leakage
  • Market early-warning & competitor signals

Typical engagements

  • AssessmentChannel data and commercial diagnostic
  • Pilot · 30-45 daysOne region, brand or distributor network
  • BuildCommercial cockpit and channel data products
  • Managed runModel monitoring and MLOps

Delivered with

SAP + DMS Data Model Enterprise Cockpit 360 Revenue Assurance CATALYST
Transform · Service line

Aftermarket, Service & Warranty

ForAftermarket headService headQuality head
On the value chainQuality & warrantyMaintenanceSales & distributionAftermarket & parts

The client problem

Parts, lubricants and service carry the margin and the loyalty, yet parts demand is forecast by gut, warranty claims are read one by one and product data differs on every channel.

Outcomes

  • Parts availability planned from the installed base
  • Warranty claims classified to failure modes and suppliers automatically
  • Consistent product content on every dealer, distributor and marketplace channel

What we do

  • Parts demand forecasting
  • Warranty analytics & recovery from suppliers
  • Product information management & syndication
  • Service-ticket and feedback classification
  • Installed-base and contract renewal signals

Typical engagements

  • AssessmentAftermarket and warranty data review
  • Pilot · 30-45 daysOne parts family or warranty stream
  • BuildAftermarket data products and agents
  • Managed runModel and agent operations under SLA

Delivered with

Product Information Management Enterprise Cockpit 360 NARRATOR
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIOCOO
On the value chainSupply chainProduction & plantMaintenanceFinance & compliance

The client problem

After go-live, plant and partner feeds change, models drift and agents need someone watching overrides, limits and evaluation results across every site and shift.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs
  • Continuous improvement driven by override and evaluation data
  • Your teams freed from L2/L3 support

What we do

  • Run & L2/L3 support
  • DataOps, including SAP, MES and DMS feeds
  • MLOps
  • AgentOps: logs, overrides, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Governance & controls SCIKIQ Data Fabric
Our assets

What makes our services faster

Every engagement starts from SCIKIQ IP rather than a blank page. These assets are how we deliver — they come with the service.

1
SCIKIQ Data Fabric

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 268 data sources, and governance, lineage, data quality, PII detection and masking, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

Manufacturing accelerators from platforms we have built — the Enterprise Cockpit 360, the Agentic Decision Platform, the SAP + DMS Data Model and the R&D Knowledge Assistant — plus cross-industry accelerators such as CLARION, COMPASS and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

How it works Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most manufacturers start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the SCIKIQ Data Fabric, integrated with SAP, MES, PLM, QMS, dealer and distributor systems; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with operations, quality, procurement and finance; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The SCIKIQ Data Fabric we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by SCIKIQ with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — ERP, plant, engineering, channel and the ledger connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation