Data Sciences Group · Asset Management + Intelligence

Build better maintenance data. Faster.

Data Sciences Group provides Enterprise Asset Management (EAM) consulting and maintenance data services, combining equipment libraries and proprietary AI tools to prepare asset, maintenance and materials data. From reliability engineering to CMMS data preparation and deployment, DSG turns source information into practical maintenance intelligence.

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We work across your EAM/CMMS platforms: SAP PM and MM, IBM Maximo, Oracle Enterprise Asset Management (eAM), Hexagon / HxGN EAM, Maintenance X / MaintainX, Blue Mountain RAM and other platforms.

Expertise. Intellectual property. Tools.

Our offerings

Asset management, reliability and criticality assessments, maintenance data builds, governance and EAM implementation support.

Our intellectual property

Reusable equipment libraries, classifications, failure frameworks, Job Plans and BOM templates, configured to your assets and operating context.

Our proprietary AI tools

Extract data from OEM manuals and drawings, analyze maintenance and procurement transactions, then create, cleanse, enrich and validate data for platform-specific load templates.

Industries

EAM and maintenance data services by industry

Utilities

Electric, gas, water and wastewater.

Transformers · Breakers · Pumps · Valves · Blowers

Oil & Gas

Upstream, midstream, downstream and pipelines.

Pumps · Compressors · Vessels · Exchangers

Mining & Minerals

Mining, crushing, conveying and mineral processing.

Crushers · Mills · Screens · Feeders

Food, Grain & Bulk Handling

Receiving, storage, conveying, processing and loading.

Elevators · Conveyors · Dust Collectors

Pharmaceutical & Life Sciences

Manufacturing, clean utilities, HVAC and process systems.

AHUs · Pumps · Vessels · Packaging

Manufacturing & Industrial

Production, material handling and plant utilities.

Motors · Gearboxes · Compressors

Enterprise Asset Management services

Services across the asset lifecycle

DSG combines EAM consulting, asset master data development, reliability engineering and maintenance planning to improve cost, risk and performance.

01 · FOUNDATION

Asset and maintenance data builds

  • Asset and functional location hierarchy development
  • Bills of Materials and material master items
  • Preventive Maintenance job plans
  • Equipment damage and cause codes
02 · RELIABILITY

Criticality analyses

  • Failure Mode and Effects Analysis (FMEA)
  • Reliability Centered Maintenance (RCM)
  • What-if analyses and HAZOPs
  • Criticality-based maintenance priorities
03 · PRACTICES

Asset management services

  • Enterprise Asset Management assessments
  • Work Management assessments
  • Asset criticality and PM optimization
  • EAM/CMMS implementation support
04 · TRANSFORMATION

Data maturity and governance

  • Value versus cost analysis for master data improvements
  • Data maturity assessments and roadmaps
  • Program and implementation management
  • Governance and sustainable data practices
Data Sciences Group

Experience · Enterprise Asset Management

Our Maintenance Build Expertise

DSG combines the knowledge of our people with reusable tools developed through more than 25 years of asset-intensive projects.

Our Knowledge

Asset and maintenance expertise

Our Tools

Templates and extraction accelerators

Asset libraries · PM task lists · BOMs · Failure classifications

Cross-industry delivery

Maintenance builds for government, energy, utilities, mining, pharmaceutical, manufacturing and healthcare organizations.

Platform experience

Our specialists bring deep maintenance-build experience with SAP PM and MM, IBM Maximo, Oracle Enterprise Asset Management (eAM), Hexagon, Maintenance X / MaintainX, Blue Mountain RAM and other EAM/CMMS platforms. Project teams configure the data for each client's target system.

People who know the assets

Engineers, technicians and planners bring firsthand equipment knowledge to asset hierarchies, PM strategies, job plans, materials and failure classifications.

Data Sciences Group

How AssetIntel works

From source data to EAM-ready maintenance intelligence

DSG starts with a reusable equipment-class library, then configures it to the manufacturer, model, operating context and installed asset.

01 · Library

Start with structured knowledge

Equipment classes, attributes, Failure Classifications, Preventive Maintenance strategies, Job Plans and BOM frameworks.

02 · Configuration

Make it specific

Map manufacturer and model, enrich from OEM documentation, reconcile customer data and account for criticality and site context.

03 · Deployment

Put data to work

Validate, convert and prepare production-ready master data for the target EAM/CMMS, with loading support.

View the five-stage workflow

Five-stage workflow

Collect. Structure. Configure. Validate. Deploy.

1

Collect

EAM extracts, drawings, OEM manuals, field data, maintenance history and materials.

2

Identify & structure

Asset register, functional hierarchy, classes and technical attributes.

3

Configure

Failure Classifications, Preventive Maintenance, Job Plans and BOMs.

4

Validate

OEM, field, criticality and subject matter review.

5

Deploy

Prepare and load structured records into the target EAM.

Data Sciences Group

As-Built and P&ID Data Extraction and Validation

Tank and pump: from drawing to asset hierarchy

As-built drawing and piping and instrumentation diagram (P&ID) extraction supports asset identification, equipment attributes and hierarchy development. Select a tag to explore the illustrative output.

TK-101 · TANK & PUMP SYSTEM / P&IDTK-101Tank · L1V-101P-101A centrifugal pump — volute casing and impellerP-101APump · L2E-101PI/PTFI/FTTI/TTPressure · L5Flow · L5Temperature · L5MMotor · L3Child of pumpMotor StarterL4 · Child of pump— Process piping- - Drive / electrical linkL5: Pressure, flow and fluid temperatureEach instrument pair includes a gauge and transmitter.
Selected from drawingTK-101
01 · Asset candidateSurge tank
02 · ClassificationVessels › Storage / surge tank
03 · Drawing attributes
Tag
TK-101
System
Transfer system
Drawing cue
Vessel symbol + outlet
04 · Proposed asset hierarchy
Level 1 · ParentTK-101 · Surge Tank
  • Level 2 · Child of TankP-101A · Centrifugal Pump
  • Level 3 · Child of PumpMotor
  • Level 4 · Child of PumpMotor Starter
  • Level 5 · Pump InstrumentationPressure Gauge and Transmitter
  • Level 5 · Pump InstrumentationFlow Gauge and Transmitter
  • Level 5 · Pump InstrumentationFluid Temperature Gauge and Transmitter
Selected from drawingV-101
01 · Asset candidateIsolation valve
02 · ClassificationValves › Manual isolation valve
03 · Drawing attributes
Tag
V-101
System
Transfer system
Drawing cue
Inline valve symbol
04 · Proposed asset hierarchy
Pharma site › Process area
Transfer system
↳ V-101 · Isolation valve
Selected from drawingP-101A
01 · Asset candidateCentrifugal pump
02 · ClassificationRotating equipment › Centrifugal pump
03 · Drawing attributes
Tag
P-101A
System
Transfer system
Drawing cue
Pump symbol on transfer line
04 · Proposed asset hierarchy
Level 1 · ParentTK-101 · Surge Tank
  • Level 2 · Child of TankP-101A · Centrifugal Pump
  • Level 3 · Child of PumpMotor
  • Level 4 · Child of PumpMotor Starter
  • Level 5 · Pump InstrumentationPressure Gauge and Transmitter
  • Level 5 · Pump InstrumentationFlow Gauge and Transmitter
  • Level 5 · Pump InstrumentationFluid Temperature Gauge and Transmitter
Selected from drawingE-101
01 · Asset candidateHeat exchanger
02 · ClassificationHeat transfer › Heat exchanger
03 · Drawing attributes
Tag
E-101
System
Transfer system
Drawing cue
Exchanger symbol
04 · Proposed asset hierarchy
Pharma site › Process area
Transfer system
↳ E-101 · Heat exchanger

Example output is illustrative. DSG reconciles candidates with as-built drawings, OEM data, field evidence and EAM records before loading.

Data Sciences Group

DSG’s Proprietary AI · OEM Documentation Intelligence

OEM Manual Data Extraction

AI-powered OEM manual data extraction turns Original Equipment Manufacturer documentation into Asset Intelligence. DSG’s proprietary AI tools help extract, analyze and organize valuable equipment data from operating manuals, maintenance manuals, parts catalogs, datasheets and supporting diagrams—turning scattered document content into structured records for review and EAM/CMMS preparation.

From document content to usable maintenance data

Extract tables and narrative instructions, standardize terminology and units, associate data with the applicable equipment and retain source references. The available data depends on the manual, its quality, revision and equipment configuration.

01 · Extractable Data

Equipment Identity & Applicability

Manufacturer, product family, model, series, serial-number ranges, nameplate fields, equipment descriptions and configuration options.

Asset Intelligence value: Match the correct manual and revision to the installed asset; distinguish optional components from supplied equipment.

02 · Extractable Data

Components & Asset Hierarchies

Assembly names, subassemblies, component relationships, exploded-view callouts, package boundaries and component identifiers.

Asset Intelligence value: Propose maintainable asset/component hierarchies and links between equipment, assemblies and spare parts.

03 · Extractable Data

Classifications & Technical Attributes

Equipment type; dimensions and weight; materials of construction; connection sizes; capacity, flow, head, pressure, temperature, speed, power, voltage and enclosure ratings where documented.

Asset Intelligence value: Populate equipment-class attributes with values, units, applicability and source references.

04 · Extractable Data

Operating Limits & Performance

Rated duty, permissible operating ranges, performance curves, efficiency information, minimum-flow requirements, ambient limits, duty-cycle restrictions and operating cautions.

Asset Intelligence value: Organize operating envelopes and performance references for engineering review and asset-specific configuration.

05 · Extractable Data

Installation & Commissioning

Foundation and mounting requirements, alignment tolerances, utilities, clearances, storage/preservation requirements, start-up checks, commissioning steps and acceptance criteria.

Asset Intelligence value: Prepare installation and commissioning checklists and identify requirements to verify in the field.

06 · Extractable Data

Preventive Maintenance Tasks & Frequencies

Inspection, cleaning, lubrication, adjustment, testing and scheduled replacement tasks; calendar, run-hour, cycle-count or condition-based intervals; task prerequisites and acceptance criteria.

Asset Intelligence value: Draft PM strategies and Job Plan tasks while retaining interval triggers, operating assumptions and OEM exceptions.

07 · Extractable Data

Condition Monitoring & Inspection Criteria

Measurement points, inspection methods, baseline values, tolerances, wear limits, alarm guidance, vibration/temperature limits and recommended monitoring methods where provided.

Asset Intelligence value: Define candidate measurement points, inspection records and condition-triggered actions for specialist review.

08 · Extractable Data

Lubricants, Fluids & Consumables

Lubricant grades/specifications, viscosities, approved fluid types, fill quantities, lubrication points, replacement intervals, filters, cleaning agents and handling requirements.

Asset Intelligence value: Build lubrication records and PM material lists with quantity, unit and compatibility constraints.

09 · Extractable Data

Corrective Maintenance & Repair Procedures

Disassembly/reassembly steps, adjustment methods, tightening torques, fits/clearances, wear-part replacement rules, repair limits and post-repair checks.

Asset Intelligence value: Draft repair Job Plans and identify repair-specific materials, special tools and inspection hold points.

10 · Extractable Data

Troubleshooting & Failure Classifications

Documented symptoms, fault codes, diagnostic checks, possible causes and recommended corrective actions from troubleshooting tables.

Asset Intelligence value: Propose Problem, Cause and Remedy classifications tied to the OEM evidence; preserve possible causes as hypotheses.

11 · Extractable Data

Parts Catalogs & Equipment BOMs

OEM part numbers, descriptions, drawing/item numbers, assembly relationships, quantities per assembly, units, model applicability, kit contents and documented supersessions.

Asset Intelligence value: Develop equipment spare-parts BOMs, PM consumable BOMs and CM repair BOMs; flag conditional replacements and reconcile material-master matches.

12 · Extractable Data

Labor, Crafts, Skills & Tools

Documented crew sizes, task times, qualifications, craft requirements, lifting equipment, test instruments, calibration needs, special tools and tool identifiers.

Asset Intelligence value: Prepare Job Plan resource requirements. Missing labor hours or craft assignments can be proposed separately using DSG libraries and expert review.

13 · Extractable Data

Electrical, Instrumentation & Controls

Terminal references, wiring diagrams, I/O definitions, sensor ranges, setpoints, protection settings, alarm/fault definitions, communication interfaces and firmware/software compatibility where included.

Asset Intelligence value: Organize controls attributes, diagnostic references and maintenance checks for electrical and controls specialists.

14 · Extractable Data

Safety & Environmental Requirements

Warnings, hazards, isolation requirements, stored-energy precautions, PPE, handling/lifting instructions, hazardous-material information, spill precautions and disposal guidance.

Asset Intelligence value: Capture OEM precautions for site-approved work procedures and link referenced safety documents.

15 · Extractable Data

Warranty, Support & Document Traceability

Warranty terms and maintenance conditions, service contacts, certificates, referenced standards, manual numbers, editions, revisions, drawing references, page/table/figure locations and referenced documents.

Asset Intelligence value: Maintain source-linked equipment documentation and identify missing, conflicting or outdated information.

Outputs for Maximo, SAP PM/MM, Oracle eAM, Hexagon, Maintenance X and Blue Mountain RAM

Reviewed extraction results can support asset and component records, classification attributes, PM schedules, Job Plans, failure classifications, material-master candidates, equipment BOMs, measurement points, document links and configured data-load files.

Keep evidence and proposed enrichment distinct. OEM recommendations and source values are retained separately from DSG-proposed classifications, labor estimates and maintenance strategies. Missing fields, conflicting instructions and uncertain matches are flagged for review. Site teams validate equipment applicability, safety requirements, maintenance intervals and parts compatibility before implementation.

OEM documents generally do not establish the installed asset’s location/tag, actual condition, transaction history, current supplier price, current lead time or site-specific risk. DSG reconciles these with drawings, field records, EAM/ERP data and supplier information.

Data Sciences Group

Materials intelligence · interactive example

From OEM manual to priced PM and CM BOMs

Select a maintenance activity to see how DSG's pre-built BOM template guides extraction, then maps the material candidates to DSG pricing for a cost estimate.

01 · SOURCE

Illustrative OEM manual excerpt

P-101A · centrifugal transfer pump · scheduled service

“Lubricate bearing housing with EP2 bearing grease, one cartridge.

Replace seal-flush filter element F-20 at the 2,000-hour interval.

Inspect coupling insert and replace only if worn.”

Sample text created for this demo; not an actual OEM instruction.

02 · TEMPLATE + EXTRACTION

Pre-built PM BOM target list

  • LubricantEP2 bearing grease · 1 cartridge
  • Filter / consumableF-20 flush element · 1 each
  • Wear part · conditionalCoupling insert · inspect; exclude from planned cost

DSG tool identifies material type, part reference, quantity and activity from the highlighted instructions.

03 · PRICED PM BOM

Extracted materials + DSG pricing

Material candidateQtyUnitCost
EP2 bearing grease1$18$18
F-20 flush element1$35$35
Estimated PM materials$53

Conditional coupling insert is flagged for review, not included in this total.

01 · SOURCE

Illustrative OEM manual excerpt

P-101A · centrifugal transfer pump · seal repair

“For mechanical-seal replacement, install seal kit MSK-100.

Renew casing gasket CG-100 and O-ring set OR-100 during reassembly.”

Sample text created for this demo; not an actual OEM instruction.

02 · TEMPLATE + EXTRACTION

Pre-built CM BOM target list

  • Repair kitMSK-100 mechanical seal · 1 kit
  • GasketCG-100 casing gasket · 1 each
  • Elastomer / sealOR-100 O-ring set · 1 set

DSG tool links each extracted part reference to the seal-replacement activity and CM BOM template.

03 · PRICED CM BOM

Extracted materials + DSG pricing

Material candidateQtyUnitCost
MSK-100 seal kit1$285$285
CG-100 gasket1$38$38
OR-100 O-ring set1$28$28
Estimated CM materials$351

Illustrative part numbers and DSG pricing examples. Actual BOMs and costs require OEM applicability, supersession, material-master and current vendor-price validation before procurement.

Data Sciences Group

Equipment Classifications

Equipment classifications by industry

Equipment taxonomy and classification define equipment types and the technical specification attributes needed to describe each asset consistently. DSG defines the desired fields, data types, engineering units and required/optional rules for each class, then populates supported values from OEM manuals, drawings and verified asset records. Choose an industry, then explore a sample attribute template below.

Utilities

Electric, gas, water and wastewater

Illustrative Classes
Power transformersSubstation, distribution and station service
Breakers & switchgearSF6, vacuum, reclosers and MCCs
Protection & controlRelays, RTUs, SCADA panels and meters
Electric networksFeeders, cables, poles and line devices
Gas regulationRegulator runs, relief valves and odorizers
Pipelines & valvesGas mains, isolation valves and actuators
Water pumpingIntake, booster and distribution pumps
Water treatmentFilters, clarifiers and disinfection skids
Wastewater processAeration blowers, screens and digesters
Storage assetsReservoirs, tanks and water towers
Process instrumentsFlow, pressure, level and analyzers
Standby & facilitiesGenerators, UPS, HVAC and fleet

Oil & Gas

Upstream, midstream, downstream and pipelines

Illustrative Classes
Wells & wellheadsTrees, chokes and artificial lift
Gathering systemsFlowlines, manifolds and pig launchers
SeparationSeparators, scrubbers and treaters
PumpsCentrifugal and positive displacement
CompressionReciprocating and centrifugal packages
Heat transferExchangers, air coolers and heaters
StorageTanks, vessels and loading systems
Pipeline stationsBlock valves, meters and regulators
Process valvesControl, isolation and relief valves
MeasurementFlow meters, analyzers and instruments
Safety systemsESD, fire and gas detection
Power & controlsSwitchgear, drives, PLCs and RTUs

Mining & Minerals

Extraction, crushing, conveying and processing

Illustrative Classes
Drilling equipmentProduction drills and compressors
CrushingJaw, cone and gyratory crushers
GrindingSAG, ball and rod mills
ScreeningVibrating screens and classifiers
ConveyingBelts, drives and transfer stations
FeedingApron, belt and vibrating feeders
SeparationFlotation cells and magnetic separators
DewateringThickeners, filters and centrifuges
Slurry systemsPumps, pipes and valves
Dust controlCollectors, fans and suppression
Mobile fleetHaul trucks, loaders and dozers
Site utilitiesSubstations, water and compressed air

Food, Grain & Bulk

Receiving, storage, processing and loadout

Illustrative Classes
ReceivingPits, hoppers and truck dumpers
ElevatingBucket elevators and leg drives
ConveyingBelt, drag, screw and pneumatic
StorageBins, silos and aeration systems
DryingGrain dryers, burners and fans
CleaningScreens, magnets and separators
MillingRoller mills, hammer mills and sifters
Mixing & batchingBlenders, scales and feeders
Dust collectionBaghouses, cyclones and fans
Process valvesSlide gates, diverters and rotary airlocks
PackagingFillers, baggers and palletizers
LoadoutRail/truck spouts, scales and samplers

Pharma & Life Sciences

Process, clean utilities and controlled environments

Illustrative Classes
Process vesselsBioreactors, mixers and hold tanks
Sanitary pumpingCentrifugal, lobe and diaphragm
Clean utilitiesWFI, purified water and clean steam
CIP / SIPCleaning and sterilization skids
FiltrationDepth, membrane and sterile filters
PurificationChromatography and separation skids
Heat transferSanitary exchangers and chillers
DryingLyophilizers and dryers
Fill & finishAseptic fillers and cappers
PackagingLabelers, cartoners and inspection
Cleanroom HVACAHUs, HEPA and pressure controls
Monitoring & powerEMS, BMS, UPS and generators

Manufacturing

Production, material handling and plant utilities

Illustrative Classes
Production linesAssembly cells and line conveyors
Machine toolsCNC mills, lathes and grinders
FormingPresses, extruders and molding
RoboticsRobot arms, end effectors and cells
Material handlingConveyors, hoists and AGVs
DrivesMotors, gearboxes and VFDs
Fluid systemsPumps, valves and hydraulics
Compressed airCompressors, dryers and receivers
Thermal utilitiesBoilers, chillers and cooling towers
ElectricalSwitchgear, MCCs and transformers
EnvironmentalDust collection and wastewater
Controls & testPLCs, sensors and test stands

Shared equipment classifications

Rotating Equipment

  • Centrifugal Pumps · view desired attributes

    ROT-PUMP · Common identity fields: asset tag, manufacturer, model and serial number.

    Required/optional status, permitted values and source references are configured for the site. Asset values are validated before loading.

  • Positive Displacement Pumps
  • Electric Motors
  • Compressors
  • Fans & Blowers
  • Gearboxes

Material Handling

  • Belt Conveyors · view desired attributes

    MAT-BELT · Common identity fields: asset tag, manufacturer, model and serial number.

    Required/optional status, permitted values and source references are configured for the site. Asset values are validated before loading.

  • Drag Conveyors
  • Bucket Elevators
  • Screw Conveyors
  • Feeders
  • Screens

Electrical

  • Transformers · view desired attributes

    ELE-TRANS · Common identity fields: asset tag, manufacturer, model and serial number.

    Required/optional status, permitted values and source references are configured for the site. Asset values are validated before loading.

  • Circuit Breakers
  • Switchgear
  • Motor Control Centers
  • Variable Frequency Drives
  • UPS & Batteries

Process Equipment

  • Tanks & Vessels · view desired attributes

    PROC-TANK · Common identity fields: asset tag, manufacturer, model and serial number.

    Required/optional status, permitted values and source references are configured for the site. Asset values are validated before loading.

  • Heat Exchangers
  • Mixers & Agitators
  • Control Valves
  • Isolation Valves
  • Regulators

HVAC & Facilities

  • Air Handling Units · view desired attributes

    HVAC-AHU · Common identity fields: asset tag, manufacturer, model and serial number.

    Required/optional status, permitted values and source references are configured for the site. Asset values are validated before loading.

  • Chillers
  • Boilers
  • Cooling Towers
  • Exhaust Systems
  • Building Pumps

Instrumentation & Controls

  • PLCs · view desired attributes

    CTRL-PLC · Common identity fields: asset tag, manufacturer, model and serial number.

    Required/optional status, permitted values and source references are configured for the site. Asset values are validated before loading.

  • Process Instruments
  • Analyzers
  • Control Panels
  • Sensors & Switches
  • Remote I/O

Each classification includes common identity fields—manufacturer, model, serial number and asset tag—plus class-specific technical attributes. Required fields, permitted values, units and source references are configured for the site and target EAM/CMMS, including Oracle eAM.

Data Sciences Group

Failure classifications · interactive examples

Problem, Cause and Remedy by equipment class

Failure coding organizes equipment problems, possible causes and remedies into a Problem, Cause and Remedy (PCR) framework. Select the first class in a shared equipment group to explore sample EAM failure records.

These starter examples are illustrative. DSG validates failure language, cause relationships and remedies with maintenance and reliability teams.

Preventive Maintenance · Job Plans · Interactive Examples

PM Job Plans by equipment classification

Preventive maintenance planning connects PM task lists and Job Plans to equipment classifications and failure codes. Explore a sample plan for each shared equipment category and the problems its tasks help detect.

Each example includes tasks, crafts, tools, material/BOM references and related Failure Classifications. DSG configures the plan to the manufacturer, model, duty and target EAM/CMMS.

Equipment Bills of Material · Interactive Examples

Equipment BOMs: planned service and corrective repairs

Equipment and spare parts Bill of Material (BOM) development links assets and maintenance activities to required materials. DSG connects PM and CM BOMs to installed equipment, Job Plans and the target EAM/CMMS material master.

Preventive Maintenance BOMs

Materials for planned maintenance

A PM BOM defines the consumables and replacement parts for a scheduled or condition-triggered maintenance activity: lubricants, filters, gaskets and planned wear-part replacements.

Linked to the PM Job Plan, it supports repeatable material planning, reservations and service-cost estimates. Quantities are planned for one execution of the defined task.

Corrective Maintenance BOMs

Parts for a defined repair

A CM BOM identifies the spare parts and consumables for a diagnosed fault or approved repair scope: a seal kit, idler assembly, cooling fan, drive belt or I/O module.

Linked to the asset, failure classification and repair Job Plan, it supports parts selection, availability checks and repair planning. The final work-order materials depend on diagnosis and the approved scope.

The same part can appear in both. A gasket or belt may be renewed during planned service or used to repair a failure. The distinction is the maintenance activity and inclusion rule. An equipment spare-parts BOM can also hold the broader list of applicable parts; an activity BOM selects what that particular task requires.

Illustrative examples for the first equipment class in each shared category. Demo item IDs are placeholders, not OEM part numbers. DSG verifies manufacturer/model, part applicability, specifications, units, quantities and material-master matches before loading.

Data Sciences Group

DSG’s Proprietary AI · Transactional Intelligence

Transactional Analysis Tools

Turn everyday procurement and maintenance transactions into actionable Asset Intelligence. Data Sciences Group’s proprietary AI tools help analyze structured records and free-text descriptions at scale, connecting what was purchased, what failed and what work was performed to the equipment involved.

Maintenance, procurement and inventory analysis

Automated extraction, normalization, record matching and pattern detection reduce repetitive spreadsheet preparation and manual record-by-record review. Reusable equipment classifications and analysis rules support cost-effective analysis across asset fleets and repeatable reviews as new transactions arrive.

Results provide candidate matches, trends and prioritized exceptions linked to source records, so procurement, maintenance and reliability teams can focus their expertise on the decisions that matter.

01 · Practical Application

Procurement of Materials

Data: Purchase orders, receipts, supplier quotes and material issues.

AI analysis: A pump seal kit is purchased under several descriptions and suppliers. AI groups likely matching items, normalizes units and compares price, lead time and emergency-buy history.

Asset Intelligence decision: Identify consolidation opportunities, improve sourcing and prioritize long-lead spares. Validate part equivalence before combining purchases.

02 · Practical Application

Free-text Purchase Order Analysis

Data: PO line descriptions, requisition notes and supplier invoices.

AI analysis: Descriptions such as “seal for P-101,” “pump seal kit” and “mechanical seal assy” are parsed for part, manufacturer, model, asset tag and service type. AI proposes material-master and equipment-class matches.

Asset Intelligence decision: Recover spend hidden in free text, separate parts from services and flag candidates for catalog items and equipment BOMs.

03 · Practical Application

Failure Analysis

Data: Work-order narratives, failure codes, downtime and repair history.

AI analysis: Pump work orders repeatedly mention leakage, seal replacement and vibration. AI groups recurring symptoms and proposed Problem, Cause and Remedy classifications, then compares patterns across similar equipment.

Asset Intelligence decision: Prioritize recurring failures for root-cause investigation and reliability improvements. Narrative evidence suggests causes; engineering review confirms them.

04 · Practical Application

Preventive Maintenance Work Order Analysis

Data: PM schedules, completed work orders, findings, labor and material usage.

AI analysis: An AHU inspection repeatedly finds clean filters, while another unit needs replacements between scheduled visits. AI compares findings, actual effort and corrective work following PMs.

Asset Intelligence decision: Identify candidates to adjust task scope, frequency, labor estimates and PM BOMs, subject to operating conditions, OEM guidance and required maintenance.

05 · Practical Application

Corrective Maintenance Work Order Analysis

Data: Repair descriptions, actual labor, material issues, downtime and repeat visits.

AI analysis: Conveyor repairs repeatedly replace the same idler assembly and require more labor than planned. AI groups comparable repairs and highlights repeat work, missing parts and planned-versus-actual effort.

Asset Intelligence decision: Improve corrective Job Plans and CM BOMs, prepare repair kits and prioritize assets with recurring repair demand.

06 · Practical Application

Inventory & Spare Parts Optimization

Data: Stock balances, issues, returns, reservations, open POs and lead times.

AI analysis: A critical pump spare has low annual usage but a long replenishment lead time; another item is frequently expedited. AI summarizes demand patterns and flags stockout exposure, duplicate items and slow-moving stock.

Asset Intelligence decision: Review stocking levels and reorder settings using demand, asset criticality and lead time together. Avoid treating low usage alone as a reason to remove critical spares.

07 · Practical Application

Repair-versus-Replace Decisions

Data: Repair costs, downtime, asset age, condition and replacement estimates.

AI analysis: Repeated motor repairs accumulate material, contractor and internal labor costs. AI consolidates the history and builds comparable lifecycle-cost scenarios using supplied replacement and operating assumptions.

Asset Intelligence decision: Identify assets for repair-versus-replace assessment and rank capital candidates by cost, reliability and operational impact.

08 · Practical Application

Maintenance Labor & Backlog Analysis

Data: Work-order priorities, planned and actual hours, crafts, dates and status history.

AI analysis: Similar transformer maintenance tasks show different labor estimates, while ready work waits for a specialist craft. AI groups comparable jobs and highlights estimate variation, repeat visits and aging critical work.

Asset Intelligence decision: Refine Job Plan labor, identify craft constraints and prioritize backlog using asset criticality and work readiness.

09 · Practical Application

Asset History & Data Quality Improvement

Data: Asset references, location codes, PO text, material issues and work-order records.

AI analysis: A pump’s purchases and repairs are scattered across an asset tag, a location and a legacy name. AI proposes record links and flags inconsistent classifications, missing failure codes and materials absent from BOMs.

Asset Intelligence decision: Build a more complete asset history and a prioritized correction list for the EAM/CMMS. Review ambiguous matches before updating master data.

From transaction history to a decision-ready action list

1 · Bring together EAM/CMMS and ERP exports, purchase orders, invoices, inventory transactions and work-order history.

2 · Analyze and connect Normalize terminology, link candidate assets and materials, classify narratives and compare costs, usage and recurring events.

3 · Review and act Validate recommendations with subject-matter experts, then prepare approved changes to classifications, failure codes, Job Plans, BOMs, stocking policies and EAM/CMMS data-load files.

Examples are illustrative applications. Analysis depends on the available records and their quality; proposed part matches, failure causes and maintenance changes are reviewed before implementation.

Data Sciences Group

DSG’s Proprietary Tools · Master Data Preparation

Asset Master Data Load Tools

DSG has developed proprietary tools to Create, Cleanse, Enrich, Validate and Populate Master Data Load Templates for IBM Maximo, SAP PM and MM, Oracle Enterprise Asset Management (eAM), Hexagon / HxGN EAM, Maintenance X / MaintainX, Blue Mountain RAM, legacy Infor EAM, IFS Cloud / IFS Applications, Fiix, eMaint, Maintenance Connection, Trimble Unity Maintain, Limble, UpKeep, and Microsoft Dynamics 365 Supply Chain Management. These tools support EAM/CMMS data migration preparation through asset master data cleansing, enrichment, validation and load-file mapping.

1 · Create

Build candidate master records from OEM manuals, drawings, DSG libraries and existing system exports.

2 · Cleanse

Standardize descriptions, units and codes; identify duplicates and inconsistent asset or material references.

3 · Enrich

Add supported attributes, hierarchy links, maintenance tasks, failure codes and applicable parts from source evidence and DSG libraries.

4 · Validate

Check required fields, valid values, unique keys, relationships, units and source applicability; resolve exceptions with subject-matter experts.

5 · Populate

Map approved records to target fields and populate the agreed load templates in the required formats and dependency order.

Typical Master Data Load Templates by platform

Representative template families are shown below. Exact columns, mandatory fields, supported objects and load methods are configured to the installed version and customer requirements.

01 · Typical Template Scope

IBM Maximo

  • Assets and Locations: asset records, parent/child relationships, operating locations and location hierarchies.
  • Classifications and Specifications: equipment classes, attributes, values and units.
  • Failure Classifications: Problem, Cause and Remedy code structures.
  • Job Plans: tasks, labor/crafts, tools, materials and safety references.
  • Preventive Maintenance: PM records, Job Plan links, frequencies and meter-based triggers.
  • Items and Spare Parts: item master, applicable asset spare parts/BOMs and inventory setup.
  • Supporting Masters: meters, measurement points, companies/vendors and document links as scoped.

DSG maps load files to the approved Maximo Integration Framework object structures, import definitions and organization/site rules.

02 · Typical Template Scope

SAP Plant Maintenance (PM)

  • Functional Locations: structure indicators, hierarchy, descriptions and organizational assignments.
  • Equipment: equipment categories, technical data, installation relationships and manufacturer/model details.
  • Classification: classes, characteristics and technical-object characteristic values.
  • Maintenance Task Lists: general, equipment and functional-location task lists; operations, work centers and material components.
  • Maintenance Plans and Items: cycles, strategies, scheduling parameters and task-list assignments.
  • Maintenance BOMs: equipment and functional-location BOM headers and components.
  • Supporting Masters: work centers, measuring points/counters, catalogs and codes, and document references as scoped.

Template layouts and supported migration objects are confirmed for the customer’s SAP release and approved migration approach.

03 · Typical Template Scope

SAP Materials Management (MM)

  • Material Master: descriptions, material types, units of measure, manufacturer part references and applicable views.
  • Plant and Storage Data: plant extensions, storage-location data and relevant stocking parameters.
  • Purchasing and MRP Views: purchasing groups, planning settings, replenishment parameters and lead-time fields.
  • Valuation and Accounting Views: valuation class and approved accounting fields where required.
  • Procurement Reference Masters: supplier/business-partner references, purchasing info records and source-list candidates as scoped.
  • Material BOMs and Cross-References: material/component relationships, alternate units and legacy-to-target material IDs.

Procurement, inventory and finance owners approve purchasing, planning and valuation values before template population.

04 · Typical Template Scope

Hexagon / HxGN EAM

  • Equipment: assets, positions, systems, locations and equipment relationships.
  • Equipment Classes and Attributes: classes, categories, custom fields and technical details.
  • Parts and Stores: parts master, store assignments, stocking data and supplier references.
  • Parts Lists/BOMs: applicable equipment parts and assembly/component relationships.
  • Task and Job Plans: instructions, activities, trades, labor, tools and planned parts.
  • PM Schedules: time-based or meter-based requirements and associated tasks/activities.
  • Supporting Masters: meters, inspection points, failure/repair codes and document references as scoped.

DSG prepares files against the customer’s approved HxGN EAM upload definitions or integration mappings.

05 · Typical Template Scope

Maintenance X / MaintainX

  • Assets: names, identifiers, parent assets, locations, types and supported custom fields.
  • Locations: location names, descriptions and hierarchy relationships supported by the import.
  • Parts: part identifiers, descriptions, quantities, locations and asset associations.
  • Part-to-Asset References: applicable spare parts and BOM cross-reference worksheets.
  • Maintenance Setup Worksheets: procedures/checklists, recurring maintenance, task steps and parts requirements for the agreed setup workflow.
  • Supporting Data: vendor references, document links and other approved fields where supported.

Native bulk-import coverage varies. DSG separates supported import templates from worksheets requiring an approved API or configuration process.

06 · Typical Template Scope

Blue Mountain RAM

  • Asset Master: asset IDs, descriptions, manufacturer/model, serial numbers, asset types, status and parent/component relationships.
  • Locations and Organizational References: site/location structures, departments, ownership and asset-location assignments.
  • Technical Attributes: equipment specifications, instrument ranges, engineering units and configured asset fields.
  • Maintenance Work Plans: approved task instructions, inspection criteria, resource references, tools and parts requirements.
  • Preventive Maintenance Schedules: maintenance frequencies, due-date rules and asset-to-work-plan assignments.
  • Calibration Requirements and Procedures: calibration intervals, measurement/test points, ranges, tolerance criteria and procedure references where supported.
  • Reference Standards: standard/instrument identifiers, applicable calibration status and procedure associations, subject to the configured data model.
  • Parts and Equipment BOM References: spare parts, consumables, quantities and asset/work-plan material associations.
  • Supporting Masters and Documentation: manufacturer/vendor references, responsible personnel, controlled procedure links and source-document references as scoped.

DSG creates, cleanses, enriches and validates these records, then populates the agreed Blue Mountain RAM load templates. Exact entities, field layouts and supported import methods are confirmed against the customer’s RAM version and configuration. Maintenance and calibration owners approve schedules, tolerances and controlled instructions before loading.

07 · Typical Template Scope

Oracle Enterprise Asset Management (eAM)

  • Asset Groups and Asset Numbers: equipment groups, asset records, manufacturer/model details, locations and parent/child relationships.
  • Asset Attributes: attribute groups, technical specifications, values and units.
  • Maintenance Activities and Routings: task instructions, operation sequences, departments, labor and equipment resource requirements.
  • Preventive Maintenance Schedules: calendar and meter-based intervals, scheduled dates and asset-to-activity associations.
  • Items and Maintenance BOMs: inventory item references, asset spare-parts lists and PM/CM activity materials with quantities and units.
  • Meters and Condition Monitoring: meter definitions, asset associations and inspection/quality-plan references as scoped.
  • Supporting Masters: failure codes, departments/resources, organization references and document attachments as supported.

DSG creates, cleanses, enriches and validates candidate records, then populates the agreed Oracle eAM load templates. Object coverage, field layouts and approved interfaces are confirmed against the customer’s Oracle E-Business Suite release and configuration. Oracle Fusion Cloud Maintenance requires its own product-specific mappings and load templates.

08 · Typical Template Scope

Infor EAM (legacy)

  • Equipment and Locations: assets, positions, systems, locations and parent/component relationships.
  • Classes and Technical Attributes: equipment classes, specifications, custom fields, units and supported values.
  • Parts and Inventory: parts master, stores, supplier references and approved stocking data.
  • Equipment Parts Lists / BOMs: applicable spare parts, assemblies, quantities and material cross-references.
  • Task Plans and Preventive Maintenance: instructions, activities, trades, labor, tools, planned materials and time- or meter-based schedules.
  • Supporting Data: meters, inspection points, failure/repair codes and document references as scoped.

DSG prepares and validates representative load templates for the customer’s installed Infor EAM release and approved import or integration approach. Legacy Infor EAM belongs to the product lineage subsequently branded HxGN EAM; migration mappings are confirmed against the actual source and target versions.

09 · Typical Template Scope

IFS Cloud / IFS Applications

  • Equipment and Functional Structures: equipment objects, functional locations, parent/child relationships and manufacturer/model identifiers.
  • Technical Attributes: equipment classifications, specifications, values, units and approved reference data.
  • Preventive Maintenance and Work Instructions: PM actions, task instructions, frequencies, meter/condition references and equipment associations as scoped.
  • Maintenance Resources: work centers, crafts, labor requirements, tools and planned material references.
  • Parts and Equipment BOMs: part candidates, spare-parts relationships, PM/CM materials, quantities and inventory cross-references.
  • Supporting Masters: suppliers, organizational references, measurements, failure codes and document links where supported.

DSG creates, cleanses, enriches and validates candidate data, then populates the agreed IFS load templates. These are representative preparation scopes; exact objects, mandatory fields, dependency order and approved migration or integration methods are confirmed for the customer’s IFS Cloud or IFS Applications version and configuration.

10 · Typical Template Scope

Fiix

  • Assets and Hierarchies: sites, equipment records, parent/child relationships, identity fields and supported custom fields.
  • Parts and Supplies: part descriptions, identifiers, units, stock references and applicable equipment associations.
  • Scheduled Maintenance: PM task lists, intervals, triggers and asset assignments.
  • Work Preparation: task instructions, planned materials and source-document references for the agreed setup scope.

DSG prepares asset, parts and scheduled-maintenance datasets against the agreed Fiix import templates. Required fields, custom-field setup and supported relationships are checked against the customer’s configuration; unsupported setup elements are delivered as mapped worksheets or through an approved integration approach.

11 · Typical Template Scope

eMaint — Fluke Reliability

  • Assets and Locations: equipment registers, locations, hierarchy relationships and technical specifications.
  • Preventive Maintenance: PM schedules, task instructions, frequencies, meter references and resource requirements.
  • Parts and Inventory: parts master candidates, equipment spare-parts relationships, quantities and stocking references.
  • Supporting Data: failure codes, suppliers, measurement points and controlled document references as scoped.

DSG prepares representative eMaint master-data and maintenance templates. Field mappings, configured forms, required values and approved import or integration methods are confirmed for the installed version and organization.

12 · Typical Template Scope

Maintenance Connection — Accruent

  • Assets and Locations: equipment records, organizational locations, parent/child relationships and manufacturer/model data.
  • PM Procedures and Schedules: maintenance instructions, frequencies, asset assignments, labor and material planning references.
  • Parts and Equipment BOMs: part records, equipment applicability, quantities, units and inventory cross-references.
  • Supporting Masters: vendors, classifications, failure language and document links within the agreed scope.

DSG creates, cleanses, enriches and validates the agreed preparation datasets for Maintenance Connection. Exact fields, procedure structures, import coverage and dependency order are confirmed against the customer’s version and configuration.

13 · Typical Template Scope

Trimble Unity Maintain — Cityworks / AgileAssets

  • Asset Registers and Network References: equipment, facilities and infrastructure records; unique identifiers and GIS or linear-network cross-references where applicable.
  • Classifications and Attributes: asset types, technical details, locations and approved inspection or condition fields.
  • Maintenance and Inspections: repeatable task instructions, maintenance schedules and inspection templates for the agreed workflow.
  • Materials and Resources: part candidates, material lists, crews, resource references and source-document associations as scoped.

DSG prepares representative datasets for the customer’s Trimble Unity Maintain, Cityworks or AgileAssets environment. GIS feature schemas, asset identifiers, spatial or linear relationships, required fields and approved loading methods are confirmed with the implementation team. These product environments use their own mappings.

14 · Typical Template Scope

Limble CMMS

  • Assets and Locations: asset registers, parent/child relationships, locations, identity fields and supported custom attributes.
  • PM Tasks and Procedures: instructions, recurring task schedules, inspection steps and asset associations.
  • Parts and Vendors: parts master candidates, quantities, asset-to-part relationships and supplier references.
  • Maintenance References: task material requirements, applicable document links and legacy-to-target cross-references.

DSG prepares asset, parts and maintenance setup data for Limble. Native import coverage, field layouts, required relationships and any API or configuration steps are confirmed against the customer’s plan and environment.

15 · Typical Template Scope

UpKeep

  • Assets and Locations: asset records, location references, equipment identifiers and supported custom attributes.
  • Preventive Maintenance: recurring maintenance candidates, task instructions, checklists and asset assignments.
  • Parts and Inventory: part candidates, units, quantities, inventory locations and equipment applicability references.
  • Supporting Data: vendor references, task resource requirements, document links and legacy identifiers as scoped.

DSG prepares reviewed master-data and maintenance setup worksheets for UpKeep. Supported imports, template columns, plan-specific capabilities and any approved API or manual configuration requirements are confirmed before loading.

16 · Typical Template Scope

Microsoft Dynamics 365

  • Assets and Functional Locations: equipment records, functional locations, parent/child structures, asset types and manufacturer/model identifiers.
  • Maintenance Setup: maintenance job types, task instructions, maintenance plans, rounds and approved resource references as scoped.
  • Spare Parts and Materials: applicable inventory items, asset spare-parts references, planned material requirements, quantities and units.
  • Supporting Masters: counters, condition measurements, fault classifications, organizational references and source-document links where supported.

Scope focuses on Asset Management in Dynamics 365 Supply Chain Management. DSG prepares and validates the agreed datasets against supported data entities and the customer’s approved data-management or integration approach. Exact entities, dependencies, inventory relationships and configuration are confirmed for the installed release. Other Dynamics 365 products require their own mappings.

Validated data, traceable mappings and load preparation

Typical deliverables include populated templates, legacy-to-target ID cross-references, field mappings, source references, validation results, an exception log and a dependency-based load sequence. Parent records and supporting reference data are prepared before dependent assets, plans, material relationships and BOM components.

Template validation is followed by the customer’s approved test-load and reconciliation process. Actual system loading, permissions, import configuration and production acceptance are agreed within the implementation scope.

Data Sciences Group