Manufacturing Data Analytics on AWS

Manufacturing Analytics
IT/OT Convergence
Predictive Maintenance

Mactores connects your PLCs, historians, MES, ERP, and quality systems into one governed data platform on AWS, then builds the manufacturing data analytics and AI your plant managers will use in the Monday morning production meeting. The production date goes into the SOW, and the fee is fixed before work starts.

Most manufacturers already have more plant data than they can use. Sensor tags pile up in historians, MES records stay locked in one site's database, and the ERP tells a different story about yield than the shift report does. Mactores puts all of it on one platform with one set of definitions, so OEE, scrap, and downtime numbers hold up in front of your VP of Operations and your CFO alike. No inbound path is ever opened into your plant network.

Put a go-live date on your plant data

Name one plant and the KPI it reviews every morning.

The first session tells you whether it is viable, what is in scope, and when it can go live.

Book a scoping call

10x: faster queries on Seagate's factory data platform
3x: faster reporting after a manufacturer's Oracle warehouse moved to Amazon Redshift
94%: of Mactores engagements shipped on or before the committed date
200+: AWS-certified practitioners
2008: Shipping on AWS since

AWS Services Partner: Premier Tier
AWS Competency: Manufacturing and Industrial Services
AWS Specialization: Agentic AI
AWS-certified practitioners: 200+

Top Mactores Clients

All 21 case studies →

Customers include Safaricom, Synaptics, Flipboard, Poshmark, Seagate, HP, Adani, DocuSign, KlearTrust, Tilia, Sterne Kessler and Total Expert.

Why Do Manufacturing Analytics Programs Stall Before They Reach the Plant Floor?

Manufacturing analytics programs stall at the scale-out, not the model. A predictive maintenance pilot works on one line, then meets eleven more with different PLC vendors, tag names, and MES versions. Without a shared asset model, common KPI definitions, and an OT security design, every new line turns into a new project. The plumbing, not the math, stops the rollout.

Mactores has watched that story repeat across eighteen years of building on AWS, and the research matches what we see in the field. Gartner found that organizations reporting successful AI initiatives invest up to four times more, as a percentage of revenue, in data and analytics foundations than those with poor outcomes. On a manufacturing floor, that foundation is IT/OT convergence: a governed connection between operational technology and the systems that run finance, planning, and quality.

What you see

Usual cause

What Mactores changes

OEE differs by plant and nobody trusts the roll-up

Usual cause: Each site calculates availability, performance, and quality its own way

What Mactores changes: One governed OEE definition in a shared semantic layer, published to every plant

The predictive maintenance pilot works on one line only

Usual cause: Tag names and asset hierarchies vary from site to site

What Mactores changes: A standard asset model based on ISA-95, mapped plant by plant

Process engineers still export to spreadsheets

Usual cause: Dashboards are slow or show yesterday's data

What Mactores changes: Streaming ingestion with minute-level freshness where the KPI needs it

Security keeps blocking the cloud connection

Usual cause: The design opens inbound paths into the OT network

What Mactores changes: Outbound-only edge gateways and segmented zones aligned to ISA/IEC 62443

The cloud bill climbs with every new sensor

Usual cause: All raw data is stored hot and queried at full resolution

What Mactores changes: Edge aggregation, tiered storage, and a cost ceiling per plant

Historian license renewals keep the old stack alive

Usual cause: Nobody scoped the retirement

What Mactores changes: Legacy retirement written into the SOW with a date

Pilot stuck on one line?

We find what blocks the scale-out, keep the parts that work, and put a date on the next plants.

Bring the pilot as it stands today.

Get a pilot review

What Do Mactores' Manufacturing Data Analytics Services on AWS Include?

Mactores' manufacturing data analytics services cover seven entry points, from a plant data readiness assessment and an industrial data platform build to OEE dashboards, predictive maintenance, supply chain analytics, historian retirement, and generative AI on plant data. Every engagement targets one business outcome at one plant, with one go-live date, and then expands site by site.

The first question we ask back is always the same: which KPI, at which plant? Choose the starting point that fits your plants today, and we'll tell you on the first call whether it is the right one.

Service	What you get	Best fit when
Plant data readiness assessment	An inventory of PLC, SCADA, historian, MES, ERP, and QMS sources, with use cases ranked by value and data availability	You have a long wish list and no clear first bet
Industrial data platform build	Edge-to-cloud ingestion, a governed data lake on Amazon S3, an ISA-95 asset model, and a shared semantic layer	Data is scattered across sites and nobody agrees on the numbers
Manufacturing analytics and OEE	Plant, line, and asset OEE dashboards with one OEE definition, downtime Pareto charts, and scrap analysis	Leadership wants one trusted daily view across sites
Predictive maintenance and quality ML	Failure prediction, anomaly detection, and in-process quality models on Amazon SageMaker AI	Unplanned downtime or scrap already has a known dollar cost
Supply chain and demand analytics	Supplier performance, inventory, and lead-time analytics joined with production data	Planning still runs on spreadsheets and late signals
Historian and warehouse modernization	Migration off on-premises historians and legacy warehouses, with parallel-run validation and decommissioning	License renewals are the only thing keeping old systems alive
Generative AI and agents on plant data	Maintenance copilots, deviation analysis, and shift handover summaries on Amazon Bedrock	The data foundation is in place and you want AI in production

Every engagement ships with:

A governed data platform in production, inside your own AWS account
Data contracts for each source system, with named owners
An asset model and KPI definitions signed off by operations
Cost dashboards showing spend per plant and per data product
Network, access, and control documentation your OT security team can review
Runbooks, an on-call handover, and hands-on training for your data and OT teams

Start with one plant

Tell us the first use case.

We will say whether it can reach production and what getting there involves.

Talk to an FDE

Which Manufacturing Analytics Use Cases Pay Back First?

Real-time production monitoring, downtime and OEE analytics, and energy analytics usually pay back first, because they run on data plants already collect and the cost of a lost hour is known. Predictive maintenance and quality analytics follow once failure and inspection history is clean. If your controller can price an hour of downtime on Line 4, start there.

If nobody can put a number on it, the business case will be a tough sell at the next capital review, however good the model is.

Use case	Data it needs	KPI it moves	Time to first value
Real-time production monitoring	PLC and SCADA signals, MES work orders	Throughput, schedule attainment	Fastest; often the first release
Downtime and OEE analytics	Machine states, reason codes, shift calendars	Availability, mean time to repair	Fast, once reason codes are clean
Predictive maintenance	Vibration, temperature, current draw, CMMS work order history	Unplanned downtime, maintenance spend	Medium; needs failure history
Quality and scrap analytics	Process parameters, inspection results, QMS records	First-pass yield, scrap rate, cost of quality	Medium
Energy analytics	Meter data, production volumes, utility tariffs	Energy per unit produced	Fast where meters already exist
Supply chain visibility	ERP, supplier portals, logistics feeds	Inventory turns, on-time in-full	Medium to long; depends on supplier data
Digital twin and simulation	Asset model, process history, physics or ML models	Changeover time, capacity planning	Longer; best once the platform is stable

A practical rule we give every plant leader: start with one KPI the plant manager already reviews daily, prove it on one site, then roll the same data products out site by site. The rollout is where a reusable asset model earns its keep, because the tenth plant onboards in a fraction of the time the first one took. Our manufacturing and industrial practice sequences releases this way so that each one funds the next, and so your board sees results inside the fiscal year instead of a multi-year roadmap slide.

How Does Fixed-Date, Fixed-Fee Delivery Work for a Manufacturing Data Platform?

Mactores delivers a manufacturing data platform on agent-native terms. Agents carry the repetitive work at scale: source discovery, tag mapping, lineage, and parallel-run validation. FDEs own the calls that need judgment, from the asset model to the OT network boundary and cutover timing. Because the volume work is automated, scope, fee, and production date can be fixed in the SOW.

Agents absorb the scale

Source discovery across historian, MES, and ERP schemas
Tag and schema mapping
Lineage extraction
Parallel-run validation and test generation
Pipeline observability

FDEs own the judgment

Asset model and KPI definitions
Edge architecture and OT network boundaries
Cutover sequencing around production schedules and shutdowns
Security and compliance sign-off
Production acceptance

That split is what makes the contract possible. Once design closes, the SOW locks scope, fee, and go-live date. Overage from a Mactores delay is ours to absorb. If the hold-up starts on your side, such as plant network access or an approver missing at a phase exit, the extra hours bill as time and materials at a rate disclosed in that same SOW.

Cutovers follow the plant calendar. We book them against your maintenance windows, planned shutdowns, and peak season, so the new platform goes live without costing the line a minute of uptime. The full terms are in our delivery commitment.

Skin in the game, in writing

See the fixed date and fixed fee for your scope before you sign anything.

Both numbers go into the SOW once design closes.

Get my fixed quote

What Are the Steps to Implement Manufacturing Data Analytics on AWS?

Implementing manufacturing data analytics on AWS with Mactores takes five phases: assess, design, build, test, and deploy. A signed exit closes each one, so operations and finance approve working results before the next phase opens. No plant goes live until it clears a ten-point checklist covering KPI sign-off, parallel-run accuracy, OT network segmentation, and a dated legacy decommission.

Step 01
Assess

What happens

Plants, systems, and data sources inventoried; use cases ranked; production date set

Owner

Shared

Signed exit

Roadmap signed
Step 02
Design

What happens

Asset model, KPI definitions, edge architecture, and security controls defined

Owner

FDEs

Signed exit

SOW countersigned, locking date and fee
Step 03
Build

What happens

Edge connectors, pipelines, data lake, semantic layer, and first dashboards or models built on real plant data

Owner

Agents and FDEs

Signed exit

Build complete in a non-production environment
Step 04
Test

What happens

Parallel run against historians and existing reports, data quality checks, load and failover tests

Owner

Agents and FDEs

Signed exit

Acceptance gate passed
Step 05
Deploy

What happens

Staged rollout by plant, live monitoring, runbook handover, legacy retirement

Owner

Shared

Signed exit

Customer-signed acceptance

The go-live checklist every plant passes:

01 KPI definitions signed by operations and finance
02 Every source mapped to the asset model, with an owner named
03 Parallel-run results matching legacy reports within an agreed tolerance
04 Data freshness measured against the target for each KPI
05 Outbound-only connectivity from the plant, with network segmentation verified
06 Role-based access tested for plant, regional, and corporate users
07 Store-and-forward tested at the edge for WAN outages
08 Cost alerts per plant in place
09 An on-call owner named and the runbook handed over
10 A decommission date set for the legacy system

The phases follow how Mactores works on every engagement, adapted here for plant systems, OT security reviews, and production calendars.

Start at assessment

Assessment ends with a signed roadmap.

Design then locks the fee and the date.

Book the assessment

Which AWS Services Power a Modern Manufacturing Data Platform?

Mactores builds manufacturing data platforms with AWS IoT Greengrass and AWS IoT SiteWise at the edge, Amazon S3 Tables on Apache Iceberg as the system of record, Amazon Redshift for finance-grade reporting, and Amazon SageMaker AI and Amazon Bedrock for models and agents. The hard engineering sits in the edge, the asset model, and governance, where factory data projects break.

The stack is chosen for what survives a WAN outage at a remote plant, an OT security audit, and a rollout to every site, so industrial IoT analytics keep working as new plants join, without a redesign. That headroom matters: Gartner predicts that by 2029, AI agents will generate ten times more data from physical environments than all digital AI applications combined. A renewable grid operator already runs decision agents on Mactores-built real-time ingestion, acting on grid signals within bounded authority.

Layer	What we use	Why it matters on the plant floor
Edge and connectivity	AWS IoT Greengrass, AWS IoT SiteWise Edge, OPC UA and MQTT connectors, AWS Outposts where latency demands it	Local processing and store-and-forward keep data flowing when the WAN drops
Ingestion and streaming	AWS IoT Core, Amazon Kinesis Data Streams, Amazon MSK, AWS Database Migration Service for MES and ERP databases	Sensor streams and transactional records land in one place
Industrial data modeling	AWS IoT SiteWise asset models, ISA-95 hierarchies, unified namespace patterns	One model for every plant, line, and asset
Storage and lakehouse	Amazon S3 and Amazon S3 Tables (Apache Iceberg), Amazon Redshift	Low-cost long-term history plus fast corporate reporting
Catalog and governance	AWS Glue Data Catalog, AWS Lake Formation, Amazon SageMaker Unified Studio	Lineage, fine-grained access, and data sharing by role
Processing	AWS Glue, Amazon EMR, AWS Lambda, AWS Step Functions	Batch and streaming transforms at plant scale
Analytics and BI	Amazon Athena, Amazon Quick Sight (formerly Amazon QuickSight), Amazon Managed Grafana	Dashboards from the shop floor to the boardroom
Machine learning	Amazon SageMaker AI, edge inference on AWS IoT Greengrass	Failure prediction and quality models close to the process
Generative AI and agents	Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock AgentCore	Copilots grounded in maintenance manuals, work orders, and sensor history
Security and audit	AWS IAM, AWS KMS, AWS CloudTrail, Amazon CloudWatch, AWS Security Hub, AWS PrivateLink	Evidence your auditors and OT security team can review

AWS IoT SiteWise, a Lakehouse, or Amazon Redshift: Which Pattern Fits Your Plants?

Option	Choose it when	Watch for
AWS IoT SiteWise as the industrial data layer	You need asset modeling, equipment telemetry, and near-real-time metrics with little custom code	Cost at very high tag counts; plan retention tiers early
Lakehouse on Amazon S3 Tables and Apache Iceberg	You need to join OT data with ERP, MES, quality, and supply chain data across many sites	Needs stronger data engineering and governance discipline
Amazon Redshift as the analytics warehouse	Corporate reporting, finance-grade KPIs, and many concurrent BI users	Not designed for raw high-frequency sensor data

Most mature programs end up combining all three, with SiteWise close to the equipment, the lakehouse as the system of record, and Redshift serving the reports that go to the board. If an aging warehouse still feeds those reports, our data warehouse migration services move it to Amazon Redshift on a fixed date.

Should You Keep Your On-Premises Historian or Move History to AWS?

Factor	On-premises historian	Data platform on AWS
Scope of data	Time-series tags for one site	Tags plus MES, ERP, QMS, and supply chain data for every site
Retention cost	Grows with on-site storage and licenses	Tiered object storage priced per GB
Cross-plant comparison	Manual exports and spreadsheets	Built in, through one asset model
Readiness for ML and generative AI	Limited	Native access from SageMaker AI and Bedrock
Who can use it	Plant engineers on the local network	Plant, regional, and corporate users, by role

Many clients keep the historian at the edge for control-room use and move long-term history and analytics to AWS, which ends the license growth without touching operations. When the lake itself is the main gap, our AWS data lake consulting engagement covers that build in more depth.

Map it to your plants

One working session maps this architecture to your plants, systems, and AWS accounts.

Bring the source systems you run today.

Review my architecture

How Do You Keep OT Data Secure and Compliant on AWS?

You keep OT data secure on AWS by never opening an inbound path into the control network. Mactores uses outbound-only edge gateways, zones and conduits aligned to ISA/IEC 62443, least-privilege access in AWS Lake Formation, and encryption with keys you own. Each data flow is mapped to the frameworks you answer to, from CMMC and ITAR to FDA Part 11.

Your OT security lead will ask three things before a single tag leaves the plant: what can reach the control network, who can see which data, and how an incident can be reconstructed later. The stakes are concrete. IBM's 2026 Cost of a Data Breach Report puts the average US breach at $11.5 million, and industrial organizations averaged $5.50 million worldwide. Four control layers settle those questions in the design itself:

Network controls.

Outbound-only connections from edge gateways, zones and conduits aligned to ISA/IEC 62443, and private connectivity into AWS.
Identity controls.

Least-privilege roles for plant, regional, and corporate users, with access enforced centrally in AWS Lake Formation.
Data controls.

Encryption with AWS KMS keys you own, classification of export-controlled and personal data at ingestion, and regional residency.
Evidence controls.

Lineage from sensor to dashboard, audit logs in AWS CloudTrail, and versioned pipeline releases.

Framework	Where it applies	How Mactores supports it on AWS
CMMC and NIST SP 800-171	Defense suppliers handling Controlled Unclassified Information (CUI)	CUI enclaves in AWS GovCloud (US) where required, CUI tagging, access logging, evidence packages for assessors
ITAR and EAR	Export-controlled technical data	AWS GovCloud (US) Regions, US-person access controls, classification at ingestion
FDA 21 CFR Part 11 and QMSR (21 CFR Part 820)	Medical device and pharmaceutical production records	Audit trails, electronic signature workflows, validated pipeline releases, quality records aligned to ISO 13485
ISA/IEC 62443	Industrial automation and control systems	Zones and conduits, outbound-only edge, hardened gateways
NIST CSF 2.0 and NIST SP 800-82	OT and IT security programs at US manufacturers	Control mapping per data flow, monitoring and incident evidence
SOC 2 and ISO/IEC 27001	Customer and supplier assurance	Change control, access reviews, monitoring evidence
EU rules for plants and products in Europe	GDPR, NIS2, EU Data Act, Cyber Resilience Act, and EU AI Act	EU AWS Regions, pseudonymized workforce data, data-sharing interfaces for connected products, incident and vulnerability evidence

Defense suppliers: the Pentagon paused CMMC Phase 2, the third-party assessment requirement, in July 2026 while it reviews the program. Phase 1 self-assessments, DFARS 252.204-7012, and NIST SP 800-171 obligations still apply, so CUI controls belong in the platform design now rather than after the next rule change.

Data residency: every pipeline, store, and log runs in the AWS Region your policy names. CUI and ITAR data can stay in AWS GovCloud (US) while non-controlled production data runs in commercial Regions, and European plants can keep their data in EU Regions.

The table shows how the design supports each framework. It is not a certification, and assessment stays with your auditors and assessors.

Bring your control framework

We'll map every plant data flow to it, control by control.

CMMC, ITAR, FDA Part 11, ISA/IEC 62443, or the set you answer to.

Map my audit scope

How Much Does Manufacturing Data Analytics on AWS Cost, and How Long Does It Take?

A single-plant foundation typically takes 8 to 14 weeks, a multi-plant platform 14 to 20 weeks, and an enterprise rollout ships in fixed-date phases beyond that. Cost moves with source-system count, tag volume and sampling rate, history to migrate, network constraints, and compliance scope. Every build is priced as a fixed fee once scoping closes.

Pricing note. The tiers below are illustrative. They show how scope moves the fee and the timeline, and the figures that count are the ones in your signed SOW.

Scope tier	Typical scope	Main cost drivers	Illustrative timeline
Single-plant foundation	One plant, up to about 10 source systems, one or two KPIs	PLC and historian variety, data cleanliness	8 to 14 weeks
Multi-plant platform	Several plants or regulated data, 10 to 30 source systems, shared asset model	Asset model variation across sites, compliance scope	14 to 20 weeks
Enterprise rollout	Global plant network, ERP and supply chain integration, AI use cases	Site count, legacy retirement, change management	20+ weeks, delivered in fixed-date phases

What moves the number:

The number and age of source systems, especially proprietary PLC protocols
Tag counts and sampling frequency
How many years of history must migrate
Network constraints at each site
Compliance scope, such as CUI, ITAR, or GxP records
The number of BI users and dashboards

Run cost. Finance gets one figure to watch after go-live: cost per plant per month, split into ingestion, storage, compute, and BI. Set it beside the KPI it moves, such as downtime hours avoided or scrap reduced, and you have a running ROI that survives budget season. Edge aggregation, tiered storage, and right-sized sampling keep it in check as sensors are added.

Value discipline matters more than most roadmaps admit. In a Gartner survey published in October 2026, only 13% of CIOs said their enterprise had realized significant or transformational value from AI. That is why each data product is tied to a plant KPI and a cost ceiling from the first sprint, and reviewed with finance and operations at every phase exit rather than once a year.

Funding. The program can often pay for itself out of spend you already carry: historian licenses due for renewal, an on-premises warehouse nobody builds on anymore, or an integration layer whose upkeep now outweighs what it delivers. Retiring those lines through application and database modernization is how many Mactores customers fund their analytics programs, and AWS Migration Acceleration Program (MAP) funding may apply to eligible migrations.

Price your plants

Your plants, your source systems, one fixed fee and one date.

The figure is set once scoping closes and recorded in the SOW.

Price my plants

Should You Build In-House, Buy an Industrial Platform, or Bring In an AWS Partner?

Build in-house if your team has OT depth and time. Buy a packaged industrial analytics platform if your equipment is supported and its data model fits. Bring in an AWS partner for a fixed production date, full ownership in your AWS account, and a path to ML and agents. Mactores walks plant leaders through this trade-off on the first call.

There is no single right answer, and any manufacturing analytics consulting firm that claims otherwise is selling something. Each route wins in different conditions.

Factor	Build in-house	Packaged industrial analytics platform	AWS partner with fixed-date delivery
Time to first production KPI	Depends on hiring and OT experience	Fast for supported equipment	Fixed in the SOW
Ownership of data and code	Full	Often tied to the vendor’s data model	Full, in your AWS account
Fit with mixed PLC and MES estates	Strong if the team has OT depth	Strong for supported connectors, weaker for custom systems	Built per site on an open asset model
Cost model	Salaries plus cloud consumption	Subscription per asset, site, or tag	Fixed fee, then your own AWS consumption
Path to ML and agents	Built from scratch	Limited to the vendor roadmap	Designed into the platform from the first release
Delivery risk	Sits with your team	Shared, with limited customization	Contractual, with overage absorbed for partner-caused delay

Questions to ask any manufacturing analytics partner before you sign:

Does the SOW name a production date alongside the fee?
If the partner causes a delay, who pays for the extra hours?
Will the platform run in our AWS account, with code and models we own?
How will OT network access work, and will any inbound path be opened?
Can you name a manufacturing reference with a measured result?
Is retirement of the legacy historian or warehouse in scope?
Which FDE is accountable for cutover, by name?

If a firm hedges on most of these, that tells you how the engagement will go. Mactores gives the same answers on every data platform modernization program it delivers.

Which AWS Credentials Matter for Manufacturing Data Work?

For manufacturing data work, look for partner tier, the AWS Manufacturing and Industrial Services Competency, and service validations on the data services you will run. Mactores is an AWS Premier Tier Services Partner with that competency, plus Data and Analytics, Machine Learning, Generative AI, and Migration and Modernization competencies and the AWS Agentic AI Specialization.

Partner tier: AWS Premier Tier Services Partner
Relevant competencies: Manufacturing and Industrial Services, Data and Analytics, Machine Learning, Generative AI, Migration and Modernization
Specialization: AWS Agentic AI Specialization
Relevant service validations: AWS Glue, Amazon Redshift, Amazon EMR, Amazon Kinesis, Amazon MSK, Amazon QuickSight, AWS Database Migration Service, AWS Lambda
Delivery record: 94% of engagements shipped on or before the committed date
Funding: AWS Migration Acceleration Program (MAP) for eligible migrations, reviewed with your AWS account team while scoping
Team: 200+ AWS-certified practitioners
On AWS since: 2008

Check any shortlisted partner in AWS Partner Solutions Finder before the first workshop; the listing shows tier, competencies, and validations as AWS records them. Mactores' full set is on its AWS partnership page.

What Do Common Manufacturing Data Terms Mean?

OEE (overall equipment effectiveness): Availability multiplied by performance and quality, showing how much planned production time is productive.
IT/OT convergence: Connecting operational technology on the plant floor with business IT systems so data moves securely between them.
Industrial IoT (IIoT): Networked sensors, machines, and gateways that collect equipment data for monitoring and analytics.
Unified namespace: A single, event-driven structure where every plant system publishes its current state, organized by site, area, line, and asset.
ISA-95: An international standard that defines the hierarchy and information exchange between enterprise and control systems.
Historian: A time-series database that stores process and equipment data from control systems, usually on site.
OPC UA and MQTT: Common industrial protocols for reading equipment data and publishing it to other systems.
Store-and-forward: Edge buffering that holds data during a network outage and sends it once the connection returns.
Digital twin: A live digital model of an asset or process, fed by real data and used for monitoring and simulation.
Data contract: A documented agreement on the schema, freshness, and ownership of data a source system provides.
Parallel-run validation: Running the new platform alongside legacy systems and comparing outputs before cutover.
CUI (Controlled Unclassified Information): Sensitive government information that defense suppliers must protect under DFARS and NIST SP 800-171.
Forward-deployed engineer (FDE): The Mactores lead who works inside your plant and data teams, makes the architecture and cutover calls, and answers for the delivery date.
Agent-native delivery: Delivery in which agents carry the repetitive discovery, mapping, and validation work while FDEs own judgment and outcomes.

How Can Mactores Help You Out?

Mactores turns scattered plant data into a governed AWS platform on a contracted date. Start with a plant data readiness assessment if data is scattered, a scale-out review if a pilot is stuck on one line, or analytics, ML, and agents if the foundation exists. Each path closes with a fixed fee and a go-live date in writing.

We have shipped data platforms on AWS since 2008, many of them in plants where the control network is off-limits, the auditors are strict, and downtime is counted in dollars per minute. The secure, compliance-heavy AI systems we have built for regulated industries carry straight into how a manufacturing engagement runs.

If this sounds like you	Where we start	What you walk away with
Plant data is scattered across sites and systems	Plant data readiness assessment	A ranked roadmap, a fixed fee, and a production date
A pilot works on one line and stalls there	Scale-out review	A keep-or-rebuild list and a committed date for the next plants
The data foundation is in place	Analytics, ML, and agents on plant data	Production models and copilots your teams use every shift

The third path is where most recent conversations with plant leaders end up, once the first two are behind them. For that path, our work on AI agents for applications picks up where the data platform leaves off, putting maintenance copilots and deviation analysis into the tools your technicians already open at shift change. Every one of those agents depends on the data underneath it being current, governed, and searchable, which is why the platform always comes first. Skip that step, and a maintenance copilot will cheerfully quote a work order that was closed last quarter.

Your team keeps the platform, the code, and the know-how after go-live. Your plant managers get numbers they no longer have to argue about in the morning meeting, and your CFO gets a fixed date and a fixed fee to hold us to.

Bring one plant and one KPI

The first session gives you a straight answer on viability, a draft scope, and a target production date.

Thirty minutes with the FDE who would run the engagement.

Book a scoping call

What Do Manufacturers Ask About Data Analytics on AWS?

What is manufacturing data analytics on AWS?

It is the practice of collecting data from plant equipment, historians, MES, ERP, and quality systems into a governed platform on AWS, then using it for dashboards, OEE tracking, predictive maintenance, quality analytics, and AI. The goal is one trusted view of production across every site, with security controls that protect the plant network.

What is IT/OT convergence, and why does it matter for manufacturing analytics?

IT/OT convergence is the secure connection of plant-floor technology, such as PLCs, SCADA, and historians, with business systems like ERP, MES, and quality management. It matters because OEE, scrap, and maintenance analytics need both sides joined on one asset model. Without it, each plant reports its own numbers and AI models learn from partial data.

How do you connect PLC, SCADA, and historian data to AWS without exposing the plant network?

We use edge gateways running AWS IoT Greengrass or AWS IoT SiteWise Edge that read data over OPC UA or MQTT and send it outbound only. No inbound connection into the control network is opened. Networks are segmented into zones aligned to ISA/IEC 62443, and data is encrypted in transit and at rest.

What is a unified namespace, and do we need one?

A unified namespace is a single event-driven structure, usually built on MQTT, where every plant system publishes its current state under a site, area, line, and asset hierarchy. You don't need one on day one, but it pays off once several plants and many consumers share the same data. On AWS, it typically maps to an ISA-95 asset model in AWS IoT SiteWise or the lakehouse.

How long does it take to implement manufacturing analytics on AWS?

A single-plant foundation with one or two KPIs typically takes 8 to 14 weeks. A multi-plant platform runs 14 to 20 weeks, and enterprise rollouts are delivered in fixed-date phases beyond that. The exact production date is set during scoping and written into the statement of work.

How much does a manufacturing data platform on AWS cost?

Build cost depends on the number of source systems, tag counts, history to migrate, network constraints, and compliance scope. Run cost covers ingestion, storage, compute, and BI. Mactores quotes builds as fixed-fee engagements after scoping and tracks run cost per plant per month, so finance can compare it directly against the downtime or scrap it reduces.

Do we need to replace our historian or MES?

No. Most engagements keep the historian and MES running at the plant for control-room and execution needs, and move long-term history, cross-plant analytics, and AI workloads to AWS. Where an on-premises historian or warehouse only survives because of license renewals, its retirement can be scoped and dated in the SOW.

What is the difference between AWS IoT SiteWise and a manufacturing data lake?

AWS IoT SiteWise collects, models, and monitors equipment data with asset hierarchies and near-real-time metrics. A manufacturing data lake on Amazon S3 stores that telemetry alongside MES, ERP, quality, and supply chain data for cross-system analytics and machine learning. Most mature programs use SiteWise close to the equipment and the lake as the system of record.

Can manufacturing data on AWS meet CMMC, ITAR, and FDA requirements?

Yes, when the controls are designed in from the start. CUI and ITAR data can stay in AWS GovCloud (US) with US-person access controls, while FDA 21 CFR Part 11 and the QMSR call for audit trails, electronic signatures, and validated releases. Each data flow is mapped to the relevant framework, and evidence is collected for assessors.

What results can we expect from predictive maintenance on AWS?

Results depend on failure history, sensor coverage, and how quickly maintenance teams act on alerts. The strongest programs start with critical assets whose downtime cost is known, measure a baseline first, and track unplanned downtime and maintenance spend after go-live. Models are retrained as new failure data arrives.

Who owns the data platform and code after go-live?

You do. The platform, pipeline code, asset models, ML models, and documentation run in your own AWS account and stay with you after handover, and the SOW records ownership and exit terms before work starts. That is what lets your data and OT teams run, audit, and extend the platform without us.

What should we prepare before a manufacturing data engagement?

One target plant, an owner for the KPI you want to move, access to the relevant source systems, and a named contact on your OT security team. A sample of historical data speeds things up. Assessment covers the rest: checking data readiness, mapping risk, and setting the go-live date.

Still have a question?

Email it with your plant count and main source systems.

An FDE replies directly.

Email lets-talk@mactores.com→

Is Your Plant Data Ready to Run the Business?

Put a date and a fee on the platform your operations team keeps asking for.

01
Bring one plant and one KPI; our FDEs work through them with you in a single session.
02
Within five business days, receive a viability call, a draft scope, and a target date.
03
Countersign a fixed-fee SOW and kick off the assessment.

Get my plant data into production or email lets-talk@mactores.com

Backed by a 30-day satisfaction guarantee, plus 90 days of free fixes for production defects after acceptance.