How it Works
How It Works
Data-driven Transformation

A single, integrated solution for digital transformation.

It’s no wonder there’s fatigue with digital transformation.
Plant data is so varied and so difficult to use that most transformation efforts fail.

Sight Machine has spent over a decade helping the world’s most progressive manufacturers put their data to use, and we’ve developed a structured method that delivers results.

Our method moves in concrete, defined steps. It builds sequentially on early wins. And it serves every stakeholder in the manufacturing enterprise. It’s product-led and collaborative, built on a platform that’s comprehensive and open, so solutions are tailored to each customer’s environment. We work in close partnership with leading technology providers and advisory firms to help our customers achieve lasting change.

That success requires an integrated approach across three distinct domains, each with its own set of stakeholders: technology infrastructure, plant productivity, and Artificial Intelligence.

When digital transformation is addressed as a whole system and the needs of every stakeholder are served, change becomes a flywheel, with early wins leading to more wins and accelerating gains.

The Digital
Transformation
Flywheel

Select any node
to learn more
Arrow
Technology Infrastructure
Lead Stakeholder:
IT

Manufacturing-specific analysis in real-time

1 of 6

Extract, pre-process, and stream data from every plant source

2 of 6

Bring together data from every source

3 of 6

Automated, AI-based tools for data labeling

4 of 6

Automated, configurable data transformation

5 of 6

Automated Digital Twin creation and analysis

6 of 6

A single Data Foundation across firms

1 of 6

Trust in data is the first step in transformation

2 of 6

Move from counting output to managing efficiency

3 of 6

Consistent, validated, real-time measurement across plants

4 of 6

OEE improvements typically range from 5-10% in the first months

5 of 6

Data Foundation drives enterprise innovation

6 of 6
Plant
Productivity
Lead Stakeholder:
Operations
Artificial Intelligence
Lead Stakeholder:
Data Science

A single set of trusted, consistent information for every stakeholder

1 of 6

Speed early models

2 of 6

A single Data Foundation for all AI in plants

3 of 6

Real-time streaming to continuously train AI

4 of 6

Domain-oriented AI expertise

5 of 6

Firm-wide use of AI on the same Data Foundation

6 of 6

Top

Technology Infrastructure

Physical Connectivity

Physical Connectivity

Aggregation

Tag Mapping

Data Transforms

Digital Twins

Analytics

Plant Productivity

Artificial Intelligence

Customer Success

Technology Infrastructure

Lead Stakeholder:
IT

Sight Machine automates every step of the data workflow, securely combining and transforming all data sources to create a single Data Foundation.

We begin with secure connectivity for your sensitive data.

Next, we apply AI-enabled tag mapping.  Manufacturers sometimes have millions of tags named at different times by different teams. Even aggregated, the data is impossible to use without deciphering these labels’ meanings. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Next, we bring OT data into our processing platform built specifically for shop-floor data.

Sight Machine’s configurable pipelines join time series and transactional data, continuously transforming data into standardized schemas that automatically update data tables when late or missing data comes in — retroactively, even months later.

Sight Machine’s pipeline-management tools are also critical to achieving transformation at scale. With hundreds of pipelines running concurrently, it’s essential to have tools that automate cloud-resource management and continuously configure and adjust pipelines.

Data Foundation means everyone in your enterprise can work with the same base of consistent, useful information across all plants and processes.

Plant Productivity

Lead Stakeholder:
Operations

Data Foundation is a breakthrough step because it provides new information, but if the information isn’t used, transformation fails.

We help plants drive productivity by adopting new KPIs that are only possible with Data Foundation’s real-time streaming data.

We next support teams in using Data Foundation to drive KPIs up. With real-time information  that they’ve never had before in hand, operators and process engineers can improve operations on every dimension: Identify bottlenecks. Change machine speed and line speed. Find what’s causing microstops. Optimize recipes, predict failure, and reduce defects and scrap.  

The benefits go further. Because the Sight Machine platform is open, manufacturers can apply AI/ML to Data Foundation and generate hypotheses about process improvements. With AI/ML, manufacturers design and run their own process experiments, with compelling results.

Most importantly, Sight Machine makes transformation safer for change leaders. Our method mitigates risk and produces fast, concrete wins. We honor the good work you’ve already done by accelerating existing initiatives and linking Data Foundation to earlier digital investments. 

Artificial Intelligence

Lead Stakeholder:
Data Science

“Good data” drives “good models.”

Sight Machine helps manufacturers integrate AI and data science by giving data scientists access to unlimited standardized data — what AI practitioners call “good data.”

We empower data scientists to move from static to real-time models, and we promote best practices for operationalizing AI models on shop floors.

Data-driven Transformation

Customer Success

Sight Machine accelerates digital transformation with a staged, programmatic approach.

Sight Machine and the leaders we support have pioneered a repeatable method for transformation: standardized data, disciplined processes to drive the use of that data, and a single, trusted Data Foundation used by every stakeholder in the firm, from operators and process engineers to data scientists and business leaders.

Sight Machine works across many different industries and use cases. Every company and every culture is unique, and the precise steps and goals should always be tailored to your circumstances.

The Digital Transformation
Customer Journey

Select any node to learn more

Aggregated Data Foundation

Automatically transform data from multiple assets, processes, and sites into a single Data Foundation

New KPIs: Standardized OEE

Drive new KPIs from Data Foundation

Real-time Streaming

Stream all OT data in real time and transform into Data Foundation

Benchmark Productivity

New, standardized KPIs and benchmarks

Improve Productivity

Drive rapid gains in throughput, quality, materials use, changeovers, recipes, and yield

AI: Change the Process

Move from improving efficiency of the process to changing it

Enterprise Scale

A single Data Foundation for the firm

Supply Chain/Value Chain

Standardized, consistent information for use across firms

Aggregated Data Foundation

Automatically transform data from multiple assets, processes, and sites into a single Data Foundation

New KPIs: Standardized OEE

Drive new KPIs from Data Foundation

Real-time Streaming

Stream all OT data in real time and transform into Data Foundation

Benchmark Productivity

New, standardized KPIs and benchmarks

Improve Productivity

Drive rapid gains in throughput, quality, materials use, changeovers, recipes, and yield 

AI: Change the Process

Move from improving efficiency of the process to changing it 

Enterprise Scale

A single Data Foundation for the firm 

Supply Chain/Value Chain

Standardized, consistent information for use across firms

Tier 1 Automotive Manufacturer

Improves Machine Speed by 7%, Reduces Scrap by 50%

This Tier 1 automotive manufacturer used real-time streaming data in its first plant to benchmark and improve the performance of 50 assets making 309 SKUs. WIth consistent, trusted information about each asset’s performance, the team improved machine speed by 7% and addressed a number of production issues continuously. 

Next, the team applied AI/ML to process data, generated subtle hypotheses about how to change the process, and with the benefit of these insights reduced longstanding scrap by 50%.

Large Enterprise Manufacturer

Scales to 20 Plants and Drives Operational Excellence

A large enterprise manufacturer modeled data on a single process area, scaled quickly to almost 20 plants and hundreds of process areas, and is now driving enterprise-wide KPIs and operational excellence off of a rich Data Foundation, generated continuously from millions of streaming tags.

World-class Process and Discrete Manufacturer

Breaks through to a New Horizon of Productivity

A world-class manufacturer began by implementing Data Foundation at one of its sites in the U.S., an IndustryWeek Best Plant in 2017 and again in 2021. With the benefit of live, streaming data, this plant has broken through to a new horizon of productivity. The manufacturer is extending Data Foundation to 20 sites and using it to address dozens of use cases.

Solve Your Most Complex Data Foundation Challenges with Experts by Your Side.

Curious about how we can help? 
Schedule a chat about your data and transformation needs.

Talk to Our Experts

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Plant Productivity

Validate Data

Trust in data is the first step in transformation. 

Trust in data depends on validation. Raw data, Data Foundation, and KPIs all need to be validated for operators and process engineers to depend on it.

New KPIs

Move from counting output to managing efficiency.

Most plants have historically measured what they can, which in most cases has been output. But output is a far cry from efficiency. Data Foundation enables plants to adopt new KPIs, especially around productivity and sustainability. KPIs like OEE can be set, adjusted, and standardized for real-time analysis of processes, plants, and the enterprise. 

Benchmark

Consistent, validated, real-time measurement across plants.

With new KPIs, plants can identify how they’re doing. As simple as it may sound, reaching agreement within the firm about what to measure is often a critical step in transformation. Real-time Data Foundation enables this step as well as refinements to KPIs as plants learn from it. 

First Gains

OEE improvements typically range from 5-10% in the first months.

With trusted information and new KPIs, new insight into issues, and continuous, real-time feedback, plants often make rapid improvements in productivity —typically gains of 5-10% in a few months. 

Run Plants as a Portfolio

Data Foundation drives enterprise innovation.

Most plants run autonomously, but with real-time Data Foundation they can be operated as a portfolio. Who’s the best producer of which product? And what are best practices that can be shared with all? Data Foundation enables manufacturers to answer these questions.

Value Chain

A single Data Foundation across firms.

Standardized Data Foundation enables cooperation across firm boundaries. With the same consistent information, suppliers, customers, equipment providers, and materials suppliers can jointly address challenges to production.

Technology Infrastructure

Physical Connectivity

Extract, pre-process, and stream data from every plant source.

Plant data environments are remarkably heterogeneous and bespoke. The first step to data-driven transformation is extracting and streaming plant data. Historically, only sources that were accessible, easily understood, and easily modeled by hand have been tapped. With modern connectivity and transformation technologies, this paradigm is turned on its head. All data becomes relevant and useful, because it can all be extracted, streamed in real time, and understood.

Aggregation

Bring together data from every source.

Plant data is uniquely heterogenous. Sight Machine brings together data from every source: controls, MES, historians, telemetry, quality, materials, and environmental. 

Tag Mapping

Automated, AI-based tools for data labeling.

Plants struggle mightily with this challenge. Thousands of data fields in each plant have been named in different ways by different people, often over decades. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Data Transforms

Automated, configurable data transformation.

Once data is aggregated, streaming, and identifiable, it needs to be transformed automatically. Most companies build data pipelines by hand. That works for IT data and small projects, but for scale in industry, it’s necessary to automate data transformation.   

Digital Twins

Automated Digital Twin creation and analysis.

Once data has been transformed into consistent, standardized Data Foundation, models of assets, lines, and plants can be built. Digital Twins built from Data Foundation are all interoperable. 

Analytics

Manufacturing-specific analysis in real-time.

Data Foundation drives real-time BI and AI with the same information. In order to trust both the data and the recommendations, it’s necessary to provide transparency into the raw data, transforms, and analysis.  

Technology Infrastructure

Physical Connectivity

Extract, pre-process, and stream data from every plant source.

Plant data environments are remarkably heterogeneous and bespoke. The first step to data-driven transformation is extracting and streaming plant data. Historically, only sources that were accessible, easily understood, and easily modeled by hand have been tapped. With modern connectivity and transformation technologies, this paradigm is turned on its head. All data becomes relevant and useful, because it can all be extracted, streamed in real time, and understood.

Aggregation

Bring together data from every source.

Plant data is uniquely heterogenous. Sight Machine brings together data from every source: controls, MES, historians, telemetry, quality, materials, and environmental. 

Tag Mapping

Automated, AI-based tools for data labeling.

Plants struggle mightily with this challenge. Thousands of data fields in each plant have been named in different ways by different people, often over decades. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Data Transforms

Automated, configurable data transformation.

Once data is aggregated, streaming, and identifiable, it needs to be transformed automatically. Most companies build data pipelines by hand. That works for IT data and small projects, but for scale in industry, it’s necessary to automate data transformation.   

Digital Twins

Automated Digital Twin creation and analysis.

Once data has been transformed into consistent, standardized Data Foundation, models of assets, lines, and plants can be built. Digital Twins built from Data Foundation are all interoperable. 

Analytics

Manufacturing-specific analysis in real-time.

Data Foundation drives real-time BI and AI with the same information. In order to trust both the data and the recommendations, it’s necessary to provide transparency into the raw data, transforms, and analysis.  

Technology Infrastructure

Physical Connectivity

Extract, pre-process, and stream data from every plant source.

Plant data environments are remarkably heterogeneous and bespoke. The first step to data-driven transformation is extracting and streaming plant data. Historically, only sources that were accessible, easily understood, and easily modeled by hand have been tapped. With modern connectivity and transformation technologies, this paradigm is turned on its head. All data becomes relevant and useful, because it can all be extracted, streamed in real time, and understood.

Aggregation

Bring together data from every source.

Plant data is uniquely heterogenous. Sight Machine brings together data from every source: controls, MES, historians, telemetry, quality, materials, and environmental. 

Tag Mapping

Automated, AI-based tools for data labeling.

Plants struggle mightily with this challenge. Thousands of data fields in each plant have been named in different ways by different people, often over decades. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Data Transforms

Automated, configurable data transformation.

Once data is aggregated, streaming, and identifiable, it needs to be transformed automatically. Most companies build data pipelines by hand. That works for IT data and small projects, but for scale in industry, it’s necessary to automate data transformation.   

Digital Twins

Automated Digital Twin creation and analysis.

Once data has been transformed into consistent, standardized Data Foundation, models of assets, lines, and plants can be built. Digital Twins built from Data Foundation are all interoperable. 

Analytics

Manufacturing-specific analysis in real-time.

Data Foundation drives real-time BI and AI with the same information. In order to trust both the data and the recommendations, it’s necessary to provide transparency into the raw data, transforms, and analysis.  

Technology Infrastructure

Physical Connectivity

Extract, pre-process, and stream data from every plant source.

Plant data environments are remarkably heterogeneous and bespoke. The first step to data-driven transformation is extracting and streaming plant data. Historically, only sources that were accessible, easily understood, and easily modeled by hand have been tapped. With modern connectivity and transformation technologies, this paradigm is turned on its head. All data becomes relevant and useful, because it can all be extracted, streamed in real time, and understood.

Aggregation

Bring together data from every source.

Plant data is uniquely heterogenous. Sight Machine brings together data from every source: controls, MES, historians, telemetry, quality, materials, and environmental. 

Tag Mapping

Automated, AI-based tools for data labeling.

Plants struggle mightily with this challenge. Thousands of data fields in each plant have been named in different ways by different people, often over decades. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Data Transforms

Automated, configurable data transformation.

Once data is aggregated, streaming, and identifiable, it needs to be transformed automatically. Most companies build data pipelines by hand. That works for IT data and small projects, but for scale in industry, it’s necessary to automate data transformation.   

Digital Twins

Automated Digital Twin creation and analysis.

Once data has been transformed into consistent, standardized Data Foundation, models of assets, lines, and plants can be built. Digital Twins built from Data Foundation are all interoperable. 

Analytics

Manufacturing-specific analysis in real-time.

Data Foundation drives real-time BI and AI with the same information. In order to trust both the data and the recommendations, it’s necessary to provide transparency into the raw data, transforms, and analysis.  

Technology Infrastructure

Physical Connectivity

Extract, pre-process, and stream data from every plant source.

Plant data environments are remarkably heterogeneous and bespoke. The first step to data-driven transformation is extracting and streaming plant data. Historically, only sources that were accessible, easily understood, and easily modeled by hand have been tapped. With modern connectivity and transformation technologies, this paradigm is turned on its head. All data becomes relevant and useful, because it can all be extracted, streamed in real time, and understood.

Aggregation

Bring together data from every source.

Plant data is uniquely heterogenous. Sight Machine brings together data from every source: controls, MES, historians, telemetry, quality, materials, and environmental. 

Tag Mapping

Automated, AI-based tools for data labeling.

Plants struggle mightily with this challenge. Thousands of data fields in each plant have been named in different ways by different people, often over decades. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Data Transforms

Automated, configurable data transformation.

Once data is aggregated, streaming, and identifiable, it needs to be transformed automatically. Most companies build data pipelines by hand. That works for IT data and small projects, but for scale in industry, it’s necessary to automate data transformation.   

Digital Twins

Automated Digital Twin creation and analysis.

Once data has been transformed into consistent, standardized Data Foundation, models of assets, lines, and plants can be built. Digital Twins built from Data Foundation are all interoperable. 

Analytics

Manufacturing-specific analysis in real-time.

Data Foundation drives real-time BI and AI with the same information. In order to trust both the data and the recommendations, it’s necessary to provide transparency into the raw data, transforms, and analysis.  

Technology Infrastructure

Physical Connectivity

Extract, pre-process, and stream data from every plant source.

Plant data environments are remarkably heterogeneous and bespoke. The first step to data-driven transformation is extracting and streaming plant data. Historically, only sources that were accessible, easily understood, and easily modeled by hand have been tapped. With modern connectivity and transformation technologies, this paradigm is turned on its head. All data becomes relevant and useful, because it can all be extracted, streamed in real time, and understood.

Aggregation

Bring together data from every source.

Plant data is uniquely heterogenous. Sight Machine brings together data from every source: controls, MES, historians, telemetry, quality, materials, and environmental. 

Tag Mapping

Automated, AI-based tools for data labeling.

Plants struggle mightily with this challenge. Thousands of data fields in each plant have been named in different ways by different people, often over decades. Using heuristics and techniques built from a decade of streaming plant data, Sight Machine automates data labeling.

Data Transforms

Automated, configurable data transformation.

Once data is aggregated, streaming, and identifiable, it needs to be transformed automatically. Most companies build data pipelines by hand. That works for IT data and small projects, but for scale in industry, it’s necessary to automate data transformation.   

Digital Twins

Automated Digital Twin creation and analysis.

Once data has been transformed into consistent, standardized Data Foundation, models of assets, lines, and plants can be built. Digital Twins built from Data Foundation are all interoperable. 

Analytics

Manufacturing-specific analysis in real-time.

Data Foundation drives real-time BI and AI with the same information. In order to trust both the data and the recommendations, it’s necessary to provide transparency into the raw data, transforms, and analysis.  

Plant Productivity

Validate Data

Trust in data is the first step in transformation. 

Trust in data depends on validation. Raw data, Data Foundation, and KPIs all need to be validated for operators and process engineers to depend on it.

New KPIs

Move from counting output to managing efficiency.

Most plants have historically measured what they can, which in most cases has been output. But output is a far cry from efficiency. Data Foundation enables plants to adopt new KPIs, especially around productivity and sustainability. KPIs like OEE can be set, adjusted, and standardized for real-time analysis of processes, plants, and the enterprise. 

Benchmark

Consistent, validated, real-time measurement across plants.

With new KPIs, plants can identify how they’re doing. As simple as it may sound, reaching agreement within the firm about what to measure is often a critical step in transformation. Real-time Data Foundation enables this step as well as refinements to KPIs as plants learn from it. 

First Gains

OEE improvements typically range from 5-10% in the first months.

With trusted information and new KPIs, new insight into issues, and continuous, real-time feedback, plants often make rapid improvements in productivity —typically gains of 5-10% in a few months. 

Run Plants as a Portfolio

Data Foundation drives enterprise innovation.

Most plants run autonomously, but with real-time Data Foundation they can be operated as a portfolio. Who’s the best producer of which product? And what are best practices that can be shared with all? Data Foundation enables manufacturers to answer these questions.

Value Chain

A single Data Foundation across firms.

Standardized Data Foundation enables cooperation across firm boundaries. With the same consistent information, suppliers, customers, equipment providers, and materials suppliers can jointly address challenges to production.

Plant Productivity

Validate Data

Trust in data is the first step in transformation. 

Trust in data depends on validation. Raw data, Data Foundation, and KPIs all need to be validated for operators and process engineers to depend on it.

New KPIs

Move from counting output to managing efficiency.

Most plants have historically measured what they can, which in most cases has been output. But output is a far cry from efficiency. Data Foundation enables plants to adopt new KPIs, especially around productivity and sustainability. KPIs like OEE can be set, adjusted, and standardized for real-time analysis of processes, plants, and the enterprise. 

Benchmark

Consistent, validated, real-time measurement across plants.

With new KPIs, plants can identify how they’re doing. As simple as it may sound, reaching agreement within the firm about what to measure is often a critical step in transformation. Real-time Data Foundation enables this step as well as refinements to KPIs as plants learn from it. 

First Gains

OEE improvements typically range from 5-10% in the first months.

With trusted information and new KPIs, new insight into issues, and continuous, real-time feedback, plants often make rapid improvements in productivity —typically gains of 5-10% in a few months. 

Run Plants as a Portfolio

Data Foundation drives enterprise innovation.

Most plants run autonomously, but with real-time Data Foundation they can be operated as a portfolio. Who’s the best producer of which product? And what are best practices that can be shared with all? Data Foundation enables manufacturers to answer these questions.

Value Chain

A single Data Foundation across firms.

Standardized Data Foundation enables cooperation across firm boundaries. With the same consistent information, suppliers, customers, equipment providers, and materials suppliers can jointly address challenges to production.

Plant Productivity

Validate Data

Trust in data is the first step in transformation. 

Trust in data depends on validation. Raw data, Data Foundation, and KPIs all need to be validated for operators and process engineers to depend on it.

New KPIs

Move from counting output to managing efficiency.

Most plants have historically measured what they can, which in most cases has been output. But output is a far cry from efficiency. Data Foundation enables plants to adopt new KPIs, especially around productivity and sustainability. KPIs like OEE can be set, adjusted, and standardized for real-time analysis of processes, plants, and the enterprise. 

Benchmark

Consistent, validated, real-time measurement across plants.

With new KPIs, plants can identify how they’re doing. As simple as it may sound, reaching agreement within the firm about what to measure is often a critical step in transformation. Real-time Data Foundation enables this step as well as refinements to KPIs as plants learn from it. 

First Gains

OEE improvements typically range from 5-10% in the first months.

With trusted information and new KPIs, new insight into issues, and continuous, real-time feedback, plants often make rapid improvements in productivity —typically gains of 5-10% in a few months. 

Run Plants as a Portfolio

Data Foundation drives enterprise innovation.

Most plants run autonomously, but with real-time Data Foundation they can be operated as a portfolio. Who’s the best producer of which product? And what are best practices that can be shared with all? Data Foundation enables manufacturers to answer these questions.

Value Chain

A single Data Foundation across firms.

Standardized Data Foundation enables cooperation across firm boundaries. With the same consistent information, suppliers, customers, equipment providers, and materials suppliers can jointly address challenges to production.

Plant Productivity

Validate Data

Trust in data is the first step in transformation. 

Trust in data depends on validation. Raw data, Data Foundation, and KPIs all need to be validated for operators and process engineers to depend on it.

New KPIs

Move from counting output to managing efficiency.

Most plants have historically measured what they can, which in most cases has been output. But output is a far cry from efficiency. Data Foundation enables plants to adopt new KPIs, especially around productivity and sustainability. KPIs like OEE can be set, adjusted, and standardized for real-time analysis of processes, plants, and the enterprise. 

Benchmark

Consistent, validated, real-time measurement across plants.

With new KPIs, plants can identify how they’re doing. As simple as it may sound, reaching agreement within the firm about what to measure is often a critical step in transformation. Real-time Data Foundation enables this step as well as refinements to KPIs as plants learn from it. 

First Gains

OEE improvements typically range from 5-10% in the first months.

With trusted information and new KPIs, new insight into issues, and continuous, real-time feedback, plants often make rapid improvements in productivity —typically gains of 5-10% in a few months. 

Run Plants as a Portfolio

Data Foundation drives enterprise innovation.

Most plants run autonomously, but with real-time Data Foundation they can be operated as a portfolio. Who’s the best producer of which product? And what are best practices that can be shared with all? Data Foundation enables manufacturers to answer these questions.

Value Chain

A single Data Foundation across firms.

Standardized Data Foundation enables cooperation across firm boundaries. With the same consistent information, suppliers, customers, equipment providers, and materials suppliers can jointly address challenges to production.

Plant Productivity

Validate Data

Trust in data is the first step in transformation. 

Trust in data depends on validation. Raw data, Data Foundation, and KPIs all need to be validated for operators and process engineers to depend on it.

New KPIs

Move from counting output to managing efficiency.

Most plants have historically measured what they can, which in most cases has been output. But output is a far cry from efficiency. Data Foundation enables plants to adopt new KPIs, especially around productivity and sustainability. KPIs like OEE can be set, adjusted, and standardized for real-time analysis of processes, plants, and the enterprise. 

Benchmark

Consistent, validated, real-time measurement across plants.

With new KPIs, plants can identify how they’re doing. As simple as it may sound, reaching agreement within the firm about what to measure is often a critical step in transformation. Real-time Data Foundation enables this step as well as refinements to KPIs as plants learn from it. 

First Gains

OEE improvements typically range from 5-10% in the first months.

With trusted information and new KPIs, new insight into issues, and continuous, real-time feedback, plants often make rapid improvements in productivity —typically gains of 5-10% in a few months. 

Run Plants as a Portfolio

Data Foundation drives enterprise innovation.

Most plants run autonomously, but with real-time Data Foundation they can be operated as a portfolio. Who’s the best producer of which product? And what are best practices that can be shared with all? Data Foundation enables manufacturers to answer these questions.

Value Chain

A single Data Foundation across firms.

Standardized Data Foundation enables cooperation across firm boundaries. With the same consistent information, suppliers, customers, equipment providers, and materials suppliers can jointly address challenges to production.

Artificial Intelligence

First AI Models

Speed early models.

Many data science teams build proof-of-concept models. These generate enthusiasm for AI, but to keep the momentum going, models need to be applied and refined with operations, scaled across multiple sites, and adapted to changing underlying conditions.

Good Data

A single Data Foundation for all AI in plants.

The principal challenge for AI is that without standardized, consistent data underneath, models remain bespoke and limited. AI works best when there is a large, consistent Data Foundation underneath. The AI community increasingly recognizes that good data is scarcer than good models.

Handle Model Drift

Real-time streaming to continuously train AI.

Factories are complex environments, with constant subtle changes. Linking AI to real-time streaming Data Foundation prevents model drift. This is a critical step in sustaining AI progress; otherwise good models become outdated fast.

Integrate with Shop Floor

Domain-oriented AI expertise.

Most data scientists are trained in data science and learn manufacturing. With time and practice, deep domain knowledge and data science expertise can be joined. Data Foundation, with frameworks for using data in plants, speeds this collaboration.

Scale to All Plants

Firm-wide use of AI on the same Data Foundation.

Successful projects are never copy-and-paste from one plant to another. The data is different. The conditions are different. But with Data Foundation and good AI practices, AI can be repeatedly applied across the fleet.

Integrate AI, BI

A single set of trusted, consistent information for every stakeholder.

Data Foundation gives operators, process engineers, data scientists, and business leaders one source of consistent, trusted information. Whether it’s real-time visibility or sophisticated AI, all analysis can be linked and shared through a common Data Foundation.

Artificial Intelligence

First AI Models

Speed early models.

Many data science teams build proof-of-concept models. These generate enthusiasm for AI, but to keep the momentum going, models need to be applied and refined with operations, scaled across multiple sites, and adapted to changing underlying conditions.

Good Data

A single Data Foundation for all AI in plants.

The principal challenge for AI is that without standardized, consistent data underneath, models remain bespoke and limited. AI works best when there is a large, consistent Data Foundation underneath. The AI community increasingly recognizes that good data is scarcer than good models.

Handle Model Drift

Real-time streaming to continuously train AI.

Factories are complex environments, with constant subtle changes. Linking AI to real-time streaming Data Foundation prevents model drift. This is a critical step in sustaining AI progress; otherwise good models become outdated fast.

Integrate with Shop Floor

Domain-oriented AI expertise.

Most data scientists are trained in data science and learn manufacturing. With time and practice, deep domain knowledge and data science expertise can be joined. Data Foundation, with frameworks for using data in plants, speeds this collaboration.

Scale to All Plants

Firm-wide use of AI on the same Data Foundation.

Successful projects are never copy-and-paste from one plant to another. The data is different. The conditions are different. But with Data Foundation and good AI practices, AI can be repeatedly applied across the fleet.

Integrate AI, BI

A single set of trusted, consistent information for every stakeholder.

Data Foundation gives operators, process engineers, data scientists, and business leaders one source of consistent, trusted information. Whether it’s real-time visibility or sophisticated AI, all analysis can be linked and shared through a common Data Foundation.

Artificial Intelligence

First AI Models

Speed early models.

Many data science teams build proof-of-concept models. These generate enthusiasm for AI, but to keep the momentum going, models need to be applied and refined with operations, scaled across multiple sites, and adapted to changing underlying conditions.

Good Data

A single Data Foundation for all AI in plants.

The principal challenge for AI is that without standardized, consistent data underneath, models remain bespoke and limited. AI works best when there is a large, consistent Data Foundation underneath. The AI community increasingly recognizes that good data is scarcer than good models.

Handle Model Drift

Real-time streaming to continuously train AI.

Factories are complex environments, with constant subtle changes. Linking AI to real-time streaming Data Foundation prevents model drift. This is a critical step in sustaining AI progress; otherwise good models become outdated fast.

Integrate with Shop Floor

Domain-oriented AI expertise.

Most data scientists are trained in data science and learn manufacturing. With time and practice, deep domain knowledge and data science expertise can be joined. Data Foundation, with frameworks for using data in plants, speeds this collaboration.

Scale to All Plants

Firm-wide use of AI on the same Data Foundation.

Successful projects are never copy-and-paste from one plant to another. The data is different. The conditions are different. But with Data Foundation and good AI practices, AI can be repeatedly applied across the fleet.

Integrate AI, BI

A single set of trusted, consistent information for every stakeholder.

Data Foundation gives operators, process engineers, data scientists, and business leaders one source of consistent, trusted information. Whether it’s real-time visibility or sophisticated AI, all analysis can be linked and shared through a common Data Foundation.

Artificial Intelligence

First AI Models

Speed early models.

Many data science teams build proof-of-concept models. These generate enthusiasm for AI, but to keep the momentum going, models need to be applied and refined with operations, scaled across multiple sites, and adapted to changing underlying conditions.

Good Data

A single Data Foundation for all AI in plants.

The principal challenge for AI is that without standardized, consistent data underneath, models remain bespoke and limited. AI works best when there is a large, consistent Data Foundation underneath. The AI community increasingly recognizes that good data is scarcer than good models.

Handle Model Drift

Real-time streaming to continuously train AI.

Factories are complex environments, with constant subtle changes. Linking AI to real-time streaming Data Foundation prevents model drift. This is a critical step in sustaining AI progress; otherwise good models become outdated fast.

Integrate with Shop Floor

Domain-oriented AI expertise.

Most data scientists are trained in data science and learn manufacturing. With time and practice, deep domain knowledge and data science expertise can be joined. Data Foundation, with frameworks for using data in plants, speeds this collaboration.

Scale to All Plants

Firm-wide use of AI on the same Data Foundation.

Successful projects are never copy-and-paste from one plant to another. The data is different. The conditions are different. But with Data Foundation and good AI practices, AI can be repeatedly applied across the fleet.

Integrate AI, BI

A single set of trusted, consistent information for every stakeholder.

Data Foundation gives operators, process engineers, data scientists, and business leaders one source of consistent, trusted information. Whether it’s real-time visibility or sophisticated AI, all analysis can be linked and shared through a common Data Foundation.

Artificial Intelligence

First AI Models

Speed early models.

Many data science teams build proof-of-concept models. These generate enthusiasm for AI, but to keep the momentum going, models need to be applied and refined with operations, scaled across multiple sites, and adapted to changing underlying conditions.

Good Data

A single Data Foundation for all AI in plants.

The principal challenge for AI is that without standardized, consistent data underneath, models remain bespoke and limited. AI works best when there is a large, consistent Data Foundation underneath. The AI community increasingly recognizes that good data is scarcer than good models.

Handle Model Drift

Real-time streaming to continuously train AI.

Factories are complex environments, with constant subtle changes. Linking AI to real-time streaming Data Foundation prevents model drift. This is a critical step in sustaining AI progress; otherwise good models become outdated fast.

Integrate with Shop Floor

Domain-oriented AI expertise.

Most data scientists are trained in data science and learn manufacturing. With time and practice, deep domain knowledge and data science expertise can be joined. Data Foundation, with frameworks for using data in plants, speeds this collaboration.

Scale to All Plants

Firm-wide use of AI on the same Data Foundation.

Successful projects are never copy-and-paste from one plant to another. The data is different. The conditions are different. But with Data Foundation and good AI practices, AI can be repeatedly applied across the fleet.

Integrate AI, BI

A single set of trusted, consistent information for every stakeholder.

Data Foundation gives operators, process engineers, data scientists, and business leaders one source of consistent, trusted information. Whether it’s real-time visibility or sophisticated AI, all analysis can be linked and shared through a common Data Foundation.

Artificial Intelligence

First AI Models

Speed early models.

Many data science teams build proof-of-concept models. These generate enthusiasm for AI, but to keep the momentum going, models need to be applied and refined with operations, scaled across multiple sites, and adapted to changing underlying conditions.

Good Data

A single Data Foundation for all AI in plants.

The principal challenge for AI is that without standardized, consistent data underneath, models remain bespoke and limited. AI works best when there is a large, consistent Data Foundation underneath. The AI community increasingly recognizes that good data is scarcer than good models.

Handle Model Drift

Real-time streaming to continuously train AI.

Factories are complex environments, with constant subtle changes. Linking AI to real-time streaming Data Foundation prevents model drift. This is a critical step in sustaining AI progress; otherwise good models become outdated fast.

Integrate with Shop Floor

Domain-oriented AI expertise.

Most data scientists are trained in data science and learn manufacturing. With time and practice, deep domain knowledge and data science expertise can be joined. Data Foundation, with frameworks for using data in plants, speeds this collaboration.

Scale to All Plants

Firm-wide use of AI on the same Data Foundation.

Successful projects are never copy-and-paste from one plant to another. The data is different. The conditions are different. But with Data Foundation and good AI practices, AI can be repeatedly applied across the fleet.

Integrate AI, BI

A single set of trusted, consistent information for every stakeholder.

Data Foundation gives operators, process engineers, data scientists, and business leaders one source of consistent, trusted information. Whether it’s real-time visibility or sophisticated AI, all analysis can be linked and shared through a common Data Foundation.