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If your manufacturing plant relies on analog troubleshooting, rather than data-driven insights, unresolved KPIs may accumulate.

The good news is that DataProphet provides tailored support at each phase of your digital maturity journey. A data-readiness assessment is the first port of call. It determines whether the infrastructure and data collection mechanisms you have in place can support a data-driven solution such as PRESCRIBE.

Our team will guide and advise you on systematic integration of your OT systems. This will provide a robust foundation for informed decision-making around your plant’s data. The collection of high-quality data will extend to cross-functional teams for actionable analysis.

Change management support includes a dedicated customer success manager and digital skills training. These will help instill a proactive, data-driven focus on KPI realization.

Celebrating measurable wins along the way, your teams will begin to see and own their improvements. Outcomes include process efficiency and assisted root cause analysis based on centralized access to primed data.

Our AI-as-a-Service is geared to move you to an optimized production paradigm. We start by helping you implement a data pipeline from the Edge to cloud to front end.

From here, advanced analytics tools and AI algorithms will inform your decision-making processes.

Real-time data collection, analysis, and sharing will enable you to modify control plans based on prescriptive guidance.

New KPI benchmarks can then be maintained across product lines for continuous improvement.

Most plants have to construct an integrated view of available production and quality data from scratch every time root cause analysis is performed. Our IIoT platform does the data orchestration heavy-lifting to construct a unified view of this data. We free you up to focus on the big picture and home in on the insights.

Our unified view contextualizes your current plant state end-to-end. We stream mature data live from robust, fault-tolerant Edge devices to a secure cloud and onto an intuitive, custom-configurable front-end.

End-to-end data-integrity and centralization is sustained by our software engineers and machine learning specialists. Plant stakeholders can rest assured they have a contextualized, current, and complete picture of their plant’s data. Optimization-critical data is rendered secure and readily available for role-based access at all times.

Our IIOT solution empowers plant personnel for fine-grained monitoring of their manufacturing processes. They trace inputs and machine setpoints to production outcomes with extreme accuracy.

Our IIOT connectivity solution’s cloud infrastructure enables the collection, centralization, and standardization of ISA 95 levels 1, 2, and 3 industrial data.

Data from multiple plants and different networks in one secure and accessible location means managers can monitor sites worldwide.

The machine learning algorithms of our expert AI engine comprise state-of-the-art deep learning, proven to generate repeatable results. Our prescriptive optimization solution measurably improves a wide range of production metrics.

Our machine learning specialists build models based on your plant’s historical operating regions and combine them with continuous evaluation of actual production.

This enables adaptive guidance that moves the plant from its current mode of operation to its holistic optimal — before problems appear on the line.

Your results are assured against targeted outcomes. Production metric optimization is pegged to the margin of improvement which you have deemed worthwhile. Assuming a specific level of compliance with the prescriptions, we stand by your results.

A unified view is the mapping between a product’s given quality value and the exact process measurements responsible for this production result.

This view is necessary for machine learning to deliver optimal recipes for data-driven process performance improvement.

To successfully model the mapping between process and quality data, these two parameter sets must strategically be aligned in time.

In simple terms, predictive analytics applied to manufacturing processes forecasts production outcomes. Prescriptive AI modeling takes this machine learning analysis a step further. Our AI provides recommendations on actions to take based on the predictions generated by deep learning discovery.

Prescriptive AI is inherently efficient for production optimization. This is because artificial neural networks provide an unparalleled ability to represent any mathematical function or probability distribution.

This makes it especially effective for dynamically controlling complex processes like manufacturing. Deep learning applied this way can home in on the hundreds of interdependencies that determine production outcomes. Additionally, it can be trained to suggest high-impact modifications to improve performance proactively.

Our process engineers consult with your manufacturing specialists to ensure that our prescriptive optimization technology is a good fit for your process. Adoption of our prescriptive solution going forward proceeds from a successful use case (i.e., one shown to deliver a significant return on your investment).

Our data-driven discovery traces relationships between parameters and production outcomes for most industrial processes with extreme accuracy.

We have successfully adapted our solutions in a variety of manufacturing sectors and their production environments. These include automotive, grey iron foundries, minerals, steel, and semiconductors.

Dataprophet typically delivers between 20-40% improvement in production process performance with significant cost savings and enhanced environmental impact. Here are some of our key results.

Data-readiness — via systematic integration of your OT systems and Edge devices — can lead to improvements in data-driven process efficiency and reduced downtime in approximately three months.

Prescriptive optimization for continuous improvement of key production metrics across the plant can be achieved in a year or less.

The time-to-value can be shorter assuming a high level of prescription compliance and the proactive buy-in of plant decision makers.

We oversee security changes using a world-class management framework that operates against version-controlled configuration definitions, known as “Infrastructure-as-Code” (IaC). This oversight provides all the necessary automated vulnerability scanning and anti-malware prevention across the production infrastructure.

Dataprophet places the entire data infrastructure behind extensively port-hardened reverse proxies. A stringent log retention policy affords the whole system security-focused traceability.

Dataprophet embeds segregation between office and product environments. Client applications and data are hosted in a separate environment safeguarding against unwanted access to critical IT and OT systems.

We apply the least-privilege principle. Users are granted the lowest access and privilege levels that will enable them to do their job. In some cases, special access is provided and revoked on a just-in-time basis. Users with elevated privileges are informed of the risks and expected responsible behaviors.

Explicit authentication is based on user accounts for internal systems, rather than network location. There are no “Trusted Networks” and no Active Directory. Multi-factor authentication is enforced for all critical systems, and strongly recommended for non-critical systems.

We employ a highly transparent configuration change and code review process. Malicious changes are unlikely to slip by without someone noticing and will likely require collusion between employees in senior roles.

Security is a priority consideration in our solutions architecture and feature design process. We endeavor to use tried and trusted components wherever feasible and follow vendor/industry best practices.

EDGE devices are cryptographically identified and authorized for the integrity of the network. They are also locked down against manual user logins, managed entirely by our automated infrastructure-as-code framework.

In the cloud, customers enjoy completely isolated streaming, storage, and retrieval, plus a dashboard environment with role-based access. We isolate client data to tenant-specific storage and databases. This data is tied to discrete locations and is encrypted at rest and in motion using the AES-256 standard.

Segregation of tenant application data is part of the basic architecture of DataProphet CONNECT. Exposure from a compromise at one client is limited to their own data and operations.

Dataprophet works closely with manufacturers to design the CONNECT VPN to accommodate challenging factory OT environments without compromising security. Data is encrypted end-to-end — in transit, and at rest.

Our solutions have been effective in cases where we don’t have access to live data environments or access to proprietary production process knowledge.

In one instance, our data scientists adapted our prescriptive AI with extensive data analysis of the six quality metrics without seeing the actual parameters. Our data scientists preprocessed obfuscated data to reduce the effects of outliers and identified appropriate imputation methods to handle missing data points.

Some processes are particularly susceptible to uncontrollable factors over time. In one case, our team successfully prescribed for seasonally different optimal regions and plant state shifts. Proceeding based on uncontrollable values, multiple optimal regions were discovered and crystallized for several distinct production periods.

Manufacturing Excellence 4.0 is a journey of data-driven continuous improvement, and the change management methods used to get there. It is reflected in delivery of world class, industry benchmarking results. These are achieved year-on-year on the shop floor, using a standardized 4IR approach that upskills the workforce for smart factory step change. Manufacturing Excellence 4.0 is a journey that normalizes and automates coordinated algorithmic business thinking between plant stakeholders.

The same principles apply to digital transformation in the workforce as to traditional Manufacturing Excellence regimes. These include instilling a sense of urgency in operational teams that work practices need to adapt to affect systems and process improvements.

Executive sponsorship is critical here. Manufacturing leadership must communicate the vision for data-driven improvement. They do this via establishing clear KPIs and emphasizing the importance of achieving these with 4IR tools in clear time frames.

Factory teams can be further motivated during this transition as digital skills training is provided and short-term wins are recognised and celebrated at each phase of digitisation.

Our goal is to help customers to optimize their operations for the digital era while integrating data-driven production holistically. We can help your people with this transition to lean industry 4.0, which is proven to yield bottom line operational improvements of up to 40%.