The real question is what value does the data in your data lake deliver? FactoryEye integrates data from across the entire organization. It then processes and contextualizes the data to ensure you get the maximum possible value from your data – both real-time actionable insights and deeper understanding for long-term planning and ongoing improvements.
Though ERP systems are central to operations, your organization also uses other systems and data sources and possibly also paper processes. FactoryEye integrates and contextualizes all IT and OT data, and provides digital forms to eliminate those paper processes. In this way, FactoryEye enables cross-departmental actionable insights that your ERP cannot provide. It provides alerts and notifications and dashboards and simplifies the generation of all the reports you need.
While MES systems are focused exclusively on the production floor, FactoryEye provides a range of additional value:
By integrating, processing, and storing data from all your IT systems, FactoryEye is able to contextualize data across departmental boundaries. This delivers coordinated and centralized insights which enable intelligent decisions at all levels across the organization.
FactoryEye generally connects to your data historians or IIoT systems to collect your data, process and enriches it with contextualization and then store it in our data lake.
The FactoryEye digital forms module allows creating digital forms for manual data entry instead of using paper forms – for all parts of the organization. This saves paper and the time for printing and distributing forms – and makes for a rapid data entry process.
Forms can be dynamic (i.e. the fields requested or ranges accepted made dependent on the previous selection or data entry) to simplify the entry process. This and data validation ensure data accuracy. Data is validated by checking that the data entered is within an accepted range, and that all necessary data is collected.
Merging data from multiple systems in a given situation gives you additional valuable insights. Some examples:
Predictive Maintenance: Machine data, such as temperature, pressure, and vibration, can be contextualized with data on the machine’s age, maintenance history, and operating conditions to predict potential issues before they cause downtime.
Quality Improvements: Quality data, such as defect rates and inspection results, can be contextualized with data on the production process, such as machine settings and material inputs, to identify root causes of quality issues and improve quality control.
Energy Savings: Electricity consumption and demand, can be contextualized with data on production schedules and machine settings to identify opportunities to reduce energy consumption and improve energy efficiency.
Delivery updates: Contextualizing location GPS data during the movement of goods and materials through the supply chain such as delivery truck tracking enables providing real-time updates to customers about the status of their orders.
Does FactoryEye impose any limits on our IT, for example, the selection of systems such as RP, MES, PLM, TPM or other?
FactoryEye is a flexible product that works with any current or future product. This flexibility is achieved by its plug & play integration engine and by its data lake which uses a schema-on-read model that allows easily modifying the effective data structure as needed.
(Schema-on-read is an approach where data is stored without a predefined schema, and the structure is applied when the data is accessed or analyzed. This provides the benefit of flexibility to adapt to changing data needs, allowing for agility and accommodating diverse data sources.)
FactoryEye uses the field-proven Magic xpi integration module which includes a flexible connection builder to allow connecting to homegrown systems as well as other systems for which an existing connector does not exist.
Our system is hosted on Azure (hosting is done per customer country). In addition to Microsoft’s security mechanism, Magic constantly monitors the environments and data to ensure stability and security. Importantly, we provide full customer data segregation using a dedicated per-customer database and processing environment.
Out-of-the-box dashboards and reports are provided, and additional ones are created as part of the deployment. In addition, our customers can create their own reports – which are validated by Magic for performance before being deployed.
The real question is what value does your current data deliver? FactoryEye processes and contextualizes the data to ensure you get the maximum possible value from your data – both real-time actionable insights and deeper understanding for long-term planning and ongoing improvements.
Moreover, with changing data requirements you’ll probably soon (if you are not yet already) be in need of a more flexible data platform – and the flexibility of a data lake using schema-on-read is exactly the type of flexibility you need today and going forward.
(Schema-on-read is an approach where data is stored without a predefined schema, and the structure is applied when the data is accessed or analyzed. This provides the benefit of flexibility to adapt to changing data needs, allowing for agility and accommodating diverse data sources.)
Beyond increasing factory floor efficiency, FactoryEye improves overall company effectiveness by promoting transparency and cross-department cooperation across the organization. Everyone has the information they need to work effectively and make intelligent, data-driven decisions.
FactoryEye is delivered as an end-to-end cloud service – with ongoing automatic updates to the latest core version. There is no need to manage your own hardware or software, nor handle the issues of multi-system interworking.
The service is hosted in an environment that is monitored 24/7. A dedicated support team is available 24/7 ensuring your SLAs are met. The environment is secure and disaster recovery is provided.
Each customer project is different and therefore we provide a dedicated customer success manager that escorts you from project initiation to deployment and then ensures your ongoing success. A cloud architect is continuously involved to ensure optimized environment performance.
In addition, an expert team is available to create any custom applications you may need.
FactoryEye is a cloud-based service providing a pre-packaged, but flexible, end-to-end data management and analytics solution (integration, data collection, processing, and contextualization, reporting & dashboarding, and personalized easy access to data from any location) that is easy to deploy and configure per user. It allows you to start small with minimal disruption to your operations and expand with no need to switch out your existing systems.
FactoryEye includes its own powerful field-proven integration engine that is not available in BI and Analytics tools. This guarantees simplified integration to all your systems.
In addition, FactoryEye performs data-level contextualization and not the analytics-level contextualization used by BI and analytics tools. This ensures that data is accurate, meaningful, and usable for analysis with several advantages such as:
Improved Data Quality: By adding context to the raw data, you can ensure that the data is accurate, complete, and consistent. This can help you avoid data quality issues that can arise during analysis.
Better Data Understanding: Data contextualization on the data level can help you better understand the data by providing additional information about the data’s source, format, and meaning. This can help you avoid misinterpretations and make better decisions based on the data.
Faster Analysis: By adding context to the data upfront, you can streamline the analysis process by reducing the amount of time required to add context manually during analysis.
Improved Collaboration: When context is added to the data itself, it can be shared more easily among team members. This can improve collaboration and communication among team members, leading to better decision-making.
Easier Maintenance: By adding context to the data at the source, you can ensure that the context remains with the data throughout its lifecycle. This can make it easier to maintain the data and keep it up-to-date as new information becomes available.