Manufacturers often find themselves overwhelmed with vast amounts of data generated from various devices and systems within their operations. This data, collected from PLCs, HMIs, IoT sensors, and meters, holds critical information about machine performance, environmental conditions, and overall production rates. Unfortunately, despite its abundance, many manufacturers struggle to analyze and utilize this data effectively, resulting in a paradox where they are data rich but information poor. The ability to make data-driven decisions and fully harness smart manufacturing technologies is significantly impaired, representing a challenge that must be addressed as smart manufacturing becomes increasingly vital for future success.
Define the Types of Decisions the Analytics System Will Support
The first crucial step in leveraging data analytics for decision-making in manufacturing is identifying the specific outcomes the manufacturer aims to achieve. This step involves a thorough understanding of the different types of insights that data analytics can provide. These typically include descriptive, diagnostic, predictive, and prescriptive analytics. Each of these plays a unique role in guiding manufacturing processes and decision-making. Descriptive analytics offer observational insights, such as machine runtime without faults or most commonly occurring faults, answering “what happened?” This foundational level helps manufacturers understand the current state of their operations.
Diagnostic analytics take it a step further by explaining why something happened, identifying the root causes of faults or inefficiencies. Once the reasons behind these occurrences are understood, predictive analytics come into play, forecasting future events based on historical data. This level of insight can be crucial for anticipating issues before they become critical, enabling preemptive actions. Lastly, prescriptive analytics suggest actionable steps to avoid predicted problems or optimize operations. For instance, it might recommend specific maintenance schedules or process adjustments. By focusing on these desired outcomes, manufacturers can better inform the selection of the required datasets and tools for analysis.
Identify and Amalgamate the Necessary Datasets into One Platform
The primary challenge in implementing an effective manufacturing intelligence system often lies in the fact that the necessary data resides in disparate systems, referred to as data silos. Data from quality testing, for example, might be stored in a manufacturing engineer’s spreadsheet to which general operators have no access. Additionally, the omitted context, such as specific temperature measurement locations, may further limit the utility of this data. Therefore, the next step involves locating these datasets and consolidating them into a singular, unified platform.
Consolidating data from various sources into a central platform is essential for comprehensive analysis. Systems like Rockwell Automation’s FactoryTalk DataMosaix can be used to centralize and organize data across a manufacturing operation into a unified namespace (UNS). This centralization allows for better data visibility and enhances the ability to draw meaningful insights. DataMosaix, for instance, accesses data from both the IT and OT networks, enabling a seamless flow of information from deep within operational networks to cloud-based applications for complex analysis. By consolidating these data silos, manufacturers can ensure that all relevant information is accessible and can be analyzed cohesively, thus turning raw data into actionable insights.
Add Any Missing Context Required to Make the Data Actionable
Even after consolidating data into a single platform, it is essential to address any further context required for the data to be actionable and useful. Contextual information can include precise details about how and where data is collected, which is critical for accurate analysis. For example, temperature data without the context of where the measurement was taken can lead to incorrect conclusions. Ensuring that data is well-tagged and enriched with necessary contextual information allows for more accurate and meaningful analysis.
Adding context may involve standardizing data from different sources to ensure consistency. Edge connectors, as used in FactoryTalk DataMosaix, can play a significant role in this process. They collect data from various devices like PLCs, HMIs, and sensors, standardize it, and transmit it to a central Edge Manager. This manager processes the data and applies local decision-making algorithms, enhancing operational efficiency directly at the source. By adding this layer of context and standardization, manufacturers can transform raw data into reliable and actionable information, enabling more precise and informed decision-making.
Choose the Suitable Platform(s) for Data Analysis
Manufacturers frequently find themselves inundated with enormous amounts of data generated from a variety of devices and systems within their operations. This data, which comes from PLCs, HMIs, IoT sensors, and meters, contains crucial information about machine performance, environmental conditions, and overall production rates. However, despite having an abundance of data, many manufacturers struggle to analyze and use this information effectively. This creates a paradox where they are rich in data but poor in actionable insights.
The inability to make data-driven decisions and fully leverage smart manufacturing technologies significantly impairs their operations. As smart manufacturing becomes increasingly critical for achieving future success, manufacturers must address this challenge. Adopting advanced data analytics tools and integrating them into their operations can help transform raw data into meaningful insights. This shift not only improves efficiency and productivity but also boosts competitiveness in an evolving market. With the right approach, data can become a powerful asset, driving innovation and operational excellence. Therefore, overcoming the challenge of data utilization is essential in the modern manufacturing landscape.
