Don’t Be Data Rich but Information Poor- Business Intelligence for Manufacturing

A vast majority of manufacturers list smart manufacturing (data-driven decision making) as important to their future success. Data generated by automated processes is collected within every PLC, HMI, IoT sensor, and meter. This data contains valuable information related to machine performance, environmental conditions, and overall production rates. However, despite its availability, most manufacturers struggle to effectively analyze, and therefore utilize this data in a meaningful way.

This lack of robust data analytics results in many of today’s automated facilities being generally data-rich but paradoxically information-poor. The ability to make data-driven decisions and fully leverage smart manufacturing technologies is therefore hindered.

E Tech Group collaborates with manufacturers to access these valuable insights stemming from operations data for problem-solving and process optimization. This is the field of industrial data analytics for manufacturing-specific business intelligence – manufacturing intelligence.

Types of Industrial Data Analytics for Smart Manufacturing

Analytics can give insights within a hierarchy of four broad categories: descriptive, diagnostic, predictive, and prescriptive information. Each of these categories is expanded upon in Table 1 below.

Descriptive Data Analytics: Automation Notifications

Today, most manufacturing sites are pursuing the first level of data analytics, obtaining descriptive insights for systems, which range in size and scale from a singular machine to a particular production line to the enterprise as a whole. At this first level, analytics tells you the “what” or “which” of a given process, event, pattern, etc., which can provide observational insights that may not otherwise be clear. For example, analytics can answer machine-level questions such as, “How long did the machine run without a fault?” or “What is the most commonly occurring fault?”

Digging into the Data: Analytics Have Many More Insights to Offer

However, as can be seen within Table 1, the potential for analytics to answer questions, solve problems and optimize processes goes much further. With the right data, analytics can offer diagnostics-level insights, providing answers for the reasons why a particular observation is occurring. Considering the machine level again, diagnostics analytics answers questions such as, “Why is the fault happening?” or “Why aren’t we meeting production benchmarks?”

Reviewing the Diagnostic, Predictive and Prescriptive columns in Table 1, we see analytics can provide even more complex insights for predicting behavior such as knowing the conditions that will produce the particular fault. It can also be used to formulate prescriptive solutions – knowing how to avoid the fault. 

Getting Started: Analyze Your Analytics Goals

An important first step towards using data analytics for decision-making purposes is to identify the specific outcomes a manufacturer aspires to achieve. These outcomes vary widely, including process optimization, root cause analysis of system failures, or making predictions. Focusing on the desired outcomes informs the selection, and therefore the source, of required datasets.

The primary challenge for implementing manufacturing intelligence is that the needed data often resides in very different systems with limited or no streamlined generalized access – commonly termed “data silos.” As an example, data from quality testing may reside in a manufacturing engineer’s spreadsheet to which general operators have no access.

Furthermore, the data may lack important context needed for the full use of analytics. Specifically, temperature data may be present but the more precise information describing the location where the measurement was taken may be missing. The capabilities of industrial data analysis is directly related to the detail, precision and accessibility of the data.

E Tech Group’s strategy for implementing a manufacturing intelligence system is focused on strategic system integration and targeted but highly contextual data collection:

• Determine the type of decisions that the data analytics system will inform. This clarification will establish how and by whom the data is used to make decisions.
• Locate the required datasets and consolidate into a singular platform.
• Address any further context needed for the data to be useful.
• Select the appropriate platform(s) to analyze the data.

Data Analytics Platforms: Rockwell Automation Case Study

While E Tech Group is an agnostic automation services company, meaning we partner with several vendors offering unique SaaS-based analytics solutions, let’s focus on FactoryTalk DataMosaix by Rockwell Automation to give context to our discussion.

FactoryTalk DataMosaix by Rockwell Automation

FactoryTalk DataMosaix is a controls integration software solution by Rockwell Automation that centralizes and organizes data from various sources across a manufacturing operation into a unified namespace (UNS), enabling better data visibility and analysis to improve decision-making and operational efficiency.

Situated between the IT and OT networks (within the iDMZ), DataMosaix can access data originating from devices residing deep within the OT network and also connect to cloud-based portions of the application for complex analysis purposes. While the concept of a UNS presents many practical issues in regard to operations control, it offers significant benefits for reporting and analysis by consolidating data from various sources into a centralized, accessible location. This centralization makes data more usable across different applications improving overall data management of automated processes.

Rockwell Automation has designed this industrial data analytics platform to be a nearly seamless integration for Rockwell-based facilities. This provides manufacturers with a distinct opportunity for a major control system upgrade with low investment in time, capital and training. 

Accessing In-Depth Data Analytics with Interfacing & Integration

DataMosaix uses edge connectors as interface points that collect data from PLCs, HMIs, sensors, and other devices operating on the plant floor. The edge connectors standardize the data and transmit it to the Edge Manager within the DataMosaix system.

Situated within the iDMZ of a facility’s network, the Edge Manager processes the data received from edge connectors and applies local decision-making algorithms to enhance operational efficiency directly at the source. For example, if data coming from an edge connector indicates that a temperature exceeds its operating range, the edge manager can make real-time decisions to adjust the cooling system accordingly, or alert staff to do so.

This Rockwell Automation product also reserves data transmission to the cloud for more intricate and complex analysis of long-term patterns and trends, encompassing everything from the approaching equipment obsoletion to streamlining resource acquisition and optimizing automated processes. The result is a robust data-management system that provides remarkable visibility and analysis of plant operations.

Getting Specific: Rockwell PlantPAx & Energy Manager Analytics

Application-specific SaaS solutions are available via subscription to visualize and interpret specific types of data for further insights. Two stalwart Rockwell data analytics products include:

• Energy Manager: Using utility meters (for electricity, gas, water) installed throughout the target system, Energy Manager provides insight into resource use per process or per machine. Potential outcomes include discernment of the most energy-expensive products to manufacture and determination of the most energy-expensive equipment to operate. Energy Manager can also help a facility find ways to streamline energy expenditure, improving sustainability.