Manufacturing Analytics
Capitalize on your Data
Powered By AI and machine learning
Add context to solve problems
“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, or chemicals to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value."
Clive Humby, Mathematician and architect of Tesco’s Clubcard
You can achieve Business Results from IIoT & Real-tIme Data
See initial payback in 3 months
Reduce your downtime in your operations by 10% to 40%
Increase Efficiency with insights into operational performance
More Predictive Maintenance when machines are monitored
Manufacturing Analytics
All about solving manufacturing issues
- Predefined Analytics and Model
- Standardize Configurable Tools for Data Scientists
Connect to wide variety of sources
- Devices: PLC/SCADA/DCS/Sensors/Edge Devices
- Databases: Time (Historian) and Event (SQL)
- Systems: ERP, CMMS, Quality
- Misc
Data Transformation and Management
Apply Analytics Tools
INS3 has an experienced team of software and engineering experts
We are available to assist you in the design, deployment, and implementation of your analytics solution.
In addition, you can leverage INS3’s experience in manufacturing systems which — combined with our extensive knowledge network of partners — drive improvements in manufacturing results and business outcomes. We have a proven process to get you there.
Analytics Evolution
- Descriptive / Diagnostic analytics are used to describe what happened in the past and why it happened. Usually, Descriptive Analytics gain insight from historical data using reporting, scorecards, or clustering.
- Real-time analytics describe what is currently happening (e.g., the current location of the product, details on the progress of the manufacturing processes, or detection of faulty parts).
- Predictive analytics entail algorithms that engage in forecasting of future incidents (e.g., the possibility of a defect showing up). Predictive analytics signals the need for an action. Often Predictive Analytics use Machine Learning techniques.
- Prescriptive analytics provide advice on the best possible actions that the end-user should take. In other words, it answers the “what should happen”. Prescriptive analytics requires a predictive model with two additional components: actionable data and a feedback system that tracks the outcome produced by the action taken.
Trending Analytics for Time Series Data
- Connect to Machines, Controllers or Sensors
- Collect Time-Series Data at the required rate
- Flexible Data Modeling fit to the Application
- Recall volumes of data in seconds for rapid analysis
Case Study | Predictive Maintenance
50% reduction in Work Orders
Sending best personnel on their schedule
Increase overall reliability
Data
- Vibration
- Temperature
- Amp Draw
- Levels and Pressures
Sources
- Existing PLC
- Added Sensors
Solutions
- Integrate with existing Historian
- Analytics to detect an issue, and likely cause
- Interface with Maximo (CMMS)
Case Study | Quality
Double digit reduction in waste or downgrading of Roasted Peanuts
Issues
- Peanuts spend a long time in the roaster
- Quality checks can take some time
- If process is setup to make bad products, you are making bad for a while before you know to adjust, and a bunch of product is already in process
Solution
- Apply Machine Learning to large data set
Talk to an Expert
Start with the problems you are trying solve, look at the data available, determine what is needed, cleanse the data and apply AI and ML techniques.
FAQ
- There are three critical steps in starting on a Industry 4.0 journey. First is make sure you have “big data” to support the analytics you will want to do in the future. Second is get the focus on solving issues and optimizing process that will have real business value, not on putting in a bunch of technology that somehow will magically do that for you. Third is have the right team of operations, IT, and other departments to keep the focus, and good partners focused on results.
There are three critical steps in starting on an Industry 4.0 journey. First is make sure you have “big data” to support the analytics you will want to do in the future. Second is get the focus on solving issues and optimizing process that will have real business value, not on putting in a bunch of technology that somehow will magically do that for you. Third is have the right team of operations, IT, and other departments to keep the focus, and good partners focused on results.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. What does that mean to manufacturers. Using data and algorithms and computerized thinking to solve issue is the root of it.
By definition Industrial Internet of Things, typically is in the cloud. IIoT is a part of Industry / Manufacturing 4.0, but the vast majority of successful project related to Industry / Manufacturing 4.0 are utilizing data that is not in the cloud, or at a minimum started on premise, and was duplicated in the cloud. It is mostly about solving manufacturing issue, and optimizing processes utilizing data. That data is not required to be in the cloud, and most is currently no in the cloud.
A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. It is less complicated than most think, but it basically having information available in a computer that you can look at the information and get a good representation of how that machine or device function during any particular timeframe.
Manufacturing Analytics comes in many shapes and sizes. You can collect data for years, and then give a good data person a process to optimize and they can do it using Excel. You can put in a good data collection system with good visualization tools to deliver the right information to the right people at the right time, empowering them to make good business decisions. It does not have to be expensive, and it can be scalable and done in a phased approach.