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.

Sign Up for the Analytics Workshop

Analytics Evolution

  1. 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.
  2. 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).
  3. 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.
  4. 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)
Learn More

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
Learn More

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.

Schedule a Call

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.