AI for Manufacturing: Data Scientists on the Production Line

AI for Manufacturing

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In manufacturing, obtaining insights from production data can deliver transformational improvements in efficiency and quality. In fact, IDC predicts that by 2021, 20% of the world’s largest organizations will operate based on data and analytics derived from artificial intelligence (AI), machine learning, and the Internet of Things (IoT).[1]

However, there are many obstacles to prevent you from realizing this value. The fact is that manufacturing data is – well – messy. In fact, our studies have found that 79% of a data scientist’s time is spent cleaning and organizing data. According to IDC, AI-based technologies such as Sight Machine can speed production by up to 25%.

AI for Manufacturing: From One-Time to Real-Time

Let’s take a simple example: Your facility gathers information from a temperature sensor that takes regular readings in a paint booth. It is reliable at first, but the data starts to drift as the sensor wears out or breaks down. Over time, it even begins surging and slacking, or showing unusual (and possibly false) values. Worse, these spiking numbers over time may become the norm. Analysts can’t be sure whether the lamps are working correctly, how long it is taking to dry the components, or whether surfaces are at risk due to improper heat. The result is unscrubbed, noisy data that is difficult to use in measuring KPIs or root cause analysis.

Managing noise from a single sensor for a single analysis is an annoying inefficiency.  Attempting to manage potentially noisy data from many sensors, over time, for a wide variety of applications is a severe impediment to operationalizing your analytics. Instead, data scientists need a solution to sustainably manage a variety of raw data challenges, allowing them to focus on the analytics instead of data wrangling. It also needs to help manage blending data from multiple sources. Such technologies allow data scientists and engineers to blend data into standard cross-facility data models.

Automatically Cleaned Data Streams

To address this, AI for manufacturing tools such as Sight Machine helps create real-time streams of clean, ready-to-use data. Our manufacturing data platform includes:

  1. A tool for building streaming data pipelines to continuously clean production data.
  2. Transformation capabilities to integrate data from multiple sources and engineer manufacturing-specific features such as machines, lines, parts, cycles, defects, etc.
  3. Tools for detecting and managing outliers, drifting values, and other draft data.
  4. Exploratory analysis tools to identify trends and drill down into interesting data patterns.
  5. Flexible tools for creating visualizations that explain the results effectively to plant leaders, supervisors, and operators, regardless of experience.

In this environment, you can more quickly address a drifting data source, such as our temperature sensor, by retraining the AI with a continuous stream of readings. An ongoing flow of information makes it easier to adapt to and accommodate data fluctuations as part of the process.

Clear and Interpretable Results

Even if the data is clean and readily usable by data scientists, subsequent analysis may not be usable by the ultimate end users on the production floor. Manufacturers are understandably conservative about trusting data in the midst of a changing, challenging process, and operators need clear and interpretable results that let them make immediate decisions – for example, locating and replacing that faulty temperature sensor.

To accomplish this, good visualizations of information are essential, based on real-time streams or on historical data “snapshots.” However, the most useful visualizations are rarely the three-dimensional rotating graphs or tables (with lots of Greek letters) that make for great marketing. In fact, the results of several of our projects, even those based on complex chains of algorithms, can be visualized with easy-to-read Pareto charts.

Managing the Future of Data

By collecting, analyzing, and interpreting the large data sets produced by factory and channel systems, tools such as Sight Machine complement the work of data scientists to automate information preparation and integration. AI allows data teams to gather insights based on amounts and types of information previously too great for humans to evaluate. This allows data scientists to convert noisy, variable data into valuable information – revolutionizing the production process and accelerating manufacturer success.

Please contact us for a live demonstration of how Sight Machine’s AI for Manufacturing platform and CI services can achieve these goals.

[1] “FutureScape: Worldwide Manufacturing Predictions 2018,” IDC Report

AI for Manufacturing

Kurt DeMaagd

Kurt DeMaagd

Chief AI Officer and Co-Founder – Kurt co-founded and has served as a professor at Michigan State University in information management, economics, and policy. Kurt is an accomplished analytics programmer.

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