The Difference Between Dashboarding, Artificial Intelligence, and Machine Learning

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In the age of the Internet of Things (IoT), there’s a lot of terminology floating around that has a nebulous definition that seems to adjust based on the audience. Some terms are even used interchangeably, and wrongfully so.

Terms like Dashboarding, Artificial Intelligence (AI), and Machine Learning (ML) are all technologies that can be used to improve manufacturing outcomes, but they are uniquely different. But what do these differences mean for your business? We’ll dive into the distinctions of each of these to show how technology can help improve business decisions.

Dashboarding vs. Artificial Intelligence

When looking at Key Performance Indicators (KPIs), you’ll often see some graphical representations highlighting the metrics stakeholders deem the most critical. These traditional dashboards can be extremely beneficial, providing a quick view of operations and identifying areas of concern. It’s important to remember that this information is typically historical data focused on what has already happened. It can be close to real-time, and it can effectively report things like units produced in an hour, temperature fluctuations over a day, or volume of scrap by batches. This way of visualizing data relies on human intervention.

In the best examples, dashboards provide a view of information based on a role. A Maintenance Engineer might want to know which equipment is performing below specifications, or which machines will need maintenance and when. For an Operations Manager, the same data might be viewed concerning performance – is equipment performing as expected on a line, how many parts are being produced per hour, or how the output of line one compares to line two. Often the information is siloed, leading to cross-functional correlations going undetected.

Simply put, a standard dashboard is a snapshot of operations that provides a quick gut check – is everything okay? Often, this scoreboard can be enough to help adjust for improvement. However, a common limitation of its effectiveness is the number of data sources it leverages. Many out-of-the-box dashboards pull from a single data source, and drilling into the details can be highly manual and labor-intensive. As production spaces become more sophisticated, the correlation between several data sources to make predictions about performance is required.

In contrast, Machine Learning and Artificial Intelligence are forward-thinking, and leverage larger data sets to understand patterns, determine why something is happening and predict what will happen next. These engines can consume large amounts of data from multiple, disparate sources at once, correlating time-series data instantly. With this immense increase in processing power, Machine Learning trains an algorithm on what to look for, collects examples of good and bad outcomes, and uses feedback to learn how to improve. The more granular the data and the more diverse the sources, the more accurate the prediction will be.

Traditional dashboards are descriptive; Machine Learning and Artificial Intelligence are predictive. They provide an intelligent view of what will most likely happen next, making it easier to decide how to move forward.

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Machine Learning vs. Artificial Intelligence

These two terms are interrelated – in fact, Machine Learning is a subset of AI. Machine Learning is a statistical algorithm written by a data scientist to determine the likelihood of certain outcomes. As it consumes data about a process, it recognizes patterns and learns the behavior of a machine, process, or environment and identifies probable outcomes. It answers the question – why are things not okay? Over time it becomes more accurate with the goal of maximizing performance based on the algorithm that defines it.

What to consider for optimal Machine Learning impact:

  • Data Integrity – Enough samples of good and bad outcomes must be collected in regular time intervals to learn and predict accurate results. There must be high-quality inputs to get high-quality outputs.
  • Retuning and Retraining – Machine Learning models should be tuned and retrained regularly. Consider a plant floor engineer, continuously learning to ensure they are up to date on the latest information to stay relevant in the industry. Machine Learning models are very similar in how they must be tuned to ensure they can run at a pace to meet production requirements and be retrained to have the latest available data for the key processes. The more accurate the information is, the better the predictive qualities it will have.

Machine Learning’s Impact on Manufacturing

Utilizing ML allows manufacturers to identify anomalies, trends, and cross-correlations that would otherwise go unnoticed in traditional data reports. Removing the potential for human error and speeding up the processing time creates the potential for major productivity gains.

Some potential outcomes of using ML:

  • Minimize product defects and scrap rates – increase manufacturing yields by identifying root cause quality issues
  • Reduce unplanned machine downtime – predict when a machine will have a failure
  • Forecast demand more accurately – plan your operations based on visibility of customer demand
  • Faster product development – increase time to market via faster and more reliable prototyping
  • Streamline logistics and supply chain operations – reduce operating costs and better manage inventory

It’s important to note that Machine Learning consumes, analyzes, and learns from data to predict what will happen – it does not make decisions. Machine Learning is the model that feeds Artificial Intelligence. Artificial Intelligence is the action taken based on those Machine Learning predictions.  

Preparing for the Future of Artificial Intelligence

From the beginning, the goal of AI has been to train machines to perform human tasks. With developments in data processing, AI can leverage the insight of Machine Learning and other tools to solve complex problems and successfully perform tasks with little or no human interaction. Where Machine Learning suggests options to improve or avoid failure, AI makes decisions and acts. As technologies continue to advance in this area, AI is incorporated into Intelligent Machines with specific boundaries of decision making and action.

When it comes to Dashboarding, Artificial Intelligence, and Machine Learning, it’s easy to misinterpret the differences, key benefits, and impact on the industry. Leveraging technologies such as Machine Learning can prepare your business for the future and accurately predict outcomes based on your actions and decisions. By leveraging the right tools, you can improve efficiency and productivity while achieving your strategic vision.

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