• Artificial Intelligence

KPI Monitoring and Data Analysis Using AI in the Manufacturing Sector

20 Jul 2022
KPI Monitoring and Data Analysis Using AI in the Manufacturing Sector

Artificial Intelligence (AI) and Machine Learning (ML) solutions become increasingly popular in manufacturing. By implementing AI, organizations automate their routine tasks, making it possible to spend less time on manual labor and more on decision-making. 

Data analysis using AI is one of the main examples of leveraging new technologies for business needs. Research conducted by McKinsey shows that key performance indicator (KPI) monitoring and AI-enabled analysis tools can reduce a company’s yield detraction by 30% and increase a product’s time-to-market by 40%. Let’s see how exactly these technologies help the manufacturing industry reach its production goals.  

How AI/ML Transform Our Approach to Data Analysis

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The need for mass production has increased with the advance of the Internet and consumerism culture. Old-fashioned ways of dealing with manufacturing processes proved to be inefficient when faced with Big Data. Companies needed to figure out how to produce more without compromising quality. 

According to Deloitte, the manufacturing industry creates 1,812 petabytes of data annually. Together with virtually infinite data resources available on the net, companies face many challenges in data processing. Some of these challenges are: 

  • large volumes of data;
  • velocity;
  • data synchronization from multiple sources; 
  • need for visual representation;
  • shortage of professionals.

The solution came with the availability of hardware and machine learning algorithms. They introduced advanced ways of dealing with challenges regarding mass production. 

AI-enabled technologies can analyze large amounts of data that can further be used for forecasting customer demand, improving quality testing, and increasing the overall productivity of manufacturing processes. Implementation of AI technology may take months, but the results often can be seen right away. By implementing an ML algorithm that predicts and provides the reasons for subscription cancellation, LinkedIn increased its subscription revenue by 8%.

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There are plenty of companies that implemented AI in their manufacturing process already. For instance, pharmaceutical company Pfizer uses AI algorithms that gather and analyze petabytes of test data. The results of the analysis help boost the company’s research and development of new drugs. Let’s view more examples of using AI.

Data Prediction

Prediction is one of the key reasons many companies are willing to apply ML algorithms. AI can conduct Big Data analysis and make predictions based on it. Insights received from processing data via AI can be visualized and become a powerful tool for decision-making.

Using data collected by sensors, AI finds the relation between the labeled outputs and input data. Based on the previous results and real-time data, the model can predict failures or successes. Companies like KONUX leverage AI technologies to process large chunks of data remotely. They also use AI to detect inconsistencies in advance and provide better support to the smart power grid. 

In addition to sensors and algorithms, companies can use external data sources, quality measurement of machine outputs, and maintenance logs. More system-related information enables AI to predict events with greater precision.

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    Anomaly Detection

    The Internet of things (IoT) and new high-tech sensors integrated into production processes increase the availability of data. A lot of data sources can help ML algorithms detect an anomaly. Analysis of sound can detect anomalies in air conditioning, gearboxes, and engines. Vibrations can be measured to detect errors in switches and machines. For example, artificial auditory cortexes simulate human sound interpretation and help Neuron Soundware improve and automate breakdown detection.

    Visual data provided by cameras and microscopes is important for detecting anomalies. Companies like Nanotronics combine 3D microscopy with deep neural networks for the most precise defect detection (down to the nanometer). The system conducts a visual inspection of the steel surface and helps improve the performance of detecting rare defects. 

    Another example would be the use of TensorFlow ML technology and camera-equipped Raspberry Pi 3 by a Japanese farmer Makoto Koike in 2015. The farmer used these tools to create an AI-enabled technology for rating the quality of vegetables. It wasn’t perfect, though, but still demonstrated the potential of AI and ML.  

    ML algorithms are not the only solution that is used for anomaly detection and data prediction. The Statistical Modeling (SM) approach existed before ML and is still used today.

    Statistical Modeling vs Machine Learning

    SM refers to using mathematical models and statistical assumptions. They help to generate sample data and make predictions about the real world. This approach is used for finding the correlation between figures to predict real-life events. The most popular statistical modeling techniques are linear regression, classification, resampling, etc. 

    ML refers to algorithms that can learn from the data without relying on standard programming practices. ML algorithms require fewer assumptions. They learn from data and can spot hidden patterns within it. The most popular ML algorithms for anomaly detection are k-NN, DBSCAN, and SVM. 

    Machine learning and statistic modeling have a common goal — predicting outcomes based on provided data. The decision to use statistical modeling or machine learning depends on the task. It makes sense to use SM instead of ML when there are:

    • low uncertainty;
    • small volumes of data;
    • relatively small number of interactions between various independent variables;
    • need for isolating the effects of variables and high interpretability.

    The purpose of SM is to help you understand your data. ML algorithms, on the other hand, are more accurate at forecasting and are better when there are large volumes of data. 

    When using linear regression in ML, we still need to address some assumptions of SM like collinear features. The combination of SM and ML approaches can be more accurate at predicting outcomes. 

    AI/ML for Effective Quality Control

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    Monitoring KPIs is important for any industry, especially manufacturing. Chemical and other manufacturing companies already have lots of regulatory standards in place in addition to their production goals. AI helps to monitor a company’s KPIs so that decision-making is easier and the production process stays on track. Let’s explore the benefits of AI/ML for the quality control process and the most important AI model performance metrics.

    Real-Time Data Insights

    Real-time analytics and near real time analytics are the process of measuring data as soon as it enters the database. It allows staff to monitor various processes in real-time and make timely decisions that can prevent breakdowns. 

    To notice changes in KPIs may take months without AI. For instance, batch analytics take days to show results. Business intelligence insights from real-time analytics guarantee that companies get KPIs as soon as possible. Here are a few examples of real-time data analysis:

    • Real-time credit scoring. It helps financial institutions to decide whether to extend credit.
    • Fraud detection. It helps to prevent financial losses at the time of sale.
    • Customer relationship management. It helps to maximize satisfaction and business results during each interaction with the customer.
    • Targeting individual customers. It helps retail outlets to provide promotions and incentives to customers while the customers are in the store. 

    Comprehensive KPI Evaluation

    Humans cannot evaluate data uninterruptedly. AI algorithms can perform cognitive functions similarly to the human mind, but they need anchor points to start learning.

    KPIs provide the anchor points in ML projects. They help define what outcomes we should expect when using the models. With AI, comprehensive KPI evaluation is a highly scalable process. 

    Long-Term Strategic Planning

    One of the challenges facing companies attempting to report on long-term value is the huge amount of data available. Long-term strategic planning becomes increasingly complicated with new trends and a growing volume of data.

    KPIs for long-term planning are based on talent, innovation, trends, society, environment, and governance. Business owners always search for a way to get accurate KPIs for their long-term strategic planning, risk management, and business growth.

    With the ability to identify meaningful indicators and make sense of this data, AI is a real game-changer. It analyzes hefty amounts of information and provides accurate, high-quality KPIs.

    Key Performance Indicators in Manufacturing

    KPIs and their monitoring are crucial for the manufacturing industry in particular. Every manufacturing company should pay attention to: 

    • Throughput. Indicates the rate of production for a machine or a system over time (i.g. 100 units/hour). It’s impossible for a human to constantly monitor this KPI without AI.
    • Demand forecasting. Estimates the future customer demand for a product so that a company can minimize its raw materials waste. Humans can do this too, but with a greater error.
    • Cycle time. Shows an average time to convert raw materials into a finished product. Knowing this KPI makes long-term planning more accurate.
    • Changeover time. Represents time spent on calibrating, programming, loading, and switching from one task to another. It complements the cycle time KPI.
    • Scrap. Measures the amount of discarded material and waste. Without AI, a company can’t be fully aware of its losses.

    AI for Manufacturing Companies: Get the Most out of Your Business Performance

    Statistics show that only 9% of manufacturing companies implement AI and ML technologies. Most organizations miss out on the full potential of their production processes. They often don’t know where to start and aren’t aware of how much they could have saved with AI. 

    At Postindustria, we offer a full range of services for the delivery of AI-enabled solutions. Our experts know everything about Big Data analysis and KPI monitoring using ML algorithms. We can help implement AI for manufacturing companies, automate your production, and increase your financial gains promptly. Leave us your contact information in the form, and we’ll contact you to discuss your project.

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