The minute you switch on your computer or smartphone and scroll the internet, you engage with algorithms. Also, as a manufacturer, you have entered the Age of Algorithms that Industry 4.0 has been serenading. Algorithms are not only a ubiquitous part of our everyday life now but are becoming imminent to business as all enterprises capture, integrate, and analyze massive amounts of data. And in the future, algorithms with machine learning are only getting faster and more accurate. The more you deploy algorithms, the more they learn and catalyze business intelligence (BI).
“Once your computer is pretending to be a neural net, you get it to be able to do a particular task by just showing it a whole lot of examples. All you need is lots of data and lots of information about the right answer, and you will be able to train a big neural net to do what you want.”
– Geoffrey Hinton, the “Godfather” of AI.
There could not have been a simpler explanation for machine learning (ML) algorithms. Geoffrey Hinton applied his knowledge of cognitive neuroscience to computing. He proposed that connectionist AI must work just like the brain using biological neural networks. Artificial neural networks (ANNs) use algorithms that can learn from the inputs they receive and adjust, enabling effective non-linear data modeling.
AI & ML algorithms are at the heart of data analytics for turning your business into intelligent manufacturing.
Different algorithms are applied to solve several manufacturing problems. Although there are many ML algorithms (each suited for a specific task), there are 5 closest to our hearts. Here is a high-level overview of each, and we will explain why they are popularly applied to manufacturing process optimization.
1) Process Memory Algorithm (PMA)
Neewee’s proprietary magic wand, the process memory algorithm (PMA) is a multivariate supervised learning algorithm for pattern recognition. It not only supports technical step mapping of production batches but also enables the analysis of multiple data variables to recognize patterns and detect hidden irregularities in the production process. Report cards get generatedto evaluate the statistical results. The algorithm compares the various parameter combinations and identifies variables responsible for the anomalies by pattern recognition. Our PMA has displayed accuracy in recommending the most appropriate model of the production plan and enabled process optimization.
2) Genetic Algorithm
The Genetic algorithm is an evolutionary algorithm of machine learning based on natural selection, the process that drives biological evolution. Multiple models get developed for different combinations of the input parameters as individual solutions. By the “Mutation Rule,” the Genetic algorithm repeatedly modifies a population of solutions. With every iteration, the population “evolves” until an optimal solution gets generated. The Genetic algorithm is deployed for solving both constrained and unconstrained production optimization problems.
3) Long Short-Term Memory (LSTM)
The long short-term memory (LSTM) algorithm is a more advanced type of Recurrent Neural Network (RNN) used in Deep Learning (DL). LSTM is a supervised learning algorithm capable of retaining information for a longer time and is built for resolving sequential prediction problems. The input time-series signal data gets used for diagnostic analytics like machine health monitoring (MHM), anomaly detection, and predictive analytics for production planning and scheduling. LSTM is trained with industrial Big Data—time series data—applied to specific production scenarios. It then provides real-time recommendations to adjust upstream or downstream process parameters for production schedule and process optimization.
N-BEATS is a univariate time-series forecasting model with a purely deep neural architecture. Instead of handling forecasting as a sequence-to-sequence problem, theN-BEATS model treats it as a non-linear regression problem. The N-BEATS model uses backward and forward residual links to build a very deep stack of fully connected layers for analyzing past data and predicting future output values. Easy to implement and fast to train, N-BEATS is an algorithm that does not depend only on time series-specific feature engineering or input scaling. Its accuracy in performance translates into significant operational savings. 
5) Reinforcement Learning
Model-based Reinforcement Learning (RL) is an unsupervised ML algorithm. It enables an autonomous system that learns from interactions with the industrial environment through trial and error and does not rely on preprogrammed training. Since experimentation on the shop floor could get expensive, Neewee creates a Digital Twin, an end-to-end virtual model for the entire manufacturing value chain. The algorithm then learns to perform from the real-time simulation of the industrial environment. RL algorithms converge toward optimal process control through continuous monitoring and adjustment of controllable parameters. RL-based approaches have delivered impressive results, impacting the production throughput.
Deep dive into Reinforcement Learning and its business benefits here… Reinforcement Learning, the Effective Solution for Process Control Optimization. – Neewee
Utilizing data-driven ML algorithms for AI-enabled intelligent manufacturing is the best Industry 4.0 strategy.
Derive maximum business benefits; book a demo now!
 Oreshkin, B., & Carpov, D. (2021, April 15). The fastest path to building state-of-the-art AI. Element AI. https://www.elementai.com/news/2020/the-fastest-path-to-building-state-of-the-art-ai
Hinton, G. (n.d.). Geoffrey Hinton Quotes. BrainyQuote. Retrieved April 8, 2022, from https://www.brainyquote.com/authors/geoffrey-hinton-quotes