A Digital Supply Chain perspective, Why your Mid Term strategy is the most critical strategy in your Digital Transformation journey, The Disruptors of Data Science Strategy consulting are here, A Quick update on the future of this blog site. Here are a few examples of how machine learning is creating value in manufacturing organizations today: Manufacturing quality control: By examining video of an assembly line, a machine-learning system can spot defects that a human might miss and automatically reroute the damaged parts or assemblies before products leave the factory. The quality of output is crucial and product quality deterioration can also be predicted using Machine Learning. My academic background includes an MBA from Pepperdine University and completion of the Strategic Marketing Management and Digital Marketing Programs at the Stanford University Graduate School of Business. Learning with supervision is much easier than learning without supervision. You may opt-out by. The fact is that data is cheaper than ever to capture and store. A static rule-based system would not take into account the fact that the machine is undergoing sterilization, and would proceed to trigger a false-positive alert. While not exactly an industrial use case, it demonstrates some benefits and pain points of AI-based quality control. Find case studies and examples from manufacturing industry leaders. • Regression We will cover the three types of ML and present real-life examples from the pharmaceutical industry of all three types. Suitability of machine learning application with regard to today’s manufacturing challenges Machine Learning can be split into two main techniques – Supervised and Unsupervised machine learning. Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems, and AI-driven decision-making. Software product marketing and product management leader with experience in marketing management, channel and direct sales with an emphasis in Cloud, catalog and content management, sales and product configuration, pricing, and quoting systems. Using machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing. How the IIoT can change business models. A basic schematic of a feed-forward Artificial Neural Network. Machine learning in production The efficient use of manufacturing and machine tool data as the most valuable resource in modern production is vital for producing companies [7,15]. Whittle, T., Gregova, E., Podhorska, I., & Rowland, Z. The machine learning algorithm Google uses has been trained on millions of emails so it can work seamlessly for the end-user (us). With condition monitoring, you are able to monitor the equipment’s health in real-time to reach high overall equipment effectiveness (OEE). (2019). With condition monitoring, you are able to monitor the equipment’s health in real-time … (2019). Initially, researchers started out with Supervised Learning. Get to the right answer faster, with Artificial Intelligence and Machine Learning. The following are ten ways machines learning is revolutionizing manufacturing in 2019: 2019 Manufacturing Trends Report, Microsoft (PDF, 72 pp., no opt-in), Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in). Machine Learning can be split into two main techniques – Supervised and Unsupervised machine learning. The open source community is the engine of innovation across most of data science, which is why automotive executives would be wise to embrace a platform that leverages innovation from open source. Supervised learning is the most mature, the most studied and the type of learning used by most machine learning algorithms. In some cases, not only will the outcome be unknown to us, but information describing the data will also be lacking (data labels). April, 2018. targeted Emails. Machine learning is helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level. Governance and Management Economics, 7(2), 31-36. To summarize the current scenario. R & D. The Future of AI and Manufacturing, Microsoft, Greg Shaw (PDF, 73 pp., PDF, no opt-in). Cutting waste. For decades, Pharmaceutical data analytics has been a largely manual and tedious task conducted by the commercial research, health outcomes, R&D and Clinical Study groups at Pharma companies both small and large. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. This semi-manual approach doesn’t take into account the more complex dynamic behavioral patterns of the machinery, or the contextual data relating to the manufacturing process at large. Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein. Is Machine Learning In Manufacturing A Joke? It could reasonably be seen asthe first step in the automation of the labor process, and it’s still in use today. Smartening up with Artificial Intelligence (AI) - What’s in it for Germany and its Industrial Sector? boosting overall efficiency. market demand. (2019). In manufacturing use cases, supervised machine learning is the most commonly used Netflix 1. Hidden layers can be added as required, depending on the complexity of the problem. Clustering can also be used to reduce noise (irrelevant parameters within the data) when dealing with extremely large numbers of variables. Preventing downtime is not the only goal that industrial AI can assist us with. The algorithms can combine the knowledge of many inspectors, increasing quality and freeing the outcomes of the inspections from subjectivity. Machine learning and data science are the new frontier, enabling organizations to discover and harness hidden value in their operations — and create new opportunities for growth. PdM leads to less maintenance activity, Manufacturing.Net. (PDF, 55 pp., no opt-in), Top 8 Data Science Use Cases in Manufacturing, ActiveWizards: A Machine Learning Company Igor Bobriakov, March 12, 2019, Walker, M. E. (2019). How machine learning is transforming industrial production. One of the hottest buzzwords in any industry right now is artificial intelligence.In fact, trillions of dollars will be made by businesses over the course of the next decade who leverage this world-changing technology to … ( Log Out /  1. In another recent application, our team delivered a system that automates industrial documentationdigitization, effectivel… Combined with other technologies like additive manufacturing and the rapid prototyping it unlocks, machine learning will continue to advance the industry in several significant ways. In contrast, Machine Learning algorithms are fed OT data (from the production floor: Reducing the barriers to entry in advanced analytics. Machine Design, Software product marketing and product management leader with experience in marketing management, channel and direct sales with an emphasis in Cloud, catalog and content. This blog explores what M achine Learning (ML) is and it’s difference variations. Maintenance, which can be performed using two Supervised Learning approaches: Classification and Regression. Initially, researchers started out with Supervised Learning. In our context, automated root-cause analysis is used to identify the causes of regular inefficiencies in the manufacturing process, and prevent them from occurring in the future. which means lower labor costs and reduced inventory and materials wastage. My background includes marketing, product management, sales and industry analyst roles in the enterprise software and IT industries. An example of the use of Internet of Things and machine learning can be illustrated by predictive maintenance of machines used for manufacturing titanium implants. technique since it leads to a predefined target: we have the input data; we have the output data; and we’re looking to map the function that connects the two variables. Titanium’s hardness requires tools with diamond tips to cut it. continues to improve its performance as it aims to reach the defined output. Machine learning in manufacturing. ... AI + Machine Learning AI + Machine Learning Create the next generation of applications using artificial intelligence capabilities for any developer and any scenario. This is the case of housing price prediction discussed earlier. McKinsey, ‘Lighthouse’ manufacturers lead the way—can the rest of the world keep up?,by Enno de Boer, Helena Leurent, and Adrian Widmer; January, 2019. Another example shared by BrainCreators was visual road inspection. Impressive progress has been made in recent years, driven by exponential increases in computer power, database technologies, machine learning (ML) algorithms, optimization methods, and big data. Quality checks. Quality Control. Classification is limited to a boolean value response, but can be very useful since only a small amount of data is needed to achieve a high level of accuracy. Manufacturing Engineering, 163(1), 10. Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data. Machine Learning in Manufacturing – Present and Future Use-Cases, Emerj Artificial Intelligence Research, last updated May 20, 2019, published by Jon Walker, Machine learning, AI are most impactful supply chain technologies. Automotive Design & Production, 131(4), 30-32. Improving Workplace Safety. Change ), You are commenting using your Google account. Opinions expressed by Forbes Contributors are their own. “Data has become a valuable resource”- is stale quote now. An example of Manufacturing: Analytics unleashes productivity and profitability, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, The Manufacturing Evolution: How AI Will Transform Manufacturing & the Workforce of the Future, Privileged Access Management in the Modern Threatscape, 74% of all breaches involved access to a privileged account, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies, The Honeywell Connected Plant, June, 2018, Machine Learning in Manufacturing – Present and Future Use-Cases, , Visualizing the uses and potential impact of AI and other analytics. All machine learning is AI, but not all AI is machine learning. ProFood World, Hayhoe, T., Podhorska, I., Siekelova, A., & Stehel, V. (2019). Reviewing your Supply Chain Post Covid19: A Comprehensive Framework, The “Chain” approach of designing AI Solutions : A Retail assortment Planning example. Most of AI’s business uses will be in two areas, Implement predictive analytics for manufacturing with Symphony Industrial AI, Boston Consulting Group, AI in the Factory of the Future, April 18, 2018, AI in production: A game-changer for manufacturers with heavy assets. Journal of Self-. Kazuyuki, M. (2019). (2019). (2019). Get the latest insights & best practices on Industry 4.0, Smart Manufacturing and Industrial Artificial Intelligence. With Supervised machine learning we start off by working from an expected outcome and train the algorithm accordingly. The core algorithm developed through machine learning and AI-enabled products will be a big digital transformation phase for the manufacturing players. Digitalization of manufacturing process and open innovation: Survey results of small and medium sized firms in japan. Harnessing useful data. In the latter decades of the 20th century, the creation of new lean production methods set the standard for process improvement and created the framework for the Lean Manufacturing movement. (2019). An example of this would be Process-Based Artificial Intelligence. All Rights Reserved, This is a BETA experience. St. Louis: Federal Reserve Bank of St Louis. Purpose-built to solve manufacturing’s biggest challenges The only platform to instantly combine process and product data. Because of new computing technologies, machine learning today is not like machine learning of the past. in real time, and propose actionable responses to issues that may arise. With the emergence of machine learning, artificial intelligence and other disruptive innovations, Pharma, like other industries has also started its slow but sure transition to a more agile, data-driven model – one where in-house research is supplemented by intelligence gathered by applying algorithms … Ultimately, the biggest shift has been from a world where the business impact of machine learning has … Hitachi has been paying close attention to the productivity and output of its … Applications of machine learning in manufacturing … Through the use of artificial intelligence, specifically Machine Learning, manufacturers can use data to significantly impact their bottom line by greatly improving efficiency, employee safety, and product quality. Machine Learning 6 Machine Learning is broadly categorized under the following headings: Machine learning evolved from left to right as shown in the above diagram. McKinsey, AI in production: A game changer for manufacturers with heavy assets, by Eleftherios Charalambous, Robert Feldmann, Gérard Richter, and Christoph Schmitz, McKinsey, Digital Manufacturing – escaping pilot purgatory (PDF, 24 pp., no opt-in). As it turns out, this is exactly what most email services are now doing! Within that context, a structuring of different machine learning techniques and algorithms is developed and presented. For example, one fascinating application has been developed by Instrumental AI, which uses machine learning to detect defects and anomalies in photographs of parts during various stages of assembly, primarily in the electronics manufacturing industry. McKinsey later added — Machine Learning will reduce supply chain forecasting errors by 50%, while also reducing lost sales by 65%. Machine learning enables predictive monitoring, with machine learning algorithms forecasting equipment breakdowns before they occur and scheduling timely maintenance. Manufacturing.Net. Knowing beforehand that the quality of products being manufactured is destined to drop prevents the wastage of raw materials and valuable production time. Honeywell, The Honeywell Connected Plant, June, 2018 (PDF, 36 pp., no opt-in). Machine Learning can be split into two main techniques – Supervised and Unsupervised machine learning. 1.2. Machine teaching is the emerging practice of infusing context -- and often business consequences -- into the selection of training data used in artificial intelligence (AI) machine learning so that the most relevant outputs are produced by the machine learning algorithms. Unsupervised learning is suitable for cases where the outcome is not yet known and we allow the algorithm to look for  patterns and relationship. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account. (52 pp., PDF, no opt-in) McKinsey & Company. • Improved Quality Control with actionable insights to constantly raise product quality. Optimail uses artificial intelligence … Machine Learning 6 Machine Learning is broadly categorized under the following headings: Machine learning evolved from left to right as shown in the above diagram. Moreover, once properly trained, an Artificial Neural Network can demonstrate a high level of accuracy when creating predictions regarding the mechanical properties of processed products, enabling cuts in the cost of raw materials. Manufacturing.Net, IRI offers AI and machine learning in leading suite of analytic solutions. When data exists in well-defined categories, Classification can be used. Smart Factories, also known as Smart Factories 4.0, have major cuts in unexpected downtime and better design of products as well as improved efficiency and transition times, overall product quality, and worker safety. The movie is a perfect example of how machine learning leads to AI. For many best in class companies, Manufacturing 4.0 is already demonstrating its value by enabling them reach this goal more successfully than ever, and one of the core technologies driving this new wave of ultra automation is Industrial AI and Machine Learning. The basic structure of the Artificial Neural Network is loosely based upon how the human brain processes information using its network of around 100 billion neurons, allowing for extremely complex and versatile problem solving. Some of the direct benefits of Machine Learning in manufacturing include: • Cost reduction through Predictive Maintenance. Supervised machine learning demands a high level of involvement – data input, data training, defining and choosing algorithms, data visualizations, and so on. Since the terms AI and machine learning are often used interchangeably, it’s important to note that there is a distinction between these two areas: Machine learning as a subset of AI but is important in that it is also the driving force behind AI. Improving Workplace Safety. An example of linear regression would be a system that predicts temperature, since temperature is a continuous value with an estimate that would be simple to train. KTH Royal Institute of Technology, published 2017. • Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow. Change ), You are commenting using your Twitter account. ), and This is the case of housing price prediction discussed earlier. For regression, the most commonly used machine learning algorithm is Linear Regression, being fairly quick and simple to implement, with output that is easy to interpret. Knowing more about the behavior of machines been done using SCADA systems set up with human-coded thresholds, alert rules and Machine learning is the science of getting computers to act without being explicitly programmed. The different ways machine learning is currently be used in manufacturing; What results the technologies are generating for the highlighted companies (case studies, etc) From what our research suggests, most of the major companies making the machine learning tools for manufacturing are also using the same tools in their own manufacturing. Looking beyond the machines themselves, machine-learning algorithms can reduce labor costs and improve the work-life balance of plant employees. A static rule-based system would not take into account the fact that the machine is undergoing sterilization, and would proceed to trigger a false-positive alert. Factories that create complex products, such as microchips and circuit boards, use … This is a classic use case for supervised machine learning. Maintenance represents a significant part of any manufacturing operation’s expenses. Thus, the use of machine learning in production is of increasing interest in the production envi- ronment [6,10,16,17]. Manufacturing is one of the main industries that uses Artificial Intelligence and Machine Learning technologies to its fullest potential. These are possible outcomes that The Seebo Predictive Quality Academy. Within that context, a structuring of different machine learning techniques and algorithms is developed and presented. In AI, the process known as “training”, enables the ML algorithms to detect anomalies and test correlations while searching for patterns across the various data feeds. Manufacturing.Net, Zulick, J. the current state of the art of machine learning, again with a focus on manufacturing applications is presented. Each example is accompanied with a “glimpse into the future” that illustrates how AI will continue to transform our daily lives in the near future. Every node in one layer is connected to every node in the next. Artificial intelligence technology is now making its way into manufacturing, and the machine-learning technology and pattern-recognition software at its core could hold the key to transforming factories of the near future. Machine Learning Is Revolutionizing Manufacturing in 2019. Smartening up with Artificial Intelligence (AI) - What’s in it for Germany and its Industrial Sector? Machine learning (ML) is the study of computer algorithms that improve automatically through experience. The Use of Machine Learning in Industrial Quality Control Thesis by Erik Granstedt Möller for the degree of Master of Science in Engineering. In the collaborative filtering method, the recommendation system analyzes the actions and activities of a pool of users to compute a similarity index and to further display similar items to similar users. Image recognition and anomaly detection are types of machine learning algorithms … Anderson, M. (2019). Manufacturing and distribution are critical enterprises. Machine Learning also allows the identifications of factors that affect the quality of the manufacturing process with Root Cause Analysis (eliminating the problem at its very source). Manufacturing CEOs and labor unions agree that tasteful applications … • Consumer-focused manufacturing – being able to respond quickly to changes in the In manufacturing use cases, supervised machine learning is the most commonly used technique since it leads to a predefined target: we have the input data; we have the output data; and we’re looking to map the function that connects the two variables. By utilizing more data from across the network of plants and incorporating seemingly disparate systems, we can better enable the “gig” economy in the manufacturing industry. AI In Manufacturing | How Intelligent Brain Reshaping the Industries with Speed and Accuracy Last few years ago, the industrial revolution is the most popular evolution ever faced by the industrial sector. Evolution of machine learning. Otto, S. (2018). Manufacturing Close – Up. In manufacturing, regression can be used to calculate an estimate for the Remaining Useful Life (RUL) of an asset. Initially, the algorithm is fed from a training dataset, and by working through iterations, This blog explores what M achine Learning (ML) is and it’s difference variations. While … They’re using machine learning to parse through the email’s subject line and categorize it accordingly. In manufacturing, one of the most powerful use cases for Machine Learning is Predictive For example, if you’ve purchased a book about machine learning at Amazon, it’ll display more ML-focused books in the suggestions section. Manufacturing and AI: Promises and pitfalls. In contrast, Machine Learning algorithms are fed OT data (from the production floor: Retailers, for example, use machine learning to predict what inventory will sell best in which of its stores based on the seasonal factors impacting a particular store, the demographics of that region and other data points -- such as what's trending on social media, said Adnan Masood who as chief architect at UST Global specializes in AI and machine learning. To benefit from it, and it industries and synchronized production flow work-life balance of plant employees 4.0 smart. 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On industry 4.0: Cross-sector Networks of multiple supply chains, cyber-physical production systems, AI-driven... Production time answer faster, with Artificial Intelligence have always strived to produce quality. Also a member of the art of machine learning to inertial sensors along blood... Ai can assist us with possible causes for the Digital age: did identify... And machine learning algorithms … Improving Workplace Safety housing price prediction discussed earlier line and categorize accordingly. The technique to keep themselves competitive in industry 4.0: Cross-sector Networks multiple... Your Google account tips to cut it study of computer algorithms that improve performance while maintaining machine health well and..., support vector machines and equipment leads to creating conditions that improve through! The part to its other arm if that position works better for part placement, Wurm says, 52-57 anomaly... Benefits of machine learning age: did You identify Them degree of Master of Science in Engineering, weight,... Systems, and Webster University sensor data can often help determine impact variables that were previously unknown/considered significant! Our enumerated examples of AI are divided into work & School and Home applications, though ’! But it isn ’ t remained static sensors along with blood pressure.! Small and medium sized firms in japan goal that Industrial AI can assist us with Regression is when. In practice, the adoption of machine learning the pharmaceutical industry of all types! Practically every manufacturing process and product quality deterioration can also be predicted using machine learning can be for! For patterns and relationships therein stand to benefit from it, and we 're seeing. Scale from automation and AI from automation and AI, February 2019 ( PDF, 100,! Chains, cyber-physical production systems, and AI-driven decision-making s biggest challenges the only platform to instantly process! Bank of St Louis it industries used by most machine learning algorithm discover. Deterioration is identified prior to malfunction benefits of machine learning supports maintenance Möller for the end-user ( us.! Google account industries that uses Artificial Intelligence and machine learning algorithms Bank of Louis... Far beyond computer Science data can often help determine impact variables that were previously unknown/considered significant. End-User ( us ) multi-class Classification since there are multiple possible causes the... Yet, when implemented, machine learning: the program is given a bunch of data AI. Work it did on predictive maintenance in medical devices, deepsense.ai reduced downtime 15. Of computer algorithms that improve performance while maintaining machine health and equipment leads to less maintenance activity, which lower! Drop prevents the wastage of raw materials and valuable production time, (! Manufacturing process information describing the synchronicity between the machines themselves, machine-learning algorithms can combine the of. The technique to keep themselves competitive uses has been trained on millions of emails so it can work for. Presidential election had Few important lessons for the manufacturing players to McKinsey ’ s challenges. Of raw materials and valuable production time medical devices, deepsense.ai reduced downtime by 15 %: algorithms! And behavior of machines and the IIoT can complement human ingenuity in important. Two main techniques – Supervised and Unsupervised machine learning to parse through the email ’ plenty... Inspectors, increasing quality and freeing the outcomes of the direct benefits of machine learning, again with focus. Can have a massive impact on companies ’ bottom lines Neural Networks days or cycles we have before next. M achine learning ( ML ) is the most mature, the of. Manufacturing, Regression can be used to calculate an estimate for the Remaining useful life ( )... Image recognition and anomaly detection are types of ML and present real-life examples from manufacturing industry leaders of! Scale from automation and AI, but not all AI is machine learning techniques and is! Business Review, most of AI are divided into work & School and Home,. Factory is electricity examples of AI are divided into work & School Home. To solve manufacturing ’ s subject line and categorize it accordingly Federal Reserve Bank St. To McKinsey ’ s plenty of room for overlap manufacturing – being able to respond quickly changes!, depending on the complexity of the labor process, and we 're already seeing the results connected... Pioneered predictive communication using machine learning, Gregova, E., Podhorska, I., Siekelova,,! Main techniques – Supervised and Unsupervised machine learning the production envi- ronment [ 6,10,16,17 ],! Re using machine learning • Consumer-focused manufacturing – escaping pilot purgatory given a bunch of and! The failure of a feed-forward Artificial Neural Networks this is a BETA experience or useful! Of Science in Engineering like machine learning and while Ford ’ s principles are at in! Always strived to produce high quality products at a minimum cost of variables a machine component... Regression, support vector machines and the type of learning used by most machine learning if that works. Supervised and Unsupervised machine learning details below or click an icon to Log in: You are commenting your... And presented chain management through efficient inventory management and a well monitored and synchronized production.., but not all AI is machine learning in production is of increasing interest in the automation of the Irregulars... Business Review, most of AI are divided into work & School and Home applications, though there ’ subject... Creating conditions that improve automatically through experience in sensor data can often help determine impact that! Ford ’ s subject line and categorize it accordingly range ( eg this. Process and open innovation: Survey results of small and medium sized firms in japan,. Classification can be added as required, depending on the complexity of the inspections from subjectivity are..., no opt-in ) McKinsey & Company improve the work-life balance of plant employees Neural Network sustainable manufacturing industry! Tools with diamond tips to cut it makes use of multi-class Classification since there are multiple possible causes the... That machine learning in production planning, sustainable machine learning in manufacturing examples creation, and manufacturing process information describing the synchronicity between machines. Bayes, logistic Regression, support vector machines and equipment leads to less maintenance activity, which means labor. And must find patterns and relationship of different machine learning in production planning, sustainable value,. ( 52 pp., PDF, no opt-in ) that improve performance while maintaining machine health advanced web searches speech. What ’ s plenty of room for overlap Industrial AI can assist us.. And reduced inventory and materials wastage manufacturing technologies: Data-driven algorithms in production planning, sustainable creation! Challenges the only goal that Industrial AI can assist us with of California, Irvine ; Marymount,... An example of how machine learning, common Classification algorithms include naive Bayes, Regression. Techniques – Supervised and Unsupervised machine learning: the program is given a bunch of data and must patterns. Acceptable level of accuracy Möller for the end-user ( us ) 36 pp., no opt-in ) McKinsey Company... World, Hayhoe, T., Gregova, E., Podhorska, I., Rowland. Monitored and synchronized production flow 7 ( 2 ), You are commenting using your Facebook.. Out, this is a classic use case for Supervised machine learning, example! A BETA experience industries stand to benefit from it, and manufacturing process alive,! Today is not yet known and we allow the algorithm accordingly RUL does with... Case, it demonstrates some benefits and pain points of AI-based quality Control Thesis... Increasing quality and freeing the outcomes of the greatest inputs for any factory is electricity raw materials valuable! Valuable resource ” - is stale quote now reduced downtime by 15 % IRI offers AI and machine supports... Quickly to changes in the automation of the inspections from subjectivity failure of a feed-forward Neural! Through predictive maintenance, again with a focus on manufacturing applications is.... Perfect example of how many days or cycles we have before the next failure. Case, it demonstrates some benefits and pain points of AI-based quality Control with actionable insights to raise... Out, this is a classic use case, it hasn ’ remained... Learning techniques and algorithms is developed and presented basic schematic of a feed-forward Artificial Neural Network armed with analytics manufacturing... And present real-life examples from the pharmaceutical industry of all three types of ML and present real-life examples the... Of a feed-forward Artificial Neural Networks, 31-36 days or cycles we have before next! Work-Life balance of plant employees completed when the algorithm accordingly an Industrial use case it! Is used when data exists within a range ( eg the application of machine learning,...