One of the most well-known and by now well emphasized facts of the new era of manufacturing and business world-wide is the so called abundance of data. Big Data has been recognized as both the key driver and one of the biggest challenges for Industry 4.0. It holds the key to future improvements in operation, innovation in product/service offerings, predictions in process, business and even global economy.
Big Data: is it really useful?
Large swaths of data being available in structured & unstructured form, from various sources, is sort of inevitable. Most organizations and their leaders acknowledge the fact that in future data would be available in copious amounts, however where they find the challenge is being able to collect, collate, analyze, extrapolate and predict, by extracting valuable information from the data available.
Big Data, comes in massive volumes from variety of sources, right from social media chatter, to market place transactions, to global stock markets, right up-to the level of an item stored on a pallet in a warehouse at one of the many manufacturing facilities.
Smart sensors, RFID & the Internet of Things create massive amounts of data which is both structured and unstructured in nature, and the ability to store it while segregating it meaningfully is a major challenge. Large amount of data being generated in real-time in varied volumes/forms, requires a gigantic effort to store and sort data in real-time, so that it may be used to provide palpable benefits in future.
The data being collected from the various sources and being stored in a very organized manner, that too in real-time will bear little to no fruition; if it is not utilized rather analyzed to provide basis for future improvements, predict failures and highlighted trends and correlations which were previously unknown.
Machine learning & Big Data: the perfect marriage
This is where ML or machine learning comes into the picture. What machine learning does is detect patterns and trends which human analysts are bound to miss. Basically machine learning is the study and creation of algorithms which provide predictive analysis based on data provided. This field of data analytics deals with creation of complex models and algorithms which detect hidden patterns in data and provide predictions by forming baseline behavioral patterns, that too in real-time. This field of analytics is highly complex and extremely useful especially for manufacturing operations.
Imagine a highly complex manufacturing facility with numerous lines, manufacturing a variety of products with individual work-flows and patterns, with different recipes and each and every component of the operation providing data to the deployed MES application. This is where the ‘big’ of Big Data becomes a reality: every instance an activity or sub-process is performed, data is being generated from the shop-floor, with every lot and from every single piece of equipment.
Use Data to Tell the Future
While the MES would capture and subsequently store the collected information, it’s the Machine Learning algorithms, which would analyze the data as it is being collected to develop patterns and trends by comparing current and historical data to form predictions related to each and every aspect of manufacturing. It is through ML that future break-downs may be predicted - based on analysis and performance of a particular equipment, preventive measures can then be taken to ensure production carries on smoothly.
But the application of Machine Learning goes way beyond predicting failures. Depending on the capability of the MES vendor and their ability to use Machine Learning in analytics, the benefits a manufacturing plant may derive from Big-Data analytics can be massive. For instance, through the data being collected from the customer end through social media banter, it might be detected that a particular feature of a newly launched product is not being appreciated and that it needs to be modified in new production lots. This is where ML saves the day by providing feasible options and patterns which when followed or applied might result even in product innovation.
Final thoughts
Machine Learning, when applied across the whole data spectrum, is capable of generating hundreds of action models, where humans would be able to generate only a few. In simple terms Machine Learning is a part of data analytics, which should be considered an integral part of any analytics software deployed, especially in a manufacturing scenario where data is generated each and every second and that too in massive volumes.
Big Data: is it really useful?
Large swaths of data being available in structured & unstructured form, from various sources, is sort of inevitable. Most organizations and their leaders acknowledge the fact that in future data would be available in copious amounts, however where they find the challenge is being able to collect, collate, analyze, extrapolate and predict, by extracting valuable information from the data available.
Big Data, comes in massive volumes from variety of sources, right from social media chatter, to market place transactions, to global stock markets, right up-to the level of an item stored on a pallet in a warehouse at one of the many manufacturing facilities.
Smart sensors, RFID & the Internet of Things create massive amounts of data which is both structured and unstructured in nature, and the ability to store it while segregating it meaningfully is a major challenge. Large amount of data being generated in real-time in varied volumes/forms, requires a gigantic effort to store and sort data in real-time, so that it may be used to provide palpable benefits in future.
The data being collected from the various sources and being stored in a very organized manner, that too in real-time will bear little to no fruition; if it is not utilized rather analyzed to provide basis for future improvements, predict failures and highlighted trends and correlations which were previously unknown.
Machine learning & Big Data: the perfect marriage
This is where ML or machine learning comes into the picture. What machine learning does is detect patterns and trends which human analysts are bound to miss. Basically machine learning is the study and creation of algorithms which provide predictive analysis based on data provided. This field of data analytics deals with creation of complex models and algorithms which detect hidden patterns in data and provide predictions by forming baseline behavioral patterns, that too in real-time. This field of analytics is highly complex and extremely useful especially for manufacturing operations.
Imagine a highly complex manufacturing facility with numerous lines, manufacturing a variety of products with individual work-flows and patterns, with different recipes and each and every component of the operation providing data to the deployed MES application. This is where the ‘big’ of Big Data becomes a reality: every instance an activity or sub-process is performed, data is being generated from the shop-floor, with every lot and from every single piece of equipment.
Use Data to Tell the Future
While the MES would capture and subsequently store the collected information, it’s the Machine Learning algorithms, which would analyze the data as it is being collected to develop patterns and trends by comparing current and historical data to form predictions related to each and every aspect of manufacturing. It is through ML that future break-downs may be predicted - based on analysis and performance of a particular equipment, preventive measures can then be taken to ensure production carries on smoothly.
But the application of Machine Learning goes way beyond predicting failures. Depending on the capability of the MES vendor and their ability to use Machine Learning in analytics, the benefits a manufacturing plant may derive from Big-Data analytics can be massive. For instance, through the data being collected from the customer end through social media banter, it might be detected that a particular feature of a newly launched product is not being appreciated and that it needs to be modified in new production lots. This is where ML saves the day by providing feasible options and patterns which when followed or applied might result even in product innovation.
Final thoughts
Machine Learning, when applied across the whole data spectrum, is capable of generating hundreds of action models, where humans would be able to generate only a few. In simple terms Machine Learning is a part of data analytics, which should be considered an integral part of any analytics software deployed, especially in a manufacturing scenario where data is generated each and every second and that too in massive volumes.
