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We have been discussing for some time now how manufacturing and production systems will undergo significant changes with the emergence of IIoT technologies, smart and connected factories and supply chains, and a steady progression from simply monitoring machines and production lines to optimizing production processes, to predictive and prescriptive analytics, to possibly autonomous lights out factories of the future. While this is indeed the progression that appears to be taking place, I see two distinct sets of predictive analytics solution sets emerging: one for product development and one for factory operations.
So let's focus initially on the factory first and how analytics is being applied in the manufacturing of products and production operations. Again, this is a case of leading PLM solutions providers offering advanced analytics solutions applied to the manufacturing processes and to operational optimization. The common objective is to use predictive and prescriptive analytics to improve the overall performance of production operations. Additionally, these analytics solutions all use various forms of machine learning algorithms and other statistical and rules-based methods to examine and identify patterns in the production process. The goal is to apply an operational intelligence approach to achieve a closed-loop mechanism that first validates the as-built to the as-designed, and second looks for ways to improve both the product and the manufacturing process.
There is a growing consensus among those in the manufacturing community that one of the largest sources of big data is contained in the vast repository of production records. This would include completed operational and executed work records, quality assurance records, work flow histories, operational deviations and variations, engineering changes, machine and tooling metrics, material data, and many other records related to the production process. Most of this data has been maintained and archived due to industries that have mandated regulatory requirements such as A&D, automotive, pharmaceutical, medical devices, and so on. Today's operational analytics applications for manufacturing are able to glean through this production process big data and using both predictive and more importantly, prescriptive analytics engines that use machine learning, multi-variant statistics, and rules-based logic to empirically reveal the origins of the most complex problems and suggest decision options to solve them.
Each of the three leading PLM providers offer some form of analytics solutions aimed at improving and optimizing product processes. Recently, Siemens PLM released the Omneo Perfomance Analytics (PA) solution, which it obtained with the acquisition of Camstar MES around a year ago. Omneo PA is able to examine big data from existing production operations and supply chains, analyzing billions of data combinations to reveal the hidden intelligence that points to product and production issues. The recent rollout of its Simcenter simulation platform will address the issue of systems-driven analytics for product development.
Dassault Systèmes offers its DELMIA Operational Intelligence solution which functions as a closed-loop mechanism for examining and discovering patterns in the production process big data. DELMIA OI uses a patented pattern-recognition technology based on machine learning algorithms to analyze past production data and identify patterns of what works (best practices) and what does not work (risk situations). These patterns translate into the form of human-readable rules. The rules are then applied to manufacturing operations in order to follow best practices and avoid risk situations.
PTC acquired ColdLight's Neuron machine learning solution around two years ago to serve as PTC's platform for automated predictive analytics. While PTC's IoT and digital twin strategy is to use Neuron more for continuously collecting and analyzing data from products in the field in order to discover patterns related to product performance and failure, PTC is more recently focusing on a new manufacturing solutions portfolio which will use predictive analytics. Additionally, PTC is partnering with GE Digital to integrate some of their manufacturing-based predictive analytics with GE's Predix platform.
So now let's move to the product development side of the coin and predictive analytics. In the case of product design it is more about product test simulation tools, multi-discipline systems engineering methods, and an emerging branch of product development called predictive engineering analytics (PEA).
One of the most sought after but elusive goals of product design engineering is to validate that you have achieved all the design criteria in the as-built product. That is, closing the loop between the as-built to the as-designed, and validating that the physical product will meet all design criteria before the product is manufactured. This is where the concept of the digital twin in now being applied to product design criteria, and the goal of "closed-loop PLM".
The digital twin is where the physical and virtual worlds of the product including its engineering design and operational functions are connected and merged to enable everything from design improvements, continuous process improvements in manufacturing, conditional states of the machine/system, to operations and maintenance. An important aspect of the digital twin concept is that products can be designed with the intent of being smart and connected while providing operational data for predictive analytics. This is the actual intent of predictive engineering analytics (PEA).
In order to meet the requirements for the IoT and a new generation of smart connected products, product designers are adopting a closed-loop, systems-driven approach. This approach involves a de-composition of all of the functional elements while considering all intended interactions from all of engineering disciplines involved, and controlling the overall system behavior. This approach minimizes risk and avoids late-stage changes by understanding boundary limits for subsystems, and also understanding the global system's behavior at every stage of the development process. Further, PEA will enable continuous design improvements that will make simulation a predictive driver of the product.
As IIoT, the digital thread and digital twin evolve within the overall ecosystems of product and process, the methodologies, including analytics, for both product development and production processes will converge. The intent and goal of the digital enterprise is to maintain a continuous and real-time digital thread that connects the lifecycle from concept through design, test, and build, to supply chain and products in field.
So let's focus initially on the factory first and how analytics is being applied in the manufacturing of products and production operations. Again, this is a case of leading PLM solutions providers offering advanced analytics solutions applied to the manufacturing processes and to operational optimization. The common objective is to use predictive and prescriptive analytics to improve the overall performance of production operations. Additionally, these analytics solutions all use various forms of machine learning algorithms and other statistical and rules-based methods to examine and identify patterns in the production process. The goal is to apply an operational intelligence approach to achieve a closed-loop mechanism that first validates the as-built to the as-designed, and second looks for ways to improve both the product and the manufacturing process.
There is a growing consensus among those in the manufacturing community that one of the largest sources of big data is contained in the vast repository of production records. This would include completed operational and executed work records, quality assurance records, work flow histories, operational deviations and variations, engineering changes, machine and tooling metrics, material data, and many other records related to the production process. Most of this data has been maintained and archived due to industries that have mandated regulatory requirements such as A&D, automotive, pharmaceutical, medical devices, and so on. Today's operational analytics applications for manufacturing are able to glean through this production process big data and using both predictive and more importantly, prescriptive analytics engines that use machine learning, multi-variant statistics, and rules-based logic to empirically reveal the origins of the most complex problems and suggest decision options to solve them.
Each of the three leading PLM providers offer some form of analytics solutions aimed at improving and optimizing product processes. Recently, Siemens PLM released the Omneo Perfomance Analytics (PA) solution, which it obtained with the acquisition of Camstar MES around a year ago. Omneo PA is able to examine big data from existing production operations and supply chains, analyzing billions of data combinations to reveal the hidden intelligence that points to product and production issues. The recent rollout of its Simcenter simulation platform will address the issue of systems-driven analytics for product development.
Dassault Systèmes offers its DELMIA Operational Intelligence solution which functions as a closed-loop mechanism for examining and discovering patterns in the production process big data. DELMIA OI uses a patented pattern-recognition technology based on machine learning algorithms to analyze past production data and identify patterns of what works (best practices) and what does not work (risk situations). These patterns translate into the form of human-readable rules. The rules are then applied to manufacturing operations in order to follow best practices and avoid risk situations.
PTC acquired ColdLight's Neuron machine learning solution around two years ago to serve as PTC's platform for automated predictive analytics. While PTC's IoT and digital twin strategy is to use Neuron more for continuously collecting and analyzing data from products in the field in order to discover patterns related to product performance and failure, PTC is more recently focusing on a new manufacturing solutions portfolio which will use predictive analytics. Additionally, PTC is partnering with GE Digital to integrate some of their manufacturing-based predictive analytics with GE's Predix platform.
So now let's move to the product development side of the coin and predictive analytics. In the case of product design it is more about product test simulation tools, multi-discipline systems engineering methods, and an emerging branch of product development called predictive engineering analytics (PEA).
One of the most sought after but elusive goals of product design engineering is to validate that you have achieved all the design criteria in the as-built product. That is, closing the loop between the as-built to the as-designed, and validating that the physical product will meet all design criteria before the product is manufactured. This is where the concept of the digital twin in now being applied to product design criteria, and the goal of "closed-loop PLM".
The digital twin is where the physical and virtual worlds of the product including its engineering design and operational functions are connected and merged to enable everything from design improvements, continuous process improvements in manufacturing, conditional states of the machine/system, to operations and maintenance. An important aspect of the digital twin concept is that products can be designed with the intent of being smart and connected while providing operational data for predictive analytics. This is the actual intent of predictive engineering analytics (PEA).
In order to meet the requirements for the IoT and a new generation of smart connected products, product designers are adopting a closed-loop, systems-driven approach. This approach involves a de-composition of all of the functional elements while considering all intended interactions from all of engineering disciplines involved, and controlling the overall system behavior. This approach minimizes risk and avoids late-stage changes by understanding boundary limits for subsystems, and also understanding the global system's behavior at every stage of the development process. Further, PEA will enable continuous design improvements that will make simulation a predictive driver of the product.
As IIoT, the digital thread and digital twin evolve within the overall ecosystems of product and process, the methodologies, including analytics, for both product development and production processes will converge. The intent and goal of the digital enterprise is to maintain a continuous and real-time digital thread that connects the lifecycle from concept through design, test, and build, to supply chain and products in field.