How Cognitive Manufacturing can optimize manufacturing operations?
Sep 27, 2019 Comments (0)
When we as a human race reached the 18th century, agricultural societies became more industrialized and urban.
The transcontinental railroad, the cotton gin, electricity, and other inventions permanently changed society. The same happened when we were introduced to the revolutionary Artificial Intelligence and its applications and Industry 4.0 was in the making. Businesses worldwide were looking to adopt the idea of Enterprise Cognitive Computing(ECC). It used AI to enhance business operations by embedding algorithms into applications that support the organizational process. ECC applications can automate repetitive, formulaic tasks and deliver outputs in huge magnitudes with accuracy.
ECC gave way for a method specifically useful for manufacturing enterprises and popularly known as Cognitive Manufacturing. It is also a cognitive approach to the integration of AI algorithms with manufacturing operations to achieve desired outputs.
Cognitive Manufacturing: Industry 4.0 Realm
Cognitive Manufacturing leverages, cognitive computing, industrial IoT and advanced analytics to digitize, understand and optimize manufacturing processes. It enables manufacturing processes to collect data from different sources and analyze this data to provide important knowledge regarding processes, machines work efficiencies, employee efficiency and workload statistics with a thorough analysis that can render suggestions to optimize each department of manufacturing paradigm.
It improves productivity, scalability with reliability and security by using AI algorithms on various operational applications. Cognitive manufacturing fully utilizes the data across systems, equipment and processes to derive actionable insight across the entire value chain from design through manufacture to support. To truly move forward to Industry 4.0 and beyond, manufacturing has to evolve into cognitive manufacturing.
Key issues for Cognitive Manufacturing:
1. Solving Business Challenges:
It helps one understand tasks, workflows, logic of business processes to optimize the same. It helps organizations to improve business metrics such as reliability, scalability, productivity, product quality and yield. Applications of cognitive manufacturing can reduce downtime and cost with easy implementations and generation of immediate benefits. For example, a technician can know error profiles, failure patterns and downtime of a machine in advance to prepare a maintenance schedule to reduce overall downtime of a machine. The approach can ensure higher first-time fix rate.
2. Product Quality Issues:
According to a survey by IBM, about 66% of executives believe, cognitive manufacturing can reduce substantial levels of defects in products. Through, cognitive manufacturing, companies can achieve high-quality products and maintain quality standards throughout the product life cycle i.e. From designing- to development-to sales-to after-sales support. The approach improves yield, reduces overall warranty costs, and helps ensure customer satisfaction for the lifetime of a product.
3. Generating Value from Manufacturing Data:
Cognitive Manufacturing taps into several known and unknown data sources to unravel new relationships between interlinked applications and processes to provide data patterns and analyses that can be valuable for a manufacturing unit or organization. Maintenance issues can be handled by the integration of historical data with the new tangible data available from sources like worker logs, machine operation time, etc. Product inspections can be performed by cognitive visual inspection systems that learn from pictures of manufactured products to identify defects and determine if the defects are tied to quality issues.
Cognitive manufacturing is all about the robust and knowledgeable system that is constantly learning from data that is derived, not only from machine sensors, but, manuals, employee logs, biometric systems, and work environment. for such robust systems needs, highly trained IT professionals and can hire developers team to develop applications with operational capabilities in sync with cognitive manufacturing techniques.
Cognitive Manufacturing Applications:
1. Asset Performance Management(APM) :
Cognitive Manufacturing uses advanced analytics and data mining techniques to use historical data of assets along with holistic data and business metrics to achieve overall equipment effectiveness (OEE). This reduces, assets downtime, reduces maintenance costs and improves overall productivity.
2. Process & Quality Improvement:
Cognitive Manufacturing helps organizations to closely monitor product manufacturing throughout the manufacturing lifecycle, to know various aspects of product quality improvements in every stage of product design, development and service. The benefits of using these techniques include better quality products and greater warranty support.
Manufacturers across the industrial sector have used cognitive processes and quality improvement techniques. The benefits include significantly increased revenues due to higher quality manufactured products; savings from lower repair and warranty costs; reduced quality control labor costs; as well as increased system uptime.
3. Resources Optimization:
Cognitive Manufacturing can render benefits in resources optimization in three sectors given below
This technique analyzes data from various sensors, worker logs, calculate break timings, records work environment parameters through different sources and integrate all the data, understand and recommend safer and better working environment, with ergonomic methods to perform tasks that can lower worker downtime and optimize work environment, improve worker health and productivity.
Energy Resources Optimizations:
Cognitive Manufacturing applications can help organizations keep track of their energy usages, asset's energy requirements and formulate an optimized energy plan that can be executed, to make most of the energy resources and yet keep the cost lower.
Floor Planning and Scheduling:
This is a very important part of manufacturing units as floor planning and scheduling optimizations can lead to better quality products in desired deadlines with higher productivity. Cognitive Manufacturing applications use manufacturing data, quality audits data, time-check logs, and machine downtime data, integrates all of them and analyze the same to provide, elaborate floor plans and scheduled plans to achieve greater outputs.
4. Supply Chain Management:
Cognitive Manufacturing helps organizations identify key imbalances of inventories and missing links in their supply chain. The supply chain can be vast, with lots of controllable and uncontrollable sources that can be developed into a seamless operational capability through cognitive manufacturing applications.
Manufacturing organizations are now adopting multi-sensor methods and advanced analytics to enhance their manufacturing systems and digitize the whole process. With the advent of the Internet Of Things(IoT) realm and cloud computing capabilities, organizations have switched over to smart manufacturing or popularly known Industry 4.0. So, if your business is a manufacturing organization, then digital transformation is inevitable.
Manoj Rupareliya is an experienced writer working at AppEmporio possessing expertise in writing on technical, financial and digital marketing niches and provides excellent guidance to hire developers and covers essential aspects for beginners to learn and develop skills for brighter future.