This week, we announced the details of two offerings designed to help companies overcome production challenges: IBM Production Optimization, and IBM Production Quality Insights. These solutions support streamlined and efficient production processes that are underpinned by Artificial Intelligence (AI) and machine learning. Together, they increase throughput, improve quality, drive down expenditures and reduce costly waste.
Introducing IBM Production Optimization
Manufacturing and production companies are facing mounting pressures to innovate. But just as the imperative for fast development increases, traditional productivity levers are becoming exhausted. Simply put, traditional manufacturing processes cannot keep up with demand, and they’re becoming less cost-effective. A McKinsey report, ‘The great re-make: Manufacturing for modern times’ puts the cost of industry-wide manufacturing losses and waste at an astonishing $5 trillion.
Against this backdrop manufacturers are looking to new Industry 4.0 solutions driven by AI. The promise of such solutions is considerable: to bring deep, powerful insights that help operations leaders pinpoint production loss, and prescribe the best action to maximize performance.
It is in answer to such challenges that we developed IBM Production Optimization: a new, AI-driven, cloud-based offering integrated with Maximo. By employing AI, this solution augments operators’ ability to identify and resolve production issues earlier.
How it works
IBM Production Optimization delivers three key capabilities:
Predict and pinpoint losses, related to process and equipment
Analyze the root causes and test alternative scenarios
Prescribe the best actions to solve the issue
The solution is built to be versatile – enabling data scientists and process engineers to optimize costs, quality or throughput. They can do this by customizing the pre-built analytics for specific assets, and changing the KPIs to maintain optimal conditions for efficiency. What’s more, there’s the added benefit of a cloud-based infrastructure and faster ROI.
The result: significantly better throughput, improved yields and reduced manufacturing costs. You can discover more about IBM Production Optimization on our website.
Introducing IBM Production Quality Insights
Manufacturers in multiple industries are facing pressures in response to poor quality production. The costs associated with poor quality are rising, as manufacturers struggle with costly scrap and rework. In addition, the reputational impact is more damaging than ever and the cost of recalling faulty products is rising.
Unfortunately, traditional quality control and inspection techniques are expensive, time-consuming, prone to error and occasionally even dangerous. But as Industry 4.0 establishes itself as the new norm, this may be about to change. McKinsey believes that manufacturers can see a 10-20% improvement in cost of quality through Industry 4.0.
High quality production requires a swift, comprehensive and dependable inspection process, and it’s here that IBM Production Quality Insights can help. This powerful set of tools brings a 360-degree view to monitoring, inspection and insights during the manufacturing, assembly and maintenance process. With the aid of AI and machine learning, it enables manufacturers to analyze the root cause of defects from wide-ranging inspection data, whether structured (process data), visual data (image data) or acoustic data.
A powerful combination of monitoring components
By bringing together the quality monitoring and inspection capabilities of Prescriptive Quality, Acoustic Insights and Visual Insights, IBM Production Quality Insights offers an integrated, comprehensive solution to help organizations improve quality inspection and insights, while reducing costs.
Let’s examine these components in greater detail:
Prescriptive Quality helps identify substandard materials before they enter the manufacturing process, eliminating poor quality components. It can also monitor the testing process on product assemblies during critical manufacturing steps. Finally, it keeps an eye on the process variables that determine product quality.
Acoustic Insightsuses sound to detect anomalies or defects within a normal product operational cycle to pinpoint issues with production quality. By accelerating the process of sound inspection, organizations can increase yield, reduce scrap, and lessen human inspection time.
Visual Insightsuses cognitive capabilities to classify incoming defects, by matching them to patterns it has been trained to spot. It significantly accelerates the expertise-based visual inspection process to quickly identify defects in parts or a finished product.
Beyond inspection: other business benefits
Production Quality Insights not only speeds up the inspection process – it also supports quality control throughout the entire manufacturing process. By providing early, specific alerts, it enables organizations to move away from problem detection and spend more time on problem resolution.
For example – using the insights from PQI, procurement can identify suppliers who deliver damaged or substandard parts. With this information, organizations can review contractual agreements or find a new supplier. Other parts of the business can benefit too:
Product design can use the insights from PQI to make small component modifications to help simplify assembly.
Process engineering can modify or recalibrate production equipment and the assembly process to eliminate variation.
Operations can determine whether a problem is associated with a prescribed procedure or a need to provide better operator training.
Maintenance can benefit through alerts of impending equipment degradation or failures that could result in reduced yield, scrap, or the need for rework.
Integrated data means more comprehensive insights
Production Quality Insights leverages AI to get the most value from multiple quality measures. By integrating varied data sources like acoustic, visual and structured data, the solution can present a more comprehensive picture to help determine root cause.
Furthermore, by combining established data acquisition methods with newer IoT technologies, it delivers timely insights to the people who can best act upon them. The Quality Early Warning System algorithms built into Production Quality Insights leverage combined data source to detect quality issues early. As a result, organizations can detect emerging problems sooner and with fewer false alarms – and facilitate a proactive response that improves production yield and quality.
The upshot? Dependable inspection processes that support proactive solutions, and improved quality-related KPIs.
A large toy manufacturer located at the northernmost point of the planet must produce in excess of 2 billion toys* each year. To meet this incredible demand, it is critical to ensure operations, facility management, and worker safety are at optimum efficiency. To top it off, they must distribute all the toys in just one […]
If buildings are getting smarter, then medical facilities have to be brilliant. That’s why University of California, San Francisco (UCSF) Health is working with IBM Maximo to create cutting-edge health facilities that aim to keep the patient care environment safe. It started when UCSF Health and IBM partnered to optimize the management of existing health […]
Today’s facilities are swiftly moving towards a smarter ecosystem, with thousands of physical asset attributes to maintain and manage. To support this complex infrastructure, data management is crucial, and has conventionally been a major issue for higher education facilities. Without quality data management, organizations will struggle to define mission-critical information and achieve supported decision making. […]