Four Essentials To Boosting Productivity in Factory Automation Production Lines

whitePaper | March 2, 2020

Today's manufacturers have had to pivot quickly to adapt to demands brought on by COVID-19. Business Intelligence and analytics have played a vital role in providing the most successful manufacturers with insights necessary to find new ways to: Improve product and service quality.Higher levels of quality

whitePaper | August 3, 2022

Automakers, OEMs, and suppliers have faced unprecedented challenges in the wake of COVID-19. From chip shortages to truck barricades on the US–Canada border cutting off the flow of products, the automotive industry has been severely impacted by challenges that are leading to a loss of sales and frustrated customers. Advanced technologies are no longer “nice to have” but are now essential as automotive manufacturers step up their game to provide customers with more for less. Patched together, legacy systems, and outdated functionality tend to create more confusion than answers. Disconnected, siloed teams struggle with unreliable data and no clear vision for gaining new customers and retaining the old.

whitePaper | December 12, 2022

While the United States semiconductor industry remains a world leader, domestic manufacturing capacity for chips has undergone a substantial decline. The US semiconductor industry remains a leader in fields like chip design, but more and more firms have moved toward a “fabless” model, in which manufacturing at semiconductors fabrication facilities (“fabs”) is outsourced internationally.

whitePaper | May 19, 2023

For the better part of a decade, manufacturers have made significant strides to drive efficiencies, boost agility, and improve product quality by implementing data-capture sensors and analytics systems. Now, they are capitalizing on that foundation to optimize interconnectivity, automation, machine learning, and real-time data to further enhance production outcomes and improve worker safety and well-being.

whitePaper | January 1, 2023

The manufacturing industry is entering a period of rapid reconfiguration, driven by the complex interaction of technological, geopolitical, macroeconomic, and financial factors. The long-term trends that have shaped international commerce during the post-Cold War period are changing. The engines of the era—the relatively free flow of capital across borders, the lengthy and complex global supply chains, the super-cycle of cheap credit, the stable supply of energy, and the steadily liberalizing global trade regime—have stalled or reversed course, as we enter a new period defined by very different trends, including the reappearance of high inflation and a new appreciation for credit risk.

whitePaper | July 7, 2022

Production scheduling primarily deals with arranging different jobs in various machines based on the timelines for each job. There can be numerous combinations of schedules possible. Production schedules can be designed to maximize the capacity utilization, minimize idle time of the machine, earliness, overall costs etc. Manual design of a schedule can be a tedious and time consuming task. There are a lot of dependencies which the scheduler needs to tweak based on a single change. Generating a production schedule targeting a specific objective function can be done using mathematical models. Mathematical models involve formulating the production scheduling problem as an optimization model wherein there is a certain objective function which needs to be optimized based on a set of constraints. Mathematical models generally outperform manually designed schedules which involve a lot of inter-dependencies among the jobs. Due to improvement in computational power, developing a production schedule using mathematical models is relatively quick and efficient.