Digital Transformation in the Manufacturing Industry

whitePaper | March 14, 2022

It is unusual to see positive economic indicators paired with historic labor and supply chain challenges. But this is the trajectory for US manufacturing in 2022 emerging from the pandemic. The recovery gained momentum in 2021 on the heels of vaccine rollout and rising demand. As industrial production and capacity utilization surpassed pre-pandemic levels midyear, strong increases in new orders for all major subsectors signal growth continuing in 2022.

whitePaper | November 28, 2022

Smart manufacturing is driving the manufacturing industry to modernize its software infrastructure. Whether you call it Industry 4.0 or the Industrial Internet of Things (IIoT), manufacturing is in the midst of a significant transformation. A key motivation in this push to modernize is that realtime data and dynamic decision making yield meaningful performance improvements for businesses.

whitePaper | August 30, 2022

Unexpected change costs in automotive manufacturing investment projects can quickly lead to spiraling costs and delays in production unless carefully managed. Any changes to production schedules and machinery must be meticulously planned. But without the correct information to base a plan, problems may multiply instead of being resolved.

whitePaper | January 17, 2023

For those of us that have been around a while, we have learned to live through the supply and demand cycles of the semiconductor industry. As we are currently in an excess demand situation, one of the big questions we all have is “When will things flip?” We stop short of saying that this will not happen this time, but some key elements are different.

whitePaper | April 4, 2022

Digital transformation is driving the adoption of Industry 4.0 in manufacturing. Multiple technologies, such as internet of things (IoT) devices and artificial intelligence (AI), are changing manufacturing processes. For instance, more is being done with robotics, 3D printing, and industrial clouds. Employees need to digest all these innovations to enable new ways for them to do their jobs properly. Around the globe, industries have implemented work-from-home policies. In most manufacturing companies, there is a mixture of employees from the business office to the factory floor, and manufacturers can now instantly enable remote work for these employees.

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.