Data, Digital threads, and Industry 4.0

whitePaper | June 1, 2023

This rapid shift from internal combustion engine (ICE) vehicles to EVs has brought a huge opportunity for automobile manufacturers to scale up their EV production. However, this is posing challenges for automobile manufacturers, as they need to invest heavily in a short time span for EV design, development, manufacturing, selling and servicing infrastructure. There are additional challenges related to battery cost, supply chain complexities and manufacturing infrastructure. Contract manufacturing is becoming an increasingly important strategy for EV manufacturers.

whitePaper | June 6, 2021

Three-dimensional (3D) printing is a powerful technology that has wide-ranging applications, including many in the pharmaceutical industry. Among the approaches that are being explored in the production of pharmaceutical dosage forms powder-based systems which utilize either drop-on-powder or selective laser sintering and liquid-based systems which use dropon-drop deposition or stereolithography are frequently applied. With liquid-based technology, either UV, laser energy or high temperature is used to induce polymerization and build the 3D structure.1 The process of 3D printing offers the potential to produce medications tailored to the needs of patients and dosage forms in various shapes, sizes and textures with different release profiles that can be difficult to produce using conventional techniques.2

whitePaper | December 13, 2019

Winsun has a 3D construction printer installed in their factory in Suzhou, China, that produces elements by 3D printing. The elements are then transported to and assembled at the building site to form the building structure. Hence, Winsun is actually 3D printing precast elements and do not 3D print on the building site. Therefore, when Winsun claimed to have done 10 buildings in 10 days, it actually meant, that they assembled 10 buildings in 10 days, out of elements 3D printed in the factory long before.

whitePaper | February 25, 2023

In the swarm of buzzwords surrounding today’s manufacturing sector, the “Smart Factory” or “Factory of the Future” buzz is especially prominent. In this white paper featuring experts from Siemens Digital Industries Software, we’ll unpack the meaning of the term, examine key considerations in understanding its value and explore a few important takeaways for how to take advantage of these technologies, including for small and medium businesses (SMBs).

whitePaper | March 17, 2022

Manufacturing industry professionals must corral and understand massive amounts of data from many systems to drive operational efficiencies, higher levels of service, supply chain resiliency, and ultimately a complete view of the business. Having the ability to explore the impact of and interplay among changes in production efficiency, product quality, customer demand, and service excellence isn’t possible without visibility into data and meaningful data analytics. This “big data” approach to manufacturing operations and supply chain optimization can deliver big results and is at the heart of the digital transformation initiatives of many industrial companies.

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.