SD-WAN for Manufacturing

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.

whitePaper | August 1, 2022

One of the most important decisions that an asset-intensive enterprise must make is how to manage and care for its assets (i.e. equipment, buildings, plants and machinery). This is a decision that affects everyone in the organization, from the plant operators, to the engineers and all the way to executives in the C-suite. The solution will be different depending on the needs of the business and how each uses its assets.

whitePaper | January 15, 2022

The Fourth Industrial Revolution, whose fields of innovation include the industrial internet of things (IIoT), artificial intelligence (AI) and the development of cyber-physical systems, is transforming the relationship between technology and human beings. The exponential development of technology is undeniably exciting: it has the potential to unlock efficiency, productivity, sustainability and growth at scale. Yet such developments also give rise to a series of challenges concerning the role and place of human beings.

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 | November 5, 2022

Many credit the birth of the digital twin to Dr. Michael Grieves, who applied the idea to the manufacturing sector in 2002; however, the argument is often made that its origins can be traced back even further. For example, during the Space Race (1955-1975), NASA would create a replica of each spacecraft they would send into orbit. These replicas were studied and used for simulation to gain insight into their performance and improve their space exploration initiatives.

whitePaper | August 1, 2020

The Industrial Internet of Things (IIoT) in manufacturing is now an imperative to staying competitive. This means that becoming a digital enterprise is a requirement, as Bain & Company estimates the IIoT to have a $200B market potential by 2021. Organizations face a choice of building their own IoT platform or buying a pre-built platform. Read this joint Microsoft and Siemens white paper, "Make vs. Buy: Understanding the Tradeoffs Between a Build-Your-Own and Pre-Built Industrial IoT Platform," to learn the advantages and disadvantages of each approach, and get an understanding of how other manufacturers have gotten started.