Stora Enso Annual Report 2019

whitePaper | September 21, 2023

Automation is improving the overall productivity of discrete manufacturing plants and facilities, delivering a range of goods from automobiles and electronics to furniture and toys to countless other items. The complexity of coordinating personnel, work procedures, material handling, and automated equipment – all while meeting production goals – make increasing digitalization and use of data across the enterprise and in production essential. Perhaps most important is the ability to put the control engineer, line worker, and design engineer in the middle of this critical data to derive actionable insights and provide real-time decision support. The constantly growing capabilities of digital technologies are creating more opportunities for discrete manufacturers to drive increasingly reliable and more efficient operations.

whitePaper | September 18, 2022

RPA is a system that allows businesses to automate tasks and processes that are normally performed by employees. RPA works across different platforms, applications, and departments. RPA integrates seamlessly with existing IT infrastructure, requiring no additional installation. Organizations do not have to invest heavily in automating essential processes

whitePaper | January 2, 2023

Additive Manufacturing (AM) and Injection Molding (IM) are often perceived as competing plastic processing technologies. However, organizations must be ready to utilize and switch between different production technologies to address changing product requirements, shift production schedules based on market demands, or respond to supply chain crises. This includes making both technologies complementary, not exclusive.

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 18, 2020

As with other manufacturing techniques, the 3D printing process can be challenging and demands a level of expertise and familiarization. This guide aims to help designers produce quality and cost-effective parts on Stratasys PolyJet 3D systems. The guide provides information for both new and experienced users. The topics covered in this guide describe tools and methods for optimizing design features, strength, and durability of printed parts, and ensuring that the parts meet your expectations.

whitePaper | July 25, 2022

Manufacturers are demanding ever-greater precision, accuracy, and reliability from industrial automation. Fortunately, sensors, robot arms, grippers, and the software to connect and guide them have become increasingly capable. In response to the labor and supply chain challenges of the past few years, as well as the demands for personalization and rapid fulfillment, many factories and other facilities are looking to adopt robots or add them to their operations. In this Special Focus Issue, we look at how AI and machine learning are enabling more precise picking, among other applications.