By extracting real value from their data, manufacturers can make accurate predictions about component life, replacement requirements, energy efficiency, utilization, and other factors that have direct impacts on production capacity, throughput, quality, sales, customer acceptance, and overall efficiency.

Low cost sensors and new wireless connectivity tools enable manufacturers to employ digital analytics more effectively than ever before. With the right tools, they can gather, cleanse, process, and visualize massive amounts of data from disparate sources that cover all phases of the product life cycle, from product design to warranty claims.

This guide explains some of the major challenges involved in applying data analytics to manufacturing processes and the benefits of developing optimized approaches to addressing those challenges.

This study assesses and compares nine suppliers of generative design software to offer an unbiased assessment and ranking Only platforms with a dedicated market focus on industrial and manufacturing were considered.

This report sets out the market positioning of each profiled company leader, mainstream, and followers ABI Research developed this Competitive Assessment (to offer a comparative assessment and ranking of the following suppliers of generative design software Altair, Ansys, Autodesk, Dassault Systèmes Hexagon, nTopology ParaMatters PTC, and Siemens.

Altair was ranked Overall Leader, Top Innovator, and Top Implementer.

Any industry that produces bulk quantities of goods such as pharmaceuticals, food, chemicals, or cosmetics, is seeking to produce these products consistently while reducing cost factors like waste and down time. Due to the nature of process manufacturing, multiple ingredients are combined to be mixed, coated, or sorted, so understanding the behavior of these processes is of paramount importance for manufacturers. Through the use of simulation modeling and Smart Manufacturing principals, manufacturers are now able to optimize these processes, leading to greater productivity and profitability.

Product engineers are under consistent pressure to reduce the costs, improve the quality and increase the throughput of manufacturing processes. This fast-paced environment is not well suited for trial-and-error manufacturing engineering.

How are engineers responding to these challenges? Is simulation and simulation-driven design for manufacturing (SDfM) well established across the industry? When simulation is deployed, does it deliver on the promises of reducing costs while improving throughput and quality? And what are the barriers to the adoption of simulation during the early stages of product development?

In this 2021 survey report conducted by Engineering.com, we discuss those questions and discover:

• Top design priorities
• Top benefits of SDfM
• Top barriers to expanding and adopting SDfM
• Risks to staying competitive in the market