Consumer goods

Consumer goods are products able to satisfy multiple needs and serve a direct purpose to the end consumer. They can be divided into two distinct groups: consumer durable goods (household appliances, computers, cellular phones, boats, sporting equipment, cars, motorcycles…) and non-durable goods, or goods for immediate consumption (groceries such as pasta, coffee, drinks, healthcare products, and so forth).

The fact that consumer goods are continuously put to the test by hundreds of thousands, if not millions of people means that any uncertainties present in their design and planning must be handled, studied and managed carefully.  The slightest error could result in a great loss of resources (of both materials and intangible assets), other than the fact that it represents, in many cases, a potential danger to the consumer’s safety.

The Consumer Goods Challenges

As globalization has increased competition in all consumer goods markets, the effectiveness of traditional design and production management tools have more and more been questioned; new, disruptive methodologies have emerged, in order to give durable competitive advantages to manufacturing market leaders.

Some of the strategic elements that make up these competitive advantages can be summarized as follows:

  • increased process and product efficiency and quality (by means of new materials, design methods and innovative production processes)
  • product costs reduction, thus affecting the final price of the good
  • reduction of time to market (from the initial idea to the final product)
  • compliance with eco-sustainability and green production requirements

Computer-Based Engineering in the Consumer Goods Industry

CAE tools have nowadays become a common industry standard to rapidly give companies the strategic elements mentioned above, as well as to completely quantify the uncertainties/tolerances of the actual and simulated processes, in order to reduce waste and to conform to production quality standards (six sigma, among others).

New CAE software tools are continuosly being developed to progressively abandon the old “trial and error” method, which is slow and wasteful, in favor of an integrated, optimized process where all the design parameters are quantified and can interact with one another. Some notable examples of an integrated approach to CAE in the consumer goods industry are here reported: 

  • in the field of household appliances, structural, thermal, fluid dynamics and acoustic analyses provide the possibility to improve performance, achieve packaging and product efficiency, and enable data-driven decision making during the design phase.  The test phase, as a result, is only used on the actual prototype for a final inspection
  • in sporting equipment production, the use of CAE is fundamental in order to simulate the behaviour of the sporting apparatus under extreme conditions, to test new materials as well as alternative shapes/modifications aimed at improving product performance before prototype production
  • in the electronics field (pc, tablet, telephony), crash, thermal and electromagnetic analyses are performed using the finite element method (FEM), in order to improve the quality and duration of the produced items
  •  in the food industry, integrating Computer Based Engineering into production flow, not only for production machinery design, but also for packaging, conservation and fine-tuning of the production process itself, can provide a lasting competitive advantage to the manufacturer

What can EnginSoft do for you?

EnginSoft is a company that has based its competitive advantage on virtual prototyping since the 1980es, and has always been diligent in the training and formation of its technical staff in order to provide companies with the best solutions and the most suitable software for their specific needs.

EnginSoft’s strength is the multidisciplinary approach that includes all areas of Computer Based Engineering.  In fact, its expertise covers all types of analyses for both product and process, including mechanical (structural inspection, exertion, crashes), thermal, thermal-structural, fluid dynamics, acoustics, electromagnetics.

Case Study

  • Performance and Shape Optimization of the Campagnolo Tri-ProPad bike shorts pad

    In 2008 Campagnolo decided to create a new sport cycling wear line (for both professionals and amateurs), starting from the design of a new and revolutionary pad for bike shorts. The new pad was conceived from the beginning to give the cyclist a feeling of lightness and comfort, coming from the choice of materials, the pad shape and its superior saddle isolation. Learn more ...

  • Optimizing the Glass Clamping of a Pyrolytic Oven

    The aim of this study was to find the best quality glass-clamping system, through parametric model optimization, for a new pyrolytic self-cleaning oven by Indesit. Like all pyrolytic self-cleaning ovens the pyrolysis process causes the oxidization of spills and dirt in the oven at temperatures of 450°to 500°C reducing them to ashes. This process produces a high thermal gradient which considerably deforms the glass and can cause ruptures. Learn more ...

  • Integrated Simulation of Commercial Pasta Manufacturing

    This study was part of the Virtual Optimization PAsta production process (OPAV) research project, which resulted in a simulation model that could be used by industrial pasta manufacturers to help them improve the quality and production process of their pasta. The model created allows the user to adjust rheological, mechanical and technological parameters that are peculiar to the pasta production process in order to optimize the characteristic of the final pasta production. Learn more ...

  • A Polynomial Chaos Approach to Robust Multi-Objective Optimization

    Almost all real world optimization problems across a wide range of disciplines contain uncertainty. This is why the engineering community at large is increasingly focusing on robust optimization and the quantification of uncertainty. Uncertainty can derive from a variety of sources such as errors in measuring, difficulties in sampling, lack of knowledge, or future events that are not completely known at the time of sampling. Learn more ...

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