All products impact the lives of their users, this is called social impact. Some social impacts are commonly recognized by the engineering community, such as impacts to a user’s health and safety, while other social impacts can be more difficult to recognize, such as impacts on families and gender roles. When engineers make design decisions, without considering social impacts, they can unknowingly cause negative social impacts. Even harming the user and/or society. Despite its challenges, measuring a program’s or policy’s social impact is a common practice in the field of social sciences. These measurements are made using social impact indicators, which are simply the things observed to verify that true progress is being made. While there are clear benefits to predicting the social impact of an engineered product, it is unclear how engineers should select indicators and build predictive social impact models that are functions of engineering parameters and decisions. This paper introduces a method for selecting social impact indicators and creating predictive social impact models that can help engineers predict and improve the social impact of their products. As a first step in the method, an engineer identifies the product’s users, objectives, and requirements. Then, the social impact categories that are related to the product are determined. From each of these categories, the engineer selects several social impact indicators. Finally, models are created for each indicator to predict how a product’s parameters will change these indicators. The impact categories and indicators can be translated into product requirements and performance measures that can be used in product development processes. This method is used to predict the social impact of the proposed, expanded U.S. Mexico border wall.
Organizations all over the world, both national and international, gather demographic data so that the progress of nations and peoples can be tracked. This data is often made available to the public in the form of aggregated national level data or individual responses (microdata). Product designers likewise conduct surveys to better understand their customer and create personas. Personas are archetypes of the individuals who will use, maintain, sell or otherwise be affected by the products created by designers. Personas help designers better understand the person the product is designed for. Unfortunately, the process of collecting customer information and creating personas is often a slow and expensive process. In this paper, we introduce a new method of creating personas, leveraging publicly available databanks of both aggregated national level and information on individuals in the population. A computational persona generator is introduced that creates a population of personas that mirrors a real population in terms of size and statistics. Realistic individual personas are filtered from this population for use in product development.
One of the purposes of creating products for developing countries is to improve the consumer's quality of life. Currently, there is no standard method for measuring the social impact of these types of products. As a result, engineers have used their own metrics, if at all. Some of the common metrics used include products sold and revenue, which measure the financial success of a product without recognizing the social successes or failures it might have. In this paper, we introduce a potential universal metric, the product impact metric (PIM), which quantifies the impact a product has on impoverished individuals—especially those living in developing countries. It measures social impact broadly in five dimensions: health, education, standard of living, employment quality, and security. By measuring impact multidimensionally, it captures impacts both anticipated and unanticipated, thereby providing a broader assessment of the product's total impact than with other more specific metrics. The PIM is calculated based on 18 simple field measurements of the consumer. It is inspired by the UN's Multidimensional Poverty Index (UNMPI) created by the United Nations Development Programme (UNDP). The UNMPI measures how level of poverty within a nation changes year after year, and the PIM measures how an individual's poverty level changes after being affected by an engineered product. The PIM can be used to measure social impact (using specific data from products introduced into the market) or predict social impact (using personas that represent real individuals).
Though little research has been done in the field of over-design as a product development strategy, an over-design approach can help products avoid the issue of premature obsolescence. This paper compares over-design to redesign as approaches to address the emergence of future requirements. Net present value (NPV) analyses of several real world applications are examined from the perspective of manufacturers and customers. This analysis is used to determine the conditions under which an over-design approach provides a greater benefit than a redesign approach. Over-design is found to have a higher net present value than redesign when future requirements occur soon after the initial release, discount rates are low, initial research and development cost or price is high, and when the incremental costs of the future requirements are low.
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