Product environmental impact reduction efforts largely focus on incremental changes during detailed design. Application of automated concept generation using a design repository and integral life cycle assessment approach is explored to evaluate and reduce environmental impacts in the conceptual phase of product design.
With increasing concerns about the environment, people are re-evaluating every aspect of their lives. Did you know that every year, an estimated 2.2 billion tons of waste is dumped in our oceans? As per the Environmental Protection Agency, in 2013, Americans generated about 254 million tons of waste with about 34% recycling rate. The UK produces about 434 million tonnes of solid waste annually with a projected growth rate of around 3% per year. The Brundtland report clearly outlined that sustainable development would only be achieved if society in general, and industry in particular, learned to produce more goods and services with less of the world's resources and less pollution and waste.
Related to the recent issues on the environmental sustainability, the attention and importance of Reusable Medical Equipment (RME) has increased rapidly. As a part of System Redesign Project funded by Veterans Engineering Resource Center (VERC), “Design Evaluation for Reusable Medical Equipment” project has been conducted. This research project aims to develop new RME design assessment and evaluation framework and Design for Reusability (DFR) and Design for Sustainability (DFS) principles. In this paper, we will present a decision support system for RME design evaluation, based on DFR and DFS principles. To illustrate the proposed new framework, GI endoscope is used in this research. In the proposed system, we apply a Rough Set Theory to identify the relationships among design and reprocessing features. Also we use feature selection technique to select the customized features from the design features and reprocessing features to be used for design evaluation.
Advanced analytics opens vast untapped potential for farmers, investors, and emerging economies to reduce the cost of goods sold. The way digital technologies are reshaping the relationship between consumers and brands has been hotly debated over the past few years, with much discussion of the reshaping of consumer decision journeys, the advent of multichannel marketing and sales, and the impact of smartphones and the mobile Internet on customer behavior. Yet an even bigger opportunity has been largely overlooked. By taking advantage of big data and advanced analytics at every link in the value chain from field to fork, food companies can harness digital's enormous potential for sustainable value creation. Digital can help them use resources in a more environmentally responsible manner, improve their sourcing decisions, and implement circular-economy solutions in the food chain.
Globalization of economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain re-engineering. As a result, decision support systems that can facilitate thesefforts are in great demand. In this paper, we identify essential elements that are required for modeling supply chains and embed them in a multi-agent framework. Our framework uses simulation analysis and provides a platform for rapidly developing customized decision supportools for different supply chain problems with limited additional effort. A subset of concepts from this framework is being utilized by IBM for making supply chain re-engineering decisions. A supply chain can be defined as a network of autonomous or semi-autonomous business entities collectively responsible for procurement, manufacturing and distribution activoffs) and common processes in different types of supply is essential to understand important issues (decision tradeities associated with one or more families of related products (see Figure I). Different entities in a supply chain op-conducted in the following three domains (which differ in chains. Our framework is based on supply chain studies erate subject to different sets of constraints and objectives.