Green profit design for lifecycle

“Green Profit” refers to economic profits generated by an environmentally sustainable business. As awareness of sustainability increases and environmental regulations become more stringent, manufacturers are faced with the challenge of making a green profit in their businesses. Recovering end-of-life products after customer use is a promising solution to this challenge. Various recovery options, including reuse, refurbishment, remanufacturing, and material recovery, can make it possible for companies to comply with environmental legislation and also gain social and economic benefits. This dissertation presents a design approach, referred to as “Green Profit Design,” to help maximize green profits from end-of-life recovery of products. Green Profit Design is a Design for Recovery approach that facilitates green and profitable end-of-life recovery of products by establishing a clear link between product design and end-of-life recovery. Product design features, including product architecture, functional performance, and material properties, greatly affect the economic and environmental performances of end-of-life recovery. Therefore, the most important factors in achieving green profit are an understanding of how design decisions affect actual end-of-life recovery and understanding the economic and environmental implications of the design. The Design for Recovery methods introduced in this dissertation evaluate product design from a recovery perspective and provide a quantitative assessment of how good or how bad a product design is in terms of both recovery profit and environmental impact. The methods can be utilized for either design improvement or design selection. An original contribution of this dissertation is that it provides the foundation for integrating the different perspectives on end-of-life recovery of different domains, i.e., design engineering, environmental engineering, and business. Another important contribution is its thorough coverage of recovery processes. In addition to technical and operational issues, the methods in this dissertation also cover the recovery processes at the front end (i.e., product take-back and reverse logistics) and the back end (i.e., remarketing of recovered items) and suggest an advanced approach for coordinating and managing the entire process more effectively. This dissertation presents two empirical studies, four Design for Recovery methods, and three extended studies on further refinement of the four methods. Using statistical analyses, the empirical studies investigate the challenges that the Design for Recovery approach must overcome. The current industry practice of electronics recovery is examined to gain a better understanding of the design issues associated with end-of-life recovery. The Design for Recovery methods focus on evaluating the design of the original product from a recovery perspective. The first three methods consider the case in which the second-life products recovered from the end-of-life products have a pre-defined design. Optimization models for evaluating a single product and for evaluating the design of a family of products are presented, and the effects of product obsolescence and deterioration at the time of end-of-life recovery are also analyzed. The fourth method is focused more on how to remarket end-of-life products, and it provides the advanced tools required for market positioning to optimize the design and the price of a second-life product. The three extended studies focus on environmental implications of end-of-life recovery and discuss appropriate timing utilizing recovery principles. End-of-life recovery is basically a strategy for extending the life of a product by reusing, refurbishing, or remanufacturing that product. The studies demonstrate that an extended lifetime may not always be environmentally sustainable, and that shortening the lifetime may actually be better in some cases. To help decide on the optimal lifetime strategy for a given product, lifecycle assessment (LCA) approaches for a large-scale system are discussed, and an analytical model is proposed for planning optimal lifetime of a given product based on the LCA approach

Changes in intravenous hydration frequency and emergency department length of stay after implementation of oral ondansetron therapy in children with dehydration due to acute gastroenteritis

Purpose Oral ondansetron is a safe and effective antiemetic drug to facilitate oral rehydration therapy in acute gastroenteritis (AGE) with mild dehydration. We investigated the effect of oral ondansetron therapy on intravenous (IV) hydration frequency and emergency department length of stay (EDLOS) in dehydrated children with AGE. Methods We reviewed 15,813 children aged 12-60 months with primary diagnosis of AGE who visited a tertiary care university-affiliated hospital emergency department. The enrolled children were divided into the pre- (from January 2009 to June 2011) and post- (from January 2016 to June 2018) ondansetron groups according to the implementation of oral ondansetron therapy in the emergency department. As primary outcomes, IV hydration frequency, EDLOS, and hospitalization rate were compared between the 2 groups. As secondary outcomes, EDLOS and hospitalization rate were compared between the children in the post-ondansetron group who underwent the therapy, and those who did not. Results Of 7,990 enrolled children, 3,300 (41.3%) were designated as the post-ondansetron group, and among them 1,093 (33.1%) underwent oral ondansetron therapy. This group showed a lower IV hydration frequency, a shorter median EDLOS compared to the other group (61.9% vs. 55.8%, P < 0.001; 223.0 minutes vs. 175.0 minutes, P < 0.001, respectively), and a higher hospitalization rate (7.9% vs. 9.9%, P < 0.001). The children in the post-ondansetron group who underwent the therapy showed a shorter median EDLOS and a lower hospitalization rate compared to those who did not (142.0 vs 205.0 minutes, P < 0.001; 2.9% vs. 13.4%, P < 0.001, respectively). Conclusion Oral ondansetron therapy may reduce IV hydration frequency and EDLOS in dehydrated children with AGE, and can be considered in those having severe vomiting

Severe painful vaso-occlusive crises and mortality in a contemporary adult sickle cell anemia cohort study.

BACKGROUND: Frequent painful vaso-occlusive crises (VOCs) were associated with mortality in the Cooperative Study of Sickle Cell Disease (CSSCD) over twenty years ago. Modern therapies for sickle cell anemia (SCA) like hydroxyurea are believed to have improved overall patient survival. The current study sought to determine the relevance of the association between more frequent VOCs and death and its relative impact upon overall mortality compared to other known risk factors in a contemporary adult SCA cohort. METHODS: Two hundred sixty four SCA adults were assigned into two groups based on patient reported outcomes for emergency department (ED) visits or hospitalizations for painful VOC treatment during the 12 months prior to evaluation. RESULTS: Higher baseline hematocrit (p = 0.0008), ferritin (p = 0.005), and HDL cholesterol (p = 0.01) were independently associated with 1 or more painful VOCs requiring an ED visit or hospitalization for acute pain. During a median follow-up of 5 years, mortality was higher in the ED visit/hospitalization group (relative risk [RR] 2.68, 95% CI 1.1-6.5, p = 0.03). Higher tricuspid regurgitatant jet velocity (TRV) (RR 2.41, 95% CI 1.5-3.9, p \u3c 0.0001), elevated ferritin (RR 4.00, 95% CI 1.8-9.0, p = 0.001) and lower glomerular filtration rate (RR=2.73, 95% CI 1.6-4.6, p \u3c 0.0001) were also independent risk factors for mortality. CONCLUSIONS: Severe painful VOCs remain a marker for SCA disease severity and premature mortality in a modern cohort along with other known risk factors for death including high TRV, high ferritin and lower renal function. The number of patient reported pain crises requiring healthcare utilization is an easily obtained outcome that could help to identify high risk patients for disease modifying therapies. TRIAL REGISTRATION: ClinicalTrials.gov NCT00011648 http://clinicaltrials.gov

PCRR E-waste Stream Analysis

The problem addressed in this paper is that the incoming stream of “feedstock” from product take-back systems is known to be widely variable, but the type and extent of that variability have not been well defined. This paper presents an analysis of data from an incoming e-waste stream for a computer refurbisher, and analyzes the type and degree of variability. The implications for design for sustainability are presented, along with a discussion of suggested future research needs.published or submitted for publicationnot peer reviewe

Optimal Line Design of New and Remanufactured Products: A Model for Maximum Profit and Market Share with Environmental Consideration

For original equipment manufacturers (OEMs), producing a line of new and remanufactured products can be an effective strategy for improving the sustainability of their business. The potential cannibalization of new product sales and the technological obsolescence of used products, however, can create barriers for OEMs to embrace remanufacturing. In order to address these challenges in OEM remanufacturing, this paper proposes a mixed-integer programming model for the optimal line design of new and remanufactured products. Aiming at two objectives, i.e., maximizing the total profit and maximizing the total market share, the model simultaneously optimizes a line of new and remanufactured products in terms of their (1) design specifications (including an upgrade plan for the remanufactured product), (2) selling prices, and (3) production quantities and the detailed production plan. With the simultaneous optimization, the model suggests an optimal way of differentiating the new and remanufactured products in order to overcome the cannibalization and obsolescence effects and to maximize the total profit and/or market share. The model also accounts for environmental impact, stipulating that the total environmental impact of manufacturing remains under a certain limit. To demonstrate the applicability and effectiveness of the model, a case study is presented using the example of a desktop computer

Monte Carlo Simulation of the Effect of Heterogeneous Too-Cheap Prices on the Average Price Preference for Remanufactured Products

A prevailing assumption in research on remanufactured products is “the cheaper, the better”. Customers prefer prices that are as low as possible. Customer price preference is modeled as a linear function with the minimal price at customers’ willingness to pay (WTP), which is assumed to be homogeneous and constant in the market. However, this linearity assumption is being challenged, as recent empirical studies have testified to customer heterogeneity in price perception and demonstrated the existence of too-cheap prices (TC). This study is the first attempt to investigate the validity of the linearity assumption for remanufactured products. A Monte Carlo simulation was conducted to estimate how the average market preference changes with the price of the remanufactured product when TC and WTP are heterogeneous across individual customers. Survey data from a previous study were used to fit and model the distributions of TC and WTP. Results show that a linear or monotonically decreasing relationship between price and customer preference may not hold for remanufactured products. With heterogeneous TC and WTP, the average price preference revealed an inverted U shape with a peak between the TC and WTP, independent of product type and individual customers’ preference function form. This implies that a bell-shaped or triangular function may serve as a better alternative than a linear function can when modeling market-price preference in remanufacturing research

Bayesian Genetic Association Test when Secondary Phenotypes Are Available Only in the Case Group

In many case-control genetic association studies, a secondary phenotype that may have common genetic factors with disease status can be identified. When information on the secondary phenotype is available only for the case group due to cost and different data sources, a fitting linear regression model ignoring supplementary phenotype data may provide limited knowledge regarding genetic association. We set up a joint model and use a Bayesian framework to estimate and test the effect of genetic covariates on disease status considering the secondary phenotype as an instrumental variable. The application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the 16th Genetic Analysis Workshop