Supply chain integration model: practices and customer values

Dissertation to obtain PhD in Industrial EngineeringIn order to increase partnership efficiency and truly meet the customers' demands, in today's business environment companies are operating in supply chains. Integration of supply chains facilitates minimizing diferent types of wastes and satisfying needs of the end customer. The first step toward supply chain integration is to understandand the customer values, and to reconfigure supply chain to support those values. The current research addresses supply chain integration through quantifying relations between supply chain practice and customer values. It employs Bayesian network and analytic network process as tools to quantify comparative relations among entities. The proposed approach starts with identifying trade-offs along customer values using Bayesian network. In parallel supply chain practices are comparatively analyzed through interviews with experts which is technically quantified using analytic network process. Thereafter, these two parallel phases join together to form a network of customer values and supply chain practices. The network is able to quantitatively identify relations among nodes; in addition, it can be used to plan scenarios and handle senstitivity analyses. This model is expected to be used by supply chain decision makers to have a quantitative measure for monitoring the influence of practices on preferences of the end customer. A survey and two case studies are discussed which go through aforementioned phases. The survey identifies and analyzes six customer values namely quality, cost, customization, time, know-how and respect for the environment. It makes input for the two cases which develop supply chain integration model for fashion and food industry. Supply chain practices are categorized into two groups of manufacturing and logistics practices. The two case studies include five manufacturing practices as cross functional operations, decrease work in process, implement standards, mixed production planning, and use recyclable materials as well as four logistics practices namely visibility to upstream /downstream inventories, information sharing with customer, implement logistics standards, and just in time.Fundação para a Ciência e Tecnologia - (MIT Project: MIT-Pt/EDAM-IASC/0022/2008

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Corresponding Author Bayesian Network and Pest Management: A case study of trap plants on locust population

ABSTRACT Bayesian network approach has been effectively used in different experimental fields such as ecological decision making. There is also now growing instance of its usage and effectiveness in pest management. The fundamental principle to optimizing outcomes of agricultural products for pest management is the consideration of all chemical methods, physical methods, mechanical methods, transgenic plants, and trap plants. Some of methods have harmful environmental effects while some others are both environmental friendly and effective. Among them the use of trap plants can be one of the major approaches for pest control. The Locust is one of the most destructive pests and has unpredictable damages on agricultural products especially in poor countries where the volume of product has critical and crucial influence of human life. This research provides a case study on using Bayesian networks on the effect of a number of trap plants have on locust population, and considerations to predict and prevent the pests' harmful effects. According to the findings, the probability methods such as Bayesian networks can contribute effectively on pest management studies particularly trap plants to enhance decision making procedure in national and international perspective