Energy and resource efficient manufacturing is becoming increasingly important due to scarcity of natural resources, stricter regulations and increasing customer demand for sustainable products. According to the Energy Information Administration, the industrial sector accounted for about one-third of total U.S. energy consumption in 2017. Research has shown that machine tool design is responsible for 40% of energy use while machining processes demand 22% of total energy consumption. The manufacturing sector is a significant contributor to environmental damage and resource use, and therefore there is ample opportunity for improving energy and resource efficiency. To address these issues, this dissertation proposes approaches to evaluate and quantify resource use of manufacturing across three hierarchical levels: process, machine tool, and production facility; these methods are used to assess current performance and provide effective approaches to reduce future resource consumption.

Depending on the scale of the system considered, different strategies can be applied to optimize the efficiency of manufacturing systems. In order to offer a systematic study, this dissertation is structured according to increasing system scale: First, at the individual process level, the design of experiments (DOE) method was applied to study the effect of the main machining parameter such as feed rate, spindle speed and drill diameter. A case study of drilling operations on multi-layer printed circuit boards (PCBs) was conducted to find the optimal cutting parameters for achieving high cutting performance that can avoid additional deburring process. A plan of experiments was performed to investigate the burr formation mechanism. In the end, a micro-drilling burr control chart was proposed for process planning.

The second part of this work focuses on machine tool level solutions. Changing the cutting parameters alone will not result in an optimal solution since there are other components in the production equipment that consume resources. Therefore, the scope moves from process planning to the machine tool level. A lot of studies tried to quantify the energy consumption of machine tools, but few of them have looked into other resources such as water. To fill this gap, a water footprint assessment of the milling machine tool was conducted, following ISO 14046 standards. A case study of face-milling on aluminum alloy was examined. The water inventory of a milling machine was calculated, and a water scarcity footprint was estimated to assess the associated impacts. Uncertainty analysis was conducted to assess the potential bias from different plant locations.

The third part of this work focuses on factory level solutions. Numerous studies have implemented value stream mapping to improve the production facility in terms of productivity, but energy information is often left out. Therefore, a construction of an environmental value stream map (E-VSM), which integrates environmental metrics into conventional value stream mapping, is presented. Through the construction of an E-VSM, a case study evaluates the performance of a cover glass manufacturing facility in China. Improvement strategies have been provided to increase productivity, as well as reduce environmental impact. Uncertainty analysis was carried out to find out how geographic locations might affect the results.

This research has developed and evaluated effective approaches for the analysis of energy, water, and other resource use in manufacturing systems on different levels. The presented work allows manufacturers to better understand the resource consumption of manufacturing activities, and provides effective strategies to reduce the associated impacts.

Chapter 1 of this dissertation seeks to understand the sources and nature of measurement errors in temperature variables, and how they can influence climate impact estimations. Measurement error in temperature variables is usually assumed to be “classical'' i.i.d. normal and “small''. This type of measurement error leads to attenuation bias, that is often small enough to be ignored. We show, however, that measurement errors in temperature variables are often large, distinctly non-normal, and vary systematically across space and time. The divergence between our empirical results and conventional wisdom stems from the fact that the construction of the temperature variables involves a series of steps, each of which introduces a distinct source of measurement error. This work is the first to formally characterize sources of measurement error in temperature variables and how they interact. Simulation results are provided that illustrate the influence of these sources of measurement errors on climate impact estimates. We further propose a correction method that can be used to obtain consistent estimates of the parameters of interest under the conditions identified.

In Chapter 2, I zoom in to examine one particular source of measurement error: measurement error induced by using coarse data to approximate daily mean temperature. Much of the historical records from weather stations around the world reports on minimum and maximum temperature and this practice is still followed by most stations. The minimum and maximum are averaged to approximate daily mean temperature, the exposure variable most commonly used in climate impact studies. Empirically, I find substantive differences between daily mean temperatures constructed using hourly temperature data and daily minimum and maximum temperature. This single source of measurement error can easily result in a 5 to 10\% bias in estimates of the impact of daily mean temperature on an output measure of economic interest, with the direction of the bias dependent on the location and season of the year.

Chapter 3 investigates climate impact models from another perspective: the representation of climate variables. The “bin” regression model has been put forward as a flexible semi-parametric method for representing a climate variable and it has emerged as the workhorse approach for empirical work (e.g., Deschênes and Greenstone, 2011). Our work is the first to formally explore econometric properties of the bin regression approach. We show that, although the bin regression approach often produces reasonable results, that the approach produces consistent parameter estimates only under very stringent and highly unlikely assumptions about the true data generating procedure. Problems with the bin regression approach are likely to be most severe in the tail bin categories, where most policy interest with respect to climate change impacts lies. We propose alternatives to bin model for the climate change impacts that produce consistent estimates and generally have better efficiency properties.

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