Due to the COVID-19 epidemic, ordering food online has become very popular. This study used a structural equation model to analyze the indicators that influence the decision to order food through a food-delivery platform. The theory of planned behavior and the technology acceptance model were both used, along with a new factor, the task–technology fit (TTF) model, to study platform suitability. Data were collected using a questionnaire given to a group of 1320 consumers. The results showed that attitudes toward on-line delivery most significantly affected the behavioral intentions of the consumers, followed by subjective norms. Among attitudes, perceived ease of use was the most significant, followed by perceived usefulness and trust. The study’s results revealed that TTF had the most significant impact on perceived ease of use, followed by perceived usefulness. This means that, if a food-ordering platform is deemed appropriate, consumers will continue to use it, and business sustainability will be enhanced.
This dissertation develops models to understand and mitigate the bullwhip effect across supply chains. The models explain the bullwhip effect that is caused by using the up to target ordering policy in standard Material Requirement Planning (MRP) systems. In the up to target ordering policy, the orders are directly driven by actual demand oscillations. We develop the models in AutoRegressive Integrated Moving Average (ARIMA) forms for a single demand item in a tandem line supply chain model. Different from supply chain models in current literature that are based on the assumption of the up to target ordering policy with some specific ARIMA models and specific numbers of stages in supply chain, the up to target ordering policy models in this dissertation can be applied to any ARIMA demand, any ordering lead time, and any number of stages in supply chains to derive the closed form expressions of the variation in inventory and the variation in orders. In addition, we propose the generalized ordering policy in which the up to target ordering policy is a special case. The generalized ordering policy permits manufacturers to smooth orders with the guaranteed stationary inventory in which smoothing orders is regarded as an effective way to mitigate the bullwhip effect. With the generalized ordering policy, manufacturers can control the tradeoffs between the variation in inventory and the variation in differencing orders which is stationary due to differencing. The generalized order models can be applied to any ARIMA demand, any ordering lead time, and any smoothing period. Two special cases of the v generalized ordering policy are also illustrated. One is the previously mentioned up to target ordering policy that minimizes the variation in inventory. Another is the smoothing ordering policy that minimizes the variation in differencing orders. We also provide generic formulas to determine the optimal smoothing weights in the smoothing ordering policy for ARIMA(p, 0, q) and ARIMA(p, 1, q) orders. Finally, this dissertation introduces the bounded MRP following the rate based planning concept. We propose a simulation based technique to set the bounds into standard MRP systems for exponential smoothing or ARIMA(0,1,1) demand. With this bounded MRP, we can mitigate the bullwhip effect and reduce the conflict between production planning and infeasible capacity planning.
Objective. Among crash types on ai highways, rear-end crashes have been found to cause the largest number of fatalities. is study aims to nd ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classi cation and regression tree (CART) was used to analyze the complicated relationship of variables of big data. e analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most signi cant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. e model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.
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