Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 74-80). / Information sharing is one of the established approaches to improve demand forecasting and reduce the bullwhip effect, but it is infeasible to do so effectively in a long supply chain. Using the polystyrene industry as a case study, this thesis explores the usage of modern natural language processing (NLP) techniques in a deep learning model, known as NEMO, to forecast the demand of a commodity -- without requiring downstream companies to share information. In addition, this thesis compares the effectiveness of such an approach with other non-deep learning approaches, specifically an ARIMA model and a gradient boosting model, XGBoost, to demand forecasting. All three models returned large forecast errors. However, NEMO tracked the volatility of actual data better than the ARIMA model. NEMO also had better success in predicting demand than the XGBoost model, returning approximately 20% better Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores. This result suggests that NEMO can be improved with better data, but other issues, such as legality of text mining, need to be considered and addressed before NEMO can be used in day-to-day operations. In its current form, NEMO can be used alongside other forecasting models and provide invaluable information about upcoming demand volatility. / by William W.J. Teo. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Indiana University-Purdue University Indianapolis (IUPUI) / Current supply chain information systems are transaction-based and suffer from lack of real-time transparency. Furthermore, they are often centralized and therefore cannot adequately scale to include a large number of small and medium size companies. This thesis presents a hybrid peer-to-peer supply chain physical distribution framework (HP3D) that addresses these increasingly critical gaps in a global market. HP3D leverages the advantages of hybrid networks through flexible peers and a light-weight index server in order to share supply chain physical distribution information in pseudo real-time among stakeholders. The architecture of HP3D consists of a hierarchy of dynamic sub-networks that evolve based on market demands and digitize the transfer of goods between suppliers and customers. These sub-networks are created on demand, emulate the end-to-end movement of the shipment and terminate when the delivery of goods is completed. A variation of blockchain technology is also proposed in order to increase the security level of the proposed framework.

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 51). / On-time delivery is a key metric in the trucking segment of the transportation industry. If on-time delivery can be predicted, more effective resource allocation can be achieved. This research focuses on building a predictive analytics model, specifically logistic regression, given a historical dataset. The model, developed using six explanatory variables with statistical significance, results in a 76.4% resource reduction while incurring an impactful error of 2.4%. Interpretability and application of the logistic regression model can deliver value in predictive power across many industries. Resulting cost reductions lead to strategic competitive positioning among firms employing predictive analytics techniques. / by Rafael Duarte Alcoba and Kenneth W. Ohlund. / M. Eng. in Supply Chain Management

The Performance Excellence Criteria for the Malcolm Baldrige National Quality Award are widely accepted as an accurate reflection of quality management best practices. Since being introduced in 1988, the Baldrige Criteria have undergone regular revisions in an attempt to reflect the evolution of these best practices. This thesis examines the evolution of the elements of the Baldrige Criteria dealing with supply chain management (SCM) by conducting a detailed content analysis of each version of the Criteria since 1988. Our analysis includes an examination of changes in point values for SCM elements of the Baldrige Award over time. We also present an analysis of the distribution of the point values in each version of the Criteria across six key dimensions of SCM, as well as an analysis of how the SCM elements in the Baldrige Criteria compare with best practices as represented by the Supply Chain Operations Reference model.

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We propose a classification framework for the operations management literature that has looked at pricing and ordering in supply chains when supply and/or demand are uncertain. We then focus on developing three new models for managing supply and demand uncertainties in supply chains. In the first model, we study a two period sourcing problem of a firm under two sets of contracts. The contracts differ in terms of acquisition costs and the level of risk that they impose on the firm. We provide the conditions where the optimum solution is unique and also explore the behaviour of the optimum solution analytically and numerically. One application of our model is in the agribusiness supply chain and we provide numerical examples based on data from the almond industry in California. In the second model we look at a joint ordering, pricing and capacity planning problem. We characterize the optimum policy both in single and multi-period cases. In addition, we study the impact of fixed production costs on the optimum policy. The third model is devoted to coordination between a buyer and a supplier where there is a possibility of improving the supplier by both players. We analyze the problem under a Stackelberg game setting where the buyer is the leader. We show that the buyer either tries to amplify the investment of the supplier by order inflation or assumes all the investment costs. We investigate the behaviour of the optimum solution under different strategies. / Thesis / Doctor of Philosophy (PhD)

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Determining safe yield of an existing water supply is a basic aspect of water supply planning. Where water is withdrawn from a river directly without any storage, the withdrawal is constrained by the worst drought flow in the river. There is no flexibility for operational adjustments other than implementing conservation measures. Where there is a storage reservoir, yields higher than the flow in the source stream can be maintained for a period of time by releasing the water in storage. The determination of safe yield in this situation requires elaborate computation. This thesis presents a synthesis of methods of drought flow analysis and yield estimation. The yield depends on both the magnitude of the deficit and its temporal distribution. A new Markov chain analysis for assessing frequencies of annual flows is proposed. The Markov chain results compare very well with the empirical data analysis. Another advantage of the Markov chain analysis is that both high and low flows are considered simultaneously; no separate analyses for the lower and upper tails of the distribution are necessary. The temporal distribution of drought flows is considered with the aid of the generalized bootstrap method, time series analysis, and cluster sequencing of worsening droughts called Waitt's procedure. The methods are applied to drought inflows for three different water supply reservoirs in Spotsylvania County, Virginia, and different yield estimates are obtained. / Master of Science

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Supply chains are considered the foundation of the global economy, and businesses with global supply chains usually encounter at least 1 disruption annually. Mitigating the negative impact of disruptions is critical to supply chain managers, as disruptions can negatively impact organizational profitability and performance. Grounded in the resource dependence theory, the purpose of this qualitative multiple case study was to explore strategies organizational and supply chain managers use to mitigate negative results from supply chain disruption. Participants were 4 supply chain managers working in 2 different international organizations located in Jordan, who used effective strategies to mitigate supply chain disruptions. Data collection involved semistructured interviews and a review of organizational documents. Data were analyzed using thematic analysis, and 2 main themes emerged: Developing relationships and collaboration and strategy to identify supply chain disruption. The implications for positive social change include the potential for organizational and supply chain managers to mitigate negative results of supply chain disruptions and improve organizational performance. Sustaining organizational performance promotes the well-being of employees, families, communities, and the economy, which can result in customer satisfaction, business growth, and stable employment.