Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / "June 2016." Cataloged from PDF version of thesis. / Includes bibliographical references (pages 63-64). / What is the appropriate raw material inventory strategy for a make-to-order manufacturing company? As companies grow in size and the business environment changes over time, many companies adapt their operating policies to remain competitive. However, some policies, such as raw material inventory policies, are left untouched as "legacies" of the company's past due to lower priorities or lack of adequate data. These raw material inventory policies are of particular importance to manufacturing firms, especially those that often operate at maximum capacity or have seasonality in demand. This research proposes a raw material inventory policy evaluation tool that allows a company to understand how certain key performance indicators are affected by various changes in its inventory policy and helps the company devise a strategy. This evaluation tool can then guide the company towards a better inventory policy in the absence of cost information and shows the results in terms of number of events. The company can then adjust various replenishment policies depending on the product's demand characteristics. In addition, the research demonstrates that inventory policy changes can be used to partially overcome supplier service level declines and demand variability. / by Vikash Chandra and Michael Tully. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 57-58). / What are the key store related parameters that drive sales for large retail chains? This question has become increasingly important to Lojas Americanas, the sponsor company. In the last few years, the company has expanded rapidly to cater to a larger group of consumers in a wide range of locations across Brazil. With this expansion, it wishes to determine the key parameters that drive sales for each department and modify its assortment policy accordingly for each store, so as to optimize total sales. This thesis investigates the sales impact of a wide range of store related parameters such as location, size, and socio-economic profile of the surrounding population. Stepwise regression analysis is used here. For this regression, AIC and the p-value threshold are used as the criteria to identify statistically significant store related parameters that influence sales. Furthermore, cross validation is performed to check the explanatory power of the model. The analysis performed yields useful results. A total of 36 different retail departments are analyzed and an adjusted R-squared value (for the validation set) of over 0.6 is obtained for a vast majority of them, indicating that the model performs well in determining the key parameters that drive sales. Furthermore, for each department, the statistically significant set of parameters is obtained and for the company's overall revenue a set of 11 key parameters is identified as highlighted in the Discussion section of the thesis. LA can use the results of this analysis to guide its product assortment policy. / by Krishna Rajendran. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 31). / The choice of the location and number of warehouses is a strategic-level decision that can have a long- lasting impact on a firm's performance. Warehouse locations and their capacities determine how products flow within a firm's supply chain, which directly influences a firm's performance in terms of cost and service level. This research applies a mixed integer linear programming method to evaluate factors that drive existing inefficiencies in a warehouse network belonging to a Thai commodity chemicals manufacturer. The objective is to determine an optimal warehouse network configuration that minimizes the firm's total transportation and warehousing cost. Inventory turns and storage capacity constraints are found to be the key drivers of inefficiencies. The optimal solution suggests that the company should retain fewer warehouses and expand capacities at these locations. As the company continues to grow, the potential benefit from expansion becomes greater. / by Dangfun Pornnoparat. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 56-57). / Supply chains are exposed to a variety of risks as they become more complex and geographically diverse. Disruptions due to these risks can be costly. Companies cannot hope to mitigate all of their supply chain risks. In order to focus risk management resources on locations in the supply chain with the most risk, companies need a comprehensive method to quantify all of their significant supply chain risks. We worked with a company in the garment manufacturing industry to map their supply chain for a few representative products. Using input from the company, we equated different risk indices with the probability of loss of a node in their supply chain. The probabilities of loss allowed us to calculate a value-at-risk at each node. Once calculated, the values-at-risk were overlaid on a visual depiction of the company's supply chain network. While previous studies have quantified and visualized risk in companies' supply chains, our research sought to combine different categories of risk in order to give a more comprehensive picture of the risk at each node. We looked at disruption risks due to natural disasters, supplier bankruptcy, and political instability. We found that commercially available indices that quantify different categories of risk can be used to inform supply chain risk management decisions. Moving from these indices to a value-at-risk model of a supply chain is not a wholly quantitative process. Therefore, the strength of the model lies more in the relative quantities of value-at-risk rather than their absolute values. Overlaying these values-at-risk over a visual depiction of their supply chain gave the company a clearer picture of where to focus risk management efforts. Other companies in other industries could apply a similar approach to build an organizational risk management tool. / by Jason Alexander Braud and Siqi Gong. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / "June 2016." Cataloged from PDF version of thesis. / Includes bibliographical references (pages 63-64). / Retailers must coordinate inbound shipments from a large number of vendors. In order to manage capacity, retailers need to have a system to prioritize inbound loads with capacitated carriers. This practice creates a constraint when the number of loads exceeds the capacity of committed carriers due to seasonality and consumer shopping behaviors. A prioritization mechanism needed to be developed to support decision making for the selection of loads when capacity is constrained. This research applied the Analytic Hierarchy Process to define prioritization logic for each inbound load and solved a Knapsack model to optimize the assignment. This decision-making model allows the retailer to properly assign load priority based on company objectives. Further, opportunities were found to optimize load priority by up to 8.3 percent as compared to the current assignment. Similar retailers can leverage this research not only to prioritize inbound loads but also to prioritize other decisions such as which initiatives to pursue. / by Richard Koury Rassey and Yong Zheng. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / "June 2016." Cataloged from PDF version of thesis. / Includes bibliographical references (pages 56-57). / The unpredictable demand pattern during promotions leads to lost sales incurred due to frequent stock-outs, affecting the revenue and the brand of both the manufacturer and the retailer. The research focuses on finding out the root-causes of stock-outs in retail stores. It uses the audit response data that informs us of various states for zero on-shelf availability. These responses are used to create a fault-tree diagram that shows how different states could be reached. The root-causes mentioned in the fault-tree diagram are classified as either qualitative or quantitative root-causes. The credibility of quantitative root causes was established through regression analysis while store visits and interviews of different players of the supply chain helped to reason out the qualitative root-causes. Quantitative factors such as replenishment frequency, store sales volume and forecast accuracy seem to indicate a good correlation with stock-outs during promotions. / by Alankrita Nigam. / M. Eng. in Logistics

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Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 50-51). / This study addresses a medical device company's need to relicense its products for export after declaring a new legal manufacturer. New license applications are approved at an unknown date with increasing probability within a finite time horizon. Approval results in the instantaneous obsolescence, or "sudden-death," of inventory bound for export. As a result, the company needs to re-align its supply chain strategy to avoid stock-outs or inventory obsolescence. This thesis develops a model that aids the organization in assessing the decisions and necessary information that can help navigate the transition. Potential responses include pushing inventory out of the system before obsolescence, or ramping down production in advance of the sudden-death event. Improved estimates of alternative distribution costs, shortage costs, salvage values, and production capacities will greatly aid the organization's ability to respond to the event scenario. Changing these factors suggest different optimal inventory policies. To illustrate this relationship, a dynamic programming model is derived based on a probability distributions for likely license approval times. The resulting model allows the organization to assess optimal inventory policies derived from various system assumptions. In the thesis, different product aggregations are used to assess inventory strategies for bulk-license application submission. Patterns are identified in the analysis of simulation runs, including the time period for starting alternative inventory ramp-up as well as ramp-down speed. The intent of the study is to provide an iterative method for experimenting with assumptions within the organization in order to drive a coordinated response to the sudden-death eVent. The method is intended to be useful to other organizations planning to transition in preparation-for events occurring with increasing likelihood within finite time horizons. / by Abraham Zamcheck. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 66-70). / Recent advances in technological capability and economics have opened up a new world of capability known as the Internet of Things (IoT). The Internet of Things is the concept that all machines can be connected to the internet, and be remotely monitored through an infrastructure of interconnected software and hardware. Many companies are just beginning to explore the economic value that the Internet of Things can unlock, with much of the initial focus on remote diagnostics and predictive maintenance, particularly in application to industrial machines. This research tests various scenarios of predictive failure accuracy, creating spare parts forecasts based off of varying predictive forecast parameters. We compare these scenarios and their respective outputs to a regular time-series forecasting scenario, inserting each type of forecast into a periodic review (R, S) inventory system. We measure the output of each forecast put into the system in terms of spare parts inventory levels and in-stock service performance. We find that as long as the true positive rate (TPR) and false positive rate (FPR) have different values, our model is able to hold a lower average inventory while providing a higher level of service. Additionally, as the difference between the two values increases, the average amount of inventory held decreases, while the level of service provided increases. A more detailed summary of the results found and the implications on service supply chain were developed, and further areas of research are discussed. / by Michael Patrick Brocks and Renzo Eliseo Trujillo Castaneda. / M. Eng. in Logistics