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

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

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 72-73). / As supply chains become more global and complex, it increases the importance of end-to-end visibility into the material, information, and financial flows of a firm. This work covers various aspects of supply chain visibility such as current status, its benefits, and the benefit-enabling mechanisms, traced through multiple stages of research, including a survey, round- table, and detailed interviews. Specifically, we developed a multi-case study analysis of supply chain visibility solutions (SCVS) and analyzed its effect on key supply chain processes. A qualitative "with and without SCVS" framework was employed to understand the changes observed in key business processes at several companies that recently implemented SCVS. This was combined with a cross-case study analysis to evaluate the effect of SCVS on the efficiency and effectiveness dimensions of process performance. We found that SCVS has a direct positive effect on data management processes via benefit-enabling mechanisms such as automation, standardization, and better raw data. Key operational processes, such as shipment and inventory management, risk management, and procurement and partner collaboration, were affected positively by SCVS via the antecedent data management process. The strength of impact of SCVS on key operational processes varies by company characteristics and the stage of implementation of SCVS. This research is most useful to companies considering implementing an SCVS to understand the potential benefits of SCVS and the underlying mechanisms. Managers can increase the relevance and robustness of results by extending the case-study analysis to include more companies with similar operating characteristics. / by Anna Stanchik. / 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 90-91). / Multi-stop truckload has been gaining importance in recent years as part of a shift away from Less-than-truckload freight. In our research, we sought to understand how the price and carrier behavior vary as the number of stops increases. Rational economic theory says that these shipments will be more expensive, and experience shows that in practice they also tend to get rejected more often. This thesis tested these two likely results together with other factors known for affecting price and rejection rates, such as lead time, clustering of the stops, etc. We used logistics regression to predict the acceptance ratio and ordinary least squares regression to model the price based on historical data. We found that there is an inherent cost associated with multi-stops, which depends on the number of stops and whether the stop is a pick or a drop. The proximity of these stops as well as the stop-off charge can also impact the price. Carrier acceptance and routing guide depth depends on the price structure and load characteristics. As the number of stops increases, it takes longer for a tender to be accepted and the shipment performance also deteriorates with an increased likelihood of late delivery especially if the initial pickup is late. Therefore, companies need to be aware of the hidden costs associated with multi-stop truckloads as they plan their transportation network. / by Xiaojia (Amy) Chen and Shang Lin (Peter) Tsai Yang. / 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 50). / The United States has one of the safest drug supply chains in the world. However, its security is threatened by new challenges such as counterfeit, diverted, and illegally imported drugs. To counter the new challenges, the Drug Supply Chain Security Act (DSCSA) was signed into law by President Obama on November 27, 2013, with a 10-year implementation timeframe. As a result, companies in the U.S. pharmaceutical industry, including drug manufacturers, distributors, and dispensers, are challenged to fully comply with the DSCSA by 2023. The compliance with the DSCSA will enable companies to operate and manage the risks of their supply chains more efficiently. Industry consortiums, such as the Healthcare Distribution Management Association (HDMA), and the industry leaders have recommended various interoperable data exchange models for the implementation of the compliance. However, domestic and international complexities make it difficult to pick the optimal model for the industry. In this research, we start with categorizing the known data exchange models that can be potentially used by the U.S. pharmaceutical industry. Second, we develop a scorecard methodology based on a framework that considers various factors across the entire supply chain. Next, we examine the categorized models using this scorecard methodology. Lastly, we conclude with recommendations on the data strategy decision for the U.S. pharmaceutical industry. / by Peter Wontae Chung and Tao Zhang. / 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 40). / For many manufacturing facilities, process improvement efforts are a luxury when faced with heightened competitive pressures and a fast-paced work environment where fires are constantly being fought. This need for speed is even more important for startup companies who are racing against time to get their product to the market. Continuous improvement and Lean projects typically span from trial-and-error experiments to changes based on lengthy analyses. Biotech startup Company XYZ has felt the effects of these forces and launched a new effort to improve its operations via continuous improvement and Lean, ultimately reducing costs and improving productivity of operations. This thesis examines one example of a process improvement effort at Company XYZ's pre-clinical manufacturing facility. This project involved characterizing the cycle time and process flow, leading to targeted actions to increase the throughput and reduce the amount of time and effort to manufacture their drugs. Tools and ideas from Lean and Six Sigma were applied and a recommendation and next steps were presented to the company. This thesis also provides a broader demonstration of how such continuous improvement efforts can fit into the pharmaceutical and biotechnology industries. / by Mimi L. Tsai. / M. Eng. in Logistics