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

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 94-97). / The craft brewing industry in New York State has grown rapidly over the past five years helped in part by New York State legislation called the Farm Brewery Act of 2012. The act imposes agricultural stipulations for breweries desiring to file for a Farm Brewing license. The hop industry will have to grow in kind to meet the Farm Brewery Act requirements. The level of growth that needs to be achieved was determined through the use of system dynamics modeling. Production volumes were calculated based on survey results from brewers and farmers. Currently, the state's hop industry is producing a surplus of hops and will be able to supply short-term brewery growth over the horizon of the next three years. It was discovered, however, that the industry is vulnerable to demand shifts and prudent action should be taken to become resilient to changes in buyer regulations and preferences: increasing the economy of scale and building collaborative relationships through farm clustering will improve the longevity of New York's hop industry. / by Nathan D. Stempel. / 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 83-85). / The company sponsoring our project is a leading chemical manufacturer, supplying a wide range of products on a global scale. One of the most interesting lines of business the company operates is to supply specialty chemicals in faraway places on few weeks' notice. The nature of these supply chains is project- driven, or, in other words, unique, non-repeatable and established for the sole purpose of fulfilling a single project. The company faces the challenge of operationalizing the set-up of such supply chains in order to reduce the amount of time and effort devoted to each supply and maximize learning from each project. This thesis seeks to address the conundrum of setting up project-driven supply chains in a more efficient, effective and easily repeatable way. Through interviews with company personnel, we identify eleven challenges and subdivide them into three categories: Business-Specific, Cross-Border, and Last-Leg. Next, through literature review and interviews with experts, we identify seventeen possible solutions to the above issues, which have the potential to improve planning and execution of project-driven supply chains in the chemical industry. Recognizing that cross-learnings from supply chains in humanitarian and military supply chains are valuable, our interviews include military and humanitarian practitioners, with the aim of increasing the variety of solutions. Using the research data as a basis, we formulate a project-driven supply chain mobilization template aimed at including most critical areas required in setting up project-driven supply chains. This template could be used as both a guideline for planning the mobilization of a supply contract, and as a validation tool to confirm the mobilization process has been thoroughly completed. The adoption of the mobilization template and process via integration with managerial and approval processes is expected to drive a more efficient and effective mobilization of project-driven supply chains for companies within the chemical industry and beyond. / by Sze Xin Mok and Ruggero Moretto. / M. Eng. in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, February 2016. (Axel Barbará). / Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, June 2015. (Tomás Dominguez Molet). / Cataloged from PDF version of thesis. / Includes bibliographical references (page 54). / Globalized companies seek growth while dealing with more complex and compelling challenges such as economic volatility, fluctuating commodity prices, supply-chain inefficiencies and increasing customer expectations. While companies cannot control all of these challenges, there is much they can do to remain competitive. Companies can gain competitive edge through improved demand forecasting and efficient inventory management. SKU segmentation is a concept that intends to demonstrate that there is a economic benefit behind treating and handling some products differently from another. Our thesis optimized SKU segmentation for a global CPG and restaurant brand. This aided inventory managers and supply planners adequately assess the varying needs of diverse products, to cluster them according to comparable needs, to reduce supply chain costs and optimize their supply chain. To explore this problem, we analyzed 53 weeks of forecast and demand data for over 15,000 SKUs. Utilizing a variety of clustering techniques, we identified a more cost-effective clustering strategy for the subject case. We analyzed the comparative costs between our theoretical classification and the actual classification used by the subject case study. Once we identified the disconnect, we calculated the incurred costs for being out of the optimal solution. This included both the effect of safety stock and the stock out costs. Our research created new insights into the comparable cost of under-forecasting and over-forecasting on safety stock and customer service level. / by Axel Barbará and Tomás Dominguez Molet. / 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 62-64). / Unilever, a large multi-national Consumer Packaged Goods (CPG) company, uses water as an essential ingredient in its products and as a critical component in its manufacturing processes. In many instances, the price of water does not reflect market dynamics insofar as water is cheaper where there is low availability and vice versa. Business continuity costs due to poor water quality or water shortages may far outweigh the direct costs that Unilever incurs in purchasing water. Hence, by performing a literature review, numerous interviews with experts and stakeholders and an extensive review of existing water valuation tools, we created a framework that is capable of calculating a comprehensive value of water for any of Unilever's 250+ manufacturing sites based on site-specific conditions. We identified and developed the three core components of our framework, namely: purchase price, processing and handling cost and business disruption cost. Our main contribution is the estimation of a business disruption cost that takes into consideration mitigation options available and a scenario analysis of different water-related events to yield the total value-at-risk. A risk- adjusted value of water would enable Unilever to optimize water use and build resilience within its manufacturing operations by incentivizing water efficiency and catchment-based water stewardship initiatives where they are needed most. As the evaluation of a comprehensive price of water is a complex challenge, this project is a first step towards building a more robust framework. We have listed several recommendations that would strengthen the framework. / by Rishi Gohil and María Carolina Méndndez Vives. / M. Eng. in Logistics