Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 70-73). / Ongoing humanitarian operations can suffer from the lack of medical item availability. The central problem thus becomes how to ensure the right item in the right place at the right time while maintaining appropriate costs. By means of a case study, this research grouped items by various item characteristics and assigned each group a common operating policy. The results of such item segmentation, and the application of common operating policies, was a theoretical increase over the current rule of thumb, single operating policy by 22% in average expected item availability and a decrease in total costs of 2-8%. Yet, similar results were achieved without segmentation. The major conclusion is that consideration of demand variability as a means to achieve greater item availability is key. The determination of appropriate costs becomes a transparent one for the decision-maker. More generally, this approach facilitates the comparison of various inventory management scenarios and the assumption of informed levels of risk. / by Brent Turner. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 87-89). / The number of natural hazards has been increasing over the last 10 years. Understanding the impact of natural hazards on retail networks is crucial to make effective planning against disruptions. We used daily sales and inventory data from a country-wide retail network and natural emergencies historic data to quantify the consequences triggered by these events in product and financial flows. We analyze sales and inventory flow through points of sale and distribution centers. We propose the Resilience Investment Model (RIM) to invest in resilience against the effects of natural hazards. This model takes into account the operational details of the organization. RIM is a two-stage multi-period inventory flow stochastic program. The resilience investments consist in acquiring additional inventory to buffer against disruptions and the use of real options contracts with suppliers to execute when a declared emergency happens. We use a set of risk profiles over the future costs to align the investment with the financials and preferences of the organization. This research shows how the risk profiles of the decision maker shape the location and distribution of backup stock in a retail network. We show that risk averse profiles reduce worst-case cost by 15% while increasing average cost by 2%. We recommend the use of risk profiles with cost targets to quantify the Value at Risk of the network due to natural hazards. / by Jorge García Castillo. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged student-submitted from PDF version of thesis. / Includes bibliographical references (pages 60-65). / Industrial digitalization concepts such as Industry 4.0 or Smart Manufacturing are currently of great interest in academia and among industrial players. These concepts are expected to boost companies' manufacturing supply chain performance factors such as availability and productivity. For instance, greater availability of assets on the shop floor makes the product flow more predictable and smooth, thus reducing the necessity for high inventory and increasing inventory turnover. Although current studies of industrial digital transformation offer a large variable theoretical construct, they lack quantitative proof of their assumptions. The main goal of this thesis is to introduce a method to quantify the expectation that digital initiatives in heavy industry impact certain manufacturing supply chain performance factors. In particular, the study examines the visualization effect on the unplanned machine downtime, planned maintenance, and machine utilization. The assumption of the decrease in unplanned machine downtime, increase in early-stage planned maintenance, and increase in machine utilization are tested using non-parametric hypotheses test - Wilcoxon Signed Rank test. Measurement of these factors is conducted using data collected from a power generation equipment manufacturer. The showcase factory participates in an overall digitalization Smart Manufacturing program and is in its early stage of implementation. The results indicate a significant increase in machine utilization and planned maintenance. However, unplanned machine downtime was not significantly reduced, although the result shows an approximation toward statistically significant change. The importance of frequent analysis becomes obvious. Future tests are necessary to study the development in later stages of implementation of Visualization. The reduction in downtime could become significant and the planned maintenance should stop increasing and start decreasing over time. The proposed method serves as a step toward academic quantitative analysis of industrial digitalization. / by Paulina Gisbrecht. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged student-submitted from PDF version of thesis. / Includes bibliographical references (pages 59-63). / Excess inventory is prevalent in both the armed forces and defense companies; it takes up space and resources that could be used elsewhere. This thesis proposes a method to reduce the excess inventory and associated costs, while maintaining instant part availability, despite design changes which alter the number of parts required. A single period model extension was created based on K-means clustering of the parts according to lead-time and cost. These groupings provided the backbone of the cost functions created in the thesis. A predictive demand function was also created so that the design change's alterations to demand would be captured. The cost function was optimized using the predicted demand, to find an optimal order quantity that met the demand requirements and was the lowest cost option. Together these single period model function extensions allowed for a 31 percent decrease in excess inventory and 34 percent decrease in total cost. Due to the nature of this report the companies' names have been removed, and the data naming conventions were altered so as to protect the nature of the parts. / by Danaka M. Porter. / M. Eng. in Supply Chain Management

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 (pages 81-82). / Medical devices companies struggle to balance between inventory and service performance, as the products are non-interchangeable and inventory investment is expensive. To find the right level of inventory, we first used unsupervised clustering method to find demand pattern uncertainty for each product. Then, we developed a simulation-based approach to determine the required inventory to achieve a required service level guarantee. We further explored policy changes in the demand fulfillment process to identify how the company can effectively improve performance without increasing inventory level. After comparing different results, we concluded that reduction of replenishment lead time is the most effective measure. The methodology can be applied to a wide range of products and sectors. / by Maria Rey and Xiaofan Xu. / M. Eng. in Supply Chain Management

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 (pages 51-52). / High volatility in order patterns leads to supply chain wide inefficiencies and high operational costs. This issue is particularly common in the consumer goods industry due to large numbers of SKUs under management and frequent promotions. By leveling out the number of weekly shipments (containing constant quantitates of top selling SKUs), a company can potentially boost operational performance while reducing costs. The research question of this thesis was therefore "Will a consistent, pre-determined customer shipment profile based on the lean leveling principle reduce variability and enable improvements in transportation cost, service level and cash (i.e. reduce working capital tied up in inventory)?" In academic literature, lean principles have been applied extensively in manufacturing settings, while the logistics domain remains a relatively unexplored lean frontier. In this thesis the team sought to realize lean-based gains by replacing large, infrequent batch deliveries with frequent small shipments, as derived from lean theory. The team created a customer shipment profile based on historical shipping data, consumption data and forecast information. The top selling items, which were the core products of subsequent analysis, were derived from a SKU segmentation. The number of required units was calculated based on the service promise. The team simulated two inventory policies: a Fixed scenario (orders are derived from historical averages) and a hybrid scenario (a fixed component based on a percentage of the historical average and a variable component). The model was validated by comparing calculated transportation cost, service level and cash with the values derived from the actual company records. The study suggests that applying the lean leveling concept may lead to reduced shipment variability. Placing orders on a fixed shipment schedule can lead to lower transportation costs and higher service levels. Cash requirements for inventory may be higher with increasing implementation of lean leveling. The optimal result for buyer and seller could be obtained with the hybrid model: At 75% fixed orders, the benefits of transportation cost, cash and service level were equally balanced. Other companies across different industries may find the thesis model useful to possibly improve operational performance while reducing costs through lean leveling. / by Melissa Botero Aristizabal and Fabian Brenninkmeijer. / M. Eng. in Supply Chain Management

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 (pages 40-41). / Supply chains in the pharmaceutical industry are growing increasingly more complex and expanding their geographic reach both in manufacturing production and to the end consumer, the patient. Physical development, manufacturing and distribution of these drugs, both of biologics and small molecules, is extremely technical in science and processes. Additionally, the industry is highly regulated with nuanced requirements that vary by country of origin and consumption, adding complexity to the drug development process. For these reasons, companies are pushing for longer range planning and forecasting of their drug pipelines, beginning the process earlier for drugs that are in pre-clinical phases of production in order to adequately plan for capacity in manufacturing and distribution. Working with data on a number of small molecules across different lines of treatment in the drug development pipeline, a discrete event simulation model was developed to simulate production quantity outputs given varying levels of stochastic parameters such as drug dosage, treatment duration, patient population, patient compliance, and competitive market share. Results from the simulations were used to assess manufacturing capacity risk given capacity and resource capabilities. The outputs of the model built in this thesis can be used to better inform capacity planning decisions for these early stage molecules. / by Emily Chen. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged student-submitted from PDF version of thesis. / Includes bibliographical references (pages 79-83). / Transportation costs constitute an important part of total logistics costs and have a dramatic impact on all kinds of decisions across the supply chain. Accurate estimation of transportation costs can help shippers make better decisions when planning transportation budgets and can help carriers estimate future cash flows. This study develops a forecasting model that predicts both contract and spot rates for truckload transportation on individual lanes for the next seven days. This study considers several input variables, including lagged values of spot and contract rates, rates on adjacent routes and volumes. The architectural approach to short-term forecasting is a neural network based on Nonlinear Autoregressive Models with eXogenous input (NARX) models. NARX models are powerful when modelling complex, nonlinear and dynamic systems, especially time series. Traditional time series models, including autoregressive integrated moving average (ARIMA), are also used and results from different models are compared. Results show that the NAR model provides better short-term forecasting performance for spot rates than the ARIMA model, while the ARIMA model performs slightly better for contract rates. However, for a longer-term forecast, the NARX model provides better results for contract rates. The results from this study can be applied to industrial players for their own transportation rate forecasting. These results provide guidelines for both shippers and carriers regarding what model to use, when to update the model with new information, and what forecasting error can be normally expected from the model. / by Xiwen Bai. / M. Eng. in Supply Chain Management

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 (pages 45-46). / Logistics providers process millions of packages daily and collect an incredible amount of data from these shipments. As new sensors are added to more and more packages, companies will now have increasingly fast access to even more data. However, how will logistics companies leverage this idea of big data to generate the most business value for their customers? Using a qualitative approach by interviewing current users of real-time monitoring devices, we were able to understand how customers perceive the value added by this technology. Moreover, we scoured a significant amount of literature on sensors, the logistics industry, and upcoming technological breakthroughs. We quickly discovered that customers do not perform extensive quantitative analysis to determine the trade-offs and financial benefit of using real-time sensors in their shipping processes. Additionally, we found that customers are unwilling to analyze this big data themselves, but instead want their logistics provider to interpret the data to provide value-added services. Therefore, logistics providers should leverage all of the data they collect, instead of simply creating value when shipments become exceptions, e.g. out of temperature range. We propose using smart contracts on a permissioned blockchain to automate business processes and reduce frictions within the shipping parties and other intermediaries. / by Tarun Tiwari and Anthony Toteda. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 27). / Changing regulatory requirements continues to be an increasingly complex issue in the medical device industry. Regulations place stress on regional supply chains across the world. Most recently, the European Parliament issued the Medical Device Regulation (EU) 2017/745 instituting new compliance framework for all devices manufactured, sold, and/or distributed in the European Union. The new framework requires the implementation of unique device identifiers and more stringent conformity assessment procedures. In addition, many device classification types have changed, post-market clinical surveillance has been instituted, and traceability through a centralized IT database is now mandated. While the the act aims to improve patient safety and efficacy across the medical device industry, it poses huge impacts across both the physical and informational flows in supply chains. This research evaluates the regulatory impact across supply chain operations using predictive modeling and machine learning. The model determines how various activities and events in manufacturing and sourcing environments contribute to supply constraints when modified to accommodate new regulatory requirements. The model also determines how product attributes contribute to performance variability. By taking a proactive approach to assess the impacts of regulatory changes, firms can optimize supply chain flows to reduce cost, lead-time, and service level risks. / by Melissa Medina. / M. Eng. in Supply Chain Management