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 79). / The security alarms services market in the United States delivers hardware equipment and services to homeowners and businesses to help monitor and enhance personal property protection. Customer satisfaction via wait time reduction, first call resolution, and cost minimization are key drivers of success to players in this market. Most companies invest heavily in customer service systems including call centers. Our client, AlarmCo, a top provider of property protection, manages an inbound call center that supports a range of questions from customers who call within thirty days from the alarm installation date. Often, security companies fail to utilize strategic solutions when managing inbound customer call traffic and default to reactive measures which unnecessarily increase customer wait times. The key question the team aims to address in this thesis is: "How can we improve the customer service experience for customers of a major security service provider in the United States?" For this thesis, MIT partnered with OnProcess Technology, a managed services provider specializing in complex, global service supply chain operations, to develop a robust framework to preemptively reduce the number of inbound customer calls, and thereby improve customer service. Using ABC segmentation, the team categorized customers by reason code and demographics. To simulate the client's call center queue, the team calculated the key inputs for the queuing model including average wait time, interarrival rates and number of servers. The team then chose and developed the M/M/n stochastic queuing model for the simulation. The M/M/n queue reflects a simple system with parallel servers, arrivals with a Poisson distribution and service times that are exponentially distributed. Next, the customer segmentation was used to develop targeted preemptive solutions. Taking into account feasibility ratings, the team assigned success rates to each solution and adjusted the inbound call data accordingly. By analyzing the outputs of the simulation before and after adjusting the dataset, the team quantified the impact of preemptive solutions on the call center queue. Ultimately, narrowing to twelve strategic preemptive solutions led to the enhancement of the as-is queuing model by reducing average wait time by up to 35%. / by Qiao Chu and Nisha Palvia. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. / Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 95-98). / As part of standard business cycles, new technologies continue to emerge that disrupt industries and capture market share from stagnant incumbents. In the trucking industry, Uber for Freight (UFF) is one of these innovative business models. Loosely defined as platforms which seek to more efficiently match shippers' loads with truck drivers, these companies are seeking to 'uberize' freight transport through algorithm-based applications. By eliminating the middleman of a carrier or broker, these startups' value proposition is cost savings and increased efficiency gained through a frictionless interface. While process automation has its upsides, many industry veterans have questioned the potential success of this business model. Furthermore, experts have expressed uncertainty regarding the operational mechanics of an UFF company as well as the true distinction between UFF and a traditional broker. This research seeks to address these questions about the UFF model by first developing a clear description of its players and processes, compiled based on interviews with existing companies in this space. Secondly, this research determines that UFF is best classified as a subdivision of brokers, providing similar services through a different business model that eliminates some degree of human intervention. More than simply automation, UFF provides additional benefits through its rating system and efficient payment processes. As a case study, this research then investigates the applicability of UFF within a specific company. The sponsor company, a large, multinational chemical company, maintains an extensive product offering that reaches customers across almost all industries. These products vary widely in format, hazardous material classification and service level requirements. Based on interviews with sponsor company representatives across functions and geographies, this research examines the challenges and benefits of incorporating UFF into a company's transportation strategy. From these learnings, it was recommended that UFF be implemented gradually, starting on a U.S. lane that transports non-hazardous products with lower service level requirements. If safety and service levels prove satisfactory, the sponsor company can scale accordingly to more complex products or lanes. While UFF has clear benefits and disruptive potential, it must be utilized with the appropriate products and customers; it is not a one-size-fits-all solution. / by Leah Davis and Joseph Lucido. / M. Eng. in Supply Chain Management / M. Eng. in Logistics

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 72-74). / Over the years, supply chain management has continued to change and evolve to become a major component in competitive strategy to enhance organizational productivity and profitability. While considerable research has been done in formulating accurate and robust demand forecasts, many areas for improvement remain in supply chain planning. In particular, many planning parameters (e.g., lead time, waste, yield, run rate, capacity, etc.), which are vital inputs into the planning process, are often not given the consideration they deserve. Oftentimes, the planned values of these parameters were not scientifically derived in the first place, or their actual values may have changed since the planned values' original inception and now differ significantly from its planned value. This research examined one type of planning parameter in particular - lead time, and showed there is room for improvement in how lead time is managed and considered within the current planning process. The research showed that using predictive analytics to predict lead time (predictive lead time) can reduce the deviation between the planned and actual values in the lead time parameter..Moreover, the analyses showed that using predictive lead time can reduce the safety stock cost, the manual labor required in exception management (re-planning), and the manual labor in purchase order management. / by Darryl Yau. / 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 48-49). / This thesis explores the impact of the Drug Supply Chain Security Act (DSCSA) on various stakeholders in the pharmaceutical supply chain. Specific attention has been dedicated to the impact on manufacturers and distributors/retailers. Although various interpretations of the DSCSA are possible, this thesis takes the perspective of a centralized data model, and tests the feasibility of implementing a centralized database under both data nesting and unit level relational models. This is in contrast to the decentralized system, which is further explored in the partner thesis, Impact of Drug Supply Chain Security Act on US pharmaceutical industry under decentralized information flow (Chang & Mohan, 2017). Both quantitative and qualitative analysis are employed in this thesis. Quantitative analysis was conducted using publically available industry data, from which the impact on overall supply chain costs was modeled. Qualitative analysis consisted of stakeholder interviews, process mapping, and time studies to determine the extent of process changes and what they should look like to conform to DSCSA. After accounting for the current state of implementation, as well as real-world constraints, the findings indicate that the best-practice scenario for Manufacturers and Distributors/Retailers to conform to DSCSA is one using a Centralized data model and nested linked-pedigrees. Although this option is estimated to be 67% costlier than the least expensive scenario, it offers a more robust and secure data model that allows for better long-term scalability. Implementation and cost concerns are also discussed in the conclusion to elaborate on trends and considerations in choosing the appropriate Serialization scenario to pursue. / by Aisha Nabiyeva and David Z.Y. Wu. / 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 47-51). / In manufacturing companies, production strategies prioritize maximizing line efficiency which favors large lot sizes and few setups. On the other hand, logistics strategies prioritize minimizing inventory costs which favors smaller lot sizes and more setups. This thesis provides a new mixed integer linear model formulation that optimizes lot sizes such that both manufacturing efficiency and inventory costs are considered simultaneously. The model solves a multi-machine capacitated lot sizing problem with novel extensions for multi-echelon inventory, transfer costs between inventory echelons, and a multi-echelon product setup hierarchy. The model includes extensions for setuptimes and multiple non-identical machine capabilities. The multi-echelon inventory extension is applicable to firms that contract a third party logistics provider's warehouse to handle seasonal inventory. In this situation, the firm has two inventory holding cost structures and desires to optimize usage of the contracted warehouse. The multi-echelon setup extension is applicable to firms that manufacture products with similar characteristics such that they share a common machine setup cost at a category or aggregated level and a unique setup cost at an item or disaggregated level. When applied to benchmarking manufacturing data, the model demonstrates improved production plans that reduce inventory and setup costs by 30% in some scenarios. This thesis emphasizes how integrating production and logistics strategies can offer significant improvement to any firm's supply chain. In particular, firms with a multi-echelon inventory or setup cost structure can benefit from a model that accounts for these important cost drivers in planning its production. / by Ian Chua and Thomas Heyward. / 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 (pages 47-49). / With the increasingly competitive landscape of e-commerce and omni-channel delivery execution, the last mile has emerged as a critical source of opportunity for cost efficiency. Unmanned aerial vehicles (UAVs) have historically been utilized for military applications, but they are quickly gaining traction as a viable option for driving improvements in commercial last-mile operations. Although extensive literature currently exists on vehicle routing problems, research integrating drones as a supplement to these routing problems is scarce. This thesis explores the feasibility of deploying drones to the last mile, by modeling the cost of serving customers with one truck and multiple drones in the context of the traveling salesman problem. The model is constructed with mixed integer linear programming (MILP) optimization and assessed with a sensitivity analysis of several key parameters. We find significant median cost savings over TSP of 30 percent in the base case, and that these effects on savings can diminish to a median 4 percent in the worst case while surging up to 55 percent in the best case. / by Justin J. Yoon. / 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 36-38). / There is a current lack of visibility in the transfer of goods from farmers to oil mills, to manufacturers, to retail outlets and finally to the consumer in the palm oil industry. While leading brands have pledged to commit to a 100% sustainable certification, only 19% of global palm oil production is certified as sustainable. Emerging technologies, such as blockchain, a distributed ledger, can transform supply chain traceability as we know it and bring more transparency through the value chain, creating value to stakeholders. From a process perspective, the proposed solution leverages the mass balance, and book and claim traceability models that RSPO has defined. From a technology perspective, the proposed solution leverages blockchain, geospatial imagery classification, and IoT technologies to keep track of the flow of physical goods and sustainable palm oil certificates. From a people perspective, the proposed solution includes a set of incentive models that could be utilized in easing change management efforts. / by Toufic Hirbli. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. / 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 48-49). / Transportation cost is one of the major costs in supply chain. Companies need to optimize every aspect of the transportation process to reduce the total logistics cost. A key aspect is optimal mode selection to minimize the cost of every lane in a company's transportation network. The traditional approach is to select the mode based on freight cost and average transit time. Besides these two factors, the transit variability associated with each mode choice also impacts the transportation cost in an indirect way. In particular, higher variability of transit time will lead to a higher safety stock level in order to keep up with service level, resulting in higher inventory holding cost. To study the impact of transit time variability, we first generated transit time distribution from data provided by a larger retailer. Then we constructed a total logistics cost equation based on transportation cost, inventory cost that incorporates both average transit time and transit time variability from the transit time distribution. Lastly, we conducted sensitivity analysis using the total logistics cost equation with respect to changes in service level, load value, and volume. Beside mode selection problem, our approach of including cost of variability in total cost calculation can be applied to general problems that deals with uncertainties. / by Tianshu Huang and Maria Bernarda Serrano. / M. Eng. in Logistics / 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 (page 56). / The oil and gas industry is characterized by unpredictable boom and busts cycles. Companies must manage capacity to be able to quickly meet increasing demand during boom cycles and survive when oil prices go down. During this time, companies resort to in-house sourcing ("Make") or buying externally ("Buy") from suppliers, whichever is rational. Since 2014, the oil field services industry has been in a period of recession, and oil prices have dropped significantly. The company's sourcing team asked us to analyze the Make-vs.-Buy scenarios. Our research has two primary objectives. First, to provide a methodical understanding of key Make-vs.-Buy decision factors for optimized capacity management during an upturn. Second, to develop a 2x2 assessment model that can assist in making the Make-vs.-Buy decision once the recession is over and prices have returned to a normal index. We interviewed research company personnel to get a better sense of their hypotheses: first, quantities ordered vary with boom/bust cycles; second, external pricing rises during boom cycles and falls during bust cycles; third, internal sourcing has a unified price that does not change with the boom/bust cycle. We tested the company's hypotheses with a limited set of product data but could not verify them. To better assess the situation, we researched the factors considered by theorists when making a Make-vs.- Buy decision. Based on this research, we identified four assessment criteria -- strategic, technological, market and economic factors -- that are intrinsic as well as extrinsic to the company throughout the entire decision making process. Furthermore, we created a model to test boom and bust circumstances and provide a better testing mechanism for boom and bust cycles. / by Akansha Nidhi and Fady Riad. / 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 (pages 60-62). / Healthcare cost are on the rise and becoming a major concern in the US economy. In fact, healthcare spending is the largest individual contributor to US gross domestic product. Various stakeholders such as government, insurance and payors are now looking at the healthcare providers and manufacturers to come up with innovative and cost-effective solutions. Medical device manufacturers rely heavily on startups to provide new technology solutions to market. Yet, survival rate of these startups is very low. Various factors contribute to the success of these startups such as: market scope, financial support, supply chain integration, patent protection, regulatory compliance, founders experience and more. This thesis investigates supply chain integration factors contributing to the success of medical device startups. Sixteen key supply chain integration factors were identified using systematic literature review and interview research methodology. A survey was conducted to a range of industry participants to validate these factors. Survey responses were then analyzed and validated using statistical application SAS JIMP. Most of the factors stem from regulatory requirements as laid down by U.S. Food and Drug Administration. Out of sixteen factors, Quality management system, good manufacturing practice and collaboration with suppliers and distributors were identified as most critical to the success of medical device startups. These supply chain integration factors were further categorized under three major categories namely compliance, cost and collaboration. A medical device startup need to manage simultaneously all supply chain integration factors under these three categories. The findings of the research would be useful to medical device startups to bring lifesaving innovative solutions in market and contribute to success of these startups. / by Sanjay Gupta. / M. Eng. in Supply Chain Management