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

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). / Supply chains are facing increasingly volatile environments. Traditional optimization solutions provide a baseline understanding for industry applications, but cost-efficient solutions require a more robust approach. In high-tech capital construction projects, the construction of facilities requires complex project schedules, forecast well in advance. These forecasts are used to hire contract workers of varying contract lengths. In this thesis, we develop a risk integration methodology for contract workforce hiring optimization, and explore the capability of generalizing this approach for other supply chain problems. We first create a risk-integrated, optimal solution for workforce hiring that strategically covers areas of high risk density in construction forecasts. We first develop a program to simulate schedule variations based on the associated risk parameters of the scheduled tasks. Using the risk statistics resulting from these simulated schedules, we build new schedule requirements using two different methods. The first method addresses the gap from a daily perspective (bottom-up), while the second method addresses it from an overall schedule perspective (top-down). These new requirements are each overlaid on the input schedule, re-optimized, and excess daily coverage is trimmed. Using both methods, we found that higher levels of risk coverage were achieved at lower costs than the traditional solutions. In the studied case for Intel, a 23% additional risk coverage was generated for equivalent cost. Ultimately, the results show that strategic risk integration can result in a lower final cost, and a generalized framework for risk integration can be applied across many supply chain problems. / by Damaris R. Zipperer and Andrew N. Brown. / 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 65-68). / Business forecasting frequently combines quantitative time series techniques with qualitative expert opinion overrides to create a final consensus forecast. The objective of these overrides is to reduce forecast error, which enables safety stock reduction, customer service improvement, and manufacturing schedule stability. However, these overrides often fail to improve final forecast accuracy. Process mis-steps include small adjustments, adjustments to accurate statistical forecasts, and adjustments to match financial goals. At best, these overrides waste scare forecasting resources; at worst, they seriously impact business performance. This report offers a framework for identifying overrides that are likely to improve forecast accuracy. A new class of metrics, Dispersion-Scaled Overrides, was developed as an input to the framework. Other inputs included statistical forecast accuracy and auto-correlation. Classification techniques were used to identify whether an override was likely to create forecast value add. These techniques were found to be approximately 80% accurate in predicting success. This result suggests that using Dispersion-Scaled Overrides alongside common forecast accuracy metrics can reliably predict forecast value add. In turn, this can maximize the value that experts add to the business forecasting process. / by Jeffrey A. Baker. / 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 46). / Traditionally, production and transportation planning processes are managed separately in organizations. In such arrangements, order processing, load planning, and transportation scheduling are often done sequentially, which can be time consuming. Establishing a proactive steady flow of products between two nodes of a supply chain can bypass this order-plan-ship process. A steady flow of products can reduce transportation costs, increase cross-dock productivity, and reduce bullwhip effect upstream in the supply chain. This thesis develops an analytical framework to calculate this steady flow. The determination of eligible SKUs in this approach is performed by analyzing each SKU's historical and forecasted demand. The level of flow of each SKU is found using optimization with the objective of maximizing total savings. The methodology was tested on a plant-to-warehouse lane of a fast moving consumer goods company. The relationship between demand characteristics and optimal steady flow was studied. It was found that as the coefficient of variation decreases, the optimum steady flow moves closer to the mean of the non-zero demand and selected forecast over the model horizon. The methodology developed in the research, with its potential to reduce transportation cost and improve warehouse productivity, also presents the opportunity for new and innovative contract types with transportation providers. / by Priya Andleigh and Jeffrey Scott Bullock. / 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 40). / For companies with a large number of Stock Keeping Units (SKUs), it is extremely challenging, if not impossible, to manage the SKUs individually. Therefore, companies stratify SKUs into different classes and manage them by class. Currently, most companies identify SKU stratification based on the single factor of sales volume. This thesis explores more comprehensive analysis methods that can consider multiple SKU characteristics. We applied four methods (Single Factor Analysis, Dual-Matrix Analysis, Analytical Hierarchy Process, and Cluster Analysis) to the data of a company in the Consumer Packaged Goods industry. The factors considered were velocity, volatility, and profit margin. Our research indicates that the Analytical Hierarchy Process is the most viable and comprehensive method for stratifying SKUs. It allows for a flexible number of stratification factors, different importance levels of the factors, and user control of the number of classes and class sizes. By applying the Analytical Hierarchy Process to SKU stratification, companies will be able to carry the right inventory for the right SKUs, and improve customer service. / by Jiaxin Jiang and Andrew Steverson. / 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 36-38). / In today's world, the e-commerce market is growing rapidly and becoming more competitive. While many players in the industry are attempting to get their share of pie, consumers are demanding faster deliveries and free shipping. This market growth and change in consumer behavior provides an exciting opportunity for companies to compete. In order to meet the new consumer demand, companies need to find better ways to deliver faster. Faster delivery times can be achieved by using an optimization model to plan delivery network and operations. Typically, this optimization model has been based on minimizing cost. However, in the current market, lowest cost is not necessarily the best driver of sales as the consumer culture enters an era of instant gratification. We argue that minimizing customer waiting time will bring better performance and win over market share by providing the quickest delivery service that is expected by the majority of consumers. We propose solving the location routing problem (LRP) aiming at minimizing customer waiting time with capacitated depots and vehicles. We take two approaches to solve this problem: mathematical model and heuristic algorithm. The mathematical model obtains the optimal solution, but it has a limitation on the size of the problem due to the NP-hardness of the LRP. Therefore, we introduce three different variations of Simulated Annealing (SA) algorithm to solve the Capacitated Latency Location Routing Problem (CLLRP). According to the comparison results on a popular benchmark test, one of the designed SAs, the Iterative Simulated Annealing algorithm, consistently provides the best combination of performance and computation time compared to the other two SAs. Therefore, this specific algorithm is further compared to the mathematical model on some problem instances. The comparison results demonstrate that the proposed algorithm performs competitively with the algorithms in the literature and the mathematical model. / by Eugene Sohn. / 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 46-47). / Drug counterfeiting is one of the major issues in the pharmaceutical industry across the world. These products could cause damages from ineffective treatments to death of patients. In order to fight against counterfeit drugs, the US government introduced Drug Supply Chain Security Act (DSCSA) mandating that all prescription drugs should be serialized. In addition, it mandates all pharmaceutical companies in the U.S. to provide tracking documents in response to a tracing request from FDA. While the act aims to improve drug security across the pharmaceutical industry, it poses a huge impact across the supply chain on both physical flow and information flow. This research evaluates the supply chain impact at an industry level. In this thesis, we evaluate the supply chain impact of Matryoshka model and Unit level model supported by a decentralized information flow. The thesis then evaluates the supply chain impact from three aspects, operational cost, IT infrastructure cost and capital investment. We reference Nabiyeva and Wu's research on centralized information flow model to conduct an exhaustive supply chain impact evaluation across the centralized model and the decentralized model. We conclude that among all these scenarios, unit level model under centralized information flow design bears the highest cost as it requires higher IT investment. On the other hand, the matryoshka model under decentralized information flow has a least supply chain impact from the cost perspective with low IT investment. / by Meng Ying Chang and Raghavendran Mohan. / 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. / Pharmaceutical supply chains are strictly regulated and work within unique constraints. Traditionally, innovator companies that are manufacturing the product have no direct interaction with the end users (treatment sites or individual patients); rather, over 90% of the orders go through intermediary wholesalers and distributors. However, with the introduction of new technologies for patients to manage their own health, federal regulations coming into effect on supplier responsibility for tracking drugs down to the user, and ever more pressure to cut costs and justify the high cost of medicine, manufacturers are actively reshaping their role in the pharmaceutical supply chain. Our objective in this thesis project was to support our Sponsor Company, a "Big Pharma" company with a wide range of medicines, to understand the key cost drivers of their current distribution channel and to explore the impact that a shift to an alternative distribution channel would have from a financial and operational standpoint. We first conducted a literature review to examine the existing research on costing methodologies, the impact of home delivery for clinical care and the drug distribution landscape. The literature shows some evidence that home delivery improves patient adherence and reduces inventory costs for suppliers. We then analyzed a targeted product's distribution network within the US by building a cost-to-serve model, which maps out the end-to-end service components conducted by the Sponsor Company. With this model we were able to test the supply chain impacts of volume change and a gradual shift to alternative distribution channels. The results of the model showed that for this particular product, working capital was a key cost driver, shifting volume to incorporate alternative distribution channels is highly beneficial; even some significant increases in operating costs are effectively neutralized by reductions in working capital for the entire channel. Aside from the model results, we recommend validating the assumptions and suggest that this 'bottom-up' costing model be extended for other products and geographies and used to inform the company's overall corporate strategic planning exercise. The cost-to-serve model framework can also be extended beyond the pharmaceutical industry to benefit consumer facing industries considering an omni-channel strategy. / by Qi Zhang and Muching Zhang. / M. Eng. in Supply Chain Management