Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 46-47). / Large pharmaceutical companies struggle to find innovative ways to reduce work-in-process inventory in their production facilities. In our research, we focus on the tradeoff between inventory and production capacity through investing in new facilities and equipment. This tradeoff will depend on the company's objectives and what it is willing to give up in return for reducing inventory. We found that increasing capacity to reduce work-in-process inventory by investing in new facilities is not always the most favorable approach in terms of net present value. However, for flexibility or lead-time improvements, it may make sense to proceed with the investment. We developed multiple scenarios considering the company's future plans to reduce inventory or grow. These scenarios provide insights into the factors that improve the attractiveness of the investments and those that do not. Our financial analysis along with the guidelines and procedures that we have developed help the sponsor company most effectively reach its goal to reduce its work in process inventory. / by Karim Kamareddine and Yihong Yao. / 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 66-67). / Order-picking is an integral operation in warehouses and distribution centers (DC), consuming considerable operating resources and expenses. Numerous studies have attempted to optimize the efficiency and reduce the cost of order-picking. In working with a partner company, this thesis evaluates a proposed mechanism for piece-picking that would achieve this end. The company has a shelf-pack number for each SKU, whereby the SKU must be piece-picked in a quantity that is a multiple of the number. The company has proposed to change this number from 1 to 2 to raise the number of units per pick and reduce the number of picks needed for a SKU. In this thesis, simulation is performed on the company's shipment data from DC to store to reveal the merits and demerits of this scheme. SKUs are segmented into different groups based on their suitability for this scheme as a means of mitigating the negative repercussions of the proposal. The scheme can reduce the number of picks and related costs needed, but it causes a shift of inventory from DC to store, thus creating an increase in store inventory. However, SKUs can be allotted into groups suitable or unsuitable for the scheme depending on the amount of savings generated for a given amount of impact on store inventory. The scheme's benefits and impact on store inventory are thoroughly examined, and their implications on DC inventory are also discussed. This thesis offers a novel perspective into piece-picking optimization, and it finds the proposed scheme viable, simple, and flexible. / by Stephanie Hsuan-Chia Chen and Eunji Han. / 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 55-57). / Manufacturers and retailers are increasingly interested in exploring different ways to optimize their fulfillment of e-Commerce orders. An approach that is often considered is drop-shipping, where the manufacturer takes on the responsibility of shipping directly to the consumer. Retailers are interested in this model as it shifts their inventory responsibility upstream and frees up working capital. Manufacturers are intrigued by drop shipping as a means of capturing lost sales on high-value, seasonal products that retailers might be under-stocking. These manufacturers currently lack the retailer-side inventory availability information to assess the extent of this opportunity. We propose a framework to show manufacturers and retailers how to examine the key issues of drop shipping such as capacity constraints, per unit distribution cost, changes in working capital, cost allocations in the supply chain and delivery time to customers. We also explore how to bridge information gaps to gauge inventory availability and lost sales using Web Extraction System data. We demonstrate our framework by partnering with a CPG manufacturer interested in implementing drop-shipping. Using their data from an existing facility and a selected retailer, we simulate drop shipping orders for a specific set of products during the holiday season that are normally fulfilled by the retailer. Firstly we show that in this scenario, the manufacturer will not exceed their current facility's capacity and will require minimal changes to their existing operations. Using Activity-Based Costing (ABC), we then find that the overall channel costs are only slightly more expensive than those in the traditional model. However, the manufacturer takes on a much larger portion of those costs than they would in the existing model. The transfer of the distribution labor and inventory holding costs from the retailer to the manufacturer drives these cost shifts. As expected, we found significant working capital benefits for the retailer when shifting to drop-shipping. To understand the potential gains that could be achieved from capturing lost sales, we paired data from a Web Extraction System with Point-of-Sale data to obtain previously unavailable retailer inventory information. Contrary to initial expectations for this scenario, the retailer displays very high inventory availability, making lost sales a weak justification for adopting this model. Lastly, using publicly available time-in-transit tables, we model the changes in delivery time that customers experience. The results show that the average delivery time increases by one day for most locations in the US. Our framework and analyses contribute to developing an understanding of the opportunities and implications of drop shipping. In addition, we introduce new techniques manufacturers can use to deal with asymmetric inventory information. / by Christopher Alan Creyts and Nora Weisskopf. / 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 71-72). / The Hershey Company faces a risk of obsolescence across its supply chain as it follows the First In First Out (FIFO) technique at its manufacturing plant distribution center instead of distributing goods based on either the demand at each retailer's end or the useable shelf life of the goods being distributed. The two different stages at which a product can turn obsolete are a) when it reaches expiry and b) during the end of a season or promotion run for a specialty product. The existing picking strategy does not differentiate between orders based on the type of products or the volume served by destination/retailers. This could lead to the risk of obsolescence or return of products in some retailers as the products reach expiry before sales at the retailer's end due to insufficient remaining shelf life. Through this project, we aim at reducing the total obsolescence of a product by proposing a new picking strategy based on the sales volume at each distribution channel and the remaining shelf life of products at the manufacturer's site. The cut-off value or the ratio of volume served by fast moving customer distribution centers to the total volume at which the obsolescence within the supply chain would be minimal was determined for a set of products using an excel simulation model. Hierarchical clustering was performed on all products to form two clusters of distribution centers based on the shipped order quantities and the fractional volume served by both the clusters was determined. The new model was proposed for those product-distribution center combinations with fractional volumes greater than the cut-off as they are most likely to benefit with reduced level of obsoletes. / by Ranjani Rajan and Ying Wang. / 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 88-92). / The oil and gas industry is very volatile; it is characterized by unpredictable cycles of sharp rises and plunges in oil prices. This cyclical nature presents a huge challenge for companies that are operating in the industry. Companies have to be able to ramp up their production quickly so that they have enough capacity to meet increasing demand when oil prices go up and be able to survive when oil prices go down. In this context, companies have to make sure that their suppliers are flexible to changing demand. Assessing supplier flexibility is one of the major challenges facing our thesis sponsor company, which is one of the largest oil field services companies in the world. Our project has two primary goals. First we would like to develop a sound understanding of common factors that characterizes flexibility of suppliers in oil and gas industry. Second, we would like to develop the first version of a self administered audit able instrument to assess the flexibility of suppliers. We developed a comprehensive list of factors influencing flexibility of suppliers through systematic literature review and interview research methodology. We then designed a survey to validate the flexibility factors using statistical measures. Finally, we developed the first version self assessment instrument using Microsoft Excel. The instrument would help our thesis sponsor company to assess the flexibility of their supply base. The findings of our research would be useful to companies operating in seasonal and cyclical industries. The research might help companies develop insights regarding flexibility of their suppliers to adapt to changing market demands, and develop strategies to balance supply and demand at minimum cost. / by Peng Bi and Remya Pushpangatha Kurup. / 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). / What is the appropriate raw material inventory strategy for a make-to-order manufacturing company? As companies grow in size and the business environment changes over time, many companies adapt their operating policies to remain competitive. However, some policies, such as raw material inventory policies, are left untouched as "legacies" of the company's past due to lower priorities or lack of adequate data. These raw material inventory policies are of particular importance to manufacturing firms, especially those that often operate at maximum capacity or have seasonality in demand. This research proposes a raw material inventory policy evaluation tool that allows a company to understand how certain key performance indicators are affected by various changes in its inventory policy and helps the company devise a strategy. This evaluation tool can then guide the company towards a better inventory policy in the absence of cost information and shows the results in terms of number of events. The company can then adjust various replenishment policies depending on the product's demand characteristics. In addition, the research demonstrates that inventory policy changes can be used to partially overcome supplier service level declines and demand variability. / by Vikash Chandra and Michael Tully. / 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 57-58). / What are the key store related parameters that drive sales for large retail chains? This question has become increasingly important to Lojas Americanas, the sponsor company. In the last few years, the company has expanded rapidly to cater to a larger group of consumers in a wide range of locations across Brazil. With this expansion, it wishes to determine the key parameters that drive sales for each department and modify its assortment policy accordingly for each store, so as to optimize total sales. This thesis investigates the sales impact of a wide range of store related parameters such as location, size, and socio-economic profile of the surrounding population. Stepwise regression analysis is used here. For this regression, AIC and the p-value threshold are used as the criteria to identify statistically significant store related parameters that influence sales. Furthermore, cross validation is performed to check the explanatory power of the model. The analysis performed yields useful results. A total of 36 different retail departments are analyzed and an adjusted R-squared value (for the validation set) of over 0.6 is obtained for a vast majority of them, indicating that the model performs well in determining the key parameters that drive sales. Furthermore, for each department, the statistically significant set of parameters is obtained and for the company's overall revenue a set of 11 key parameters is identified as highlighted in the Discussion section of the thesis. LA can use the results of this analysis to guide its product assortment policy. / by Krishna Rajendran. / 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 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