Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2006. / Includes bibliographical references. / OEMinc's new business model is a dramatic departure from that used in the past. The company has moved steadily upstream in the supply chain, leaving more and more of the manufacturing effort to suppliers. Literature shows that extraordinary productivity gains in the production network, or value chain, are possible when companies are willing to collaborate in unique ways, often achieving competitive advantage by sharing knowledge, research and assets. For its newest product, Excelsior, OEMinc has moved to an extended enterprise model involving dozens of Partners. Approximately half are Component Partners (CPs), who supply systems and components. The remainder are Assembly Partner's (APs), who integrate these components into sub-assemblies. Many components are purchased by OEMinc and drop-shipped by CPs to APs, then installed in subassemblies. For the purposes of this analysis, Critical Safety Inventory is defined as inventory held at a site that buffers against disruptions in quality or upstream delivery and is not needed for production at that time. / (cont.) More specifically, the need for CSI is driven by the following: *variability in delivery time, resulting in late parts at the AP site or at OEMinc; *part non-conformances, which result in parts being unavailable for installation; and/or *part damage upon installation. The challenge OEMinc faces, which this project attempts to address, is: "How can OEMinc mitigate supply chain risk in the context of reduced information and control?" This project focuses on inventory management as a tool for mitigating risk. Therefore, the project definition has been further defined as follows: To develop an effective safety inventory policy for OEMinc-owned, drop-shipped components within the Excelsior Supply Chain, with the goal of supporting production, reducing inventory cost, and enabling continuous improvement. As outlined above, OEMinc's move to the extended enterprise business model is a significant step towards its vision of being a large-scale systems integrator. The success of this transition is important for OEMinc's long-term future, in addition to being an enabler for the Excelsior. The following approach was used: 1) Case Studies: Components were selected based on characteristics that bracketed the types of issues that might be seen in the supply chain at OEMinc. / (cont.) It was expected that examination of these supply chains would reveal particular issues representative of a wider selection of components. 2) Simulation analysis: A generic simulation model was created for components under the Excelsior Business model. The simulation was used to determine how many shipsets of inventory should be held at the AP site for a varying lead times, expedite lead times and risks of non-conformance. 3) Benchmarking: Representatives of peer companies were interviewed and site visits were conducted to gather information on how they manage their relationships with partner suppliers, with special attention paid to inventory management, partner management, incentives and data exchange. 4) Metrics Analysis: OEMinc's existing metrics system was assessed to determine what changes might be made given the business model shift for the Excelsior program. 5) Implementation: Based on the results of the preceding steps, a set of guidelines was developed for Partners to reach the desired state with respect to CSI management. Using a Systems Dynamics framework, the supply chain was analyzed to determine what incentives should be applied to encourage the desired supplier behavior. / by Ian A. MacDonald. / S.M. / M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2007. / Includes bibliographical references (p. 61). / In any production environment, managing demand variability is a delicate balancing act. Firms must constantly weigh potential obsolescence costs of unused inventory (should sales not materialize) against potential expedite costs or lost sales (should demand outpace available inventory). For build-to-order manufacturers such as Dell, the balancing act is even more challenging. While it offers a wide array of products, Dell does not hold its safety stock in the form of finished goods inventory. Instead, safety stock is held as parts inventory, sitting in supplier-owned supplier logistics centers. As a result, supplier stocking decisions may impact Dell's ability to respond to forecast variability. Other factors, such as globalization, product proliferation, and geo-manufacturing, all magnify the impact variability has on the forecasting process. This thesis discusses two methods of dealing with demand variability. First, it examines the potential application of statistical modeling techniques to the part-level forecasting process. / (cont.) In particular, it looks at the use of time series models to forecast part-level demand. While the results did not merit a recommendation to utilize time series forecasts across the board (in lieu of the current process), certain supplemental applications of such forecasts would benefit Dell. Second, it examines how hedging is currently utilized as a means to account for demand variability. While beneficial to Dell on the surface, a consistent hedge to the forecast is potentially detrimental to its vendor relationships. It has the direct impact of driving excess inventory onto the books of its vendors and it has the indirect impact of higher per part costs to Dell. It also exposes Dell to part shortages due to supplier decommits. To help counter these effects, the thesis identifies potential changes to the hedging process that Dell should consider. / y Marshall Einhorn. / S.M. / M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Manufacturing Program at MIT, 2007. / Includes bibliographical references (p. 59-60). / Intel's Colorado Springs wafer fabrication facility, known internally as F23, has undertaken several initiatives to reduce cycle time including High Precision Maintenance (HPM), content reduction through the application of Manufacturing Excellence (mX), effective utilization of production equipment, and aggressive inventory control. Each of these efforts has contributed to the marked improvement F23 achieved throughout 2006. F23's cycle time efficiency, the ratio of raw process cycle time to actual fab cycle time, improved from 12% (worst amongst Intel facilities) to greater than 35% (best amongst Intel sites), and overall cycle time was reduced by more than 61% in 2006. Inventory control was found to have a major impact on factory cycle time and performance. F23 controls its factory work-in-process, WIP, inventory through the F23 Wafer Starts Protocol. F23 utilizes Little's Law (Cycle Time = Inventory / Output) to identify target WIP inventory levels required to achieve particular cycle time goals. The target inventory is then achieved by modulating wafer starts. To do this, the Wafer Starts Protocol monitors the inventory of the overall fab and the constraint operations and suggests the amount of wafers to start for each shift. / (cont.) Maintaining the target inventory level drives the overall factory cycle time towards the cycle time goal. Using the starts protocol, F23 has reduced its inventory by 44% while ramping factory output. During the implementation of this wafer starts protocol, F23 began tracking a new inventory metric to determine factory performance. Critical WIP ratio was introduced to evaluate the factory's inventory relative to the theoretical minimum inventory based upon a given factory output level and raw process time. F23 also found that this metric provides an effective comparison of inventory level between fabs. The Fab23 Wafer Starts Protocol is one of the ways in which F23 has applied Manufacturing Science tactics and principles to drive cycle time improvements. F23 has found that inventory control can have significant impacts on factory cycle time. This is one of the reasons why F23 was able to achieve dramatic cycle time improvement. / by Matthew J. Ward. / S.M. / M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Manufacturing Program at MIT, 2008. / Includes bibliographical references (p. 122-123). / Total landed cost and supply chain risk analysis are methods that many companies use to assess strategic sourcing decisions. For this project, landed cost is defined as those costs associated with material movement from a supplier to a designated PerkinElmer, Inc. (PKI) manufacturing site. Tools or models that are available in the technology marketplace are often too cumbersome to incorporate with a company's existing technology architecture or are too simplistic to compute an accurate landed cost. For PerkinElmer, as their Analytical Sciences business continues to grow globally, they are continuously reviewing their supplier portfolio and assessing their procurement strategy. The landed cost and risk analysis tool consists of two components, a cost model and a risk analysis model. Both models were developed to allow PKI to better understand the savings opportunities associated with a supplier selection. When performing supply chain modeling and cost optimization, it was necessary to be able to evaluate multiple scenarios that can influence a sourcing decision. Therefore, by changing parameters such as transportation mode, lead time, inventory carrying cost, freight cost, order frequency, and order quantities in the dynamic cost model, PKI is able to understand supply chain cost trade-offs. The model developed for this project is dynamic to allow multi-variable scenarios to be assessed simultaneously, thus increasing the overall analysis efficiency. For the risk analysis model, approximately 20 different factors were considered as a part of a risk portfolio. This concept adapts traditional financial investment portfolio management theory by considering how much operational impact one factor may have on PKI. / (cont.) The concept is to consider a diversified portfolio, so all of the possible risk incurred by a sourcing decision does not reside in any one "category" (logistics, inventory, etc.). The outcome of the model is an index and adjusted cost, providing PKI with an estimate of the potential cost of doing business with a supplier based on their risk profile. / by Brian Feller. / S.M. / M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008. / Includes bibliographical references (p. 57). / One of the biggest opportunities for this consumer goods company today is reducing retail stockouts at its Direct Store Delivery (DSD) customers via pre-selling, which represents approximately 70% of the company's total sales volume. But reducing retail stock-outs is becoming constantly more challenging with an ever-burgeoning number of SKUs due to new product introductions and packaging innovations. The main tool this consumer goods company uses to combat retail stock-outs is the pre-sell handheld, which the company provides to all field sales reps. The handheld runs proprietary software developed by this consumer goods company that creates suggested orders based on a number of factors including: * Baseline forecast (specific to store-item combination) * Seasonality effects (i.e., higher demand for products during particular seasons) * Promotional effects (i.e., lift created from sale prices) * Presence of in-store displays (i.e., more space for product than just shelf space) * Weekday effects (i.e., selling more on weekends when most people shop) * Holiday effects (i.e., higher demand for products at holidays) * Inventory levels on the shelves and in the back room * In-transit orders (i.e., orders that may already be on their way to the customer) The more accurate that the suggested orders are, the fewer retail stock-outs will occur. This project seeks to increase the accuracy of the consumer demand forecast, and ultimately the suggested orders, by improving the baseline forecast and accounting for the effect of cannibalization on demand. / by Susan D. Bankston. / S.M. / M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008. / Includes bibliographical references (p. 61). / The main objective for prototyping is to get the most value out of the opportunity. Value may take the form of information, performance, displaying production readiness or proving capability for the amount of resources consumed and time required. The extents to which the aforementioned variables add customer value differ from project to project. Therefore, it is important to understand what the customer values most in the effort and modify the process to best achieve the prioritized results. Achieving customer value in the prototyping process is critical to Raytheon's Advanced Products Center (APC) business because it is likely that the customer will bring production into the facility. Misalignment with customer expectations will be avoided by tailoring the process around the metrics that the customer prioritizes. Confusion and inconsistency will be limited by having a clear and understood process. The intent of this thesis is to provide a means of tailoring the process to best achieve customer value given the characteristics of the project. / by Brian Lane Jordan. / S.M. / M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2001. / Includes bibliographical references (p. 65-68). / In its attempt to sustain the growth expectations in an increasingly competitive market, Intel Corporation has invested in a number of new products and services, which differ significantly from its core semiconductor business. In 2000, Intel purchased 12 companies, amounting to an investment value of approximately 6 billion dollars. Associated with these various businesses ventures are new customers, new channels, different product attributes, uncertain demand profiles, and various service requirements, all of which have a direct impact on Intel's strategy for physical distribution. The existing logistics infrastructure is not equipped to optimally distribute the greater product mix and respond to the added complexities. The integration of third party logistics providers into its current distribution network may enhance Intel's ability to respond to the growing needs of the diversified product offerings. This thesis explores the decision to outsource logistics activities. It presents both the financial and non-financial, strategic and operational factors, which affect the decision. The decision framework presented is applied to the current business situation at Intel and an outsourcing strategy, which both addresses the current distribution challenges, and compliments the overall strategy of the corporation, is recommended. The description of the recommended distribution strategy is followed by a discussion of the complications associated with executing the strategy and guidelines for effective supplier management in a logistics context. The decision to outsource is just one of the decisions analyzed in the development of distribution strategies. Studying the strategies utilized by other organizations can enhance the competitiveness of a firm's logistics network. This paper also describes the methodology developed for exchanging logistics information with other companies, and based on the evidence from the specific study conducted by Intel's Planning and Logistics Group, discusses the critical success factors for future external studies. / by Andrea Lynn Vlasak. / M.B.A. / S.M.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2009. / Includes bibliographical references (p. 71-72). / Dell's long-term success depends on its customers' future buying patterns. These patterns are largely determined by customers' satisfaction with the after-sales service they receive. Previously, Dell has been able to deliver high customer satisfaction but has done so at a high expense, further reducing the low margins on their consumer product line. Dell's Global Consumer Services and Support organization (GCSS) is constantly innovating to lower its operating costs while maintaining customer satisfaction. Their task is difficult to achieve in part because of the broad scope of problems that Dell's customer service agents (CSAs) tackle and the grey areas of support boundaries. In order to identify and correct the root-causes of these contact-center costs, Dell needs the ability to measure the specific cost of supporting individual customers. Yet, no such customer-centric data framework exists at Dell, or indeed in the contact center industry. However, it is possible to create just such a customer focused data framework by applying an automated value stream mapping (VSM) analysis to a large sample of contact-center activity data from Dell's data warehouse. The resulting data set is a collection of digital value stream maps representing the end-to-end customer service experience of each contact-center customer. After performing the proposed data transformations, these customer-focused metrics (CustFM) are shown to yield significant insights into previously unidentifiable cost reduction opportunities available across Dell's global contact-center network. / by Avijit Sen. / S.M. / M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2009. / Includes bibliographical references (leaves 72-73). / Over the past few years, the Intel Fab-17 facility has aggressively pursued lean methodology to reduce the manufacturing costs associated with its aging 200mm diameter wafer process. One area ripe with improvement opportunities is the processes supplying and managing Test Wafers, which are non-production wafers used to verify production tools and operations. With four test wafer types, hundreds of different sequences of operations (defined as routes), and varying consumption trends, thousands of decisions must be made daily to ensure Test Wafers are available on time and with the proper base characteristics. To further illustrate the magnitude and importance of Test Wafer systems, roughly the same number of Test Wafers are introduced each time period into the fab as production wafers. Through direct observation and process mapping techniques, I identified two system level projects, each containing enormous cost and performance improvements to the entire facility. Project One: Reallocating excess inventory In analyzing the Test Wafer inventory quantity and consumption rates in primary stockroom, I noticed that certain routes had excess inventory while others were deficient, thus leading to significantly more expensive Test Wafers types to be used instead. In order to maximize realized cost savings, I developed a linear optimization program which distributed excess Test Wafer inventory to areas of need. Different re-allocation costs, initial material specifications, and forecasted consumption needs constrained the quantity and location for this redistribution. / (cont.) Per the optimization program's recommendations, I led a team to re-allocate the largest excess Test Wafer inventory area to twelve different locations. The savings for this project correspond to over a year's worth of test wafer inventory now available for these routes and banks. Project Two: Determining supply decisions from a Days of Inventory (DOI) metric The previous process for supplying Test Wafers into the fab was complicated, lacked standardization, required significant human intervention, and led to tool performance impacts despite high operating costs. To address these issues, I designed, developed, and implemented a program which prioritized and calculated thousands of test wafer decisions based upon a Days of Inventory (DOI) metric. By prioritizing actions based on the time until stock out, cost-effective decisions were made while ensuring Test Wafers are available at a tool when needed. The program forecasted short term consumption using an Exponentially Weighted Moving Average (EMWA) and pulled real-time inventory and available Test Wafer material to support the calculations and decision logic. After a successful fab-wide pilot, the "DOI Scheduler" program has now replaced the previous test wafer supply process. As a result, internal fab test wafer inventory will decrease by approximately 35% (as of March 2009, inventory has dropped by 15% and continues downward), Test Wafer availability will improve by approximately 75%, and 4 to 5 hrs a week of labor resources have been saved. / (cont.) Equally important, the prior non-standard process is now standardized, enabling future Test Wafer improvement projects and allowing root cause analysis on previously unsolvable problems. / by David W. Johnson. / M.B.A. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2001. / Includes bibliographical references (leaf 58). / by Michael M. Vander Wel. / S.M.