Mitigating Space Industry Supply Chain Risk Thru Risk-Based Analysis

Using risk-based analysis to consider supply chain disruptions and uncertainty along with potential mitigation strategies in the early stages of space industry projects can be used avoid schedule delays, cost overruns, and lead to successful project outcomes. Space industry projects, especially launch vehicles, are complicated assemblies of high-technology and specialized components. Components are engineered, procured, manufactured, and assembled for specific missions or projects, unlike make-to-stock manufacturing where assemblies are produced at a mass production rate for customers to choose off the shelf or lot, like automobiles. The supply chain for a space industry project is a large, complicated web where one disruption, especially for sole-sourced components, could ripple through the project causing delays at multiple project milestones. This ripple effect can even cause the delay or cancelation of the entire project unless project managers develop and employ risk mitigations strategies against supply chain disruption and uncertainty. The unpredictability of when delays and disruptions may occur makes managing these projects extremely difficult. By using risk-based analysis, project managers can better plan for and mitigate supply chain risk and uncertainty for space industry projects to better manage project success. Space industry project supply chain risk and uncertainty can be evaluated through risk assessments at major project milestones and during the procurement process. Mitigations for identified risks can be evaluated and implemented to better manage project success. One mitigation strategy to supply chain risk and uncertainty is implementing a dual or multi-supplier sourcing procurement strategy. This research explores using a risk-based analysis to identify where this mitigation strategy can be beneficial for space industry projects and how its implementation affects project success. First a supply chain risk assessment and mitigation decision tool will be used at major project milestones to show where a multi-sourcing strategy may be beneficial. Next, updated supplier quote evaluation tools will confirm the usage of multiple suppliers for procurement. Modeling and simulation are then used to show the impact of that strategy on the project success metrics of cost and schedule

From Offshoring to Reshoring: A Conceptual Framework for Manufacturing Location Decisions in a Slow-Steam World

Reshoring, the act of moving manufacturing operations from an offshore location to the nation of the parent company, is rapidly becoming one of the most researched topics in business. Reshoring describes the reversal of a previous offshoring decision, whereby a firm either relocated its own manufacturing operations overseas or outsourced a significant portion of production to offshore suppliers. With looming uncertainty in global consumer demand and diminishing returns in offshore markets, reshoring is gaining exposure as a viable strategy for firms experiencing a diluted competitive advantage as grounded costs approach market equilibrium. With academic literature on reshoring only beginning to emerge, many questions remain unanswered. This study was designed to address some of those gaps by developing a conceptual framework linking the antecedents of reshoring to firm performance. Both the resource-based view of the firm and transaction cost economics were used to provide the theoretical basis for determining the direct and intervening factors contained in the conceptual model. To empirically test the conceptual model, a longitudinal event study was conducted using archival data for 96 firms incorporated in the United States that relocated manufacturing to United States between the years 2007 and 2013. The event study was conducted by gathering financial data for sample firms as well as closely matched firms which served as industry controls, thereby providing a to isolate the financial impact of reshoring for each sample firm. Once these abnormal returns were analyzed using Wilcoxon Signed-Rank tests, the structural model was tested using partial least squares structural equations modeling. This dissertation contributes to the global sourcing literature in several ways. First, the event study results strongly support the theory that American firms can significantly improve performance by relocating manufacturing to the United States. Next, although strategic drivers were not supported, path modeling using PLS-SEM provides statistical support for the proposed economic drivers of reshoring. Finally, significant moderating effects were identified, offering further guidance to firms considering reshoring decisions while expanded the academic literature on reshoring

SUPPLY CHAIN RISK MANAGEMENT IN AUTOMOTIVE INDUSTRY

The automotive industry is one of the world\u27s most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies

Developing lean and responsive supply chains : a robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet

Procurement risk management in a petroleum refinery.

We analyze a petroleum refinery's procurement strategy, explaining how risk management affects optimal sourcing from long-term, spot, and swap contracts. We use time series analysis to model the interaction between petroleum prices, transportation costs, and gross product worth. These models are then used to generate the scenarios incorporated in the stochastic program applied to compute the conditional value-at-risk. We prove the necessary and sufficient conditions for the optimal procurement and risk management strategies, and show that risk aversion can be better represented by the weighted average between expected profit and conditional value-at-risk, deriving the respective ISO curves. We estimate that an increase in the degree of risk aversion decreases the use of swap contracts. Our model is applied to the analysis of a refinery based in Singapore. Using regression analysis, we show we cannot reject the hypothesis of a statistically significant relationship between the way Saudi Arabia prices the long-term contracts and the shape of the forward curve. We then study how risk aversion influences the procurement strategies, profitability, and risk exposure of the refinery. Finally, we analyze the pricing of long-term (forward) contracts by Saudi Arabia, and study how the country could benefit from a different pricing policy

An optimization model for strategic supply chain design under stochastic capacity disruptions

This Record of Study contains the details of an optimization model developed for Shell Oil Co. This model will be used during the strategic design process of a supply chain for a new technology commercialization. Unlike traditional supply chain deterministic optimization, this model incorporates different levels of uncertainty at suppliers’ nominal capacity. Because of the presence of uncertainty at the supply stage, the objective of this model is to define the best diversification and safety stock level allocated to each supplier, which minimize the total expected supply chain cost. We propose a Monte Carlo approach for scenario generation, a two-stage non-linear formulation and the Sample Average Approximation (SAA) procedure to solve the problem near optimality. We also propose a simple heuristic procedure to avoid the nonlinearity issue. The sampling and heuristic optimization procedures were implemented in a spreadsheet with a user’s interface. The main result of this development is the analysis of the impact of diversification in strategic sourcing decisions, in the presence of stochastic supply disruptions

Understanding Managerial Decisions about Global Sourcing: Offshoring and Reshoring of Production

As international commerce continues to emerge due to telecommunication and transportation breakthroughs, the eagerness of companies to send particular business functions offshore increases. Offshoring is the removal of a company function (particularly, manufacturing) from a domestic location to a remote destination. Since many developing economies contain low labor wages, companies in the United States and Europe are able to leverage cost savings by paying low compensation to foreign production employees. The low cost concept, though, does not always offer significant financial reward. For companies with particular product types, business models, or limited experience, offshoring proves to be an expensive mistake that is difficult to reverse. Even so, some U.S. enterprises are reshoring their production function to combat the issues faced in the foreign manufacturing sector. This study aims to investigate the problems of offshoring and proposes a “systems-view” decision framework for global sourcing

Risk and Visibility in Global Supply Chains: An Empirical Study

Working with international suppliers in global supply chains, manufacturing firms now are faced with substantial supplier risks which could be triggered by disruptions in either their suppliers or the supplier’s market. Reactive actions to the risks, however, have usually been shown to be inefficient and sometimes ineffective. In this dissertation, therefore, I develop a theoretical framework linking some key relationship-specific capabilities to supplier risk. My contention is that the capabilities, when developed, can help proactively mitigate the risk. Thus, the model in this study is grounded in the resource-based and the relational views. In this study, the survey method has been employed to collect data from 66 manufacturing firms in the United State who are sourcing from international suppliers. Procedural and statistical methods have been employed to guard against typical empirical issues including non-response bias, common method bias, and problems in validity and reliability of measurement instruments. Structural equation modeling with partial least squares was employed to test the model with bootstrapping to estimate t-values for the paths. The analysis results showed support for the model. A conclusion from the study is that visibility is the critical relationship-specific capability that needs to develop for buying firms to mitigate supplier risk proactively. This is because it may not be substitutable by other mechanisms like goodwill trust, and other capabilities, including absorptive capacity and IT integration, will only operate via visibility to influence risk performance. Moreover, visibility is a significant capability that helps mitigate risk regardless of the relationship duration between the buyer and the supplier and of the market conditions under which the supplier is working. This study thus adds to the risk literature with discussions of supplier risks. Nuances have also been added to the resource-based and relational views by developing the theoretical relationships among the identified capabilities and by examining the contextual conditions under which the relationships are working to mitigate supplier risk. Managers from both sides of a dyadic relationship may benefit from the study by utilizing the tools and the study results to monitor and mitigate supplier risk