Model To Monetarily Aggregate Risks Of Procurement To Support Decision Makers

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Model To Monetarily Aggregate Risks Of Procurement To Support Decision Makers

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Author : Philipp von Cube
language : en
Publisher: Apprimus Wissenschaftsverlag
Release Date : 2019-11-05

Model To Monetarily Aggregate Risks Of Procurement To Support Decision Makers written by Philipp von Cube and has been published by Apprimus Wissenschaftsverlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-05 with Technology & Engineering categories.

The present thesis provides a model to monetarily aggregate procurement risks to support decision makers. A material flow oriented view forms the fundament of the model. The model is designed to aggregate delay, quality and cost related procurement risks considering their uncertainty. Procurement risks are aggregated to form a monetary risk distribution. Decision-makers can select procurement strategies that are adequate for their risk situation, depending on their affinity for risk to mitigate procurement risks.

Advances In Design

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Author : Hoda A. ElMaraghy
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-07-02

Advances In Design written by Hoda A. ElMaraghy and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-07-02 with Technology & Engineering categories.

Advances in Design examines recent advances and innovations in product design paradigms, methods, tools and applications. It presents fifty-two selected papers which were presented at the 14th CIRP International Design Seminar held in May 2004. This book will be bought by postgraduate and senior undergraduate students studying product design. It will also be of interest to researchers and practitioners working in the field of product design.

Procurement Risk Management Using Commodity Futures

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Author : Yihua Xu
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-27

Procurement Risk Management Using Commodity Futures written by Yihua Xu and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with categories.

This dissertation, "Procurement Risk Management Using Commodity Futures: a Multistage Stochastic Programming Approach" by Yihua, Xu, 許意華, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: ABSTRACT This study addresses the procurement risks that arise from variations in customer demand and fluctuations in the prices of material to be purchased, and seeks ways to effectively manage these risks. Procurement is prone to risks due to the uncertainties in, for example, demand, price and delivery. The effective management of these risks is hence a critical provision within the framework of procurement planning. However, what generally interests a procurement manager, when attempting to match closely product supply with customer demand, is the lowest cost that could possibly be attained. This mindset is found to concur with traditional models for procurement planning, which tend also to focus on cost minimization or the maximization of profit. With the potential risks largely ignored, such traditional models are clearly inadequate in the dynamic and precarious environment in which procurement is to be performed. This study describes a procurement planning approach that takes into account the risks arising from the fluctuations in procurement prices and customer demand volatility during a procurement undertaking. From the perspective of risk management, procurement is concerned with minimizing the downside risk exposure by means of hedging the associated risks so as to avoid possible losses. The specific risk hedging method developed in this study is based on the commodities and derivatives markets, which have grown rapidly and flourished in the age of e-commerce. This method is based on the static financial risk-hedging models that deal with a fixed hedged quantity. However, in making operational decisions in which the purchased quantity fluctuates due to customer demand, hedging has to be performed dynamically and this forms a significant extension to the available models. To allow and support operational procurement decision making as well as financial risk hedging in the presence of commodity markets, an integrated procurement risk management framework is developed. The development of this framework involves three major research issues (i) the establishment of a quantitative procurement risk management framework; (ii) the modelling of the stochastic behaviour of commodity prices and customer demand; and (iii) in II matching the two stochastic quantities mentioned above, the modelling of the procurement planning and financial risk hedging problem, jointly represented as a multistage stochastic program. The solutions obtained from this stochastic programming model can be evaluated according to the specified profit/risk profiles of a decision maker. To model the stochastic behaviour of commodity prices, the Gibson-Schwartz two-factor model and the Schwartz-Smith two-factor model are employed for storable commodities and non-storable commodities respectively. State-space form models and Kalman filtering are used to estimate the parameters of the empirical price models based on historical commodity price data. Two commodities are studied in this research. One is copper which is storable, and the other is electricity which is non-storable. Using the empirical price models, scenarios can be generated for stochastic program optimization. Numerical experiments are carried out to demonstrate the benefit that could be gained from the use of the integrated procurement risk management approach developed in this study. It is found that, when compared with pure operational pla

Managing Risks In The Public Procurement Of Goods Services And Infrastructure

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Author :
language : en
Publisher:
Release Date : 2023

Managing Risks In The Public Procurement Of Goods Services And Infrastructure written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

Representing approximately 12% of GDP across OECD countries, public procurement is an important pillar of public service delivery. However, successful public procurement is threatened by risks in areas as diverse as compliance, sustainability, and operations. Governments can address these challenges by identifying, assessing, treating, and monitoring risks throughout the procurement process. To do so, they use general tools such as risk registers and risk matrices, as well as more targeted measures aimed at specific challenges, such as supply chain risks. The procurement of complex goods, services and infrastructure involves different and often more consequential risks linked to market structures, the size and length of contracts, and the interconnected nature of decision making. In addressing this broad array of risks, the development of a national risk management strategy is a crucial step to ensure a co-ordinated and consistent approach.

Risk Analysis For Supplier Selection Problem Using Failure Mode And Effects Analysis Fmea

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Author : Wei Zeng
language : en
Publisher:
Release Date : 2013

Risk Analysis For Supplier Selection Problem Using Failure Mode And Effects Analysis Fmea written by Wei Zeng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

With the onset of globalization, the proper selection of suppliers is more critical and important than before. An enterprise can generally select the suppliers by setting up the criteria based on pricing, quality, reliability, and knowledge of industry trends. At the same time, the impact of unexpected and unpredicted risks involved in the suppliers’ performance such as cost increase, delivery delay and poor quality cannot be ignored. The purpose of this thesis is to develop a quantitative approach to support decision makers to evaluate and select the best supplier under the risky environment.To gain the knowledge of risk analysis, supplier selection, Multi Criteria Decision Making (MCDM) and Failure Mode and Effects Analysis (FMEA), a systematical and comprehensive literature research was conducted. Moreover, interviews with purchasing managers were performed at a major chemical product manufacture.In this work, an FMEA risk analysis methodology was proposed, and it was used to cope with a real case of supplier selection. FMEA evaluation schemes were created for calculating “discounts” for the criteria scores according to the seriousness of the risk situations. The result not only ranking suppliers by risk-discounted scores, but also can inform decision makers that where risks come from, how serious they are and the possibility of detecting and controlling the risks.This practice indicates that it can be applied to support decision makers consider more issues than just choose the “best” supplier by using numerical tool, which opens important future research directions.

Disruption And Operational Risk Quantification And Mitigation Models For Outsourcing Operations

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Author : Ragip Ufuk Bilsel
language : en
Publisher:
Release Date : 2009

Disruption And Operational Risk Quantification And Mitigation Models For Outsourcing Operations written by Ragip Ufuk Bilsel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.

More companies choose outsourcing to gain cost advantages, focus on their core competencies and maintain competitive edge. Although outsourcing provides many benefits, it also makes the buyer more dependent to the outside firms and increases his exposure to supply chain risks. This dissertation provides quantitative techniques to measure those risks and mathematical models to incorporate risks in supply chain decision making. Outsourcing risks are grouped under two main categories: operational risks which represent risks due to day to day global supply chain operations and disruption risks which are related to rare, but catastrophic events that may disrupt supply chains and cause heavier damage than the operational risks. In this dissertation, we first present a general risk quantification scheme and a classification based on four major risk components: severity of impact, frequency of occurrence, detection time and recovery time, and implement this scheme to quantify disruption risks. Severity of impact is modeled using the Generalized Extreme Value Distribution which is appropriate for modeling minima and maxima of rare events. Frequency of occurrence is modeled as a Poisson process. A Markov chain is used to model information propagation in supply chains and detection time is modeled using the mean first passage time concept. Risk recovery time is assumed to be exponentially distributed and a conceptual model to compute the parameter of the exponential distribution is also developed. Another important issue in risk management is mitigation plans. Once a supplier faces a disruption, the buyer should have an alternative strategy to follow. In this dissertation, we propose two multiobjective mathematical models to optimally generate supplier assignment and mitigation plans under two different purchasing strategies. The first strategy, called single sourcing, assumes that the buyer assigns an order for a product to one and only one supplier; that is, order splitting among suppliers is not allowed. The second strategy, called multiple sourcing, is a generalization of the single sourcing model where the buyer can split an order among a predetermined number of suppliers. Both models consider four objectives: minimizing total cost, lead time and risk value, and maximizing quality of purchased items. The multiobjective models are solved using four variants of goal programming and their solutions are discussed. Operational risks are more common in supply chains and can be modeled using traditional probability distributions. In this dissertation, we extend the multiobjective mathematical models developed earlier to stochastic programming models. Uncertainty in customer demand and production capacity are assumed to cause operational risk. Initially, demand and capacity parameters are modeled as normal random variables and chance constraints are formed to include those stochastic data in the mathematical models. Deterministic equivalents of those chance constraints are derived to numerically solve the models. When no correlation among random variables is assumed, the deterministic equivalent models are linear mixed integer programs which can be solved efficiently using commercial optimization software. If correlation is included, deterministic equivalent models become nonlinear mixed integer programs which are computationally more challenging. Alternative linearization procedures are proposed to transform the deterministic equivalents of the nonlinear models to linear mixed integer programs. This results in an increase in the problem size. Deterministic equivalent models are also solved using goal programming techniques. It is observed that the optimal solutions to the deterministic models are infeasible to the stochastic models. This indicates that previous supply chain decisions are no longer valid when uncertainty is considered in decision making. We also present a robust model where the normality assumption on demand and capacity random variables is removed. This robust model is valuable when the decision makers have information only on the mean and the variance of demand and capacity. The robust model is more conservative and provides poorer results compared to the stochastic models under normality.