Combined Pricing and Portfolio Option Procurement

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90569/1/poms1255.pd

Dual Sourcing Using Capacity Reservation and Spot Market: Optimal Procurement Policy and Heuristic Parameter Determination

This contribution focuses on the cost-effective management of the combined use of two procurement options: the short-term option is given by a spot market with random price, whereas the long-term alternative is characterized by a multi period capacity reservation contract with fixed purchase price and reservation level. A reservation cost, proportional with the reservation level, has to be paid for the option of receiving any amount per period up to the reservation level. A long-term decision has to be made regarding the reserved capacity level, and then it has to be decided - period by period - which quantities to procure from the two sources. Considering the multi-period problem with stochastic demand and spot price, the structure of the optimal combined purchasing policy is derived using stochastic dynamic programming. Furthermore, a simple heuristic procedure is developed to determine the respective policy parameters. Finally, we present a comprehensive numerical study showing that our heuristic policy performs very well

The Effect of Material Price and Product Demand Correlations on Combined Sourcing and Inventory Management

Both material sourcing and inventory management are important competitiveness factors, and it is a significant challenge to integrate the two areas. In sourcing, combined strategies using long-term contracts and the spot market received increasing attention recently, typically concentrating on the financial effects. However, there is limited research on the consequence of combined sourcing considering both purchasing and inventory effects from an operations point of view. In this paper, we analyze the effect of uncertainty on the combined sourcing decision under stochastic demand and random spot-market-price fluctuations and exploit the benefits of forward buying in periods with low spot-price realizations, but also of intended backordering in case of a high spot price. Since the decision on capacity reservation has to take into account the short-term utilization of each source which in turn depends on the available long-term contract capacity, decision making faces highly complex interactions between long-term and short-term decisions.From finance research, we find scarce evidence that the spot prices of commodities evolve independently over time. Rather, price correlation across time periods is found, and a popular way to describe these price dynamics is to model it as a mean reverting process. Thus, in this contribution we will respectively extend common i.i.d. price models from operations management studies and will additionally consider the effect of correlation between demand and price. In this paper, we provide a managerial analysis showing the effects of demand and spot market price correlations on the optimal procurement policy and provide managerial insights. We model the combined sourcing problem as a stochastic dynamic optimization problem and analyze the optimal procurement strategy by means of stochastic dynamic programming. The behavior of the optimal policy confirmed several previous assumptions, though some interesting and important managerial consequences arise due to demand and price correlations. Based on the policy analysis, a numerical study will reveal to which extent inobservance or misspecification of an existing level of correlation might result in performance losses in operational decision making. These observations play an important role under the trend of increasing volatility and dynamic changes on the spot market but also in the customer’s behavior

A study on options hedge against purchase cost fluctuation in supply contracts.

He, Huifen.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 44-48).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Literature Review --- p.4Chapter 1.2.1 --- Supply Contracts under Price Uncertainty --- p.5Chapter 1.2.2 --- Dual Sourcing --- p.6Chapter 1.2.3 --- Risk Aversion in Inventory Management --- p.6Chapter 1.2.4 --- Hedging Operational Risk Using Financial Instruments --- p.7Chapter 1.2.5 --- Financial Literature --- p.9Chapter 1.3 --- Organization of the Thesis --- p.10Chapter 2 --- A Risk-Neutral Model --- p.12Chapter 2.1 --- Framework and Assumptions --- p.12Chapter 2.2 --- "Price, Forward and Convenience Yield" --- p.14Chapter 2.2.1 --- Stochastic Model of Price --- p.14Chapter 2.2.2 --- Marginal Convenience Yield --- p.16Chapter 2.3 --- Optimality Equations --- p.17Chapter 2.4 --- The Structure of the Optimal Policy --- p.21Chapter 2.4.1 --- One-period. Optimal Hedge Decision Rule --- p.21Chapter 2.4.2 --- One-period Optimal Orderings Decision Rule --- p.23Chapter 2.4.3 --- Optimal Policy --- p.24Chapter 3 --- A Risk-Averse Model --- p.28Chapter 3.1 --- Risk Aversion Modeling and Utility Function --- p.28Chapter 3.2 --- Multi-Period Inventory Modelling --- p.31Chapter 3.3 --- Exponential Utility Model --- p.33Chapter 3.4 --- Optimal Ordering and Hedging Policy for Multi-Period Problem --- p.37Chapter 4 --- Conclusion and Future Research --- p.40Bibliography --- p.44Chapter A --- Appendix --- p.49Chapter A.l --- Notation --- p.49Chapter A.2 --- K-Concavity --- p.5

Planning of outsourced operations in pharmaceutical supply chains

In this dissertation, we focus on the planning and control of supply chains where part of the supply chain is outsourced to a contract manufacturer(s). Supply Chain Management deals with the integration of business processes from end-customers through original suppliers that provide products, services and information that add value for customers (Cooper et al., 1997). In a narrow sense, a supply chain can be ‘owned’ by one large company with several sites, often located in different countries. Planning and coordinating the materials and information flows within such a worldwide operating company can be a challenging task. However, the decision making is easier than in case more companies are involved in a supply chain, since the sites are part of one organization with one board and it is likely that the decision makers have full access to information needed for the supply chain planning. Outsourcing is an ‘act of moving some of a firm’s internal activities and decision responsibilities to outside providers’ (Chase et al., 2004) and it has been studied extensively in the literature.Outsourcing is developing in many industries, but in this dissertation, we focus on outsourcing in the pharmaceutical industry, where outsourced supply chain structures are rapidly developing. Recent studies show that the global pharmaceutical outsourcing market has doubled from 2001 to 2007 and it is expected to further increase in the upcoming years. In the pharmaceutical industry, the outsourcing relationship is typically long-term and customers often require high service levels. Due to high setup costs, production is conducted in fixed large batch sizes and campaign sizes. The cumulative lead time within the supply chain is more than one year, whereas the customer lead time is about two months. In this industry, production activities are outsourced for three main reasons. First, intellectual property legislation requires outsourcing the production activities to a contract manufacturer that owns the patent for specific technologies that are needed to perform the production activities. Second, expensive technologies or tight (internal) capacity restrictions also result in outsourcing. Third, to limit the supply uncertainty, companies outsource to have an external source producing the same product next to an internal source. This dissertation deals with the planning and control of outsourced supply chains, which are supply chains where part of the supply chain is outsourced to a contract manufacturer. Most supply chain operations planning models from the literature assume that the supply chain is planned at some level of aggregation and that further coordination is conducted at a more detailed level by lower planning levels. These concepts implicitly assume that the lower planning level and the operations are conducted within the same company with full information availability and full control over the operations, which is not case when part of the supply chain is outsourced. Hence, the objective of this dissertation is to obtain insights into the implications of outsourcing on the supply chain planning models. First, we review the literature on outsourcing research and we find that little is known on the operational planning decisions in an outsourced supply chain and on the implications of outsourcing on the operations planning. The literature on outsourcing at the operational level uses outsourcing purely as a secondary source to control performances such as the delivery reliability. Consequently, we discuss two case studies that we conducted into outsourced supply chains to understand the implications of outsourcing on the supply chain operations planning function, where the contract manufacturer is the only source of supply. The main implications of the planning and control of outsourced supply chains can be summarized in three categories: limited information transparency, limited control over the detailed planning and priorities at the contract manufacturer, and contractual obligations. Below, we discuss these in more detail. In order to decide on the release of materials and resources in a supply chain, it is required that the decision maker is able to frequently monitor the status of the supply chain. In an outsourced supply chain, the outsourcer does not have access to all relevant information of the entire supply chain, especially not to the available capacity in each period, also because the contract manufacturer serves a number of different (and sometimes even competing) outsourcers on the same production line. Moreover, the contract manufacturer plans and controls its part of the supply chain based on rules and priorities that are unknown to the outsourcer. This results in facing an uncertain capacity allocation by the outsourcer. Another implication is that the contract manufacturer requires by contract to reserve capacity slots prior to ordering. These reservations are subject to an acceptation decision, which means that part of the reservation quantity can be rejected. The accepted reservation quantity bounds the order quantity that follows later on. Therefore, another main insight from the case study is that in an outsourcing relationship, the order process consists of different (hierarchically connected) decisions in time. In the ordering process, the uncertain capacity allocation of the contract manufacturer should be incorporated. Hence, the order release mechanism requires a richer and more developed communication and ordering pattern than commonly assumed in practice. In a subsequent study, we build on this insight and we design three different order release mechanisms to investigate to what extent a more complicated order release function improves (or deteriorates) the performance of the supply chain operations planning models. The order release mechanisms differ in the number of decision levels and they incorporate the probabilistic behaviour of the contract manufacturer. Based on a simulation study, we show that a more advanced order release strategy that captures the characteristics of outsourcing performs significantly better than a simple order release strategy that is commonly used in practice. We also discuss the conditions for a successful implementation of the more advanced order release strategy. In another study, we study the case where the contract manufacturer is a second source next to an internal manufacturing source for the same product and where the outsourcer faces inaccurate demand forecasts. The two sources are constraining the supply quantities in different ways. Its own manufacturing source is more rigid, cheaper and tightly capacitated, whereas the contract manufacturer is more flexible but more expensive. In that study, we compare the performance of two different allocation strategies by a simulation study in which we solve the model in a rolling horizon setting. The results show that the rigid allocation strategy (the cheaper source supplies each period a constant quantity) performs substantially better than the dynamic allocation strategy (each period the allocation quantities are dynamic) if the parameters are chosen properly. In another study, we study the outsourcer’s problem of deciding on the optimal reservation quantity under capacity uncertainty, i.e., without knowing what part of the reservation will be accepted. In that study, we develop a stochastic dynamic programming model for the problem and we characterize the optimal reservation and order policies. We conduct a numerical study where we also consider the case where the capacity allocation is dependent on the demand distribution. For that case, we show the structure of the optimal policies based on the numerical study. Further, the numerical results reveal several interesting managerial insights, such as that the optimal reservation policy is little sensitive to the uncertainty of the capacity allocation from the contract manufacturer. In that case, the optimal reservation quantities hardly increase, but the optimal policy suggests increasing the utilization of the allocated capacity. We also study the outsourced supply chain from the contract manufacturer’s perspective. In that study, we consider the case where the contract manufacturer serves a number of outsourcers with different levels of uncertainty. The contract manufacturer faces the question of how to allocate the contractual capacity flexibility in an optimal way. More precisely, we focus on the contract manufacturer’s decision to make the acceptation decision under uncertainty. The more the contract manufacturer accepts from an outsourcer, the more risk is taken by the contract manufacturer, as the outsourcer might not fully utilize the accepted reservation quantity. However, we assume that the outsourcer is willing to pay an additional amount to compensate the contract manufacturer for that risk. We develop a mixed-integer programming model, which optimizes the allocation of capacity flexibility by maximizing the expected profit. Offering more flexibility to the more risky outsourcer generates higher revenue, but also increases the penalty costs. The allocated capacity flexibilities are input (parameters) to the lower decision level, where the operational planning decisions are made and demands are observed. The simulation results reveal interesting managerial insights, such that the more uncertain outsourcer gets at least the same capacity flexibility allocated as the less uncertain outsourcer. Moreover, we have seen that when the acceptation decision is made, priority is given to the less uncertain outsourcer, because that information is the most valuable. However, we see the opposite effect when orders are placed, namely that priority is given to the more uncertain outsourcer, i.e., the most paying outsourcer, as no uncertainty is involved anymore. These insights are helpful for managers of contract manufacturers when having contract negotiations with the outsourcers. We believe that the results and insights that we obtained in the various research studies of this dissertation can contribute to solving the broader real-life problems related to the planning and control of outsourced supply chains. We also discuss potential managerial implications of our findings explicitly addressing the management decisions that may be affected by using the insights from our studies. Considering the operational implications of outsourcing when taking the strategic outsourcing decision will lead to a different and a better estimate of the transaction costs and probably to a different strategic outsourcing decision. Based on our research, we think that the transaction cost estimate will be higher if the outsourcer and the contract manufacturer do not agree on operational issues, such as the multi-level order release mechanism. From a tactical point of view, the outsourcer may include the options of postponement and cancellation in the contract, even if the contract manufacturer would charge little extra for these options. The results show that the benefits of including these options are substantial. Moreover, we showed that controlling a contract manufacturer operationally in the same way as an internal manufacturing source leads to a nervous ordering behaviour with a lot of changes and a lot of panicky communication between the outsourcer and the contract manufacturer. Combining the insights from different studies, one can also conclude that including little reservation cost is beneficial to both parties; it leads to a win-win situation. The outsourcer with a high level of demand uncertainty secures sufficient capacity allocation from the contract manufacturer and avoids more expensive penalty costs. For the outsourcer with less demand uncertainty, it is wise to set the contract such that the reservation costs are subtracted from the total paid amount. Moreover, this outsourcer may gain competitive advantage if his competitors operate in the same market by securing sufficient capacity allocation (by paying little reservation costs). For the contract manufacturer, including reservation cost is also beneficial, as it leads to a better match between the outsourcer’s reservation and ordering behaviour

Inventory Management and Financial Hedging of Storable Commodities

Under revision for resubmitting to Management Science</p

Leveraging risk management in the sales and operations planning process

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 71-72).(cont.) Lastly, we visited SemiCo, a leading global supplier of high performance semiconductor products, to gain first-hand insight into the S&OP process of a large multinational company and complete a brief case study about how risk management is currently being utilized within this company's S&OP process. Finally, we synthesized these four sources of information in order to develop a common framework and recommendations that companies can use for understanding the best practices for incorporating risk management into the S&OP process.The objective of this thesis project is to analyze how companies can utilize risk management techniques in their sales and operations planning process (S&OP). S&OP is a strategy used to integrate planning and processes across functional groups within a company, such as sales, operations, and finance. A large body of academic and industry literature already exits, proving that S&OP can integrate people, processes, and technology leading to improved operational performance for a business. However, little research has been done in the area of applying risk management techniques to the S&OP process. When companies use S&OP in order to align their demand, supply, capacity, and production, based on various factors such as history, pricing, promotions, competition, and technology, they rarely factor in uncertainty and risk into the S&OP process. Furthermore, for those companies that do implement risk management in the S&OP process, there is no consensus in the business community about how to do this accurately and effectively. Our basic approach to understanding risk management and its place in the S&OP process will be four-fold. First, we conducted a literature review in order to gain basic S&OP process understanding and current risk management strategies. Next, we conducted thirteen hour-long phone interviews with practitioners and thought leaders in the field of sales and operations planning in order to gain insight into how companies currently discuss, assess, and act upon uncertainty within the S&OP process. Third, we conducted an online survey of various companies and consultants working in the field of S&OP to see how they currently discuss and incorporate uncertainty into their S&OP work.by Yanika Daniels and Timothy Kenny.M.Eng.in Logistic

Selection of Wood Supply Contracts to Reduce Cost in the Presence of Risks in Procurement Planning

Les activités d'achat dans l'industrie des pâtes et papiers représentent une part importante du coût global de la chaîne d'approvisionnement. Les décideurs prévoient l'approvisionnement en bois requis jusqu'à un an à l'avance afin de garantir le volume d'approvisionnement pour le processus de production en continu dans leur usine. Des contrats réguliers, flexibles et d'options avec des fournisseurs de différents groupes sont disponibles. Les fournisseurs sont regroupés en fonction de caractéristiques communes, telles que la propriété des terres forestières. Cependant, lors de l'exécution du plan, des risques affectent les opérations d'approvisionnement. Si les risques ne sont pas intégrés dans le processus de planification des achats, l'atténuation de leur impact sera generalement coûteuse et compliquée. Des contrats ad hoc coûteux supplémentaires pourraient être nécessaires pour compenser le manque de livraisons. Pour aborder ce problème dans cette thèse, dans un premier projet, un modèle mathématique déterministe des opérations d'approvisionnement est développé. L'objectif du modèle est de proposer un plan d'approvisionnement annuel pour minimiser le coût total des opérations relatives. Les opérations sont soumises à des contraintes telles qu’une proportion minimale de l'offre par chaque groupe de fournisseurs, des niveaux cibles des stocks, de la satisfaction de la demande, la capacité par la cour à bois et la capacité du procédé de mise en copeaux. Les décisions sont liées à la sélection des contrats d'approvisionnement, à l'ouverture de cour à bois et aux flux du bois. Dans un deuxième projet, une évaluation du plan d'approvisionnement à partir du modèle déterministe du premier projet est effectuée en utilisant une approche de simulation Monte Carlo. Trois stratégies contractuelles différentes sont comparées : fixes, flexibles et une combinaison des deux types des contrats. L'approche de simulation de ce projet évalue la performance du plan par la valeur attendue et la variabilité du coût total, lorsque le plan est exécuté pendant l'horizon de planification. Dans un troisième projet, une approche de programmation stochastique en deux étapes est utilisée pour fournir un plan d'approvisionnement fiable. L'objectif du modèle est de minimiser le coût prévu du plan d'approvisionnement en présence de différents scénarios générés en fonction des risques. Les décisions lors de la première étape sont la sélection des contrats dans la première période et l'ouverture des cours à bois. Les décisions de la deuxième étape concernent la sélection des contrats commençant après la première période, les flux, l'inventaire et la production du procédé de la mise en copeaux. iii L'étude de cas utilisée dans cette thèse est inspirée par Domtar, une entreprise des pâtes et papiers située au Québec, Canada. Les résultats des trois projets de cette thèse aident les décideurs à réduire les contraintes humaines liées à la planification complexe des achats. Les modèles mathématiques développés fournissent une base pour l'évaluation de la stratégie d'approvisionnement sélectionnée. Cette tâche est presque impossible avec les approches actuelles de l'entreprise, car les évaluations nécessitent la formulation de risques d'approvisionnement. L'approche de programmation stochastique montre de meilleurs résultats financiers par rapport à la planification déterministe, avec une faible variabilité dans l'atténuation de l'impact des risques.Procurement activities in the pulp and paper industry account for an important part of the overall supply chain cost. Procurement decision-makers plan for the required wood supply up to one year in advance to guarantee the supply volume for the continuous production process at their mill. Regular, flexible and option contracts with suppliers in different groups are available. Suppliers are grouped based on common characteristics such as forestland ownership. However, during the execution of the plan, sourcing risks affect procurement operations. If risks are not integrated into the procurement planning process, mitigating their impact is likely to be expensive and complicated. Additional expensive ad hoc contracts might be required to compensate for the lack of deliveries. To tackle this problem, the first project of this thesis demonstrates the development of a deterministic mathematical model of procurement operations. The objective of the model is to propose an annual procurement plan to minimize the total cost of procurement operations. The operations are subject to constraints such as the minimum share of supply for each group of suppliers, inventory target levels, demand, woodyard capacity, and chipping process capacity. The decisions are related to the selection of sourcing contracts, woodyards opening, and wood supply flow. In the second project, an evaluation of the procurement plan from the deterministic model from project one is performed by using a Monte Carlo simulation approach. Three different strategies are compared as fixed, flexible, and a mix of both contracts. The simulation approach in this project evaluates the performance of the plan by the expected value and variability of the total cost when the plan is executed during the planning horizon. In the third project, a two-stage stochastic programming approach is used to provide a reliable procurement plan. The objective of the model is to minimize the expected cost of the procurement plan in the presence of different scenarios generated based on sourcing risks. First-stage decisions are the selection of contracts in the first period and the opening of woodyards. Second-stage decisions concern the selection of contracts starting after the first period, flow, inventory, and chipping process production. The case study used in this thesis was inspired by Domtar, which is a pulp and paper company located in Quebec, Canada. The results of three projects in this doctoral dissertation support decision-makers to reduce the human limitation in performing complicated procurement planning. The developed mathematical models provide a basis to evaluate the selected procurement strategy. This task is nearly impossible with current approaches in the company, as the evaluations require the formulation of v sourcing risks. The stochastic programming approach shows better financial results comparing to deterministic planning, with low variability in mitigating the impact of risks