Inventory, investment, and pricing policies for lot-size decision makers

In this dissertation, inventory, investment, and pricing policies for lot-size decision makers are examined based on classical economic order quantity. Specifically, we focus on investment in setup operations, investment in quality improvement, and market dependent products such as substitutes and complements. We examine various impacts of investment and competition on inventory policies and derive managerial insights and economic implications. Throughout this dissertation, deterministic mathematical programming is used as the primary analysis technique and optimal policies are obtained through this technique;For the inventory and investment relationships, we construct and analyze inventory and investment in setup operations policies, inventory and investment in quality improvement policies, and inventory and capital investment allocation policies in setup and quality operations under ROI maximization. The resulting contributions are the establishment of an ROI model with/without the capital budget constraint and characterization of the unique global optimal solution when there exists an option to invest in setup operations. We also show how the inventory level is reduced when it is optimal to invest additional money in setup operations and/or quality improvement;For the inventory and competition relationships, on the other hand, we design and analyze two duopoly models for two profit maximizing sellers when products are substitutes or complements. The resulting contributions are formulation of inventory and pricing policies for substitutes and complements. Furthermore, we obtain the closed-form inventory and pricing policies at equilibrium when symmetric demand and cost are assumed

Market-based allocation mechanisms for lot-size decision makers and electric power utilities

The objective of this dissertation is to investigate how lot-size decision makers and electric power utilities determine critical economic quantities (e.g., the order quantities for the lot-size decision makers and the transmission service charges for the electric power utilities). A primary motivation of this study is to improve the economic efficiency (often measured in terms of cost saving) for lot-size decision makers and electric power utilities;For lot-size decision makers, we extend the traditional economic order quantity (EOQ) model by considering various aspects of model environments such as pricing policies. First, we investigate the inventory and disposal policies for a buyer who is just informed of a temporary sale. It is shown how the buyer determines the optimal inventory and disposal quantities so as to exploit the temporary sale. This inventory model is extended by focusing on the period between the announcement and commencement of a sale. By analyzing the optimal solutions for this extended model, it is shown how the pre-announcement can be utilized to maximize cost saving. Next, we examine an inventory and investment in setup operations model under profit maximization and under return on investment maximization. From the optimality conditions, the optimal order quantity, investment level, and various interesting managerial insights are obtained. Finally, we consider a published multi-product EOQ model with constraints, and examine its optimal inventory and pricing policies. We show that there are two critical errors, and provide correct design and analysis by re-formulating and re-solving the entire model;On the other hand, for electric power utilities, we propose an elementary two-stage trilateral brokerage system for electric power transactions by considering buyers, the sellers, and intermediate transmission utilities. By employing economic analysis and linear programming at each stage, we show that significant gains in economic efficiency can be achieved. Next, we extend this basic model by allowing multiple bids from buyers and sellers and by optimizing over all possible transmission routes to maximize the cost savings. By way of a series of numerical examples, we show how this extended model can improve the economic efficiency of the brokerage system significantly

Market-based pricing strategies for lot-size producers and electric power suppliers

http://www.worldcat.org/oclc/2828194

An inventory system with time-dependent demand and partial backordering under return on inventory investment maximization

Producción CientíficaIn this article, we study an inventory system for items that have a power demand pattern and where shortages are allowed. We suppose that only a fixed proportion of demand during the stock-out period is backordered. The decision variables are the inventory cycle and the ratio between the initial stock and the total quantity demanded throughout the inventory cycle. The objective is to maximize the Return on Inventory Investment (ROII) defined as the ratio of the profit per unit time over the average inventory cost. After analyzing the objective function, the optimal global solutions for all the possible cases of the inventory problem are determined. These optimal policies that maximize the ROII are, in general, different from those that minimize the total inventory cost per unit time. Finally, a numerical sensitivity analysis of the optimal inventory policy with respect to the system input parameters and some useful managerial insights derived from the results are presented.Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (project MTM2017-84150-P

Dynamic process improvement

Bibliography: p. [25].by Charles H. Fine and Evan L. Porteus

Profitability index maximization in an inventory model with a price- and stock-dependent demand rate in a power-form

Producción CientíficaThis paper presents the optimal policy for an inventory model where the demand rate potentially depends on both selling price and stock level. The goal is the maximization of the profitability index, defined as the ratio income/expense. A numerical algorithm is proposed to calculate the optimal selling price. The optimal values for the depletion time, the cycle time, the maximum profitability index, and the lot size are evaluated from the selling price. The solution shows that the inventory must be replenished when the stock is depleted, i.e., the depletion time is always equal to the cycle time. The optimal policy is obtained with a suitable balance between ordering cost and holding cost. A condition that ensures the profitability of the financial investment in the inventory is established from the initial parameters. Profitability thresholds for several parameters, including the scale and the non-centrality parameters, keeping all the others fixed, are evaluated. The model with an isoelastic price-dependent demand is solved as a particular case. In this last model, all the optimal values are given in a closed form, and a sensitivity analysis is performed for several parameters, including the scale parameter. The results are illustrated with numerical examples.Ministerio de Ciencia, Innovación y Universidades y Fondo Europeo de Desarrollo Regional (FEDER) - (project MTM2017-84150-P

Essays on the effective integration of risk management with operations management decisions

textIn today's marketplace, firms' exposure to business uncertainties and risks are continuously increasing as they strive to meet dynamically changing customer needs under intensifying competitive pressures. Consequently, modern supply chains are continuously evolving to effectively manage these uncertainties and the allied risks through both operational and financial hedging strategies. In practice, firms extensively use operational hedging strategies such as operational flexibility, capacity flexibility, postponement, multi-sourcing, supplier diversification, component commonality, substitutability, transshipments and holding excess stocks as operational means for risk management. On the other hand, financial hedging which involves buying and selling financial instruments, carrying large cash reserves or adopting conservative financial policies, changes the cash flow stream of the firms and may help to reduce the firms exposure to business risks and uncertainties. Overall, in this dissertation we explore how risk management can be integrated with operating decisions so as to improve the firm value creating more wealth for the shareholders. In the first essay, we focus on capacity flexibility as a means of operational hedging for risk management in an MTO production environment under demand uncertainty. We demonstrate that capacity flexibility may not only be used to hedge against the demand uncertainty, but may also be employed to effectively protect against possible suboptimal operating decisions in the future. In the second essay, we focus on operational hedging in financially constrained startup firms when making short-term production and long-term investment decisions. We provide an analytical characterization of the optimal investment and operating decisions and analyze the impact of market parameters on the operations of the firm. Our findings highlight an interesting operational hedging behavior between the process investment decisions and the short-term production commitments of the firm when they are faced with financial constraints. Our third essay focuses on the value of integrated financial risk management activities by publicly traded established firms under the risk of incurring financial distress cost. Different from the existing operations management literature, we study the risk management by a public corporation within the value framework of finance; hence our findings do not require any specific assumptions about the investors' utility functions. Moreover, we contribute to the operations management research by examining the impact of the costs of financial distress on hedging and operating plans of the firm. Overall, in this dissertation, we examine the effective integration of operational and financial risk management so as to improve the firm value creating more wealth for the shareholders.Information, Risk, and Operations Management (IROM

A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand

The product-mix planning and the lot size decisions are some of the most fundamental research themes for the operations research community. The fact that markets have become more unpredictable has increaed the importance of these issues, rapidly. Currently, directors need to work with product-mix planning and lot size decision models by introducing stochastic variables related to the demands, lead times, etc. However, some real mathematical models involving stochastic variables are not capable of obtaining good solutions within short commuting times. Several heuristics and metaheuristics have been developed to deal with lot decisions problems, in order to obtain high quality results within short commuting times. Nevertheless, the search for an efficient model by considering product mix and deal size with stochastic demand is a prominent research area. This paper aims to develop a general model for the product-mix, and lot size decision within a stochastic demand environment, by introducing the Economic Value Added (EVA) as the objective function of a product portfolio selection. The proposed stochastic model has been solved by using a Sample Average Approximation (SAA) scheme. The proposed model obtains high quality results within acceptable computing times

Modeling Industrial Lot Sizing Problems: A Review

In this paper we give an overview of recent developments in the field of modeling single-level dynamic lot sizing problems. The focus of this paper is on the modeling various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research