13,779 research outputs found

    A decision-making approach for investigating the potential effects of near sourcing on supply chain

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    Purpose - Near sourcing is starting to be regarded as a valid alternative to global sourcing in order to leverage supply chain (SC) responsiveness and economic efficiency. The present work proposes a decision-making approach developed in collaboration with a leading Italian retailer that was willing to turn the global store furniture procurement process into near sourcing. Design/methodology/approach - Action research is employed. The limitations of the traditional SC organisation and purchasing process of the company are first identified. On such basis, an inventory management model is applied to run spreadsheet estimates where different purchasing and SC management strategies are adopted to determine the solution providing the lowest cost performance. Finally, a risk analysis of the selected best SC arrangement is conducted and results are discussed. Findings - Switching from East Asian suppliers to continental vendors enables a SC reengineering that increases flexibility and responsiveness to demand uncertainty which, together with decreased transportation costs, assures economic viability, thus proving the benefits of near sourcing. Research limitations/implications - The decision-making framework provides a methodological roadmap to address the comparison between near and global sourcing policies and to calculate the savings of the former against the latter. The approach could include additional organisational aspects and cost categories impacting on near sourcing and could be adapted to investigate different products, services, and business sectors. Originality/value - The work provides SC researchers and practitioners with a structured approach for understanding what drives companies to adopt near sourcing and for quantitatively assessing its advantage

    Risk-Smoothing Across Time and the Demand for Inventories: A Mean-Variance Approach

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    The standard production smoothing model of inventory demand cannot represent the added incentives for smoothing risks or explain the impact of market shocks that independently affect expectations and uncertainty. Those limitations are overcome by modeling inventory demand as a problem in deterministic optimal control, with the risk-averse firm maximizing utility that is a separable function of the mean and variance of returns and the firm controlling on two decision variables, production and inventory investment. Support for the mean-variance approach comes from regressions using Survey of Professional Forecasters data to show how changes in the mean forecasts of the GDP price deflator and changes in the disagreement among deflator forecasts can explain changes in aggregate inventory investment over time. Further support comes from the ability of the model to explain the excess volatility of industry output over sales—a fact at odds with the production smoothing theory.

    Value at Risk and Inventory Control

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    The purposes of this paper are two-fold. On the one hand, we shall provide a decision analysis justification for the Value at Risk (VaR) approach based on ex-post, disappointment decision making arguments. We shall show that the approach is justified by a disappointment criterion. In other words, the asymmetric valuation between ex-ante expected returns above an appropriate target return and the expected returns below that same target level, provide an explanation for the VaR criterion when it is used as a tool for VaR efficiency design. Second, this paper provides applications to inventory management based on VaR risk exposure. Although the mathematical problems arising from an application of the VaR approach, tuned to current practice in financial risk management, are difficult to solve analytically, solutions can be found by application of standard computational and simulation techniques. A number of cases are solved and formulated to demonstrate the paper’s applicability.Inventory; VaR; Disappointment

    Estimated U.S. Manufacturing Production Capital and Technology Based on an Estimated Dynamic Structural Economic Model

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    Production capital and total factor productivity or technology are fundamental to understanding output and productivity growth, but are unobserved except at disaggregated levels and must be estimated before being used in empirical analysis. In this paper, we develop estimates of production capital and technology for U.S. total manufacturing based on an estimated dynamic structural economic model. First, using annual U.S. total manufacturing data for 1947-1997, we estimate by maximum likelihood a dynamic structural economic model of a representative production firm. In the estimation, capital and technology are completely unobserved or latent variables. Then, we apply the Kalman filter to the estimated model and the data to compute estimates of model-based capital and technology for the sample. Finally, we describe and evaluate similarities and differences between the model-based and standard estimates of capital and technology reported by the Bureau of Labor Statistics.Kalman filter estimation of latent variables

    Beyond the Cost of Price Adjustment: Investments in Pricing Capital

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    The literature on costs of price adjustment has long argued that changing prices is a complex and costly process. In fact, some authors have suggested that we should think of firms’ price-setting activities as “producing” prices, similar to the way firms use production processes to produce goods and services. In this paper we explore one natural extension of this view, that besides observing costs of price adjustment, we should also expect to see firm-level investments in capital expenditures into these “pricing” production processes. We coin the term “pricing capital” for these investments, and suggest that they can improve the efficiency of the “pricing production” activities by both reducing the costs of adjusting prices, and improving the effectiveness of price adjustments in future periods. Using two types of data sources, we find compelling evidence of the existence as well as the importance of pricing capital in firms. The existence of firm-level “pricing capital” has the potential of fundamentally altering the way we think about pricing and price adjustment in many areas of economics. It suggests looking toward the “pricing capital” to decipher the likely degree and causes of price rigidity and its variation across price setters, markets, and industries. Moreover, “pricing capital” introduces a new, higher-level, pricing decision made by individual firms. Decisions to invest in pricing capital compete with traditional capital investment decisions that have long been studied in economics, such as capital investments in plant, equipment, and R&D. Furthermore, since pricing capital is a choice variable, it implies that costs of price adjustment often used in models of price rigidity are endogenous. As such, pricing capital offers new insights into the micro-foundations of the costs of price adjustment. The most provocative implication of the new theory of pricing, however, is that the allocative efficiency of the price system itself may be determined endogenously by individual price setters who choose whether and how much to invest in pricing capital.Cost of Price Adjustment, Menu Cost, Managerial and Customer Costs of Price Adjustment, Pricing Capital, Pricing Production Process (PPP), Price Rigidity, Sticky Prices, Rigid Prices, Microfoundations of the Costs of Price Adjustment, Allocative Efficiency, Price System, Endogenous Price Adjustment Cost

    (S,s) Pricing: Does the Heterogeneity Wipe Out the Asymmetry on Micro Level?

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    In this paper I present a model of asymmetric pricing. Firms here follow the (S,s) pricing rule with different lengths of tails. I use numerical simulations with four-state shocks to detect the link between the present asymmetry in pricing on the micro level and asymmetry in aggregate output movements. This paper investigates whether the asymmetry on firm level can result in asymmetry on the macro level and what is the role of heterogeneity of agents in the process. It looks at two kinds of asymmetries on the aggregate level: (i) asymmetric output responses to positive and negative monetary shocks and (ii) asymmetric responses to shocks during different phases of business cycle. The basic conclusion is that to some extent the first type of asymmetry can be attributed to the asymmetry of adjustment bands and that heterogeneity softens the effect, but the second type of asymmetry is the result of (S,s) pricing behaviour of firms, thus of heterogeneity itself.(S,s) pricing, Asymmetry, Four-state shocks, Heterogeneity

    The Design of a Carbon Tax

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    We consider the design of a tax on greenhouse gas emissions for a developed country such as the United States. We consider three sets of issues: the optimal tax base, issues relating to the rate (including the use of the revenues and rate changes over time) and trade. We show that a well-designed carbon tax can capture about 80% of U.S. emissions by taxing fewer than 3,000 taxpayers and up to almost 90% with a modest additional cost. We recommend full or partial delegation of rate setting authority to an agency to ensure that rates reflect new information about the costs of carbon emissions and of abatement. Adjustments should be made to the income tax to ensure that a carbon tax is revenue neutral and distributionally neutral. Finally, we propose an origin-based system for trade with countries that have an adequate carbon tax and a system of border taxes for imports from countries without a carbon tax. We suggest a system that imposes presumptive border tax adjustments with the ability of an individual firm to prove that a different rate should apply. The presumptive tax could be based either on average emissions for production of the item by the exporting country or by the importing country.

    Price Adjustment at Multiproduct Retailers

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    We empirically study the price adjustment process at multiproduct retail stores. We use a unique store level data set for five large supermarket and one drugstore chains in the U.S., to document the exact process required to change prices. Our data set allows us to study this process in great detail, describing the exact procedure, stages, and steps undertaken during the price change process. We also discuss various aspects of the microeconomic environment in which the price adjustment decisions are made, factors affecting the price adjustment decisions, and firm-level implications of price adjustment decisions. Specifically, we examine the effects of the complexity of the price change process on the stores’ pricing strategy. We also study how the steps involved in the price change process, combined with the laws governing the retail price setting and adjustment, along with the competitive market structure of the retail grocery industry, influence the frequency of price changes. We also examine how the mistakes that occur in the price change process influence the actions taken by these multiproduct retailers. In particular, we study how these mistakes can make the stores vulnerable to civil law suits and penalties, and also damage their reputation. We also show how the mistakes can lead to stock outs or unwanted inventory accumulations. Finally, we discuss how retail stores try to minimize these negative effects of the price change mistakes.Cost of Price Adjustment, Price Adjustment Process, Menu Cost, Posted Prices, Multiproduct Retailer, Price rigidity, Sticky Prices, Frequency of Price Changes, Time Dependent Pricing, Retail Supermarket and Drugstore Chains
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