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The USDA Forest Service Forest Inventory and Analysis (FIA) program collects data on a wealth of variables related to trees in forests. Some of these trees produce nontimber forest products (NTFPs) (e.g., fruit, bark, and sap) that are harvested for culinary, decorative, building, and medicinal purposes. At least 11 tree species inventoried by FIA are valued for their bark. For example, slippery elm (Ulmus rubra Muhl.) is included in FIA forest inventories, and the bark is used for its medicinal value. Despite widespread use of NTFPs, little quantitative information about abundance, distribution, and harvest is available to support sustainable management. Methods for using the FIA database to monitor and explain the situation regarding selected NTFPs are presented. The focus is on using FIA data to assess for (1) geographic distribution, (2) abundance, (3) applicable metrics (e.g., square feet of bark), and (4) change over time. ; USDA Forest Service, Southern Research Station's Forest Inventory and Analysis Unit in Blacksburg, Virginia ; We give special thanks to Caitlin Carey and Laura Wade for their hard work and dedication in completing figures and tables for each of the 19 NTFP tree species reports. We are grateful for the insight and dedication of the reviewers and editors who worked diligently on improving our article. We also thank the USDA Forest Service, Southern Research Station's Forest Inventory and Analysis Unit in Blacksburg, Virginia, for their support of this work. ; Public domain authored by a U.S. government employee

ABSTRACTThis study compares the performance of one constant model with two adaptive models. MAD and bias were used as criteria for evaluating performance. The results of testing the adaptive models with the constant model were contrary to the expected better performance of the adaptive models; no significant differences were found between the models over known horizontal, trend, and seasonal demand patterns. However, some of the practical advantages of adaptive forecasting models in business suggest their continued use.

Inventory management has always been a major component of the field of operations research and numerous models derived from the industry aroused the interest of both the researchers and the practitioners. Within this framework, our work focuses on several classical inventory problems, for which no tractable method is known to compute an optimal solution. Specifically, we study deterministic models, in which the demands of the customers are known in advance, and we propose approximation techniques for each of the corresponding problems that build feasible approximate solutions while remaining computationally tractable. We first consider continuous-time models with a single facility when demand and holding costs are time-dependent. We present a simple technique that balances the different costs incurred by the system and we use this concept to build approximation methods for a large class of such problems. The second part of our work focuses on a discrete time model, in which a central warehouse supplies several retailers facing the final customers demands. This problem is known to be NP-hard, thus finding an optimal solution in polynomial time is unrealistic unless P=NP. We introduce a new decomposition of the system into simple subproblems and a method to recombine the solutions to these subproblems into a feasible solution to the original problem. The resulting algorithm has a constant performance guarantee and can be extended to several generalizations of the system, including more general cost structures and problems with backlogging or lost-sales. ; Nous considérons des problèmes de gestion des stocks multi-échelon à temps périodique avec des demandes non stationnaires. Ces hypothèses sur la demande apparaissent notamment lorsque des prévisions sur la demande sont utilisées dynamiquement (de nouvelles prévisions sont fournies à chaque période). La structure des coûts comprend des coûts fixes et variables d'approvisionnement, des coûts de stockage et des coûts de mise en attente des demandes. Le délai ...

Forest inventory plots are widely used to estimate biomass carbon storage and its change over time. While there has been much debate and exploration of the analytical methods for calculating biomass, the methods used to determine rates of wood production have not been evaluated to the same degree. This affects assessment of ecosystem fluxes and may have wider implications if inventory data are used to parameterise biospheric models, or scaled to large areas in assessments of carbon sequestration. Here we use a dataset of 35 long-term Amazonian forest inventory plots to test different methods of calculating wood production rates. These address potential biases associated with three issues that routinely impact the interpretation of tree measurement data: (1) changes in the point of measurement (POM) of stem diameter as trees grow over time; (2) unequal length of time between censuses; and (3) the treatment of trees that pass the minimum diameter threshold ("recruits"). We derive corrections that control for changing POM height, that account for the unobserved growth of trees that die within census intervals, and that explore different assumptions regarding the growth of recruits during the previous census interval. For our dataset we find that annual aboveground coarse wood production (AGWP; in Mg ha−1 year−1 of dry matter) is underestimated on average by 9.2% if corrections are not made to control for changes in POM height. Failure to control for the length of sampling intervals results in a mean underestimation of 2.7% in annual AGWP in our plots for a mean interval length of 3.6 years. Different methods for treating recruits result in mean differences of up to 8.1% in AGWP. In general, the greater the length of time a plot is sampled for and the greater the time elapsed between censuses, the greater the tendency to underestimate wood production. We recommend that POM changes, census interval length, and the contribution of recruits should all be accounted for when estimating productivity rates, and suggest methods for doing this. ; European Union ; UK Natural Environment Research Council ; Gordon and Betty Moore Foundation ; CASE sponsorship from UNEP-WCMC ; Royal Society University Research Fellowship ; ERC Advanced Grant "Tropical Forests in the Changing Earth System" ; Royal Society Wolfson Research Merit Award

Inventory management has always been a major component of the field of operations research and numerous models derived from the industry aroused the interest of both the researchers and the practitioners. Within this framework, our work focuses on several classical inventory problems, for which no tractable method is known to compute an optimal solution. Specifically, we study deterministic models, in which the demands of the customers are known in advance, and we propose approximation techniques for each of the corresponding problems that build feasible approximate solutions while remaining computationally tractable. We first consider continuous-time models with a single facility when demand and holding costs are time-dependent. We present a simple technique that balances the different costs incurred by the system and we use this concept to build approximation methods for a large class of such problems. The second part of our work focuses on a discrete time model, in which a central warehouse supplies several retailers facing the final customers demands. This problem is known to be NP-hard, thus finding an optimal solution in polynomial time is unrealistic unless P=NP. We introduce a new decomposition of the system into simple subproblems and a method to recombine the solutions to these subproblems into a feasible solution to the original problem. The resulting algorithm has a constant performance guarantee and can be extended to several generalizations of the system, including more general cost structures and problems with backlogging or lost-sales. ; Nous considérons des problèmes de gestion des stocks multi-échelon à temps périodique avec des demandes non stationnaires. Ces hypothèses sur la demande apparaissent notamment lorsque des prévisions sur la demande sont utilisées dynamiquement (de nouvelles prévisions sont fournies à chaque période). La structure des coûts comprend des coûts fixes et variables d'approvisionnement, des coûts de stockage et des coûts de mise en attente des demandes. Le délai d'approvisionnement est supposé constant. Le problème consistant à déterminer la politique optimale qui minimise les coûts sur un horizon fini peut être formulé grâce à un programme dynamique. Dans le cadre déterministe, les problèmes auxquels nous nous intéressons sont le plus souvent NP-difficiles, ce qui fait rapidement exploser l'espace d'état. Il devient alors nécessaire de recourir à des heuristiques. Nous nous orientons vers la recherche d'algorithmes d'approximation combinatoires pour le problème One Warehouse Multi Retailers et plus généralement pour des systèmes de distribution divergents. Nous nous intéresserons dans un premier temps à des systèmes de distribution à deux étages avec un entrepôt central et des entrepôts secondaires qui voient la demande finale. Dans un deuxième temps, des structures logistiques plus complexes pourront être considérées. L'objectif sera de proposer des heuristiques originales, basées sur des techniques de répartition des coûts, de les comparer numériquement à la politique optimale sur de petites instances et, si possible, d'établir des garanties de performance.

AbstractIn this article, we deal with the inventory control of one single product in a distribution network consisting of one warehouse and n retailers where the retailers face continuous demands with constant rates. We study two policies for this problem that are derived from the application of the classical economical ordering quantity formula at each location: the first policy is purely single‐echelon, whereas the second synchronizes the reorder intervals between the different installations and thus constitutes a first step toward a multiechelon perspective. We show that those policies are, respectively,
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‐ and 1.275‐optimal. We then conduct computational experiments and demonstrate that our second policy exhibits excellent average performance and improves substantially over the pure single‐echelon policy: in particular it deviates from Roundy's celebrated algorithm by less than 5%. This result indicates that in this simple setting, a great part of the benefit of shifting to a multiechelon perspective can be obtained by the most natural synchronization scheme between the different installations, and adapting single‐echelon policies can be efficient at managing the inventory in simple distribution networks, confirming observations of practitioners. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

This case recounts an actual sequence of events that unfolded in the early 1990s. The facts of this case were drawn primarily from two enforcement releases issued by the Securities and Exchange Commission (viz., AAER Nos. 1015 and 1037) and from the 1991–1994 annual reports of Perry Drug Stores, Inc. In 1992, Perry Drug Stores ranked as the 14th largest drug store chain in the nation. During that year, Perry's management discovered that the company's inventory was significantly overstated. Physical inventories of Perry's more than 200 retail outlets revealed a $20.3 million inventory shortage, representing 14 percent of the company's book inventory. The company's year-end book inventory figure was an estimate yielded by the gross profit method. Perry's executives decided not to record a book-to-physical inventory adjustment to recognize the shortage during fiscal 1992. Perry's independent auditors were aware of this decision and did not object to it. When the company's 1993 physical inventory confirmed the inventory shortage, Perry wrote off the $20.3 million shortfall, treating it as a change in accounting estimate. Perry's audit firm approved this financial statement treatment of the write-off. This case focuses on inventory accounting and control issues, in particular application of the gross profit method. In addition, the case introduces auditing issues relevant to inventory.

AbstractSeveral papers have studied inventory classification in order to group items with a view to facilitating their management. The generated classes are then coupled with the specific reorder policies composing the overall inventory control system. However, the effectiveness of inventory classification and control system is strictly interrelated. That is to say, different classification approaches could show different performance if applied to a different set of reorder policies, and vice versa. Furthermore, when the cost structure is subjected to uncertainty, a pure cost‐based analysis of the inventory control system could be corrupted.This paper presents a multicriteria framework for the concurrent selection of the item classification approach and the inventory control system through a discrete‐event simulation approach. The key performance indicators provided by the simulator (i.e., average holding value, average number of backorders, and average number of emitted orders) are indicative of the multidimensional effectiveness of the adopted inventory control system when coupled with a specific classification approach. By this way, a multicriteria problem arises, where the alternatives are given by exhaustively coupling the item classes, which are generated by different classification approaches, with the reorder policies composing the inventory system. An analytical hierarchy process is then used for selecting the best alternative, as well as for evaluating the effect of the weights assigned to the key performance indicators through a sensitivity analysis.This approach has been validated in a real case study with a company operating in the field of electrical resistor manufacturing, with a view of facilitating the management of items showing intermittent demand.

Savannakhet Province, Lao People's Democratic Republic (PDR), is a small area that is connected to Thailand, other areas of Lao PDR, and Vietnam via road No. 9. This province has been increasingly affected by carbon dioxide (CO2) emitted from the transport corridors that have been developed across the region. To determine the effect of the CO2 increases caused by deforestation and emissions, the total above-ground biomass (AGB) and carbon stocks for different land-cover types were assessed. This study estimated the AGB and carbon stocks (t/ha) of vegetation and soil using standard sampling techniques and allometric equations. Overall, 81 plots, each measuring 1600 m2, were established to represent samples from dry evergreen forest (DEF), mixed deciduous forest (MDF), dry dipterocarp forest (DDF), disturbed forest (DF), and paddy fields (PFi). In each plot, the diameter at breast height (DBH) and height (H) of the overstory trees were measured. Soil samples (composite n = 2) were collected at depths of 0–30 cm. Soil carbon was assessed using the soil depth, soil bulk density, and carbon content. Remote sensing (RS; Landsat Thematic Mapper (TM) image) was used for land-cover classification and development of the AGB estimation model. The relationships between the AGB and RS data (e.g., single TM band, various vegetation indices (VIs), and elevation) were investigated using a multiple linear regression analysis. The results of the total carbon stock assessments from the ground data showed that the MDF site had the highest value, followed by the DEF, DDF, DF, and PFi sites. The RS data showed that the MDF site had the highest area coverage, followed by the DDF, PFi, DF, and DEF sites. The results indicated significant relationships between the AGB and RS data. The strongest correlation was found for the PFi site, followed by the MDF, DDF, DEF, and DF sites.