Inventory Variables Research Articles

Estimation of standing wood volume and species composition in managed nemoral multi-layer mixed forests by using nearest neighbour classifier, multispectral satellite images and airborne lidar data / Puistute liigilise koosseisu ja tüvemahu hindamine k-lähima naabri meetodil mitmerindelistes majandatavates segametsades

Abstract. Nearest neighbour techniques are useful for constructing maps of forest inventory variables based on sample plot and auxiliary data from remote sensing. The most problematic issue of the nearest neighbour technique is possible systematic bias in the estimated values. In this study a 15 by 15 km test site in nemoral multilayer mixed forests was established in Laeva, Estonia. A set of 444 circular sample plots was used as reference set. Airborne lidar data and Landsat-8 Operational Land Imager image were used to construct five different feature variable sets consisting of original variables and alternatively principal components. The response variables were wood volume of first tree layer, wood volume of the second tree layer and main species code. A special test was carried out where a substantial amount of Silver birch dominated plots were removed from the reference set. The wood volume prediction validation was carried out on 89 forest growth sample plots and on 2290 forest stands. Species composition prediction was validated on 986 forest stands. As in many previous studies the results confirmed superiority of airborne lidar variables over spectral variables for wood volume estimation. The first three principal components of airborne lidar variables and first five principal components of all possible original feature variables contained over 99% of the information and performed well in imputations. The imputed wood volume at small values was overestimated and underestimated at large values regardless of used reference set. The feature variable sets containing spectral data performed better for species composition imputation. There was a forest age dependent discrepancy in predicted species proportions: birch and spruce proportions were underestimated in young stands and overestimated in older stands while proportion of aspen had exactly the opposite errors. The lack of fit depended slightly on the feature variable sets. The birch dominated plot partial removal from the reference set changed the predicted proportion of species but did not remove the forest age dependent lack of fit. The result can be important for the studies in which bootstrap samples are used to estimate error statistics for nearest neighbour technique based forest inventory variable maps.

Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps

In this paper, we first provide an empirical evidence of the existence of intraday jumps in the crude oil price series. We then show that these jumps, in conjunction with realized volatility measures, are important in modeling the convenience yield over the 2001–2010 period. Our empirical results indicate that lagged jump mean only explains around 16% of the weekly convenience yield. Our best specification, including variation in inventories, 8-week realized variance and the 250-day jump mean is able to explain around 61% of the weekly convenience yield. Importantly, our results are not driven by the simultaneous determination of the various variables at work as we only use lagged variables in all regressions.

Inventory record inaccuracy in supply chains: the role of workers’ behavior

Purpose – The purpose of this paper is to explore the effect of inventory record inaccuracy due to behavioral aspects of workers on the order and inventory variance amplification. Design/methodology/approach – The authors adopt a continuous-time analytical approach to describe the effect of inbound throughput on the inventory and order variance amplification due to the workload pressure and arousal of workers. The model is numerically solved through simulation and results are analyzed with statistical general linear model. Findings – Inventory management policies that usually dampen variance amplification are not effective when inaccuracy is generated due to workers’ behavioral aspects. Specifically, the psychological sensitivity and stability of workers to deal with a given range of operational conditions have a combined and multiplying effect over the amplification of order and inventory variance generated by her/his errors. Research limitations/implications – The main limitation of the research is that the authors model workers’ behavior by inheriting a well-known theory from psychology that assumes a U-shaped relationship between stress and errors. The authors do not validate this relationship in the specific context of inventory operations. Practical implications – The paper gives suggestions for managers who are responsible for designing order and inventory policies on how to take into account workers’ behavioral reaction to work pressure. Originality/value – The logistics management literature does not lack of research works on behavioral decision-making causes of order and inventory variance amplification. Contrarily, this paper investigates a new kind of behavioral issue, namely, the impact of psycho-behavioral aspects of workers on variance amplification.

Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning: the case of moose

Determining the spatial distribution of large herbivores is a key challenge in ecology and management. However, our ability to accurately predict this is often hampered by inadequate data on available forage and structural cover. Airborne laser scanning (ALS) can give direct and detailed measurements of vegetation structure. We assessed the effectiveness of ALS data to predict (1) the distribution of browse forage resources and (2) moose (Alces alces) habitat selection in southern Norway. Using ground reference data from 153 sampled forest stands, we predicted available browse biomass with predictor variables from ALS and/or forest inventory. Browse models based on both ALS and forest inventory variables performed better than either alone. Dominant tree species and development class of the forest stand remained important predictor variables and were not replaced by the ALS variables. The increased explanatory power from including ALS came from detection of canopy cover (negatively correlated with forage biomass) and understory density (positively correlated with forage biomass). Improved forage estimates resulted in improved predictive ability of moose resource selection functions (RSFs) at the landscape scale, but not at the home range scale. However, when also including ALS cover variables (understory cover density and canopy cover density) directly into the RSFs, we obtained the highest predictive ability, at both the landscape and home range scales. Generally, moose selected for high browse biomass, low amount of understory vegetation and for low or intermediate canopy cover depending on the time of day, season and scale of analyses. The auxiliary information on vegetation structure from ALS improved the prediction of browse moderately, but greatly improved the analysis of habitat selection, as it captured important functional gradients in the habitat apart from forage. We conclude that ALS is an effective and valuable tool for wildlife managers and ecologists to estimate the distribution of large herbivores.

A real-time SPC inventory replenishment system to improve supply chain performances

Inventory replenishment rules contribute significantly to the bullwhip effect and inventory instability in supply chains. Smoothing replenishment rules have been suggested as a mitigation solution for the bullwhip effect but dampening the bullwhip effect might increase inventory instability. This paper evaluates a real-time inventory replenishment system denoted as SPC that utilizes a control chart approach to counteract the bullwhip effect whilst achieving competitive inventory stability. The SPC employs two control charts integrated with a set of decision rules to estimate the expected demand and adjust the inventory position, respectively. The first control chart works as a forecasting mechanism and the second control chart is devoted to control the inventory position variation whilst allowing order smoothing. A simulation analysis has been conducted to evaluate and compare SPC with a generalized (R, S) policy in a four-echelon supply chain, under various operational settings in terms of demand process, lead-time and information sharing. The results show that SPC is superior to the traditional (R, S) and comparable to the smoothing one in terms of bullwhip effect, inventory variance, and service level. Further managerial implications have been obtained from the results.

Estimating dissolved organic carbon inventories in the East China Sea using remote‐sensing data

AbstractThe coastal ocean is characterized by a high dissolved organic carbon (DOC) concentration due to large terrestrial inputs and high primary production. Monitoring the dynamic variation of DOC inventories in coastal oceans provide more information on carbon flux, but is very challenging in practice. We propose a method of estimating DOC inventories in the East China Sea (ECS) by integrating the surface DOC distribution with a vertical model of the DOC profile via satellite data. Surface DOC concentration was retrieved from satellite‐derived chromophoric dissolved organic matter and chlorophyll concentration. Two vertical DOC profile models (uniform model and stratified model) were established based on water‐density profiles, and the usage of these two models was corresponded to water mass classification determined by a water mass index. The average bias of the satellite‐derived DOC inventory, in the euphotic layer was 23.8% in spring, 24.8% in autumn and 14.7% in winter, and in the water column was 15.6% in spring, 12.3% in autumn, and 10.2% in winter. The total satellite‐derived DOC inventory integrated in the water column was about 31.84, 31.96, 28.59, and 31.18 Tg in four seasons in the ECS (5.84 × 105 km2). Sensitivity analysis indicated that the remote‐sensing method of estimating DOC inventory is stable and reliable. The available long‐term and large‐scale satellite‐derived DOC inventories in marginal sea would provide us basic information on carbon cycle, and the difference between the seasonal DOC inventories would help to understand the DOC export and relative biogeochemical processes in the ECS.

SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains

Demand signal processing contributes significantly to the bullwhip effect and inventory instability in supply chains. Most previous studies have been attempting to evaluate the impact of available traditional forecasting methods on the bullwhip effect. Recently, some researchers have employed SPC control charts for developing forecasting and inventory control systems that can regulate the reaction to short-run fluctuations in demand. This paper evaluates a SPC forecasting system denoted as SPC-FS that utilizes a control chart approach integrated with a set of simple decision rules to counteract the bullwhip effect whilst keeping a competitive inventory performance. The performance of SPC-FS is evaluated and compared with moving average and exponential smoothing in a four-echelon supply chain employs the order-up-to (OUT) inventory policy, through a simulation study. The results show that SPC-FS is superior to the other traditional forecasting methods in terms of bullwhip effect and inventory variance under different operational settings. The results confirm the previous researches that the moving average achieves a lower bullwhip effect than the exponential smoothing, and we further extend this conclusion to the inventory variance.

The impact of information sharing and inventory control coordination on supply chain performances

The lack of coordination in supply chains can cause various inefficiencies like bullwhip effect and inventory instability. Extensive researches quantified the value of sharing and forecasting of customer demand, considering that all the supply chain partners can have access to the same information. However, only few studies devoted to identify the value of limited collaboration or information visibility, considering their impact on the overall supply chain performances for local and global service level. This paper attempts to fill this gap by investigating the interaction of collaboration and coordination in a four-echelon supply chain under different scenarios of information sharing level. The results of the simulation study show to what extent the bullwhip effect and the inventory variance increase and amplify when a periodic review order-up-to level policy applies, noting that more benefits generate when coordination starts at downstream echelons. A factorial design confirmed the importance of information sharing and quantified its interactions with inventory control parameters, proving that a poor forecasting and definition of safety stock levels have a significant contribution to the instability across the chain. These results provide useful implications for supply chain managers on how to control and drive supply chain performances.

Geographical gradients in boreal forest albedo and structure in Finland

Land surface albedo is an essential climate variable controlling the planetary radiative energy budget, yet it is still among the main uncertainties of the radiation budget in the current climate modeling. To date, albedo satellite products have not been linked to extensive forest inventory data sets due to the lack of ground reference data. Here, we used comprehensive and detailed maps of forest inventory variables to couple forest structure and MODIS albedo products for both winter and summer conditions. We investigated how the relationships between forest variables and albedo change seasonally and along latitudinal gradients in the forest biomes of Finland between 60° and 70° N. We observed an increase in forest albedo with increasing latitude in winter but not in summer. Also, relationships between forest variables and the black-sky albedo or directional–hemispherical reflectance (DHR) at different latitudes were tighter in winter than in summer, especially for forest biomass. Summer albedo was only weakly correlated with the traditional inventory variables. Our findings suggest that the relationships between forest variables and DHR depend on latitude.

Association between component costs, study methodologies, and foodborne illness-related factors with the cost of nontyphoidal Salmonella illness.

Nontyphoidal Salmonella spp. are one of the most common causes of bacterial foodborne illness. Variability in cost inventories and study methodologies limits the possibility of meaningfully interpreting and comparing cost-of-illness (COI) estimates, reducing their usefulness. However, little is known about the relative effect these factors have on a cost-of-illness estimate. This is important for comparing existing estimates and when designing new cost-of-illness studies. Cost-of-illness estimates, identified through a scoping review, were used to investigate the association between descriptive, component cost, methodological, and foodborne illness-related factors such as chronic sequelae and under-reporting with the cost of nontyphoidal Salmonella spp. illness. The standardized cost of nontyphoidal Salmonella spp. illness from 30 estimates reported in 29 studies ranged from $0.01568 to $41.22 United States dollars (USD)/person/year (2012). The mean cost of nontyphoidal Salmonella spp. illness was $10.37 USD/person/year (2012). The following factors were found to be significant in multiple linear regression (p≤0.05): the number of direct component cost categories included in an estimate (0-4, particularly long-term care costs) and chronic sequelae costs (inclusion/exclusion), which had positive associations with the cost of nontyphoidal Salmonella spp. illness. Factors related to study methodology were not significant. Our findings indicated that study methodology may not be as influential as other factors, such as the number of direct component cost categories included in an estimate and costs incurred due to chronic sequelae. Therefore, these may be the most important factors to consider when designing, interpreting, and comparing cost of foodborne illness studies.

Using Short-lived Radionuclides to Estimate Rates of Soil Motion in Frost Boils

Cryoturbation in high-latitude soils is crucial for the long-term cycling of elements, but the rates of soil motion are poorly constrained. Here, we test whether the rate of frost creep, soil erosion and vertical soil mixing in frost boils can be estimated using short-lived radionuclides (Cs-137 and Pb-210). We find a small-scale variation in Cs-137 and Pb-210 inventories in the lower levels of the eroding regions of frost boils in comparison to the expected depositional sites; hence, the distribution of the radionuclides appears to reflect a lateral transport of atmospheric fallout from the centre of the boil (inner domain) towards the surrounding soil (outer domain). C-14 dating of the soil indicates that fallout of Cs-137 was mobile in the soil and that Pb-210 moved with the soil matrix. A soil creep model and a surface soil erosion model are derived and applied to the lateral and vertical distributions of Pb-210 in the frost boil. Both models predict the expected trajectories of soil motion and provide rates of creep, erosion and mixing at a mm yr(-1) to cm yr(-1) scale. The distribution of Pb-210 provides new insights about the processes and rates of soil mass movement in frost boils, if sound mass-balance models are applied. Copyright (C) 2014 John Wiley & Sons, Ltd.

A control-relevant approach to demand modeling for supply chain management

The development of control-oriented decision policies for inventory management in supply chains has drawn considerable interest in recent years. Modeling demand to supply forecasts is an important component of an effective solution to this problem. Drawing from the problem of control-relevant parameter estimation, this paper presents an approach for demand modeling in a production-inventory system that relies on a specialized weight to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecasts to inventory management policies based on internal model control or model predictive control. A systematic approach to generate this weight function (implemented using data prefilters in the time domain) is presented and the benefits demonstrated on a series of representative case studies. The multi-objective formulation developed in this work allows the user to emphasize minimizing inventory variance, minimizing starts variance, or their combination, as dictated by operational and enterprise goals.

Spatial patterns of 137Cs inventories and soil erosion from earth-banked terraces in the Yimeng Mountains, China

The Yimeng Mountains is one of China's most susceptible regions to soil erosion. In this region, slopes are composed of granite- or gneiss-derived soils that are commonly cultivated using earth-banked terraces. Based on the 137Cs measurement for nine reference cores, the present study analysed the spatial patterns of 137Cs inventory and soil erosion using 105 sampling points in a seven-level earth-banked terrace system. The mean 137Cs inventory, standard deviation, coefficient of variation, and allowable error for the nine reference cores were 987 Bq m−2, 71 Bq m−2, 7%, and 6%, respectively, values that may reflect the heterogeneity of the initial 137Cs fallout deposit. Within each terrace, the 137Cs inventory generally increases from the rear edge to the front edge, accompanied by a decrease in the erosion rate. This results from planation by tillage and rainfall runoff during the development of the earth-banked terraces. Across the entire seven-level terrace system, 137Cs inventories decrease from the highest terrace downwards, but increase in the lower terraces, whereas erosion rate displays the opposite trend. These trends are the result of the combined effects of the earth-bank segmented hillslope, the limited protection of the earth banks, and rainfall runoff in combination with tillage. The high coefficients of variation of 137Cs inventories for the 21 sampling rows, with a mean value of 44%, demonstrate the combined effects of variations in original microtopography, anthropogenic disturbance, the incohesive soils weathered from underlying granite, and the warm climate. Although earth-banked terraces can reduce soil erosion to some extent, the estimated erosion rates for the study area are still very high.

Beyond Information Sharing: An Empirical Analysis of Vendor‐Managed Inventory

Using a unique, item‐level data set, we examined benefits to downstream firms (distributors) from the decision‐transfer component of vendor‐managed inventory (VMI), the feature that distinguishes VMI from other information‐sharing, collaborative supply chain programs. Our major findings are that the decision‐transfer component of VMI adds significant benefits to the downstream firm in terms of inventory and stockout reductions above and beyond information sharing, and that these two benefits may be realized at different times following VMI implementation; that is, inventory reduction, initially, may be the major benefit to distributors from VMI, while the benefits of stockout reduction may more likely be realized after the first year of implementation. In addition, VMI provides benefits to the upstream firm (manufacturer) by reducing the downstream firm's inventory variability, a likely contributor to the bullwhip effect. Based on our empirical analysis, the decision‐transfer component of VMI, on average, reduces inventory levels by 7%, stockouts by 31%, and inventory variability by 9%.

The interaction of speculators and index investors in agricultural derivatives markets

AbstractThrough the analysis of the weekly Commodity Futures Trading Commission reports on 12 US traded agricultural commodities, we revisit the heated debate on the impact of index flows on commodities prices. After introducing a novel stock‐to‐use proxy that may be used to represent inventory variations at the intra‐month level, we show that speculators, contrary to index investors, are sensitive to commodity‐specific fundamental information. Their endogeneity to commodities markets hinders the estimation of their market impact. Regarding the market impact of index flows, the endogeneity problem is alleviated in two ways: first, we restrict the scope to agricultural commodities, for which index flows are more exogenous to market prices; second, we introduce two novel instrumental variables that are computed from index flows outside the market under analysis. We find that index investment flows are offset by commercial players, not speculators. The serial correlation of index flows may explain the tendency of speculators to synchronize with index investors. There is strong evidence of an index flows' impact in those commodities markets where speculative and index positions are the most correlated. The market impact of index flows is located in periods of liquidity stress, as is the correlation between speculative and index positions. Overall, our results demonstrate an impact of index investors on some agricultural prices and suggest that the synchronicity between speculative and index positions is an important determinant of this impact.

Exercise addiction in team sport and individual sport: Prevalences and validation of the exercise addiction inventory

Exercise addiction is characterized by increasing exercise amounts which take priority over other areas of life. It is mostly observed in individual sports, such as running and weight-lifting, whereas addiction in team sport has not previously been investigated. The aim of this study was to estimate the prevalence of exercise addiction in a team sport (football) compared to an individual sport (fitness) and to test the psychometric properties of the Exercise Addiction Inventory in these populations. The Exercise Addiction Inventory was completed by 274 men in the age group of 16–39 years (98 football players and 176 fitness exercisers). The prevalence of exercise addiction was statistically equal in the two groups (7.1% in football and 9.7% in fitness). The Cronbach's alpha showed good internal reliability in both groups and factor analysis found one underlying factor interpreted as exercise addiction. Less of the variance was explained in the football sample with low loading on item 2: conflicts (0.2). We compared the structure of the scale between the two groups by using structural equation models and we found that exercise addiction can explain the six Exercise Addiction Inventory (EAI) variables. Thus, the EAI is recommended for identification of addiction in both populations but in football players the item of conflicts could be replaced. Motivation for exercise in the football group seemed to be enjoyment and competition, while the fitness group exercised for health and weight reasons. Different motivation for exercise might explain the psychometric differences of the scale, but further studies are warranted.

Consistency of stand density estimates and their variability in forest inventories in Slovenia

The national estimates of forest resources in Slovenia are based on data aggregation from forest inventories conducted in forest management units of Slovenia Forest Service (SFS) and on the parallel forest inventory established within the Forest and Forest Ecosystem Condition Survey (FECS) as a continuation of international forest monitoring programme. Since the difference in sampling design and temporal dynamics of data collection is of key importance for inventories at the level of forest management units compared to large-area forest inventories, the consistency of stand densities was checked in both concepts. Comparing growing stock estimates in FECS on the systematic 4-km grid of sampling plots from SFS forest management planning, no overall significant differences in temporally comparable periods have been detected. We propose a basis enabling consistent estimates of structural forest characteristics at the national level for the process of forest inventory harmonization in Slovenia.

Modeling and analysis of the causes of bullwhip effect in centralized and decentralized supply chain using response surface method

The “bullwhip” effect is a major cause of supply chain deficiencies. This phenomenon refers to grow the amplification of demand or inventory variability as it moves up the supply chain. Supply chain managers experience this variance amplification in both inventory levels and orders. Other side, dampening variance in orders may have a negative impact on customer service due to the increase in the inventory variance. This paper with simulating a three stage supply chains consisting of a single retailer, single wholesaler and single manufacturer under both centralized and decentralized chains. In this paper, it is intended to analysis the causes of bullwhip effect from two dimensions of order and inventory variance using the response surface methodology. The results show that in both supply chains, rationing factor is considered as the least important cause of bullwhip effect. While the wholesaler’s order batching and the chain’s order batching are considered as the main causes for the bullwhip effect in the decentralized and centralized chains, respectively.

Leaf Area Index (LAI) Estimation in Boreal Mixedwood Forest of Ontario, Canada Using Light Detection and Ranging (LiDAR) and WorldView-2 Imagery

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers for tactical planning. This paper focuses on estimating LAI from: (i) height and density metrics derived from Light Detection and Ranging (LiDAR); (ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and (iii) a combination of these methods. For the Hearst Forest of Northern Ontario, in situ measurements of LAI were derived from digital hemispherical photographs (DHPs) while remote sensing variables were derived from low density LiDAR (i.e., 1 m−2) and high spatial resolution WorldView-2 data (2 m). Multiple Linear Regression (MLR) models were generated using these variables. Results from these analyses demonstrate: (i) moderate explanatory power (i.e., R2 = 0.53) for LiDAR height and density metrics that have proven to be related to canopy structure; (ii) no relationship when using SVIs; and (iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with narrow ranges of LAI for model calibration. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at high spatial resolution for decision makers in the forestry community. This method can be easily incorporated into simultaneous modeling efforts for forest inventory variables using LiDAR.

Relaxations for two-level multi-item lot-sizing problems

We consider several variants of the two-level lot-sizing problem with one item at the upper level facing dependent demand, and multiple items or clients at the lower level, facing independent demands. We first show that under a natural cost assumption, it is sufficient to optimize over a stock-dominant relaxation. We further study the polyhedral structure of a strong relaxation of this problem involving only initial inventory variables and setup variables. We consider several variants: uncapacitated at both levels with or without start-up costs, uncapacitated at the upper level and constant capacity at the lower level, constant capacity at both levels. We finally demonstrate how the strong formulations described improve our ability to solve instances with up to several dozens of periods and a few hundred products.