Inventory Variables Research Articles

Significant temporal variability leads to estimation bias in greenhouse gas emissions from aquaculture pond systems

Rising demand for aquatic products has expanded aquaculture, significantly elevating greenhouse gas (GHG) emissions from the aquaculture ponds. However, the emission estimation shows huge uncertainty. This study employed a meta-analysis of 1060 datasets of CO2, CH4 and N2O fluxes to examine how temporal variability affects GHG emissions from China's aquaculture ponds and to identify key environmental drivers. The results reveal that China’s aquaculture ponds are significant sources of GHGs to the atmosphere, with fluxes of CO2, CH4, and N2O from coastal pond systems at 5.50, 7.41 mg m² h⁻¹, and 16.72 μg m² h⁻¹ during the farming period, and −8.96, 4.33 mg m² h⁻¹, and 44.98 μg m² h⁻¹ during the non-farming period, respectively. Regarding inland pond systems, the fluxes of CO2, CH4, and N2O were 50.48, 5.19 mg m² h⁻¹, and 36.35 μg m² h⁻¹ during farming period, and 0.90, 1.03 mg m² h⁻¹, and 51.46 μg m² h⁻¹ during non-farming period, respectively. Total GHG annual emissions were 42.17 Tg CO2-eq over a 100-year time scale, predominantly from CH4 at 74.11 %, with CO2 contributing to 9.63 %, and N2O to 6.63 %. Post-cultivation drainage significantly shifts biogeochemical conditions and emission patterns, reducing total GHG emissions. Ignoring the non-farming period leads to overestimated CO2 and CH4 emissions, and underestimated N2O emissions. Our study provides new insights into GHG estimation from aquaculture ponds, highlighting the importance of considering temporal variability in GHG inventories, and supporting the development of management-based mitigation strategies.

Inventory Accounting Method Selection: The Relationship Between Cost of Goods Sold Variability and Inventory Variability

Inventory Accounting Method Selection: The Relationship Between Cost of Goods Sold Variability and Inventory Variability

A STUDY ON OUTBOUND CUSTOMER COMPLAINT DUE TO INVENTORY VARIANCES AT APOLLO TIRES NDC1

This study defines into outbound customer complaints arising from inventory variances at Apollo Tyres NDC1, with the primary objective of understanding and addressing the underlying issues. Through a secondary objective, it aims to identify root causes, assess customer impact, apply the DMAIC methodology, and evaluate improvement measures. Using a structured approach, the study examines factors contributing to inventory variances and their implications on customer satisfaction and loyalty metrics. By implementing the DMAIC framework, inventory management processes are systematically analysed to identify areas for improvement. Improvement measures, including macro implementation, wall-to-wall audits, and the proposed Outbound Integrated Optimization and Navigation System (OIONS), show promising results in reducing inventory variances and mitigating customer complaint.

Understanding the impacts of coastal deoxygenation in nitrogen dynamics: an observational analysis

Biological production and outgassing of greenhouse gasses (GHG) in Eastern Boundary Upwelling Systems (EBUS) are vital for fishing productivity and climate regulation. This study examines temporal variability of biogeochemical and oceanographic variables, focusing on dissolved oxygen (DO), nitrate, nitrogen deficit (N deficit), nitrous oxide (N2O) and air-sea N2O flux. This analysis is based on monthly observations from 2000 to 2023 in a region of intense seasonal coastal upwelling off central Chile (36°S). Strong correlations are estimated among N2O concentrations and N deficit in the 30–80 m layer, and N2O air-sea fluxes with the proportion of hypoxic water (4 < DO < 89 µmol L−1) in the water column, suggesting that N2O accumulation and its exchange are mainly associated with partial denitrification. Furthermore, we observe interannual variability in concentrations and inventories in the water column of DO, nitrate, N deficit, as well as air-sea N2O fluxes in both downwelling and upwelling seasons. These variabilities are not associated with El Niño-Southern Oscillation (ENSO) indices but are related to interannual differences in upwelling intensity. The time series reveals significant nitrate removal and N2O accumulation in both mid and bottom layers, occurring at rates of 1.5 µmol L−1 and 2.9 nmol L−1 per decade, respectively. Particularly significant is the increase over the past two decades of air-sea N2O fluxes at a rate of 2.9 µmol m−2 d−1 per decade. These observations suggest that changes in the EBUS, such as intensification of upwelling and the prevalence of hypoxic waters may have implications for N2O emissions and fixed nitrogen loss, potentially influencing coastal productivity and climate.

Fenotipos sobresalientes de Pinus chiapensis y Pinus douglasiana en rodales bajo manejo forestal en Oaxaca

In some areas of Oaxaca, Mexico, it is necessary to implement plantations that use seeds of genetic quality, to increase productivity and phenotypic quality. The objective was to select outstanding phenotypes of Pinus chiapensis (Martínez) Andresen and P. douglasiana Martínez, evaluating quantitative and qualitative characteristics. In 2021, 15 superior phenotypes per species were selected to evaluate inventory variables (height, diameter), density (trees ha-1), age and chip increments, recording climate and site data. Crown area (m2), basal area (m2) and total volume with bark (VTcc, m3) were determined for each tree; site index (SI) was determined based on anamorphic curves, density guides and average VTcc. Data were analyzed with Student's t tests, Pearson's correlation (a = 0.05), cluster analysis, and principal component analysis. The species showed significant differences (p ≤ 0.01) in dasometric characteristics. Using the average volume as a selection criterion, six P. chiapensis trees presented volumes above average (≥ 9.76 m3) and seven P. douglasiana trees presented volume above average (&gt; 4.14 m3) and clear stem height (18.7 m), in places with high precipitation; while comparing morphological, of site and climatic characteristics, 10 individuals of P. chiapensis and six of P. douglasiana with excellent quality IS were selected. The selected trees have outstanding characteristics of wood quality and volume, so it is convenient to use them to collect seeds in the study area.

Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach

This research investigates the effectiveness of the Random Forest algorithm in optimizing e-commerce inventory management. In a digital business that continues to grow, inventory management is crucial for smooth operations and customer satisfaction. The Random Forest algorithm, a development of the CART method by applying bagging techniques and random feature selection, was tested to predict inventory. An experimental design is used to test the algorithm's performance algorithms performance, using data relevant to the observed inventory variables. The analysis involves evaluating the performance of algorithms in predicting and preventing stockouts. The results show that the Random Forest algorithm provides more accurate inventory predictions than traditional methods. Comparison with linear and rule-based regression reveals higher accuracy, making this algorithm a promising choice for e-commerce inventory management. These findings imply that the Random Forest Algorithm can be an effective solution in overcoming the complexity and fluctuations of digital markets. Practical recommendations include a deep understanding of the data, engagement of trained human resources, and training strategies for optimal use of these algorithms. This research also contributes to the literature by expanding understanding of the application of the Random Forest algorithm in various contexts, including forest basal area prediction, supply chain management, and backorder prediction. In conclusion, the Random Forest algorithm has great potential to improve e-commerce inventory management, opening up opportunities for broader application in the digital business world. Proactive adoption of these algorithms can have a positive impact on operational efficiency, customer satisfaction, and a company's competitiveness in an ever-evolving market.

Mechanisms Underpinning the Net Removal Rates of Dissolved Organic Carbon in the Global Ocean

AbstractWith almost 700 Pg of carbon, marine dissolved organic carbon (DOC) stores more carbon than all living biomass on Earth combined. However, the controls behind the persistence and the spatial patterns of DOC concentrations on the basin scale remain largely unknown, precluding quantitative assessments of the fate of this large carbon pool in a changing climate. Net removal rates of DOC along the overturning circulation suggest lifetimes of millennia. These net removal rates are in stark contrast to the turnover times of days to weeks of heterotrophic microorganisms, which are the main consumers of organic carbon in the ocean. Here, we present a dynamic “MICrobial DOC” model (MICDOC) with an explicit representation of picoheterotrophs to test whether ecological mechanisms may lead to observed decadal to millennial net removal rates. MICDOC is in line with &gt;40,000 DOC observations. Contrary to other global models, the reactivity of DOC fractions is not prescribed, but emerges from a dynamic feedback between microbes and DOC governed by carbon and macronutrient availability. A colimitation of macronutrients and organic carbon on microbial DOC uptake explains &gt;70% of the global variation of DOC concentrations, and governs characteristic features of its distribution. Here, decadal to millennial net removal rates emerge from microbial processes acting on time scales of days to weeks, suggesting that the temporal variability of the marine DOC inventory may be larger than previously thought. With MICDOC, we provide a foundation for assessing global effects on DOC related to changes in heterotrophic microbial communities in a future ocean.

Investigation of discretization uncertainty in Monte Carlo neutron transport simulations of the Molten Salt Fast Reactor (MSFR)

In the present work, an assessment of the Neutronic Benchmark of the Molten Salt Fast Reactor (MSFR) was performed using mesh based Monte Carlo Neutron Transport (MCNT) calculations with numerical uncertainty quantification due to discretization in neutronic parameters. Calculations with Constructive Solid Geometry (CSG) models where made as a baseline for the developed mesh based models. The numerical uncertainty given by the mesh utilization is evaluated using an extended version of the Grid Convergence Index (GCI). The fuel salt reprocessing is evaluated regarding a constant reprocessing rate. The fuel salt inventory variation with time for the developed models (CSG and meshed) is presented. The differences caused by the discretization procedure are noticeable, which shows that mesh based MCNT require careful mesh sensitivity evaluation and further validation.

Application of Analytical Hierarchy Process and Frequency Ratio Model for Predictive Flood Susceptibility Mapping Using GIS for the Khazir River Basin, Northern Iraq

Flood damage assessment is considered the essential tool for evaluating risk to civil and agricultural systems in land use planning. The validity of the studies’ outcome depends on the availability of data and their spatial distribution. The present study came to compute flood susceptibility maps utilizing two application models: (i) the frequency ratio, and (ii) the analytical hierarchy process. These models were then tested in the Khazir River basin using GIS with a selection of twelve flood conditioning factors. The flood inventory variables layer and flood-causing factors were created using remote sensing data, a digital elevation model, and secondary data from various sources. Then, the flood inventory map was highlight divided into training and test data, with 105 flood sites (70%) used for training and 45 sites (30%) used for testing. After applying the areas under the curve for the frequency ratio and analytical hierarchy process models, which were 90.6% and 88.9%, respectively, the final flood sensitivity maps showed similar results for the two models, which confirm the effectiveness of the adopted methodology. The study found a considerable spatial variance in flood sensitivity maps, as (21.06%) of the flooded areas are classified as having very low sensitivity to flooding, (24.09%) are classified as having low vulnerability to floods, and (23.79%) are classified as having moderate vulnerability, (24.10%) classified as highly vulnerable to flooding, and (6.96%) classified as highly vulnerable to flooding. Flood danger ranged from very low in mountain locations to very high in plain areas close to the riverbanks. Obtained results could be improved if a land-use planning policy will be applied, in order to establish a master plan for water resources development to avoid flood damage.

Variation in phoneme inventories: quantifying the problem and improving comparability

Abstract For over a century, the phoneme has played a central role in linguistic research. In recent years, collections of phoneme inventories, originally designed for cross-linguistic purposes, have increasingly been used in comparative studies involving neighbouring disciplines. Despite the extended application of this type of data, there has been no research into its comparability or tests of its reliability. In this study, we carry out a systematic comparison of nine popular phoneme inventory collections. We render them comparable by linking them to standardised formats for the handling of cross-linguistic datasets, develop new measures to test both size and similarity, and release the organised data in supplementary material. We find considerable differences in inventories supposedly representing the same language variety, both in terms of size and transcriptional choices. While some of these differences appear to be predictable, reflecting design decisions in the different collections, much of the observed variation is unsystematic. These results should sound a note of caution for comparative studies based on phoneme inventories, which we suggest need to take the question of comparability more seriously. We make a number of proposals for improving the comparability of phoneme inventories.

Analysis of the Effect of Liquidity, Inventory Variability on Profit Before Tax for FIFO &amp; Average Inventory Accounting Methods

This study aims to determine the effect of liquidity inventory variability on profit before tax for the Fifo and average inventory method. The sample used in this study is a pharmaceutical company listed on the Indonesia Stock Exchange during the research period 2016-2020, which presents reports of financial statements that have been audited successively, with eight companies as research samples using the purposive sampling method. The results of research with discriminant analysis Liquidity have a significant effect on Profit Before Tax for the FIFO Inventory Method, and the average discriminant analysis of Inventory Variability has a substantial effect on Profit Before Tax for the FIFO Inventory Method and the average in the Pharmaceutical sector listed on the Indonesia Stock Exchange for the period 2016 -2020.

Within-individual variation of measured depression symptoms: A systematic review and meta-analysis

BackgroundDepression screening instruments are commonly used to assess the presence and severity of depression symptoms. However, there is little information on variation in depression screening scores over time within the same individual. MethodsA systematic review and meta-analysis was performed of studies reporting the within-subject variability of the Beck's Depression Inventory (BDI), Hospital Anxiety and Depression Scale (HADS), Patient Health Questionnaire-9 (PHQ-9) and Patient Health Questionnaire-2 (PHQ-2). Multiple databases were searched from inception to 14th July 2023. Title and abstract screening was performed in duplicate, full text screening and data extraction by one reviewer and verified by a second. Risk of bias was assessed with a modified COSMIN tool. ResultsOf 2798 titles and abstracts and 157 full text articles screened, 41 met the inclusion criteria. No studies were on patients with depression, most had only two measurements, less than two weeks apart. The pooled estimates of ICCs (Intraclass Correlations) and 95 % confidence intervals for BDI, HADS, PHQ-9 and PHQ-2 were 0.89 (0.84–0.93), 0.89 (0.85–0.93), 0.84 (0.81–0.88) and 0.75 (0.56–0.93), respectively.Studies in healthy subgroups showed lower ICC than those with physical illness. LimitationsAssessment of variability is not the main aim of most papers assessing depression measurement instruments, so it is possible that some relevant papers have been missed. ConclusionsWithin-subject scores for BDI, HADS and PHQ-9, show generally excellent agreement over periods of up to two weeks. However published data on within-subject variability is lacking over longer time periods and for patients with depression.

An analysis of the supply chain dynamics of remanufacturing with multiple collectors

This paper addresses how decisions related to the collection of used products (namely the location and number of collection sources) affect the dynamic performance of remanufacturing. More specifically, we address the case where a manufacturer produces both new and remanufactured, as-good-as-new, products and delivers them to the final customers via a supplier (or suppliers). A percentage of these products is collected at the end of their life by multiple collection sources – either the suppliers, third-parties in charge of the collection, or a combination of both – that may differ in the time required to carry out their collection activities. In this scenario and assuming an optimal operation of all nodes in the supply chain, we derive an exact formulation for two well-known indicators of the so-called bullwhip effect, i.e. the amplification of the order variance and the inventory variance experienced by the manufacturer as compared to that of the final customer demand. The analysis of the so-obtained formulae highlights the importance of the collection lead times (which may be employed as a proxy of the location of the collection source, or its effectiveness in the collection process), as only the number of collection sources with different collection lead times affects the bullwhip effect, being irrelevant how collection sources with the same collection lead times allocate their collection quotas of end-of-life products among themselves. An expression for the optimal allocation (in terms of reduction of the bullwhip effect) of the collection quotas of end-of-life products to each collection source is given, showing the positive effect of increasing the number of groups of collection sources with different collection lead times. In order to reap these benefits, it is required to perform a balanced allocation of the collection of end-of-life products among these groups. These results offer a number of managerial insights for the design of a collection system for the end-of-life products in a remanufacturing setting from a dynamic performance perspective.

Pengaruh Harga, Persediaan Barang dan Citra Merek Terhadap Keputusan Pembelian Konsumen Melalui Minat Beli Sebagai variabel Intervening

Consumers buy goods or services to fulfill their needs. In the process of fulfilling these needs, consumers employ various methods. One of them is seeking information related to the product they intend to purchase, such as its price and availability in the store they plan to visit. This is because the majority of consumers opt for affordable items with comparable quality, thus saving time and effort. This research adopts a qualitative approach, focusing on hypothesis testing with measurable data to draw conclusions that can be generalized. The population in this study is infinite, encompassing all consumers of the BASMALAH store in Jember Regency. The sampling method used is incidental sampling, which selects samples based on chance. The representative sample size for the study is 21 x 5 = 105 respondents. Data analysis is conducted using path analysis, developed from regression analysis, and hypothesis testing is assisted by SPSS version 23. The analysis results indicate that the price variable has a significant influence on consumer buying interest, with a standardized coefficient of 0.219. The inventory variable also significantly affects consumer buying interest, with a standardized coefficient of 0.346. The brand image variable has an effect, but it is not significant in influencing consumer buying interest, with a standardized coefficient of 0.158. The price variable significantly influences consumer purchase decisions, with a standardized coefficient of 0.384. The inventory variable has an effect, but it is not significant in influencing purchase decisions, with a standardized coefficient of 0.207. Brand image has an effect, but it is not significant in influencing consumer purchase decisions, with a standardized coefficient of 0.188. The price variable significantly influences purchase decisions through buying interest. The inventory variable has an effect, but it is not significant in influencing purchase decisions through buying interest.

Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database

The forest reflectance and transmittance model (FRT) is applicable over a wide swath of boreal forest landscapes mainly because its stand-specific inputs can be generated from standard forest inventory variables. We quantified the accuracy of this model over an extensive region for the first time. This was done by carrying out a simulation study over a large number (12,369) of georeferenced forest plots from operational forest management inventories conducted in Southern Finland. We compared the FRT simulated bidirectional reflectance factors (BRF) with those measured by Landsat 8 satellite Operational Land Imager (OLI). We also quantified the relative importance of several explanatory factors that affected the magnitude of the discrepancy between the measured and simulated BRFs using a linear mixed effects modelling framework. A general trend of FRT overestimating BRFs is seen across all tree species and spectral bands examined: up to ∼0.05 for the red band, and ∼0.10 for the near infrared band. The important explanatory factors associated with the overestimations included the dominant tree species, understory type of the forest plot, timber volume (acts as a proxy for stand maturity), vegetation heterogeneity and time of the year. Our analysis suggests that approximately 20% of the error is caused by the non-representative spectra of canopy foliage and understory. Our results demonstrate the importance of collecting representative spectra from a diverse set of forest stands, and over the full range of seasons.

Modeling time series radon inventory and constraints on the submarine groundwater discharge mass balance of a well-mixed, highly dynamic estuary

Although often diffuse and over-shadowed by surface-water inputs, submarine groundwater discharge (SGD) represents a significant source of freshwater and solutes to many sensitive marine environments. Radon (222Rn) is an effective naturally occurring tracer of SGD due to its non-reactive nature and dominant terrestrial source. However, limited continuous measurements and poor constraints on temporal 222Rn variability in shallow-turbulent estuaries introduce large uncertainties to SGD rate estimation. We hypothesize that nonlinear regression analysis (e.g., generalized additive model [GAM]) and machine learning (ML) algorithms could explain radon trends and resolve limitations of the traditionally used mass balance for estimating SGD in shallow and vertically mixed estuaries, thus enabling preliminary insights related to wind-driven mixing losses using sporadic groundwater tracer measurements and publicly available hydroclimatic parameters. Cross-correlation functions and a generalized additive model [GAM] were employed to quantify atmospheric and hydrologic controls and predict, respectively, 222Rn inventory variability modulated by windspeed and direction. Two ML models, neural network (deep-learning neural network [DNN]) and decision tree (random forest [RF]) based models, were trained on an intermittent 24-month 222Rn (n = 10,660, 30 min interval) dataset (2019–2021) collected from a well-mixed northwestern Gulf of Mexico estuary (Corpus Christi Bay, Texas) with publicly available inputs (e.g., windspeed/direction, tide levels, water temperature, creek discharge and barometric pressure, n = 35,088). Both ML models can predict observed radon concentrations with r2 > 0.90, however, only the RF model is able to predict within the observed range of the 222Rn magnitude dataset and finds the relevant hydroclimatic parameters (e.g., streamflow and wind direction) highly responsible for the seasonal and daily variability in the inventory, respectively. The DNN model can more closely predict extremely high inventories, but it overpredicts lows, resulting in numerous negative values, which suggests that large data gaps in the groundwater tracer cause overfitting and underperformance of this model in a wind-mixed estuary. Using the GAM predicted 222Rn dependency on wind direction and speed, the adjusted mass balance reduces overestimation of wind-mixing losses and results in overall lower fluxes. This research provides new opportunities to: 1) predict radon inventories in regions with large temporal data gaps using only publicly available hydroclimatic parameters and 2) better constrain SGD estimates in windy coastal areas, allowing for insights that can be used to plan field excursions and management strategies for coastal water and solute budgets.

On the equivalence of the proportional and damped trend order-up-to policies: An eigenvalue analysis

We investigate the equivalence of the order-up-to (OUT) replenishment policy with damped trend forecasting (OUT-DT) to the proportional OUT (POUT) policy via an eigenvalue (zero-pole) analysis. We show when the damped trend forecasting parameters are selected from a specific region of the parameter space, the Bullwhip Avoidance(BA) region, the OUT-DT policy potentially possesses some desirable characteristics. Under an independent and identically distributed (i.i.d.) random demand (the simplest random demand) we show the OUT-DT policy: (a) can eliminate the bullwhip effect, (b) has the same dynamic response as the POUT policy (because of the superposition principle, this holds true for all demands, not just i.i.d. demands), (c) forecasting parameters can be set so as to minimize the sum of the inventory and order variances, and (d) has a similar order and inventory variance as the POUT policy when non-linear constraints are present. We investigate the case when the forecasting parameters are selected from the BA region and correlated demand with one auto-regressive term, one integrated term, and two moving average terms, ARIMA(1,1,2), is present. We reveal the effect of finite lead times on the bullwhip effect (order variance) using an approach based on the order of the eigenvalues (the zeros and poles). We reveal either: (a) the bullwhip effect is always present and always increasing in the lead time or (b) a smoothing effect can be present with short lead times (and the order variance may even be decreasing in the lead time) but the bullwhip effect may return with long lead times.

Size of sampling units aproppriate for management level forest inventory in Eastern Amazon, Brazil

This study aimed to indicate the ideal sample size for continuous forest inventories in the tropical forest in Amapá. Data were obtained from a monitoring inventory started in 2010 and completed in 2016, in five permanent sampling units of 100m x 100m with an inclusion level of DBH ≥ 10cm. Randomly allocated sample subunits with five different dimensions ranging from 400 m2 to 1600 m2 were simulated within these units. The measurement times of each sampling unit, from the first to the last three, were timed. Dimensions were analyzed for their accuracy and relative efficiency. Given the above and depending on the purpose of the inventory, the standard sampling unit of 1 ha (100 m x 100 m) is recommended for the inventory variables. The 20 m x 20 m (400 m2) square sampling unit was the most suitable for monitoring growth variables (annual increment in diameter, basal area, and volume), indicating that these sampling units for continuous inventories can become an essential alternative for use in monitoring inventory in the region.

Automatic Extraction of Forest Inventory Variables at the Tree Level by Using Smartphone Images to Construct a Three-Dimensional Model

This paper focuses on the current urgent demand for the accurate measurement of forest inventory variables in the fields of forestry carbon sink measurement, ecosystem research, and forest resource conservation, and proposes the use of images to construct a three-dimensional measurement model of forest inventory variables, which is a new method to realize the automatic extraction of forest inventory variables. This method obtains sample site information by using high-definition images taken in the forest by a smartphone, which significantly improves the field operation efficiency and simple operation, and effectively alleviates the problems of long field operation times, complicated operations, and expensive equipment used by current methods for obtaining forest inventory variables. We propose to optimize the Eps parameters of the DBSCAN algorithm based on the MVO algorithm for point cloud clustering to obtain single wood point clouds, which improves the accuracy of the model and can effectively solve the problem of large interference from human factors. The scale coefficients of the image and the actual model are obtained by the actual measurement of tree height and diameter at breast height to complete the construction of the three-dimensional measurement model of the stand and are then combined with the AdQSM algorithm to realize the automatic extraction of forest inventory variables, which provides a new interdisciplinary method for the comprehensive extraction of forest inventory variables. The accuracy of the model measured in the experimental sample site of Fraxinus mandshurica Rupr was as follows: the absolute error of tree height measurement ranged from 0.05 to 0.37 m, the highest relative error of measurement was 2.03%, and the average relative error was 1.53%; for the absolute error of diameter at breast height, measurement ranged from 0.007 to 0.057 m, the highest relative error of measurement was 7.358%, and the average relative error was 3.616%. The method proposed in this study can be directly applied to the process of acquiring and visualizing the variables of forest inventory in the field of ecological research, which has good flexibility and can meet individual research needs.

The more the better? The impact of the number and location of product recovery options on the system dynamics in a closed-loop supply chain

The hybrid closed-loop supply chain (CLSC) system, commonly observed in the consumer electronics and automotive industries, may include one or more product recovery options, such as product repair and core remanufacturing. From system dynamics perspective, one important strategic decision is how many recovery options (and in which locations) should be invested to minimise poor dynamic behaivor such as bullwhip effect. This motivates us to investigate the impact of the number and locations of recovery options on the dynamic behaviour. We adopt step and sinusoidal functions as customer demand, representing a sudden sustained shift in demand and seasonally unadjusted demand, respectively. We also consider different return rates for each echelon of the CLSC. Our simulation results indicate that the bullwhip effect and inventory variance can be significantly reduced in all CLSC scenarios in comparison to a traditional open loop supply chain. However, a increased number of product recovery options does not improve the dynamic performance of the CLSC. Moreover, the bullwhip effect and inventory variance may be reduced if product recovery options are located at downstream echelons that are closer to end customers (e.g. retailers) and have high return rates. Further, return rates play a key role in mitigating the bullwhip effect, consistent with the prior literature. Our findings provide strategic insights for managers seeking to improve their CLSCs performance from system dynamics perspective.