Inventory Location Research Articles

E-tailer’s Inventory Location and Pricing With Strategic Consumers

This research examines how strategic consumer behavior influences e-tailers’ decisions on inventory storage locations, pricing strategies, and inventory levels. The e-tailer opts for either a single central warehouse, which has lower holding but higher shipping costs due to its distance from consumers, or a mix of central and proximal local warehouses, which reduces shipping costs but incurs higher holding costs. We find that local warehousing prompts consumers to delay purchases in hopes of discounts, compelling e-tailers to lower prices to encourage early buying. Consequently, the firm may not utilize the local warehouse, even when it comes at no cost. Intriguingly, we find that increased local storage expenses or diminished product durability could paradoxically elevate firm profits. Our numerical analysis highlights the benefits of strategically distributing inventory across both central and local warehouses, especially under conditions of reduced demand uncertainty. Moreover, we establish that our key insights persist in the case of multiple local warehouses.

Integrating supplier selection decisions into an inventory location problem for designing the supply chain network

Integrating supplier selection decisions into an inventory location problem for designing the supply chain network

BLOCKCHAIN TECHNOLOGY IN SUPPLY CHAIN MANAGEMENT: A COMPREHENSIVE REVIEW

This paper explores the transformative potential of blockchain technology in addressing inefficiencies, opaque processes, and fraud susceptibility in the traditional supply chain ecosystem. Blockchain technology, with its distributed ledger technology (DLT), provides secure, transparent, and tamper-proof record-keeping capabilities throughout the supply chain. Its applications include traceability and transparency, inventory and logistics optimization, smart contracts for automated payments, and counterfeit prevention and brand protection. Blockchain's decentralized and immutable ledger facilitates traceability, offering an unalterable record of the journey of goods. Real-time visibility into inventory levels and locations empowers better forecasting, demand planning, and logistics management, leading to cost reductions and improved efficiency in warehousing, delivery routes, and resource utilization. Smart contracts enable automated, secure, and transparent execution of predefined contractual conditions, resulting in efficiency gains, cost savings, and contractual compliance. Blockchain also creates a secure digital fingerprint for products, combating counterfeiting and ensuring product authenticity throughout the supply chain. The case study of Everledger in the diamond industry exemplifies how blockchain technology contributes to ethical sourcing and reduces the risk of conflict diamonds. However, blockchain faces challenges such as scalability, regulatory uncertainties, cost, and privacy concerns. Future directions involve integrating blockchain with artificial intelligence and the Internet of Things, developing industry-specific blockchain solutions, and exploring hybrid blockchain models. By addressing these challenges through collaborative research, development, and policy initiatives, stakeholders can unlock the full potential of blockchain in supply chain management, creating a resilient and robust supply chain ecosystem.
 Keywords: Blockchain, Supply Chain Management, Traceability, Efficiency, Trust, Case Studies, Best Practices, Agriculture, Pharmaceuticals, Manufacturing, Challenges, Future Directions

Efficient Humanitarian Logistics: Multi-Commodity Location–Inventory Model Incorporating Demand Probability and Consumption Coefficients

Background: A logistics network plan could be a major key issue due to its effect on supply chain effectiveness and responsiveness. This study aims to investigate the inventory location in the humanitarian logistics response stage using a three-level logistics network to integrate location–allocation problems such as warehouse location and shelter allocation to each facility, and then determine the inventory level in each warehouse. Methods: In this research, the center and its distribution, as well as the reduction in service-level costs due to inventory deficit, have been considered to increase the level of shelter services. In order to investigate the network, in this study, bi-objective mixed-integer linear programming (BOMILP) is presented. Results: The first objective is to reduce location costs and inventory costs that take into account probable demand, consumption factors, and transportation costs, and the second objective is to raise the level of services offered to victims in the model. The software programs GAMS win32, 25.1.2 and MATLAB have been utilized with numerical examples in various dimensions. Conclusions: To maximize the efficiency and quality of the service, first, the model was numerically solved, and then the location where the most commodities could be transported at the lowest possible cost was identified.

Omni-channel inventory management of perishable products under transshipment and substitution

We consider a retailer selling two substitutable products through an offline and online channel. The products are stored at separate inventory locations and transshipments from the offline channel to online customers are possible, i.e. we propose a two-location inventory model incorporating customer-driven inventory interactions (demand substitution initiated by the customer) and retailer-driven inventory interactions (transshipment, i.e. demand substitution initiated by the retailer). We compare this model to a system incorporating customer-driven substitution only and use empirical data about customers’ preferences and perceptions of grocery shopping in online and offline channels to understand the trade-offs between various substitution forms in omni-channel retailing. In a numerical study, we discuss the optimal order quantities and resulting expected profits and quantify the value of a retailer-driven transshipment strategy. Although the empirical results reveal that online demands are relatively low and product substitution rates are particularly high, our results confirm that a transshipment system can achieve higher expected profits than when considering customer substitution only. Finally, we discuss the performance of policies that ignore product and/or channel substitution. We find that having complete information about product substitution is crucial for a grocery retailer’s omni-channel order quantity decision.

Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.

Solution Space Management to Enable Data Farming in Strategic Network Design

During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry.

Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region

Soil and water conservation is one of the major tasks of hydrologic soil groups (HSGs), which play an increasing role in the hydrological process. This research attempts to produce maps of soil classification into four hydrologic groups in the Upper Oum Er-Rbia basin (Morocco), which has many environmental risks due to specific climatic conditions and anthropogenic activities. The study was based on soil physical parameters (texture, organic matter, and bulk density) identified by extensive field surveys and remote sensing data (Digital Elevation Model, Landsat-9, European Reanalysis-5, and Soil Moisture Active Passive). The soil category mapping was prepared using four machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), K-nearest neighbor (K-NN), and naïve Bayes (NB). From 202 hydrologic soil groups, inventory locations were constructed and then randomly divided into a ratio of 70/30% for the training and validating models. Twenty-nine causative factors and indices were prepared and subdivided into four dataset types: whole variables (A), important variables (B), noncollinear variables (C), and HSG-specific variables (D) for HSG classification. The ML models’ performance and robustness were evaluated using six accuracy metrics: Overall Accuracy (OA), Precision, Recall, F1-Score, Kappa coefficient, and Area Under the Curve (AUC) value. The experimental results revealed that the performance of models varies according to the datasets used. Based on the AUC results, all models performed comparably well, with very high accuracies. However, RF had a better classification performance (OA = 0.788), followed by NB (OA = 0.757), SVM, and K-NN (OA = 0.742), especially when using datasets related to HSGs. In addition, according to maps, HSG D had the highest rate and dominated the study area. However, saturated hydraulic conductivity is the main contributor to modeling HSG. Hence, the developed approach and maps produced from this study provide a new contextual planning tool for decision-makers to devise adaptation strategies to avoid environmental risks and conserve soil water resources.

An exact criterion space search algorithm for a bi-objective blood collection problem

Blood shortages are one of the main challenges in blood supply chains. To supply required blood, blood center managers face conflicting collection objectives involving costs and patients’ lives. Moreover, some challenging decisions must be made, such as routing, blood collection, location of blood stations, transshipment policy and holding inventory. In this study, a bi-objective mixed integer mathematical model is developed to address all the above-mentioned issues in the network. The model focuses on precisely determining the location of collection stations (permanent and temporary), transshipment and inventory of the blood center, routes and the quantity of collected blood to optimize the two objectives of decreasing total blood shortages and costs. Next, a subtour cut generation (SCG) method is introduced to enhance the solution approach of the problem. It leads to solving large instances more efficiently. The problem is critical and complex since missing even a non-dominated solution may lead to both negative financial impact and noncompensable outcomes. To extract all non-dominated solutions, an exact criterion space search method, called the triangle splitting method (TSM), is adopted. Several experiments are investigated for some instances from the literature. In addition, a real case study is considered. Results indicate that designing the network using the proposed mathematical model can significantly reduce total blood costs and shortages. Consequently, by utilizing the proposed methodology, blood center decision makers will have the opportunity to choose the most preferred point among extracted non-dominated solutions. Furthermore, the performance of the adopted TSM was compared with an improved non-dominated sorting genetic algorithm (NSGA-II) and lexmin epsilon constraint (LEPS). The results confirm that the adopted TSM algorithm performs much better than the improved NSGA-II and LEPS, considering bi-objective performance measures.

PERANCANGAN SISTEM INFORMASI INVENTARIS BARANG BERBASIS WEB PADA DISPORA SUMUT

The design of the goods inventory system is the best solution for the North Sumatra Youth and Sports Office, which starts in a manual process which causes many problems that arise from the manual inventory process, from loss of goods data to the loss of the goods themselves. The web-based inventory is an access that can be monitored by all stakeholders in the North Sumatra Youth and Sports Office. The inventory information system that was built can record transactions of incoming goods, outgoing goods, stock of goods, use of goods, replacement of goods, submission of goods, data collection of inventory room locations and reports so that they can find out and provide fast, precise and accurate information every day. In this study using the method of observation and literature study with the flow of identifying problems, determining research objectives, studying literature, analyzing problems. The usefulness of this research also has an impact on each person in charge of the inventory of the North Sumatra Youth and Sports Office, making it easier for the head of equipment. All processes and stages based on a web database will have a positive impact on the inventory process at the North Sumatra Youth and Sports Office.

Network inventory deployment for responsive fulfillment

Customer expectations on fulfillment responsiveness is growing with prevalent home delivery practices via e-retails or omnichannel operations. Tight responsiveness requirements, such as x-hour delivery, requires physical availability of inventory near demand locations. This raise a need for a broad and dense inventory network and a smart inventory deployment model. Although building such a decentralized network is financially unviable for most companies, the recently emerging Physical Internet can enable affordable access to a decentralized network by transforming the asset-driven logistics to service-driven logistics. Under such context, we build a Newsvendor-based inventory model with partial pooling, induced by responsiveness requirements, followed by a pragmatic heuristic inventory solution, W-solution. Through numerical experiments we investigate the advantage of the decentralized network and W-solution over the centralized network and an allocation-based inventory model, a pre-allocation model. The results demonstrate that decentralization can increase profit significantly by capturing more demand offering more responsive fulfillment. That is, extra sales exceeds the cost saving from risk-pooling in centralized system. The W-solution saves extra cost by exploiting pooling smartly than the pre-allocation model, especially under decentralized network. We also find that the W-solution does not always result in less inventory than the pre-allocation model but rather it depends on sales margin unlike the common rule of thumb for inventory pooling. Further, through the case of a drop-ship manufacturer in the US market, we provide empirical evidence of the benefit of decentralization over the risk pooling when responsive fulfilment is required.

옴니채널 추진에 따른 최적 공급사슬 구조

We investigate the optimal supply chain structure in inventory location with the advance of omnichannel in the retail industry. Based on a dyadic supply chain consisting of a supplier and buyer(s) considering asymmetric information and flexible production lead times, we show that a dichotomous inventory location, combining VMI (Vendor Managed Inventory) and RMI(Retailer Managed Inventory), is optimal for the whole system in the presence of nonstationary demand across periods and asymmetric information between the suppplier and the buyer(s). Next, on the request of mass merchandisers to increase material availability under omnichannel initiatives, we show that more investment in reducing the production lead time is necessary when customer demands are less correlated across periods. It seems that such guideline for the investment in material availability can be more valuable with an increasing trend of omnichannel initiatives to revamp consumer experience in an uncertain market environment.

Two-echelon inventory location model with response time requirement and lateral transshipment

This study presents a new model for a two-echelon location-inventory system with response time constraints. This system controls inventory with a (S-1, S) policy and comprises of a finite collection of customers, a finite collection of service facilities and a single plant. This paper's main novelty is the incorporation of lateral transshipment into a two-echelon location-inventory system with response time requirement. By using a continuous-time Markov process approach, we determine expected on-hand inventory level in steady state, expected lateral transshipment level in steady state and expected backorder level in steady state. We utilize these steady state levels to formulate a mixed integer nonlinear programming model which incorporates lateral transshipment into an integrated location-inventory system with response time constraint. The model minimizes the total system cost and simultaneously determines: optimal location and number of service facilities, the optimal assignment of customers and base-stock level. We exploit the model's properties using Lagrange decomposition and we show that the model is convex. The model is tested on a real-world scenario using GAMS and our model returned lower costs following comparisons with a model without lateral transshipment. We also establish that lateral transshipment results to consistency of expected cost with varying response time requirement.

Experimental investigation on flow-induced vibration of a flexible catenary riser conveying severe slugging with variable gas superficial velocity

A flexible catenary riser conveying gas–liquid mixture may experience the severe slugging induced vibration (SSIV) within a specific gas and liquid superficial velocity range. In this work, the SSIV of a flexible catenary riser with aspect ratio of 200 and a horizontal upstream pipeline was experimentally investigated to shed some light on the nonlinear multiphase flow-induced vibration and the correlation with the dynamic behaviour and the flow evolution. The non-intrusive optical measurement with high-speed cameras was employed to capture the vibration displacements of evenly distributed markers along the riser as well as the flow characteristics of gas–liquidmixture in the riser. The first and second types of severe slugging (SSI and SSII) were observed in the standard case (vsg = 0.177 m/s and vsl = 0.094 m/s) with an occurrence frequency ratio of 1:3. Large amplitudes are excited in the slug formation and gas blowout stages, and the latter presents more violent vibration with a faster travelling waves’ propagation speed. When the gas superficial velocity increases while maintaining the liquid superficial velocity, the transition stage is significantly elongated, characterized by the alternate appearance of partially filled gas–liquid mixture and short liquid slugs. Nevertheless, each liquid slug, no matter it is long or short, makes its contribution to the vibration response, presenting the coincidence of the recurrence frequency and the vibration frequency. The response amplitude is associated with the liquid slug length as well as the location of liquid inventory. The contributions of the three regimes (SSII, short liquid slugs and partially filled mixtures) are mainly associated with their occupied time. The severe slugging is the predominant contributor at vsg≤ 0.708 m/s, while its contribution is negligible at higher gas superficial velocities.

An inventory hub location model for multi‐coal‐fired coastal power plants: a case study in Guangdong district

AbstractThe increasing demand for electric power is an established trend in China, of which coal power accounts for a large proportion. This paper proposes an inventory hub location model considering multi‐type coal to minimize the total transportation and inventory cost in a multi‐coal‐power supply chain. A second‐order cone programming method is adopted to convert the original model. The exact solutions of the small‐scale real case show that the model can greatly reduce the total costs by CPLEX. For real‐world large‐scale cases, a customized greedy randomized adaptive search simulated annealing algorithm is devised and tested to obtain near‐optimal solutions within a reasonable time. The research has strong practical guiding significance for the management of the thermal coal supply chain.

Spatial assessment of gross vertical reservoir heterogeneity using geostatistics and GIS-based machine-learning classifiers: A case study from the Zubair Formation, Rumaila oil field, southern Iraq

The study of oil-field reservoir heterogeneity is an important task in the oil industry as it affects waterflooding, developing injection production systems, and optimizing hydrocarbon production. In this study, vertical reservoir heterogeneity was quantified using the Lorenz statistical index, empirical Bayesian kriging, and seven machine-learning classifiers (Classification and Regression Trees, Boosted Regression Trees, Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbors, and Support Vector Machine with three different kernels (linear, radial, and polynomial) under the geographic information system platform. The main pay zone of the Zubair Formation in the Rumaila oil field from southern Iraq was used as a case study. The degree of heterogeneity was first quantified using the Lorenz index, and a borehole-heterogeneity inventory location map was prepared according to the determined Lorenz index. Information about five factors influencing the heterogeneity, namely, porosity, permeability, volume of shale, reservoir-unit thickness, and depth to the top of reservoir unit, was collected based on available cores, nuclear magnetic resonance log, gamma-ray logs, and drilling-information logs. Factors from these sources were interpolated to show their spatial distribution using the empirical Bayesian kriging technique. The relationship between the borehole inventory map of vertical heterogeneity and the five factors was examined using the seven machine-learning classifiers. Two statistical-error measures, namely, accuracy and Cohen's kappa, were used to verify the performance of the classifiers in both training and testing stages. Results proved that Random Forest, Support Vector Machine with radial kernel function, and Logistic Regression were the best models. The probabilities of the best performance models were then interpolated and classified into five heterogeneity zones: Very low, low, moderate, high, and very high. The high-very high classes for each of these models approximately occupy 60% of the oil field and are mainly distributed in the middle and north of the field, whereas the other classes encompass about 40% of the field and mostly occur in the south. This distribution of classes is most likely related to the distribution and complexity of former depositional environments. • The spatial vertical heterogeneity of a clastic reservoir unit is quantified in this study. • Five factors including porsoity and permeability are used as influencing factors for modeling heterogeneity. • The high zone of heterogeneity encompasses 60% of the reservoir unit.

Point-of-use hospital inventory management with inaccurate usage capture

Many hospital supply chains in the US follow a “stockless” structure, often implemented with the acquisition of new systems promising improved efficiencies and responsiveness. Despite vendor promises, supply chain gains from new technology are often unfulfilled or result in a reduction of performance. A critical component of achieving promised gains is the hospital’s ability to accurately and consistently capture hospital inventory use. In practice, recording demand with perfect, 100% accuracy is infeasible, so our models condition on the level of accuracy in a particular hospital department, or point-of-use (POU) inventory location. Similar to previous literature, we consider actual net inventory and recorded net inventory in developing the system performance measures. We develop two models, optimizing either cost or service level, and we assume a periodic-review, base-stock (or par-level) inventory policy with full backordering. In addition to choosing the optimal order-up-to level, we seek the optimal frequency of inventory counts to reconcile inaccurate records. Results from both models provide insights for supply chain managers in the hospital setting, as well as hospital administrators considering the adoption of similar technologies or systems.

A competitive stochastic bi-level inventory location problem

ABSTRACT This paper aims to address an inventory competitive facility location problem with the order quantity in a stochastic environment. In this problem, the facility location, customer design, and inventory control decisions have been integrated for two decision makers, i.e. the leader and the follower. To this end, a bi-level nonlinear nonconvex mixed integer mathematical model was proposed. Next, an alternative bi-level mixed-integer linear model was obtained. The enumeration branch-and-cut method in the field of bi-level linear programming was developed to come up with optimal or near-optimal solutions. To determine the branching choice and specialized cuts, a preference function was used. Computational results of some data tests were reported, and the results were compared with those of previous famous methods to show the efficiency of the algorithm. According to the computational results, the proposed algorithm could find effective solutions for solving such problems, which could compete with other well-known published algorithms.

Spatial prediction of landslide susceptibility using projected storm rainfall and land use in Himalayan region

Landslide susceptibility assessment (LSA) is a method used to reduce landslide vulnerability defined as landslide spatial prediction with the help of associative factors. The goal of the analysis is to forecast potential (2040, 2060, 2080, and 2100 AC) rainfall and land use and land cover (LULC) with the aid of the CSIRO-MK3.6.0 General circulation models (GCMs) climatic model and the dynamic conversion of land use and its effects (Dyna-CLUE) model. The purpose of this work is to produce landslide susceptibility map (LSM) of potential cycles correlated with expected rainfall and LULC data utilizing the binary logistic regression (BLR) models in the Upper Rangit River basin of eastern Himalayan region, India. Including rainfall and LULC total nineteen factors have been incorporated, these are mainly topographical, hydrological, geological, and environmental factors. A total 671 landslide locations have been mapped which divided randomly as training (70%) and validation (30%) datasets of LSM. Current (2018) LSM was validated by 30% of landslide inventory location and the result of area under curve of receiver operating characteristic (ROC) indicated that the LSM has 92.4% and 89.6% of the success and prediction rate respectively. To demonstrate the accuracy of the BLR landslide susceptibility (LS) model, this model was used to generated future LSMs based on projected of rainfall and LULC. The result of projected representative concentration pathway (RCP)-based rainfall depicted that the increasing trend of rainfall in the future period and the moderate to very high LS zones has also increased from 2040 to 2100. This study will be used to further study of landslide hazard (LH) studies with climatic approaches, and will also contribute to regional planning and development of current and future Upper Rangit River basin.

A Critical Review of a Holistic Model Used for Assessing Multimodal Transport Systems

The purpose of this paper is to present a rigorous and critical review of an established cost/time-distance model. The model offers a perspective on the inter-relationships between transport modes, nodes, methods and cargo volumes, types and forms. Organisations can review their door to door supply chain costs by applying the model described in the paper. The reviewed multimodal transport cost model is based on a relatively simple framework but demonstrates that other existing models of modal choice, multimodal transport and inventory location all oversimplify the transport process. The critical components of the model are the transport from origin to destination, consignment loading/unloading, intermodal transfer and performance variability. Other activities such as storage, value addition and customs clearance can be added into the model as required. Several findings emerge from the development of the model and provide much greater clarity concerning the cost-structure of door-to-door multimodal transport services along economic corridors. The model is shown to be a useful tool for identifying theoretical alternative locations for facilities such as inland terminals. The relative unit costs of operating respective freight transport modes lead to a series of classic door-to-door cost profiles stemming from the modal mix, which varies according to shipment distance, volume, cargo value-density and other variables.