Inventory Costs Research Articles

Optimalisasi Manajemen Persediaan Bahan Baku Pada Usaha Pabrik Mie Sinar Mandiri Kabupaten Tana Toraja

This research was conducted to find out and analyze how raw material inventory management at the Sinar Mandiri Noodle Factory Business uses the Minimum-Maxsimum method. The method used in this research is a quantitative descriptive method. The results of this research show that using the Min-Max method is more optimal than the method applied by the Sinar Mandiri Noodle Factory Business. By using the Min-Max method, you can save raw material inventory costs in 2023 amounting to IDR 6.687.111..-. Based on these results, the Sinar Mandiri Noodle Factory Business should use the Min-Max method so that raw material inventory management can be more optimal and inventory costs can be minimized so that company profits can be maximized.

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

Pricing in spatial classification system in non-symmetric market demand based on the calculations of double interval grey numbers

PurposeThe contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.Design/methodology/approachThis paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.FindingsThis study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.Originality/valueThe model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.

Analisis Just In Time (JIT) Persediaan Krudung di CV. Age Group

Every company always needs inventory, without having inventory, entrepreneurs will be faced with risks such as one time not being able to fulfill their customers' desires. This can happen, because goods are not always available at all times, which means entrepreneurs will lose the opportunity to gain the profits they should get. Therefore, the company must have an inventory management strategy in which the company must ensure that it is able to provide supplies with quality, quantity and time. Therefore CV. Age Groub must pay attention to storage capacity in order to meet demand for supplies of goods, and more attention must be paid to inventory management so that it remains well organized. By using the Just In Time approach. This research aims to optimize the efficient use of warehouse space to store goods with maximum capacity. The results using the Just In Time system approach are more efficient in calculating total inventory costs for the CV age group. The results obtained from analysis using the Just In Time method vary because they are based on various aspects. The greatest savings value is in the aspect of minimum inventory capacity from normal inventory with 66 deliveries which can save approximately 87% of the company's total costs, while from inventory which is added assuming damage, loss and defects of 5% with 73 deliveries can be achieved. save costs by 88%. So if you use the Just In Time system you will save costs of approximately 87-88%.

Study on Optimizing Inventory to Minimize Waste in the Fresh Produce Supply Chain

Abstract: The increasing demand for fresh produce, particularly greens, presents unique challenges in inventory management due to their short shelf life and susceptibility to spoilage. Inefficient management often results in significant waste, leading to financial losses for retailers and increased environmental burdens from the disposal of spoiled goods. Despite various strategies being employed in the agri-food sector, gaps remain in understanding how to optimize inventory levels to minimize waste at the retails level while maintaining high service levels for customers. This research aims to develop an inventory management model tailored to fresh produce that can mitigate these issues by balancing stock availability and minimizing wastage, particularly in large retail shops. To address the research problem, a mixed-integer linear programming (MILP) model was developed, focusing on three service levels—90%, 92%, and 95%—to evaluate the trade-offs between customer satisfaction and total inventory costs. The model incorporates key constraints, including stock balance, shelf-life limitations, and demand satisfaction. The model was validated using actual data, including stock, sales, and wastage records for the month of August, 2024. The objective function aimed to minimize total costs, encompassing holding costs, wastage, and lost revenue. The findings indicate that higher service levels, while reducing stockouts, significantly increase costs due to excess inventory and waste. The most cost-effective solution was observed at the 90% service level, which balanced waste reduction and customer satisfaction. The study also emphasizes the importance of integrating total cost reduction and minimize wastage. Future work should focus on incorporating consumer behavior patterns and extending the model to multi-echelon inventory systems, which include both distribution centers and retail outlets, to capture a more holistic view of the supply chain.

The impact of transshipment and staggered deliveries on pharmaceutical supply chain performance

ABSTRACT This paper studies the impact of proactive transshipment and staggered deliveries on the pharmaceutical supply chain performance. Therefore, a mathematical model considering transshipment and staggered deliveries is developed to minimise the total inventory management costs of a pharmaceutical supply chain under uncertain demand. To deal with this problem, a two-stage stochastic model is presented and solved using the sample average approximation. The proposed solution shows an important gain of 5% on average in the total inventory management costs when incorporating transshipment of drugs between hospitals and a gain of 32% on average when considering both transshipment and staggered deliveries.

Impact of an enterprise controlled substance management system on labor and inventory costs.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To evaluate the impact of an enterprise controlled substance (ECS) management system with integration of analytics software on labor requirements and inventory cost within a health-system pharmacy. A prospective, pre-post observational study was designed to assess the impact of implementing a solution that connects disparate systems with the integration of analytics software. Three study modules were implemented over approximately 18 months. The intervention consisted of implementation of new CS vaults, a centralized server, an automated central medication inventory system, and inventory optimization analytics software. The number of transactions and time spent on CS reports were compared before and after implementation to determine labor and inventory efficiencies. Both of the study facilities had a decrease in CS daily stockouts and an increase in inventory turns compared to baseline, while the total number of transactions (vends) at the central vault and decentralized dispensing cabinets increased. The addition of analytics allowed for establishment of informed changes to periodic automated replenishment levels. Additionally, both facilities saw a reduction in the number of expired medications, and there was subsequently a reduction in the total reverse distributor costs. Finally, both facilities had a reduction in the amount of time spent on manual tasks associated with reconciling and managing discrepancies. An inventory management system integrated with an advanced analytics tool provided a reduction in the time spent on receiving, storing, and reconciling CS records while the number of transactions increased. The ECS solution enhanced the visibility of the chain of custody while closing the loop between reporting and receiving inventory, eliminating or reducing the frequency of manual processes.

Integrating spare part inventory management and predictive maintenance as a digital supply chain solution

Purpose This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC). Design/methodology/approach A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution. Findings Several major organisational, infrastructure and cultural obstacles were revealed, and an optimum scenario for the integration of spare part inventory management with PdM was recommended. Practical implications The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and addressing shortage of spare parts. Originality/value This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.

Analysis of Inventory Control in the Downstream Fuel Supply Chain : An Indonesian Prespective

This research focuses on determining the level of inventory in a downstream supply chain for fuel products in Indonesian oil and gas company prespective. The variability in supply and demand is a problem in maintaining the stability of services to the consumers. High inventory costs that are completely borne by the company are also a problem that needs to be controlled. Therefore, determining inventory levels is very important to mitigate the variability of supply and demand that occurs with optimum inventory costs. This research designs a probabilistic model by considering variability in supply and demand to determine safety stock so that the correct level of Fuel Product inventory at each Storage Point can be determined. This research method is quantitative research based on empirical data. It is hoped that the research results will be able to obtain inventory control which can be taken into consideration by oil and gas company in managing the downstream supply chain of fuel products.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.

ANALISIS PENGENDALIAN PERSEDIAAN SIMPLISIA PRODUK WEDANG UWUH (STUDI KASUS: CV SALAMA NUSANTARA)

CV Salama Nusantara had not conducted calculations regarding inventory, and there were still raw materials accumulating in the warehouse (overstock), which could result in a reduction in profit and an increase in company expenses. Additionally, it was observed that there was a shortage of raw materials (stockout), leading to production interruptions and the company incurring higher costs for reordering in smaller quantities. This research aimed to understand the inventory system when using ABC and EOQ methods and the difference in Total Inventory Cost (TIC) values between the company’s and EOQ methods The study showed that the ABC method in the simplisia inventory system classified category A with 66.58% of the investment value for one type of simplisia, requiring greater control compared to categories B and C. The use of EOQ for ginger simplisia resulted in the highest EOQ value of 134.72 kg with an ordering frequency of 7 times. The comparison of Total Inventory Cost (TIC) for simplisia such as red ginger, cardamom, sappanwood, cinnamon, cloves, and nutmeg showed that the EOQ method resulted in a lower TIC compared to the company’s policy, with differences of Rp2,365, Rp12,973, Rp14,116, Rp65,494, Rp4,768, Rp16,055, and Rp66,048, respectively.

Variable‐weight combined forecasting model with causal analysis and clustering for refined oil sales forecasting

AbstractForecasting refined oil sales is essential in energy supply chain management. However, accurate forecasting is limited by several factors, including multiple influences of external features, heterogeneity of different gasoline stations, and difficulty in balancing linear and nonlinear forecasting. To address these issues, we propose a novel variable‐weight combined forecasting model. In the first stage, the model incorporates causal analysis and clustering methods to provide a quantitative description of multiple effects of external features and highly correlated aggregation of homogeneous data. Subsequently, based on the patterns of external feature influences learned from historical data, variable‐weight combined forecasting is realized to balance linear and nonlinear forecasting dynamically. Experiments based on real sales data procured from several regions demonstrate that the proposed model outperforms other benchmark and widely used models in terms of forecasting accuracy and statistical significance. The ablation experimental results confirm the significance of causal analysis, clustering, and variable‐weight combined forecasting in improving the balance between linear and nonlinear forecasting. Moreover, our results indicate that improving the quality of clustering can yield greater benefits than improving the amount of training data. Finally, we also explore whether the forecasting superiority translates into better inventory control, and our results show that the proposed optimization model can effectively balance inventory cost and service level, while also better suppress the bullwhip effect.

SWMR Algorithm Approach for the Vendor Managed Inventory Problem when Static Demand Rates

In a two-phase supply chain with Vendor Managed Inventory (VMI) policies, the supplier takes on the duty of keeping its retailers' inventory up to date and making sure they never run out of stock. This is in addition to his inbound inventory. This study aims to combine the retailers in various set partitions or to maximize their distribution and inventory holding costs within the same set partition. This paper focuses on a two-stage supply chain made of a single warehouse and multiple retailers (SWMR) facing a static demand and implementing VMI. We proposed a model for coordinating the system with a two-phase solution method. The first phase is to minimize the overall inventory costs and, in the second phase, a constructive vehicle routing problem (VRP) heuristic is used to optimize the distribution costs by satisfying some additional restrictions. Results of this implementation of VMI in an illustrative supply chain case are shown and discussed in detail.

Bond market structure and volatility

AbstractWe apply variance ratio methodologies to examine market quality in the US corporate bond market. We find that the open‐to‐open to close‐to‐close return variance ratio is greater than one suggesting that the corporate bond market is less efficient during the opening hours than during the closing hours. We show that the higher variance ratio at the open is related to the market power of dealers at the open and the sources of power are from lower cost of inventory, lower asymmetric information, and more flexibility to intermediate a trade. Dealers appear to exert less market power for bonds with low volume and credit rating. The results are consistent with dealers behaving strategically to unload risky assets and take on safer assets.

Inventory, market making, and liquidity in OTC markets

We develop a search-theoretic model of a dealer-intermediated over-the-counter market. Our key departure from the literature is to assume that, when a customer meets a dealer, the dealer can sell only assets that it already owns. Hence, in equilibrium, dealers choose to hold inventory. We derive the equilibrium relationship between dealers' costs of holding assets on their balance sheets, their optimal inventory holdings, and various measures of liquidity, including bid-ask spreads, trade size, volume, and turnover. Using transaction-level data from the corporate bond market, we calibrate the model to quantitatively assess the impact of post-crisis regulations on dealers' inventory costs, liquidity, and welfare.

Optimasi Ukuran Lot Persediaan dalam Model Integrasi Single Vendor dan Single Buyer dengan Mempertimbangkan Kekurangan Buyer

<p><strong>The supply chain is the main factor influencing the production process in achieving goals. This research develops a model to determine the optimal production lot size in the supply chain system between vendors and buyers by considering the inventory shortage at the buyer. Minimizing the combined inventory cost is the goal of optimizing the production lot size by developing an integration model to determine the optimal lot size. The proposed model uses a periodic delivery method from the vendor to the buyer and allows for inventory shortages at the buyer. An iterative algorithm is applied to find the optimal lot size and minimum integrated inventory cost. The results showed that the initial model with n=1 resulted in a total cost of 2252 and an optimal production size (Q*) of 457. The proposed model reduces the total cost to 1605 with an optimal production size (Q*) of 737. Sensitivity analysis showed that an increase in the frequency of deliveries to vendors and vendor set-up costs contributed to a rise in total cost, while an increase in vendor storage costs led to a decrease in production lot size. This research can improve the efficiency of production planning and inventory management in the supply chain.</strong></p><p><strong><em>Keywords</em></strong> - <em>Inventory, Shortages, Single Buyer</em><em>, Single Vendor.</em></p>

A deterministic fluid model for production and energy mode control of a single machine

Improving machines’ energy efficiency through dynamic energy mode control to meet demand requirements with minimal energy consumption is a promising approach. This study considers a machine operating in working, idle, off, and warmup energy modes with different energy consumption in each mode. A deterministic fluid model is developed to analyze an energy mode control policy that determines when to keep the machine working, off or idle, and switch to other modes based on the inventory/backlog level to minimize the total energy, inventory, and backlog costs. This approach facilitates the derivation of closed-form expressions for the optimal thresholds and the associated costs. This modeling approach allows us to prove that a policy that operates the machine between the working and off modes or the working and idle modes is always better than a hybrid policy that operates the machine in working, off, and idle modes simultaneously. We use the solution of the deterministic fluid model to propose an approximate policy for machines with stochastic production, warmup, and demand processes. We compare the results of the proposed approximation method with the optimal solution of a stochastic system where the production and warmup times are exponential and the demand inter-arrival times have Erlang distribution. The optimal solution for the stochastic system is determined by solving a Markovian Decision Process (MDP). Our numerical experiments show that the proposed approximation method predicts the optimal policy type for the stochastic case with a 89.3% accuracy, and the average error between the optimal cost and the cost obtained with the approximation method is 1.37% for 729 different cases tested. Furthermore, the computational efficiency of the proposed approximation is around 250 times better than the effort to determine the optimal policy using an MDP approach. We propose this approximation method where the control parameters are given in closed form as an easy-to-implement and effective policy to control energy modes to minimize the total energy, inventory, and backlog costs. Furthermore, we present the deterministic fluid modeling approach as a versatile approach to analyze energy mode control problems.

Inventory classification with artificial intelligence: Conceptual framework

In today's world where competition is increasing harshly, it is important to achieve sustainable profits and keep costs competitive. Under these conditions, the importance of supply chain elements is increasing day by day. Management of inventory costs, which constitute a large volume among cost items, affects the performance of companies. The first step to focus on to keep inventory costs under control and improve is inventory classification. Inventory classification, which is at the top of the supply chain elements and closely affects the subsequent phases, it is critical in determining supply chain performance. Thanks to inventory classification, material groups are determined and stock strategies for these groups are clarified. Incorrect inventory classification causes materials to be assigned to incorrect groups, which negatively affects inventory costs and subsequent phases of the supply chain, causing an increase in costs. The most used methods for inventory classification are ABC analysis, multi-criteria inventory classification and optimization. However, the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification. The advantages brought by artificial intelligence methods have also created distinctive contributions to inventory classification studies. This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification.

Unsupervised machine learning with different sampling strategies and topographic factors for distinguishing between landslide source and runout areas to improve landslide inventory production

This study derived 12 topographical and hydrological factors related to landslides from a 10-m digital elevation model. Three unsupervised machine learning algorithms were employed to distinguish between the features of landslide sources and runout areas for Typhoon Morakot. Two sampling strategies were designed in this study. The first strategy involved creating a data set by pairing landslide sources and runout areas on the basis of the size of polygonal areas recorded in the inventory and then exploring the effectiveness of feature separation with k-means++ as the primary method and EM (expectation maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) as supplementary methods. Because of practical challenges associated with the inability to determine whether remote sensing results correspond to landslide sources or runout areas, the first sampling strategy was used to test the feasibility of the adopted algorithms. In the second sampling strategy, the effectiveness of feature separation was tested by dividing polygon samples into several intervals on the basis of their sizes, thereby verifying the practicability of the adopted algorithms in real-world operations. This study verified that combining unsupervised machine learning algorithms with topographic factors facilitates the effective separation of landslide sources and runout areas, thereby enhancing the quality of landslide inventories and reducing the cost of landslide inventory creation. Moreover, it indicated that meter-scale topographic data yield classification results similar to those obtained from manually created landslide inventories based on centimeter-scale stereo aerial photos. The proposed method effectively balances the cost and quality of landslide inventories.