Optimal Inventory Level Research Articles

Optimizing the Hospital Blood Bank Stock in Korea: A Comparative Analysis of the Uniform 5-Day Stock Index and a Novel Blood Stock Index.

Maintaining optimal blood inventory levels in hospitals is important to prevent blood shortage and wastage. We aimed to provide an efficient blood inventory management strategy for hospital blood banks nation-wide by comparing the current use of 5-day issuable stock (IS) with Lim's IS as a novel target IS. The average and CV of daily usage (DU) were calculated from information entered into Korea's Blood Management System by 194 participating hospitals in 2019 and 2020. Using these data, Lim's IS was calculated by determining the simulated annual average blood shortage day nearest to 1 for each blood group in each hospital. The 5-day IS (5IS) was estimated by multiplying the average DU in 2018 by five to count the shortage days in 2019. The average DU (0.3-231.3 units) and corresponding CV (0.33-7.14) in the participating hospitals were inversely proportional (r=-0.699 to -0.695). The hypothetical averages of 5IS and Lim's IS were 27.0±41.2 and 24.7±20.8, respectively (P=0.006). The shortage days for 5IS and Lim's IS were 8.9±22.7 and 1.0±1.9, respectively (P<0.001). While 5IS was unacceptable for universal application, Lim's IS remained near one shortage day and is considered more efficient than 5IS. Hospitals should implement indicators that consider DU and its variations. This is the first study to introduce Lim's IS as an indicator of optimal blood inventory, and the data are expected to provide guidance for effective blood inventory management nationwide, particularly during blood shortages.

Inventory management of new products in retailers using model-based deep reinforcement learning

This study addresses the optimal inventory management problem for new smartphone products as an effective example of a supply chain with a short product life cycle. The determination of the optimal inventory level leads to a reduction of lost opportunities and defective inventory, which is an important issue from a profit improvement perspective. Mathematical optimization and reinforcement learning approaches have been proposed for inventory management; however, most of these approaches focus on products that are regularly sold over a long period. Thus, when the target is a new product, it is difficult to optimize inventory control from its day of release due to a lack of sufficient data for learning. To solve this problem, we focus on model-based deep reinforcement learning with high sample efficiency and propose an inventory management method for new products that combines model learning in an offline environment and planning in an online environment. Simulations using real-world historical sales datasets demonstrate that the proposed method outperforms existing methods in terms of profitability, efficiency, and customer satisfaction. In particular, the proposed method improves total rewards and inventory turnover by ¿5% each than the heuristic method while maintaining the same stock-out rate. In addition, the results demonstrate that the proposed method can maintain stable inventory control for multiproduct and multistore supply chains.

Risk‐averse two‐stage stochastic programming for the inventory rebalancing of bike‐sharing systems

AbstractAs the popularity and usage of bike‐sharing systems increase, a better decision‐making model tailored for the successful operations of bike‐sharing systems is needed. This study is motivated to address operator‐based inventory rebalancing of bike‐sharing systems, and the main objective is to develop a mathematical optimization model designed to derive an optimal daily inventory rebalancing plan. Specifically, this study proposes a risk‐averse two‐stage stochastic programming to determine optimal initial inventory levels for each station to minimize operational costs for relocating bikes and expected penalty costs due to unmet requests. This study adopts the conditional value at risk to properly measure the risk associated with unmet requests to implement risk‐averse stochastic programming. Numerical experiments are conducted based on scenario data generated by empirical distributions fitted to trip data from the Houston BCycle to validate and evaluate the proposed model. The results show that the proposed model can be successfully applied to inventory rebalancing to improve the usability of bike‐sharing systems.

A Heuristic Model for Spare Parts Stocking Based on Markov Chains

Spare parts management has gained significant attention in recent years due to the considerable costs associated with backorders or excess inventory. This article addresses the challenge of determining the optimal number of spare parts to stock, assuming that the parts can be repaired. When an item fails, it is promptly sent for repair in a workshop. The time between failures and the repair time are assumed to follow an exponential distribution, although it should be noted that the results could be adapted to other distributions as well. This study introduces a heuristic method to find the optimal inventory level that minimizes the total cost, considering holding inventory, backorder, and repair costs. The research offers a valuable decision-making framework for determining the number of spare parts needed to minimize inventory costs, based on just two parameters: (1) the ratio of time to repair and time to failure, and (2) the ratio of the inventory holding cost of a spare part per day to the daily cost of an idle machine. To the best of our knowledge, there are no similar methodologies in the existing literature. The proposed method is straightforward to implement, employing graphs and simple computations. Therefore, it is anticipated to be highly beneficial for practitioners seeking a quick and reliable estimator of the optimal number of spare parts to stock for critical components.

Inventory management for stockout-based substitutable products under centralised and competitive settings

Inventory planning in fashion markets is highly challenging, owing to uncertain demand; yet, in making inventory decisions, retailers may be able to capitalise on high substitutability between products. This research develops single-period inventory-management models describing a market with two substitutable products, under stockout-based substitution; i.e. when a customer’s preferred product is out-of-stock, s/he may choose to purchase the substitute. Two settings are considered: centralised (a single retailer who sells both products) and competitive (two retailers, each selling one product). For each setting, we derive closed-form analytical solutions for the inventory levels that maximise expected profit. The model is further enriched with sales data from an online apparel retailer offering substitutable products (a sneaker in different colours), and we analyse the sensitivity of the optimal inventory levels and profits to parameter values. Key findings include the following: (i) Under competitive conditions, both retailers always order positive inventory so as not to lose customers. However, in a single-retailer setting, there are situations in which the retailer orders inventory for only one product. (ii) The optimal inventory levels and corresponding profits are highly sensitive to consumers’ willingness to substitute between products. These findings provide concrete insights that can guide fashion brands’ inventory-management decisions.

Working Capital Management and Firm Profitability: An Empirical Review

Working capital management is very important for the survival of a company no matter the size of that company. Inadequate working capital or illiquidity is a major issue confronting Many Nigerian companies. The main objectives of this study is to review the impact of working capital management on the profitability. The variables used in the study were cash conversion cycle, accounts receivable, inventory and accounts payable proxies to working capital management while, return on equity and return on assets as proxies to profitability. The study adopted conceptual approach where data collected from already existing data on the impact of working capital management on profitability. it is quite clear that a positive correlation exists between working capital management and firms’ profitability. Finally, a company which maintain sufficiently low inventory levels will reduce the holding cost of the inventory which results to higher profitability. The managements of companies should improve their cash conversion cycle, focus more on credit transactions with their vendors, and decrease their receivables days’ by offering a discount to those who paid early and those paid in advance, and should maintain optimum level of inventory in order to maximize their profitability.

Implementation of the Just In Time Method to Efficient Production Costs (Literature Review Study)

In production activities, the problem of inventory is one thing that must be considered in order to avoid waste in the production process. This study shows the control of raw material inventory in a production to get the optimal level of inventory. The just in time (JIT) method reduces the amount of inventory so that it can reduce costs as efficiently as possible. Inventory control with the just in time (JIT) method is the development of the economic order quantity (EOQ) method by reducing shipping lots. Based on this research, it is known that with the same data, using just in time (JIT) the costs incurred are smaller than the costs incurred previously. Costs previously incurred were Rp. 14,019,680.00 using economic order quantity (EOQ) the costs incurred were Rp. 8,497,593,00 while using just in time (JIT) the costs incurred were Rp. 3,800,239, 00 From these results it can be seen that with the just in time (JIT) method the production costs incurred are more efficient. The solution using the just in time (JIT) method is done with the help of MATLAB software.

Effects of Working Capital Management on Profitability of NBFIs in Rwanda

Working capital management is a key factor in determining a company's financial management. Inefficient or inefficient management of working capital impacts profitability and liquidity. To achieve optimal working capital management, company management must carefully manage the trade-off between profitability and liquidity. The general objective of this study was to determine the Effects of Working Capital Management on Profitability of NBFI in Rwanda. The study adopted Credit theory and Transaction Cost Theory. The study adopted a descriptive research design using both quantitative and qualitative approaches. The study assumed a target population of 96 individuals, a multistage random sample of 38 executives from various categories. Survey data were collected using a structured questionnaire. To test whether there is an impact on the profitability of the NBFIs in Rwanda, the correlation between inventory management policy and profitability and the correlation analysis between lending policy and profitability were used. Working capital has been found to have a significant relationship with profitability. Based on the research findings, it can be concluded that working capital is a positive, significant predictor of profitability. The findings of the study suggested that there is a positive and significant relationship between credit policy and profitability. A positive significant linear relationship between inventory control policies and profitability of NBFI in Rwanda was observed. The NBFI financial managers should regularly review their credit policies to ensure they are optimal. NBFI's finance managers should take precautions to help them maintain optimal inventory levels for both raw materials and finished goods.

The Joint Decision-Making Support through Piecewise Objective Optimization Model for Integrated Supplier Selection, Inventory Management, and Production Planning Involving Discounts

The decision-makers in manufacturing industries continuously optimize every supply-chain part to achieve optimal profit. In this paper, three crucial activities in the supply chain are observed as profit contributors: supplier selection, inventory management, and production planning. Decision-making support is needed to optimize those activities, especially when prices/costs involve discounts. Therefore, this study aims to develop integrated decision-making support for supplier selection, inventory management, and production planning involving discounted prices. The problem was considered with multi-supplier, multi-raw material, multi-product, and multi-observation time instant. The objective was based on maximizing the profit for the entire activity, i.e., from the raw material procurement and storage to the production. This supply chain was modeled as mixed-integer linear programming with a piecewise objective function representing the profit, which was maximized. It was also modeled with a bunch of constraint functions, including product demand satisfaction. The proposed model was tested with computational simulations using randomly generated supply chain data. The primal simplex algorithm was also employed to calculate the real value of the optimal decision, which was combined with the Branch-and-Bound approach to calculate the appropriate integer solution. The results showed that the optimal decision was achieved, namely (1) The optimal quantity of raw materials ordered to each supplier, (2) The optimal production quantity, and (3) The optimal inventory level, which provided the maximal profit for the whole optimization time horizon. This indicated that the proposed decision-making support model is implementable for industrial decision-makers.

A novel crossover operator for genetic algorithm: Stas crossover

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly.

Model predictive control based integration of pricing and production planning

&lt;abstract&gt;&lt;p&gt;We witness an increased emphasis on the integration of different areas of manufacturing firms with the intention of avoiding suboptimal solutions. In particular, due to new technologies which make it easy to change the price of a product in real time, the integration of pricing and production planning may be garnering the most interest. We are proposing in this paper a way to model the dynamics of the price. Thus, the price and the inventory level are considered as state variables whereas the supply (production) rate is the control variable. The demand rate is dynamic and state-dependent. Using a model predictive control approach, the optimal supply rate, and thus the optimal price and inventory level, are obtained. Different examples are provided under different scenarios for the supply rate and for the demand rate.&lt;/p&gt;&lt;/abstract&gt;

Inventory Control Using ABC Classification and Min-Max Stock Method in The Manufacture of Armored Vehicle Body Hull at PT XYZ

Research and development efforts are currently underway to enhance inventory control systems. However, fluctuations in demand often pose challenges to achieving optimal inventory management. To address this issue, it is crucial to establish efficient control measures that maintain an optimal inventory level. This study focuses on finding an effective approach for PT XYZ to manage stockpiles of raw materials for armored vehicle body hulls, aiming to prevent shortages or excessive stockpiling. The research uses the ABC categorization system and the min-max stock approach as its material inventory control strategies. Inventory classification, inventory turnover, the amount of safety stock, minimum stock, maximum stock, and order quantity are all determined using this procedure. By conducting a case study on PT XYZ's material inventory control for Panzer body hulls, it was identified that 3 materials fell under the important category (A), 3 materials in the moderate category (B), and 20 materials in the less critical category (C). The findings of this research are that the ABC method and min-max stock can be used as a reliable foundation for establishing inventory control parameters. Companies are advised to evaluate and consider the rules for each material, such as material costs, rare or not material types, and a history of delays from suppliers. These factors play a significant role in determining appropriate inventory control measures.

An Inventory Optimization Model for a Textile Manufacturing Company

Inventory management is a crucial issue in most businesses from factories in industry to small and large organizations in the production or service sector. A company should determine an optimum inventory level between excessive inventory that takes up physical space, costs much, and lack of inventory which disrupts the supply chain causing unavailability of the product that makes customers change their idea and buy from another supplier.&#x0D; In this study, we first classify the items that the company manufactures by using ABC analysis and develop a mathematical model to minimize total cost to enable a better inventory management. We use ABC analysis method to evaluate products in a textile company in terms of importance and to track these products according to their priorities. Accordingly, we propose a mixed integer programming model to determine production quantities and inventory levels with minimum cost. The results shows that the company in concern can improve its total production and inventory costs by 3.8 percent.

An enhanced solution approach for multi-period discrete manufacturing product assembly inventory routing problem with supply failure risks and customer demand uncertainty

PurposeThis study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).Design/methodology/approachA five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.FindingsThe simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.Research limitations/implicationsA two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.Originality/valueThe proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

BIM Based Inventory Management in Construction of Residential Highrise Building in India

According to a study, construction materials account for more than 65 percent of a project's entire cost. Material Planning efficiency is critical to completing a project on scheduled time and on budget. This research paper focuses primarily on inventory control and materials planning using BIM process as this are the important pillars of material management. The S-curve analysis is used to determine how much the estimated materials cost differs from the market materials cost. Engineers and contractors are interviewed to determine the main reasons for this. Inventory management techniques such as ABC classification and economic order quantity (EOQ) analysis are performed. In most cases, S-curve analysis shows that actual material costs are greater than estimated material costs. BIM-based ABC and EOQ analyses are used to continuously maintain sufficient inventory, protect inventory materials from damage, reduce inventory costs, solve inventory problems, and maintain optimal inventory levels. A sensitivity check is applied to the results of the EOQ analysis. “The use of ABC classification and EOQ analysis can help to alleviate the stock-out of A class and B class materials that occur on building sites. Following the implementation of these easy inventory control strategies, the total inventory expenditure is reduced”.

Manage Inventories with Learning on Demands and Buy-up Substitution Probability

Problem Definition: This paper considers a setting in which an airline company sells seats periodically, and each period consists of two selling phases, an early-bird discount phase and a regular-price phase. In each period, when the early-bird discount seat is stocked out, an early-bird customer who comes for the discounted seat either purchases the regular-price seat as a substitute (called buy-up substitution) or simply leaves. Methodology/Results: The optimal inventory level of the discounted seats reserved for the early-bird sale is a critical decision for the airline company to maximize its revenue. The airline company learns about the demands for both discounted and regular-price seats and the buy-up substitution probability from historical sales data, which, in turn, are affected by past inventory allocation decisions. In this paper, we investigate two information scenarios based on whether lost sales are observable, and we provide the corresponding Bayesian updating mechanism for learning about demand parameters and substitution probability. We then construct a dynamic programming model to derive the Bayesian optimal inventory level decisions in a multiperiod setting. The literature finds that the unobservability of lost sales drives the inventory manager to stock more (i.e., the Bayesian optimal inventory level should be kept higher than the myopic inventory level) to observe and learn more about demand distributions. Here, we show that when the buy-up substitution probability is known, one may stock less, because one can infer some information about the primary demand for the discounted seat from the customer substitution behavior. We also find that to learn about the unknown buy-up substitution probability drives the inventory manager to stock less so as to induce more substitution trials. Finally, we develop a SoftMax algorithm to solve our dynamic programming problem. We show that the obtained stock more (less) result can be utilized to speed up the convergence of the algorithm to the optimal solution. Managerial Implications: Our results shed light on the airline seat protection level decision with learning about demand parameters and buy-up substitution probability. Compared with myopic optimization, Bayesian inventory decisions that consider the exploration-exploitation tradeoff can avoid getting stuck in local optima and improve the revenue. We also identify new driving forces behind the stock more (less) result that complement the Bayesian inventory management literature. Funding: Z. Luo acknowledges the financial support by the Internal Start-up Fund of The Hong Kong Polytechnic University [Grant P0039035]. P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15508518]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505318] and the National Natural Science Foundation of China [Grant 71971184]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1169 .

Omnichannel retail operations with mixed fulfillment strategies

Many retailers are implementing omnichannel (OC) strategies including buy online, pick up in store (BOPS), buy online, ship from store (BOSS) and buy online, ship to store (STS). We study the impact of mixed fulfillment OC strategies (e.g., BOPS-STS, BOSS-BOPS-STS) on the retailer’s store operations. We build a stylized model where the retailer operates both a physical store and online channels, and faces two segments of customers: fixed-channel customers and OC-type customers. We find that both BOPS-STS and BOSS-BOPS-STS may induce customers to shift fulfillment options for time-insensitive or experience products when pickup hassle cost is low. In addition, both BOPS-STS and BOSS-BOPS-STS can expand the market for time-sensitive or experience products. We show that BOSS-BOPS-STS may increase the retailer’s optimal inventory level for time-sensitive or experience products when in-store hassle cost is high, and will decrease the optimal inventory level for time-insensitive or non-experience products when both pickup and in-store hassle costs are low. These two mixed strategies may increase or reduce the retailer’s profits, depending on whether they drive effects of option shift and market expansion and whether their fixed costs exceed certain thresholds. Finally, we find that different OC strategies interact and their effects on the retailer’s store operations and customers’ channel choices vary across product types.

Dynamic inventory system with pricing adjustment for price‐comparison shoppers

AbstractWith numerous price‐comparison websites and applications, consumers today are frequently conducting price‐comparison shopping. As a result, retailers face an increasing challenge in predicting consumer demand and determining the optimal product price and inventory level accordingly. To address this issue, this paper proposes an inventory model with joint decisions of price and inventory to optimize the retailer's long‐run average profit under price‐comparison consumer shopping. We first formulate the demand arrival process for a retailer under price‐comparison shopping to be affected by not only its own price but also its competitors'. Based on this demand arrival process, we then formulate the retailer's long‐run average profit and derive properties of its optimal solution. Our model focuses on capturing the impact of price‐comparison consumers on a retailer's optimal price and inventory decisions. In particular, we allow competitors' prices to affect the retailer's demand via two key factors: the manufacturer's suggested price and the variability of the outside lowest price. According to our results, when the suggested price increases, the retailer should lower its price to obtain more price‐comparison customers from competitors, whereas when the variability of outside lowest price increases, the retailer should raise its price to increase per unit profit from nonprice‐comparison customers.

The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective

Strengthen the resilience of supply chains was observed to be critical issue by firms to confront disruptions triggered by unprecedented demand and severe disasters. However, the extraordinarily challenging disruptions of COVID-19 pandemic, unlike any disasters seen in recent times. This study aims to provide a practical solution to supply chain (SC) disruptions by estimating the best forecasting models for demand fluctuations in the context of food and beverages. A method is proposed to predict SC disruptions and enhance SC resilience. Double exponential smoothing (DES) and the ARIMA model are adopted as forecasting approaches to estimate demand and optimum inventory quantities during three different periods of disruption associated with the COVID-19 outbreak. A downstream SC involving 2 distributors and 56 retailers is considered to elaborate inventory measurements (optimal inventory levels and total costs). The results demonstrate that distributors can reduce costs by dispensing with some retailers, particularly those who order low quantities and thus incur unjustified expenses. Furthermore, high accuracy is obtained, with minimal differences between the real data and the model’s forecast. Existing research has largely ignored supply disruptions in the distributor-retailer relationship. This study provides distributors and SC managers important knowledge on SC disruptions and identifies appropriate forecasting methods to increase SC resilience. It also provides distributors and other SC managers unprecedented insights on tackling crises of stability like COVID-19.

Constrained Stochastic Inventory Control Models for Multi-Item with Variable Demand

Constrained multi-item inventory system with variable demands are considered. The demand rates of five selected multi-item - Cowbell, Milo, SMA, Cerelac and Golden Morn were modeled as Weibull, Normal and Lognormal probability distributions respectively with the aid of chi-squared multinomial goodness-of-fit test. The respective probability distributions with estimated location parameters: 491.55, 536.92, 10.5, 2.1926 and 5.3103 were used as the basis of probabilistic inventory models to obtain dynamic EOQ for each item under each constraint subject to Kuhn, Karush and Tucker (KKT) conditions as against the use of simple averages in deterministic inventory models. The optimal values of these constraints: available warehouse space (124sq.ft), specified level of inventory (94 units), limited capital (76,671.52 naira) and number of orders (1/month) were obtained using the optimal EOQ values to establish optimal inventory level and constraints level/capacity for each item in order to avoid shortage or excess stock.