Explore the vast range of titles available.

Purpose: The exact estimation of the fill rate in the lost sales case is complex and time consuming. However, simple and suitable methods are needed for its estimation so that inventory managers could use them. Design/methodology/approach: Instead of trying to compute the fill rate in one step, this paper focuses first on estimating the probabilities of different on-hand stock levels so that the fill rate is computed later. Findings: As a result, the performance of a novel proposed method overcomes the other methods and is relatively simple to compute. Originality/value: Existing methods for estimating stock levels are examined, new procedures are proposed and their performance is assessed.

This study examines several papers to study inventory models with back orders to provide a simpler solution procedure for EOQ/EPQ inventory models by using Arithmetic Geometric Mean inequality or Cauchy Bunyakovsky Schwarz inequality. We will point out that a paper implicitly adopted the fill rate from a published article without proper citation to convert two-variable minimum problems into one-variable problems. Hence, his simpler solution procedure is standing on the shoulder of a giant. We also provide detailed examinations for other three related papers that was related to our studied article to point out their contributions and questionable results.

Alcohol use disorders (AUD) are prevalent and responsible for substantial morbidity and mortality; yet efficacious treatments are underused. Previous studies have identified demographic and clinical predictors of medication fills, yet these studies typically do not include patients who were prescribed a medication but did not fill it. To examine rates of and factors associated with prescription order and prescription fill for medications for AUD (MAUD) among individuals diagnosed with AUD in outpatient settings. In a cross-sectional analysis, we used multivariate logistic regression to identify factors associated with prescription order and fill. We used data from the Optum Labs Data Warehouse that linked 2016-2021 de-identified claims and electronic health record (EHR) data, allowing us to observe prescription orders and whether they were filled. We identified 14,674 patients aged ≥ 18 who had an index outpatient encounter with an AUD diagnosis in the EHR. We computed the proportion for whom a MAUD prescription was ordered within 1 year of index visit, and for whom one was filled within 30 days of the order. 5.8% of the sample had a MAUD prescription order within 1 year of their index visit. Among those with an order, 87% filled their MAUD prescription within 30 days of receipt (i.e., 5.1% of full sample). After multivariable adjustment, receipt of a MAUD prescription order was more likely for patients who were female (adjusted odds ratio (aOR) [95%CI] = 1.44 [1.24-1.67]), or had moderate or severe AUD (1.74 [1.50-2.01]). Patients receiving an order were more likely to fill it if they had a comorbid mental disorder (1.64 [1.09-2.49]). The low rate of prescription orders was notable. Low use of MAUD appears to result chiefly from prescription order decisions, rather than from prescription fill decisions made by patients.

In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity to meet predetermined fill rate requirements? We develop a new approach for network capacity configuration and allocation and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services. We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that “long chain is almost as good as the fully flexible network”: for given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last-mile delivery operations and in semiconductor production planning, using real data from two companies. This paper was accepted by Terry Taylor, operations management.

The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d’Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049$/kWh as compared to 0.15 $ /kWh of an existing system in the literature.

Resource pooling strategies have been widely used in industry to match supply with demand. However, effective implementation of these strategies can be challenging. Firms need to integrate the heterogeneous service level requirements of different customers into the pooling model and allocate the resources (inventory or capacity) appropriately in the most effective manner. The traditional analysis of inventory pooling, for instance, considers the performance metric in a centralized system and does not address the associated issue of inventory allocation. Using Blackwell’s Approachability Theorem, we derive a set of necessary and sufficient conditions to relate the fill rate requirement of each customer to the resources needed in the system. This provides a new approach to studying the value of resource pooling in a system with differentiated service requirements. Furthermore, we show that with “allocation flexibility,” the amount of safety stock needed in a system with independent and identically distributed demands does not grow with the number of customers but instead diminishes to zero and eventually becomes negative as the number of customers grows sufficiently large. This surprising result holds for all demand distributions with bounded first and second moments. This paper was accepted by Martin Lariviere, operations management.

This study addresses the challenge of aligning inventory forecasting with sustainability and service level goals in re-order point systems. It introduces a semiparametric forecasting method based on exponential smoothing and M-estimation, designed to directly model reorder levels under fill rate (P2) constraints. The proposed approach is benchmarked against state-of-the-art techniques, including Generalized Autoregressive Score (GAS) models, volatility-adjusted smoothing, and DeepAR—a deep learning model for probabilistic time series forecasting. Using monthly demand data from the M3 competition, empirical evaluation demonstrates that the semiparametric method achieves high service level accuracy with low inventory and logistics costs, particularly under short lead times. DeepAR shows strong performance in minimizing inventory levels but tends to underestimate stock requirements under high service level targets. A hybrid strategy combining forecasts from multiple models proves robust across scenarios, reducing forecast risk. The findings highlight the potential of integrating traditional statistical methods with AI-based approaches to support resource-efficient inventory management. By minimizing excess stock and backorders, the proposed methods contribute to reducing environmental impact, offering practical solutions for organizations seeking to balance operational efficiency with sustainability.

Manufacturing companies often provide supplies of raw materials so that the production process runs smoothly. Initial observations on halal frozen food businesses that use chicken and shrimp as the main ingredients show that material procurement activities are carried out daily and materials are purchased more when prices on the market are down. The company’s historical data shows a shortage of chicken raw material supplies in April due to increased demand, while raw material supplies were insufficient, and raw material prices tended to be high. This research was conducted to measure the performance of raw material inventory at halal frozen food companies using the inventory turnover rate, inventory days of supply, and fill rate methods and to calculate the optimal amount of raw material inventory with EOQ, safety stock, and reorder point (ROP). The inventory turnover rate calculation for chicken raw materials shows the best value in June 2022 of 68.35, the results of the analysis of the best inventory turnover rate for shrimp raw materials in March 2022 are 68.79. The calculation of inventory days of supply for chicken raw materials shows the best value in December 2022 of 0.98 days, calculation of inventory days of supply for shrimp shows the best value in February 2023 of 0.91 days. The fill rate is 100%. The research results show that inventory performance in the halal frozen food business is good. To avoid raw material shortages in the future, companies can use the Economic Order Quantity guide in ordering raw materials.

PurposeThe impact of product variety decisions on fill rate, inventory and sales performance in a consumer goods company has been examined. From a marketing perspective, it is possible to leverage sales, reach new segments and consequently increase competitiveness when there is a greater product variety on the market. However, operations and logistics professionals indicate potential impacts on the supply chain, such as production, storage and distribution complexity. The nature of the product variety-cost-sales performance relationship is not clear, and empirical evidence about whether and how operations cost and sales performance increases with variety is inconclusive.Design/methodology/approachThe multiple linear regression and the Tobit regression techniques were applied over a seven-year horizon of data from a business intelligence platform of a consumer goods company.FindingsOur results show that sales performance is negatively associated with product variety. The total effect of product variety on sales performance has been examined, including both the direct effect and the indirect effect through inventory and fill rate. Therefore, the findings provide a comprehensive understanding of the impact of product variety on operations and sales performance.Originality/valueSeveral studies have researched the impact of product variety on fill rate, inventory and sales performance separately; however, the research of the impact and the relationship of these factors is scarce and limited.

Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics.