Incentive-based programmes enable grid operators to influence electricity demand during periods of high electricity loads, stabilising the grid and facilitating a better integration of renewable energy sources. While research has shown that these incentives are effective for power grids and that they can be implemented with appropriate restrictions for the incentivized entity, earlier research has not considered the impact these programmes may have on downstream members of the supply chain. This paper investigates a three-stage supply chain consisting of one manufacturer producing products to order for several wholesalers, who in turn satisfy the demand of their customers. The manufacturer receives and schedules orders from the wholesalers while also considering incentives in the form of load deferral when scheduling production orders to benefit from incentives set by the grid operator. A model was developed and numerical simulations were performed to examine how these incentive systems affect inventory levels and potential stockouts at the wholesalers. Additionally, we analyse how established inventory control policies are able to respond to uncertainty triggered by the incentive-based programme. Our findings provide initial insights into the broader implications of incentive-based programmes on supply chain dynamics.

Fast food businesses need to implement a series of inventory control policies that monitor and determine the level of raw material inventory needed to ensure proper management and avoid disruption to the company's operations. The objectives of this research include: 1. to determine the results of implementing an internal control system for raw material inventory; 2. to determine whether the internal control system is running well or not. The research object was carried out at one of thefast food restaurant in Madura, namely M2M Pamekasan Branch. The type of research is qualitative with a descriptive approach. Data sources are primary and secondary data. Data collection techniques through structured interviews. Data analysis techniques include: data collection, data analysis, data evaluation to drawing conclusions. The results of the study show: 1. The internal control system for raw material inventory implemented by the company includes: a. control environment that includes the attitudes of management and employees towards the importance of control in the company; b. risk assessments carried out by company management must be able to identify various risks faced by the company; c. control procedures which are various processes of efforts carried out by company management to enforce supervision/control of company operations; 2. With the implementation of the internal control system for raw material inventory, it greatly helps the company in ordering raw materials, checking until receiving goods from the head office, everything runs well and smoothly.

Demand forecasting for medical and consumable supplies in healthcare institutions is a challenging problem due to irregular usage patterns, seasonality, sudden demand spikes, and data sparsity, and inaccurate forecasts may lead to stock-outs, excessive inventory costs, and disruptions in patient care. This study proposes an anomaly-aware, hybrid and ensemble-based forecasting and decision support framework for short-term hospital inventory demand prediction using real-world operational data obtained from a hospital inventory management system. The proposed approach integrates density-based anomaly detection, material-level behavioral feature extraction, supervised time series transformation, and a multi-model ensemble architecture combining linear models, tree-based methods, and boosting-based learners, with model selection and weighting performed via time-series cross-validation. To ensure operational robustness, a multi-layer fallback strategy incorporating classical exponential smoothing and conservative heuristics is employed for data-scarce scenarios, and an interpretable rule-based forecast confidence score together with an integrated ABC–XYZ segmentation scheme is used to directly link forecasts with inventory control policies. Experimental results on real hospital inventory data demonstrate that the proposed framework significantly improves forecasting stability and accuracy compared to single-model approaches, particularly for heterogeneous and irregular consumption patterns, while providing a practical, explainable, and operationally actionable solution for hospital inventory management.

Optimizing spare parts inventory is critical for balancing system availability and resource efficiency in digitalized environments. Under the ( s , S ) inventory control policy, this paper develops a joint optimization model of reliability and spares inventory control for a k / n : G retrial system with the Bernoulli vacation mechanism. This model holds significant reference value and application prospects in the design of cloud computing systems. Through probabilistic analysis and Markov process methodology, we analyze the state probability and derive key performance and reliability indices of the system. Based on the important cost elements and performance indices, we provide the mathematical expectation of the total system cost per time unit. Then, we establish a cost-minimization inventory policy optimization model constrained by joint system availability and spare parts availability, using an Adaptive Hybrid Branch-and-Bound (AHBB) algorithm to determine the optimal inventory policy. Numerical experiments are provided to demonstrate the newly proposed model and analyze the effects of different parameters on the system reliability indices. Next, we give the optimal inventory ( s * , S * ) and the corresponding minimum cost for different k / n : G systems under the joint reliability constraint of spare parts and system. Finally, the cost-effectiveness of our scale-adaptive ( s , S ) policy and the efficiency of the proposed AHBB algorithm through comparative experiments is demonstrated.

Background :The application of principles for the implementation,management, and maintenance of point-of-care (POC) testing systemservice is unclear in tertiary healthcare settings in Nigeria. Hence, thestudy assessed these principles through the perceptions of healthcareprofessionals (HCPs).Methods:The cross-sectional study was conducted among 300 HCPs inthe University of Uyo Teaching Hospital (UUTH), southern Nigeria,using a pre-tested self-administered questionnaire that assessed the majorprinciples for POC system service. The questionnaires assessed theimplementation principles (determination of healthcare need, presenceof POC Organizing and implementation committee, POC testing policy/accountability protocols, direct involvement of Health care Professionals(HCPs) and the training and certification of operators), the managementprinciples (establishment of quality assurance and audit policies,establishment of maintenance and inventory control policies,establishment of documentary protocols) and the maintenance principles(accreditation and/or regulation of POC testing systems/devices and thecentral laboratory's involvement in effecting all the principles).Categorical data were summarized in frequency and percentages andpresented in tables and figures.Results:Regarding implementation principles, most respondents affirmednot having determined the healthcare need, clinical/operational/economic benefits, performance requirements, clinical risks, and costsbefore deployment (63.3%), not having any POC organizing/implementation coordinating committee (83.3%), no POC testing policy/accountability protocols (96.7%), and no training/certification ofoperators for POC systems/devices in the hospital and/or theirdepartments/units (91.7%) (p<0.001). On the management principles, mostrespondents affirmed negatively to having established quality assurance/audit policies (83.3%), maintenance/inventory control policies (91.7%),and documentary protocols for POC systems/devices in the hospital and/or their department/unit (96.7%) (p<0.001). Concerning the maintenanceprinciples, most respondents affirmed to no accreditation/regulationpolicy (73.3%) and involvement of the central laboratory regarding thePOCT systems/devices within the hospital and/or their departments/units (78.4%) (p<0.001).Conclusion:The level of application of POC principles is low withinUUTH based on current findings. This highlights a critical gap in currentoperational practices, posing potential risks to the quality of patientdiagnostic data. Immediate development/implementation of targetedprograms and enhanced compliance protocols to address these deficienciesis recommended.

Background :The application of principles for the implementation,management, and maintenance of point-of-care (POC) testing systemservice is unclear in tertiary healthcare settings in Nigeria. Hence, thestudy assessed these principles through the perceptions of healthcareprofessionals (HCPs).Methods:The cross-sectional study was conducted among 300 HCPs inthe University of Uyo Teaching Hospital (UUTH), southern Nigeria,using a pre-tested self-administered questionnaire that assessed the majorprinciples for POC system service. The questionnaires assessed theimplementation principles (determination of healthcare need, presenceof POC Organizing and implementation committee, POC testing policy/accountability protocols, direct involvement of Health care Professionals(HCPs) and the training and certification of operators), the managementprinciples (establishment of quality assurance and audit policies,establishment of maintenance and inventory control policies,establishment of documentary protocols) and the maintenance principles(accreditation and/or regulation of POC testing systems/devices and thecentral laboratory's involvement in effecting all the principles).Categorical data were summarized in frequency and percentages andpresented in tables and figures.Results:Regarding implementation principles, most respondents affirmednot having determined the healthcare need, clinical/operational/economic benefits, performance requirements, clinical risks, and costsbefore deployment (63.3%), not having any POC organizing/implementation coordinating committee (83.3%), no POC testing policy/accountability protocols (96.7%), and no training/certification ofoperators for POC systems/devices in the hospital and/or theirdepartments/units (91.7%) (p<0.001). On the management principles, mostrespondents affirmed negatively to having established quality assurance/audit policies (83.3%), maintenance/inventory control policies (91.7%),and documentary protocols for POC systems/devices in the hospital and/or their department/unit (96.7%) (p<0.001). Concerning the maintenanceprinciples, most respondents affirmed to no accreditation/regulationpolicy (73.3%) and involvement of the central laboratory regarding thePOCT systems/devices within the hospital and/or their departments/units (78.4%) (p<0.001).Conclusion:The level of application of POC principles is low withinUUTH based on current findings. This highlights a critical gap in currentoperational practices, posing potential risks to the quality of patientdiagnostic data. Immediate development/implementation of targetedprograms and enhanced compliance protocols to address these deficienciesis recommended.

This research was conducted with the primary objective of analyzing and evaluating the existing raw material inventory control policy at Pentol Bakso Bapak Rizki Business, and subsequently formulating an optimal inventory policy to minimize total costs. The study employed a descriptive quantitative approach through a case study methodology. Data concerning ordering costs, carrying costs, and annual raw material usage were collected via interviews and documentation, then quantitatively analyzed by applying the Economic Order Quantity (EOQ), Safety Stock (SS), and Re Order Point (ROP) methods to measure the efficiency of the current policy against the recommended optimal policy. The analysis results indicate that the inventory policy currently implemented by the business, with an ordering frequency of 48 times per year, incurs a total annual inventory cost of Rp 26,140,042. Based on the EOQ calculation, the optimal order quantity is determined to be 147 kg per order, with a significantly reduced recommended ordering frequency of 6 times per year. The adoption of this method successfully lowers the total inventory cost to Rp 25,233,452, yielding a potential cost saving of Rp 906,590 annually. Furthermore, the calculated Safety Stock of 6 kg and a Re Order Point at the 9 kg level provide robust guidelines for the business to ensure uninterrupted production flow and minimize the risk of raw material shortages.

This study addresses the problem of excess chicken egg inventory at agent warehouses caused by the mismatch between fluctuating demand and incoming supply, which frequently leads to product damage and financial losses. The research aims to design an inventory control policy based on demand forecasting and probabilistic inventory modelling to determine an ideal stock level. A quantitative approach is employed by first applying the two‑period Moving Average method, which produces an MSE of 6.17 and a MAPE of 0.10 with a forecast of 65 crates for the ninth period, and then integrating these forecasts into a probabilistic inventory model at a significance level of to obtain a reorder point of 214 crates and a safety stock of 60 crates. The results show that this integrated approach enables the agent to trigger replenishment precisely when on‑hand inventory reaches the reorder point, thereby maintaining an adequate service level while reducing overstock and the risk of spoilage. The combined use of a quantitative forecasting model and a probabilistic inventory framework constitutes the main contribution of this study, offering an integrated decision support scheme for determining both the optimal order timing and the appropriate buffer stock level in a perishable‑goods context.

We study a periodic review stochastic inventory control system for a single product at a single location with both fixed cost and random yield. If the random yield follows either two-point or uniform distribution, then some research works have been done in the literature. For other random yield distributions, the structure of the optimal inventory control policy has been an open problem for over three decades. A Negative Dominance (ND) property has been identified for the expected total cost function, which is approximated by a piecewise linear function, in each period. Under some very mild requirements about the random yield distribution and the single-period cost function, a period-dependent lower bound for initial inventory levels in any period is provided such that the expected total cost function indeed has the ND property at any initial inventory level below this lower bound and a period-dependent upper bound for initial inventory levels in any period is also provided such that the optimal order quantity is zero at any initial inventory level above this upper bound. We are able to show that these two bounds will not tend to infinity when the number of periods tends to infinity and, through numerical experiment, we show that the gap between these two bounds is quite small. Even further, with the help of this ND property, the search of the optimal order quantity at any initial inventory level below this lower bound in any period is as simple as the case with zero fixed cost even if the random yield follows neither uniform nor two-point distribution.

Inventory control is a policy designed to manage and regulate the supply, storage, and accessibility of items in an organisation. Its primary purpose is to ensure that there is always enough stock on hand to meet demand while minimising the costs associated with holding and maintaining inventory. XYZ, a pharmaceutical distributor, faces challenges in optimising its inventory control policy, resulting in frequent stockouts, overstocks, and product damage during distribution. This study aims to design an optimised inventory control policy to enhance the Company's drug inventory system performance. The research begins by classifying inventory items using the ABC method and calculating the inventory turnover ratio (TOR). A scatter diagram is then constructed based on historical demand data to identify product demand patterns. Subsequently, three forecasting methods are applied, and the most accurate method is selected based on the Mean Absolute Percentage Error (MAPE). Inventory issues are categorised, with Class A drugs managed using the Q model and Classes B and C using the P model. The Hadley-Within method with backorder is employed for inventory problem-solving. The proposed inventory policy results in a total inventory cost of IDR 339,221,858 and a TOR of 4.26. In contrast, the existing system incurs a total inventory cost of IDR 1,338,286,901 with a TOR of 8.7. Implementing the optimised policy would lead to a cost reduction of 74.65%, equating to IDR 999,065,044, and improved inventory efficiency. This study provides a practical approach for pharmaceutical distributors to enhance inventory management, reduce costs, and optimise supply chain performance.

We study the sample complexity of offline learning for a class of structured Markov decision processes (MDPs) describing the inventory control system with fixed ordering cost/setup cost, a fundamental problem in supply chains. We find that a naive plug-in sampling-based approach applied to the inventory MDPs leads to strictly lower sample complexity bounds compared to the optimal bounds recently obtained for the general MDPs. More specifically, in the infinite-horizon discounted cost setting, we obtain an O ~ ( min { ( S ¯ − s _ ) 2 ( 1 − γ ) 2 ϵ 2 , 1 ( 1 − γ ) 4 ϵ 2 } ) sample complexity bound, where ( S ¯ − s _ ) 2 corresponds to the number of state-action pairs in a generic MDP with state space S and action space A . As such, O ~ ( ( S ¯ − s _ ) 2 ( 1 − γ ) 2 ϵ 2 ) improves on the optimal generic reinforcement learning (RL) bound Θ ~ ( ( S ¯ − s _ ) 2 ( 1 − γ ) 3 ϵ 2 ) (when directly applying Θ ~ ( | S | | A | ( 1 − γ ) 3 ϵ 2 ) here) by a factor of ( 1 − γ ) − 1 , and O ~ ( 1 ( 1 − γ ) 4 ϵ 2 ) is able to completely remove the dependence on state and action cardinality. In the infinite-horizon average cost setting, we obtain an O ~ ( ( S ¯ − s _ ) 2 ϵ 2 ) bound, improving on the generic optimal RL bound Θ ~ ( ( S ¯ − s _ ) 2 t m i x ϵ 2 ) (when directly applying Θ ~ ( | S | | A | t m i x ϵ 2 ) here) by a factor of t m i x , and hence removing the mixing time dependence. By carefully leveraging the structural properties of the inventory dynamics in various settings, we are able to improve on those “best-possible” bounds developed in the RL literature. Our results demonstrate the drawbacks one could face by blindly following RL algorithms and the necessity of designing sample efficient algorithms that properly incorporate the special structures of the inventory systems.

This paper proposes a novel inventory control policy called the dynamic order policy. The proposed policy dynamically adjusts the reorder quantity based on two threshold levels (s∗, s, S, S∗), enhancing its flexibility compared to the traditional policy (s, S). The new policy allows placing a larger order when the inventory drops below a threshold of s∗ and places a standard order when the inventory falls between the thresholds of s∗ and s. A genetic algorithm is employed to optimize the inventory decision parameters due to its ability to solve complex, nonlinear discrete problems. The model is tested under various simulation scenarios by demand rate, lead time, and customer volume. The results demonstrate that the proposed policy reduces the total average cost by up to 6.7% compared to the traditional policy. This dynamic framework presents a promising alternative for managing uncertain inventory environments with backlogs and variable lead times.

Amidst the global focus on sustainable development and environmental well-being, the adoption of green Supply Chain (SC) management has emerged as a pragmatic solution for mitigating Greenhouse Gas (GHG) emissions across various operational facets. This study employs a comprehensive approach, concurrently examining GHG emissions in production, transportation, and warehousing within a two-echelon SC framework involving a single vendor and buyer. Their joint objective is to optimize profitability while adhering to governmental regulations targeting GHG emissions reduction. The vendor’s visits to downstream sites are pivotal in fulfilling ordered products dispatched following fixed lead times. The buyer faces stochastic demand and employs a periodic inventory review policy, making service-level decisions contingent on market demand volatility and vendor visit intervals. The vendor’s production processes, requiring energy consumption, prompt investments in green technology to curtail emission rates. Governmental involvement extends to environmental safeguarding through tax policies. Introducing a game-theoretic approach, this study illuminates decision-making processes among SC stakeholders regarding replenishment strategies and emission reduction measures. Mathematical models and solutions for decentralized and centralized setups scrutinize how the SC leader orchestrates a profit-sharing contract to incentivize follower engagement in a comprehensive optimization strategy. The application of the proposed approach is investigated using real-world data from the pharmaceutical SC. In particular, a case study of the inventory control policy at the University of Michigan’s Central Pharmacy is presented to validate the model empirically. The results highlight the significant potential of green investments in reducing GHG emissions and emphasize the critical role of government incentives in driving these investments. The proposed coordination mechanism is shown to markedly enhance supply chain performance. Analytical findings indicate that government incentives lower both the minimum and maximum profit-sharing thresholds necessary for effective coordination, whereas high emission taxes without complementary incentives may discourage collaboration. Sensitivity analyses further reveal how holding costs, emission intensities, and energy prices differently affect service levels and green investments across decentralized and centralized structures. Notably, the study quantifies a 3% decline in service level when GHG emission taxes increase from 0.05 to 0.2, illustrating the need for balanced policy design in pharmaceutical supply chains. Computational experiments, calibrated with real-world pharmaceutical data, validate the model and demonstrate up to a 52% increase in total supply chain profit, over 20% improvement in service level, and reductions of up to 70% in transportation-related and 11.5% in production-related GHG emissions. These findings offer actionable insights for aligning environmental sustainability with profitability through contract-based coordination mechanisms.

Spare parts management is crucial in today's production environments to ensure high machine availability. The joint consumption of spare parts due to maintenance activities is often considered to optimise their inventory control performance. The design of inventory replenishment policies for clusters rather than for individual parts to deal with jointly incurred costs is known in the literature as the joint replenishment problem. We propose a two-step framework that hierarchically clusters a set of parts according to several similarity measures and optimises the inventory control policy for each cluster in each hierarchy level. Various hierarchical clustering approaches are considered, and, for each level of the generated hierarchies, two different joint replenishment policies are used, namely the continuous review ( Q , S ) policy and the periodic review ( T , S ) policy. We conduct an empirical investigation using spare parts data from an automotive company to test the performance of the proposed framework. Through a multi-scenario analysis, we demonstrate that: (i) the clustering of spare parts with joint replenishment leads to a substantial cost reduction in comparison with the standard ABC classification, k-means clustering and single-item replenishments, regardless of the reordering policy and (ii) different clustering approaches lead to very similar performance from a total cost perspective.

PT Solusi Bangun Indonesia is a cement manufacturing company that utilizes FABA (Fly Ash and Bottom Ash) as one of its supplementary raw materials. A significant issue faced by the company is the overstock of FABA, which contributes to increased storage costs and poses a risk of environmental pollution. This research aims to develop an optimal inventory planning strategy for FABA by integrating time series forecasting methods with a min-max inventory control policy. The study utilizes historical FABA demand data from July 2023 to June 2024. Three forecasting techniques are applied: moving average, weighted moving average, and exponential smoothing. Accuracy evaluation indicates that the moving average method with a parameter of n = 3 produces the lowest forecasting error, as measured by MSE and MAD. The forecasting model is further validated using the Moving Range Chart to ensure its stability and reliability. The calculated safety stock at a 95% service level is 191.34 tons, with a minimum inventory of 758.83 tons and a maximum of 1,326.31 tons. This forecasting-based min-max policy effectively enhances inventory efficiency, mitigates risks of overstock and stockout, and supports operational sustainability.

In this paper we examine whether empirical method can replace bootstrapping in intermittent demand stock control based on simulation. Thus, we generate artificial demand data with 30; 50 and 70 % of zero demand periods and simulate reorder point/fixed order quantity inventory control policy using past stock movement simulation and the local search to obtain the optimal trade-off between holding and ordering costs and the required fill rate for order lead time 2; 6; 12 and 18 periods. The outputs from simulation experiments prove that empirical method outperforms bootstrapping in term of the consumption of computational time while maintaining similar ability to overestimate lead time demand. Thus, empirical method can become a suitable substitute of bootstrapping in the local search. Moreover, it can be successfully used to generate an initial reorder point in a more on a one-way neighbourhood search oriented optimization. As empirical method copes both with theoretical and empirical demand distributions and does not require a deciding on number of sampling runs, an optimization of smoothing constants based on a selection of an appropriate accuracy metric, an adoption of a demand classification schemes or a data aggregation it is well predetermined to become an important part of a simulation-optimization software solution focusing on sporadic demand inventory control in large scale real life tasks.

Uncontrollable factors like government policies, economic conditions and natural disasters significantly affect market demand in inventory management. We propose a stochastic framework with a market variable for real-time demand updates, using Bayesian estimation to infer the variable before each periodic review. This integrates with the location-scale demand distribution to estimate the expected location and scale parameters. By minimising time-varying expected costs in a single-period setting, we derive optimal ordering quantities for both periodic and continuous schemes. Numerical optimisation determines maximum order-up-to levels, enabling optimal multi-period inventory control policies. Theoretical analysis shows that during early market recovery, extended review periods may reduce orders, while overly short periods can obscure negative trends by accumulating demand. Numerical experiments indicate risk-averse behaviour in negative markets, with proactive decisions mitigating losses. Our real-time update strategy outperforms conventional stationary assumptions, with continuous ordering excelling under high demand uncertainty by enhancing real-time inventory management and mitigating market fluctuations. These findings offer valuable insights for inventory managers to optimise decision-making and reduce costs in volatile market environments.

The inventory location problem under periodic inventory control policy (R, S): modeling, solving and analysis

This study addresses the limitations of rigid classification boundaries in the VIKORSort method, a variant of the VIKOR multi criteria decision making approach, by incorporating fuzzy set theory to enhance classification flexibility and improve decision making effectiveness. The proposed method redefines the crisp boundaries of VIKORSort using fuzzy set theory with a trapezoidal fuzzy membership function, which gives the percentage of belonging to each class for each alternative. This enables smoother transitions between predefined classes and offering a more nuanced representation of alternatives’ characteristics. An empirical investigation evaluates the inventory performance of the fuzzy and non-fuzzy variants of VIKORSort, using data from a central warehouse managing 4799 spare parts in the textile industry. Inventory performance is analyzed under a reorder point inventory control policy and a target Cycle Service Level (CSL) constraint. Three CSL scenarios are considered to calculate the costs associated with the classification approaches. The results reveal that the proposed VIKOR-Fuzzy Sorting method outperforms the conventional approach in both cost efficiency and flexibility. In addition to providing a more precise depiction of alternatives near classification boundaries, the fuzzy method enables more effective assignment of target service levels in inventory management. This study thus offers practitioners a robust solution for addressing complex sorting challenges, particularly in inventory systems, by resolving boundary issues inherent in traditional approaches. Additionally, this approach enables businesses to develop more effective service level policies, enhancing overall operational performance. • Fuzzy boundaries aid smooth transitions between predefined classes more than crisp. • Classification of inventory according to inherent characteristics. • VIKOR excels in compromise under conflicting criteria versus other MCDM.

The imbalance between supply and demand for slot resources and empty containers has led to resource waste and excessive operating costs for liner companies. Moreover, intense competition among ports has made both container ship slots and port equipment resource allocation inefficient. To address these challenges, this paper aims to solve the collaborative optimization problem of slot allocation and empty container repositioning within port clusters concerning inventory control. A cooperative possession strategy and a hybrid (T, s) inventory control policy are incorporated in this paper. A novel mixed-integer programming model is proposed, enabling us to simultaneously track slot allocation, empty container repositioning, empty container leasing, and slot renting. To solve the model, a new branch-and-bound algorithm based on Lagrangian relaxation and the ascendancy principle (BBLRAP) is developed. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm. The results show that the new collaborative optimization method, incorporating the cooperative possession strategy and (T, s) inventory policy, can increase liner company revenues by expanding market share, reducing costs, and improving the utilization of slot resources, ultimately achieving a win–win outcome for both liner companies and their partners. Compared to state-of-the-art studies, the following paper makes new contributions to proposing a cooperative possession strategy within port clusters for the first time. This paper ensures that liner companies and partners achieve a win–win situation in the cooperative game, expanding market shares and improving customer satisfaction.