Variable Demand Research Articles

Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Optimal Design of an Off-Grid Solar Energy System Integrated with a Diesel Generator for Urban Areas in Pakistan

The growing energy demand in Pakistan, coupled with the challenges posed by reliance on imported fossil fuels, necessitates the exploration of alternative energy solutions. This study presents the design and techno-economic analysis of an off-grid hybrid energy system tailored for a residential neighborhood in Karachi, Pakistan. The system integrates individual solar energy solutions for seven housles, supplemented by a shared diesel generator to ensure a reliable power supply, particularly during load-shedding and grid outages. The study utilizes HOMER Pro software to model and optimize various configurations, taking into account local solar insolation levels and seasonal variability in energy demands. The optimized system includes photovoltaic (PV) panels, battery storage, and a shared diesel generator with individual connections and meters, with each house receiving a customized solution based on its specific energy requirements, ranging from 15 kWh/day to 45 kWh/day. The shared diesel generator is designed to reduce the need for large battery banks, thereby minimizing both capital and operational costs. The results demonstrate a cost-effective solution with a Levelized Cost of Energy (LCOE) of $0.2959/kWh and a Net Present Cost (NPC) of $15,562.55 over a 25-year period. This research highlights the potential for implementing such systems in urban areas of Pakistan, offering a sustainable, reliable, and economically viable alternative to conventional energy sources.

Solving the Price-Setting Newsvendor Problem with Parametric Operational Data Analytics (ODA)

We study the data-integrated, price-setting newsvendor problem in which the price–demand relationship is described by some parametric model with unknown parameters. We develop the operational data analytics (ODA) formulation of this problem that features a data-integration model and a validation model. The data-integration model consists of a class of functions called the operational statistics. Each operational statistic maps the available data to the ordering decision. The validation model finds, among the set of candidate operational statistics, the ordering decision that leads to the highest actual profit, which is unknown because of the unknown demand parameters. This ODA framework leads to a consistent estimate of the profit function with which we optimize the pricing decision. The derived quantity and price decisions demonstrate robust profit performance even when the sample size is very small in relation to the demand variability. Compared with the conventional approach with which the unknown parameters are estimated and then the decisions are optimized, the ODA framework produces significantly superior performance in the mean, standard deviation, and minimum of the profit, suggesting the robustness of the ODA solution especially in the small-sample regime. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02227 .

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue.

Biofuel has gained significant attention as a potential source to meet fuel demands instead of fossil fuel. The price of biofuel and alternative fuel have a considerable impact on biofuel demand. Thus, it is important to design a biofuel supply chain network that incorporates the biofuel price into an elastic demand. More precisely, a variable demand, including customer importance level (to the environment), biofuel price, and substituted fuel price, is considered in this research. Furthermore, this research presents a bi-objective mixed-integer quadratic formulation that aims to maximize the total profit of the supply chain and carbon absorption in harvesting areas. The problem is solved by the ε-constraint and lagrangian relaxation methods due to its complexity. Moreover, substituted fuel price uncertainty is addressed by two-stage stochastic programming. Finally, a real case study utilizing the data envelopment analysis approach is applied to assess the efficiency and currency of the addressed model. Several consequences are illustrated in the case study, such as rich areas for exporting algae, suggesting hub locations for biofuel production, etc.

Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework

The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addressing critical supply chain issues, including inventory optimization, demand variability, and regulatory compliance. The proposed framework details essential components—data collection, processing, integration with existing systems, and real-time decision-making—and outlines critical success factors for effective implementation. By offering insights into the transformative potential of predictive analytics, this paper provides actionable recommendations for pharmaceutical companies seeking to build resilient and responsive supply chains. Future research directions are also proposed, emphasizing the need for adaptable predictive models and the ethical implications of AI applications in pharmaceuticals. Keywords: Pharmaceutical Supply Chain, AI-driven Predictive Analytics, Machine Learning, Demand Forecasting, Inventory Optimization, Regulatory Compliance.

Coordinated planning for offshore wind and electricity–hydrogen system based on SDDP: A case study of coastal provinces in China

The coordinated development of offshore wind and electricity–hydrogen systems (OW-EHS) has the potential to enhance the utilization of renewable energy sources and achieve carbon neutrality. This article presents a case study of nine coastal provinces in China and designs four scenarios to analyze the economic benefits and potential hydrogen production capacity of OW-EHS. A multi-stage stochastic dual dynamic programming based approach is proposed for integrating OW-EHS, exploring offshore wind energy as both a primary electricity source for coastal regions and a clean, sustainable energy source for electrolyzers. It accounts for variations in hydrogen demand, wind uncertainty, load profiles, and electricity price dynamics, offering a holistic perspective on the feasibility and benefits of collaborative offshore wind and electricity–hydrogen systems. A comparative analysis highlights the effectiveness of the proposed planning approach, demonstrating its capacity to maximize the overall system benefits, encompassing both the optimal levelized cost of energy and hydrogen production. The new discoveries elucidate the unique attributes and advantages of each coastal province in OW-EHS. This research provides a tailored blueprint for coastal provinces in China to harness their offshore wind potential and accelerate progress towards carbon neutrality.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

A Stochastic Sustainable Closed-Loop Supply Chain Networks for Used Solar Photovoltaic Systems: Meta-Heuristic Comparison and Real Case Study

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

Intra-articular knee injections in patients with primary osteoarthritis in a tertiary clinical setting: a best practice implementation project.

This project aimed to improve physicians' adherence to evidence-based practices regarding the selection and administration of intra-articular knee injections for patients with osteoarthritis. The project also aimed to empower patients by increasing their awareness of these evidence-based practices. In the management of knee osteoarthritis, intra-articular injections are commonly used when initial treatments prove inadequate. However, issues such as patients' demand for rapid relief and variability in physicians' familiarity with optimal practices often lead to suboptimal treatment decisions and potential adverse outcomes. This project followed the JBI Evidence Implementation Framework, which is grounded in an audit, feedback, and re-audit process. Initially, physicians were surveyed on the topic under consideration, and hospital records were reviewed. Subsequently, targeted interventions were implemented, including briefings and the distribution of informative flyers. The baseline audit indicated low adherence with all audit criteria. Various parameters, such as adherence to best clinical practices, informed decision-making by patients, and the recommendation of alternative treatment modalities, were significantly lacking. Following the interventions, substantial improvements were observed, including higher rates of adherence to best clinical practices by physicians for multiple criteria, such as stepwise referral to conservative therapy options and the content of intra-articular injections. Adherence to best clinical practices can be improved through educational briefings for physicians on the most current evidence-based treatment practices for injectable substances in knee osteoarthritis. The active engagement of patients, facilitated by informative flyers and physician assistance, contributed to better involvement in their treatment choices. However, the participation of health care institutions and providers as well as lifelong education practices may be necessary for more comprehensive and sustainable changes. http://links.lww.com/IJEBH/A277.

Cardiovascular Response and Locomotor Demands of Elite Basketball Referees During International Tournament: A Within- and Between-Referee Analysis.

There is little knowledge about within- and between-referee variation (WBRV) in cardiovascular responses (CVR) and locomotor game demands (LMD). Thus, the primary aim of this study was to assess the WBRV of CVR and LMD in male basketball referees during elite international games in preparation [e.g., warm-up (WU) and re-warm-up (R-WU)] and active game phases. The secondary aim was to explore quarter-by-quarter differences in CVR and LMD. Thirty-five international male referees took part in this study (age, 40.4 ± 5.4 years; body height, 184.9 ± 5.7 cm; body weight, 85.1 ± 7.5 kg; BMI, 24.0 ± 1.7 kg × m-2; fat%, 18.8 ± 4.7% and VO2max, 50.4 ± 2.2 L × kg-1 × min-1. In total, 76 games (e.g., 228 officiating cases) were analyzed during the FIBA elite men's competition. They officiated 4.5 games on average (range 3-9 games). Each referee used the Polar Team Pro system to measure CVR [e.g., heart rate (HR), time spent in different HR intensity categories] and LMD (e.g., distance covered, maximal and average velocity, and number of accelerations). Results showed that the referees had bigger WBRV during the active and preparation (e.g., W-U than R-WU) phase when variables of higher CVR and LMD intensity were observed (e.g., time spent at higher HR zones, distance covered in higher speed zones). The WBRV, CVR, and LMD were higher during WU than R-WU. Moreover, the referees had a lower CVR and LMD in the second half. In conclusion, the referees should establish and follow consistently a game-to-game preparation routine and attempt to spread their on-court preparation time equally within the crew. A half-time preparation routine should be improved to re-establish a sufficient activation level similar to that achieved in pre-game preparation.

An Exploratory Study of Contextual Control Modes in Teamwork.

This study investigated the relationship between human team member contextual control and team performance under time constraints. Contextual control modes, which describe different strategies for action selection in dynamic environments, characterize how humans maintain performance under variable demands. Control modes have not yet been studied in teamwork settings. Modeling of the cross-level interaction between team members' control modes and emerging team behaviors can improve understanding of effective teamwork in dynamic environments. A human-subjects study explored the relationship between individual contextual control and team performance. Questionnaires about contextual control were used to elicit individual control modes. Analysis compared team members' control modes and investigated how control modes changed under varying time pressures. Participant's control modes differed in their look ahead horizon, the extensiveness of prior action evaluation, and their prior experience. Many team members shifted control modes during trials, resulting in both convergence and divergence of paired control modes. No effects on communication rate were found due to changes in team members' control modes, but partially significant findings may suggest that the control mode divergence affects performance. Teams can operate in multiple control mode configurations that change dynamically according to context. Further research with an increased sample size is warranted to analyze how time constraints influence team members' control modes and overall teaming processes and whether divergence of team control mode is favorable under time pressures. Further study of contextual control in teams may help improve team design to better support teams in coping with time constraints in dynamic environments.

Physical Activity and Pregnancy Norms Among Daasanach Semi-Nomadic Pastoralist Women in Northern Kenya.

In subsistence populations, high physical activity is typically maintained throughout pregnancy. Market integration shifts activity patterns to resemble industrialized populations, with more time allocated to sedentary behavior. Daasanach semi-nomadic pastoralists living in northern Kenya face lifestyle heterogeneity due to the emergence of a market center. We investigate how Daasanach women manage the energetic demands of pregnancy with subsistence labor tasks and how market integration relates to variation in energetic demands, physical activity, and coping strategies. We conducted nine focus group discussions with 72 pregnant women. We also deployed wrist-worn fitness trackers with 21 pregnant women in two community types: central or peripheral to the market center to capture variation in market integration. Data from focus group discussions were analyzed using thematic analysis. We used multiple linear regression to examine the relationship between gestational age and physical activity. We identified themes of increased fatigue, diet restrictions, and assistance with labor tasks during pregnancy. Gestational age negatively predicted mean daily steps, with a decrease of 1160 ± 437 steps per day with each consecutive pregnancy month. Stratified by community type, gestational age only negatively predicted mean daily steps for peripheral communities, with a decrease of 1443 ± 629 steps per day with each consecutive pregnancy month. Results suggest that physical activity differs with market integration early, but not late, in pregnancy. Daasanach women cope with the energetic demands of pregnancy by reducing physical activity late in pregnancy and receiving assistance with labor tasks from family and neighbors.

The Potential for the Use of Hydrogen Storage in Energy Cooperatives

According to the European Hydrogen Strategy, hydrogen will solve many of the problems with energy storage for balancing variable renewable energy sources (RES) supply and demand. At the same time, we can see increasing popularity of the so-called energy communities (e.g., cooperatives) which (i) enable groups of entities to invest in, manage, and benefit from shared RES energy infrastructure; (ii) are expected to increase the energy independence of local communities from large energy corporations and increase the share of RES. Analyses were conducted on 2000 randomly selected energy cooperatives and four energy cooperatives formed on the basis of actual data. The hypotheses assumed in the research and positively verified in this paper are as follows: (i) there is a relationship between hydrogen storage capacity and the power of RES, which allows an energy community to build energy independence; (ii) the type of RES generating source is meaningful when optimizing hydrogen storage capacity. The paper proves it is possible to build “island energy independence” at the local level using hydrogen storage and the efficiency of the power-to-power chain. The results presented are based on simulations carried out using a dedicated optimization model implemented by mixed integer programming. The authors’ next research projects will focus on optimizing capital expenditures and operating costs using the Levelized Cost of Electricity and Levelized Cost of Hydrogen methodologies.

A Durable Textile With Advanced Thermal Functions and Electromagnetic Shielding.

In the face of increasingly variable cold climates and diverse individual temperature regulation demands, personal thermal management (PTM) textiles with electromagnetic shielding have obtained significant attention. However, the PTM textiles face several challenges, including single heating mode, insufficient durability, and complex preparation processes. Herein, an all-day PTM textile Cotton@PDA/AgNPs (CPANS) with energy-free PRH, energy-saving solar heating, compensatory electricalheating, electromagnetic interference (EMI) shielding, and outstanding durability is fabricated by sequentially growing polydopamine (PDA) and silver nanoparticles (AgNPs) on the cotton fabric (CF). The CPANS exhibits low mid-infrared emissivity (36.6%) and high absorptivity (70.8%), which guarantees the energy-saving heating capability. Moreover, the conductivity of the CPANS is ≈11109Sm-1, enabling an electrical heating temperature of ≈177°C under a low voltage of 1.1V and superb EMI shielding effectiveness (≈60dB). The remarkable adhesive properties of the PDA ensure that the desired durability of the CPANS remains high even after rigorous physical treatments. This innovation shows enormous potential for wearable integrated garments in the future and offers a new ideal for PTM fabrics in the cold.

A Comparison of Match External Load Demands Across Women's Collegiate Field Sports.

Kuhlman, NM, Jagim, AR, Jones, MT, Feit, MK, and Fields, JB. A comparison of match external load demands across women's collegiate field sports. J Strength Cond Res XX(X): 000-000, 2024-Historically, soccer (SOC), lacrosse (LAX), and field hockey (FH) have been grouped together as "field sports" due to their intermittent nature of match play and similar physiological demands. However, questions have emerged regarding potential variations in demands between sports. The purpose of this study was to compare external match workloads between collegiate women's SOC, LAX, and FH, and examine within-sport positional differences. Athletes (SOC: n = 15; LAX: n = 15; FH: n = 14) wore a global positioning system device during all matches throughout a competitive season. External load metrics included in the analysis were total distance (TD), sprint distance (SD), accelerations (>3 m·s-2), sprint efforts (SE), player load (PL), PL per minute (PL/min), top speed (m·s-1), and distances covered in select speed zones. Soccer performed higher external load volumes (TD, PL) (p ≤ 0.025) but incurred lower intensities (SD, SE, accelerations) (p < 0.001) compared with LAX and FH. Within-sport positional differences were apparent in SOC (p < 0.001-0.018), LAX (p < 0.001-0.045), and FH (p < 0.001-0.004). However, FH exhibited more positional variance in external load metrics compared with SOC or LAX. Field hockey midfielders covered greater volumes (TD, PL) compared with attackers and defenders (p < 0.001), while attackers sustained greater intensities (SD, SE, top speed) compared with midfielders and defenders (p < 0.004). Results indicate that there are significant differences in external workload parameters between sports and across positions. Practitioners should tailor sport-specific and position-specific programs to match demands, and future research should explore the proposed categorizations of LAX as a "fast collision invasion sport," FH as a "fast contact invasion sport," and SOC as a "slow contact invasion sport."

Predicting financial performance with intellectual capital using machine learning

Purpose This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success. Design/methodology/approach Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance. Findings Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees. Originality/value By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

An integrated policy approach for sustainable decarbonization pathways of energy system in a city under climate change scenarios

An integrated assessment model has been developed to estimate useful energy demand, followed by an optimization model for planning the energy supply. Four scenarios have been developed. The findings of this study highlight the impact of behavioral, climate change, and socio-economic factors on energy demand and supply. Specifically, these factors can lead to significant variations in demand, such as a more than 3.48-fold difference in cooling demand or a 1.75-fold difference in the transportation demand by cars across various scenarios.The use of a dual-fuel car and electric motorcycle is suggested in all scenarios. Waste processing has been done with a landfill in all scenarios except in the final years of the net-zero emission target scenario, where an incinerator is proposed. The supply model for electricity generation in the early years suggests the combined cycle power plant followed by solar energy at the end. However, in the low-cost electricity scenario, it is always suggested to import electricity. The sensitivity analysis showed that the emission penalty and electricity price are the most important parameters. The results of this study will help policymakers and decision-makers in energy system planning and provide researchers with appropriate information, tools, and methods for future research.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&amp;CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Development of an Excel Dashboard-Based Warehousing Administration System Using the Design Thinking, EOQ, and POQ Approaches

Warehousing is an important part of a manufacturing process where ideal warehouse management will improve the company's logistics in times of crisis. This research seeks to gain an in-depth understanding of the problems faced by warehousing and create user-focused solutions. Furthermore, EOQ and POQ methods are used to optimize stock and order management. EOQ helps determine the economical number of bookings to be made, while POQ considers demand variability in determining order schedules. The results of this study show that with Design Thinking, it was found that the needs of users are the use of an easier application system and stock filling standards so that the accuracy of stock is better maintained. It can be concluded that the EOQ and POQ methods can overcome understock and overstock. However, the EOQ method has less risk than POQ, this is because the company does not spend a lot of funds and takes with little frequency so it is more effective and the warehouse movement space can be more minimal.

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.