Demand Fluctuations Research Articles

Designing Supply Chain Resilience With a Quality Function Deployment (QFD) Approach at PT. XYZ

PT. XYZ is a leading company in the palm oil industry, but there are still problems that need to be addressed in its supply chain management. The problems include 3% product returns from cooking oil shipments to distributors under the brands Sania cooking oil, Fortune cooking oil, Sovia cooking oil, and Siip cooking oil. In addition, there are fluctuations in demand, an increase in demand of 170% with a demand of 20,774 tons and a decrease in demand of 59% with a demand of 7,225 tons at an average monthly production of 12,195 tons. This research aims to design Supply Chain Resilience with a Quality Function Deployment (QFD) approach. This QFD approach uses a 3-level HOQ. HOQ 1 assesses the relationship between customer needs and potential risks, resulting in risk prioritization. HOQ 2 assesses the relationship between potential risks and risk causes, resulting in the prioritization of risk causes. HOQ 3 assesses the relationship between risk causes and resilience measures, resulting in the prioritization of resilience measures. From the research results, 20 resilience measures were obtained and the 5 highest ranks were obtained as priority resilience measures, namely insurance, marketing quick response, product quality, customer relations, preventive maintenance and repair. The results of the resilience measure design are expected to be used as risk mitigation for the company.

Analysis of demand for qualified tourism organizedby branches of the Polish Tourist and Sightseeing Society(using fuzzy relations)

The study uses the fuzzy relations method to examine the demand for qualifiedtourism in 16 voivodeships of Poland. Data collected by PTTK was analyzed in the field of12 forms of tourism in five defined research areas (A1 – mountain tourism; A2 – tourism foradvanced users; A3 – water tourism; A4 – walking; and A5 – extreme tourism). The analysesshowed that in all regions of the country, the greatest demand was for walking and foradvanced tourism users. At the same time, the largest number of people participated in thesetwo types of tourism. There were large fluctuations in demand for trips and events, especiallydue to the pandemic situation in the country and the economic situation. Thanks to fuzzyrelations, it is possible to study phenomena composed of variables whose values are collectedby organizations and statistical offices.

Are Data Assets Useful?—Evidence Based on Corporate Cost Stickiness

ABSTRACT As the digital economy continues to grow, data resources are becoming increasingly important as factors of production. The existing body of literature lacks comprehensive research on how digital data actually becomes a production factor. This study aims to fill that gap by examining how data assets affect corporate cost stickiness. Our findings show that data assets have a significant impact on reducing cost stickiness, and this conclusion remains robust even after conducting various rigorous tests. Mechanism tests indicate that data assets help companies in effectively managing the bullwhip effect resulting from demand fluctuations in the supply chain. Moreover, companies that possess and utilize data assets have better inventory turnover, enhancing their capacity for flexible production. Furthermore, the inhibitory effect of data assets on cost stickiness is more pronounced in regions characterized by a high level of digital economy development and among enterprises facing higher operational uncertainty. This highlights the important role of data assets as novel production factor in managing costs and allocating resources effectively. In conclusion, this research demonstrates the value of data assets in cost management and resource allocation for businesses in the digital era.

Feasible Actuator Range Modifier (FARM), a Tool Aiding the Solution of Unit Dispatch Problems for Advanced Energy Systems

Integrated energy systems (IESs) seek to minimize power generating costs in future power grids through the coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, and even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear. If a unit is flexibly operated without respecting limits on materials, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the bounds on power variations (explicit constraints),the solution of the unit dispatch problem needs to meet the limits on the variation of key process variables, including temperature, pressure and flow rate (implicit constraints).The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher–validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., balance of plant, gas turbine, and high-temperature steam electrolysis. Finally, the FARM module provides dedicated tools for visualizing the response of the constrained variables of interest during operational transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an ecological interface design (EID) for IES units.

Evaluation of wavy wall configurations for accelerated heat recovery in triplex-tube energy storage units for building heating applications

Efficient thermal energy storage (TES) is crucial for integrating intermittent renewable energy sources and managing fluctuations in energy supply and demand. Among TES methods, latent heat TES (LHTES) using phase change materials (PCMs) is highly promising due to its high energy storage density and nearly isothermal operation during phase transitions. However, the inherently low thermal conductivity of PCMs hinders heat transfer rates, negatively impacting charging and discharging performance. This study investigates novel wavy channel geometries for the PCM container in a triplex-tube LHTES unit to enhance heat transfer and improve solidification rates during the discharge process. A comprehensive computational model based on the enthalpy-porosity method is developed to simulate the PCM (paraffin wax RT35) solidification inside wavy channels with sinusoidal, zigzag, and step-function profiles. The effects of channel geometry, heat transfer fluid (HTF) velocity, and inlet temperature on solidification rate and heat recovery are systematically evaluated. Results demonstrate that the step-function geometry significantly accelerates solidification compared to straight channels, reducing discharge time by 65.1 % and increasing heat recovery rate by 147.9 %. Decreasing waviness width from 15 mm to 5 mm further reduces solidification time by 70.1 %, while increasing waviness height from 5 mm to 15 mm leads to a 74.4 % reduction. Moreover, increasing HTF flow from Re = 250 to 1000 enhances heat recovery by 92.3 %, and lowering inlet temperature from 20 °C to 10 °C improves it by 76.9 %. The findings provide valuable insights for designing efficient LHTES units with wavy channel geometries for renewable energy integration and thermal management applications.

Indonesia's Breakthrough in Optimized Yarn Forecasting for Textile Demand Accuracy

PT. XY, a textile company specializing in woven sarongs, faces fluctuating demand during Islamic religious celebrations, impacting production. In Ramadhan 2023, production increased by 30%, but warp yarn availability was insufficient. This study forecasts warp yarn production over twelve periods, comparing Double Exponential Smoothing Holt’s (DES) and Holt-Winter’s Exponential Smoothing (WES) methods, optimized using the golden section method. Using historical data from January 2021 to April 2023, WES with golden section parameters (α1 = 0.67387, β1 = 0.08756, γ2 = 0.85408) achieved the best accuracy with a MAPE of 5.5437%. The WES method is recommended for improving production planning at PT. XY, with future research suggested to explore production correlations and procurement costs. Highlight: Demand Fluctuation: PT. XY experiences significant demand changes during Islamic religious celebrations. Forecasting Methods: Comparing DES and WES methods for predicting warp yarn production. Optimal Accuracy: WES with golden section optimization achieved the lowest MAPE of 5.5437%. Keywoard: Textile Industry, Warp yarn forecasting, Production Planning, Holt-Winter's method, Golden section optimization

RISK ASSESSMENT IN FINANCIAL DECISION MAKING ANALYSIS ON FISHERMAN PERSPECTIVE IN COASTAL REGION: A CASE STUDY

Assessing financial decision-making from a fisherman's perspective in coastal regions involves considering a range of risks. Market volatility, driven by fluctuations in fish prices and changing consumer demands, poses immediate challenges. Environmental factors, such as climate change and natural disasters, can disrupt fishing conditions, impacting yields. Regulatory uncertainties, including shifts in fishing quotas and environmental compliance standards, add complexities. Technological advancements may necessitate investments, while limited access to credit and interest rate fluctuations present financial risks. Study is done with secondary data such as case studies, literature reviews, books, blogs and internet sources. Findings show that it is reasonable to expect that conflicts in fisheries management will increase with time as human population grows and seafood demand increases. KEYWORDS: Risk Assessment, Financial Decision Making, Fisherman, Population, Coastal Region.

Potentials of using real-time data to increase the update frequency of production planning and control strategies in MTO: a discrete event simulation study

In recent years, production planning and control strategies have gained increasing importance within manufacturing enterprises to stay competitive by enhancing the ability to meet and quickly adapt to the hyper-dynamic requirements of highly volatile markets. In this context, traditional production planning and control strategies like material requirements planning often result in long lead times and high work in progress due to their weak responsiveness to short-term fluctuations in demand. However, new production planning and control strategies and concepts based on the usage of real-time data, as a fundamental principle of Industry 4.0, may have the potential to compensate for the shortcomings of traditional approaches. By using a discrete event simulation based on the data of an electronics manufacturing company, the potential of using real-time data to increase the update frequency in different production planning and control strategies in make to order production systems is analyzed.

A cloud-oriented data-analysis framework to analyze peak demand dynamics in institutional building clusters

Peak loads in higher education institutional building clusters (IBCs) possess considerable economic repercussions on their overall operations. Thus, identifying electrically inefficient buildings presents a significant opportunity to curtail peak loads and promote energy efficiency in IBCs. Existing literature implements clustering algorithms to comprehend the electrical demand dynamics of buildings to analyze disparity in their load behavior. These techniques perform well in single building environment, however, fall short in comprehending the demand dynamics for building clusters, specifically during peak loads. This study introduces a cloud-oriented quantile-based data analysis framework, specifically designed for simultaneously evaluating demand profiles of multiple buildings within IBCs. Quantile-based metrics namely, Value-at-Risk, Conditional Value-at-Risk and Conditional Value-at-Risk standard deviation are implemented to comprehend the electrical fluctuations and quantify the electrical impact of each building during campus-wide peak demands. A cross-building comparison is established by linearly ranking buildings following two key criteria: (i) buildings with frequent demand fluctuations and (ii) buildings exerting a high electrical impact on overall IBC demand. Both criteria are equally weighted while ranking to identify the most inefficient buildings during peak loads. The framework is implemented in a Canadian university and resulted a substantial 50 MW demand reduction through recommissioning and retrofitting of inefficient buildings.

Assessing resiliency in scale-free supply chain networks: a stress testing approach based on entropy measurements and value-at-risk analysis

Supply chain (SC) resiliency and risk management have garnered increasing attention recently. While several studies have explored the use of scale-free network models to design and optimise SC networks, there remains a lack of a generalised stress-testing method that can be applied to various types and sizes of SCs. To address this, we propose a novel approach to assess the resiliency of scale-free SC networks based on entropy measurements. By considering various sources of disruptions, including demand fluctuations, capacity constraints, and transportation disruptions, we create a stress test that measures the network's resiliency. Our method categorises the SC into plants, ports, and customers and analyses the impact of disruptions on individual nodes within similar categories. We utilise real-world SC logistics data and map it into a scale-free network representation. By calculating the global vulnerability of the entire SC network and the localised impact of node vulnerability, we gain insights into how disruptions propagate and affect interconnected nodes. We use value-at-risk (VaR) and conditional-VaR (CVaR) to identify high-risk nodes, highlighting nodes at risk of extreme performance fluctuations or failures. The analytical results have led to developing six propositions, which serve as references for practitioners and help generalise the findings.

Search for innovative solutions to improve the energy system of Ukraine: World experience

This study analyses the global experience of introducing innovations in the energy sector to identify effective solutions that can be applied to improve the energy system of Ukraine. The research methods included a comparative analysis of international experience in the energy sector, an assessment of the possibilities of adapting innovative technologies to Ukrainian conditions, and an examination of technical and economic aspects of integrating new solutions. The study analysed the experience of countries such as Germany, Denmark, the United States, Finland, the Netherlands, Sweden, Japan, China, France, and South Korea, which have succeeded in implementing innovative solutions in the energy sector, in particular, in the integration of renewable energy sources (RES), the development of smart networks, energy decentralisation, energy storage technologies, and energy efficiency improvement. It is identified that the most efficient approaches include the integrated use of renewable energy sources in combination with energy storage, which ensures the stability of energy supply even in conditions of uneven electricity production. The importance of smart networks for optimising energy distribution and reducing losses is also emphasised. The decentralisation of energy and the development of energy cooperatives contribute to improving energy security and community engagement. The study also showed that the introduction of smart energy management systems at the consumer level helps to increase the flexibility of the energy system and effectively respond to fluctuations in demand. In addition, it is determined that the successful implementation of innovative projects in the energy sector requires state support through subsidies and regulatory incentives. Based on international experience, it is concluded that combining technical solutions with effective management and political initiatives is key to ensuring the sustainable development of the Ukrainian energy system

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios.

OPTIMAL CONTROL DESIGN FOR FREQUENCY REGULATION IN ELECTRIC POWER SYSTEM WITH LOW INERTIA

Electricity is a very important element in this era because almost all aspects of modern life depend on electricity. Therefore, electricity plays a very important role in improving people's quality of life and maintaining an efficient and productive life. An efficient and reliable electrical system is essential to ensure adequate electricity availability and maintain system reliability. Therefore, planning, designing and operating electrical systems must be carried out carefully to ensure stability, reliability and efficiency. However, a decrease in frequency in the electrical system sometimes occurs when there is a sudden change in load. This can affect system stability. Therefore, Load Frequency Control (LFC) and Linear Quadratic Regulator (LQR) analysis is needed to maintain the stability of the electrical system frequency. The combination of these two techniques, namely LFC with LQR modeling, provides a better solution for maintaining frequency stability and optimizing electrical system performance. LFC analysis regulates power generation settings automatically to compensate for fluctuations in load demand and maintain a stable frequency, while LQR is a control technique used to minimize system errors and optimize system performance. Therefore, LFC with LQR results in system performance increasing very significantly with a faster response, undershoot that can be reduced to 0.001 and a better settling time of 300s in area-1 and 450s in area-2 and rise time reaching 270s in area-1 and 405s in area-2 as well as the use of LQR can maintain the system frequency at its nominal limit and the presence of New Renewable Energy (EBT) has an effect in the form of a greater undershoot level than without EBT.

Identification of Construction Project Risks in the Indian Context: A Case Study on Building Construction

Abstract: This paper presents a comprehensive investigation into the risk factors prevalent in building construction projects within the Indian context, focusing on their identification, ranking, and management strategies. Through data collected from 206 construction professionals via an online questionnaire, the study employs robust reliability analysis, yielding a high Cronbach's Alpha coefficient of 0.898, affirming the questionnaire's internal consistency and reliability. Utilizing the Relative Importance Index (RII) and Safety Risk Score (SRS), the research ranks critical risk factors, including poor stakeholder communication, inaccurate cost estimation, funding delays, and market demand fluctuations, underlining the necessity of addressing these challenges to mitigate potential impacts on project outcomes. The paper emphasizes the significance of proactive risk management strategies to ensure project success and resilience amidst evolving circumstances, providing valuable insights into construction project risks specific to the Indian landscape and offering recommendations for enhancing risk management practices in the field.

Comparative Analysis of Liquefied Natural Gas (LNG) Distribution Scheme using Milk-Run and Hub-Spoke Methods on Small-Scale LNG Carrier in Eastern Indonesia

Liquefied Natural Gas (LNG) has been seen as a promising alternative in the face of these challenges. However, attention remains focused on the efficiency of LNG distribution, especially in the face of unpredictable demand fluctuations. Currently, with demands for increasing distribution efficiency, Indonesia, one of the largest LNG producers in Southeast Asia, faces a particular challenge in achieving efficiency in meeting domestic energy needs. To address the problem, the Indonesian government has initiated a program to streamline energy infrastructure development, focusing on LNG distribution in Eastern Indonesia. The study will use two distribution schemes, Milk-Run and Hub-Spoke, with different types of ship capacity to calculate operating costs. This study focuses on comparing the effectiveness of the two popular distribution schemes, the Milk-Run and the Hub-Spoke, especially in optimizing LNG distribution in the area. The data in this study was obtained from institutions that openly publish data in a digital system. The findings suggest that the Milk-Run distribution scheme was identified as an optimal solution for LNG delivery to multiple points from a single source for Northern and Southern Papua. The results of this study are expected to provide practical guidance to the parties concerned in their efforts to improve the efficiency of LNG distribution in eastern Indonesia and further study in other regions of Indonesia.

Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management

Accurately predicting electrical power demand is crucial to making related forecasts and to effective sustainable energy management. Most relevant state-of-the-art studies deploy models that do not use optimizing parameters and do not incorporate strategies for using forecast results. This study was designed to develop a novel electricity consumption forecasting model, the Symbiotic Bidirectional Gated Recurrent Unit, which integrates Gated Recurrent Unit, Bidirectional Technique, and Symbiotic Organisms Search algorithms. The results of tests on a series of evaluation criteria showed the proposed model performed significantly better than six comparison models when parameter optimization was used. For all three sector datasets, the proposed model generated the most-accurate predictions of all models. In practical terms, when supply is expected to exceed demand, the prediction results may be used to adjust power plant output to reduce wastage. Conversely, when demand is expected to exceed supply, Time-of-Use tariffs may be implemented based on time-of-day and seasonal fluctuations in demand to facilitate reductions in peak usage and level out overall demand.

Laboratory study of cyclic underground hydrogen storage in porous media with evidence of a dry near-well zone and evaporation induced salt precipitation

Large-scale storage of ‘green’ hydrogen is essential in our quest to transition to renewable energy sources and achieve the net-zero emission targets. As such, Underground Hydrogen Storage (UHS) in geological porous media, such as in depleted oil and gas fields or saline aquifers, provides a cost-effective solution to store such large volumes of hydrogen (H2) to buffer seasonal fluctuations in renewable energy supply and demand. Due to the novelty of the topic, many research gaps persist, but we focus on H2 transport characteristics in the subsurface, hydrogen recoverability during cyclic operations, chemical interactions, and acoustic velocity as an indicator of H2 saturation. In this study, we investigate UHS using a triaxial core-flooding experiment to address the above-mentioned research gaps by replicating field operations in the laboratory setup at representative subsurface conditions. Unstable displacement due to immiscible two-phase flow led to early H2 breakthrough at 18% of H2 saturation, ultimate H2 saturation of 36% and withdrawal efficiency of 78% during the first cycle. However, cyclic charging and discharging led to an eventual H2 withdrawal efficiency as high as 95%. Observed chemical interactions during our 3-day experiments were negligible, but our results also indicated a strong potential for evaporation-induced salt precipitation after injection of dry H2 gas, leading to decreased reservoir porosity and permeability over time. Our ultrasonic measurements confirmed the sensitivity of P-wave velocity to H2 saturation, which decreased by 3.5% when the H2 saturation increased to 36%. This indicates that H2 plume and leaks in UHS should be possible to monitor using 4D seismic surveys. Our results help to better understand H2 flow, storage, and transport during cyclic UHS. The results indicate that H2 withdrawal efficiency increases as the UHS reservoir matures and provides evidence of a dry near-well zone and evaporation induced salt precipitation in a UHS reservoir.

Information transmit strategy of e-commerce platform with financially constrained supplier

A signaling game model is constructed in the framework of a supply chain system consisting of a financially constrained supplier and an e-commerce platform acting as marketplace and a creditor simultaneously. The primary concern of this work is to investigate how the e-commerce platform, which has superior information about market demand, indirectly communicates this information to the financially constrained supplier through its marketing efforts. Under both separating and pooling equilibria, the decisions and profits of each member of the supply chain are firstly considered. Furthermore, using an “intuitive criterion”, the e-commerce platform’s dominant strategy and its influencing factors are analyzed. The results show that when there is a significant difference in the marginal marketing costs, the platform tends to choose the separating equilibrium. When the difference in marginal marketing costs is relatively slight, the e-commerce platform’s dominant strategy depends on the capital of the supplier and the level of market demand fluctuations. Specifically, the e-commerce platform will select the pooling equilibrium when market demand fluctuations are minimal, but the platform will select the separating equilibrium when market demand fluctuations are substantially large. If the supplier’s capital is increased within a certain range, the e-commerce platform is more motivated to select the pooling equilibrium. In addition, the platform usage fee ratio, the platform loan interest rate and the supplier’s prior beliefs are important factors influencing the platform’s decision.

Challenges in Manpower Management: Discuss the Challenges Faced by the Automotive Industry in Managing Manpower Due to Rigid Labor Laws and Complex Legal Systems, Especially Concerning Temporary Increases in Workforce to Meet Demand Fluctuations

The automotive industry is at the forefront of technological innovation and economic growth. However, it faces significant challenges in manpower management, particularly due to rigid labor laws and complex legal systems. These challenges are exacerbated when addressing temporary workforce increases to meet demand fluctuations. This paper aims to discuss the multifaceted issues that arise in the context of labor flexibility, talent acquisition, and retention strategies within the automotive sector. Firstly, the industry contends with the difficulty of attracting and retaining talent in a competitive landscape where technology companies and startups often offer more appealing opportunities. The rapid pace of technological advancements demands a workforce that is adaptable and skilled in new and evolving competencies. However, traditional labor laws often do not accommodate the flexible arrangements required to attract such talent. Secondly, the high turnover rate within the automotive industry further complicates manpower management. The need to cultivate leaders from within is hindered by generational differences and a redundant skill set that is increasingly becoming automated. This situation is compounded by the industry’s struggle to maintain employee health and safety, which is paramount given the physical nature of manufacturing jobs. Lastly, the legal complexities associated with temporary staffing to manage demand peaks present a significant hurdle. The industry must navigate a labyrinth of employment regulations that vary by region and often restrict the ability to swiftly adjust workforce levels in response to market needs. The automotive industry must develop robust strategies to overcome these challenges. This includes fostering organizational resilience, reimagining talent management, and advocating for labor law reforms that reflect the dynamic nature of the industry and its workforce requirements. The ability to adapt to these challenges will be a determining factor in the industry’s capacity to sustain innovation and growth in the coming years.

Risk Analysis of Electricity Demand at Public Electric Vehicle Charging Stations (SPKLU): CVaR Model Approach

The electricity demand at Public Electric Vehicle Charging Stations (SPKLUs) exhibits significant volatility, which is driven by several aspects including electricity demand patterns at specific time intervals, load variability, SPKLU capacity, and other related factors. The variability of these swings can present hazards for SPKLU operators in relation to energy administration as well as operational and financial hazards. The objective of this study is to assess the risk associated with energy demand fluctuation at SPKLUs by employing the Conditional Value-at-Risk (CVaR) model technique. CVaR, or Conditional Value at Risk, is a quantitative measure of risk that calculates the predicted loss value in the most unfavorable situation. It is commonly employed to enhance the risk management approach of SPKLUs. The electricity demand at SPKLU exhibits significant volatility, with an average fluctuation of 10.15% and a standard deviation of 49.67%. The CVAR, calculated at -121.19% for a confidence interval of 1%, represents the maximum potential loss that could be experienced during worst-case electrical demand conditions, highlighting the substantial fluctuations in demand. The study initially implemented the CVaR model to analyze power demand at SPKLU, providing novel perspectives on risk reduction for critical infrastructure and proposing unique strategies for managing demand fluctuations in a reliable and efficient manner. The results also offer comprehensive insights into risk exposure and facilitate the formulation of well-informed and strategic risk management plans.