Demand Volatility Research Articles

Algorithm aversion during disruptions: The case of safety stock

Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.

Better safe than sorry? The effect of asymmetric cost management on firm resilience in manufacturing firms

PurposeGlobal business practices show that during periods of demand volatility, manufacturing firms often engage in asymmetric cost management (ACM), a behavior in which costs increase faster than they decrease when demand decreases by the same amount. However, whether managing asymmetric costs will enhance or impede firm resilience remains an open question. We aim to investigate the impact of ACM on firm resilience and its boundary conditions.Design/methodology/approachUsing unbalanced panel data of 2,273 Chinese manufacturing listed companies from 2002 to 2021, we conduct an empirical analysis using a double fixed effects model.FindingsOur findings reveal that ACM has a negative effect on firm resilience. This suggests that in coping with external environmental fluctuations, ACM fails to fulfill its expected role effectively. Instead, it manifests as a severe agency problem affecting firm resilience. Further, we find that managerial myopia and digitalization diminish the negative effect, while customer instability exacerbates it.Originality/valueThis study contributes to the literature on the organizational resilience of manufacturing firms by providing an in-depth understanding of cost management and emphasizing the need to consider agency issues carefully when managing asymmetric costs.

Distributionally robust optimization of a newsvendor model under capital constraint and risk aversion

This paper proposes a capital-constrained newsvendor model with risk aversion under a partially known demand distribution with only knowledge of its mean and variance. We adopt the robust conditional value-at-risk (RCVaR) to characterize the vendor’s risk aversion. Firstly, we obtain the closed-form RCVaR optimal order quantity that depends on the demand volatility level: When demand volatility is low, the vendor has four financing-ordering strategies contingent upon different capital levels. When demand volatility is medium, the vendor does not seek bank loans and is left with two ordering strategies. When demand volatility is high, the vendor does not bother placing an order at all. Then, we investigate the impact of capital constraint, risk aversion and demand volatility on the RCVaR optimal order quantity. Finally, we demonstrate the robustness of the RCVaR optimal ordering policy by numerical experiments based on both randomly generated and real-world data.

STRATEGI KEUANGAN DAN PERTUMBUHAN: DAMPAK STRUKTUR MODAL, PROFITABILITAS, DAN UKURAN PERUSAHAAN TERHADAP NILAI PERUSAHAAN

Firm’s value is an important indicator that reflects the company's performance and future prospects. Determining firm’s value is influenced by various factors, including capital structure, profitability, and company size. This research focuses on the basic materials sector, which plays an important role in the economy and often faces the challenges of price and demand volatility. In the 2021-2022 period, this sector faces various dynamics that affect firm value. By using purposive sampling methods and multiple regression analysis, this research aims to identify the influence of capital structure, profitability and company size on firm value in the basic materials sector. The main finding of this research is that capital structure has a significant effect on firm value, while profitability and company size do not have a significant effect. These results contribute to understanding the relationship between financial factors and firm value in the basic materials sector. The implication of these findings is that investors can consider capital structure as a factor in making investment decisions, while company management needs to pay attention to capital structure management, profitability and company size to avoid a decline in firm value.

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.

Integrated batch production planning and scheduling with machine-learning based production capacity region considering wind energy system

Continued greenhouse gas emission has led to increased global warming. Towards the goal of carbon emissions reduction and environmental sustainability, replacing high-polluting fossil fuel by clean energy has become a top priority in the decision-making of the current chemical industry. This work presents an optimization framework to address the integration of batch chemical production planning and scheduling considering energy consumption, which is a vital operational problem in chemical production. Assisting the linkage between planning layer and scheduling layer, a novel linear support vector machine based production capacity region calculation method is proposed to identify feasible regions of the scheduling layer. Uncertain factors in both the production system and the energy system are considered, including demand volatility of chemical products, energy supply fluctuations and time-varying energy market prices. To evaluate each strategy and the proposed model, simulation experiments are performed in three representative case studies. The results reveal that the integrated model considering energy consumption shows superior performance in different energy configurations, with or without wind energy generation and battery storage systems.

Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors

Efficient sizing and economic analysis of community battery systems is crucial for enhancing energy efficiency and sustainability in rooftop PV panel-rich communities. This paper proposes a comprehensive model that integrates key technical and economic factors to optimize the size and operation of the prosumer-owned battery, maximizing the financial returns over the life span of the battery. Sensitivity and uncertainty analyses were also conducted on a number of factors that are constantly changing over the years such as per-unit cost of the battery and interest rate. Monte Carlo simulations were utilized to replicate the unpredictable PV generations and the volatility of house load demands. The developed model is evaluated under three scenarios: a shared community battery for all houses, individual batteries for each house, and a combined system with an additional large load. Particle Swarm Optimization (PSO) is utilized to maximize the formulated objective function subject to the considered constraints. The findings indicate that integrating community batteries offered a substantial economic advantage compared to individual home batteries. The additional revenue stream of incorporating larger consumers looking to reduce their carbon footprint (e.g., commercial) returned a further augmented net present value (NPV). The influence of different tariff structures was also assessed and it was found that critical peak pricing (CPP) was the most prolific. The outcomes offer valuable insights for policymakers and stakeholders in the energy sector to facilitate a more sustainable future.

EXPRESS: Disturbance in Multitier Supply Chain under Competition

The COVID-19 outbreak has raised concerns over the global pharmaceutical supply chain where raw ingredients are produced in a few geographically concentrated regions. Disturbance in upstream production along with the surging demand for medical treatments have increased the risk of medicine shortfalls, and this situation has prompted the need for a more diversified supply chain with geographically dispersed competing suppliers. In this research, we investigate how disturbance spans a supply chain network where supply quantities are endogenously determined by competition behaviors. We evaluate the expected shortfall between post-disturbance supply and intrinsic demand, with an adapted algorithm which pushes pseudoflows in the network and partitions nodes into strong and weak branches in a spanning tree, to solve the equivalent maximum flow problem. Parametric analysis based on simulations reveals that the severity of disturbance loss can be significantly offset with supplier competitions, although the marginal benefit of competition decreases fast in the number of suppliers. In addition, configuration inefficiencies can magnify upstream shortfalls and supply chain efficiency can be improved by pushing high-cost firms to stop production. Our findings indicate that a supply chain with a moderate level of competition and balanced configuration can be robust against disturbance and demand volatility.

Can solar water kiosks generate sustainable revenue streams for rural water services?

Providing a sustainable supply of safe drinking water in rural Africa depends on sufficient revenue from user payments to maintain services. While handpumps have been the primary source of drinking water for rural Africans for decades, local revenue generation has been unstable, contributing to service disruptions and welfare losses. We examine the effect of upgrading manual handpumps to solar kiosks in rural Mali from 2019 to 2023. We model 452 monthly records of observed payments and metered water usage to estimate changes in volumetric use and revenue generation. Average revenues increase four-fold indicating stronger financial performance with solar kiosks. In contrast, we find no significant increase in the volume of water people use when a handpump is upgraded to a solar kiosk. We estimate that a 1 °C temperature increase is associated with a $9 increase in average monthly revenue and 366 more litres of water used every day per waterpoint. Our study suggests that rural Malians are more inclined to pay for water from professionally managed solar kiosks. However, seasonal volatility in water demand and uncertainty in the long-term revenue effect suggests caution in assuming solar kiosks are a definitive solution to the nuanced and dynamic nature of water user behaviours in rural Africa.

Empirical analysis of electricity demand in Albania. the impact in ecosystem

Albania represents the case of a developing country with a volatile demand for power facing an unstable domestic supply, especially during the last decade. Understanding future patterns of electricity usage is crucial in various planning contexts, the most important of which would be security of supply. In this framework, acknowledging the future national demand for electricity is needed for electricity providers for them to plan the sufficient and security of electricity supply. The aim of this paper is electricity demand modelling and forecasting in Albania. The model developed for the determinants of electricity demand for Albania allows understanding of the patterns of consumption behaviour. The main demand drivers of the electric consumption considered here are macroeconomic and demographic factors. The current work empirically estimates that the main determinants of electricity consumption in Albania for the period 1990-2014 are GDP, population and remittances. Results coefficients are then used to forecast electricity consumption for the period 2015-2030. We believe that the main contribution of the proposed estimation forecasts would be for correctly informing policy makers regarding electricity demand patterns in Albania and in forming expectations on a vital sector such as electricity.

Minitab 20 and Python based-the forecasting of demand and optimal inventory of liquid aluminum sulfate supplies

In a company, inventory management is crucial due to the significant impact on various aspects of the business. Similarly, the Indonesian water supply company (PDAM) requires effective inventory management to ensure the supply of liquid aluminum sulfate chemicals. The probabilistic statistical inventory control (SIC) model is commonly used for inventory management. However, previous research on chemical inventory models in PDAMs often relied on simple linear regression to forecast demand data, which fails to capture the inherent volatility in demand. Therefore, this research aimed to predict demand data using the seasonal autoregressive integrated moving average (SARIMA) method and determine the optimal policy for supplying liquid aluminum sulfate chemicals. The results showed that the best demand forecasting model was SARIMA (2,1,2) (1,1,0)12 with a mean absolute percentage error (MAPE) value of 8.19%. The finding of the optimal inventory policy showed a safety stock value of 11,922.35 kg, a reorder point value of 49,511.20 kg, and an order quantity of 21,526.59 kg, leading to a total cost of IDR 11,132,034,145.45. The sensitivity test also showed that variations in lead time, price, μ, and σ parameters directly influence changes in total cost, reorder point, and safety stock. These calculations were conducted using Minitab and Python software.

Product refurbishment in an imperfect production inventory system under carbon regulations: An interval type-2 fuzzy environment

This study develops an imperfect production inventory model focusing on the proper handling of imperfect products and addressing carbon emissions in different stages of a production process. The proposed inventory system consists of three stages: production, screening, and refurbishing. The produced products undergo a screening process, with perfect quality items being delivered to the primary market, while imperfect quality products are routed through a refurbishing process, subsequently sold in the secondary market. It is observed that in the existing literature on refurbished products, all such types of products have been sold at the end of the business period, this means that there is no continuous demand of refurbished product at the remaining time points of the business period which does not support the practical situation in a real business system. In this regards, our main objective in this study is consideration of continuous demand of refurbished products along with perfect ones. Again, we incorporate interval type-2 fuzzy uncertainty in both markets’ demand to better reflect market fluctuations introducing a new defuzzification method. In this framework, we evaluate the effectiveness of two emission regulatory strategies: carbon tax and carbon cap and trade policies. The findings of this study offer valuable insights into various aspects. Notably, our analysis reveals that if the difference between the primary and secondary markets’ demands reduces to a large extent, a negative impact on the profit is observed. It is also shown that an increase in demand for refurbished products allows for a higher permissible rate of imperfections in production. Comparing the outcomes under two emission regulations, we find that the cap and trade policy is beneficial from both economic and environmental perspectives. Additionally, it becomes evident that the manufacturer should carefully consider the duration of each inventory stage, as it leads to a significant increase in overall emissions. Regarding the uncertain nature of the market, it is observed that demand volatility in the secondary market negatively affects the profit.

Experimental Investigation of a Distributed Architecture for EV Chargers Performing Frequency Control

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers.

A simulation-based optimization model for balancing economic profitability and working capital efficiency using system dynamics and genetic algorithms

Economic uncertainty has been increasing, as evidenced by recent fluctuations in global markets and unpredictable economic indicators such as volatile demand, stock market fluctuations, and unpredictable interest rates. Economic profitability and working capital efficiency are pivotal indicators of a business’s financial health, both of which are adversely impacted by economic uncertainty. However, these metrics may diverge as distinct objectives drive them. There exists a gap in the literature regarding effective strategies for managing the trade-off between these metrics under economic uncertainty. This study addresses this gap by introducing a simulation-based optimization model that integrates system dynamics simulation and genetic algorithms. The proposed model aims to balance economic profitability and working capital efficiency within inventory management under partial trade credit. A recent real case study demonstrates the model’s applicability and reveals its superiority over conventional system dynamics simulation modeling. With its capacity to inform strategic and tactical decision-making, this model emerges as a valuable tool for supply chain and financial managers seeking to ensure financial stability amidst economic volatility.

Inventory Prediction Using a Modified Multi-Dimensional Collaborative Wrapped Bi-Directional Long Short-Term Memory Model

Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22.

Optimal allocation of offshore wind power and energy storage considering source-load power stochasticity

Abstract Large-scale offshore wind generation has been integrated to power grids in China. The annual increase in electric vehicles, air conditioning systems, and other electrical facilities has intensified the randomness and volatility of power supply and demand, presenting significant challenges to the safe and economical operation of power systems. Energy storage systems serve as regulators in the power grid, yet the electrical performance and costs associated with various storage technologies differ considerably. Consequently, the strategic planning and development of energy storage solutions tailored to specific local conditions have emerged as critical areas of research. This paper presents an in-depth analysis of power characteristics across source loads, explores an optimized configuration approach for energy storage, and validates this method through a numerical example. The findings confirm the effectiveness of the proposed configuration strategy and offer pertinent recommendations for the implementation of energy storage solutions.

Incentive contract design for reducing carbon emissions in the supply chain under asymmetric information

AbstractIn light of the volatility of market demand and carbon trading prices, this study examines the effects of two carbon policies—the carbon tax policy and the cap‐and‐trade policy—as well as various information structures on the supply chain's carbon emission reduction incentives. A brand incentivizes the carbon abatement effort of an upstream supplier through a two‐part contract in the form of a fixed salary and a proportional incentive. We discover that, under some circumstances, the brand may benefit more from the unobservability of supply chain information. The brand bundles the risk of uncertainty with the supplier through the proportional incentive. In classical incentive contracts, only the cost is considered and the positive effect of risk conveyance is often ignored. In our study, the brand needs to investigate a trade‐off between the benefit of risk conveyance and the drawback of the incentive cost. On one hand, the proportional incentive characterizes the incentive cost. The brand increases the incentive intensity due to the unobservable effort of the supplier. On the other hand, it also has a risk conveyance effect. Specifically, the proportional incentive decreases with the correlation coefficient under the cap‐and‐trade policy. Additionally, there are situations in which consumer surplus benefits from the observability of supply chain information. However, supply chain information transparency is harmful to consumer surplus as supplier risk aversion increases.

Enabling industrial decarbonization: A MILP optimization model for low-carbon hydrogen supply chains

This study develops a an optimization model focused on the layout and dispatch of a low-carbon hydrogen supply chain. The objective is to identify the lowest Levelized Cost of Hydrogen for a given demand. The model considers various elements, including electricity supply from the local grid and renewable sources (photovoltaic and wind), alongside hydrogen production, compression, storage, and transportation to end users. Applied to an industrial case study in Sweden, the findings indicate that the major cost components are linked to electricity generation and investment in electrolyzers, with the LCOH reaching 5.2 EUR/kgH2 under typical demand conditions. Under scenarios with higher peak demands and greater demand volatility, the LCOH increases to 6.8 EUR/kgH2 due to the need for additional renewable energy capacity. These results highlight the critical impact of electricity availability and demand fluctuations on the LCOH, emphasizing the complex interdependencies within the hydrogen supply chain. This study provides valuable insights into the feasibility and cost-effectiveness of adopting hydrogen as an energy carrier for renewable electricity in the context of decarbonizing industrial processes in the energy system.

Impact of Capital Position and Financing Strategies on Encroachment in Supply Chain Dynamics

Channel encroachment and financing decisions are prevalent in practice. Utilizing the Stackelberg game framework, we investigate the impact of a retailer’s capital position and financing strategies on supply chain dynamics in which a supplier considers establishing a direct sales channel. We find that the retailer’s equilibrium financing strategy is impacted by demand volatility and the initial working capital. The supplier’s encroachment decision hinges on the entry cost when neither trade credit financing nor bank credit financing is available. When both types of credit are available, the choice of financing is a complex interplay involving initial working capital, entry cost, and demand volatility. Notably, the supplier’s decision to encroach may shift from a binary stance of either encroaching or not encroaching, or it may oscillate from encroachment to non-encroachment and back to encroachment, particularly with an increase in demand volatility when the entry cost is moderate. The novelty of this study lies in its integration of supplier channel decisions with retailer operational decisions and financing strategies, examining how the capital position and financing strategies impact channel decisions. This study provides managerial insights into the interplay between supplier’s channel dynamics and retailer’s financial considerations, shedding light on unexplored aspects of channel management. In future studies, some assumptions in this study can be modified to obtain more managerial insights.

Climate change adaptation projects of transport facilities: is a demonstrator necessary?

AbstractTransport facilities are increasingly threatened by the climate-change related natural disasters in recent years. In order to incentivize the transport facility operators to engage in climate change adaptation, governments may provide subsidies to the adaptation investment. Due to the substantial uncertainties in the outcomes of the adaptation projects, the government can set up an “experiment” project, which can be used as a demonstrator. In this paper, we develop real-option game models to investigate the transport facility operators’ adaptation investment under static stochastic demand and time-varying stochastic demand. Meanwhile, the government’s optimal decisions on whether to set up a demonstrator and the related subsidy policies are also examined. We find that under static stochastic demand, the government prefers to set up a demonstrator if the marginal benefit of the adaptation capacity is larger than its marginal cost, or the demonstration effect is high enough. Under time-varying stochastic demand, the government prefers to set up a demonstrator when the demand volatility is low. Moreover, increasing demand volatility requires less (or more) subsidy, when the demonstrator is (or is not) set up. Increasing demand volatility leads to postponement of the adaptation timing and more adaptation investment, irrespective of whether there is a demonstrator.