Residues Control for Perishables in Retail Through Dynamic Pricing

This conceptual paper analyzes the use of dynamic pricing in soccer competitions in the United States, emphasizing the role of business analytics in enabling data-driven ticket pricing methods. Based on economic, marketing, and sports management perspectives, we identify a gap in the literature about dynamic pricing in soccer events, which have traditionally depended on static pricing models. Our research examines how dynamic ticket pricing might enhance income and attendance for soccer competitions while ensuring fairness for fans. Our research reveals that dynamic pricing enables organizers to maximize revenue from high-demand matches by increasing ticket prices to align with real-time market value and to enhance attendance for low-demand matches by strategically reducing prices and providing discounts. These modifications are facilitated by business analytics, encompassing demand forecasts and price optimization algorithms, which guarantee that current data informs pricing decisions on consumer willingness to pay. Our analysis emphasizes that dynamic pricing in sports functions not merely as a mechanism for price augmentation but may also augment consumer value and accessibility, as demonstrated by its capacity to lower ticket prices when warranted. Drawing on existing literature, the paper provides a theoretical framework and practical recommendations, highlighting areas for future empirical research. We offer practical insights into the implementation of dynamic pricing in soccer, emphasizing the significance of analytical models, effective stakeholder engagement, and policy considerations to balance revenue objectives with fan experience. These findings enhance sports management literature by applying dynamic pricing theory to U.S. soccer and providing a framework for utilizing business analytics in the optimization of sports revenue.

In a city center that is usually flooded with traffic, finding a parking space is a challenge for most commuters. Searching for parking generates additional urban traffic, increases energy consumption and vehicle emissions, and wastes time. This study examines how parking systems can be used as an effective tool for reducing traffic congestion. It proposes a new, smart approach to parking using real-time dynamic pricing and route guidance in a multi-agent system to better align supply and demand, thereby reducing both congestion and the amount of time spend by drivers searching for parking. To examine the effects of dynamic parking pricing on road traffic, we conducted a multi-agent open-source framework to simulate spatiotemporal distributions of daily individual activity. By extending MATSim, we were able to model parking usage more realistically. We developed several micro-simulation scenarios to evaluate strategy performance for both on-street and off-street parking in the city center of Tunis, Tunisia. The results show that dynamic pricing policies can be used to plan and manage parking areas more efficiently, minimizing the dispersion of parking occupancy lots. Variations in parking prices were shown to increase parking lot vacancies and reduce traffic congestion. Moreover, the system can also generate increased revenue for government and private-sector parking authorities while improving driver satisfaction. Finally, the parking dynamic pricing strategy promises a convenient mobility experience, while increasing efficiency and reducing the negative externalities of urban transportation.

Dynamic and targeted bundle pricing of two independently valued products

Matching electricity supply and demand is crucial for deploying renewable energy sources. Tools such as dynamic and market-based pricing help engage energy end-users in this effort and enable demand response. Moving away from a predominant focus on the technical aspects of demand response, in this article, we explore dynamic pricing offerings in the light of energy democracy and retailers’ views on making electricity provision less inconspicuous. To this end, we examine the market offers of 59 Finnish electricity retailers, including their customer segmentation and understanding of the barriers to broader uptake of dynamic pricing. Our findings indicate that markets are in ferment; the electricity retailers are innovating and trialling new electricity products, but there are also significant concerns over vulnerable energy end-users. We also find evidence that a move towards more dynamic pricing is not a general trend. On the contrary, electricity retailers have diverse portfolios, and an increasing number of them promote products such as 'super-flat' tariffs, in which prices are detached from both the timing and volume of consumption. Dynamic pricing and transparency in energy balancing can contribute to increased energy literacy and energy democracy. However, current market offerings are not organised, and they are not developing in ways that would be conducive to the broad utilisation of these demand response tools among domestic end-users of electricity.

This paper studies the reference effect on dynamic pricing in revenue management for cases with limited capacity and stochastic demand. We first present a single‐period fixed pricing (FP) model in finite horizon with fixed capacity and stochastic demand, and show that there is a unique optimal solution. The model is then extended to a discrete‐time dynamic pricing (DP) model as a benchmark case. We subsequently propose a DP model with reference effect (DPR), investigate the properties of the revenue function, and present a computational scheme to compute the dynamic optimal price. Numerical experiments are conducted to exhibit how the reference effect may influence the initial price, pricing trend, price dispersion, and expected revenue, with the FP, DP, and DPR policies in the same environment of a fixed capacity facing stochastic demand. We also present, through numerical examples, a comparison between deterministic demand and stochastic demand scenarios under reference effect, and with a fixed initial price versus a variable initial price.

Dynamic pricing has been used to control congestion in mobile wireless networks (MWNs). In dynamic pricing, users are incentivized to time-shift their non-critical usage from the peak hours when the price is high, to off-peak hours when the price is low. The current dynamic pricing (SDP) schemes only consider the congestion level in the call-originating cell and neglect the call-destination cell in the dynamic price computation. The main problem with these schemes is that, when majority of the users in a congested cell are callees, dynamic pricing is ineffective because callers and not callees pay for network services, and resources used by callers and callees are the same for symmetric services. For example, application of dynamic pricing does not deter a callee located in a congested cell from receiving a call, which originates from a caller located in an uncongested cell. To address this problem, we propose a source-destination based dynamic pricing (SDBDP) scheme, which considers congestion levels in both the call-originating and call-destination cells to compute the dynamic price paid by a caller. An analytical model based on Markov decision chain has been developed in this work. Numerical simulations have been carried out in MATLAB for arbitrary caller-callee distributions in a typical day. Call blocking and call dropping probabilities have been used as the performance metrics to compare the current SDP schemes to the proposed SDBDP scheme. Results obtained show that the proposed SDBDP scheme achieves better congestion control than the existing SDP schemes, under arbitrary caller-callee distribution.

El impacto de la fijación dinámica de precios en las percepciones de los clientes de restaurantes y en el sentimiento hacia los preciosResumenPropósitoEste estudio examina cómo la fijación dinámica de precios impacta en las percepciones de los clientes de los restaurantes y en el sentimiento hacia los precios a través de métricas de reputación en línea. Además, para profundizar en el debate sobre la fijación dinámica de precios, se propone una definición novedosa explorando las formas específicas de discriminación que pueden manifestarse en diferentes industrias.Diseño/metodología/enfoqueUtilizando un conjunto de datos exhaustivo de reseñas de restaurantes obtenidas de TripAdvisor, el estudio se centra en los restaurantes afiliados a uno de los mayores grupos de restaurantes en España. Empleamos un método cuasiexperimental (diferencias en diferencias) para estudiar cómo las estrategias de precios dinámicos influyen en las percepciones de valor de los clientes basándonos en las calificaciones numéricas. Mientras tanto, empleamos un modelo BERT en el componente textual de las reseñas para desentrañar los matices emocionales de la fijación dinámica de precios.HallazgosLos resultados no revelaron un impacto causal de las estrategias de precios dinámicos en las percepciones de los clientes. Además, el análisis de sentimiento no muestra una visión negativa aumentada después de introducir la fijación dinámica de precios en los restaurantes en comparación con el grupo de control. Contrariamente a lo que sugiere la literatura previa, nuestros hallazgos indican que la implementación de precios dinámicos no afecta negativamente las percepciones o los sentimientos de los clientes respecto a los precios en los restaurantes.Limitaciones/implicaciones de la investigaciónLa configuración cuasiexperimental del estudio presenta desafíos inherentes para establecer la causalidad que requieren una investigación más profunda utilizando entornos experimentales controlados. Sin embargo, nuestro estudio revela que los clientes de restaurantes no perciben la fijación de precios dinámica como injusta. Este hallazgo es crítico para los gerentes de restaurantes al considerar la implementación de la fijación de precios dinámica y estrategias de gestión de ingresos. Además, nuestro estudio resalta la importancia de considerar no solo las calificaciones numéricas sino también el análisis del sentimiento del cliente. Este enfoque más holístico para evaluar el impacto de las estrategias de precios puede dar a los gerentes de restaurantes una comprensión más profunda de las reacciones de los clientes. Además, se proporciona una definición de fijación de precios dinámica más rigurosa, aclarando su naturaleza y su distinción en el uso de diferentes discriminaciones de precios.Originalidad/valorEste estudio contribuye a la comprensión en evolución del impacto de las estrategias de fijación de precios dinámicos en las percepciones y sentimientos de los clientes en la industria restaurantera. Su objetivo es llenar el vacío en la comprensión de las reacciones de los clientes a los precios determinados algorítmicamente (a través de sistemas de gestión de ingresos (RMS) como DynamEat) en esta industria. La combinación de inferencia causal y análisis de sentimientos ofrece una perspectiva novedosa, arrojando luz sobre las conexiones matizadas entre la implementación de la fijación de precios dinámicos y las emociones de los clientes.

Electricity deregulation makes it more feasible to apply differential rates across customers. Dynamic prices can be used to reflect the changes in marginal energy costs of a power system. Some dynamic pricing pilot projects reveal that dynamic prices can actually reduce or shift electricity usage. However, system-load peaks and local-area load peaks could occur at different times. When both peaks are coincident, two objectives can be achieved simultaneously by applying dynamic pricing: total system demand and local demand reduction. However, when these peaks are non-coincident, local demand requires investment. We propose and extended dynamic pricing scheme by taking area- and time-specific marginal distribution capacity costs into account for addressing locational investment deferral. Elasticity model which uses actual test data from pilot projects is adopted to estimate load reduction due to dynamic pricing.

In recent years, ride-on-demand (RoD) services such as Uber and DiDi are becoming increasingly popular. Different from traditional taxi services, RoD services adopt dynamic pricing mechanisms to manipulate the supply and demand on the road, and such mechanisms improve service capacity and quality. Seeking route recommendation has been widely studied in taxi service. In RoD service, the dynamic price is a new and accurate indicator describing the supply and demand, but it is yet rarely studied in providing clues for drivers to seek for passengers. In this paper, we propose to incorporate the impacts of dynamic prices as a key factor in recommending seeking routes to drivers. We first justfiy why it is necessary to recommend seeking routes and consider dynamic prices, by analyzing real service data from a typical RoD service. We then design a reinforcement learning model based on order and GPS trajectories datasets, and take into account dynamic prices in the design. Results prove that our model improves both driver earnings and seeking strategies. On driver earnings, the reinforcement learning model increases revenue efficiency by up to 34.52%, and considering dynamic prices leads to another increase of 6.19%. On seeking strategies, drivers are encouraged to serve local demand first, and they are redistributed more evenly and effectively.

This paper deals with a regional demand response method based on dynamic electricity pricing in a multi-period energy market. In the proposed method, first, the power demand and supply in each area are determined considering power flow using the retail and wholesale electricity prices in a day-ahead market. Next, to adjust for power deviations caused by errors in power generation, our proposed method solves the problem of optimal allocation between power demand reduction by consumers and increased power supply from balancing generators in a real-time market. This paper also shows a distributed algorithm for obtaining the optimal values of each market player in both the day-ahead and real-time markets, and finally, numerical simulation results demonstrate the effectiveness of the proposed method.

Dynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalances instead of preventing them. Combining game theory and time series analysis of dynamic pricing data from on-demand ride-hailing services, we explain this apparent contradiction. We derive a phase diagram demonstrating how and under which conditions dynamic pricing incentivizes collective action of ride-hailing drivers to induce anomalous supply shortages. We identify characteristic patterns in the price dynamics reflecting these supply anomalies by disentangling different timescales in price time series of ride-hailing services at 137 locations across the globe. Our results provide systemic insights for the regulation of dynamic pricing, in particular in publicly accessible mobility systems, by unraveling under which conditions dynamic pricing schemes promote anomalous supply shortages.

Demand Response (DR) is the adjustment of consumer electricity demand through the deployment of one or more strategies, e.g. direct load control, policy implementation, dynamic pricing, or other economic incentives. Widespread implementation of DR is a promising solution for addressing energy challenges such as the integration of intermittent renewable energy resources, reducing capacity cost, and improving grid reliability. Understanding residential consumer preferences for shifting product usage and how these preferences are distributed amongst a population are key to predicting the effectiveness of different DR strategies. In addition, there is a need for a better understanding of how different DR programs, system level objectives, and preference distributions will impact different segments of consumers within a population. Specifically, the impacts on their product use behavior and electricity bill. To address this challenge, a product based approach to modeling consumer decisions about altering their electricity consumption is proposed, which links consumer value to their products, instead of directly to the amount of electricity they consume. This model is then used to demonstrate how population level preference distributions for altering product use impact system level objectives.

In the challenging environment of attended home deliveries, pricing delivery options can play a crucial role to ensure profitability and service quality of retailers. To differentiate between standard and premium delivery options, many retailers include time windows of various lengths and fees within their offer sets. Even though customers prefer short delivery time windows, longer time windows can help maintaining flexibility and profitability for the retailer. We classify pricing strategies along two dimensions: static versus dynamic price setting and whether an offer set can include one or multiple price points. For static pricing, we implement price configurations that reflect current business practice. For the dynamic pricing, we adapt routing mechanisms that consider the flexibility within the underlying route plan during the booking process and set delivery fees accordingly. To evaluate the pricing strategies under plausibly realistic conditions, we model customer behavior through a nested logit model. This model represents customer choice as sequential decisions between premium and standard time windows. We perform a computational study considering realistic travel and demand data to investigate the effectiveness of static and dynamic time window pricing. Finally, we offer managerial insights and an outlook into applying strategic analysis to decide on price setting strategies.

The study examined the effect of pricing strategies on the revenue of maize marketers in Benue State, Nigeria. A multistage and purposive sampling technique was used in selecting 400 registered maize Marketers in six Local Government Areas of the State. Primary data were collected using structured questionnaires and analyzed using descriptive and inferential statistics. The findings revealed that majority (91.7%) applied cost plus pricing strategy in marketing of maize; (87.0%) used competitive pricing strategy, while (57.1%) used value based pricing strategy. The results also showed that 56.1% adopted bundle pricing strategy; (49.4%) applied geographical pricing strategy; few marketers used dynamic pricing (16.4%), psychological pricing (6.5%), premium pricing (2.6%), penetration pricing (1.6%), and price skimming (0.5%) respectively. The findings revealed that marketers who used dynamic pricing strategy made the highest sales revenue of ₦122,484.803, marketers that used penetration pricing recorded the least sales revenue of ₦104,333.33. Evidence from the study shows that dynamic pricing strategy enhance sales revenue of maize marketers by ₦15,104.32. It was recommended that government should enhance maize marketer’s access to extension services in the state by conducting training sessions focusing on enlightening maize marketers on more profitable pricing strategies to adopt. The government should also encourage continued education among maize marketers in the state through workshops so as to enable them make informed marketing decisions.

This paper explores the development of a web application for dynamic parking pricing, leveraging ReactJS, ExpressJS, MySQL, Google Maps API, and machine learning techniques. The system addresses the challenge of static parking fees by incorporating real-time data and a trained model to predict optimal parking prices. The core functionality hinges on a multiple linear regression model. This model is trained on historical parking data, considering factors such as parking space availability, time of day, base fare, air pollution levels, and user ratings. By analyzing these variables, the model predicts an accurate price for each parking space. The web application utilizes ReactJS to provide a user-centric interface. Users can search for parking spaces and view dynamic pricing in real time. Integration with Google Maps API offers a visual representation of available parking locations along with their corresponding prices. ExpressJS serves as the backend server, facilitating communication between the ReactJS front end and the MySQL database. The database stores historical parking data used for model training, along with real-time updates on parking availability and user ratings. This project contributes to the field of intelligent parking management by proposing a data-driven approach. The system promotes efficient resource allocation and user convenience through dynamic pricing based on various influencing factors. This approach aims to establish a fair and adaptable pricing structure for parking spaces, ultimately improving overall parking management. Keywords: ReactJS, ExpressJS , Google-Maps-API, Multiple Linear Regression , Dynamic Fare , Parking Management