Current Pricing Models Research Articles

Maximizing Efficiency in the Suez Canal: A New Approach to Evaluate the Impact of Optimal Time-Varying Tolls on Ship Arrival Times

In the existing literature, an optimal time-varying toll scheme has been proposed for the Suez Canal to address the inefficiency of numerous ships queuing and waiting at the anchorage area to enter the canal. The primary objective of this tolling strategy is to alleviate the significant issue of ships queuing at the canal’s anchorage area. This stands in contrast to the current tolling system employed by the Suez Canal, which primarily aims to recover the management and operational costs associated with ship passage through the canal. However, the existing literature has yet to explore how the arrival times of ships at the anchorage area will change after implementing the optimal time-varying toll scheme. The goal is to ensure that the equilibrium cost of each tolled ship does not result in losses and achieve maximum efficiency in eliminating queueing at the anchorage area. To address this gap, this paper adopts the principle of cost equilibrium conservation and utilizes the Point-Slope Form to derive two mathematical formulas representing all ships’ post-toll arrival times at the anchorage area of the Suez Canal. These formulas are specifically derived for two categories of tolled ships: those that enter the canal earlier than the latest entry time regulated by the canal authorities and those that enter later. The derived formulas are concise and comparative, strengthening the theoretical underpinnings of the current pricing model for a queuing canal. Furthermore, they serve as valuable references for canal authorities in devising pertinent measures, such as organizing the scheduling of canal pilots, to facilitate the implementation of the optimal time-varying toll scheme.

Are Current International Pricing Paradigms for an Efficacious Disease‐Modifying Anti‐Alzheimer’s Treatment Equitable? Are Quality‐Adjusted Life‐Years Worth Considering?

AbstractBackgroundThe June 2021 approval of aducanumab [Aduhelm], initially priced at $56,000/year, followed by the January 2023 approval of lecanemab [Leqembi], initially priced at $26,500/year, generated renewed debate over reasonable and appropriate pricing for disease‐modifying anti‐Alzheimer’s treatments. For equitable pricing of an anti‐Alzheimer’s treatment, what are the optimal patient‐centered pricing paradigms?MethodA literature search of pricing models for anti‐Alzheimer’s, plus other, medicines indicates there are (at least) seven drug pricing models, with value‐based pricing being the most popular. In many countries new medicines are only compared within a certain indication, which may lead us to unknowingly pay substantially more for equal benefit in one disease versus another(1).ResultIn tandem with Leqembi’s expedited review and approval, there was substantial discussion as to what would be reasonable and appropriate pricing. Analysts at the Institute for Clinical and Economic Review [ICER] reported the drug would be cost‐effective at the $8,500 to $20,600 range; their analyses were based on preliminary data. Eisai analysts reported that lecanemab has a societal medicinal value of approximately $37,000, noting that it slowed cognitive decline by 27% over eighteen months(2). The top five selling drugs for multiple sclerosis have an average annual price of $64,760; the average annual cost for the top five cancer drugs is > $165,000.Measurement of outcomes and costs are essential for value‐based pricing. For dementia, the International Consortium for Health Outcomes Measurement [ICHOM] notes that patient‐centered outcome measures represent the ultimate measure of quality and they are always multidimensional. Quality‐adjusted life‐years [QALYs] seem to be absent from the computation of pricing for anti‐dementia treatments. Although consensus on QALY calculations is evasive, currently extant data are compelling. In Australia, the 2023‐adjusted QALY range is $32,618 to $55,415; United Kingdom $49,631 to $98,158, Germany $25,562 to $82,079, Sweden $51,494 to $59,416 and in Japan $56,819 to $67,643(3‐5). Utilizing an 18‐month treatment‐period, a range of QALY valuations will be presented, plus comparisons with the two other clinical indications.Conclusion The inclusion of QALYs for valuation of Alzheimer’s disease medicines warrants consideration; and, The full value of anti‐dementia treatments may be understated in current pricing models.

A factor pricing model based on double moving average strategy

The heterogeneous market hypothesis states that there are different types of investors with different investment term-structures in the stock market. Based on the hypothesis, we analyze the trading strategies of moving averages with different term structures, and we find that the trading strategies formed by the 1-month short-term moving average with 3-, 6-, 9-, 12-, 18-, and 24-month long-term moving averages can gain significant excess returns in the Chinese market. Accordingly, this paper adopts the moving average factor formed by the method of Liu’s to expand the current four-factor pricing model of the Chinese market. The results of spanning regression show that the introduction of the short-term average with a period of 1-month and the long-term moving average with a period of 3- and 12-month can significantly improve the pricing power. The GRS test developed by Gibbons et al. (1989) also shows that the augmented six-factor model with moving average factors can pass the test at the significance level of 5%, and the average absolute value of intercept terms decreases by about 50% compared with the results of the four-factor model. At the same time, under different macro-state dependence, the overall performance of the augmented six-factor model with moving averages is still better than that of the four-factor pricing model proposed by Liu et al.’s. Our main contribution is to introduce double moving average factors to capture the behaviors of investors’ different term structures and add these factors to the most competitive asset pricing model for enhancing the pricing power. It is of great significance to supplement the pricing model in the Chinese market.

A pricing model for agricultural insurance based on big data and machine learning

<p>Agricultural insurance is a crucial element of policies that promote and protect agriculture. It protects agriculture from risk and distributes agricultural hazards. The rural economy’s stabilization has been a significant stabilizer function. But as agriculture insurance has quickly advanced, a number of issues have unavoidably come to light. Agricultural insurance still offers a wide range of products and services available today. Big data will play a significant supporting role in the pressing need to innovate and improve goods and services. Other information supporting agricultural insurance includes agricultural data connected to it. The two previously most often utilized agricultural index insurances are regional yield insurance and weather index insurance. They struggle with risk pricing mostly due to a lack of appropriate empirical data, complicated dependence linkages between various hazards, and the prevalence of basis risk. A comprehensive study and review of pertinent research findings are carried out by modelling regional yield risk, building weather indicators and their distribution fitting, modelling agricultural dependence risk, and measuring and reducing basis risk. This article highlights the flaws in the current pricing models as well as the problems that need to be addressed in future studies. The need to further develop agricultural index insurance’s risk modelling techniques and increase the objectivity and precision of the pricing outcomes cannot be overstated in terms of their practical importance.</p>

Not one, but many: developing a multi-indication pricing model for medicines in Belgium.

Back ground: Current pricing and reimbursement models that focus on one indication at a time are not suited to address the market access of multi-indication medicines. Therefore, the aim of this study is to co-create with Belgian stakeholders a multi-indication pricing model and procedural pathway, to identify conditions for implementation, and to illustrate the multi-indication pricing model with a case study. Methods: Different multi-indication pricing models were identified from the literature, case studies and pilots in other countries. Semi-structured interviews were conducted with 21 representatives from the National Institute for Health and Disability Insurance, insurance funds, clinicians, patients, the policy cell of the Minister of Health, pharmaceutical industry and academia. These provided insight in the opinions of stakeholders about possible multi-indication pricing models and their feasibility in the Belgian context. Agreement on the preferred multi-indication pricing model and procedural pathway was reached in a multi-stakeholder round table. Results: The international review generated four main multi-indication pricing models that vary in terms of whether a uniform price or differential prices are applied, whether prices are adjusted for the volume and/or value of the medicine in each indication, and whether a proactive or retroactive dynamic pricing approach is used. However, Belgian stakeholders preferred a fifth model, which sets a single price as the volume- and value-weighted average price across all indications at launch. Over time, the price is adapted based on volume and value of the medicine in real-life practice for each indication. To implement this model, a legal framework, horizon scanning and early dialogue, data infrastructure, an evidence plan for the medicine, technical expertise and governance model need to be developed. Conclusion: Although the multi-indication pricing model preferred by Belgian stakeholders raises the administrative burden, it allows for the price of a medicine to vary during the lifecycle based on its initial and real-life performance in multiple indications.

Essence controls in pharmaceutical enterprise systems

The healthcare access is fundamental rights for every human being. It is Government’s responsibility to provide authentic Medicines and good healthcare system to its people as essential part of human life. Currently many governments across the countries are coming up with many welfare schemes to provide good healthcare services as a fundamental right. Pharmaceutical industry is also considered an essential part of healthcare services as it continuously invents new drugs to improve people’s life. New research and their potential outcomes raising new hopes of curing critical and rare diseases. Pharmaceutical industry must adopt innovative technologies in enterprise systems which help to control the process of medicine invention from research stage to final drug approval. Enterprise system secure the research information and patients confidential data, maintain Good manufacturing Practice (GMP) in production to manufacture breakthrough medicine for improving human life expectancy and quality of life. Enterprise system also provide accurate information’s for internal and external audit. Enterprise system in pharmaceutical business process effectively reduce defects and Corrective and Preventive Actions (CAPA). New drug development and alternatives of expensive medicines are offering cost effective and affordable treatment plans to poor population. However, adopting new technologies in enterprise systems and improving existing infrastructure is another key challenge for poor and developing countries [1]. The complex and lengthy process of drug launch and huge investment on research and development make process more vulnerable when results are discovered without any prominent health improvements [2]. Newly launch drugs are trend to be costly and unaffordable for poor patients who need them and put a high pressure on health care budgets. These trends raise questions about incentives at work in the pharmaceutical sector and the sustainability of current pricing models.

Digital maturity index for Russian public companies

Subject. The article addresses the digital maturity index, which is necessary for measuring the level of digitalization of Russian issuers to further explore the digital premium in current pricing models in the Russian stock market. Objectives. The purpose is to develop an original model for assessing the level of digital maturity of Russian issuers to compare the efficiency of ongoing digital transformation in the domestic market. Methods. The study draws on the comparative analysis of data from 51 domestic issuers operating in 11 sectors of the Russian economy. Results. Based on my original model, I compiled a Digital Maturity Index to rank domestic issuers by the level of digital development. This index enables to analyze the efficiency of digital transformation in general, and to compare companies with their competitors. Conclusions. The domestic economy has great development potential due to the ongoing digital transformation of Russian companies. The high level of training of domestic specialists in digitalization has contributed to domestic companies being among the pioneers of digital transformation. Companies in the financial and telecom sectors are among the leaders in the digitalization of business in the domestic market. Domestic steel companies also show significant rates of digitalization. The offered digital maturity index model sets the stage for the development of the topic of modern multifactor models that include a company's digital maturity factor.

Analysing the Economic Viability of Implicit Demand Response Control of Thermal Energy Storage in Hot Water Tanks

Demand-responsive control of electrically heated hot water storage tanks (HWSTs) is one solution, already present in the building stock, to stabilise volatile energy networks and markets. This has been put into sharp focus with the current energy crisis in Europe due to reduced access to natural gas. Furthermore, increasing proportions of intermittent renewable energy will likely add to this volatility. However, the adoption of demand response (DR) by consumers is highly dependent on the economic benefit. This study assesses the economic potential of DR of centralised HWSTs through both an analysis of spot price data and an optimisation algorithm approximating DR control. The methods are applied to a case study apartment building in Norway using current pricing models and examine the effect of the demand profile, electricity prices, heating power and storage capacity on energy cost and energy flexibility. Unit cost savings from DR are closely linked to the variation in unit energy price during the optimisation period. Increasing the storage capacity or the heating power increases the flexibility with a diminishing rate of return. However, increasing storage capacity does not result in cost savings as additional heat losses are greater than the saving from shifting demand, except for during highly volatile electricity price periods. Changing the minimum setpoint temperature improves the cost curve as a greater thermal storage capacity can be achieved without increasing heat loss. Systems utilising a smaller heating power are more economical due to the dominant role of the monthly price related to the peak energy demand of the system.

Investigating peer-to-peer power transactions for reducing EV induced network congestion

Current electricity pricing models take into account costs of generation, transmission, emissions and a fixed cost of distribution based on time of day and type of primary source. However, the authors postulate that increasing fast charging for EVs will lead to increased overloading and voltage drops observed in networks at the low voltage level. To this end, the authors propose a novel method that encourages reactive power injection to minimize voltage drops and active power injection to minimize cable loading by using a centralized, double-sided optimized algorithm. This model allows for peer-to-peer power delivery, incentivized using pricing factors, encouraging local optimization-based solutions to network overloading as well as increased islanding in the network as a means of improving grid resilience and allowing optimal EV charging and discharging. Incentivized peer-to-peer transactions are then recorded in a centralized blockchain, with instantaneous power serving as the transactional commodity for both fixed consumers and generators (houses) and mobile consumers and generators (EVs). The mathematical model, as well as the impact of such a decentralized network compared to a business-as-usual network shows the potential of decongestion of 5–10% in terms of a reduction in voltage drops and cable loading with even a low EV penetration level (<5% of available transportation) by using existing resources as V2G and voltage stability resources.

Current prices versus minimum costs of production for CFTR modulators

BackgroundWhile the clinical benefits of CFTR modulators are clear, their high prices render them inaccessible outside of the world's richest countries. Despite this, there is currently limited evidence regarding global access to these transformative therapies. Therefore, this study aims to estimate the minimum costs of production of CFTR modulators, assuming robust generic competition, and to compare them with current list prices to evaluate the feasibility of increased global access to treatment. MethodsMinimum costs of production for CFTR modulators were estimated via an algorithm validated in previous literature and identification of cost-limiting key starting materials from published routes of chemical synthesis. This algorithm utilised per kilogram active pharmaceutical ingredient costs obtained from global import/export data. Estimated production costs were compared with published list prices in a range of countries. ResultsCosts of production for elexacaftor/tezacaftor/ivacaftor are estimated at $5,676 [$4,628-6,723] per year, over 90% lower than the US list price. Analysis of chemical structure and published synthetic pathways for elexacaftor/tezacaftor/ivacaftor revealed relatively straightforward routes of synthesis related to currently available products. Total cost of triple therapy for all eligible diagnosed CF patients worldwide would be $489 million per year. Comparatively, the annual cost at US list price would be $31.2 billion. ConclusionsElexacaftor/tezacaftor/ivacaftor could be produced via generic companies for a fraction of the list price. The current pricing model restricts access to the best available therapy, thereby exacerbating existing inequalities in CF care. Urgent action is needed to increase the availability of triple combination treatment worldwide. One strategy based on previous success is originator-issued voluntary licenses.

Cost-Effectiveness of Liposomal Cytarabine-Daunorubicin (CPX-351) Compared to Conventional Cytarabine-Daunorubicin Chemotherapy in Acute Myeloid Leukemia

Cost-Effectiveness of Liposomal Cytarabine-Daunorubicin (CPX-351) Compared to Conventional Cytarabine-Daunorubicin Chemotherapy in Acute Myeloid Leukemia

Enhanced Spatial–Temporal Map-Based Video Analytic Platform and Its Local- Versus Cloud-Based Deployment with Regional 511 Camera Network

This paper explores the cloud- versus server-based deployment scenarios of an enhanced computer vision platform for potential deployment on low-resolution 511 traffic video streams. An existing computer vision algorithm based on a spatial–temporal map and designed for high-angle traffic video like that of NGSIM (Next Generation SIMulation) is enhanced for roadside CCTV traffic camera angles. Because of the lower visual angle, determining the directions, splitting vehicles from occlusions, and identifying lane changes become difficult. A motion-flow-based direction determination method, a bisection occlusion detection and splitting algorithm, and a lane-change tracking method are proposed. The model evaluation is conducted by using videos from multiple cameras from the New Jersey Department of Transportation’s 511 traffic video surveillance system. The results show promising performance in both accuracy and computational efficiency for potential large-scale cloud deployment. The cost analysis reveals that at the current pricing model of cloud computing, the cloud-based deployment is more convenient and cost-effective for an on-demand network assessment. In contrast, the dedicated-server-based deployment is more economical for long-term traffic detection deployment.

Understanding and Analysing Resource Utilization, Costing Strategies and Pricing Models in Cloud Computing

This paper is primarily focused on understanding the basics of cloud computing economics – specific pricing models and general cost structure and compares between basic cost structures. It also examines 15 pricing models dependent on various bases and attempts to consolidate the different pricing methods used to guarantee maximum profitability to both the service provider and consumer. To avail the services of cloud computing from the cloud service provider, the organization has to incur individual costs. There are three types of costs: fixed, variable, and semi-variable costs. Each type is computed and analyzed. There are more than 30 pricing models though the pay-as-you-go model, subscription model, and pay-for-resources models are in demand. To consider and discuss the basics of cloud computing, emphasizing its economics and pricing models considering various aspects and costing components, using and estimating plans from various cloud Registering suppliers. The paper also considers identifying gaps, if any, present in the current pricing models that are highly preferred with other pricing models that are designed but not implemented due to practical conditions and fear of higher percentage of risk and error and to compare the basic prices provided by the cloud service providers.

Research on Advantages and Difficulties of Implementing Full-cost Comprehensive Information System in Power Engineering

The article analyses the current status and existing problems of the current pricing model of electric power projects in our country, introduces the advantages of the full-cost comprehensive unit price method, and demonstrates that the implementation of the full-cost comprehensive unit price method for project pricing is simple, operability, and meets market needs. The market pricing principle is more conducive to the reasonable determination and effective control of the project cost. Finally, it expounds the difficulties that need to be studied to solve the current implementation of this model, and proposes corresponding countermeasures. At the same time, we applied the full-cost comprehensive unit price to actual cases, and demonstrated the effectiveness of the full-cost comprehensive unit price in power engineering management through modelling and simulation.

Data Services Sales Design With Mixed Bundling Strategy: A Multidimensional Adverse Selection Approach

In the era of the Internet of Things (IoT), an immense amount of data is generated from numerous sensors and devices. Data as a service (DaaS) represents a new market whose time has come, and DaaS-based businesses are emerging quickly. Businesses across sectors begin seeing their data not only as fundamentally valuable but economically viable to distribute. Due to the exponential growth of the DaaS market, the current pricing models gradually become less suitable for the selling of data sets. A more sophisticated pricing strategy is needed to unlock the value of that data for the data vendor’s (DV’s) revenue growth and their customers’ benefits such as online service providers (SPs). In this article, we aim to maximize the DV’s profits by designing a mixed sales mechanism, which allows the DV to sell data sets separately or bundled. Particularly, we apply a multidimensional adverse selection model from contract theory to model the data set trading between DVs and SPs. The DV’s surplus maximization problem is solved in the single-product case first, then extended to the multiproduct case. Furthermore, the analysis of the solution of the pricing strategy in single-product and multiproduct cases is provided. Finally, the simulation results show that the proposed pricing model can improve the DV’s profits efficiently.

The correlation between clinical benefit and financial cost of cancer drugs.

7071 Background: The cost of many cancer drugs is very high, but it is unclear if these costs are associated with commensurate improvement in outcomes. We aimed to assess the association between the cost of cancer treatments and their clinical benefit, using the NCCN Evidence Blocks value assessment framework. Methods: The NCCN Evidence Blocks include 4 measures of clinical benefit: Efficacy, Safety, Quality of Evidence, and Consistency of Evidence. The NCCN assigns scores on each measure ranging from 1 (least favorable) to 5 (most favorable). We obtained the NCCN Evidence Blocks scores as of December 31, 2018 for all recommended cancer treatments for the 30 most prevalent cancers in the US. For each treatment, we calculated total treatment costs (including drugs, administration fees, and supportive care medications) using Medicare reimbursement rates. We categorized treatments as either “time-limited” or “time-unlimited” according to whether their costs are best reflected as per full treatment course (often, adjuvant/neoadjuvant treatments) (time-limited) or per month of therapy (often, treatments for advanced disease) (time-unlimited). We used generalized estimating equations, with clustering within treatment indications, to estimate the association between Evidence Blocks scores and treatment costs, modeling the expected change in cost associated with a one-unit increase in the score on an Evidence Blocks measure. Results: There were 541 time-unlimited and 845 time-limited treatments. Among time-unlimited treatments, monthly treatment cost ranged from $4 to $64,630. Monthly treatment cost was positively associated with Efficacy ($3,036, 95%CI: $1,782, $4,289) and Quality of Evidence ($1,509, 95%CI: $171, $2,847) but negatively associated with Safety (-$1,470, 95%CI: -$2,790, -$151) and Consistency of Evidence (-$2,003, 95%CI -$3,420, -$586). Among time-limited treatments, cost per course of therapy ranged from $0 to $775,559, and no measure was significantly associated with cost. Evidence Blocks scores accounted for little of the variation in treatment cost (linear model R-squared = 0.10 for time-unlimited, and &lt; 0.01 for time-unlimited). Conclusions: The association between NCCN Evidence Blocks measures and treatment cost was inconsistent, and accounted for little of the cost variation among treatments for the same indication. The clinical benefit of cancer treatments does not appear to be a primary determinant of treatment cost, suggesting that current pricing models may be inadequate to incentivize the development and utilization of high-value treatments.

Estimating costs of multi-component enterprise applications

AbstractEstimating the cost of a multi-component application (e.g., its resource or energy consumption) is fundamental in nowadays enterprise IT, especially if we consider that current pricing models are mainly pay per-use. While this is still manageable on small applications, it is really hard to manually estimate the cost of large-scale enterprise applications involving hundreds of interdependent application components. In this article, we formalise the problem of estimating costs of multi-component applications, by representing the structure of an application as a typed directed graph, and by allowing to associate different types of costs with different application components. We show that costs can be fully customised, and that associating different costs with the same application leads to different cost estimation problems defined on that application.We then present an approach for solving cost estimation problems on multi-component applications, which is based on terminating and confluent graph transformations. We also present a prototype implemenation of our approach, which we use to run a case study based on a third-party application.

A Conceptual Model for Pricing Health and Life Insurance Using Wearable Technology

AbstractA health risk score was created to investigate the possibility of using data provided by wearable technology to help predict overall health and mortality, with the ultimate goal of using this score to enhance the pricing of health or life insurance. Subjects were categorized into low‐, increased‐, and high‐risk groups, and after results were adjusted for age and sex, Cox proportional hazards analysis revealed a high level of significance when predicting mortality. High‐risk subjects were shown to have a hazard ratio of 2.1 relative to those in the low‐risk group, which can be interpreted as an equivalent increase in age of 7.8 years. Our findings help to demonstrate the predictive capabilities of potential new rating factors, measured via wearables, that could feasibly be incorporated into actuarial insurance pricing models. The model also provides an initial step for insurers to begin to consider the incorporation of continuous wearable data into current risk models. With this in mind, an emphasis is placed on the limitations of the study in order to highlight the areas that must be addressed before incorporating aspects of this model within current pricing models.

A Collaborative Pricing Framework for In-Network Caching in Information- Centric Networking

As a promising future Internet architecture, information-centric networking (ICN) takes the content as the focal point while shifting away from the traditional host-centric paradigm. The content-oriented feature and in-network caching from ICN enable new economic or pricing models among Internet service provider (ISP), content provider (CP), and users. The current pricing models in ICN usually focus on that users directly pay the content price to the CP, which leaves the ISPs out of the payment processes, resulting in ISP’s reluctance to deploy ICN. Furthermore, the existing models largely ignore the interaction between the pricing strategy and user consumption behaviors, such as choosing cheaper ISPs or giving up the acquisition of the content with high prices. In order to design a collaborative pricing framework to prompt ISP’s deploying ICN, we first develop a content acquisition scheme in conjunction with the lump-sum and retail strategies for ICN. Within this scheme, a user consumption model to characterize the user’s behaviors in using the Internet and the utility function for all ICN entities, including the CP, access ICN, and transit ICN, are formulated to maximize the revenues of all entities. Our extensive analysis and numerical results show that the proposed collaborative pricing framework significantly outperforms the existing scheme in joint caching and pricing strategies .

Energy-proportional profiling and accounting in heterogeneous virtualized environments

The costs of current data centers are mostly driven by their energy consumption (specifically by the air conditioning, computing and networking infrastructure). Yet, current pricing models are usually static and rarely consider the facilities’ energy consumption per user. The challenge is to provide a fair and predictable model to attribute the overall energy costs per virtual machine (VM) in heterogeneous environments. In this paper we introduce EPAVE, a model for Energy-Proportional Accounting in VM-based Environments. EPAVE allows transparent, reproducible and predictive cost calculation for users and for Cloud providers. It provides a full-cost model that does not account only for the dynamic energy consumption of a given VM, but also includes the proportional static cost of using a Cloud infrastructure. It comes with PowerIndex, a profiling and estimation model, which is able to profile the energy cost of a VM on a given server architecture and can then estimate its energy cost on a different one. We provide performance results of PowerIndex on real hardware, and we discuss the use cases and applicability of EPAVE.