Market Scenario Research Articles

EXAMINING THE ROLE OF MARKETING RESEARCH ON INCREASED PROFITABILITY IN NICON INSURANCE COMPANY KADUNA STATE NIGERIA

The objective of this study was to examine the influence of marketing research on the profitability of NICON insurance in Kaduna State, Nigeria. The study adopted survey research sampling the opinion of 14 staff of NICON Insurance, composed of senior management staff, administrative staff, and marketing staff. Their opinion was collected using a structured questionnaire. The data obtained was analyzed by tabulation and percentages, and the formulated hypotheses were tested using the Pearson Correlation Coefficient method. The findings revealed that marketing research exerts a statistically significant effect on NICON insurance profitability. More so, the study found that marketing research has a statistically significant effect on NICON insurance total premium. The study concluded that for insurance firms to maximize profit at an optimum level, they have to expand the scope of marketing research and development in order to eliminate the risk involved in today’s marketing trend. The study therefore recommended that NICON Insurance Kaduna State should fully embrace the marketing research tasks, and establish departments or units dedicated to this crucial task, staffing them with qualified and experienced personnel. They should also step up their research to deliver timely information on the development of insurance products and other aspects of their business. Furthermore, through market scenario research, they have to make a consistent effort to track the level of client satisfaction with their offerings.

Cost estimation for launch vehicle families considering uncertain market scenarios

When optimizing the staging of a launch vehicle, usually a reference mission with a fixed payload mass is used to determine the specific launch cost (or a surrogate thereof, e.g. total mass or dry mass). While this approach neglects suboptimal loading, it delivers sufficiently good results for the evaluation of an individual launcher if the design mission is well known. However, this approach does not capture the full range of payloads that a launch vehicle is expected to launch during its operational lifetime. In order to estimate the cost and subsequently optimize the staging of such launch vehicle families, a full launch scenario has to be considered. The cost of an individual launch vehicle loses importance and the deciding factor is the total cost associated with launching the specified scenario. However, estimates of the future market inevitably include large uncertainties. These uncertainties and their impact are ideally considered and quantified when assessing the specific recurring cost of a single launch vehicle. The consideration of a whole launch market scenario is especially relevant when evaluating a family of launch vehicles, an option currently being discussed for future European launchers. In this case, the distribution of the payloads onto the different members of the launch vehicle family has to be optimized in order to reach the lowest possible cost. Within this manuscript, a method that allows the determination of the recurring cost of such a launcher family based on a launch market scenario including uncertainties is proposed. Based on the launch numbers derived from the combinatorial optimization, parametric cost models for recurring and non-recurring cost are adapted and applied to three possible future launch vehicle families.

Techno-Economic Analysis of a Thermally Integrated Solid Oxide Fuel Cell and Compressed Air Energy Storage Hybrid System

Natural-gas-fueled solid oxide fuel cell (SOFC) systems have the potential for high-efficiency conversion of carbon to power due to the underlying electrochemical conversion process while readily facilitating carbon capture through the separation of the fuel and oxidant sources. Compressed air energy storage (CAES) technology can potentially store significant quantities of energy for later use with a high round-trip efficiency and lower cost when compared with state-of-the-art battery technology. The base load generation capability of SOFC can be coupled with CAES technology to provide a potentially flexible, low-carbon solution to meet the fluctuating electricity demands imposed by the increasing share of intermittent variable renewable energy (VRE) production. SOFC and CAES can be hybridized through thermal integration to maximize power output during periods of high electrical demand and then store power when either demand is low or renewable generation reduces power prices. The techno-economics of a low-carbon hybrid SOFC and CAES system was developed and investigated in the present study. The proposed hybrid system was found to be cost-competitive with other power-generating base-load facilities when power availability was considered. The hybrid system shows increased resilience to changes in a high VRE grid market scenario.

Optimal dispatch of combined heat and power generating units with prohibited operating zones using improved heap‐based optimizer

AbstractIn the present market scenario, energy cost has been increased and also restrictions have been faced in fossil fuels energy sources. This leads to evolvement of combined heat and power generating units having higher efficiency as compared to the existing systems. The optimal dispatch of CGUs involves a number of intricate constraints, making it a practical optimization problem. So, an improved heap‐based optimizer algorithm has been implemented in this work for solving optimal dispatch problem by accomplishing a proper balance between initial and final stages of global search and convergence, respectively. This problem provides optimal scheduling of heat and power generating units and seeks to minimize the overall fuel cost supply of combined units considering their operational limits. Here, three test systems, that is, 7‐units, 24‐units and 48 units system have been taken into consideration to verify the performance of proposed IHBO algorithm. The impact of prohibited zones on power dispatch has been analysed for both the system in an electricity market. In addition, the valve‐point loading effect, mutual dependency of produced heat and power of combined units and also transmission power losses in transmission lines have been studied. Comparisons among different methods have been made based on their respective solutions obtained and from that it manifests the robustness and superior performance of the proposed technique. Verification studies showed that impact of prohibited zones on generating units, increases the production cost with respect to without prohibited zones. However the proposed technique yields least production cost among other techniques.

Virtual Power Plant for Optimizing Power Flow in Large Scale Distributed Networks

The control issues in the network caused by the increasing proportion of dispersed production & sources of clean energy need coordinated handling of these assets. Virtual power plants (VPPs) may be thought of as systems that comprise resources like electric vehicles, energy storage, controlled loads, and other distributed generators. Operation, resource unpredictability, energy management, involvement in electrical markets, etc. are just a few of the many obstacles VPPs must overcome. The challenge of the study is to optimize the performance of VPPs in managing dispersed clean energy sources, and the proposed model employs a distributed control approach and particle swarm optimization (PSO)-based learning reinforcement technique to address this challenge by maximizing VPP output and operational efficiency in various energy market scenarios. In this research, we explore the topic of ongoing electrical power in massively distributed systems based on VPPs. To maximize the VPP output & reach an ideal operational point, the authors of this work used a distributed control approach. The purpose of this article is to examine VPP performance under a variety of markets for energy configurations. Further, a PSO-based learning reinforcement technique is used to examine the benefits and drawbacks of VPPs’ power management.

Optimal decisions for hybrid manufacturing and remanufacturing with trade-in program and carbon tax

With the practical implementation of carbon taxes and the adoption of trade-in programs, the decision environment for manufacturing and remanufacturing has become increasingly complicated. Taking the carbon tax and the trade-in program into consideration, this study develops manufacturing-remanufacturing models for two market scenarios (i.e., emerging market and mature market) when cores for remanufacturing are limited by used product collection, and finds that the determining factors of optimal decisions are different across the two market scenarios. In addition to the carbon tax and carbon emissions of remanufactured product, the optimal decisions are also affected by the segment size of the replacement consumer in the mature market. Furthermore, we find that carbon tax policy does not always encourage the manufacturer to work on remanufacturing, and it may prevent remanufacturing under certain circumstances. Moreover, the impacts of supply chain parameters on optimal decisions, total carbon emissions, and profits are explored. The carbon tax has similar impacts across the two market scenarios; however, the carbon emissions of remanufactured product and the remaining value of the used product show different impacts. From the social welfare perspective, the optimal carbon tax depends on the regulator’s environmental concern.

Organizational change management model proposal: action-research in a public exam company.

Purpose – The presented research aimed to propose a model for organizational change management for a public examination company, which has experienced a significant increase in its activities and business environment in recent decades. Design/methodology/approach – The adopted methodological approach was action research. The collected data allowed for the identification of the dominant culture within the organization and the assessment of its internal and external environment. This information informed the selection of an appropriate change management model. Findings – Two results emerged from the research, one of general use and the other specific to the context: i. a method for choosing change management models, and ii. a detailed management model outlined for the analyzed organization. Originality/value – The academic contribution consisted of developing a method for searching and selecting a management model. It is noteworthy that this method itself represents an initial outcome of the research with a high level of applicability, not limited to companies in the public examination sector. Regarding the final product, the selected and detailed model's primary contribution lies in guiding the organization in making adjustments to remain competitive and continue its operations. The outlined steps include the necessary care and attention to details in implementing changes, aiming to effectively contribute to the success of the adaptations required by the market scenario. Keywords: Organizational change. Change management. Organizational culture.

A novel randomized weighted fuzzy AHP by using modified normalization with the TOPSIS for optimal stock portfolio selection model integrated with an effective sensitive analysis

Building an ideal stock portfolio is a difficult and important undertaking in the world of financial decision-making. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique and the Randomised Weighted Fuzzy Analytic Hierarchy Process (AHP) are combined in this research to create a powerful model for selecting the best stocks for a portfolio. To improve the portfolio's robustness under various market scenarios, our model also incorporates sensitivity analysis. Given the inherent uncertainty in financial markets, the suggested approach starts by using modified fuzzy AHP based on introducing the randomized weights and the novel normalization to get expert views and assign suitable weights to the underlying selection criteria. Based on the established criteria and associated fuzzy scores, the TOPSIS approach is then used to rank and choose the best stocks. Our model also includes sensitivity analysis to evaluate the performance of the portfolio under various market conditions. Extensive empirical tests are performed utilizing historical stock market data to assess the effectiveness of our strategy. The outcomes show the suggested model's superiority in building stock portfolios that beat conventional approaches in terms of risk-adjusted returns.

Digital transformation or not? Manufacturer's selection strategy under carbon cap-and-trade mechanism

Purpose The digital economy is profoundly transforming the manufacturing industry's fundamental concepts and value creation logic, making digital transformation (DT) strategy a crucial decision for manufacturers. And faced with increasingly severe environmental issues, DT may become an important means to achieve sustainable development. This paper mainly discusses the strategic choice of the manufacturer's DT and analyzes the impact of DT on carbon emissions.Design/methodology/approach Based on the carbon cap-and-trade mechanism, the authors have constructed two decision models to study the DT strategy of the manufacturer, further exploring the impact of the mechanism on the DT strategy and production strategy of the manufacturer. Finally, the authors discussed the effect of manufacturers' DT on their carbon emissions.Findings The authors found that the manufacturer should initiate DT to enhance their competitiveness, regardless of whether they are in a low digital technology scenario or a high digital technology scenario. Notably, DT can enhance the ability of the manufacturer to respond to external emergencies. In a low digital technology market scenario, both carbon emissions per unit of product and carbon price are positively affecting the digitization level of the manufacturer. In a high digital technology market scenario, the manufacturer will initiate a full degree of DT. Moreover, the impact of DT on total carbon emissions varies in markets with different levels of digital technology.Originality/value Innovatively, the authors divided the DT of the manufacturer into market scenarios with low digital technology and high digital technology. Provide the manufacturer with DT decisions according to different scenarios. At the same time, it verifies the uncertainty of DT on carbon emission and enriches the related research.

Self-learning Agents for Recommerce Markets

Nowadays, customers as well as retailers look for increased sustainability. Recommerce markets – which offer the opportunity to trade-in and resell used products – are constantly growing and help to use resources more efficiently. To manage the additional prices for the trade-in and the resale of used product versions challenges retailers as substitution and cannibalization effects have to be taken into account. An unknown customer behavior as well as competition with other merchants regarding both sales and buying back resources further increases the problem’s complexity. Reinforcement learning (RL) algorithms offer the potential to deal with such tasks. However, before being applied in practice, self-learning algorithms need to be tested synthetically to examine whether they and which work in different market scenarios. In the paper, the authors evaluate and compare different state-of-the-art RL algorithms within a recommerce market simulation framework. They find that RL agents outperform rule-based benchmark strategies in duopoly and oligopoly scenarios. Further, the authors investigate the competition between RL agents via self-play and study how performance results are affected if more or less information is observable (cf. state components). Using an ablation study, they test the influence of various model parameters and infer managerial insights. Finally, to be able to apply self-learning agents in practice, the authors show how to calibrate synthetic test environments from observable data to be used for effective pre-training.

Blockchain-Based Renewable Energy Certificate Trade for Low-Carbon Community of Active Energy Agents

The future distribution grid is a peer-to-peer (P2P) community formed by a large number of active energy agents (AEAs), and renewable energy certificate (REC) trading is an efficient way to realize a low-carbon AEA community. AEAs can trade not only electricity but also RECs among themselves to economically and efficiently meet the renewable portfolio standard (RPS) requirements. Aiming to lower the market barrier and increase the trading benefits for market participants, this paper proposes a blockchain-based renewable energy certificate (BCREC) that supports divisible and multiple transactions. The trade process includes four stages: setup, pre-transaction, transaction, and post-transaction. A scheme based on blockchain oracles and smart contracts is implemented to achieve decentralized BCREC issuance and transaction and to support a more flexible trading market. By exploring two typical market scenarios, we verify the advantages of BCREC trading and evaluate its impacts on AEA profits and market efficiency.

Optimal production cooperation strategies for automakers considering different sales channels under dual credit policy

The dual credit policy has significantly facilitated the market penetration of new energy vehicles. However, the best strategy for production collaboration, particularly when considering diverse sales channels, remains underexplored. In this study, a duopolistic market scenario is examined where a new energy vehicle automaker sells directly to end-users and a fuel vehicle automaker resells to consumers through a car dealer. We developed a game-theoretical model to analyze optimal decisions under three distinct strategies: pure competition (pc), commission sales (cs), and mixed production (mp). The findings are as follows: (1) The new energy vehicle credits rate and fuel consumption standards exhibit similar effects. While impacting the range effort levels minimally, these standards notably reduce the fuel-saving rate of fuel vehicles, questioning the efficiency of restrictive policies. (2) With the rise in credit trading prices, there is an increase in equilibrium decisions and profits for both the pc and mp strategies, suggesting that higher credit prices incentivize automakers to optimize their strategies, leading to better results. (3) The strategic choice of the automakers is heavily influenced by the credit trading price. The energy vehicle automaker tends to prefer the pc strategy, while the fuel vehicle automaker's preference alternates between mp and cs, depending on market conditions.

Techno-economic assessment of a biomass gasification-based polygeneration system for greenhouses

Decentralized biorefineries are an attractive solution to reduce the carbon footprint of agricultural greenhouses, by converting the abundant local residual biomass into value-added products such as biofuels. While the thermodynamic performance of many biomass system configurations has been investigated in the literature, techno-economic assessments are needed to validate the potential of such facilities. This approach is applied to a small-scale polygeneration system for the use-case of a southern Quebec greenhouse. The polygeneration system can produce heat, electricity, methanol, hydrogen, and a CO2-rich stream. A TRNSYS model is used to simulate the heat demand of a 20 000 m2 greenhouse. The work presented here compares the polygeneration system with a conventional solution based on biomass combustion. Nine configurations between 1120 and 5600 kW of thermal output power are investigated for both system types. Twenty-seven market scenarios are generated from the combination of three pricing scenarios for electricity, methanol, and hydrogen: pessimistic, baseline, and optimistic. Fixed capital investment (FCI), operation expenses (OPEX), and NPV are presented. In the most pessimistic market scenario, the best polygeneration system would have an NPV of −8.2 M CAD. In the baseline and optimistic scenarios, the same polygeneration system would have an NPV of 17.6 M CAD and 40.2 M CAD, respectively. By comparison, the best combustion-based solution would have the highest NPV at −5.0 M CAD. Those results highlight that small decentralized polygeneration systems are promising. However, their profitability is heavily dependent on future energy market developments.

Evaluating Industry 4.0 Manufacturing Configurations: An Entropy-Based Grey Relational Analysis Approach

Worldwide manufacturing and service sectors are choosing to transform the existing manufacturing sector, particularly reconfigurable manufacturing systems using the technologies of the next generation Industry 4.0. In order to satisfy the demands of the fourth industrial revolution, model evaluation and assessing various candidate configurations in reconfigurable manufacturing systems was developed. The proposed model considers evolving consumer demands and evaluates manufacturing configurations using a gray relational approach. For the case at hand, it is evident that considering all possible dynamic market scenarios 1 to 6, the current manufacturing configuration, i.e., alternative 1, has 89% utilization, total 475 h of earliness and 185 h of lateness in the order demand delivery to the market, and a total of 248 throughput hours and around 1143 bottleneck hours. The main challenge is to make a perfect match between the market demands, variations in product geometry, manufacturing processes and several reconfiguration strategies/alternatives. Furthermore, it is evident that alternative 1 should be reconfigured and that alternative 3 is the best choice. Alternative 3 exhibits 86% system utilization, a total of 926 h of earliness and 521 h of lateness in the order demand delivery to the market, and a total of 127 throughput hours and around 853 bottleneck hours. A simulation framework is used to demonstrate the efficacy of each possible reconfigurable production setup. The sensitivity analysis is also carried out by adjusting the weights through principal component analysis and validating the acquired ranking order. Thus, if the decision makers want to provide a preference to all criteria, the order of the choices of configurations is found to be alternative 3, alternative 1, alternative 4, alternative 2 and alternative 5.

Orphan Drugs: A Thematic View

Orphan diseases are all medical conditions that impact 0.65 to 1 out of every 1000 people, according to the World Health Organization. Since these are not financially stable and feasible, they are typically not explored for their pathophysiology or the emerging therapy alternatives. On January 28, 1983, in the USA a law known as the Orphan Drug Act was formed to promote the study, creation, and approval of medications for rare diseases. There are currently as many medications for other infectious diseases as there are for tropical infectious diseases, with 11 pharmaceuticals (4.87%) recognized as orphan drugs. Several medications having orphan status are used to treat illnesses that no longer meet the requirements for orphan designation. Some businesses also manufacture and produce orphan medications. Nuclear cloning, rational drug design, and high throughput screening are all methods used in drug discovery programs that could lead to the development of new medicines. This article discusses orphan drugs, medical orphans, rare diseases, the orphan drug act, orphan drug design, the global market scenario of orphan drugs and various databases that contain information about orphan drugs. Patients who have been ignored by for-profit medication discovery efforts have hope for the future.

Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach

The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns, and it rules out, by construction, systemic risk, which can negatively affect its out-of-sample performance. In the present work, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios, we simulate a large number of returns via a Monte Carlo method. This is done by obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting and then combining them with a fitted copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both profitability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs.

The modified homotopy perturbation method and its application to the dynamics of price evolution in Caputo-fractional order Black Scholes model

BackgroundFollowing a financial loss in trades due to lack of risk management in previous models from market practitioners, Fisher Black and Myron Scholes visited the academic setting and were able to mathematically develop an option pricing equation named the Black–Scholes model. In this study, we address the solution of a Caputo fractional-order Black–Scholes model using an analytic method named the modified initial guess homotopy perturbation method.MethodologyForemost, the classical Black Scholes model relaxed for European option style is generalized to be of Caputo derivative. The introduced method is established by coupling a power series function of arbitrary order with the renown He’s homotopy perturbation method. The convergence of the method is demonstrated using the fixed point theorem, and its methodology is illustrated by solving a generalized theoretical form of the fractional order Black Scholes model. The applicability of the method is proven by solving three different fractional order Black–Scholes equations derived from different market scenarios and its effectiveness is confirmed as feasible series of arbitrary orders that accelerate fast to the exact solution at an integer order were obtained. The computation of these results was carried out using Mathematica 12 software. Subsequently, the obtained outcomes were utilized in Maple 18 software to conduct a series of numerical simulations. These simulations aimed to analyze the influence of the fractional order on the dynamics of payoff functions regarding the share value as the option approached its expiration date under varying market constraints. In all three scenarios, the results showed that option values decrease as the expiration date approaches the integer order. Furthermore, the comparative outcomes reveal that Caputo fractional order derivatives control the flexibility of the classical Black–Scholes model because its payoff curve exhibits more sensitivity to changes associated with market characteristic parameters, such as volatility and interest rates.RecommendationsWe propose that the results of this work should be examined and implemented by mathematicians and economists to better comprehend the influence of Caputo-fractional order derivatives in understanding the dynamics of option price evolution of financial assets.

Applications of Microalgae in Foods, Pharma and Feeds and Their Use as Fertilizers and Biostimulants: Legislation and Regulatory Aspects for Consideration.

Microalgae are a rich resource of lipids, proteins, carbohydrates and pigments with nutritional and health benefits. They increasingly find use as ingredients in functional foods and feeds as well as in cosmetics and agricultural products including biostimulants. One of their distinct advantages is their ability to grow on wastewaters and other waste streams, and they are considered an environmentally friendly and cheap method to recover nutrients and remove pollutants from the environment. However, there are limits concerning their applications if grown on certain waste streams. Within, we collate an overview of existing algal applications and current market scenarios for microalgal products as foods and feeds along with relevant legislative requirements concerning their use in Europe and the United States. Microalgal compounds of interest and their extraction and processing methodologies are summarized, and the benefits and caveats of microalgae cultivated in various waste streams and their applications are discussed.

Silvopasture offers climate change mitigation and profit potential for farmers in the eastern United States

Silvopasture—integrating trees, forage, and grazing livestock on the same piece of land—is increasingly popular, given its potential to store carbon (C) and improve farmers’ livelihoods. We examined the C and economic implications of adding different silvopastoral systems to existing pastures in historically forested areas of the eastern United States (U.S.). We assessed nine distinct systems, varying by species and product (timber, nuts, and fodder for livestock), for two market scenarios: one based on current demand and one that assumes increased demand for products from silvopasture systems. For each system, we assessed C storage (biomass) and economics (internal rates of return (IRR) with and without C payments). We find that silvopasture in the eastern U.S. could expand by 5.6–25.3 million hectares under base case and full adoption scenarios (equaling a 6% increase in the global footprint of silvopasture), and could capture up to 4.9 or 25.6 Tg CO2e yr.−1, respectively. Expansion of silvopasture in these scenarios would come largely from demand for fodder as a supplemental feed, as well as specialty timber products. Per ha mitigation potential varied widely (0.5–6.5 tCO2e ha−1 yr.−1), due to species differences in C accumulation rates. Economics differed too, with some systems offering short break-even timelines (e.g., 7–9 years for fodder systems), and others costing more up front but having greater long-term returns (e.g., Chestnut). Furthermore, while some systems are profitable without any price on C (e.g., fodder-based silvopasture offers 6–14% 10-year IRRs without a price on C), higher payments for C would likely be necessary to unleash broad investment in timber and nut-based silvopasture. Our analysis included planting, maintenance, and harvest costs and tree product revenue. Future work is needed to fully incorporate additional considerations, like loss of grazing use during establishment, shade-induced effects on forage production, and livestock productivity. Furthermore, specific economic, ecological, site- and operation-level considerations are critical to evaluate the appropriateness of silvopasture systems for a given setting. This analysis suggests that across the eastern U.S., silvopasture could offer both climate change mitigation and enhanced profitability for farmers, with notable differences in the system-specific magnitude of opportunity.

Deep reinforcement learning applied to a sparse-reward trading environment with intraday data

Deep reinforcement learning (DRL) has made remarkable strides in empowering computational models to tackle intricate decision-making tasks. In quantitative trading, DRL trading agents have emerged as a means to optimize decisions across diverse market scenarios, culminating in developing profitable trading strategies by assimilating knowledge from past experiences. This study introduces an innovative trading system centered around the Deep Q-Network (DQN) algorithm called Extended Trading DQN (ETDQN). ETDQN stands out by its ability to adapt its learning process to trade effectively across varying market conditions, with feedback received exclusively upon trade liquidation. This contrasts with models that inundate agents with continuous feedback signals. ETDQN leverages distributional learning and several other independent extensions to enhance its DRL capabilities, streamlining its decision-making process. The model accomplishes this by prioritizing experiences encompassing diverse sub-objectives, facilitating the accumulation of maximum profit while obviating the need for intricate reward fine-tuning. Through extensive training on three distinct financial time series signals, ETDQN demonstrates its proficiency in identifying trading opportunities, particularly during periods of heightened price volatility. Notably, the model exhibits a more assertive approach towards managing annual returns volatility compared to the conventional DQN model, outperforming it by a factor of 1.46 and 7.13 concerning average daily cumulative returns, as evidenced in the historical data of Western Digital Corporation and the Cosmos cryptocurrency, respectively.