Purchasing Strategies Research Articles

A hybrid method for forecasting coal price based on ensemble learning and deep learning with data decomposition and data enhancement

PurposeCoal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.Design/methodology/approachThe proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.FindingsThe ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.Originality/valueThe proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

Coal price forecasting using complete ensemble empirical mode decomposition and stacking‐based ensemble learning with semisupervised data processing

AbstractGlobally, coal is a critical energy source, and the profits of related enterprises are highly related to changes in the coal price. A robust coal purchasing cost forecasting method may enhance the coal purchasing strategies of coal‐consuming enterprises and obtain key information for reducing global carbon emissions. However, forecasting the price of coal is a challenging task due to the noise and high random fluctuation of coal price data. To overcome these obstacles, this research proposes a novel forecasting method combining data decomposition, semisupervised feature engineering, and ensemble learning to forecast coal prices. Initially, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is employed to decompose the coal price series to reduce the complexity. Second, considering the fluctuation of coal price is influenced by various factors (such as transportation cost and coal mine production), the proposed system incorporates an adaptive data fusion module to fuse data from multiple sources. Finally, a stacking‐based ensemble learning model is adopted in the method to increase the forecasting accuracy by combining the forecasting results of multiple models. The Bohai‐Rim Steam‐Coal Price Index was used to validate the proposed method, and the result of the case study shows that the proposed method provides superior performance than the other nine baseline models in all measured indices. The outcomes of ablation tests indicate the precision of each algorithm is improved by combining CEEMDAN, which proves that the decomposition algorithm is necessary.

The influence of information technology on supply chain resilience through purchasing strategy, production flexibility, and supply chain responsiveness

In today's rapidly changing business environment, companies need to be dynamic, adapting their internal processes to respond effectively to external changes. Information Technology has become crucial for businesses to enhance their ability to manage and adapt to change. This study examines East Java manufacturing companies that have heavily invested in sustainable IT systems to enhance their supply chain responsiveness and resilience through improved purchasing strategies and production flexibility. The study collected data from companies that had implemented IT for at least three years, with respondents being permanent employees with a minimum of two years' experience. Analysis of 108 survey responses using SmartPLS 4 revealed significant impacts of IT implementation on purchasing strategy (0.610), production flexibility (0.363), and supply chain responsiveness (0.164). Furthermore, purchasing strategy influenced production flexibility (0.367), supply chain responsiveness (0.348), and resilience (0.166). Production flexibility also affected supply chain responsiveness (0.348) and resilience (0.343), while responsiveness impacted resilience by 0.306. These findings provide a practical contribution for functional managers in companies regarding the importance of IT investment in developing effective purchasing, production, and marketing strategies to meet market demands swiftly. The research contributes to supply chain strategy and resilience theory while highlighting the significance of strong collaboration with external partners for top management.

Examining Personal Values Influence on Purchase of Green Foods in Egypt: Sustainability-Based Perspective

The study aimed to examine the green food purchase behavior in Egypt. Considering the theory of planned behavior (TPB), the study tested the impact of personal values (egoistic, altruistic, and bio-spheric), intention to pay a premium, and green food availability on green food purchase behavior. In addition, the study tested the impact of green food availability as a moderator between green food purchase intention and green food purchase behavior. A total number of 366 survey questionnaires were collected online from customers who buy green food in Egypt. The data were analyzed by structural equation modeling to test the research hypotheses using Smart PLS 4.0. The findings showed that biospheric values positively influenced green food purchase behavior, while egoistic and altruistic values don’t have that significant impact. Additionally, the willingness to pay more for readily available green food options increased eco-friendly purchasing behavior. However, green food availability as a moderator does not significantly impact the green food intention-behavior relationship. Nonetheless, the study supported the TPB by showing that positive attitudes towards green food successfully translate into the intention and the act of purchasing it. This study tried to explain green food purchase behavior in Egyptian society from a holistic perspective. It provided insight for marketers to help them understand customer behavior and build suitable strategies for green food purchases.

Application of game theory in modern electrical power system (a review)

In recent decades, because of the speedy progress of the smart grid and the deepening of the reforms of the energy system, demand-side users can contribute to the collaboration of the energy network, with the main right to public energy procurement and the main right to sell energy. The demand side of the smart grid and the open energy market provides users with more adoptions, and game theory is expected to become an important tool for optimizing multi-stakeholder decision-making and solving many problems in this area. In this regard, this review article first reviews the recent development of game theory application in modern power systems, in addition to discussing in detail the typical gaming behavior of the current energy demand side. Second, it focuses on the application of game theory mainly in three aspects: distributed energy users, high-energy energy users, and medium- and low-energy users. Game theory is used in optimizing the distributed energy coordination, optimizing the energy purchasing strategy, responding to the needs of commercial and residential users, and ensuring network communication. Later, a comprehensive analysis of recent trends in game theory application in power systems is carried out in detail.

A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy

Under the policy of renewable portfolio standard (RPS), many microgrid (MG) users with high electricity consumption are installed with renewable energy (RE) generation devices to complete their RE consumption responsibility and reduce carbon emissions. In view of the conflict between the goals of microgrid operators (MGO) and MG users, it is a challenge to achieve economic optimization for both MGO and MG users at the same time when they participate in system dispatch and complete RE consumption responsibility. Therefore, this paper proposes a bi-level noncooperative dispatch strategy that takes into account user satisfaction and RE consumption willingness. In this bi-level model, the upper level is led by an MGO who decides electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. First, a novel comprehensive evaluation model is formulated to evaluate users' willingness to consume green electricity. Based on this, users' consumption behavior of green and thermal electricity is modeled independently not only in electricity trading but also in power dispatching. Second, optimization objectives are established with the goal of maximizing MG users' welfare, including user satisfaction improvement and energy purchase cost reduction, as well as maximizing the revenue of the MGO. Then, a Stackelberg game model is constructed, and a differential evolution algorithm nested CPLEX is used to solve the Stackelberg game problem of balancing the interests of both parties. Finally, our simulation analysis shows that under the dispatch strategy in this paper, load peak-valley difference is reduced. In addition, when the green electricity generated within the system is sufficient, based on a high level of users' willingness to consume green electricity, the total consumption of green electricity is increased by 29.5% and the overall welfare of users is increased by 0.75%.

The Impact of Marketing Strategy on Consumers' Purchasing Decisions in the Computer Gaming Aspect

Video game marketing strategies, particularly concerning social media and E-sports culture, have become a focal point of contemporary research. Researchers have highlighted the potential and progress in utilizing these digital platforms to engage consumers and enhance game popularity. However, there remains a significant gap in understanding the specific mechanics of how such strategies lead to commercial success and the extent to which they influence consumer behaviour. This study delves into the multifaceted dynamics shaping consumer behaviour in digital gaming. Drawing from the Cognitive Evaluation Theory, it explores how intrinsic motivations like autonomy and engagement drive consumer behaviour. It also examines innovative in-game purchase strategies, notably the Battle Pass and loot boxes, and their profound influence on player spending. Social media is scrutinized as a critical marketing tool, with user and expert reviews shaping purchase decisions. The importance of vibrant gaming communities and E-sports culture is highlighted, underlining their contribution to a game's cultural significance. Lastly, the study considers brand building in E-sports, shedding light on how renowned brands foster emotional bonds with consumers, ultimately securing loyalty and enhancing their market position. The goal is to provide industry players with a holistic understanding of the evolving digital gaming industry, aiding them in devising effective strategies to navigate this complex terrain.

Research on Resource Allocation of Autonomous Swarm Robots Based on Game Theory

To address the issue of resource allocation optimization in autonomous swarm robots during emergency situations, this paper abstracts the problem as a two-stage extended game. In this game, participants are categorized as either resource-providing robots or resource-consuming robots. The strategies of the resource-providing robots involve resource production and pricing, whereas the strategies of the resource-consuming robots consist of determining the quantity to be purchased based on resource pricing. In the first stage of the game, the resource-providing robots use the Cournot game to determine the resource production according to market supply and demand conditions; in the second stage of the game, the resource-providing robots and the resource-consuming robots play the price game and establish the utility function of the swarm robots to seek the optimal pricing and the optimal purchasing strategy of the swarm robots. After the mathematical derivation, this paper demonstrates the existence of a single Nash equilibrium in the constructed game. Additionally, the inverse distributed iterative search algorithm solves the game’s optimal strategy. Finally, simulation verifies the game model’s validity. This study concludes that the designed game mechanism enables both sides to reach equilibrium and achieve optimal resource allocation.

PENERAPAN DATA MINING DENGAN METODE NAIVE BAYES CLASSIFIER PADA PENJUALAN BARANG UNTUK OPTIMASI STRATEGI PEMASARAN

Along with technological developments, market developments are also increasing rapidly and competition is starting to occur between entrepreneurs and business people, which requires entrepreneurs to take advantage of existing technology. At this time the Universal Homeware Store does not realize that this data can be processed effectively to find out the development of a business or business that is being run, besides that the procurement of goods that customers need is often unavailable. One of the data processing technologies is by applying data mining. Data mining is a form of extracting information from data that is large enough to obtain data patterns so as to produce information data that is useful for making decisions and increasing profits in developing marketing strategies. One of the uses of data mining is using the Naive Bayes Classifier algorithm. Naive Bayes Classifier is one of the existing algorithms in data mining which is used to deal with uncertainty problems by emphasizing the concept of probability of use and measuring the level of accuracy so that it can predict purchasing strategies from past sales analysis. with research results Using 36 data and with rapid minner tools produces an accuracy value of 80%

How can better calf care be realized on dairy farms? A qualitative interview study of veterinarians and farmers

Improving health and welfare outcomes for replacement and surplus dairy calves is important for the sustainability of the dairy industry. Dairy farmers and veterinarians hold expertise in calf management and have valuable perspectives on how to practically motivate improvements. The objective of this study was to determine strategies that could improve the care calves receive on dairy farms from the perspective of dairy farmers and their herd veterinarians. Two veterinary clinics specializing in dairy practice in British Columbia, Canada, and 21 of their client dairy farms participated in the project. Following a meeting in which calf colostrum management was discussed between farmers and their herd veterinarian, participant interviews were conducted. Separate interviews were conducted for the farmers (n = 27 farmers from 21 farms) and their herd veterinarians (n = 7, with 1 to 5 farms that each vet worked with enrolled in the study) using tailored semi-structured question guides. Interviews (n = 42) were transcribed and coded following inductive thematic analysis methodology. The themes identified included strategies for farmers, veterinarians, and calf buyers, as well as contexts that influenced the dairy farmers' internal motivation to provide good calf care. Results indicated that farmers could optimize their calf management through fostering engagement of calf care personnel or by enlisting technology. Veterinarians could provide support to farms by being actively involved in calf monitoring, assisting in developing operating protocols, and setting goals, and especially by using farm-specific data to guide their management recommendations. Calf buyers could communicate with and provide accountability to farmers and improve their purchasing strategies to encourage farms to raise more vigorous surplus calves. Farmers' personal values, social networks, and relationships with different dairy industry stakeholders influenced their concern about the standards of their calf care practices. These findings provide guidance on how dairy farmers could achieve or be prompted to achieve improvements in their calf care practices.

코로나 팬데믹 이후 국제 유연탄 시장의 변화 및 대응 방안 연구

Bituminous coal is an important energy source with significant ripple effects on domestic power generation and industry. However, it is prone to price fluctuations due to climate change and changes in production capacity in major producing countries. In particular, since the outbreak of COVID-19, international prices have skyrocketed due to the closure of mines and port disinfection measures in major producing countries. This study aims to provide an overview of the international bituminous coal market and find insights focusing on recent decarbonization trends and changes in major exporting and importing countries after the COVID-19 pandemic. Our suggestions are: First, a diversified purchasing strategy should be established. Second, power generation firms should take advantage of the high ash coal price decrease. Third, firms should use fixed price and variable index-linked contracts. Finally, firms should seek to secure stable and diversified sources of environment-friendly coal.

A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions

Two of the most critical issues encountered in the day-ahead scheduling of regional integrated energy systems are the uncertainty of renewable energy resources and complexity of load demand. Furthermore, varying operating conditions also pose challenges for economical day-ahead scheduling. This paper proposes a scenario-based day-ahead scheduling approach for regional integrated energy systems to minimize operating costs by mining historical data. A hybrid dynamic energy hub model with variable efficiency that integrates an extreme gradient boosting (XGBoost) algorithm and analytical formulation is proposed. We developed a scenario-based scheduling optimization model in which climate data and load data are predicted using XGBoost and the probability distributions of their predicted errors are estimated using a Gaussian mixture model. Monte Carlo simulation and K-means clustering were used to generate and reduce scenarios and a success-history-based adaptive differential evolution algorithm was adopted to search for optimal solutions for day-ahead scheduling. Furthermore, a weighted average electricity purchasing strategy was adopted to address uncertainty and further improve operating economy by adjusting the output of gas turbines and electricity purchasing for actual scheduling. Case studies were conducted to verify that the proposed approach can reduce daily operating costs and enhance the operating economy of regional integrated energy systems.

Multistep Multiagent Reinforcement Learning for Optimal Energy Schedule Strategy of Charging Stations in Smart Grid.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users' satisfaction ratio.

Strategi Wisata Edukasi Kampung Lele Dalam Persaingan Industri Wisata Halal Kediri (Tinjauan Porter Five Force dan Pondasi Rancang Bangun Ekonomi Islam)

Competition in the halal tourism industry in Indonesia is currently busy. The needs of the Muslim community in the halal industry, recreational areas include aspects that must be considered. In addition to meeting the needs of the Muslim community, halal tourism also encourages economic growth. An effective competitive strategy will create the advantages of a tourist spot with its competitors. This study aims to examine the competitive strategies carried out by a tour in Kediri Regency which is developing quite well including during the pandemic. Tourism in Kediri has several strategies in competing with fellow tourism industries. So it is very relevant when reviewed in the Porter Five Force Approach which is a method of strength in competition among industries. In addition, as a form of competition in the halal industry, the basic values of Islamic economics become the foundation of Islamic economic design to become the basis for the economic and business activities of Muslim communities. The results of this study explain that the competitive strategy of Wisata Edukasi Kampung Lele Kediri through the Porter Five Force approach has realized five aspects of strength which include strategies to face new competitors, strategies to face substitute products, bargaining power strategies for buyers, bargaining power strategies for suppliers, and facing similar industry competition. Based on the design foundation, the basic values of Islamic economics such as tawhid, adl', nubuwwah, khilafah, and ma'ad have been well realized by Wisata Edukasi Kampung Lele Kediri.

Optimal energy management in smart buildings with electric vehicles based on economic and risk aspects using developed whale optimization algorithm

With the development of smart electricity consumption technology, consumers can benefit from an economically optimized home energy management system, considering their well-being and utilizing electrical appliances. The multi-objective modelling of energy management in smart buildings with the presence of electric vehicles is the main topic of this paper, which also takes risk and economic considerations into account. To make the problem more realistic, various practical and nonlinear constraints are taken into account, such as production limits, flexible loads, and cost-risk limitations and indicators. Since solving this problem with classical methods is challenging and prone to suboptimal solutions, a developed whale optimization algorithm is suggested. In the proposed developed approach, efforts are made to enhance its capabilities and prevent it from converging to local points using local search. Finally, the proposed method and model are discussed and examined under different scenarios. The results demonstrate that when consumers have a positive attitude towards energy consumption, the net cost significantly decreases compared to a conservative attitude. The battery is used during times of favourable prices when users perceive more solar energy availability. Moreover, when users adopt different price risk indicators, these indicators have varying effects on the electricity purchasing strategy of the user.

AI Carbon Footprint Management with Multi-Agent Participation: A Tripartite Evolutionary Game Analysis Based on a Case in China

AI is playing an important role in promoting sustainable development, but the carbon footprint caused by AI is scaling quickly and may partly offset the effort to reduce carbon emissions. However, recommendations for limiting the AI carbon footprint are lacking. In order to address this gap in the literature, this paper first constructs a tripartite evolutionary game model by taking governments, AI industry alliances, and consumers into consideration, and then exploring the impacts of key factors on these three players’ strategy selection based on the case of smart air conditioner consumption in China. The results show that the behavior of governments has an important influence on the behavior of AI industry alliances and consumers. The ideal consequence is that governments adopt an unregulated strategy, AI industry alliances adopt a green development strategy, and consumers adopt a green purchase strategy. Regulation by governments is indispensable for limiting the AI carbon footprint during an early stage but becomes dispensable when the system reaches an optimal state. Although a tendency toward green consumption, image benefit, regulatory cost, carbon price, and the subsidies given to consumers and AI industry alliances can largely influence the strategy selection of governments, governments are most sensitive to carbon prices and the subsidies given to consumers. AI industry alliances are not sensitive to subsidies, reputation improvement, and reputation loss but are most sensitive to carbon prices. Consumers are most sensitive to green consumption tendencies, self-satisfaction, and utility but are not sensitive to subsidies.

What is the right purchasing strategy for your company? The fit between strategic intent, strategic purchasing and perceived environmental uncertainty

PurposeThe authors empirically examine purchasing strategy typologies based on strategic intent (i.e. competitive priorities) and practices used to achieve these priorities. The authors further investigate the implementation conditions of such strategies based on perceived uncertainty and strategic purchasing.Design/methodology/approach The authors utilize case study data from 11 international service and manufacturing firms with global supply chains. Each company was profiled based on the level of perceived environmental uncertainty, the characteristics of strategic purchasing, the use of relevant purchasing practices and its ability to create value through purchasing.FindingsThe study findings show that four purchasing strategy types exist: Purchasing Rationalization, Supply Base Optimization, Purchasing as a Service and World-Class Supply Base Management. Lower levels of perceived environmental uncertainty favor the adoption of rationalization strategies (i.e. Purchasing Rationalization and Supply Base Optimization), while increased uncertainty leads companies to switch to relationship-focused strategies (i.e. Purchasing as a Service and World-Class Supply Base Management). Further, that specific components of strategic purchasing (i.e. strategic planning, maturity, status and report level) enable the successful implementation of different strategy types.Originality/valueThis research contributes to the existing literature by outlining the different types of purchasing strategies and the external and internal factors that need to be considered to achieve strategic alignment and value creation in purchasing, and by classifying purchasing strategy types at the functional level based on empirical evidence.

THE HALAL PURCHASING STRATEGIES ON MANUFACTURING PERFORMANCE

Abstract 
 Purpose – The purpose of this study is to introduce a new framework to optimize the design of halal purchasing strategies towards the manufacturing industries performance.
 
 Design/methodology/approach – The study utilizes large group of executives and managers in the purchasing and supply chain department from manufacturing firm in Malaysia.
 Findings – The finding shows that the purchasing strategies (effective negotiation, collaborative relationship and interaction, effective cost management, supply base management, and strategic sourcing management) and halal policy (responsibility, scope, assurance, and method of assurance) determine the manufacturing purchasing performance. More empirical research is needed to further refine the Halal Purchasing Strategies for different product–market combinations. Second, conduct qualitative research to various industries.
 
 Research limitations/implications – This research shows that halal purchasing strategies is different from conventional purchasing method, which requires a halal policy and specific design parameters for effective negotiation, collaborative relationship and interaction, effective cost management, supply base management, and strategic sourcing management.
 
 Originality/value – The halal purchasing strategies can be an important instrument to design and manage halal purchasing in extending halal integrity from source to point of consumer purchase. It could be implemented as a practice mediating the impact on manufacturing performance in order to remain competitive in the challenging and competitive business environment.

Book recommendation and purchase of intelligent image recognition technology under the background of 5G environment

In addition to a more extreme network experience and larger network capacity, 5G can also be used to rapidly transmit massive global text data at higher transmission speeds and higher reliability than previous communication technologies, allowing more communication devices around the world to operate at a faster rate. The transmission speed is safe and reliable to connect with each other. The rapid development of image recognition technology will be more effective in improving the operational efficiency of various industries. At present, scholars have used 5G technology and image recognition technology in the field of book management. This paper designs a book image recognition recommendation scheme based on 5G technology, in order to improve the problems of inaccurate book sales targets and low book classification efficiency of merchants from various perspectives. The image recognition book recommendation system combined with 5G technology is mainly divided into two steps: image recognition and book recommendation. The ultra-high speed provided by 5G technology can greatly improve the speed and accuracy of image recognition. Significantly reduces program running time. This paper first analyzes the related concepts of 5G, image recognition and recommendation algorithm, then sorts out the book image recognition method, and then designs the book recommendation purchase method. Combined with 5G technology, the image recognition and recommended purchase strategy are optimized. Finally, an online questionnaire is used to investigate the merchants using this method. The survey results show that the merchants who use this identification and purchase recommendation method have better performance in book classification, book personality recommendation, customer loyalty and book sales rate have greatly improved.

Airport operations with electric-powered towing alternatives under stochastic conditions

Air transportation has become a common travelling medium for ordinary public in most regions around the world since the deregulation of civil aviation in the US in 1978. While the global economy and the ordinary people have benefited from the growing air-transportation, the negative impacts of such change have not gone unnoticed. Today, civil aviation is a significant contributor to several environmental issues such as conflict with wildlife, noise pollution, and Greenhouse Gas Emissions (GGE). The civil aviation industry is responsible for 2–3% of all greenhouse gas emissions in the world. Moreover, the emissions at airports have an even more significant impact on the local population and the surrounding environment. It is estimated that a typical airplane consumes between 4% and 7% of its fuel for ground operations. The GGE released by airports is not only contributing to global warming, but also impacting the health of local communities living next to airports. Accordingly, this paper discusses operational and technological improvements at airports with the objective of minimizing the negative impact of airports on the environment. First, electrification of taxiing operations is discussed. Next, a Mixed Integer Linear Programming (MILP) model which aims at assigning electric powered tow-tractors for airplanes to complete taxiing operations with minimum jet-fuel usage is introduced. The impact of stochastic conditions on the taxiing operations has been discussed and the impact of traffic density has been incorporated in the model. Finally, an optimum number of tow-tractor purchase strategy is recommended for airports.