东方电气新能科技有限公司风电后市场业务跨部门协同机制优化研究

In a business, each department usually reserves resources to deal with its own risks. However, if there is a lack of coordination and adaptive allocation, there may be a situation where resources are idle. Therefore, the adaptive allocation algorithm of cross-departmental risk management and control resources under end-to-end collaborative control is studied. With intelligent decision center as the core, the end-to-end collaborative control architecture of power enterprises is designed. In this framework, a coevolutionary algorithm is introduced to calculate the optimal reallocation rate parameter of contribution from adaptive allocation. An adaptive resource allocation model for inter-departmental risk management and control of electric power is established, and a heuristic algorithm is used to solve the model, and the optimal resource adaptive allocation result is obtained.

No abstract available

This paper examines the incorporation of Power BI in managing IT project performance and flexibility, which has brought a paradigm shift in managing organizations’ projects. It also looks into how Power BI facilitates project agility by providing means to display status using dynamic dashboards and integration with project management tools, financial applications, and operation databases. Another aspect where Power BI is utilized is when it comes to risk forecasting to prevent risks in the future. Automated reports enhance communication to ensure stakeholders are presented with timely and relevant visual reports to make relevant decisions. Power BI decreases data silo and improves contingency strategy transitions, as well as quickens project responsiveness through cross-departmental collaboration. Ultimately, it enhances the project management cycle by increasing flexibility and productivity and enhancing coordination, thus becoming an essential application in the dynamic IT environment.

This study explores the integration of Business Intelligence (BI) and Operations Research (OR) as a driver of sustainability within the evolving framework of Logistics 4.0. As logistics systems face pressures from environmental regulations, digital transformation, and stakeholder expectations, the intersection of data analytics and optimization emerges as a critical lever for sustainable operations. Grounded in a Delphi study conducted in a Portuguese logistics firm, this research captures expert consensus across five dimensions of BI implementation: data infrastructure, real-time decision-making, operational transparency, stakeholder coordination, and sustainability performance monitoring. Methodologically, this study employed two iterative Delphi rounds with 61 cross-functional professionals directly engaged with the organization’s BI systems, particularly Microsoft Power BI. Findings indicate that integrating BI with OR models enhances organizational capacity for proactive scenario planning, carbon footprint reduction, and ESG-aligned decision-making. The results also underscore the importance of cross-departmental collaboration, data governance maturity, and user training in fully leveraging BI for sustainable value creation. By providing both theoretical insights and practical guidance, this study advances the emerging discourse on data-driven sustainability in logistics. It offers actionable insights for logistics managers, sustainability strategists, and policymakers seeking to operationalize digital sustainability and embed intelligence-driven approaches into resilient, low-carbon supply chains.

No abstract available

No abstract available

Driven by China’s long-term energy transition strategies, the construction of large-scale clean energy power stations, such as wind, solar, and hydropower, is advancing rapidly. Consequently, as a green, low-carbon, and flexible storage power source, the adoption of pumped storage power stations is also rising significantly. Operations management is a significant factor that influences the performance of pumped storage power stations in various domains, including environmental protection, economic benefits, and social benefits. While existing studies have highlighted the importance of stakeholder partnering in operations management, a systematic exploration of the causal relationships between partnering, operations management, and the performance of pumped storage power stations—especially from a multi-energy complementarity perspective—remains untouched. This paper strives to shed light on the vital role of stakeholder partnering in augmenting the operations management and overall performance of pumped storage power stations, thereby contributing to China’s dual carbon goals. A comprehensive conceptual model was developed by reviewing the relevant literature to empirically examine the causal relationships among partnering, operations management, and power station performance, which was validated using data from the Liaoning Qingyuan Pumped Storage Power Station, which is the largest of its kind in Northeast China. The findings suggest: (1) Effective partnering among stakeholders, particularly with grid companies, significantly influences the operations management of pumped storage power stations, with deficiencies in partnering mainly attributed to the lack of effective communication channels and problem-solving mechanisms. (2) The level of operations management in China’s pumped storage power stations is relatively high, averaging a central score around 4.00 (out of a full score of 5) on operations management indicators. However, there is a need to concentrate on enhancing multi-energy complementarity coordination, digital management system development, and profitability. (3) Path analysis further unveils that partnering not only improves operations management but also boosts the performance of pumped storage power stations. These findings suggest a wide range of practical strategies for operations managers at pumped storage power stations to forge partnerships with stakeholders and integrate complementary resources, aiming to achieve excellence in performance.

The article formulates the prerequisites for the introduction of digital logistics technologies and services in the energy market, which consist in a high level of competition and tariff pressure, require the search for new opportunities to reduce capital and operating costs. The state and interstate regulation of the energy market, which manifests itself in the adoption of strategic trade and political decisions, requires the development and implementation of market mechanisms for the adaptation of supply chain participants and the creation of new economic ties. A customer-oriented approach to the design of supply chains in the energy market defines the introduction of digital technologies and services to create a more comfortable customer environment: increased transparency and ease of use, interconnection with other services, cheaper energy. There are defined the main directions of the introduction of digital logistics technologies and services. It is revealed that the differences in the design of supply chains in the energy market, taking into account their renewability, are based on the object of material flow management. In the supply chains of non-renewable energy sources, the object of management is resources (coal, oil and petroleum products, gas and gas chemistry products, etc.) and processes aimed at their exploration, production, processing, transportation, storage and consumption. In the supply chains of renewable energy sources, the object of management is the generated electric energy, as well as equipment and technologies for its production and storage – turbines for nuclear and hydroelectric power plants, wind generators, solar panels, storage devices for storing electric energy, etc. It has been inferred that certain digital logistics technologies and services in international supply chains on the energy market will take place subject to the resource renewability.

PurposeThis paper explores the impact of multiple team membership (MTM) on the productivity of team members in engineering consulting firms. MTM refers to employees participating concurrently in multiple teams, a concept closely linked to projectification. Despite the fact that this concept can enhance collaboration, it also introduces coordination challenges that may negatively affect productivity.Design/methodology/approachThrough an inductive approach involving 12 semi-structured interviews with engineering consulting professionals specializing in water and energy infrastructure projects, this paper examines the factors affecting team member productivity in an MTM setting. Following the interviews, a Delphi technique was employed, engaging 16 experts to rank the factors and sub-factors identified from the interview data. This two-stage approach ensured a comprehensive and validated assessment of productivity factors.FindingsThis study develops 8 factors process model grounded in structuration theory to explain the socio-technical mechanisms by which multiple team membership shapes productivity outcomes in engineering consulting firms specialized in water and energy infrastructure projects. Key findings surface micro-foundations, tensions in technology provisions, planning processes, and career development that inform theoretical advances and practical improvements.Originality/valueThis research contributes empirically insights into managing MTM in expert service contexts. Applying Giddens' structuration theory, this study reveals how agency and structures shape productivity across organizational, team, and individual levels. In practice, this study provides recommendations for improving productivity within projectified environments, mainly for team members working in an MTM environment in engineering consulting firms specializing in water and energy infrastructure projects.

In the context of carbon emission reduction, both servitization and digitalization of manufacturing enterprises are important ways to promote the high-quality development of manufacturing enterprises. The coordinated development of digitalization and servitization may provide a feasible path for enterprises to realize energy conservation and emission reduction across the value chain. Based on the 393 valid questionnaires issued by the Association of Manufacturing Enterprises and government agencies, this paper uses the methods of Grey Relational Analysis and Topsis to construct three major indicator systems of manufacturing enterprise service, digitalization and high-quality economic development. Based on the Cobb–Douglas production function regression analysis, the relevant assumptions involved in the model of promoting the service of manufacturing enterprises and the model of promoting the digitalization of manufacturing enterprises are verified. The empirical results show that, firstly, the digitalization capability of manufacturing enterprises can positively affect the servitization performance of manufacturing enterprises. Secondly, the digital capability of manufacturing enterprises has a significant positive impact on both breakthrough service innovation and incremental innovation of manufacturing enterprises. Thirdly, breakthrough service innovation and incremental service innovation play a partial mediating role. Fourthly, the degree of servitization of manufacturing enterprises has a significant positive impact on the digital demand of supporting customer behavior service business and supporting customer product service. There are three possible contributions of this paper. Firstly, it has further enriched and deepened the research on the interactive mechanism of digitalization and service of manufacturing enterprises. The second is to design a scientific and systematic evaluation system for the digitalization and service level of Chinese manufacturing enterprises, and measure and evaluate the coupling and coordination level of digitalization and service level of Chinese manufacturing enterprises based on this. Thirdly, it reveals the promotion of the coordinated development of digitalization and service in manufacturing enterprises to the high-quality development of manufacturing enterprises in China and puts forward that the coordinated development of digitalization and service in manufacturing enterprises is a way to promote the high-quality development of manufacturing enterprises.

Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative, model for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities. The latter type of maintenance arises due to the network context: When an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network. So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to ‘as good as new.’ By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to those of suboptimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.

Under the new situation, the traditional electric power service products can no longer meet the personalized needs of multiple customers. This paper mainly studies the key links of new power service product design, establishes a scientific and rational power service product design system, helps grid enterprises to enhance service capabilities, and fully meet the needs of power customers. Introduction From the management point of view, customer-centered is the fundamental reason for the existence of enterprises, even the only reason. Affected by such factors as open electricity market, lower transmission and distribution prices and slower growth of electricity, power grid business is facing increasingly fierce market competition. Customers have become the core resources of power grid companies, and power customer service has become the core competitive point. At the same time, the increasing dependence of society on electricity requires higher reliability of power supply, more and more complex power grids, more and more types and numbers of access equipment, and changes in the form of power grids. Traditional power services are facing great challenges. How to design new power service products has been mentioned as an important position. Scientific design methods of power service products are particularly important for the development of power grid enterprises. There are many studies on customers and power services, such as literature [1]. In view of the large deviation between customer perception and service perception within enterprises, the path of implementing customer perception management of key contacts in the whole process is designed. Documents [2], [3], [4], [5] have studied the power supply service mode and service situation. In view of the new situation, the research on the design method of power service product has not yet seen the monographic research literature. Change of Customer and Customer Demand From the point of view of customer life cycle, from expanding new installations to continuously using electricity to selling customers, customers have business management needs at each stage, which needs the cooperation of various departments to complete. These services include industry expansion service, emergency repair service, equipment maintenance service, business consultation service, electricity billing service and so on. All of the above are traditional basic services provided by power grid enterprises. Great changes have taken place in customers faced by power grid enterprises, mainly in four aspects: energy use mode, interaction mode, consumption mode and thinking mode. International Conference on Advanced Education, Management and Humanities (AEMH 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Social Science, Education and Humanities Research, volume 352

The Indonesian government has increased fuel prices through Decree 218.K/MG.01/MEM.M/2022. This is in response to an increase in the energy subsidy budget of IDR 502.4 trillion. Additionally, current geopolitical conditions have greatly influenced the price of energy commodities in the world market. This has caused the government to change fossil fuel usage to electrical energy. This study aims to analyze the relationship between the implementation of TQM and organizational performance in PT XYZ, as well as identify the elements of TQM that have the most significant impact on improving organizational performance. By understanding this relationship, this research is expected to provide recommendations for PT XYZ to enhance its competitiveness and operational effectiveness. The research method used is a survey by distributing questionnaires to PT XYZ employees. The data obtained was analyzed using the Structural Equation Model (SEM) method to test the correlation between variables. The study results show that implementing TQM positively and significantly affects organizational performance. Factors such as top management commitment, quality awareness, communication, and employee engagement have a dominant role in improving job satisfaction and the company's business results. The conclusion of this study confirms that TQM can be an effective strategy for increasing organizational competitiveness by improving customer satisfaction and service quality. The implications of this study highlight the importance of strengthening relationships with external providers to increase competitive advantage. In addition, the management of PT XYZ is advised to maintain a quality culture and management system that has been proven to positively impact the company's performance.  

The customer services business middle platform is an important digital approach and effective tool for the electric grid enterprise to improve the customer services, enhance the electric power supply services, promote the marketing lean management and optimize the energy consumption policies, helping to push forward the digital transformation of the enterprise. The paper first introduces the design methodology of the business middle platform based on the whole process of business domain modelling, service architecture, service design, service implementation and service governance. It describes the evolution of opening the service capabilities of the shared service centres from the multichannel business platform to the customer services business middle platform, and elaborates the core service capabilities of these shared service centres. It gives the hierarchical design of the business middle platform architecture, and takes the key business scenarios such as electricity charging and accounting, electricity business expansion as examples to elaborate the technical solutions of the business middle platform, and puts forward the key design points and the technical design elements. Taking the innovative scenarios such as two-dimensional barcode for validating electric power supply transfer, residential energy efficiency management, and electric power demand response as examples, it illustrates the means of supporting various agile product innovations and application development with the opened service capabilities of the business middle platform. In the future, the business middle platform will be required to strengthen the horizontal collaboration with other business middle platforms to provide strong support for cross-professional business of the electric grid enterprise, and to output the service capabilities via the capability aggregation platform, for striving to build an open and win-win energy internet application ecosystem.

The purpose of the research is to establish the relationship between organizational structure and administrative management in the electric power supply service in Peru, 2019. The population and the sample consisted of 771 and 126 employees, respectively, of an electric power supply company in Peru. A correlational design was used. The method used is inductive, hypothetical-deductive, analysis-synthesis, and the technique used is the survey. The results found are: 8.7% of the collaborators state that their level of organizational structure is deficient, 88.9% is regular and 2.4% is good; as for administrative management, 5.6% of the collaborators state that their level of administrative management is bad, 88.1% is regular and 6.3% is good. A positive relationship of 0.793 (good) was established between organizational structure and administrative management. The conclusion is that the employees consider that it is vital today to comply with their work as well as to know their organizational structure well in order to avoid disorder, improvisation and command authority.

With the development of the times and changes in the situation, electric power services present new needs and characteristics, large state-owned enterprises in the electric power industry deepen their strategic objectives and put forward major strategic development layouts, this paper proposes a set of indicator system from the perspective of total factor productivity, combined with the company's strategic system, to evaluate the strategy of large power grid enterprises. In this paper, the data of four input-output indicators and three environmental variables of 13 electric power listed enterprises from 2018 to 2022 are selected as research samples in the arithmetic example, and the three-stage DEA method is used to evaluate the efficiency of the grid company, and the results of the research show that: electric power listed companies are generally at a high level of total factor productivity, the number of years of establishment and the gross regional product will reduce the input redundancy, and the government subsidy will increase the input redundancy of enterprises, the efficiency of listed electric power companies is improved after removing the influence of environmental variables, and the efficiency level of enterprises is underestimated. In terms of technology, enterprises can increase investment in technology research and development, and actively introduce and cultivate technical talents; in terms of government subsidies, they can reasonably allocate resources and optimize the direction and strength of investment; in terms of enterprise management, they can reform the enterprise system mechanism to improve enterprise efficiency.

The electric power industry is undergoing profound transformations driven by big data, posing challenges to the traditional power grid marketing management model. These challenges include neglecting market demands, insufficient data support, and inadequate customer service. The application of big data technology offers innovative solutions for power grid marketing management, encompassing critical aspects such as data collection and integration, storage management, analysis, and mining. By leveraging these technologies, power grid enterprises can precisely understand customer needs, optimize marketing strategies, and enhance operational efficiency. This paper explores strategies for power grid marketing management based on big data, addressing areas such as customer segmentation and personalized services, as well as market demand forecasting and response. Furthermore, it proposes implementation pathways, including essential elements such as organizational structure and team building, data quality and governance systems, training, and cultural development. These efforts aim to ensure the effective application of big data technology and maximize its value.

No abstract available

Under the background of the new technology era of cloud, big things, mobile intelligence, RPA (RoboticsProcessAutomation) technology, as an important and mature application in the field of artificial intelligence, can help financial personnel to free themselves from a large number of simple and complex transactional work and invest in Financial analysis, scientific decision-making and other high value-added work. At present, financial robot products based on RPA technology can be extended to be compatible with OCR, voice, intelligent customer service, deep learning and other functions, supporting the establishment of risk management and control systems and intelligent application scenarios, and ultimately improve the cross-business collaboration capabilities and operation automation efficiency of financial management. Effectively control financial risks, improve the efficiency of data asset use and financial analysis and decision-making capabilities, and provide power companies with good management and economic benefits. This article first analyzes the advantages and technical characteristics of RPA technology, then summarizes the practical application of financial robotics technology in power companies, explores the role of RPA technology in financial digital transformation, and studies its risk management and control models, which are of great significance to improving the comprehensive management level of power grid companies.

The choice and implementation of the appropriate policy are largely determines the efficiency of business processes in the management of production assets of an energy service company. The actualization of the company's choice of the right policy occurred with the approval of new rules for the organization of electric power facilities operation. The problem of choice is aggravated by an approach based on a combination of production experience and expert opinions, which in some cases are poorly coordinated. The authors have developed a methodological basis for choosing one of the alternative maintenance and repair policies of power equipment "by periodicity, operating time" or "by technical condition", taking into accounts the strategic priorities of the owner company. The presented methodology serves as a universal tool for comparing alternatives according to a single set of criteria using the principles of a risk-based approach. The paper considers a generalized algorithm for solving the complex problem of ensuring a reliable power supply to industrial consumers for each of the alternative policies. The main theoretical positions are illustrated by the example of a typical oil production power supply system using real data and operational constraints. The obtained results confirm the possibilities of the methodology for the reasonable choice of the most effective maintenance and repair policy of equipment and its readiness for practical application in the conditions of power facilities real operation.

Asset management is a strategic component in ensuring the sustainability and operational efficiency of electric energy companies, especially in facing the challenges of increasing demand and the need for reliable electricity services. This article examines the implementation of ISO 55001 as a structured asset management system framework, with a Balanced Scorecard (BSC) approach as an integrated performance measurement tool from four perspectives: financial, customer, internal business processes, and learning and growth. The research was conducted at an electric energy provider organization, by analyzing Key Performance Indicators data for 2022–2023. The results of the BSC measurement show positive trends, such as increasing ROI, EBITDA Margin, Customer Satisfaction Index, and human capital readiness, but also reveal weaknesses such as the still high ratio of power outages and Equivalent Forced Outage Rate (EFOR). SWOT analysis is used to identify internal factors (strength and weakness) and external factors (opportunity and threat). Quantitative SWOT results show that the company is in quadrant I (aggressive strategy), with internal strengths and external opportunities dominating. Based on these findings, alternative strategies are formulated that are adjusted to each BSC perspective and ISO 55001 pillars (Cost, Performance, Risk). Strategies include optimizing asset life cycles, reducing maintenance costs, increasing distribution system reliability, and developing Human Resource capabilities. The integration of ISO 55001 and BSC has been proven to provide a holistic approach in designing data-based and risk-based strategies. These findings not only provide theoretical contributions to the development of modern asset management systems, but are also practically relevant for corporate policy makers in improving the efficiency, reliability, and competitiveness of organizations in the energy sector.

With the continuous advancement of new power system construction, rapid emergence of emerging businesses and technologies, increasing demands for lean management in information operations, and evolving trends toward complex paths under heterogeneous environments, management analysis methods are increasingly extending to big data technology. The information operation management model has gradually evolved into a big data environment. This paper proposes the construction of an operation and maintenance (O&M) indicator system, O&M data governance and unified technologies, as well as applications of O&M data across various business scenarios such as equipment profiling analysis, system profiling analysis, user profiling analysis, and customer profiling analysis. These initiatives guide resource allocation, highlight data value, provide evidence for frontline O&M personnel and management decision-makers, enable multi-dimensional precision evaluation and control of various $O \& M$ assets, and ultimately enhance O&M efficiency and overall information operation standards.

No abstract available

Cloud service delivery has become an essential aspect of modern businesses, driving innovation and transforming operational efficiencies. In this context, the Project Management Office (PMO) plays a critical role in ensuring the successful delivery of cross-functional cloud projects. This paper aims to identify the best practices in PMO for managing cloud service delivery across various functional domains. By synthesizing existing literature, case studies, and empirical evidence, we present a framework for optimizing PMO practices, ensuring alignment with business objectives, and fostering collaboration between IT, business, and external cloud service providers. The study emphasizes governance, risk management, cross-functional communication, and the adoption of agile methodologies as key factors in enhancing cloud service delivery. The findings indicate that PMOs, when effectively executed, can significantly contribute to the success of cloud projects by providing structure, transparency, and continuous improvement.

To solve the problem of conflicting goals and disjointed cross domain optimization in the coordinated development of power marketing and comprehensive energy, this paper proposes a collaborative development model based on multiple swarm particle swarm optimization algorithms. This model constructs a closed-loop architecture of "data perception optimization decision-making execution feedback", integrating multi-dimensional data such as user electricity load, package preferences, combined cooling, heating, and power system output, wind and solar energy storage status, and achieving multi-objective optimization of comprehensive cost, energy utilization efficiency, and carbon emissions through a master-slave population collaborative particle swarm algorithm. We have researched and designed various engineering implementation schemes for swarm particle swarm optimization algorithms, clarified parameter configuration and operation flow, and verified the performance of the patterns through experiments. The results show that the proposed model improves the comprehensive cost reduction rate by 5.2 to 4.8 percentage points, energy utilization efficiency by 6.6 to 4.1 percentage points, and dynamic response delay by 65% to 34% compared to traditional particle swarm optimization algorithm and adaptive particle swarm optimization algorithm. It maintains linear scalability in large-scale user scenarios and effectively supports cross domain collaborative optimization of power marketing and comprehensive energy.

In an era of rapidly evolving consumer preferences and digital transformation, traditional marketing performance metrics often fall short of capturing the multidimensional nature of strategy success. This review paper proposes a comprehensive framework for Developing Multidimensional Key Performance Indicators (KPIs) by integrating cross-functional organizational insights with continuous behavioral feedback loops. We begin by synthesizing the state of the art in KPI design, highlighting the limitations of single dimensional metrics such as return on investment (ROI) and customer acquisition cost (CAC). We then explore how insights from finance, operations, human resources, and customer service can enrich marketing KPIs by providing a holistic view of organizational performance. Next, we examine behavioral feedback mechanisms—including real-time customer interactions, sentiment analysis, and employee engagement data—to demonstrate how iterative learning cycles can refine KPI relevance and predictive power. Through a structured comparison of existing multidimensional KPI models, we identify best practices and common pitfalls in their implementation. Finally, we offer a step by step guideline for marketers to co create, monitor, and adjust KPIs in alignment with dynamic market conditions and strategic objectives. Our proposed framework aims to enable decision makers to drive more informed, agile, and customer centric marketing strategies, ultimately fostering sustainable competitive advantage.

In recent years, escalating global climate change and the frequent occurrence of extreme weather events have posed severe challenges to the stability of power systems. Power systems urgently need to transition from traditional "robustness" designs to more adaptive and restorative "resilience" designs. This study constructs a three-dimensional "technical-operational-organizational" analytical framework to systematically explore the impact of power grid enterprise resilience on the business environment. The research finds that vulnerabilities in grid infrastructure, inefficiencies in operational management, and inadequacies in the policy environment are the main constraints on resilience, while electricity service costs, supply reliability, and user satisfaction are key dimensions for optimizing the business environment. In the short term, resilience can be enhanced by strengthening critical nodes and optimizing emergency resources. In the medium to long term, sustainable development should be driven by digital twin technology, resilient grid standards, and cross-departmental collaboration mechanisms. The study provides theoretical foundations and practical pathways for power grid enterprises to address climate risks and improve the business environment.

In today's data-driven business environment, centralized data platforms play a pivotal role in facilitating informed decision-making and enhancing cross-functional collaboration. This paper proposes a conceptual model for designing centralized data platforms that prioritize cost savings and operational efficiency while addressing the dynamic needs of diverse organizational functions. Drawing inspiration from Prime Video's innovative data infrastructure strategies, the model emphasizes a scalable and modular architecture that integrates advanced analytics, automation, and real-time data accessibility. The proposed model highlights the critical components required for a robust centralized data platform, including data governance frameworks, secure and seamless data sharing protocols, and AI-driven insights generation. By unifying data silos across departments, the platform fosters synergy between marketing, operations, product development, and customer service, enabling holistic organizational growth. The model also incorporates mechanisms to balance cost optimization with enhanced functionality, such as cloud-based solutions for scalability and edge computing for localized data processing. A key feature of this framework is its emphasis on fostering collaborative decision-making. It introduces dynamic dashboards and visualization tools that allow stakeholders to access actionable insights tailored to their roles, thereby streamlining interdepartmental communication and goal alignment. Additionally, the model addresses challenges related to data security, user accessibility, and system reliability, proposing solutions like role-based access controls, encryption standards, and continuous monitoring through AI-based anomaly detection. The study leverages lessons learned from implementing centralized platforms at Prime Video to illustrate practical applications and the tangible benefits of this approach. Enhanced content recommendation systems, optimized resource allocation, and improved customer engagement strategies serve as compelling examples of the model's potential.

The rapid expansion of cloud infrastructure networks has transformed global supply chain operations, yet reverse logistics processes remain fragmented and inefficient. Reverse logistics—the return, repair, recycling, or disposal of digital and physical assets—is critical in maintaining service continuity, reducing operational costs, and advancing sustainability goals. This review explores a program management model for optimizing end-to-end reverse logistics in cloud infrastructure networks. By integrating program management principles with advanced digital tools such as artificial intelligence, blockchain, and digital twins, organizations can streamline asset recovery, enhance transparency, and enable predictive decision-making. The study emphasizes the importance of cross-functional governance structures, standardized workflows, and real-time monitoring to mitigate bottlenecks across data centers, cloud hardware, and distributed service ecosystems. Key focus areas include lifecycle management of servers and networking equipment, secure data decommissioning, and sustainable recycling pathways. Additionally, the review highlights the role of performance metrics and program-level alignment in ensuring scalability and resilience. The proposed model contributes to bridging gaps between technical, environmental, and business objectives, offering a holistic approach to reverse logistics. Ultimately, this framework supports both economic efficiency and environmental stewardship in the evolving landscape of cloud infrastructure networks.

Electric power companies often need to deal with a large amount of data, which are stored in the data centre stage is extremely dispersed, and the problem of difficult to fetch and use the data arises. This paper proposes an intelligent recommendation algorithm based on electric power data. Firstly, based on the constructed business logic relationship mapping, this paper calculates the similarity between the business demand information and data content, carry out correlation analysis on cross-departmental and cross-professional data, and obtain the data collection of the department where the business demand is located and the associated departments. Then this paper optimizes the intelligent recommendation algorithm based on deep neural network, pays attention to the implicit relationship between the data through the introduction of the attention mechanism to get the data collection associated with the exact business demand. Finally, it realises the intelligent extension of data fields for different business needs and provides intelligent recommendation for data with different business needs. The experimental results show that the algorithm has better intelligent recommendation performance, higher precision and superior efficiency. The method can intelligently recommend associated business data, and help users in the power problem scenario to obtain relevant information quickly and accurately, which has certain practicability and popularization value.

This paper develops an integrative framework proposing actionable strategies for dismantling knowledge silos within corporate environments. Guided by a qualitative methodology, data was gathered from knowledge management (KM) professionals across physical and digital domains using a triangulation approach. The findings illuminate the critical importance of executive-level engagement and sustained commitment to KM initiatives, while simultaneously highlighting the fundamental role of interpersonal trust in overcoming barriers to collaboration and intellectual exchange. We identify five key strategic approaches that demonstrate symbiotic relationships across two organizational movements—Top-Down and Bottom-Up—which, when implemented cohesively, can effectively mitigate detrimental knowledge silos and enhance cross-functional knowledge flows. This work contributes original perspectives to the currently limited literature on addressing intra-organizational knowledge barriers. By situating knowledge silos and knowledge hiding within the broader context of knowledge risks, this study emphasizes the importance of comprehensive knowledge risk management in sustaining knowledge-driven innovation and organizational effectiveness. For future research, we propose investigating optimized technological systems that can effectively support and reinforce embedded knowledge-sharing routines within organizational contexts.

Organizations adopting artificial intelligence face a fundamental structural challenge: traditional hierarchies and coordination mechanisms often stifle the experimentation and rapid iteration AI implementation requires. Emerging evidence suggests that small, cross-functional teams with high autonomy—typically comprising senior engineers, domain experts, and experienced product managers—deliver faster time-to-value and stronger early returns on AI investments than centralized, top-down approaches. This article examines the organizational design principles enabling these teams to succeed and addresses the critical gap in enterprise-scale coordination mechanisms. Drawing on organizational theory, agility research, and practitioner accounts from technology, financial services, and healthcare sectors, we propose a dual-operating system model that preserves the benefits of autonomous pods while building connective tissue for resource allocation, knowledge sharing, and strategic alignment. The article concludes with evidence-based recommendations for leaders navigating the transition from experimental AI initiatives to institution-wide capability.

In today's society, electricity is a necessity that cannot be ignored. To ensure its availability and reliability, regular maintenance and efficient material management in power plants are essential. Material costs significantly impact operational costs, making expenditure control strategies extremely crucial. In supplier selection, organizations often overlook important factors beyond cost and risk. Rezaei proposes to combine the purchasing portfolio matrix with the supplier potential matrix, a method that has been tested in various sectors but has not been widely explored in operation and maintenance (O&M) services. To address this gap, this article presents a comprehensive framework that integrates resource-based view and transaction cost economics theories. Conducted through a case study of an O&M services company in Indonesia, the main objective is to improve the understanding of supplier selection in power plant O&M, enrich the method, and make a significant contribution to the field empirically. The application of the method improved efficiency by reducing warehouse inventory balances by an average of 10% per year, reducing workplace accidents, lowering the carbon footprint, and increasing customer satisfaction scores by 5% in terms of spare parts/material availability.

: As China’s Belt and Road Initiative (BRI) continues to influence worldwide infrastructure expansion, intercultural collaboration has emerged as a critical success factor in joint venture operations. This study examines the relationships between Cultural Intelligence (CQ) and work performance among Indonesian employees in an Operation and Maintenance (O&M) China-Indonesia joint venture within a power generation company established under the BRI framework. Employing a quantitative research design, data were gathered through the Cultural Intelligence Scale (CQS) and Individual Work Performance Questionnaire (IWPQ) to assess the four dimensions of CQ (Meta-cognitive, Cognitive, Motivational and Behavioral) and their association with task and contextual performance. The data were examined using Partial Least Squares – Structural Equation Modeling (PLS-SEM). It shows that CQ positively influences employee performance, with the motivational dimension (Drive CQ) showing the strongest effect. While the impact of each CQ dimension varies, the findings emphasize that overall CQ development is vital for supporting employee effectiveness in cross-cultural work environments. The study identifies a lack of structured intercultural training and language support as persistent challenges that limit the full realization of employee potential. These insights highlight the strategic value of integrating CQ development into organizational learning and HR practices, particularly in multicultural joint venture settings. The research contributes to the larger discourse on workforce localization and cross-cultural competency in emerging economies and provides actionable recommendations for enhancing collaboration and productivity in multinational operations.

Asset Integrity Management (AIM) is vital for ensuring the safety, reliability, and longevity of critical industrial infrastructure. As industries face increasing operational complexities and sustainability demands, integrating advanced technologies becomes essential for maintaining asset integrity while optimising efficiency. This paper explores the intersection of technological innovation and operational excellence in AIM, focusing on sectors such as oil and gas, power generation, and renewable energy. Key advancements, including predictive maintenance, digital twins, Internet of Things (IoT)-enabled monitoring, and robotics, are transforming asset management by enhancing real-time monitoring, predictive analytics, and overall system performance. These innovations contribute to cost reduction, minimise downtime, and improve environmental sustainability. Additionally, this study touches on the role of supply chain coordination in supporting effective AIM, particularly in areas such as procurement, lifecycle planning, and system integration. A structured asset lifecycle framework is essential for aligning supply chain processes with AIM objectives. Each stage, from acquisition and integration to maintenance and decommissioning, requires coordinated supply chain strategies to optimise asset utilisation, minimise waste, and ensure seamless operations. Real-world case studies demonstrate the successful implementation of advanced AIM solutions, while challenges such as integration complexities, cybersecurity risks, and high initial costs are also examined. Ultimately, the study underscores the need to strategically balance technological innovation with operational excellence to ensure long-term asset reliability, safety, and sustainability.