Business Scenarios Research Articles

Research on BIM sub-model data of construction engineering based on MVD

BIM technology has been widely applied to various stages of construction projects in China. The data required at each stage and scenario of construction projects varies, necessitating the definition of target BIM data according to data needs. When BIM software generates IFC models, different MVD templates can be configured to output BIM sub-models. To study the characteristics of data configuration for different MVDs, this article takes construction projects as an example and designs an analysis experiment for BIM sub-model output based on MVD. It analyzes the differences and characteristics of IFC model data from both the overall project level and the professional component level according to key indicators, with the aim of providing references for construction project personnel to select matching MVDs, ensuring the effectiveness of BIM sub-models, and meeting the needs of business scenario applications.

Cloud Platform Business Scenario Modeling Based on Multi-Model Fusion Recommendation Algorithm

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

Power text information extraction based on multi-task learning

In order to improve the analysis and processing speed of power system fault text in actual business scenarios, a power fault text information automatic extraction model based on pre-training and multi-task learning is proposed. The pre-training model is used to learn the context information of power text words, and the first-order and second-order fusion features of words are mined to enhance the representation ability of features. The multi-task learning framework is used to combine the learning of named entity recognition and relationship extraction tasks to achieve mutual complementation and mutual promotion between entity recognition and relationship extraction, thereby improving the performance of power fault text information extraction. Finally, the model is verified by the daily business data of a power grid data center. Compared with other models, the accuracy and recall rate of power fault text entity recognition and relationship extraction are improved.

Metaverse Insights: Research Advancements, Innovation Trends and Industry Implications

The metaverse is a universe of communication across boundaries. The provision of new experiences with the highest level of immersion through virtualisation, a greater degree of flexibility to produce and distribute information, and a space for new social communication all occur here. With the aid of technology and gadgets, people can engage digitally and visually in the virtual environment provided by metaverse. It is considered to be an ‘extension of the internet’ in fact. The metaverse has no restrictions based on location. It is based on an ‘immersive digital environment’ that is unbroken, without constraints and without borders. Although it seems like something out of a science fiction film or book, it is actually the way that communication will be done in the future. This article is an attempt to analyse the selective literature from the Scopus database to form an opinion about what can be the trends of research in metaverse and what are the areas of applicability of this phenomenon in business scenarios. It highlights the research areas mushrooming in the field of metaverse.

STC-BERT (Satellite Traffic Classification-BERT): A Traffic Classification Model for Low-Earth-Orbit Satellite Internet Systems

The low-Earth-orbit satellite internet supports the transmission of multiple business types. With increasing business volume and advancements in encryption technology, the quality of service faces challenges. Traditional models lack flexibility in optimizing network performance and ensuring service quality, particularly showing poor performance in identifying encrypted traffic. Therefore, designing a model that can accurately identify multiple business scenarios as well as encrypted traffic with strong generalization capabilities is a challenging issue to resolve. In this paper, addressing the characteristics of diverse low-Earth-orbit satellite traffic and encryption, the authors propose STC-BERT (satellite traffic classification-BERT). During the pretraining phase, this model learns contextual relationships of large-scale unlabeled traffic data, while in the fine-tuning phase, it utilizes a semantic-enhancement algorithm to highlight the significance of key tokens. Post semantic enhancement, a satellite traffic feature fusion module is introduced to integrate tokens into specific low-dimensional scales and achieve final classification in fully connected layers. The experimental results demonstrate our approach’s outstanding performance compared to other models: achieving 99.31% (0.2%↑) in the USTC-TFC task, 99.49% in the ISCX-VPN task, 98.44% (0.9%↑) in the Cross-Platform task, and 98.19% (0.8%↑) in the CSTNET-TLS1.3 task.

Exploratory Study of Metaverse in Business in Indonesia Using Scenario Planning Approach (Metaverse Case Study in Digital Business & Technology Division PT Telkom Indonesia)

The Metaverse, as the next step after the internet (Web1) and Social Media (Web2), gained attention after Facebook rebranded to Meta in 2021, signaling a focus on its development. While still in its early stages, with limited business applications beyond marketing, this research explores potential metaverse scenarios in Indonesia through PT Telkom’s Digital Business & Technology Division. Using the TAIDA method and a qualitative approach with primary interviews and secondary data, the study highlights Indonesia’s strong potential for metaverse growth, driven by internet adoption, VR/AR technology, cryptocurrency, and government support. Key uncertainties include low adoption, early-stage development, and unclear regulations. Four business scenarios are analyzed, focusing on promotion inefficiency, digital gaming interest, cybersecurity concerns, and potential platform discontinuation. Business scenarios encompass a booming metaverse, metaverse as a game, metaverse as a virtual world threat, and social media dominance. Scenario 1 analysis reveals PT Telkom’s focus on developing metaverse with risks related to promotional inefficiency. Scenario 2 involves developing metaverse’s digital game with the risk of low public interest. Scenario 3 emphasizes cybersecurity with risks related to societal disinterest. Scenario 4 envisions discontinuing metaverse with risks tied to customer misunderstanding. The primary objectives target awareness, cybersecurity, and customer understanding while ensuring platform reliability and regulatory guidance.

Optimal ordering strategy for deteriorating items with maximum lifetime using trade credit financing under imprecise environments

In many business scenarios, a retailer is permitted specific credit period to pay back for the products bought earlier. This facility enables retailers to continue their business operations even when they are unable to raise funds or secure a business loan. To boost the market's demand, promotional effort is a very effective business strategy to the retailer for maximizing the profit. On the other hand, a sudden and significant rise in customer demand for an inventory leads to shortages. Moreover, inventory relevant unit cost coefficients become imprecise due to insufficient data, human error etc. Nowadays, neutrosophic set quantifies the impreciseness more realistically. Considering these facts, an imprecise EOQ model for deteriorating items with maximum lifetime is formulated under trade credit facility. In addition, this article allows shortages, which are linearly time-dependent partially back-logged. Here, the unit cost coefficients are expressed as single-valued trapezoidal neutrosophic numbers. Furthermore, particular models are derived under different environments - intuitionistic, fuzzy, and crisp. Step-by-step solution procedures are suggested for all models to obtain optimal solutions. Models are numerically illustrated with real-life data, and some sensitivity analyses are performed. Managerial insights demonstrate that depletion time always depends on demand. Again, the present study suggests to reduce demand by halting the promotional activities during the shortage period and choose products with a larger lifetime.

A Gnn-Enhanced Ant Colony Optimization for Security Strategy Orchestration

The expansion of Internet of Things (IoT) technology and the rapid increase in data in smart grid business scenarios have led to a need for more dynamic and adaptive security strategies. Traditional static security measures struggle to meet the evolving low-voltage security requirements of state grid systems under this new IoT-driven environment. By incorporating symmetry in metaheuristic algorithms, we can further improve performance and robustness. Symmetrical properties have the potential to lead to more efficient and balanced solutions, improving the overall stability of the grid. We propose a gnn-enhanced ant colony optimization method for orchestrating grid security strategies, which trains across combinatorial optimization problems (COPs) that are representative scenarios in the state grid business scenarios, to learn specific mappings from instances to their heuristic measures. The learned heuristic metrics are embedded into the ant colony optimization (ACO) to generate the optimal security policy adapted to the current security situation. Compared to the ACO and adaptive elite ACO, our method reduces the average time consumption of finding a path within a limited time in the capacitated vehicle routing problem by 67.09% and 66.98%, respectively. Additionally, ablation experiments verify the effectiveness and necessity of the individual functional modules.

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

IDIOMS IN BUSINESS ENGLISH TEXTBOOKS: THE CASE OF MARKET LEADER AND BUSINESS RESULT

Idioms represent an important part of effective communication between native speakers, but they are quite necessary among non-native speakers too. Their crucial role in making communication flow easily makes them attractive and important to be studied. Their use in not very common among non-native speakers, because they are usually taught at higher levels, such as C1 and above. This paper is a systematic analysis of idioms representation in Business English textbooks used in higher education, both Market Leader and Business Result. The focus is in identifying their presence in these textbooks, followed by an analysis and categorization of idiomatic expressions across various units and topics. A thorough approach of their complex nature and connection to real-world business scenarios is taken in order to see the efficacy of their inclusion in the textbooks. The paper contains extractions, which show how these expressions are introduced to students in these two textbooks, and if they are equivalent to the requirements in the Common European Framework of Languages. This research’s objective is to help teachers, educators and curriculum see how idiomatic language is treated in Business English textbooks. Also, the study adds a contribution of raising awareness of the role idioms play in language acquisition and the implications for effective business communication.

Detecting Process Duration Drift Using Gamma Mixture Models in a Left-Truncated and Right-Censored Environment

Within the realm of business context, process duration signifies time spent by customers between successive activities. This temporal perspective offers important insight to customer behavior, highlighting potential bottlenecks, and influencing business management decisions. The distribution of these process duration often changes over time due to factors such as seasonality, emerging legislation, changes to supply chains, and customer demand. Referred to as concept drift, these variations pose challenges for robust process modeling, understanding, and refinement. Subsequently, gamma mixture models are widely employed to model durations. These source data can, however, become left-truncated and right-censored within any specific observation window thereby necessitating a (well-known) modification to the likelihood function. The approach reported in this article leveraged this adapted likelihood across a series of observation windows, applying the likelihood ratio test to identify duration changes/concept drift. Due to its flexibility in modelling any duration distribution, the gamma mixture model was used with Nelder–Mead optimized likelihood for the left-truncated and right-censored data. The number of gamma components was determined by the Bayesian information criterion. The proposed framework underwent validation through simulated exponential samples, leading to recommendations for its practical application. Subsequently, we applied the methodology to three real-life event logs exhibiting diverse characteristics. Experimental results showcase the effectiveness of our approach in terms of data fitting, as compared to Kaplan–Meier curves, and in detecting instances of drift. This comprehensive validation underscores the practical utility and reliability of our framework for dynamic business scenarios.

After-sales service and brand reputation: a case of kitchen appliance industry

PurposeTo build long-term relationships and gain a competitive edge, marketers need to provide customers with unique and distinct experiences that they cannot find in other companies. According to the literature, after-sales service helps to achieve these goals. By modeling the linkages between after-sales service, service quality, customer attitude and purchase intention, this study aims to understand how customers perceive after-sales service in decision-making in kitchen appliance industry.Design/methodology/approachThrough purposive sampling, 324 respondents, primarily female, answered a structured questionnaire about their perception of after-sales service for kitchen appliance products. Previously well-established, validated scale measures from the extant literature were used. The responses were gathered using a seven-point Likert scale.FindingsAccording to the findings, after-sales service quality is vital in kitchen appliance buying decisions. Accordingly, the higher the quality of service perceived by the customer, the more favorable the brand's attitude and purchase intention will be. Additionally, brand reputation was found to be an essential moderator between customer attitude and purchase intention, suggesting that the reputation of the kitchen appliance brand plays a positive and significant role in consumers’ purchase intentions.Originality/valueIt is well known that after-sales service plays a crucial role in current business scenarios, but empirical research on kitchen appliances has been scarce. This study aims to fill a void in the existing literature by investigating the relationships between after-sales service, after-sales service quality, customer attitude and purchase intention in the domain of kitchen appliances.

SecretFlow-SCQL: A Secure Collaborative Query Platform

In the business scenarios at Ant Group, there is a rising demand for collaborative data analysis among multiple institutions, which can promote health insurance, financial services, risk control, and others. However, the increasing concern about privacy issues has led to data silos. Secure Multi-Party Computation (MPC) provides an effective solution for collaborative data analysis, which can utilize data value while ensuring data security. Nevertheless, the performance bottlenecks of MPC and the strong demand for scalability pose great challenges to secure collaborative data analysis frameworks. In this paper, we build a secure collaborative data analysis system SCQL with a general purpose. We design more efficient MPC protocols and relational operators to meet the demand for scalability. In terms of system design, we aim to implement a system with security, usability, and efficiency. We conduct extensive experiments on SCQL to validate our optimization improvements: (1) Our optimized secure sort protocol sorts one million 64-bit data in only 4.5 minutes, 126× faster than EMP (9.4 hours). (2) The end-to-end execution time of the typical vertical scenario query is reduced by 1991× from the state-of-the-art semi-honest collaborative analysis framework Secrecy (rewritten with Additive Secret Sharing protocol), with appropriate security tradeoffs. (3) We test the system in the WAN setting with input size = 10 7 to demonstrate the scalability. We have successfully deployed SCQL to address problems in real-world business scenarios at Ant Group.

Research on the Construction of a Digital Twin System for the Long-Term Service Monitoring of Port Terminals

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations.

Matching inventory and demand in a Fast Moving Consumer Goods company: A Decision Support System

Demand fulfilment and its core, order promising, is a key business function that directly impacts the company’s ability to generate revenue through sales. For companies operating under a Make-To-Stock (MTS) production strategy, it is tantamount to matching both on-hand and projected stock to customer demand. The prevalent manner to carry out this task is to use either First-Come-First-Served policies, or simple allocation rules embedded in commercially available software. However, order promising is, in general, quite complex due to uncertainties both in demand and supply, and it can become extremely challenging when demand exceeds the stock. Therefore, several contributions have established the advantages of order promising using optimisation models and/or sophisticated algorithms. However, these works assume that customers can be clustered according to their profitability/importance and it remains open to test whether this also holds for homogeneous customers. Furthermore, due to the exploratory nature of most studies, they assume rather stylised scenarios (such as e.g. single product-orders, partial shipments) that do not clarify how model-based order promising would perform in a realistic setting.In this paper we present a decision framework for order promising in a Fast Moving Consumer Goods (FMCG) company in the brewery sector where customers cannot be clustered and that incorporates many realistic features (such as e.g. multi-product orders, on-full delivery, or perishability constraints). The decision framework relies on several iterations of a MILP model that allocates the stock to customer and reserves some portion of the stock for orders arriving later on. These functionalities are embedded in a Decision Support System (DSS) that also helps with the renegotiation of the orders that cannot be served. Results reported from the usage of the DSS show its ability to obtain high service levels and to favourably compare to other order promising policies. Furthermore, in order to analyse the instance sizes that can be solved in reasonable time, and the parameters that influence the performance and results of the MILP model, an extensive computational experience has been carried out using a testbed that covers a wide range of business scenarios.

Using business games in administration teaching: A case study of the entrepreneurial Matrix Game

Business games, also known as business simulation games, are educational tools used in teaching business administration that simulate real-life business scenarios. They provide a controlled environment for students to practice decision-making, problem-solving, and critical thinking skills in a risk-free environment. In this article we applied the Entrepreneurial Matrix Company Game to a sample group of undergraduate business students. This game is a learning methodology that simulates the step-by-step creation of a new business model in a controlled environment. It is an interactive tool that allows participants to experience different situations and challenges that may arise when opening a new business. Our objective was to identify the importance of such a tool for entrepreneurial motivation, and at the end of this research we identified that the company game Matriz Empreendedora can help identify potential obstacles and opportunities, allowing student-entrepreneurs to better prepare themselves to launch a new business.

Improving MSME Accounting Financial Recording Skills Based on Android Applications

Purpose: This community service project aimed to enhance the financial recording skills of Micro, Small, and Medium Enterprises (MSMEs) in Desa Kapita through hands-on training with Android-based accounting applications. Research Design and Methodology: The project employed a mixed-methods approach, including pre-and post-training evaluations via questionnaires, interviews, and practical exercises. Involving 25 SME owners in Desa Kapita, Kecamatan Bangkala, and Kabupaten Jeneponto, the training focused on the Wave application for transaction recording, expense management, and financial report generation. The program featured interactive workshops, simulated business scenarios, and follow-up mentoring. Findings and Discussion: Post-training evaluations revealed significant improvements in financial recording skills. 80% of participants developed a solid understanding of basic accounting concepts, and 85% effectively utilized the Wave application for managing finances. The training enhanced the accuracy and efficiency of transaction recording and financial report generation, leading to improved financial oversight and business decision-making. Implications: The findings indicate that targeted training can effectively close the digital literacy gap among SME owners, promoting the use of modern financial tools. Future efforts should incorporate regular mentoring and the creation of local support networks to maintain and broaden the benefits of digital financial management solutions for MSMEs.

Application Research of Multi-Model Fusion Technology in Real-Time Prediction of Dispatch Order Operation Duration

Abstract Power grid dispatching is a challenging task that requires dispatchers to be forward-looking in multiple aspects, with the operation of dispatch orders being one of its core tasks. Currently, the prediction of dispatch order duration relies mainly on the manual maintenance of experienced dispatchers, which is not only labor-intensive but also demands a high level of expertise. As a result, the effectiveness of existing systems in this regard is difficult to guarantee. However, due to the complexity of business scenarios, there has been no deployment of systems based on artificial intelligence algorithms. To address this issue, this paper employs multi-model fusion technology to achieve real-time prediction of dispatch order operation duration and has deployed it in Zhejiang Electric Power Company. The main contribution of this paper lies in proposing a strategy to solve the problem of regression prediction after differentiating feature fusion. Additionally, the paper provides a detailed description of how the relevant algorithms were deployed in Zhejiang Electric Power Company and presents the results of practical applications, demonstrating the effectiveness and practicality of the proposed solution.

Business expansion strategy of two-sided platform considering technology R & D effect and congestion effect

PurposeA new model for two-sided platforms is presented, incorporating both the original business and an expanded (new) business. Previous studies have neglected the impact of technology R & D on the new business aspect of two-sided platforms. This study addresses this gap by examining the technology R & D effect on the new business while also considering the congestion effect on the original business. It investigates the optimal pricing and user scales for both the original and new businesses. Additionally, the profits of the original business, the new business, and the overall two-sided platform are analyzed.Design/methodology/approachPrevious studies have overlooked the technology R & D effect on the new business aspect of two-sided platforms. Therefore, this study focuses on examining the technology R & D effect on the new business. Additionally, the congestion effect on the original business is considered. To determine the optimal prices for both sides of the original and new businesses, a game sequence model is introduced.FindingsThe optimal price on the service buyers (defined b side) of new business increases only with regard to technology R & D effect on b side of new business increasing. The optimal price on b side of new business is equal to half of the b-side R & D effect. The optimal profit of original business decreases with regard to the technology R & D effect of new business and cross-market network effect on the services provider (defined s side) between original business and new business increasing, respectively. To gain optimal profit of two-sided platform, the two-sided platform adopts some strategies to improve the congestion.Originality/valueA new two-sided platform model is stated, in which the technology R & D effect is embraced. The expanded business scenario of two-sided platform is considered, that is, the two-sided platform has one business firstly and a new business is developed or improved of two-sided platform. To solve the prices strategy of original business and new business, a game sequence of the original business and new business is presented.