Business Decision Support Research Articles

Implementasi CRUD (Create, Read, Update, Delete) pada Aplikasi Toko Sembako Berbasis Visual Basic.NET dan MySQL

Advances in information technology have opened up great opportunities to increase efficiency and smooth operations in various sectors, including grocery stores. However, many grocery stores still use manual, error-prone, and time-consuming methods for managing inventory and recording sales transactions. To overcome this problem, a grocery store merchandising application was developed and interacted with a database using Microsoft Visual Studio and SQL Server. This application was designed to meet the needs of store owners, cashiers and customers. The main features developed include product management, inventory management, sales transactions, and sales reporting. Interface design using Windows Forms by prioritizing usability and data security. The design process involves several stages: user needs analysis, database design, feature implementation, testing, and refinement. The result of this design is a desktop application that can improve operational efficiency in grocery stores by providing faster and more accurate inventory and transaction management and reporting. real-time to support business decisions. Therefore, it is hoped that this application can be an effective solution for grocery stores to face operational obstacles in the digital era.

PERENCANAAN STRATEGIS SI/TI PADA TOKO “LATINA BAKERY” DENGAN MENGGUNAKAN METODOLOGI WARD AND PEPPARD

The development of technology and information systems is currently growing very rapidly, most small, medium, and large organizations and companies have used it to support and facilitate their business processes. “Latina Bakery” is one of the business entities that is currently facing significant challenges in facing increasingly competitive market competition. Latina Bakery has implemented SI/TI to support the success of the store's vision and mission, but in its application, many of the systems used are still not effective and efficient. The main challenges faced lie in two fundamental aspects: First, limitations in the utilization of adequate technological infrastructure, and second, not optimal management and utilization of data to support business decision-making. This condition causes difficulties in adapting and competing amid market dynamics that continue to snowball. To overcome these problems, this research implements the Ward and Peppard methodology, a comprehensive approach consisting of: SWOT analysis (Strengths Weaknesses Opportunities, and Threats), Value Chain Analysis, McFarlan Analysis, and Five Forces Model Analysis. The main objective of this research is to produce an in-depth analysis to formulate an Information Systems and Information Technology (IS/TI) strategy aligned with the vision, mission, and business objectives. It can be a recommendation to move away from the business.

Impact of Management Information Systems on Enterprise Decision Support

This thesis examines the impact of management information systems (MIS) on decision support in business, focusing on the role of MIS in improving data availability, enhancing data analysis capabilities, improving decision-making efficiency and supporting collaborative decision-making. Through real-world case studies of large manufacturing companies and retail businesses, the paper demonstrates how MIS implementation can significantly improve productivity, inventory management, and customer satisfaction. The manufacturing company in Case 1 improved production line efficiency and inventory management, reduced inventory costs, and enhanced decision support by integrating an MIS platform. The retail enterprise in Case 2 achieved sales growth, inventory optimisation and customer satisfaction through real-time data analytics and CRM systems. The paper concludes that the application of MIS in modern enterprises can effectively improve the quality of decision-making and operational efficiency, and provide important support for enterprises in complex market environments. Through an in-depth analysis of MIS functions and their impact, the paper provides useful references and lessons for enterprises in the implementation and optimisation of decision support systems.

Risk Analysis in Customer Relationship Management via QRCNN-LSTM and Cross-Attention Mechanism

Risk analysis is an important business decision support task in Customer Relationship Management (CRM), involving the identification of potential risks or challenges that may affect customer satisfaction, retention rates, and overall business performance. To enhance risk analysis in CRM, this paper combines the advantages of QRCNN-LSTM and cross-attention mechanisms for modeling. The QRCNN-LSTM model combines sequence modeling with deep learning architectures commonly used in natural language processing tasks, enabling the capture of both local and global dependencies in sequence data. The cross-attention mechanism enhances interactions between different input data parts, allowing the model to focus on specific areas or features relevant to CRM risk analysis. By applying QRCNN-LSTM and cross-attention mechanisms to CRM risk analysis, empirical evidence demonstrates that this approach can effectively identify potential risks and provide data-driven support for business decisions.

Relaciones entre la auditoría de gestión y toma de decisiones. Enfoque teórico

Companies need to obtain effective results in the management of their activities and operations. They are in a continuous process of change and evolution, immersed in an interrelated environment that provides crucial information. In this context, it is essential that they exercise control and be selective when making decisions about strategies to implement. The management audit is presented as a key administrative tool to support business decision-making, which highlights the need to have information that facilitates and enriches these investigations. Under this premise, it was proposed to analyze the theoretical foundations of management auditing and managerial decision making from the perspectives of the relevant theories. This analysis is crucial to understand and adequately represent the theoretical relationships between its convergent categories. The research focused on management auditing as a strategic tool for decision-making in small and medium-sized enterprises (SMEs) in the Ecuadorian banana sector. The study was developed with a qualitative approach and descriptive nature, using a logical documentary and historical method. To do this, it was supported by sources such as scientific journals and other studies related to the topic. The content and structure of the study reflect the dynamics of the various theoretical relationships found. In conclusion, an evident evolution of this discipline over time in organizational structures has been demonstrated. Theoretical relationships provide frameworks for evaluating and improving management and decision-making, but their applicability and effectiveness in a specific sector may vary. Furthermore, the derivations obtained can serve as a reference and contribute to scientific knowledge for future researchers interested in delving deeper into the topic.

Solar Water Heater Using an IoT

Abstract: Traditional water heaters use a lot of electricity to heat water. Traditional water heaters require a heating coil to heat water, which uses at least 2000 watts. It also requires human intervention. For this reason, we utilize solar energy. Solar energy is not only a clean and renewable alternative to fossil fuels, but it is also environmentally friendly, has a wide range of applications, and has low maintenance costs. Therefore, the aim of this project is to convert solar energy into electrical energy and utilize solar energy to heat water. The solar hot water system includes IoT, solar panels, various modules and various sensors. Based on this measurement, we provide a smart hot water system through a combination of Internet of Things technologies and business processes and decision support systems. Maintaining a traditional water system is inconvenient but often effective when regular human intervention is required. In this project work, the complete workflow of the system is presented.

Dinamika Pengetahuan Akuntansi, Pengalaman Usaha, dan Motivasi Kerja: Analisis Penggunaan Laporan Informasi Akuntansi oleh Pelaku UMKM di Kota Lhoksukon

Understanding the influence of accounting knowledge, business experience and work motivation on the use of accounting information reports by Lhoksukon City MSMEs is essential to promote business effectiveness and continuity. This study is directed at examining the interaction between accounting knowledge, business experience, and work motivation in the context of the use of accounting information reports, with the aim of providing a new perspective regarding these elements interacting to support business decisions. Using quantitative methods, questionnaires were distributed to 74 MSMEs from a total of 291 MSMEs in Lhoksukon, and analyzed using multivariate statistics to identify relationships and influences between variables. The research results show: Adequate accounting knowledge, extensive business experience and high work motivation contribute significantly to the intensity of use of accounting information reports in making business decisions. The importance of work motivation as a mediator that strengthens the effect of accounting knowledge and business experience on the use of accounting information is also identified. These findings underline the need for training and education programs that focus on increasing accounting knowledge for MSMEs and building work motivation as a basis for the effective use of accounting information in business activities. This research makes a significant contribution to the MSME accounting and management literature, as well as providing practical guidance for policy makers, educational institutions and MSME actors in designing capacity development strategies and work motivation to maximize the benefits of accounting information for business success.

The Interplay of Human, Organizational, Environmental, and Technological Factors in Business Intelligence Adoption: a Systematic Literature Review

Purpose: The objective of this research is to identify the most common factors in the literature influencing Business Intelligence Adoption (BIA) and present the main approaches, analysis methods and frameworks with model recently used in the study of BIA. Theoretical framework: Business intelligence (BI) has become a significant subject of study for both practitioners and researchers, due to its capabilities to apply data analytics and produce key information to support business decision-making. The literature indicates that most organizations have not succeeded in fully leveraging the benefits of BI, Furthermore, there is a limited availability of studies that investigate the human aspect. We also notice the absence of a relevant review of previous literature on the methods and approaches to analyse the BIA context, as well as the determination of adoption factors with a particular emphasis on human axis. The review aims to identify the most common factors in the literature for Business Intelligence Adoption (BIA) and present the main approaches, analysis methods and frameworks with model recently used in the study of BIA. Methods: This study adopts a systematic literature review by selecting and analysing 32 relevant studies published between 2012 and 2022 from two reputable databases, Science Direct and Web of Science. Content analysis was employed to scrutinize the selected article and identify the factors and characteristics that influence the adoption of Business Intelligence. Findings: The research unveiled over seventy TOE factors and twenty-four distinct human factors relevant to Business Intelligence Adoption. These findings help elucidate the multifaceted nature of BIA and the key elements that drive its adoption. Practical implications: This study enhances the existing body of knowledge by providing a thorough analysis of methodologies and approaches used in BIA research, while also advancing the understanding of adoption factors, especially those related to the human dimension. Originality/Value: The originality lies in its emphasis on the main approaches, analysis methods, and recently used models in the study of BIA. Furthermore, it enhances the field's originality and value by categorizing adoption factors within the TOE framework and giving special attention to human factors.

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

Traditional data warehouses (DWs) have played a key role in business intelligence and decision support systems. However, the rapid growth of the data generated by the current applications requires new data warehousing systems. In big data, it is important to adapt the existing warehouse systems to overcome new issues and limitations. The main drawbacks of traditional Extract–Transform–Load (ETL) are that a huge amount of data cannot be processed over ETL and that the execution time is very high when the data are unstructured. This paper focuses on a new model consisting of four layers: Extract–Clean–Load–Transform (ECLT), designed for processing unstructured big data, with specific emphasis on text. The model aims to reduce execution time through experimental procedures. ECLT is applied and tested using Spark, which is a framework employed in Python. Finally, this paper compares the execution time of ECLT with different models by applying two datasets. Experimental results showed that for a data size of 1 TB, the execution time of ECLT is 41.8 s. When the data size increases to 1 million articles, the execution time is 119.6 s. These findings demonstrate that ECLT outperforms ETL, ELT, DELT, ELTL, and ELTA in terms of execution time.

Sentiment analysis to support business decision-making. A bibliometric study

<abstract> <p>Customer feedback on online platforms is an unstructured database of growing importance for organizations, which, together with the rise of Natural Language Processing algorithms, is increasingly present when making decisions. In this paper, a bibliometric analysis is carried out with the intention of understanding the prevailing state of research about the adoption of sentiment analysis methods in organizations when making decisions. It is also a goal to comprehend which business sectors, and areas within the company, they are most applied, and to identify what future challenges that in this area may arise, as well as the main topics, authors, articles, countries, and universities most influential in the scientific literature. To this end, a total of 101 articles have been gathered from the Scopus and Clarivate Analytics Web of Science (WoS) databases, of which 85 were selected for analysis using the Bibliometrix tool. This study highlights the growing popularity of sentiment analysis methods combined with Multicriteria Decision Making and predictive algorithms. Twitter and Amazon are commonly used data sources, with applications across multiple sectors (supply chain, financial, etc.). Sentiment analysis enhances decision-making and promotes customer-centric approaches.</p> </abstract>

The Interplay of Human, Organizational, Environmental, and Technological Factors in Business Intelligence Adoption: A Systematic Literature Review

Finalidade: O objetivo desta pesquisa é identificar os fatores mais comuns na literatura que influenciam a Adoção de Inteligência Empresarial (BIA) e apresentar as principais abordagens, métodos de análise e estruturas com modelo recentemente usado no estudo da BIA. Purpose: The objective of this research is to identify the most common factors in the literature influencing Business Intelligence Adoption (BIA) and present the main approaches, analysis methods and frameworks with model recently used in the study of BIA. Theoretical framework: Business intelligence (BI) has become a significant subject of study for both practitioners and researchers, due to its capabilities to apply data analytics and produce key information to support business decision-making. The literature indicates that most organizations have not succeeded in fully leveraging the benefits of BI, Furthermore, there is a limited availability of studies that investigate the human aspect. We also notice the absence of a relevant review of previous literature on the methods and approaches to analyse the BIA context, as well as the determination of adoption factors with a particular emphasis on human axis. The review aims to identify the most common factors in the literature for Business Intelligence Adoption (BIA) and present the main approaches, analysis methods and frameworks with model recently used in the study of BIA. Methods: This study adopts a systematic literature review by selecting and analysing 32 relevant studies published between 2012 and 2022 from two reputable databases, Science Direct and Web of Science. Content analysis was employed to scrutinize the selected article and identify the factors and characteristics that influence the adoption of Business Intelligence. Findings: The research unveiled over seventy TOE factors and twenty-four distinct human factors relevant to Business Intelligence Adoption. These findings help elucidate the multifaceted nature of BIA and the key elements that drive its adoption. Practical implications: This study enhances the existing body of knowledge by providing a thorough analysis of methodologies and approaches used in BIA research, while also advancing the understanding of adoption factors, especially those related to the human dimension. Originality/Value The originality lies in its emphasis on the main approaches, analysis methods, and recently used models in the study of BIA. Furthermore, it enhances the field's originality and value by categorizing adoption factors within the TOE framework and giving special attention to human factors.

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

With the rapid development of China's new energy vehicle industry, charging infrastructure has gradually become a key factor affecting the further development of the new energy vehicle industry. Scientific and reasonable forecasts of the future development scale of the domestic charging infrastructure market have become an important element supporting the business planning of various related parties such as vehicle enterprises, charging pile enterprises, and the energy industry. Based on the distribution data of electric vehicle traveling and charging characteristics obtained from the industry research, this study proposes a set of charging infrastructure prediction model based on the principle of energy supply-demand balance, predicts the development scale of charging infrastructure in the country and key cities, and further analyzes the market competition and future market margin of the key cities, and evaluates the difficulty of future market entry in the key cities, which provides a basis for further development of the charging infrastructure industry. The difficulty of entering the future market in each key city is assessed, providing business decision support for industry participants. In the future, the charging infrastructure development scale prediction results proposed by this study can be used as the input parameters of the charging pile layout planning model to enhance the scientific nature of the charging infrastructure layout and help the industry to develop in a high-quality manner.

Perencanaan Business Intelligence untuk Strategi Pengembangan Produk Unggulan Menggunakan Algoritma Support Vector Machine

The government continues to encourage the government to accelerate Small and Medium Enterprises (SMEs) flagship products to enter the digital market, because the current digitalization of SMEs cannot be ignored, SMEs must be able to change their business to the digital realm. The strategy for developing superior SME products needs to be well planned, because a good and measurable strategy is very important for policy making. Development uses an approach that emphasizes that the driving force of development is commodities that are considered to be superior, both at the domestic, national and international levels. Superior products that have quality and are needed by customers require the right selection process, so we need a method to produce this information. Business Intelligence (BI) is a global term for all processes, techniques, and tools that support business decision making based on information technology. The strategy for developing superior products for SME products uses the criteria of production data, income, and SME respondent data in determining the priority list. A good product assessment based on priority can be done by BI using the classification method with the Support Vector Machine (SVM) algorithm. The research method carried out for planning the development of superior products for MSMEs in Tasikmalaya City is a research development research. To complete the classification of the right superior product development strategy, then Support Vector Machine algorithm can be used with test results using the Confusion Matrix with an average accuracy of 97%.

Toward a concept of digital twin for monitoring assembly and disassembly processes

Quality is one of the key factors in the customer’s selection process between competing products. Producing high-quality, defect-free products that meet consumer expectations is crucial for manufacturing companies to gain a competitive advantage. Accordingly, developing appropriate defect generation models is essential in modern manufacturing companies to predict defects and plan efficient quality control and production. On the other hand, with its ability to support new business models and decision support systems, digital twin technology is one of the new technologies emerging to support digital transformation. Faster optimization algorithms, more powerful computers, and a massive increase in available data are just some of the features of digital twins that can be used to advance simulation toward real-time quality control and optimization of products and production systems. This paper aims to model the generation of defects of product variants in assembly and disassembly processes and evaluate their integration within a digital twin system to prevent the occurrence of defects and ensure product quality. The proposed strategy is expected to improve the optimization, monitoring, and diagnostic capabilities of complex product variants’ assembly and disassembly systems, realizing an upgrade from a single physical implementation to a combination of physical and digital.

Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction

Review helpfulness has been measured commonly relying on quantitative indicators at the review level. Helpful reviews qualified by such simple indicators, however, may not necessarily yield accurate sales predictions, owing to the ever-evolving review information quality, customer demand, and product attributes. Positing that reviews with higher customer attention should be more influential to customers’ purchase intention and product sales, we propose to leverage customer attention to better realize the potential of multimodal reviews for sales prediction. We conceptualize customer attention at the holistic review set, review subset, individual review, and review element levels, respectively, and induce four indicators of customer attention, that is, timeliness, semantic diversity, voting-awareness, and varying multimodal interaction. We then propose a novel deep learning method, which incorporates these customer attention indicators using neural network attention mechanisms specifically designed for multimodal-review-based sales prediction. Empirical evaluation based on a large data set in a case study predicting hotel sales (specifically, monthly occupancy rate) shows that, in terms of both prediction performance and representation learning performance, our proposed method outperformed benchmarked state-of-the-art deep learning methods. As multimodal reviews become increasingly prevalent, this method serves as a tool for adequately leveraging such multimodal data to support business decision making.

DATA-DRIVEN SMART MANUFACTURING: CASE STUDY OF WORKFORCE MANAGEMENT PROCESS IN AN ITALIAN LEATHER GOODS COMPANY

AbstractDigitalization is one of the fundamental pillars of Industry 4.0. Within smart factories, Big Data Analytics systems play a key role in supporting the decision-making process of various stages of business processes. In this context, this research aims to identify solutions able to process large volumes of data from digital business processes with the final goal of adding value to the organisation. More specifically, the research deals with the implementation of a digital manufacturing tool able to digitize the workforce management process. The research has been applied in the case study of an Italian manufacturing company operating in the leather goods sector through the digitalization of the workforce management by a cloud-based platform. The implementation of the tool increases the efficiency of the production process, provides efficient management and integrates workforce data into one system. The implemented tool generates a large volume of data, the final goal is to make data user-friendly to support business decisions. Digitisation provides an exchange of information to support managers to make confident decisions.

Intelligent engineering management modeling for computer technology and data mining

The author suggests using computer technology and data mining in intelligent engineering management to increase the management capacity of engineering projects and decrease the consumption of construction expenses. The emphasis is mostly on theoretical and analytical approaches to problems of practical concern for data mining, perhaps in combination with other conventional tools, and the associated applications to engineers and managers of various industrial sectors. This has immediate benefits for both academic and applied data mining researchers as well as research students. The author developed a BIM project management system based on Browser/Server architecture and Client/Server architecture in conjunction with BIM technology and the BIM 4D model. To execute complete project management of business management, real-time control, and decision support, the system must establish and use its business logic and data exchange link. Each module in the 4D construction dynamic management subsystem recognizes dynamic management of the construction process, and the working duration of each module is determined by calculating by using the unified engineering decomposition principle, rationalizing the relevant data through time parameters, recording the acquired management data and storing it in the BIM database, and connecting the BIM database bi-directionally through the web server through the system integration. The results showed that when the time is 20[Formula: see text]min, the throughput of the system is higher than [Formula: see text][Formula: see text]kb/s. The system can effectively improve the project management ability, reduce the construction cost and construction period of the project, and the system responds quickly.

Growth of content marketing through LinkedIn channel

Social media analytics is the ability to gather and find meaning in data gathered from social channels to support business decisions — and measure the performance of actions based on those decisions through social media. LinkedIn is a social website which had been launched for the purpose of creating professional network and connect, like education and Business. LinkedIn marketing is a great tool for large B2B companies looking to promote their brands by reaching professionals through ad campaigns. This social website allows business people to perform various tasks like posting promotional videos, customising attractive company profile, participating in active chats with the live customers. This suggests that people on LinkedIn are generally interested in informative and educational content, which seems fitting for a professional social work. This research also emphasizes on content marketing applications such as SlideShare, Pulse and company pages which will support B2B. The Objective of this study is to analyse the various promotional strategies used by B2B business through LinkedIn channel. This study also concentrates on the benefits of existing customers flowing through LinkedIn. The sample size of this study is restricted to 100 and the area of the study is restricted to Tamilnadu. This Study enhances that for many Influencers and content creators, social media channels are the primary source of business and income. In this note, LinkedIn has a strong base of followers and these followers will help to increase the brand audience. Henceforth, this study brings out the benefits of digital marketing and gaining social knowledge.

Design and Implementation of Enterprise Financial Decision Support System Based on Business Intelligence

Purpose: Business intelligence and decision support systems are now recognized as critical enterprise infrastructure. Data is increasingly being used by business enterprises to react to key operational and strategic operations of their customers, markets, and stakeholders. Theoretical framework: Business intelligence has advanced as the volume of data generated by smart technologies and the Internet has increased exponentially. Design/Methodology/Approach: When using AI in the business world, privacy concerns arise when sensitive data is transmitted to a third-party vendor who has no connection to the company. Financial decision support data is managed by these AI service providers. Even while it may seem like a drawback, artificial intelligence technology actually has several advantages. Findings: The results of this research show that a network can improve the financial management of a corporation. Using many applications of AI technology helps bring down the overall cost of operations. When it comes to financial services, both the number of IT workers needed and the number of necessary pieces of hardware (servers) can be reduced, resulting in a marginal drop in capital expenditures. Research, Practical & Social implications: When it comes to financial services, both the number of IT workers needed and the number of necessary pieces of hardware (servers) can be reduced, resulting in a marginal drop in capital expenditures. It is now simpler and quicker to get your hands on relevant financial information, which should lead to greater efficiency. Originality/value: With our proposed approach, the accuracy of financial decision support in company increases to 99.84% from 88.94%, while the implementation time and cost are reduced.

EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVE

Sentiment analysis has been rapidly employed for business decision support. New data mining researchers are yet to have an adequate understanding of the various applications of sentiment analysis while utilising social media data. As a result, it is critical to define the data mining and text analytics research trend holistically using existing literature. The study explores sentiment analysis research for its application in transforming social media data and identifies relevant research aspects through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis are mainly related to innovation, transparency, and efficiency. Our review also highlights the distinctiveness of sentiment analysis for synthesising social media information to investigate various features, including the knowledge-domain map that detects author collaboration networks in the past.