Strategic Business Decisions Research Articles

Artificial Intelligence in Strategic Business Decisions: Enhancing Market Competitiveness

Abstract: Integrating Artificial Intelligence (AI) into strategic decision-making is essential for enhancing market competitiveness in today's dynamic business environment. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics optimize operations, personalize customer experiences, and drive product innovation. Machine learning algorithms analyze vast data to uncover patterns, aiding better decision-making. Predictive analytics forecasts market trends and consumer behaviors, allowing companies to anticipate demand and streamline supply chains, reducing risks like overproduction and stockouts. NLP-powered chatbots improve customer interactions by handling routine inquiries, freeing human agents for complex issues, and enabling personalized marketing. AI also accelerates product development by analyzing market data and consumer feedback, simulating scenarios, and predicting outcomes. Operational efficiency is enhanced through automation and optimized workflows, saving costs and increasing productivity. Despite these benefits, challenges such as data privacy, algorithmic bias, significant investment, and a shift to a data-driven culture must be managed. Effective AI integration offers significant competitive advantages, positioning companies to leverage predictive analytics, personalized customer interactions, and operational efficiency for growth and innovation.

Accelerating Fintech optimization and cybersecurity: The role of segment routing and MPLS in service provider networks

This review paper explores the profound impact of advanced financial modeling techniques on strategic business planning and performance. The paper begins with an overview of the evolution of financial modeling, highlighting the importance of advanced techniques such as Monte Carlo simulations, real options analysis, and machine learning models. It delves into the role of technology, including artificial intelligence and big data analytics, in enhancing financial modeling capabilities. The discussion then shifts to how these advanced models support strategic business planning by providing deeper insights, improving risk management, and facilitating better decision-making. The paper also examines the challenges of implementing advanced financial modeling, including data quality issues and skill gaps, and offers practical business recommendations. Future trends in financial modeling, such as increased AI integration and the incorporation of ESG factors, are also explored. The review concludes by emphasizing the significant benefits of advanced financial modeling in driving strategic business decisions and improving overall business performance. Keywords: Advanced Financial Modeling, Strategic Business Planning, Monte Carlo Simulations, Machine Learning Models, Risk Management.

Mitigation of supply chain challenges in cell therapy manufacturing: perspectives from the cord blood alliance.

Cellular therapies rely on highly specialized supply chains that often depend on single source providers. Public cord blood banks (CBB) manufacturing the first cell therapy to be highly regulated by the FDA and related international agencies are a prime example of being subject to this phenomenon. In addition to banking unrelated donor cord blood units for transplantation, CBBs also source and characterize starting materials for supply to allogeneic cell therapy developers that often employ customized technologies offered by just a small number of manufacturers. As such, these supply chains are especially sensitive to even minor changes which often result in potential major impacts. Regulations can shape supply chain efficiencies, both directly via the definition of restricted technology and process requirements and indirectly by steering strategic business decisions of critical supply or service providers. We present 3 current supply chain issues with different root causes that are swaying efficiencies in cord blood banking and beyond. Specifically, the shortage of Hespan, a common supplement used in cord blood processing, the decision by the provider to stop supporting medical device marking of the Sepax system broadly used in cord blood banking, and a new European ruling on phasing out plasticizers that are critical for providing flexibility to cord blood collection bags, are all threatening downstream supply chain issues for the biologics field. We discuss overcoming these hurdles through the prism of unified mitigation strategies, defined, and implemented by multi-factorial teams and stakeholders, to negotiate resolutions with providers and regulators alike.

THE ROLE OF DIGITIZATION IN MANAGEMENT AND STRATEGIC DECISION-MAKING IN MODERN MANAGEMENT

This research aims to assess the impact of digitization on management efficiency and strategic decision-making, a pivotal aspect in the contemporary business world. By integrating digital technologies with traditional mathematical models and analyzing survey data from 250 management professionals, the study provides a comprehensive understanding of the multifaceted effects of digitization.The principal outcomes from the mathematical analyses, including Marginal Cost Analysis, Break-Even Analysis, and Linear Programming, indicate substantial improvements in operational efficiency following digitization. Specifically, Marginal Cost Analysis shows a significant decrease in the cost of production, and Break-Even Analysis reveals a reduction in the break-even point by approximately 15%, underscoring enhanced cost-effectiveness. Linear Programming results demonstrate a 20% improvement in resource allocation efficiency. The survey analysis complements these findings, revealing a positive perception of digitization in enhancing managerial adaptability, efficiency, and decision-making effectiveness, with average rating improvements of 0.3, 0.4, and 0.5 points, respectively, on a 5-point scale. Regression analysis further highlights the positive influence of technological affinity on decision-making effectiveness. However, ANOVA results suggest variability in the impact of digitization across different industry sectors, necessitating sector-specific digital strategies.Digitization significantly elevates management efficiency and strategic decision-making capabilities. The integration of digital tools with traditional management techniques results in more informed, efficient, and strategic business decisions. However, the varied impact across sectors and the rapid pace of technological advancement calls for continuous adaptation and sector-specific strategies. These findings provide vital insights for businesses navigating the digital era, emphasizing the strategic integration of digital tools in management practices for sustained success and competitiveness.

Mendorong Motivasi Wirausaha UMKM untuk Memajukan Ekonomi Masyarakat di Kelurahan Joglo Kota Jakarta Barat

Micro, Small, and Medium Enterprises (MSMEs) are business entities managed by individuals, families, or small-scale business units, classified based on annual turnover, asset volume, and workforce. Effective human resource management is essential for MSMEs in systematically organizing finances, enabling business owners to understand their financial conditions and business developments. Easy access to information on assets, liabilities, equity, cash flow, and profit fluctuations provides crucial support for MSME entrepreneurs in making strategic business decisions. This activity not only motivates entrepreneurs to maintain business continuity but also guides them towards achieving desired business and economic goals. The drive to engage in entrepreneurship stems from a strong individual desire to realize self-potential through creative and innovative thinking, creating new products that have a social and economic impact on the local community. The courage to explore and implement new ideas is reinforced by strong entrepreneurial motivation, creating a positive cycle between motivation and action that strengthens the character of resilient and high-quality entrepreneurs.

Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Research on the Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method is an effort to understand and utilize sales data in making more informed and strategic business decisions. The main goal of this research is to extract hidden patterns from large sales data sets, which cannot be discovered by manual analysis alone. This research process is divided into several key stages, namely Data Selection, Preprocessing, Transformation, and Data Mining. The research results show that the Apriori method is effective in finding purchasing patterns. In terms of the frequency of 2 itemsets, the highest support value was found to be 1, which indicates that the combination of the two products is always purchased together in all transactions. For 3 itemsets and 4 itemsets, the high support value of 0.9 also indicates the existence of product combinations that are often purchased together. In terms of confidence, 2 itemsets show the highest value of 1.25, indicating that purchasing one product has a high tendency to be followed by purchasing other products. For 3 itemsets and 4 itemsets, the confidence values show a slightly lower trend but are still significant. Furthermore, lift analysis provides additional insight into the strength of association between itemsets, with 4 itemsets showing the highest lift value of 1.30, indicating the product combination has a very strong association compared to random expectations. This research confirms the potential of the Apriori method in finding valuable sales patterns, which can help companies make strategic decisions for increasing sales and customer satisfaction.

Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency

Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.

Sales Data Analysis Using BI and WEKA

In today's competitive business landscape, organizations rely heavily on data-driven decision-making to gain a competitive edge. Sales data analysis serves as a cornerstone in this endeavor, offering invaluable insights into consumer behavior, market trends, and the overall health of a business. This abstract highlights the significance of sales data analysis and its role in informing strategic business decisions. The abstract begins by outlining the importance of sales data analysis in understanding customer preferences, identifying emerging market trends, and optimizing sales strategies. It emphasizes the need for businesses to harness the power of data analytics tools and techniques to extract actionable insights from vast volumes of sales data. Furthermore, the abstract discusses the various dimensions of sales data analysis, including sales performance metrics, customer segmentation, and product performance analysis. It underscores the importance of leveraging advanced analytical methods such as predictive analytics, machine learning, and data visualization to unlock hidden patterns and correlations within sales data. Moreover, the abstract explores the practical applications of sales data analysis across different industries, ranging from retail and e-commerce to manufacturing and finance. It showcases real-world examples of how organizations have successfully utilized sales data analysis to enhance revenue generation, streamline operations, and improve customer satisfaction. The performance of the forecasting models is evaluated using appropriate metrics and validated against holdout datasets to ensure robustness and reliability. Model selection and tuning are conducted to optimize forecasting accuracy and mitigate potential biases. The forecasting is done using weka tool. Keywords – Insights, Visualization, Analytics, Sales trends.

Exploring Big Data Technology in Human Resources Decision-Making

With the widespread application of big data technology, human resources decision-making faces new opportunities and challenges. This paper explores the application of big data technology in human resources management and its potential impact, providing an in-depth analysis of the transformation of the role of HR managers in the era of big data. Firstly, the application of big data enables HR professionals to make more effective strategic business decisions by eliminating biases in decision-making and providing profound real-time insights. This, in turn, attracts and retains talented individuals, enhancing business efficiency. Secondly, big data plays a crucial role in the recruitment process, assisting management in avoiding recruitment errors. Personalized data analysis helps identify talents that match specific job requirements, ultimately improving long-term performance. Furthermore, the application of big data technology facilitates organized internal mobility of human resources within a unit. By analyzing employee data, organizations can gain better insights into employees' work capabilities and development potential, offering diverse opportunities for career development to enhance employee satisfaction, loyalty, and maintain competitiveness.

Business analytics and decision science: A review of techniques in strategic business decision making

Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.

The Impact of Artificial Intelligence (AI) on the Accounting System of Saudi Companies

As a major player in the world market, Saudi Arabia has seen substantial adoption of artificial intelligence AI) technology in its commercial environment. This study intends to thoroughly examine the specific effects of AI on Saudi business accounting systems. This paper offers comprehensive knowledge of the consequences of AI application in the accounting sector through a thorough examination of the body of existing literature. It examines how traditional accounting methods are affected by AI-driven automation, data analysis, and decision-making processes in the Saudi Arabian environment. The viewpoints and experiences of first-hand participants in integrating AI into Saudi enterprises’ accounting systems are provided by this study through a survey distributed to important stakeholders, such as accounting professionals, technology specialists, and business leaders. This study also emphasizes how incorporating AI technology into accounting procedures may affect workforce dynamics, skill needs, and organizational structure as a whole. One of the most significant research findings is the ability of AI to process enormous volumes of data quickly and accurately, allowing for improved financial analysis, risk assessment, and forecasting. This facilitates wiser and more strategic business decisions. AI also simplified accounting processes and decreased the need for human labor, saving Saudi enterprises money. As a result, resource allocation was optimized and overall financial performance was enhanced.

Operational excellence (OpEx) through entrepreneur’s strategic business decision making and emotional contagion in the service industry

Petroleum products play a critical role in the global industry. India is the third most significant crude oil importer in the world, of which 22 % from Russia in October 2022. Emotional Contagion refers to an entrepreneur influencing the emotions and behaviours of another entrepreneur and employees in the company to achieve operational excellence. Entrepreneurs' decision-making styles vary from analytical, conceptual, directional, and behavioural to attain the corporate goal. The utilitarian theory states that entrepreneurs, as utilitarians, lead the company with a highly ethical, moral, good environment & culture and, at the same time, target for the company's profit. We have considered twenty five companies from India and the Middle East region to study Entrepreneur's strategic business decision styles and emotional Contagion. We have used a Stratified sampling technique to collect data. We have used JAMOVI for the data analysis. This paper examines entrepreneurs' strategic business decision styles and emotional Contagion toward Utilitarianism in the Oil and Gas service industry. It provides positive and negative effects for which we have recommended that the government and MSMEs must focus on providing psychological training to entrepreneurs to achieve the highest positive emotional contagion, safe and healthy organizational culture positively

The Effect of Capital Expenditure, Sales Growth, And Leverage on Cash Holdings

The maintenance of cash holdings is an essential element in ensuring the financial stability of a company. The utilisation of cash holdings might be advantageous for companies seeking to capitalise on investment prospects or finance their expansion strategies. The maintenance of a robust cash holdings enables organisations to promptly capitalise on favourable investment prospects that may emerge within the market. The purpose of this study is to examine the effect of capital expenditure, sales growth, and leverage on cash holdings. The research population is non-cyclical sector companies, totalling 61 companies in Indonesia Stock Exchange (IDX). Data testing was carried out based on secondary data collected, namely the annual reports of each company between 2017 and 2021. The technique used is quantitative by measuring research variables indicators and testing them with statistical analysis. This research utilized a sample of 61 companies that were selected by the purposive sampling method. Multiple regression was used to analyze the data. The findings of this research show that capital expenditure has a negative effect on cash holdings, the sales growth rate has a positive effect on the cash holdings of the company, and leverage has no effect on cash holdings. The findings of this study will provide valuable insights for policy-makers and stakeholders in making informed strategic business decisions regarding cash holdings.

أثر المحاسبة الإدارية التنافسية على اتخاذ قرارات الأعمال الاستراتيجية

With the increasing complexity of business transactions, and increasing business risk, the role of competitive management accounting is becoming increasingly important, especially in making strategic business decisions. As a part of strategic management accounting, competitive management accounting positions the company to a place which has the best view of the existing and potential competitor activity, by creating a strong information base on competition, all to create added value and improve the company operations. This paper aims to highlight the importance of competitive management accounting in strategic decision-making, and based on case studies in the world and empirical research conducted in Iraq, to highlight the importance of competitive management accounting in the world and to answer the question of whether the companies in Jordon use the information from competitive management accounting in the process of decision-making?

Discuss the Motivations and Risks Behind Microsofts Acquisition of Activision Blizzard

With the rapid growth of the gaming sector and advancements in technology, the global gaming market has reached unprecedented heights, capturing the attention of millions worldwide. Simultaneously, the concept of metaverse has become a topic of intense discussion, holding the promise of transforming virtual experiences. This paper study thoroughly examines Microsoft's strategic move to acquire Activision Blizzard, scrutinizing its multifaceted impact from various perspectives. Motivated by the pursuit of synergy effects, market expansion, and preparation for the metaverse, Microsoft aims to harness the potential of Activision Blizzard's intellectual properties and fortify its gaming portfolio, potentially positioning itself ahead of Sony in the fiercely competitive gaming console market. Nevertheless, the integration of diverse corporate cultures and the ever-present specter of antitrust scrutiny pose significant challenges on the horizon. This compelling case study offers invaluable insights into the decision-making processes of major corporations, shedding light on the intricacies of the dynamic gaming industry and the unfolding metaverse landscape. As the gaming realm and technology continue to thrive, this landmark acquisition becomes a pivotal example in understanding and navigating strategic business decisions in the rapidly evolving digital era.

Landak Industrial Park (LIP) Development Strategy

Landak Industrial Park (KIL; Kawasan Industri Landak) is a national strategic project for Industrial Estates in West Kalimantan and is included in the List of 14 Priority Industrial Estate Developments Outside Java Island in the 2020-2024 National Medium-Term Development Plan (RPJMN; Rencana Pembangunan Jangka Menengah Nasional). The construction of KIL is experiencing delays, and to prevent the project from stalling, the main sources of industrial growth are new investment and increased utility from the current capacity. This study’s approach is both qualitative and quantitative. The analysis technique used is SWOT analysis using Internal Strategic Factor Analysis Summary (IFAS) and External Strategic Factor Analysis Summary (EFAS), developed using spatial analysis techniques from the concept of economic aggregation, which then generates value propositions and makes them the basis of a marketing strategy that includes market segmentation (segmenting), setting market targets (targeting), and creating a unique position (positioning). The result is from the SWOT analysis. KIL is in the turnaround quadrant, a position with a strategy of maximizing opportunities because it has major internal weaknesses. The geographical reach of other economic groups or business clusters that are quite close together is a trigger for the development of KIL. Scenarios from the KIL development strategy are expected to support strategic business decisions.

Landak Industrial Park (LIP) Development Strategy

Landak Industrial Park (KIL; Kawasan Industri Landak) is a national strategic project for Industrial Estates in West Kalimantan and is included in the List of 14 Priority Industrial Estate Developments Outside Java Island in the 2020-2024 National Medium-Term Development Plan (RPJMN; Rencana Pembangunan Jangka Menengah Nasional). The construction of KIL is experiencing delays, and to prevent the project from stalling, the main sources of industrial growth are new investment and increased utility from the current capacity. This study’s approach is both qualitative and quantitative. The analysis technique used is SWOT analysis using Internal Strategic Factor Analysis Summary (IFAS) and External Strategic Factor Analysis Summary (EFAS), developed using spatial analysis techniques from the concept of economic aggregation, which then generates value propositions and makes them the basis of a marketing strategy that includes market segmentation (segmenting), setting market targets (targeting), and creating a unique position (positioning). The result is from the SWOT analysis. KIL is in the turnaround quadrant, a position with a strategy of maximizing opportunities because it has major internal weaknesses. The geographical reach of other economic groups or business clusters that are quite close together is a trigger for the development of KIL. Scenarios from the KIL development strategy are expected to support strategic business decisions.

Business values as a strategic resource in indigenous black South African family businesses

Purpose: To identify the business values underlying the strategic decisions of indigenous black South African (IBSA) family businesses and to explore a possible relationship between these values and business outcomes.Design/methodology/approach: The study adopted a qualitative methodological approach and utilised seven IBSA family business cases. Data were collected from 17 selected participants using semi-structured interviews. The reflexive thematic analysis method was used to analyse the data transcripts.Findings/results: Ten business values were identified as underlying the strategic decisions of IBSA family businesses, namely customer focus, innovation and creativity, business traditions, continuous learning, employee empowerment and development, open communication, business sustainability, teamwork, partnerships and business reputation. These values are considered a strategic resource because they influence the strategic business decisions made and ultimately contribute to the business outcomes and long-term survival of the family business.Practical implications: The study provides insights into the relationship between business values, strategic decisions and business outcomes in the context of IBSA family businesses. Therefore, family business owners and leaders should ensure that behaviours, actions and strategic decisions are aligned to their business values so that these values can serve as the strategic resource that scholars claim they are.Originality/value: While several studies on values have been conducted in European or Western contexts, this study provides insights into the possible relationship between business values, strategic decisions and business outcomes from an indigenous African family business perspective.

Pricing model, engagement model and firm performance: evidence from Indian data science industry

PurposeData science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP.Design/methodology/approachIndian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software.FindingsThe findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP.Originality/valueBy developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry.

PENTINGNYA PENYUSUNAN LAPORAN KEUANGAN UNTUK PENILAIAN KINERJA USAHA UMKM DI RW.08 KELURAHAN KAYU PUTIH, PULO GADUNG, JAKARTA TIMUR

The success of MSME (Micro, Small and Medium Entities) entrepreneurs in dealing with the impact of the economic crisis either in 1998 or in 2008/2009 is of course inseparable from the commitment of MSME entrepreneurs themselves to continue to improve the performance of their MSME businesses. Assessment of MSME business performance is very important for MSME entrepreneurs in making strategic business decisions correctly and correctly. The right and correct decision can increase the progress of the MSME business, but on the contrary, wrong decision making can pose a risk of failure of the MSME business.So that the decisions taken by MSME entrepreneurs are not wrong, it is necessary to analyze the performance of their MSME businesses. The information needed in analyzing the business performance of MSMEs can be seen in the financial reports prepared by these MSMEs. In practice there are still many MSME entrepreneurs who still think that the preparation of these financial reports is not very important, so that in the end these MSME entrepreneurs cannot analyze their business performance in real terms. For this reason, The Accounting Study Program, Economics and Business Faculty, Trisakti University in collaboration with The Management of Rukun Warga (RW) 08, Kayu Putih Village, Pulo Gadung District, in East Jakarta, held a Community Service (PKM) Program by providing training to MSME entrepreneurs in Rukun Warga (RW) 08, Kayu Putih Village, Pulo Gadung District, East Jakarta, so that these MSMEs can understand and understand the importance of compiling Financial Reports for MSMEs in accordance with applicable Financial Accounting Standards in analyzing real financial performance. The results after the implementation of the PKM were in line with expectations, where participants were very enthusiastic about asking questions about the accounting process, the concept of business entities and answering the given accounting exercise questions, where the results of the assessment obtained an average score of 62 to 73.