Strategic Management Capabilities and Competitive Advantage of Manufacturing Firms in South-East, Nigeria (Published)
Manufacturing firms in the South-East Nigeria operate in an increasingly turbulent environment characterized by intense competition, technological change, rising production costs, exchange-rate volatility, infrastructure deficiencies, changing consumer preferences and pressure to improve product quality and market responsiveness. In such an environment, possession of physical resources alone may not guarantee superior competitive performance; firms require managerial capabilities that enable them to formulate appropriate strategies, allocate resources effectively, respond to environmental changes, innovate and sustain valuable organizational competencies. The study examined the relationship between strategic management capabilities and competitive advantage of manufacturing firms in South-East Nigeria. The study was anchored principally on the Resource-Based View (RBV) and Dynamic Capabilities Theory. A descriptive cross-sectional survey design was adopted. The study population comprises 184 registered manufacturing firms in South-East Nigeria and the target respondents were managers, directors/CEOs, and other managerial personnel of selected manufacturing firms across the five states of South-East Nigeria: Abia, Anambra, Ebonyi, Enugu and Imo states. The sample size of 126 firms was determined using the Taro Yamane formula at a 5% level of precision. Proportionate sampling was subsequently employed to allocate the sample across the five states. A structured questionnaire using a five-point Likert scale was used for primary data collection. Descriptive statistics such as frequency tables and percentage, and multiple regression were employed to test the hypotheses at the 5% level of significance. The findings of the study indicated that strategic planning capability (t-value 3.135), environmental scanning capability (t-value 3.335), strategic decision-making capability (t-value 3.235) and strategic implementation capability (t-value3.241) have positive and significant effect on competitive advantage. However, environmental scanning has the strongest explanatory power over other strategic management capabilities because it enables manufacturing firms to know what is changing, why it is changing and how they should respond before competitors do, thereby providing the information and adaptability necessary for superior competitive advantage. The relatively stronger effect of environmental scanning was also explained by its enabling role in the other strategic management capabilities. Effective strategic planning and strategic decision-making require accurate and timely environmental information, while strategic implementation requires the organization to execute strategies that remain relevant to prevailing environmental conditions. The study recommended that manufacturing firms should develop stronger strategic intelligence, implementation discipline, innovation capability, managerial learning systems and continuous environmental scanning.
Keywords: Competitive Advantage, Environmental Scanning, Manufacturing Firms, South East Nigeria, Strategic management capabilities, strategic decision-making, strategic planning, strategy implementation
Leveraging AI for Strategic Decision-Making in Biopharmaceutical Program Management: A Framework for Risk and Opportunity Analysis (Published)
Strategic planning remains essential for the biopharmaceutical industry because it runs programs through its highly complex regulatory structures based on extensive data. Drug development together with clinical trials and regulatory procedures contain various uncertainties that demand predictive methods capable of handling changing risks alongside emerging prospects. The emergence of Artificial Intelligence (AI) brought revolutionary changes to data analysis and outcome forecasting together with operational optimization improvements to organizations. The research develops an organized system for implementing AI-based methods in biopharmaceutical program management to boost decision-making accuracy while improving operational efficiency and speed. Real-time insights emerge from machine learning and natural language processing systems combined with advanced analytics data methods that assist biopharmaceutical operating entities to assess risks, identify opportunities and enhance their predictive capabilities. The utilization of AI enables organizations to discover new opportunities in addition to minimizing their risks. AI systems use predictive algorithms to mine data from patents and clinical trials and scientific publications which helps identify new therapeutic opportunities and unmet market requirements and potential business partnerships. Organizations gain strategic direction for portfolio management through these insights which allows them to select high-potential programs while they adjust rapidly to changing market needs.AI delivers significant value to clinical trial optimization as a critical healthcare application. The execution of clinical trials extends for long periods of time and requires large financial investments because recruitment problems combine with deviations from study protocols alongside management difficulties. AI systems utilize predictive models to determine candidate enrollment prospects in addition to suggesting ideal research sites and customized trial parameters matching experimental designs to treatment requirements through examination of healthcare datasets along with previous trial measurement records. NLP technology enables more efficient clinical trial design by helping with medical record screening as well as literature review tasks.The monitoring of regulatory agency updates and global approval patterns and jurisdictional policy shifts through AI helps development of regulatory strategies. The ongoing analysis enables businesses to modify their regulatory submission approaches and pathways so theymatch emerging regulatory requirements and expectations. The proposed framework starts AI adoption through specific use cases which grows alongside developing AI capabilities. The successful implementation depends heavily on data scientists working together with clinicians and regulatory experts and program managers.
Keywords: biopharmaceutical program management, framework for risk and opportunity analysis, leveraging AI, strategic decision-making