114 research outputs found

    Motivators And Inhibitors For Business Analytics Adoption From The Cross-Cultural Perspectives: A Data Mining Approach

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    In the increasingly knowledge-based world economy, the multinational firm\u27s success often hinges on its business intelligence capability nurtured by business analytics (BA). Despite the growing recognition of BA\u27s role in enhancing the firm\u27s intellectual capital and subsequent competitiveness, it is still unknown what truly motivates and inhibits BA adoption. This study aims to identify key influencing factors for BA adoption such as organizational characteristics, information security/privacy, and information technology maturity (knowledge level). In so doing, this study employed data mining and data visualization techniques to develop specific patterns of BA adoption practices based on a combined sample of 224 Korean firms and 106 U.S. firms representing various industry sectors. This study is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the cross-national study of BA practices among both Korean and U.S. firms

    Smart Warehousing as a Wave of the Future

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    Background: The unprecedented supply chain disruptions caused by the prolonged COVID-19 pandemic forced many firms to change their way of doing business dramatically. These changes include quickly responding to the growing demand for online orders and the corresponding direct shipments to customer locations. These changes have been further accelerated by rapid technological innovations resulting from the fourth industrial revolution (Industry 4.0). One of the most notable technological transformations that we have witnessed is the growing popularity of smart warehousing concepts. Although smart warehousing may represent a wave of the warehousing future, the published literature rarely documents its underlying principles, specific application targets, and potential impacts on supply chain performance. This research aims to identify key drivers of the digital warehousing revolution and describe important value propositions for warehousing automation. Methods: To help companies develop smart warehouses successfully as an integral part of a supply chain link, I conceptualize an ideal smart warehousing system, design its basic architecture, propose specific milestones for monitoring the progress of smart warehouse development, and then, identify critical success factors for its full utilization in today’s volatile warehousing environment. This paper employed qualitative content analysis to conceptualize smart warehousing development and establish a smart warehousing framework. Results: A smart warehouse will bring many managerial benefits, including warehousing cost efficiency, labor productivity, and agility in the era of the knowledge economy. Conclusions: This paper will enable companies to accelerate digital transformation and improve their competitiveness amid the post-pandemic industrial revolution

    Examining the Impact of Energy Price Volatility on Commodity Prices from Energy Supply Chain Perspectives

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    Oil has historically been the most significant primary energy source for our daily lives and business activities. However, recent skyrocketing oil prices have been one of the greatest concerns among policymakers, business executives, and the general public due to their impacts on daily necessities, including food, clothing, and automobile transportation. As a result, fast-rising inflation on the global scale is attributed to mounting oil prices. Even though many countries have made a conscious effort to tame oil prices and the subsequent inflation, their efforts are often in vain due to some uncontrollable situations. These situations include the ongoing war between Ukraine and Russia, where Russia began weaponizing its oil resources and limiting oil supplies to its neighboring European countries. Faced with the current energy crisis, a growing number of policymakers and business executives have attempted to develop energy-induced risk mitigation strategies. With this in mind, the primary purpose of this paper is to investigate what may have caused oil price hikes and to determine how significantly oil prices influence commodity prices. This paper then proposes ways to mitigate energy-induced supply chain risks by analyzing four decades of secondary data obtained from multiple sources

    Influential Factors in Decision Support Capabilities of a Mobile-Enabled Interactive Analytical Dashboard System

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    With the growing complexity of the decision-making process in business applications, a mobile-enabled analytical dashboard system becomes a popular visualisation tool to harness the power of big data for timely and more informed decisions. To maximise the usefulness of an analytical dashboard system as a decision support tool, this paper identifies a host of factors influencing the decision support capabilities including the user\u27s device type, IT knowledge, daily mobile usage, country of origin, gender, number of computer classes taken, and year in school. The results indicate that a user\u27s device type and computer knowledge significantly influence both the efficiency and effectiveness of an interactive analytical dashboard system to enhance its decision support capabilities. The user\u27s country of origin also influences the effectiveness of the dashboard\u27s decision support capabilities

    A COMPARATIVE ANALYSIS OF DISCRETE AND BATCH PICKING AND THE IDENTIFICATION OF FACTORS INFLUENCING ORDER PICKING EFFICIENCY

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    In a typical warehouse environment, order picking represents one of the highest prioritized activities due to its impact on warehousing productivity, operating costs, and order fulfillment.  Order picking generally involves determining a sequence of visitations with inventory locations where the ordered items are stored and then retrieved with the assurance of correct product specifications and quantity according to the customer order.  In the era of e-commerce, order picking has become highly labor-intensive and contributed to warehouse productivity declines due to the increased piece-by-piece picks. To mitigate the adverse impact of inefficient, unorganized order picking, we employed the alternative batch picking method and then compared its efficiency to that of a conventional discrete order picking method. This paper validated the usefulness of the proposed method by its application to order picking problems encountering the Korean processed food manufacturer. Based on a series of simulation experiments with actual data, we verified the comparative efficiency of batch picking and identified a couple of key factors affecting order picking efficiency

    Examining the Role of Disruptive Innovation in Renewable Energy Businesses from a Cross National Perspective

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    With a growing demand for safe, clean, and affordable energy, countries across the world are now seeking to create and rapidly develop renewable energy (RE) businesses. The success of these businesses often hinges on their ability to translate RE into sustainable value for energy consumers and the multiple stakeholders in the energy industry. Such value includes low production costs due to an abundance of natural resources (e.g., wind, water, sunlight), and public health benefits from reduced environmental pollution. Despite the potential for value creation, many RE businesses have struggled to create affordable energy as abundant as that which is produced by traditional fossil fuels. The rationale being that traditional RE sources emanating from natural resources tend to rely on unpredictable weather conditions. Therefore, to help RE businesses deliver sustainable value, we should leverage disruptive innovation that is less dependent on natural resources. This paper is one of the first attempts to assess the impact of disruptive innovation on RE business performances based on the survey data obtained from multiple countries representing both emerging and developed economies

    Developing the Profiles of Business Analytics Adopters and Non-Adopters using Data Mining Tools

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    Despite the growing popularity of business analytics (BA) in the increasingly knowledge-based economy, many firms are still skeptical about its strategic value and thus hesitant to adopt BA. To have a true sense of which firms are likely to adopt and then utilize the BA for their competitiveness, this paper identifies BA user characteristics in terms of the user\u27s firm size, organizational readiness, financial resources, and information technology expertise/infrastructure. In so doing, this paper conducted a series of cluster and decision tree analyses to develop specific profiles of BA adopters and non-adopters based on a sample of 224 Korean firms representing various industry sectors. This paper is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the empirical study of BA practices among Korean firms

    An Inter-Organizational Decision Support System for Global Supply Chain Management

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