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<p>Discrepancies between enterprise resource planning (ERP)-denoted production capacities and actual performance remain a persistent challenge in manufacturing, particularly for low-runner products and manual production lines. Despite ERP systems’ crucial role in production planning and control (PPC), in practice, the accuracy of their data is often challenged by dynamic shop-floor realities, infrequent updates, and limited feedback mechanisms. Previous studies have largely focused on theoretical models or simulations, while empirical studies on low-runner production and the systemic consequences of ERP inaccuracies across departments are sparse. This study examines two manufacturing case studies—pharmaceutical and chemical sectors—to quantify ERP inaccuracies, identify root causes, and develop a theoretical model illustrating how inefficiencies propagate across the value chain. The findings reveal that insufficient production data, human-dependent updates, and manual corrections create a self-reinforcing negative feedback loop that undermines planning accuracy, operational efficiency, and digital transformation initiatives. This cycle prompts departments to rely on non-ERP sources, further fragmenting data management and exacerbating resource misallocation, scheduling delays, and interdepartmental misalignments. Greater automation and larger batch sizes improve ERP alignment, while sporadic production and manual processes worsen discrepancies. The proposed model highlights the need for real-time feedback loops, predictive analytics, and IoT-enabled solutions to enhance ERP responsiveness, supporting the transition toward intelligent, adaptive PPC systems in Industry 4.0. Through these findings, the study enriches current knowledge of the systemic inefficiencies characterising traditional ERP system setups and demonstrates the value of dynamic PPC systems enabled by Industry 4.0 technologies.</p>

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Unravelling the negative spirals from ERP inaccuracies in production planning: a theoretical model and insights from case studies

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Breno Renato Strüssmann,
  • Mads Andersson,
  • Lars Hvam,
  • Anders Haug

摘要

Discrepancies between enterprise resource planning (ERP)-denoted production capacities and actual performance remain a persistent challenge in manufacturing, particularly for low-runner products and manual production lines. Despite ERP systems’ crucial role in production planning and control (PPC), in practice, the accuracy of their data is often challenged by dynamic shop-floor realities, infrequent updates, and limited feedback mechanisms. Previous studies have largely focused on theoretical models or simulations, while empirical studies on low-runner production and the systemic consequences of ERP inaccuracies across departments are sparse. This study examines two manufacturing case studies—pharmaceutical and chemical sectors—to quantify ERP inaccuracies, identify root causes, and develop a theoretical model illustrating how inefficiencies propagate across the value chain. The findings reveal that insufficient production data, human-dependent updates, and manual corrections create a self-reinforcing negative feedback loop that undermines planning accuracy, operational efficiency, and digital transformation initiatives. This cycle prompts departments to rely on non-ERP sources, further fragmenting data management and exacerbating resource misallocation, scheduling delays, and interdepartmental misalignments. Greater automation and larger batch sizes improve ERP alignment, while sporadic production and manual processes worsen discrepancies. The proposed model highlights the need for real-time feedback loops, predictive analytics, and IoT-enabled solutions to enhance ERP responsiveness, supporting the transition toward intelligent, adaptive PPC systems in Industry 4.0. Through these findings, the study enriches current knowledge of the systemic inefficiencies characterising traditional ERP system setups and demonstrates the value of dynamic PPC systems enabled by Industry 4.0 technologies.