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Exploring AI Integration in SME Production Planning: Design Spaces and the Role of Workers

Marco Fries et al.

Computer Supported Cooperative Work2025https://doi.org/10.1007/s10606-025-09512-6article
AJG 2ABDC B
Weight
0.44

Abstract

The pressure to improve products, services, and processes to remain competitive in the global market has fueled demand for affordable, high-quality, customized products with excellent availability and customer service. Production Planning and Scheduling (PPS) considers a wide range of internal and external factors in its attempts to align production with market demand. Advanced Planning and Scheduling (APS) systems have emerged to support PPS but suffer from issues such as deterministic views and practical uncertainties. Expectations are high that Artificial Intelligence (AI) and Machine Learning (ML) will support complex production planning tasks by analyzing operational data to generate optimal plans. However, there is currently little empirical research on production planning practices and what role AI might play. Our paper highlights current challenges in production planning practices and outlines design spaces for using AI and ML to support these practices. Based on an empirical study of three German small and medium-sized enterprises (SMEs) in the metal processing industry, we uncover how AI might estimate processing time and rework probability and thus we outline current design spaces for AI in production planning.

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https://doi.org/https://doi.org/10.1007/s10606-025-09512-6

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@article{marco2025,
  title        = {{Exploring AI Integration in SME Production Planning: Design Spaces and the Role of Workers}},
  author       = {Marco Fries et al.},
  journal      = {Computer Supported Cooperative Work},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10606-025-09512-6},
}

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.