This paper introduces DocPlan, a vision-language training dataset designed to emulate the stepwise reasoning strategies employed by human during visual document understanding. DocPlan was constructed to enable vision-language models (VLMs) to effectively learn the structured reasoning process that humans typically follow when interpreting documents. We reorganized existing document understanding datasets into 133K examples annotated with the following four sequential stages: Recognition → Planning → Extraction and Reasoning → Conclusion. To evaluate the effectiveness of DocPlan, we fine-tuned the direct prediction-based VLMs to perform stepwise reasoning generation across these stages. The experimental results demonstrated that our approach achieved up to a 34.4% improvement in accuracy on document-based benchmarks.