Improving operations planning with learning curves: overcoming the pitfalls of ‘messy’ shop floor data
研究了在详细零部件生产层面使用学习曲线分析时，如何通过数据聚合方法提高对学习率估计的准确性，从而增强决策者信心，对运营管理者和数据分析师有参考价值。
Abstract While most of the previous research on learning and experience curves examines cost improvements at the product level, we investigate the use of learning curve analysis at the detailed component part production level. Using extensive shop floor data from a medium‐sized commercial firm, we discovered that the ‘messy’ data (i.e. high level of data variance) at the detailed levels often lead to reduced decision maker confidence in the estimates of the learning rates. However, we also found that by applying simple aggregation methods, we could better determine the accuracy of the predicted learning curve rates. Increased confidence in the learning curve estimates is made possible by comparison of regression estimates made at the detailed data level to those made at various aggregated data levels. Based upon our analysis of the empirical data, we are able to provide insights into the practical use of learning curve analysis and associated data aggregation with ‘messy’ shop floor data.