مدل مقدار تولید اقتصادی برای اقلام با موضوع کیفیت ناقص به منظور یادگیری اثرات

Salameh and Jaber [2000. International Journal of Production Economics 64, 59–64] developed an inventory situation where items received are not of perfect quality (defective), and after 100% screening, imperfect quality items are withdrawn from inventory and sold at a discounted price. This paper extends the work of Salameh and Jaber by assuming the percentage defective per lot reduces according to a learning curve, which was empirically validated by data from the automotive industry. Mathematical models are developed with numerical examples provided and discussed. The developed model was compared with the model of Salameh and Jaber to emphasize the importance of learning.

The economic order quantity (EOQ) is the first and the simplest model available in the inventory literature. It has been the cornerstone for numerous models with a reasonably good survey provided in Silver et al. (1998). The popularity of the EOQ model is perhaps attributed to its simple mathematics, which allows managers to compute their order quantities using a business calculator. The simplicity of the mathematics is the result of the assumptions in developing the EOQ model, which are viewed by many as being unrealistic (e.g., Jaber et al., 2004). One of these assumptions is that shipments of raw materials or components received by a buyer conform to quality specification and therefore contains no defects.

This paper extended the model of Salameh and Jaber (2000) by assuming that the percentage defective in a shipment reduces in conformance with a learning curve. This behavior was observed in practice with data collected from the automotive industry. The logistic learning curve was found to fit the data quit well. The power form learning curve (Wright, 1936) fit the data the worst. Although, the learning curve of Wright (1936) has been widely used and accepted by many, it cannot be viewed as a universal learning curve. However, by separating the data into two sets (incipient and learning and maturity as described in Fig. 1), the power form learning curve fitted the data for the learning and maturity phases well.