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In today's competitive business environment, customers require dependable on-time delivery from their suppliers. Delivery lead time is defined to be the elapsed time from the receipt of an order by the supplier to the receipt of the product ordered by the customer. Delivery lead time is composed of a series of internal (manufacturing and processing) lead times and external (distribution and transportation) lead times found at various stages of the supply chain. Early and late deliveries introduce waste in the form of excess cost into the supply chain; early deliveries contribute to excess inventory holding costs, whereas late deliveries may contribute to production stoppages costs and loss of goodwill. To protect against untimely deliveries, supply chain managers often inflate in process inventory levels and production flow buffers.
Recent empirical research has identified delivery performance as a key management concern among supply chain practitioners (see Lockamy and McCormack (2004), Vachon and Klassen (2002), Verma and Pullman (1998)). A conceptual framework for defining delivery performance in supply chain management is found in Gunasekaran et al. (2001). Delivery performance is classified as a strategic level supply chain performance measure. Delivery reliability is viewed as a tactical level supply chain performance measure. The framework advocates that to be effective supply chain management tools, delivery performance and delivery reliability need to be measured in financial (as well as non-financial) terms.
Failure to quantify delivery performance in financial terms presents both short-term and long-term difficulties. In the short term, the buyer-supplier relationship may be negatively impacted. According to New and Sweeney (1984) a norm value of presumed performance is established by default when delivery performance is not formally measured. This norm stays constant with time and is generally higher than the organization's actual delivery performance.
It has been demonstrated that supplier evaluation systems have a positive impact on the buyer-supplier relationship, and buyer-supplier relationships ultimately have a positive impact on financial performance (Carr and Pearson, 1999). In the long term, failure to measure supplier delivery performance in financial terms may impede the capital budgeting process, which is necessary in order to support the improvement of supplier operations within a supply chain.
In this research we develop a cost-based performance metric for evaluating delivery performance and reliability to the final customer in a two-stage supply chain that is operating under a centralized management structure. Contemporary management theories advocate the reduction of variance as a key step in improving the performance of a system (see, for example, Blackhurst et al. (2004), Hopp and Spearman (1996), Lee et al. (1997), Sabri and Beamon (2000)).
In union with these prevailing theories, delivery performance is modeled as a cost-based function of the delivery variance. The financial benefit of reducing variability in delivery performance is demonstrated within the context of a continuous improvement program. We also quantify the effects of managerial neglect. Managerial neglect is defined as the opportunity cost of management neglecting to improve delivery performance through the reduction of delivery variability.
This article is organized as follows: First, an analytical model based on the expected costs associated with untimely delivery is developed. Next, improvement in delivery performance is modeled using a learning-based model for reducing the delivery variance. Net present value theory is used to incorporate the time value of money into the model framework to provide a guideline for the amount of investment required to improve delivery performance. Then, the economic consequence of failing to improve delivery performance through the reduction of delivery variance is studied. In the concluding section, we summarize the findings of this research and present directions for future research.
Delivery windows are an effective tool for modeling the expected costs associated with untimely delivery. Several researchers advocate the use of delivery windows in time-based manufacturing systems (see, for example, Jaruphongsa et al. (2004), Lee et al. (2001), Fawcett and Birou (1993), Corbett (1992)). Metrics based on delivery (order) windows capture the most …