“It’s very attractive to study for several reasons. First, speed is a well-established competitive priority in the supply chain literature and most companies want to impress customers with speed.”
Dr. Thomas Ngniatedema, faculty member in the Kettering University Department of Business, partnered with researchers from Kent State and West Liberty University to publish an article that models the improvement of the delivery process in supply chains.
The paper, entitled “A Modeling Framework for Improving Supply Chain Delivery Performance,” will be published in the spring 2015 issue of International Journal of Business Performance and Supply Chain Modeling.
“The delivery process is a crucial component of the overall management and control of supply chains,” Ngniatedema said. “It is one of the five supply chain processes (plan, source, make, deliver, and return) found in the Supply Chain Operations Reference-model (SCOR).”
Ngniatedema chose to focus on delivery because of its utmost importance to customer satisfaction.
“It’s very attractive to study for several reasons. First, speed is a well-established competitive priority in the supply chain literature and most companies want to impress customers with speed,” Ngniatedema said. “Second, the supplier’s conformance to a delivery schedule is an issue of top concern to the practitioner because it has a direct impact on customer satisfaction.”
As both early and late deliveries are disruptive to supply chains, Ngniatedema uses these delivery deviations to analyze a supplier’s conformance to a schedule using three types of deliveries: 1) early; 2) on-time; and 3) late. From these scenarios, the research team formulated a model to determine the financial investment required to improve supply chain delivery performance.
For example, if a product arrives early, it may contribute to excess inventory holding costs. However, if the product arrives late, it may contribute to production stoppage costs and loss of goodwill.
“Our model is very attractive because it can be used to financially quantify the benefits of reducing the variance of delivery time,” Ngniatedema said. “Delivery time is a random variable but we can model how to invest on variance reduction in the delivery process within integrated supply chains.”
The current model only applies to domestic supply chains and not ones that cross international borders. Ngniatedema and his research team are expanding this model to global supply chains.
“Our model is capable of guiding supply chain managers to assign investment to delivery performance in order to achieve improvements to overall supply chain performance,” Ngniatedema said.