Abstract
In an inventory system, the lead time is the time between the moment when a decision is made about the quantity to order (or produce) and the moment when this amount is available to satisfy demand. This lead time has an impact on inventory (holding and shortage) costs as well as on the purchase or production costs. Usually, shorter lead times result in lower inventory costs together with higher purchase or production costs. The lower inventory costs are caused by a reduced exposure to demand uncertainty, while the higher purchase or production costs are due to expediting the production and/or delivery process which typically comes with additional expenses.
In this paper, we consider a periodic review dynamic order-up-to inventory system in which shortages are backordered. Such a dynamic system is appropriate when demand is non-stationary and demand forecast distributions are a crucial input to the inventory system. We propose a framework to quantify the effect of reducing (or extending) the lead time on the exposure to forecast uncertainty in terms of a justified increase (or required decrease) in unit purchase or production cost. This difference in unit purchase or production cost quantifies the offset to compensate for the change in exposure to forecast uncertainty, and hence the change in inventory costs. The framework is based on the work of de Treville et al. (2014) which deals with valuing lead time in the single-period newsvendor problem.
We apply the framework to a case study and illustrate its use as decision support tool for deciding what lead time to use. In the case study, sales history and open order data are available to make demand forecasts. As more and more orders for a specific period are collected when approaching this period, naturally, the open order data strongly affects the evolution of forecast uncertainty in lead time, making the framework of particular interest.
In this paper, we consider a periodic review dynamic order-up-to inventory system in which shortages are backordered. Such a dynamic system is appropriate when demand is non-stationary and demand forecast distributions are a crucial input to the inventory system. We propose a framework to quantify the effect of reducing (or extending) the lead time on the exposure to forecast uncertainty in terms of a justified increase (or required decrease) in unit purchase or production cost. This difference in unit purchase or production cost quantifies the offset to compensate for the change in exposure to forecast uncertainty, and hence the change in inventory costs. The framework is based on the work of de Treville et al. (2014) which deals with valuing lead time in the single-period newsvendor problem.
We apply the framework to a case study and illustrate its use as decision support tool for deciding what lead time to use. In the case study, sales history and open order data are available to make demand forecasts. As more and more orders for a specific period are collected when approaching this period, naturally, the open order data strongly affects the evolution of forecast uncertainty in lead time, making the framework of particular interest.
Original language | English |
---|---|
Title of host publication | International Symposium on Forecasting 2020 |
Publisher | International Institute of Forecasters |
Number of pages | 1 |
Publication status | Published - 28 Oct 2020 |
Event | International Symposium on Forecasting - Virtual Duration: 26 Oct 2020 → 28 Oct 2020 Conference number: 40 https://isf.forecasters.org/overview/about/locations/ |
Conference
Conference | International Symposium on Forecasting |
---|---|
Abbreviated title | ISF |
Period | 26/10/20 → 28/10/20 |
Internet address |