A Dynamic inventory optimization method applied to printer fleet management

Current optimization methods for inventory management of toner cartridges for printer fleets typically focus on aggregate cartridge demand. However, with the development of printer technology, toner consumption algorithms are being developed which can accurately quantify the amount of toner that has been consumed over time, based on print job characteristics. This research introduces a dynamic inventory optimization approach for a fleet of printers over a rolling time horizon. Given, the consumption algorithm for the printer system, the cumulative toner consumed per cartridge per printer can be tracked. A forecasting method is developed which utilizes this toner consumption data for individual printers to forecast toner cartridge replacement times. Taking into account the uncertainty related to demand, demand forecast and lead time, an optimization model has been developed to determine the order placement times and order quantities to minimize the total cost subject to a specified service level. An experimental performance evaluation has been conducted on the parameters of the dynamic inventory management algorithm. Based on the results of this evaluation, the implementation of this dynamic inventory optimization methodology could have a positive impact on printer fleet management

Dynamic warehouse optimization using predictive analytics.

The forward area is a small area of a warehouse with a low picking cost. Two approaches that are investigated for selecting the SKUs of this area and the allocated space are the static and the dynamic approaches. In the case that decisions about the forward area are made periodically (e.g. yearly) and the products\u27 demand patterns are completely ignored, the FRP is static. We developed two heuristics that solve the large discrete assignment, allocation, and sizing problem simultaneously. Replenishing the same product in the same place of the forward area brings about a ``Locked layout of the fast picking area during the planning horizon. By using a dynamic slotting approach, the product pick locations within the warehouse are allowed to change and pick operations can accommodate the variability in the product demand pattern. A dynamic approach can introduce the latest fast movers to the forward area, as an opportunity arises. The primary objective of this dissertation is to formally define the dynamic FRP. One main mission of this research is to define a generic dynamic slotting problem while also demonstrating the strengths of this approach over the static model. Dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics. Applying different order data with different demand volatility, we show that the dynamic model always outperforms the static model. The benefits attained from the dynamic model over the static model are greater for more volatile warehouses