Mapping the additive manufacturing supply chain
The additive manufacturing (AM) supply chain is an interconnected set of independent supply networks of goods and services, catering to the demands of its constituents and end consumers of products generated using AM technology. This supply chain includes and is not limited to machine vendors, material manufacturers, software providers, logistics operators, service bureaus and research centers. In this project an interactive google maps is being developed which includes the physical location of the various members of the supply chain are displayed.
Use the map below to view full featured supply chain map. Use to open feature control. Click on map icons for company details.
3-D part nesting for additive processes
The ability to generate optimal (near-optimal) arrangements of parts in an additive manufacturing process build chamber can have a significant impact of the overall cost of production. In this project supported by the Edward P. Fitts Scholars program we are developing novel methods for this 3-D nesting problem that allows interchange of a variety of nesting objectives and constraints for selected additive processes such an electron beam and laser based systems. Given the complexity of the problem and the amount of CPU time that can be dedicated for nesting, we are developing and evaluating meta-heuristics and comparing them to existing approaches described in the literature.
Newsvendor models with restricted replenishment
Seasonal goods’ ordering decisions are often complicated by their inherent short lifecycle. In the classical newsboy problem, there is only one opportunity to order at the beginning of the season. Any unmet demand will incur a penalty cost, while any leftover inventory at the end of the season will be salvaged. One extension of the newsboy problem is to allow a single replenishment sometime during the season in addition to the initial order, for a total of two ordering opportunities. Under this condition, we are developing analytical results for the optimal initial ordering quantity, as well as the optimal replenishment amount. We also considering interesting aspects of the results and developing some intuition of the problem. We are further developing the model to include extensions such as a fixed replenishment ordering cost and holding costs. Furthermore, it is often desirable to weigh the benefits of additional ordering opportunities to the cost of doing so. We are developing an analytical framework and conduct numerical experiments which provide an explicit benefit of additional ordering opportunities. This analysis does not require the determination of a fixed ordering cost, allowing one to weigh the benefits of ordering opportunities with some arbitrary burden (cost/effort/feasibility/desirability) of conducting those orders. Further extensions of the model include the opportunity for markdowns within the season, providing an optimal markdown and ordering policy for a given problem instance. We consider the case where demand in each period throughout the horizon has a known distributional form, however the parameters of these distributions may either be known or unknown. In the unknown parameter case, we present simulation results which show the expected profit under varying conditions of initial forecast error and forecast updating schemes.
Determining the value of product substitution for a stochastic manufacturing/remanufacturing system
We consider an infinite horizon periodic-review inventory control problem for a hybrid manufacturing/remanufacturing system with product substitution where demand and returns are stochastic. In this system, a remanufactured item is considered to have an inferior value from the customer’s viewpoint and thus has a lower selling price than a new item, which leads to a segmented market for manufactured and remanufactured items. The manufacturer considers the use of a one-way product substitution strategy, according to which the demand for remanufactured items is satisfied by new items at the reduced price if the remanufactured item inventory runs out of stock and the new item inventory is positive. The problem is formulated as a discrete-time Markov Decision Process in order to find the optimal inventory policies for both with and without product substitution. The profitability of using the product substitution strategy is investigated through a numerical study based on real data from an automobile parts manufacturer.