Mehmood Khan, Doctoral Student, Ryerson University, Toronto, Ontario, Canada
Mohamad Y. Jaber, Associate Professor, Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada

In this paper, a two level supply chain situation is considered. A manufacturer/vendor is assumed to receive raw material parts from a number of suppliers for assembling a single product. The parts obtained from the supplier are not of a perfect quality. That is, each supplier may have an approximately fixed percentage of defectives in each lot supplied. An inspection process is carried out at the vendor’s end to take out these imperfect parts while manufacturing the product. These screened parts are separated as a single lot in each production cycle (Salameh and Jaber, 2000). A different percentage of defectives from each supplier gives rise to some unused parts left with the vendor in each cycle. These parts are utilized in the next cycle.

Different coordination mechanisms (Khouja, 2003) for controlling the supply chain inventory are studied. A cost minimization model would be given for each. Numerical examples will be given to compare the coordination mechanisms.

The screening process is believed to result in the perfect quality products manufactured by the vendor. Empirical results revealed that the percentage of defective items per lot decreases with cumulative number of shipments conforming to a learning curve. Therefore, this paper also investigates the models developed herein by assuming the percentage of defective items per shipment reduces according to a learning curve.

Guoqing Zhang, Associate Professor, Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, Ontario, Canada
Sanjida Rouf, Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, Ontario, Canada

We consider a supply planning problem for a closed-loop supply chain that consists of a manufacturer, its external suppliers, and a remanufacturing facility. The manufacturer, facing an uncertain market demand and return, has two options for supplying parts: either ordering the required parts to external suppliers or remanufacturing returned products and bringing those back to ‘as new’ conditions. We propose a general framework for this multi product, closed loop system and develop a non-linear programming (NLP) model for the production planning problem to maximize the total expected profit. We develop an iterative solution approach based on Kuhn-Tucker conditions. The solution not only provides optimal production level, but also decides the quantity of parts to be remanufactured and quantity of parts to be purchased from external supplier. A numerical example is presented to show the effectiveness of the modeling and solution approach. We also conduct sensitivity analysis to illustrate the interacting effects among critical parameters in the model and management insights.