Its unexcelled mechanical and physical properties, in addition to its biocompatibility, have made stainless steel 304 a prime candidate for a wide range of applications. Among different manufacturing techniques, electrical discharge machining (EDM) has shown high potential in processing stainless steel 304 in a controllable manner. This paper reports the results of an experimental investigation into the effect of the process parameters on the obtainable surface roughness and material removal rate of stainless steel 304, when slotted using wire EDM. A full factorial design of the experiment was followed when conducting experimental trials in which the effects of the different levels of the five process parameters; applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity were investigated. The geometry of the cut slots was characterized using the MATLAB image processing toolbox to detect the edge and precise width of the cut slot along its entire length to determine the material removal rate. In addition, the surface roughness of the side walls of the slots were characterized, and the roughness average was evaluated for the range of the process parameters being examined. The effect of the five process parameters on both responses were studied, and the results revealed that the material removal rate is significantly influenced by feed (p-value = 9.72 × 10−29), followed by current tension (p-value = 6.02 × 10−7), and voltage (p-value = 3.77 × 10−5), while the most significant parameters affecting the surface roughness are current tension (p-value = 1.89 × 10−7), followed by pulse-on time (1.602 × 10−5), and pulse-off time (0.0204). The developed regression models and associated prediction plots offer a reliable tool to predict the effect of the process parameters, and thus enable the optimizing of their effects on both responses; surface roughness and material removal rate. The results also reveal the trade-off between the effect of significant process parameters on the material removal rate and surface roughness. This points out the need for a robust multi-objective optimization technique to identify the process window for obtaining high quality surfaces while keeping the material removal rate as high as possible.

In a global economy, competitive and dynamic environment, Supply Chain Management (SCM) is a key strategic factor for increasing organizational effectiveness. Many organizations have not succeeded in maximizing their supply chain's potential because they have not developed the required performance evaluation metrics and measures needed to integrate their supply chain to maximize effectiveness and efficiency. This search is directed to develop a framework capable of evaluating the organization performance and introduce efficient performance measurement system enabling organization management to control, monitor, and improve the organization performance in order to achieve their strategic goals. Only when the requirements and constraints of the market place are understood an enterprise can attempt to develop a strategy that will meet the needs of both the business success and the end customer.