Volume 1 , Issue 1 Page 22
“ Multi-Echelon Inventory Control In Supply Chain Management ” Khawar Naeem
Inventory is the lifeblood in an organization . It costs about forty percent of the total organization ’ s assets . Our project was to optimize the inventory in multi-echelon and to decide a tradeoff between inventory level and service level of the customer demand . We designed the supply chain starting from raw material receiving from supplier up to the finish good delivery to the customer , integrating every activity . The demand was forecasted , produced and filled using various scientific models . An Expert System , AM ( Application Manager ) was used to develop a tool named “ Inventory Control System ” to automate all the addressed activities .
Currently the industry is working on the experience base knowledge of experienced people with being in state of relying on the upper management decision . The thesis provided the recommendation purely based on engineering knowledge . The current situation was critically analyzed and areas of improvement were highlighted . The critical parameters related to Supply Chain were devised . The inventory level at various echelons was reduced which in turn resulted in cost reduction . Further the arrival of raw material was organized using techniques of Economic Order Quantity ( EOQ ), Safety stock ( SS ) and Reorder Point ( ROP ) to have timely production which reduced overall Lead Time . The unmet demand due to the limited capacity of plant was outsourced .
“ Rejection Analysis of PET Bottles Company Using Six Sigma Approach ” Yousuf Wasil
The number of rejects in a PET Bottle Industry had to be minimized by selecting the optimal level of input variables of the process . A total of four input variables were first selected through a brain storming session with the production management and quality cell staff of the industry . These variables were statistically analyzed by changing their levels within operating range , and the change in the number of rejects was respectively observed for each variable . The four variables were Injection Pressure , Melting Temperature , Operator line and Raw material Resin Type . Among these three proved to be statistically significant variables which affected the number of rejects .
The second phase of project was then aimed to select the optimal level of each of the selected significant factors that will give the minimum number of rejects . The optimal levels were obtained through the classical model of Design of Experiment which reduced the average of 11.24 rejects per hour to an average of 3.8 rejects per hour . By implementing these levels a financial impact of 0.8 million rupees will be cut down annually .