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.