Global logistics aim to optimize and control the material/product flow and the information flow so that materials/products can be moved at a desired pace, in a proper fashion and at the right volume. The production processes in the medical equipment manufacturing industries are highly automated and dynamic and the orders are customized. This study develops an intelligent global logistics system for scheduling and planning of a production system to minimize logistics cost. The optimization technique for production scheduling in the supply chains of medical equipment shows how an intelligent logistics system can effectively solve real world problem. An intelligent modeling environment is developed to know the real supply chains dynamic scenario in medical equipment manufacturing which proposes a novel environment for intelligent operational production planning. The proposed system is verified with real world case study. The research provides an effort to optimize the logistics performance of one of GE Medical System's (GEMS) key products, X-Ray Scanner. The product has three models that are made in three different locations. GEMS does not manufacture all the parts needed for assembly at one location. This means that some of the parts are procured from other locations and these are then assembled at a particular facility, tested for quality and sent to the customer i.e. the hospital. When an order is received for a specific model, GEMS has to identify the best location for its assembly and manufacture of an X-Ray model to maintain high customer service level and at the same time minimize the overall logistics cost. The following objectives are identified to solve GEMS's logistics problems: • To build an LP optimization model using Arena OptQuest for minimizing the overall logistics cost • Identify the uncertainty of factors that affect the overall logistics cost using Design of Experiments • Propose Best Practices in the industry for global logistics Due to the constraints on the level of data that was available for the limited duration of the project, the team focused on project formulation as an important step. As a result, even though the optimal solution will not be the best solution, GEMS can use the factors considered in the formulation steps to improve the optimal solution and further minimize its logistics cost in the future. A Gantry, Console, Table and Accessories form one X-Ray Scanner. There are three different models that can be assembled from this product mix. The tables and accessories are the same for any kind of model with variations in Gantries and Consoles. These parts are made in four locations. A gantry and console of similar types are made in the same manufacturing location. In order to identify the various factors that influence the overall logistics cost, the team built a cause and effect diagram. The six possible causes are identified that have a direct influence on the overall logistics cost. They are Capacity, Lead time, Inventory Cost, Manufacturing Cost, Forecast and Shipping Cost. Based on the determination of important factors and the availability of data, the model has been developed. The logistics and supply chain networks are Develop for their distribution center and plants and optimize their logistics networks using LINDO. The objective is to minimize total logistics cost for a specific product type, given the capacity constraints in the specific plants. DOE model is used to identify the effect of each factor on the response variable, i.e. the total logistics cost. After an extensive brainstorming session, we identified five key factors (Interest Rate, Traffic Volume, Handling Cost, Distribution, Cost, Labor Cost) influencing the overall logistics cost. We assigned weights to each factors and carried out a 1/2 factorial design of experiments. The results of the DOE indicate that the effect of labor cost, distribution cost and the handling cost have a significant impact on the overall logistics cost. From the results, we observe that labor cost has the greatest impact on the optimal cost. In the future, these factors need to be taken care of. The results from the optimization model and DOE study have to be approached with caution bearing in mind that the study was done only with the available data on hand. But since our project formulation phase has been pretty intensive, GEMS can use the methodology to improve its logistics performance.
|حالة النشر||Published - 2004|
|الحدث||IIE Annual Conference and Exhibition 2004 - Houston, TX, United States|
المدة: مايو ١٥ ٢٠٠٤ → مايو ١٩ ٢٠٠٤
|Other||IIE Annual Conference and Exhibition 2004|
|المدة||٥/١٥/٠٤ → ٥/١٩/٠٤|
ASJC Scopus subject areas