1. TEMPLATE DESIGN © 2008 www.PosterPresentations.com Cross-Dock Modeling and Analysis AUTHOR: Mehdi Charfi ADVISORS: Dr. Peter Hahn and Dr. Monique Guignard Electrical & Systems Engineering, Operations & Information Management, University of Pennsylvania, Philadelphia, PA, USA ABSTRACT Cross-docking is a consolidation practice in logistics that facilitates the transfer and sorting of products from suppliers to distribution centers, eliminating warehouse holding, minimizing costs, and allowing for the realization of more efficient deliveries. There are several logistic and integration problems inherent in the cross- docking process, including scheduling uncertainty, inefficient staffing and record keeping, and limited integration of statistical data. The previous phases of this project have addressed the design of a computer-based discrete event simulation model (Intelligent Transportation System) of a freight cross-dock at the National Retail Systems (NRS) cross-docking facility in Bergen County, NJ. In this phase of the project, the discrete event simulation model will be exercised with actual or close-to-actual data in the form of Microsoft Excel datasheets that describe originating truck arriving volumes, carton quantities, destination volumes, and carton sizes. Upon creating a realistic model of the cross- docking facility, the model will be analyzed to determine suitability and rationality. With this foundation in place, simulation and optimization models can assist NRS in realizing optimization benefits in areas including door assignment, expansion, and productivity. OBJECTIVES BACKGROUND INFORMATION Cross-docking is a logistics technique used in the retail and trucking industries to rapidly consolidate shipments from disparate sources and realize economies of scale in outbound transportation. Cross-docking essentially eliminates the costly inventory-holding functions of a warehouse, while still allowing it to serve its consolidation and shipping functions. As displayed in the figure above, the idea is to transfer shipments directly from incoming to outgoing truck trailers, without in between storage. Goods typically spend less than 24 hours in a cross-dock, sometimes less than an hour. With the process of moving shipments from the receiving dock (strip door) to the shipping dock (stack door), bypassing storage, cross-docking reduces inventory carrying cost, transportation cost, and other costs associated with material handling. DISCUSSION The most noteworthy obstacle faced during this project was the insufficient amount of data received to aid in the modeling process. Through in-depth research on the cross-docking technique, along with the use of statistical theories, a single day’s worth of data was used to create sample data sets replicating one day of operation. Ideally, numerous data sets should have been received so that the model could be more realistic and accurate to the cross-dock facility’s performance. Several theories were discussed until our methods became reasonable. However, further work can certainly be done to improve the current model pending the collection of more data from NRS. METHODS The goal of this project was to develop and format a model of the cross-docking facility to be used as input data for a simulation which was created using ExtendSim (simulation software). The model is to be randomized so that it can produce sample days of operations. Given the incoming and outgoing item data from one day of operations at NRS (Figure 2), a statistical model was developed through assumptions, intuition, and statistical techniques. In an ideal situation, NRS would have provided enough data (i.e. several weeks of data) to create sample data sets with more accurate means and standard deviations. However, it can be expensive for NRS to gather such data. Our aim was to create a model that makes the most sense in the absence of data. FUTURE WORK The developed model can now be used as input data for the ExtendSim simulation. Further communication with NRS is crucial to improving the current model to develop a more realistic model. Once the model is synchronized with the simulation through computer programming techniques, the final product will allow NRS to realize optimization benefits in areas including door assignment, expansion, and productivity. RESULTS CONTACT INFORMATION Due to insufficient data resulting from an inconsistency in communication from NRS, the data from one day of operations has served as the mean for the desired sample data. Furthermore, the standard deviations have been calculated from the data to assist in creating new sample data sets. Using Microsoft Excel and several statistical assumptions, a model was formed to randomize a day’s worth of input data. To validate the model, 200 new columns were generated to replicate the Incoming Carton Quantity. The average of each row was taken and compared to the original data (Figure 3). The similarities validate the methods used in creating the model in accordance with the assumptions made throughout the process. Mehdi Charfi University of Pennsylvania | Class of 2013 Candidate, B.S.E. Systems Science Engineering School of Engineering and Applied Science mcharfi@seas.upenn.edumcharfi@seas.upenn.edu | (571) 331-0880 Researching the cross-dock system Gathering realistic data Formatting realistic data to the inputs required by ExtendSim simulation software Applying statistical techniques to ensure that simulation results and conclusions are adequately verified Figure 2. One Day Volume of Receipted and Shipped Goods Figure 1. Cross-docking Diagram Figure 3. Validation of the Model