Vin Logic Simulation Lessons Learned

Vin Logic Simulation

Lessons Learned and Insights Gained from the VinLogic Simulation Model

The VinLogic simulation was invaluable in gaining insights into how operations management functions within a complex series of scenarios aimed at creating supply chain optimization and modeling function. Throughout this analysis the lessons learned in these areas in addition to transportation management will also be addressed. The VinLogic Model is specifically designed to provide insights into how best to analyze and complete supply chain optimization strategies in complex distribution and logistics systems. The Simulation Dynamics simulation shows how transportation and delivery constraints affect overall supply chain optimization as well (Vinlogic, 2012).

Transportation and Delivery Lessons Learned

Applying constraint-based modeling and optimization techniques to the complex problems of delivery performance, logistics and transportation is possible through the use of the VinLogic Simulation Model. What is particularly useful about this simulation tool is the ability to model both from a workflow and graphical perspective how various permutations and combinations of strategies for optimizing logistics can improve delivery, logistics and transportation performance. The simulations also includes examples that show how quickly the underlying concepts can scale for regional, national and global supply chains. Inherent in the structure of the simulation are also allowances for network-wide or supplier-based production and shipping delays, effects of wide variations in production schedules, and many examples of how supply chain contingencies affect overall logistics network optimization. The model-based approach to transportation and logistics planning that encompass rail, truck and sea vessels also allows for the defining of pricing and timing parameters as well (Vinlogic, 2012). Being able to triangulate across these many constraints replicates the complexity of making these decision in global enterprises as well. The areas of being able to optimize production workflows to support the broader network impact on all logistics activities showed why recursive screens and navigation for a simulator like this. In addition, this simulator allows for constraint modeling to the per-product and per-lot level as well, using a ruled-based configuration technology that can be modified by the user to reflect their specific process workflows (Vinlogic, 2012). It is foreseeable that this type of simulation and constraint-based modeling environment would be potentially able to predict when and if an experience effect would occur throughout an entire supplier network as well. The ability of companies to attain a high level of knowedlge sharing, in effect becoming knowledge generation networks, is well-known in the areas of auto manufacturing, specifically at Toyota (Dyer, Nobeoka, 2000). Simulations such as these show the potential to predict if and when a company will attain this level of process and knowledge sharing performance.

Assessing the Pros and Cons of Simulations as Learning Platforms

The primary strength of constraint-based models is their ability to interpret many potentially conflicting constraints and still arrive at an optimal solution for logistics and transportation management initiatives and programs. This simulation also illustrates how the trade-offs inherent in the constraints have a major impact on the overall profitability of a company over time. Another aspect of this simulation showed how a bottleneck at any one point can cost tens of thousands or even millions in lost sales and inventory carrying costs. Finally, the simulation showed very clearly how the logistics and distribution dynamics in a company can either drastically contribute or detract from overall profitability and performance.