Vin Logic Simulation Lessons Learned and Insights

Vin Logic Simulation

Lessons Learned and Insights Gained from the VinLogic Simulation Model

The intent of this analysis is to provide an overview of lessons learned in the areas of operations management, specifically in the areas of transportation and delivery as a result of viewing and studying the VinLogic Simulation Model. The VinLogic Model is predicated on the concepts of the supply chain optimization and modeling, a patented series of technologies created by Simulation Dynamics (Vinlogic, 2011).

Lessons Learned in Transportation and Delivery

At its most fundamental level, the VinLogic Simulation Model is highly effective in illustrating, both from a graphical and workforce perspective, how constraint-based modeling and optimization techniques can increase logistics, transportation and delivery performance. The examples show are easily interpreted across national and global logistics and supply chains, illustrates how delays, production schedules and supply chain contingencies all act as constraints on the broader optimization model. By taking a model-based approach to logistics and transportation networks, the constraints of trick, rail and vessel modes of transport can be modeled in with corresponding pricing and time considerations as well (Vinlogic, 2011). In addition, the optimization of each production workflow and its corresponding impact on broader network dynamics and constraints are easily modified and modeled in a recursive series of screens of the simulator. The simulator also allows for greater constraint-based modeling on a per-product level than many rules-based configuration and simulation technologies do, allowing the product model to be modified, not just the process workflow (Vinlogic, 2011). In actual use for enterprise- and network-wide implementations., this approach is preferable as it dramatically increases overall visibility and optimization of the network over time, creating an experience effect. If managed to its optimal levels, the logistics and transportation networks can over time become knowledge-sharing and knowledge generation networks (Dyer, Nobeoka, 2000).

Simulations As Learning Platforms and Tools: Assessing Pros and Cons

The simulation makes it clear that there are constraint-based models that can interpret dozens of factors and still create an optimal path or strategy for transportation and logistics strategies. It also provides insights into how financial performance of a business, as illustrated in the inventory valuation section, can drastically impact the profitability of a business in any given month. A bottleneck in a logistics network can cost hundreds of millions in lost asset performance and availability. The simulation did an excellent job of showing how quickly a distribution network can contribute or detract from overall company profitability.