Supply Chains are becoming increasingly competitive
and the focus is now on squeezing every dollar out of each function
involved therein. Optimization is no longer a luxury but has become
the order of the day. Therefore, the supply chain managers are closely
looking even at the smallest opportunity available to analyze, evaluate,
implement and integrate to add to the savings - possibly within the
existing system of their supply chains.
One such opportunity available to supply chain managers is optimal loading and dispatching from manufacturing plants and points of transshipments. Organizations today are striving/battling to ensure that full truckload shipments are dispatched having the right goods to reach the destination in the right time in order to achieve following three conflicting objectives:
Simple back-of-the-envelope calculation of transportation spends for multi-location, multi-SKU organizations demonstrate that this figure (transportation spends on primary freight in absolute terms) is huge and promises serious savings to any supply chain manager. Studies, however, have revealed that optimum placement of SKUs and floor loaded products is often also an ignored area within the logistics function. Maximized volumetric efficiency while transporting/storage can yield significant savings. Using historic data based on loads dispatched and product characteristics and attributes (weight - volume constraints), it is possible to assess the impact of results that can be achieved using a modeling-based Decision Support System for creating Optimized Load Plans.
Packing a container with multiple shape/size objects with maximum efficiency comes in the knapsack class of problems. Key areas of concern could be maximizing cubic volume utilization, at the same time ensuring that legally permissible limits are met. Appropriate constraint building ensures that
At the same time, similar information about containers, trucks, railway wagons etc. could be captured such as unloadable void spaces, roll up door, steps in a step-van, issues related to keeping center of gravity within prescribed limits, etc. Considerations regarding drop locations and therefore sequential loading / priority loading could also be incorporated.
The solution envisaged creates ready databases and masters for different SKUs thereby creating a repository, or takes this information from the legacy systems if already captured. The benefits envisaged here are mainly from a cost savings perspective, and increased efficiency, while loading and transporting. However, time and opportunity costs for loading vehicles could cause the total impact (benefits) to be much higher. Other soft benefits could be in the form of lower damages while transportation, since certain constraint based planning would have been done through optimal packing and loading.
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