Transportation

Description
Usually, there are many good and innovative ideas available on how to improve a supply chain operation. Most companies have smart, experienced supply chain analysts on staff that regularly identify the key drivers causing service problems and cost overruns.

The real problem usually lies in establishing the trade-off points. The means available to improve a customer’s service level are known; however, the curve trading off service and additional cost is usually very difficult to define. With this difficulty comes paralysis, and paralysis causes a familiar phenomena… "business as usual." When you can't quantify the trade-offs, no one will advocate taking the risks!

Transportation Policy improvements offer an illustration of this point. In this Transportation Application case study, a North American distributor of retail apparel has been employing a cross-docking strategy to service its west coast customers from a single, centralized west coast DC. It has been argued that shipping directly to certain customers could be a way to improve service. However, what is the additional cost? How much will service actually improve?

Get missing transportation costs
A classic problem in network optimization is how to determine transportation average unit costs for a transportation mode that has no history. Until now, calculating costs for proposed alternatives required extensive spreadsheet modeling (and some intelligent guesswork.)

With Guru, there is a better way - simulate the alternatives! Built-in network simulation can accurately characterize the shipment profiles for proposed lanes, incorporating variance and time interdependencies into the model, in a way that is extremely difficult or impossible with two-dimensional spreadsheet models. [figure 2]

By using simulation, you can calculate the average unit costs for new transportation lanes and new site locations, giving you accurate costs to input into the network optimization automatically!

Optimize transportation mode selection
Supply Chain Guru's powerful network optimization capabilities apply linear/mixed integer programming to select least cost modes. Using Guru, you have flexibility to define transportation lane capacities, for all products, or for just one specific product.

You can define multiple transportation modes for a single transportation lane, a standard feature of network optimization. However, the real power comes into play when incorporating Guru's extensive costing and flow requirement capabilities. [figure 3]

With Guru, you can set up a piecewise linear step function to represent bulk discounts. Additionally, you can combine these cost profiles with complex flow requirements such as:

	Min Flow: require a minimum quantity of throughput transported through the lane
	Fixed Flow: require a specified quantity of throughput transported through the lane
	Max Flow: set a maximum throughput transported through the lane
	Conditional Minimum Flow: in order to utilize this lane, a minimum quantity must be reached, otherwise, the lane will not be used