In the first post in this series (Part I) I looked at the opportunities to reduce freight cost from traditional transportation management, but the really big opportunities may lie outside of your transportation team's control. In this post, we'll look at some additional (and very possibly larger) opportunities.
By the time a request hits the Transportation Team the damage has been done. It’s already been decided that something needs to move, how it needs to move and when it must depart/arrive. This is where you can really save.
Don’t ship things you don’t need to.
This may be obvious but one of the best ways to save money on transportation is to do less of it. How much of your transportation is driven by real need? How much could you avoid by tightening up your forecasting process and inventory policies (see [Inventory modeling is not "Normal"] and [Inventory modeling in action]). What about making better decisions about re-deployment ("Balancing" safety-stocks across DCs).
Don’t expedite when you don’t need to.
Air-freight is very expensive, expedited (team) truckload freight is better, but even truckload freight that must move NOW will probably not give you time to find the best rate. With a little lead-time you can save a lot of money. Does it really need to be there by 9:00 am tomorrow morning? Even if the answer right now is ‘Yes’ what can we do to avoid getting in that situation tomorrow?
Bypass steps in the chain
Does your freight shoot like an arrow from production to shelf? No? I thought not. If you were to track a case from production through a manufacturer’s DC to a retailer’s DC to store it has probably doubled back at least once. If you have sufficient volume (and lead-time) skip a step you can save on both freight and handling expenses. Optimally sourcing each order needs you to consider what it will cost to source (and replenish) that order from ALL viable shipping locations not just the default location. This is a great analytic/optimization problem but to be successful you need it embedded in your order processing system.
Managing peaks in demand
Your transportation team will typically use a number of carriers on each lane they manage and the wide variation in freight rates for these carriers may surprise you. Cheaper rates are associated with carriers that really want that volume: perhaps because it naturally fits with their networ,k filling trucks that would otherwise travel empty. Once that capacity is used up, they won’t want to cover any more freight on that lane today, it would cost too much to position the equipment. The more erratic your demand, the more likely that you have to tender loads to relatively expensive carriers or abandon your plan altogether and buy freight on the open (“spot”) market.
Can you have any control over these peaks in demand – you bet! You can handle this within your own network relatively easily. When shipping to customers, retailer typically have shipping windows when their orders must be received: ship a few loads a day earlier, a few loads a day later, smooth out the demand within a lane and stop having to beg for capacity as often.
Fill those trucks – really fill them
How full is a full truck? One particularly bad guideline I encountered said a truck was full when there was 38,000 lbs of product in it. If I can find a way to, legally, load 46,500 lbs in the trailer it’s as though 8,500 lbs of product just shipped free, effectively saving 18% (8,500/46,500 = 18%) in freight cost.
OK, this is a very extreme case to make a point, but why would anyone plan to 38,500 lbs? Well it’s because the actual constraints around load building are complex, relating not just to product weight but distribution of weight in the rig and the 3D jigsaw puzzle to physically fit product in the space available and avoid damage in transit .
In the case of the 38,000 lb rule of thumb, some of the product was low density and hit space limits before it hit weight restrictions. Of course not all the product had that problem, but the rule was generally used.
This needs a good analytic/optimization tool to get right, but the savings can be substantial. There will be more on this in a subsequent post. What's this worth? The range varies a lot, but perhaps up to 5%.
Optimize your network
Every time there is a significant change to your network, an acquisition, a divestiture or just significant growth/decline it's worth running the analytics again to make sure your distribution network is in tune with your needs. Do you need to add, remove or expand storage locations? Is it time to change production policies on which products are made where? For smaller changes in your supply chain, routinely fine-tuning the product flow to avoid unnecessary storage and handling can yield great results. An optimization model can include manufacturing, warehousing and transportation costs to find the lowest cost option overall.
Savings here can be huge (if changes have made your network seriously inappropriate for the supply chain it supports), but even ongoing fine-tuning is worth a few percentage points.
There can be substantially more money to be saved in transportation from changes made outside of the transportation team than within it. Look to better forecasting, inventory-optimization, deployment, order-processing, maximizing truckloads and network optimization to save real money.
What do you think? Have I missed something? Does this fit with your experience?