Supply Chain Luck (good and bad)


It's 7:50 am and it's not going to be a good day. While reviewing your scorecard , you notice a big hit in a key service metric. Your boss has clearly noticed it too, a meeting request just hit your calendar for 08:05. Now, you did have some problems earlier in the year but they seemed to clear up and for the last few months you've been enjoying the rosy glow that comes from near perfect service levels on your products. This month, sales spiked, inventory crashed to zero and you started cutting orders. People are not happy. What changed? What went wrong?

It may be that you missed a predictable shift in demand ("Promo? What promo?") or perhaps you do not have safety-stock levels set correctly (your bad) or, and please consider this carefully,you might just have been unlucky.  Now, I'm not suggesting that every nasty surprise you encounter is bad luck ; neither is every bit of good news due to your utter mastery of your supply chain, but sometimes... stuff happens.

Call it what you will - luck, chance, risk, volatility, variability, uncertainty, randomness or noise - stuff happens. However good your sales forecasting may be, it will be wrong. Even with robust planning, you will, occasionally, have issues with supply, weather events, product quality, equipment failure. You can and should minimize it, mitigate it, expedite around it, yes, even measure it, but you cannot, ever, ever, eliminate it.

Bottom line - if, for any metric, you can't separate "unusual, but still within the expectations of the system" from "now that's really weird" you can spend a lot of time and money trying (and failing) to control it.

I came across a great example of this yesterday, looking at the results of a simulation study for a supply chain inventory/replenishment system. The chart below shows days 200 to 1000 of a simulation run.

The 4 component charts (top to bottom) show:

  • daily demand for a product with a reasonable level of variation. This is sampling from the same exact statistical distribution throughout time - those occasional spikes are purely random and within the bounds of "reasonable" for this distribution.
  • The results in inventory. The solid blue line shows on-hand inventory, the dashed line is on-hand and on-order together. You can see that as total inventory drops below the re-order point an order is triggered, on-order inventory shoots up and some days later it is transferred into on-hand inventory. You can also see that sometimes inventory drops below the safety stock line, sometimes it is above and occasionally inventory drops to 0 and orders are cut.
  • shorts are order-cuts due to insufficient inventory. Shorts can only happen at the end of a replenishment cycle, and in fact for most of this 800 day period there are no shorts at all. Some of them are fairly small (less than 5) others not so much.
  • fill-rate calculated on a moving 180 day window. E.g. the fill-rate metric shown on day 400 is based off performance for the prior 180 days (221 to 400). For this model, the re-order-point (ROP) was set to meet a 97.5% fill-rate (in the long-term) but in this chart, it's all over the place.

Now, you may think 180 days is long-term but with this system ordering approximately every 20 days it's only had about 9 chances to test that ROP level in any given 180 day window. That's clearly not enough to get a stable metric. Note that if you average the result across the whole 1000 days, roughly 50 order-cycles, you get 98.1%. That's still over 0.5% points off from what I intended and actually, luck is on our side, it could have been much further from the "truth".

When I repeat this simulation 100 times (with different random numbers), I get a wide variety of results for the 1000 day fill-rate.

It's centered around the right place, so the good news is that my math is working, but for any 1000 day run, random variation could lead me into thinking I have a problem when the underlying system is in control and no corrective action is needed. (Anyone want to explain an average 96% fill-rate for 3 years to the boss?)

Dealing with uncertainty is the realm of statistics and I know that many of you do not want to go there. I do understand your pain (even if I don't feel it so much): trust me that in writing this post my largest issue has been the amount of text I have written, re-written and deleted because it contains too many statistical references.

Let's keep it simple then: generally, larger samples give you a better/tighter estimate of a metric than do small ones. For example, your company-wide fill-rate metric for the month is probably quite stable/reliable. The further down you drill into metrics for specific products, locations or dates the smaller your sample and the less stable the metric becomes. Looking at an individual product for last week (or perhaps even the last 6 months) is not giving you reliable information as to whether your supply chain is under control so try not to treat every little bit of bad news like a fire drill.

Visualizing Forecast Accuracy. When not to use the "start at zero" rule ?

I recently joined a discussion on Kaiser Fung's blog Junk Charts ,  When to use the start-at-zero rule,  concerning when charts should force a 0 into the Y-axis.  BTW - If you have not done so, add his blog to your RSS feed, it's superb and I have become a frequent visitor.

On this particular post, I would completely agree with his thoughts was it not for this one metric I have problems visualizing, Forecast Accuracy.   Forecast Accuracy is a very, very widely used sales-forecasting metric that is based on a statistical one, so let's start there.  

The statistical metric (Mean Absolute Percentage Error) looks at the average absolute forecast error as a percentage of actual sales.    Some of the errors will be positive and some negative but by taking the absolute value we lose the sign and just look at the magnitude of error.  (We handle optimism or pessimism in the forecast with a different "bias" metric).  

There is occasionally heated discussion in the sales forecasting community about exactly how this should be calculated but let's save that for another day as all forms I am familiar with have the same properties with regard to plotting results.

  • perfect forecasts would have no error and return 0% MAPE, this is our base.
  • there is no effective upper bound on the metric (you can have 400% MAPE or more)

If we were to look at this across a range of product groups (A thru K) it might look something like this.  The Y-axis is forced to start at 0 and the length of the bars have meaning, Product D really does have almost twice the error rate of product A.  This plots out very nicely, it's hard to misunderstand  and the start-at-zero rule certainly does apply.

Now convert MAPE into a Forecast Accuracy with this simple calculation.

Forecast Accuracy = 1 - MAPE

I can only assume this metric was created in the sense of "bigger numbers are better".  It's in widespread use, it's part of the business forecasting language, and no, I can't change it.  Perfect forecasts are now at 100% and there is no lower bound on the metric, it can easily be negative.

This causes me a problem.  Check out the chart below: this is the same data as before but now expressed as Forecast Accuracy rather than MAPE in a standard Excel chart.  Excel is trying to help (bless it) and put the 0 value in without my help.  Work in supply-chain and you will see a lot of these.

The zero value has no special meaning on this metric, so starting at 0 is very misleading:  80% accuracy (20% MAPE) is not twice as good as 40% accuracy  (60% MAPE).

Allowing the minimum of the y-axis to float  does not solve this either (below)

I really don't know what this is trying to tell me... some product groups are better than others perhaps ?  Certainly, relative size is meaningless.

"Abandon it" you say  "go to a line chart".  Line charts often have floating axes and they do not emphasize relative size nearly as much as a bar-chart does (below).

Perhaps it's less confusing/misleading than the previous charts but I still don't like it. because there is data I want to compare relative sizes for (the MAPE) and line-charts seem most useful when trying to show patterns.  I have no reason to expect a useful pattern to form from product categories: I just sorted then alphabetically.

My thanks to the contributors on Junk Charts for helping me clarify my thinking on this.  I don't know that there is a great answer but as it's one I run into all the time I do want to find a better solution.  (FYI - It's just hit me that there are another set of supply-chain metrics for order fill-rates than have the exact same problem)

How about forcing the upper limit on the Y-axis to 100% and letting the lower limit float? I am trying to emphasize the negative space between the top of the bar and 100%, essentially the error rate.

I'm not entirely happy though, those heavy bars do draw the eye, and you would have to educate the user to read the negative space.  How about a dot-plot instead ?

You would still have to learn how to read it properly ...

Or how about this?  Inspiration or desperation?  I'm now plotting the bars down from the 100% mark, emphasizing MAPE while still using the Forecast Accuracy scale.  I'm not entirely sure yet, but I think I like it and if I generalize the "start at 0" idea to "start at base" it may even fit the rule.

What do you think?  Which version best handles the compromise between a user's desire to see the metric they know and my desire to show them relative error rates?  Have you a better idea?  I would love to hear it - this one really bugs me !  Can you think of any other examples of metrics where 0 is meaningless?

Recommended Reading: The Definitive Guide To Inventory Management

A little over 15 years go now, I was set the task to model how much inventory was needed for all of our, 3000 or so, products at every distribution center.  Prior to this point, inventory targets had been set at aggregate level based off experience and my management felt it was likely we had too much inventory in total and what we did have was probably not where it was most needed. (BTW - they were absolutely right and we were ultimately able to make substantial cuts in inventory while raising service levels).

I came to the project with a math degree, some programming expertise, practical experience simulating production lines, optimizing distribution networks, analyzing investments and with no real idea of how to get the job done.  The books I managed to get my hands on gave you some idea how to use such a system but no real idea how to build it.  They left out all the hard/useful bits I think.  So, I set about to work it out for myself with a lot of simulation models to validate that the outputs made sense.

I still work occasionally in inventory modeling and I'll be teaching some components this fall, so I have been eagerly awaiting this new book : The Definitive Guide to Inventory Management: Principles and Strategies for the Efficient Flow of Inventory CSCMP, Waller, Matthew A. and Esper, Terry L. (Mar 19, 2014)

Full disclosure here: one of the authors, Dr Matt Waller is a friend and colleague of mine.  He brings an astonishing level of expertise to many areas of supply-chain management and inventory modeling is clearly no exception.

Together, Matt and Terry Esper have produced a book that (had I possessed it 15 years before it was published) could have short-cut my inventory modeling project by approximately 6 months.

This is not a long book, not quite 200 pages in fact, but it is no lightweight.  If you just want an overview of the topic you could skip the math, but my guess is that if you do that, you will never really understand.  The math is not particularly hard and it's presented in a sort of hybrid math/Excel fashion that I find easy to follow.  I'll also say that I hit my first "ahah!" moment before I got to page 20.  I won't embarrass myself by telling you what it was but something that had bothered me for years suddenly clicked into place.

Unlike most discussion on this topic, this book looks at inventory modeling from the manufacturer's or supplier's point of view right through to the retail shelf.  They also provide a number of means to estimate components of inventory from historical data so you can assess how well your planning and execution system are tracking to plan: something I was aware of but had never really thought through how useful it could be.  Details on how to conduct your own simulation studies in Excel and an overview to the most commonly used forecasting approaches that feed the inventory models round it out.

It's all here, what you need to understand (and if you so wish, build) a system to optimize your inventory  holding.  I highly recommended it.

Recommended Reading: Supply Chain Network Design

I've done a lot of  supply chain network design projects and consider myself to be an expert. Had I had this book from the start, I may have got to expert status a lot faster.

With experience in supply-chain and an academic background that includes mathematical-optimization, when the need arose to build supply chain network optimization models I just did it.  Then I learned many, valuable, real-world lessons the hard way- by getting it wrong.

There are a number of books available that cover this area: I have dipped into a few, as needed, and I have not read most of them so I really can't say this is the best book available on the subject.  I can say that this is one of the very few analytic books on any subject that I have read cover to cover.  

Network design is perhaps not as hot a topic now as it was 10 years ago.  That's just my perception, but while the hype right now is around "big data", network design continues to deliver major savings to organizations.  Network design finds where your facilities should be and how product should flow through them to support your business at the lowest cost.  The more rapidly your business is changing the more often this is worthwhile: an acquisition or divestment will almost always justify the expense with a significant ROI.  A 10% reduction in supply chain cost is common..  Even on a stable business there can be significant saving (transportation, labor and warehousing) in adjusting product flow on a relatively frequent, annual basis.

Note that while the authors (all from IBM) have extensive experience building software products to help you do supply-chain network optimization this book is not a sales brochure for LogicNet, in fact, it's barely mentioned.

The math needed to run an optimization model is not simple but it is accessible to those who want to learn and this book does take you through step by step a mathematical programming model that gets increasingly sophisticated.  The necessary theory is all there.

What attracted me was that it goes beyond the theory and has lots of details around project execution:.  the need for sensitivity analysis; the difficulty of getting reliable transportation rates ; sensible data aggregation strategies; why you must have an optimized "baseline"; and numerous others.  These are all areas that analysts get wrong - as I did.  Some learn from the experience, others send out the results anyway.

Managers who hope to become better buyers/consumers of network-design projects (remembering that  your analysts may also be making newbie mistakes) skip the math sections and you can still understand what can be modeled and why you would want to.

For analysts actively involved in building optimization-models the mathematical formulations are extremely helpful. Even if you choose to build models with a software package that tries to hide the harder math from you, the guidance around data and the art of modeling is worth the price and the time to read it.

If you have a network design project in mind and a plane journey coming up - make the investment.  

Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain (FT Press Operations Management)

by Michael Watson, Sara Lewis, Peter Cacioppi and Jay Jayaraman (Sep 1, 20112)

Ignore SNAP and your product may not be on the shelf when it's most needed - and that means lost sales.

SNAP is the “Supplemental Nutrition Assistance Program” (formerly known as “Food Stamps”) in the United States which puts food on the table for 46 million people every month. 

SNAP can drive big spikes in sales at the store. These spikes are large but short-lived and often pass undetected by reporting and forecasting systems.    

Our whitepaper covers the causes of SNAP spikes, why they vary so much across regions and products, how to identify sales spikes and what you should be doing to maximize sales.

Visit our website for more information.



Truckload Transportation - are you paying to ship air ?

How full is a "full" truck?   Not sure?  That's a shame, because when you contract for truckload freight, you pay for the whole vehicle, whether you fill it or not.  

 As I'll show you, the regulations around what constitutes "full" for weight are very complex.  In addition, the 3D jigsaw puzzle to pack product into the trailer space, distributing weight correctly and minimizing damage is exceptionally challenging.  

Get it wrong and you are paying to ship air.  

It is much easier to plan to approximate rules or guidelines than to figure out what's really going on and it is common in the CPG industry to plan transportation loads based on these approximate rules.   Unfortunately, these approximate rules are very dependent on what you are trying to load (as well as who made up the "rule") so you will rarely encounter the same "rule" twice.  Typically what you will see is based on weight, pallet positions or cube.  Rules that restrict what's loaded to maximum limits like:

  • 40,000 lbs of product
  • 2,500 cubic ft of product
  • 48 pallets of product.

None of these are right and they routinely result in shipping air.

Depending on the product and the vehicle,  I can safely, and legally, load much more than 40,000 lbs of product, (much) more than 2,500 cubic feet many more than 48 pallets.  

One particularly bad example I have encountered said

"a truck is full when there is 38,000 lbs of product in it"

.  If I can find a way to, legally and safely load 45,000 lbs in the trailer it’s as though 7,000 lbs of product just shipped


, effectively saving 15% ((7,000/45,000 = 15.5%) in freight cost.

So, why is this so difficult to get right?  Let's look at the regulations around weight.

Weight Limits

If your product is "heavy", you will probably hit a weight limit in loading.  "Heavy" in this instance is


20 lbs per cubic foot or more.  Much less than this and you will probably hit a space limit first (see below).

The government sensibly places restrictions on how heavy a loaded vehicle may be and how that weight must be distributed to be carried safely.   

To get a feel for  the complexity involved, here is a link to the relevant p

age from the US Department of Transportation.  Stay just long enough to get confused then head back here :-)   

Bridge Formula Weights

To summarize (and simplify):

  • The total weight of the vehicle (including product, packaging, pallets, fuel, driver, etc...) must not exceed 80,000 lbs.
  • There are limits for the weight on individual axles (shown below in this graphic from the US Department of Transport)
Note for the mathematically inclined:
If anyone is really interested in being able to calculate what weight should be on each axle given a particular layout of product in the trailer you need a little physics/engineering math.  Here is a great example of how a beam transfers load with an interactive calculator.
With the right math, you can calculate the center of gravity for the product and how that weight would be distributed to the axles.  Move the center of gravity forwards (e.g. by moving heavier product to the front) and you take weight of the rear wheels and transfer it to the tractor unit.  Quite how that weight then gets distributed to to axles 1 through 3 depends on where the "kingpin" connection (between the trailer and the tractor unit) is relative to the tractor units axles. You can build such a model in Excel.

So, can you plan to a total rig weight of 80,000 lbs?  No

, sorry,  there are only so many options for how to layout product in the vehicle and there may be no layout that balances weight well enough across all axles to max out the 80,000 lb overall limit.You need to find the layout that gets closest to that limit and live with the loss.

Alternatively, you may run out of space in the vehicle before you get close to a weight limit.

Space Limits

If product is reasonably light (low density) we will probably be constrained by space before weight becomes a problem..

A reasonably standard trailer's internal dimensions are approximately

  • 52' long
  • 8' wide
  • 8' (usable) height

That's a little over 3,300 cubic ft of space available to you.  However, you are typically loading with palletized product and you will not get to use most of this space.   Let's look at how well you can use the floor space first.

A standard pallet for US grocery is 40" wide, 48" long. The ability to fill the trailer floor-space depends a lot on how well these palletized units fit.  

If pallets will fit in the trailer "wide:wide" it's possible to put 30 pallet footprints in a 53' trailer.

If  the trailer is too narrow or product overhangs the pallet, you have to go to other configurations.   "Chimney stacking" maxes out at 28 footprints.  

If you can't turn pallets at all, "narrow:narrow" loads max out at 26 footprints (although with any overhang on the pallets at all you will likely get 24/25)

In each case there is floor space you cannot use.  Load "narrow:narrow" and you have already lost almost 20% of the available space.

Now let's look at vertical space.  This is much more variation in the height of trailers and in the height of doors to those trailers.  If the door height is restrictive , perhaps because the door rolls up inside the trailer, that will limit the product you can get into that trailer.  Let's assume for now that we can safely get to 8' high. 

How much of the vertical space we can use depends on what we are loading.  Some product cannot be stacked, pallet on pallet, without causing damage.  Some pallets are too tall to allow anything (except an unusually short pallet) to fit stacked on top of it.  Very short pallets may be able to stack 3 or even 4 high.

If I assume 40" high palletized product, double stacked, we can use 80" of the 96" vertical space (83%).

Combine that with "narrow:narrow" loading and we max out a truck at 83% * 83% = 69% space utilization. From a "cube" standpoint the trailer is only 70% full and it's at capacity.  Hence rules like "the trailer is full at 2,300 cubic feet of product" when the trailer has over 3,300 cubic feet of air.

Depending on product dimensions and the ability to stack product we may be able to load much more than 2,300 cubic feet or much less.

Reducing Damage

We could spend a lot of time on this, but for now suffice to say, we would like to load the trailer so that product is less likely to get crushed, to move as the vehicle corners or to land in a heap at the front when the driver must, necessarily, brake.  This puts additional constraints on what product can go where in the vehicle, reducing, again, the weight you can carry and/or the volume you can load into the available space.

Equipment  limitations

If the pallet handling equipment (for either shipper or receiver) can't stack pallets or can't handle them turned, you will necessarily lose a lot of payload capacity.  Unless this is for very short trips where you can pay for the freight-cost with handling labor savings, you need to invest in warehouse equipment.

Not all trucks/trailers are the same.  By design they can be very different, tractor units weighing anywhere between 11,000 and 20,000  lbs.  Trailers can be vary by a few thousand lbs too.  Even equipment of the same make, model and year can be different depending on its setup (kingpin position) and the addition of aftermarket parts.  (Mount a new fuel tank too near the front and see it use up the limited capacity you have on the steering axle).  If weight is an issue for you, you will need to work with your carriers to understand what equipment they are bringing in.  Lightweight equipment is worth more to you, heavyweight equipment should be avoided or contracted at a low enough rate to offset the loss in carrying capacity..

Pulling it all together

Each of these sets of restrictions, weight, space, damage and equipment are complex: accurately modeling any of them is a challenge.  Depending on what you load: heavy or light product, slightly oversize, stack-able or not,  the factors that constrain you continually move.  And, beyond modeling it, you need to optimize: to find the selection and layout of product that maximizes your vehicle loading.

My Take

If you're lucky enough to have consistently-sized, palletized product with consistent low or high density that is consistently  stacked and loaded on consistent carrier equipment  you may be able to build a simple rule that really does max out your truck loading.  Good for you!   For the rest of us, such "rules" are poor guesses at best and can leave a lot of money on the table.  

You are NOT going to build this in Excel unless you have a lot of functional knowledge, very advanced skills in mathematical-optimization and the ability to program your own optimization code.  I have built a load optimization tool (It's a weakness, I like to know how things work).  I have also built my own tools for inventory-optimization, neural-network modeling, genetic-optimization, forecasting and many other needs..  Some of these tools are still in production use today, others were essentially learning opportunities.  In this instance, I chose to buy the software because it has richer functionality than what I chose to build myself.

(On a technical note, application-specific,heuristic optimization routines solve these problems well and quickly.  Mixed-Integer-Programming can get you most of the way, but personally I can't see how to embed some of the damage-reduction ideas into a linear objective function and as the heuristic works so well, you don't need the overhead or complexity of integrating a math programming tool) 

Any load building “optimizer” that asks you to specify a maximum weight or cube or number of pallets is really just automating these approximate rules rather than helping you truly max out the load.  An optimizer that is implemented as a stand-alone package is interesting but not really useful: you need this capability integrated into order-processing, deployment and transportation planning to be effective.  

You need the right tool for the job.  These tools do exist, they work and it's not worth your time or effort to write them again.

There are a number of tools in the market that work in this space.  Google

"load building optimizer"

to see some.  I have not reviewed them all, far from it, but I can tell you that not all "optimization" is the same.  Having "optimize" in the sales literature does not mean it will do a great job for you or that any actual optimization is really taking place.

If you want a quick recommendation I suggest you talk to

Transportation | Warehouse Optimization

. they have a great load building tool and can extend this further into optimizing case-pick routing, pallet builds and shipping locations.

Let's assume you are doing a reasonably good job today without a load optimizer.  What would an extra 5% off your freight spend be worth to you?  Enough to invest in the right tool for the job?

A final thought

These rules-of-thumb can be very persistent.  Having implemented a system to drive increased payload coming out of one manufacturing site, I was perplexed some months later to see that the load factor had shrunk back to where it started.  It turns out that the warehouse supervisor at the plant was adjusting each and every load plan manually to fit with his interpretation of the “rules”.

How to save real money in truckload freight (Part II)

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.

My take

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?

How to save real money in truckload freight (Part I)

How can you save real money in truckload transportation?   In this post, let’s look at the areas that your transportation team manages directly.

Transportation procurement

I’ve seen a number of supply-chain consulting projects conclude (wrongly) that concentrating purchasing power for transportation into fewer hands would drive significant savings, of the order of 10%.  Are there economies of scale in the truckload market? Yes, but primarily at a lane (origin-destination) level: if you are buying freight for Portland to Los Angeles you do NOT get a better rate because you also want to move freight from Cleveland to New York.

You can save some money in administering freight by concentrating it into one team, you may be able to drive more rapid change in management processes or new systems but economies of scale in purchasing – I don’t think so.

On the other hand, In a recent post [How much money can you save from a Transportation Procurement Rate-Bid?] I looked at a study from C.H.Robinson and Iowa-State researchers that concluded that regular freight bids can reduce your freight bid, to the tune of about 3% over a company that does not conduct regular freight bids.  That number looks right on the money to me, absolutely worth doing, especially if your freight is a large proportion of supply chain cost but it's not really BIG.

I also posted on the challenge of getting good benchmarks for truckload freight [...the challenge of transportation rates] and highlighted the CHAINalytics consortium that provides both excellent benchmarks AND quantifies various strategies for driving rates lower.  For the totality of your freight bill, there may be another 1-2 % points to be had there too.

Dedicated routes / dedicated equipment

If you have enough volume it may be possible to set up dedicated equipment and routes and if you can keep these assets busy it will cost substantially less than if you contract separately for 1 way loads.  I have seen very few opportunities to do this cost effectively: it saves money in the few lanes where it makes sense but it’s probably not going to move the needle in terms of overall costs.

“Carrier friendly” freight / locations

During the last freight capacity crunch, I heard a lot about “carrier friendly freight”.  Think in terms of

  •  Loads that are quick to load/unload. 
  • Shipping Locations that are quick to get through with no waiting
  • Quick payment cycles.

This probably helps but I have not yet met anyone who can put a quantifiable  savings number to any of these “carrier friendly” initiatives.  I suspect that in total they may have a small impact, I’m unsure whether it offsets the cost of creating  it.

TMS optimization

Transportation management systems now include a range of algorithms to help with optimization.  Pulling together smaller orders into single shipments that deliver at multiple stops (“stop trucks”) is a great example of where such systems can drive value.   Or, perhaps it can automatically switch modes for you from truck to inter-modal or rail when you have sufficient lead-time. 

There is real money to be had here, but it does take a lot of setup.  All the constraints around carriers and shipping/receiving locations need to be embedded in the system and maintained on an ongoing basis or it will generate loads that can’t be shipped, moved or received.

Also, understand that the optimizer reviews the options available to you to find the “best” or at least a “good” solution automatically.   If you have given the system relatively few options to consider (by locking down when and how loads must ship), it will have relatively little opportunity to find savings.

My take

A superb transportation purchasing team may be able to save 5% on cost in comparison to a relatively weak team.  To save 10% requires a comparison to almost complete incompetence or a congruence of market and macro-economic forces that will unravel within 12 months.  (You got “lucky”, but it won’t last)

What do you think?   Have you seen opportunities I've missed?

Check out Part II for additional opportunities that may lie outside your Transportation Team's control

How much money can you save from a Transportation Procurement Rate-Bid?

How much money can you really save from a transportation procurement bid? Probably not as much as you might like, but enough to pay for the bid with a good return.

I recently returned from the CSCMP conference in Atlanta where I attended a great session, jointly presented by folks from C.H. Robinson and researchers from Iowa State University.  They have taken a very similar statistical modeling approach to the one I covered in a recent post [..the challenge of transportation rates] to answer questions around the impact of transportation bids and this result is in the public domain. 

You can download a copy of their white paper "Stale Rates Research: Benefits of Frequent Transportation Bids” here.   This study uses relatively little data (~$1 billion in spend) but it all comes from one Transportation Management System (TMS) which should allow for cleaner and richer data up front.

To skip to the chase, the team found that there is an immediate impact to transportation rates from conducting a bid, but:

  •  The immediate rate reduction is relatively modest at $15 per shipment on an average lane cost of about $900 (~1.7 %)
  • The value of the cost reduction decays quickly and is completely gone in about 12 months as conditions change, individual lane rates are adjusted or the lowest-rate carriers take proportionally fewer loads .
  • Across a year, the immediate impact of a freight rate bid is only about $5 per shipment.

That does not seem like much on an average $900 spend  per shipment.

However, they also found that there is a consistent discount associated with frequent (at least yearly) transportation bids that does not decay.  Presumably this reflects a level of comfort from carriers that any rates they agree to now can be revised in a reasonable time-frame.  Shippers that hold at least annual freight bids :

  • save, on average, $25 per shipment in addition to the immediate  impact
  • save about $30 per shipment in total (about 3.3% on the average shipment of $900.)

My take

If you have a billion $ in freight spend , 3.3% is about $33 million.  As freight bids seems to cost tens of thousands of dollars and certainly not millions, that looks like a good return and I imagine C.H.Robinson  will see some of that business to help repay their investment in this study.

Then again, if anyone is telling you that their spiffy new procurement or reverse-auction system is going to help you save 10%-15% on your freight bills, you may not want to commit to that saving with your boss.

I think 3% freight savings is right on the money: definitely worthwhile but perhaps not the biggest thing you should be working on in the supply chain.  What do you think?