Next Generation DSRs - An Analytic name is not enough

You need not always build your analytic tools, sometimes you should buy in. If the chosen application does what you need that often makes good economic sense... as long as you know what you are buying.

Let's be clear, an Analytic name does NOT mean there are any real Analytics under the hood.

For many managers, Analytics is akin to magic. They do not know how an analytics application works in a meaningful way and have no real interest in knowing. At the same time, there is no business standard for what makes up "forecasting", "inventory optimization", "cluster analysis", "pricing analysis", "shopper analytics", "like products" or even (my favorite) "optimization".  Don't buy a lemon!

In the worst examples, there is nothing under the hood at all. One promotion-analytic tool I came across recently proudly proclaimed that you (the user) could calculate the baseline and lift for each promotion however you saw fit and then just enter the result into their system. They presented this as a positive feature, but calculating a meaningful baseline and lift is the difficult part!!

I've seen similar approaches for:

• off-shelf alerting tools that ask you how long of a period of zero sales is abnormal (so they can report exceptions)
• supply chain systems that need you to enter safety-stocks or re-order-points (so they can figure out when to order).  
• assortment optimization tools that want you to input product substitution rates.

Hmmm, is a car without an engine still a car?

Many applications use pseudo-analytics. After all, how hard can it be? "cluster analysis" , that's finding groups of things right? I reckon I can figure that out, no stats required. Yeah, right, of course you can... FYI - meaningful, useful clusters may be a little more difficult. It's not that cluster analysis is particularly hard, but neither is it something you can knock together without the right tools or any statistical understanding.

Sadly, I have seen real world examples of pseudo-analytics too in pricing analytics, off-shelf alerting, demographic analyses, inventory optimization and forecasting.

The right tool for the right job. There are many good analytic applications available, but you can still make it useless if it does not suit the task you have in mind. Using a time-bucket oriented optimization program to schedule production runs with sequencing comes to mind. OK, relatively few people are going to understand that one and it's not a DSR application, but it is real, the software vendor did not come out shouting that there would be a problem and 2 years down the line that project was abandoned.

Are DSRs worse than other applications?

I think this kind of feature-optimism, is a general issue in buying any analytic app but my perception is that it is a bigger problem in the DSR space.  Perhaps because the DSR is trying to offer so much analytic functionality to so many functional areas?  Is a DSR really going to handle forecasting, pricing-analytics, cluster-analysis, weather-sensitivity-modeling, promotional analytics, inventory optimization, assortment selection and demographic analysis (note - not a complete list), all as packaged software, for $50K a year?   Not unless they can scale that investment across a huge user-base.  Some will be good, others not so much - be warned.   

Spotting a lemon

An expert in the field (with analytic and domain knowledge) can spot a lemon from quite a distance. If you do not possess one you would be wise to invest in some consulting to bolster your purchasing team. For those applications that pass the sniff-test, the proof of any analytic system is in it's performance. Define rational performance criteria, test, validate, pilot and never, ever, ever rely on a software vendor ticking the box in your RFP.

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

Order Optimization: Smaller (standard) order quantities, more full-pallet orders AND reduced retailer inventory

If you work in the Supply Chain between CPG and Retail this probably sounds too good to be true, but stick with me and I'll show you a win:win opportunity.

Background

When CPG product leaves a manufacturing facility, much of it is packed into corrugated cardboard cases.  Multiple cases of the same product are placed tightly together into layers on top of (typically) wooden pallets.  (To hold them together in transit they are then completely wrapped in a stretch-film not unlike the plastic film you may use to cover leftovers in your refrigerator)

Ideally, this is the last time the CPG company wants to touch these cases as manual handling is expensive.  If customers order whole pallets, the product can be quickly and cheaply shipped by operators using lift-trucks and just as cheaply unloaded at the customers facility.  

When a customer orders in quantities other than full pallets some additional handling is required.  There are varying levels of automation available though much of this is manual: a warehouse operator will physically "pick" individual cases and "pack" them onto new pallets.  When the customer receives this product they have a more difficult, expensive and error-prone job to figure out what they have received and put it away.

So, why do customer not just order full pallets?  Imagine that they average weekly demand for a product at a customer warehouse is 4 cases.  The pallet pictured above actually has 48 cases on it, so a full pallet has enough inventory to last about 12 weeks.  That's a lot of inventory and the amount of money a Retailer has tied up in their inventory is a big driver of their profitability and stock price.  

So, the standard order quantity agreed between a Retailer and a CPG is a big driver of handling costs and Retailer inventory cost.

• The CPG wants to ship more orders as full pallets (and reduce their cost of handling).  
• The Retailer might appreciate a reduction in  handling costs when receiving full pallets but they are heavily focused on reducing their own inventory; a key driver for their financials and stock price.  They can't do this by ordering more than they need.

This lack of alignment means that discussions around "standard order quantity" ( I'll call it "SOQ" from here onwards) often end with a win:lose situation.

Finding a win:win 

So, how do you find a win:win?  There are 2 keys to this:

• Demand variability: simply put, the Retailer sometimes needs less product than average and sometime they need more.  This variability  can be large - individual orders can easily be double or even triple "average" demand.  Ignoring this variability is causing a big problem.

• Ordering in multiples: Let's look at how the order processing system handles orders when demand is greater than the SOQ.  In many systems they will order multiples.  If the SOQ is set to 3 cases the only orders you can ever see are for 3, 6, 9, 12, 15 etc....  (FYI - You can make a very good guess as to what the SOQ is even if you don't know for definite by looking at order history and calculating the "Highest Common Factor".)

Now with these 2 keys in mind, consider a product with not quite enough average demand to justify full pallet ordering:

Typically CPG's will push to round-up to full-pallets (a "win" for the CPG but a "lose" for the Retailer).  The Retailer may well stand their ground and insist on SOQ being no more than  X days of average demand (a "lose" for the CPG).

Consider this option:  what happens if instead you reduce the SOQ to a half pallet ?

• sometimes demand is low and orders actually are for half a pallet.
• often demand will be higher, triggering the order processing system to order multiples of the SOQ, probably a full pallet, or possibly 1.5 or even 2 pallets.
• as the ordered quantities more closely match demand, retailer inventory goes down.

The difference between setting an SOQ that will easily (and often) round-up to full pallet quantity and routinely ordering "almost a full pallet" can be significant both in handling and retailer inventory costs.   

Smaller (standard) order quantities, more full-pallet orders AND reduced retailer inventory.  Sounds like a win:win doesn't it ?