Efficient Customer Order Routing

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Efficient Customer Order Routing

Moving past short-sighted customer ordering routing decisions


Efficient and reliable routing of customer orders is critical in today’s retailing environment. Even in one of the largest and most efficient supply networks, Amazon, shipment costs are 14% of retail sales. That’s a big number, and for most retailers who are not at the scale of Amazon, those costs are even higher. With 42% of retailers saying faster shipping is a top priority, these cost are set to go even higher. As part of our series on the cutting edge of omni-channel fulfillment, we’ll take a look at how changing from a myopic view to a more complete picture can increase the efficiency of order routing decisions.

Current State of the World

While there is great variability in order routing techniques, most boil down to applying a set of rules to determine the inventory eligible to use and then selecting the sourcing location with either the lowest shipment cost or shortest delivery time. These decisions are made one order at a time, but the challenge is we don’t just have that one order. We will have a whole set of orders coming and by ignoring these future orders, we’ll make decisions that could increase our cost and/or impact the quality of service for our most important customers.


Assume we have two fulfillment centers - one in Elizabethtown, PA and the other in Cedar Rapids, IA. The Elizabethtown DC only has 1 unit on hand while Cedar Rapids, IA has 10 units. An order for standard 4 day delivery comes in from a customer in Atlanta, GA. The delivery time from both DCs with UPS Ground is 2 days, but Elizabethtown is slightly less expensive at a cost of $10.26 vs. $10.80 at Cedar Rapids. So, the order is routed to the Elizabethtown DC.

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Then, another order comes in for the same product from a customer in Bangor, ME, but, she wants next day service. Since inventory is only available from Cedar Rapids, it’s shipped from there via Next Day Air from there at a cost of $65.76.

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However, with perfect foresight, the most cost effective strategy would have been to ship the first order from Cedar Rapids, and the next day delivery from Elizabethtown. The total cost to fulfill both orders, $40.55, would have been 45% less than the short sighted solution.

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The Cutting Edge

Of course it's not possible to know exactly where every future order will come from and the real life scenarios are much more complicated. However, it is possible to forecast the location of customer demand and incorporate the impact to the fulfillment costs into the decision of how to route an individual order. So, how can we do it?

First, a forecast of the demand by zip code and the customer’s desired service level (i.e. next day, 2 day, 4 day, whenever) is needed. While it sounds easy, it’s really hard to create a good forecast at that level of granularity, and, it often requires grouping zip codes together so that an accurate forecast can be created. Not only do we need the one number forecast, we also need the chances that we sell 0, 1, 2, or more units in each zip code, ie: the distribution of demand. This enables finding the decision that achieves your goal over all the possible demand outcomes, instead of assuming that a specific pattern is going to occur.

Next, the financial impact, including to future orders, of fulfilling a specific order from a specific location can be calculated utilizing linear programing techniques. Alright, that’s nerdy…But, it’s just a way to search through tons of possible answers and find the one that delivers the best result. One of the neat things of doing this is it can produce the expected value of having one more or less unit of inventory at a location. That’s the key value we need to understand the impact of selecting the order from that location!

Finally, these costs can be incorporated into your existing order routing rules in the DOM resulting in more efficient decisions. You’ll be able to quickly see the improvements!

As an added bonus, the forecast used by the DOM solution can also be used by the replenishment solution to ensure that inventory is located in the right place from the get go.

About Us

Cognira is a boutique consulting and software analytics company that focuses exclusively on retail merchandising and supply chain. Cognira’s goal is to make science easy for retailers, and to help them get the most value from their advanced science solutions. We bring decades of experience leveraging science, analytics and scalable technology to improve retail decisions.


This work is inspired by Jason Acimovic and Stephen C. Graves’s Making Better Fulfillment Decisions on the Fly in an Online Retail Environment.

3 Ways Excess Data Can Stunt Your Growth

3 Ways Excess Data Can Stunt Your Growth

We love data. But we also know too much data can be a bad thing.  Here are three ways we’ve seen retailers using data to their detriment.

And some ideas on how to avoid these pitfalls.

1. Analyzing anecdotes

One data challenge we've seen is people manually reviewing too much data anecdotally.

Examining sales volumes for the last time you ran that promotion is valuable. But a single person can only consider a few data points at a time. And that data is open to interpretation, which is often biased.

At one retailer, team members in three functional areas, were each reviewing historical anecdotes, estimating the impact of the same up-coming promotions, and arriving at different conclusions.

Analyzing anecdotes uses valuable time. And bias can lead you in the wrong direction.

Strong analytics will curb reliance on anecdotes.

Your teams will always want to see sales history. Making it efficiently available brings a lot of value. To deliver even more value, provide demand insights based on proven modeling techniques. These will steer teams to better informed, less biased decisions. This investment in data analysis, paired with an explanation of insights will also curb reliance on so much historical data.

2. Divining too many details

Want to know whether a discount with a recipe sent to Jasmine’s mobile account will inspire her to buy that lemon-infused Tunisian olive oil when she walks into your Kansas City store the second week in July - if July 4th falls on a Wednesday – and it’s raining, but just a little bit?

While many systems can mechanically model customer demand at very, very specific levels of detail, taking into account nearly unlimited factors, often the data cannot support reliable results.

Systems with erratic recommendations are burdensome to manage. And these systems don’t get used, leading to less effective decision making.

A balanced approach will help ensure rich and reliable results.

Well-equipped implementers, data analysts and solution providers can balance detailed insights and dependable results in a variety of ways.

However, when one retailer reached out for help with their forecast challenges, they painfully admitted - “Our last implementor was very good – they did everything exactly as we asked them.” 

Pushing for very specific approaches to demand modeling may pressure your implementor or data teams to make you happy in the short term. Instead, being clear about where you are looking for value, how you will leverage the forecast, and your bandwidth to manage exceptions will help produce detail-rich forecasts in which you and your teams can have confidence.

3. Bigger is better syndrome

With so much data available to retailers today, some can become eager to leverage more of it than is necessary to address today’s most pressing opportunities. And that data can be difficult and time-consuming to access.

Data-intensive analysis can leave other important, more easily addressed challenges unattended. Valuable insights are left unmined and unseen. Out of tune systems continue to misbehave, getting you into who knows what kind of trouble.

The right data for the job will help give you higher returns on your effort.

Petabytes of data and shiny, new tools can be enchanting – and in many cases, are worthwhile. But the most powerful and valuable approaches to leveraging data in retail are not all new. Being open to a variety of data sources and solutions can help you get the most value for your investment.

Can you think of other ways too much data can stop progress or bring harm to your business?


A little bit about Cognira

We’ve helped retailers get value out of big data and little data. Using the most modern cognitive and machine learning tools. And time-proven, robust statistical techniques.

What Your Inventory Can Tell You About Your Promotional Strategies

What Your Inventory Can Tell You About Your Promotional Strategies

Many retail challenges, like promotions, impact and require coordination across many areas of the business. We find it helpful to work across functional areas and bring different perspectives to the table. Below are some thoughts about promotions from the POV of an Inventory Manager.

Inventory Managers can see that there is no turning back time on the discount clock.  Promotions are totally ingrained into our customer’s buying behavior, even (or especially) for the most loyal of customers.  What do we see?

Customers will wait for promotions on products they already want to buy.

We’ve seen products where up to 90% of sell-through happens at discounted prices, products which consumers will not buy at regular price.  These items could be fashion items or consumable products that are on sale frequently. 

If customers know significant price drops are coming, they can wait to buy the fashion item.  And the consumable product has a shelf life that extends until the next promotion, why not stock up?   

Other circumstances we've seen are retailers who have one or two major sales per year, during which they do the majority of their business.  They sell expensive items at a good discount.


Why do Inventory Managers care?

As we condense our sales of our items or business into fewer time periods the volatility increases, and demand is more challenging to predict.  Achieving high service levels for our customers is more difficult and expensive. - It’s harder to move inventory through the supply chain quickly in response to sales. Sometimes, to chase demand, we have to use alternate sourcing, which increases costs and strains the supply chain.

Customers are given offers they cannot refuse.

Limited time offers and bundled offers can convince customers to buy. They may purchase items they don't really want or spend more than they planned. 

 Free shipping on orders over $50

$20 off when you buy $50

40% off marked items, today only!

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This is awesome, right?  From a customer standpoint, the risk is limited, because they can always take the items back, and we do get sales we might not have otherwise.

How does this impact Inventory Management?

It’s not unusual for 25% of online fashion sales to be returns and returns for these types of promotions are even worse.  

This impacts our stocking strategies. Sometimes we cannot track it or transfer it because the items aren't ranged to sell in that location.  We have to mark it down to sell it. Other times we have stores package it to fulfill online demand. 

It’s almost too easy to connect with Customers

The email you send out today impacts what customers buy tomorrow.  Anywhere from single person to all loyalty or potential customers could be targeted.  When customers share promotions on social media and third party sites publish them, the promotion audience can grow amazingly fast.


What burden does this place on Inventory Management?

We may see sales spike and discounts applied, without insight into the promotional types or vehicles.  If we don't know in advance what offers, we can't respond in time to get inventory in the best locations.  Even if when we do know a promotion is coming, the results are difficult to forecast, because we don't know important details.

What can retailers do?

These practices add a lot of uncertainty, causing us to lose sales or increase costs.  It’s a balancing act; we need to work together across functional areas to make the best decisions. 

We can:

Share data and create good analytics

If the data is not centralized, each group may only see a part of the puzzle.  Don’t assume everyone is looking at what you are.  Information is the first step.

Are we able to answer important questions with our data? 

  • Does pantry loading drive traffic or higher spend overall?  Under what circumstances?
  • Are we running promotions on competing products at the same time?
  • What promotions are driving cherry pickers?
  • How much inventory is orphaned by returns coming into stores?  What is the impact of these un assorted items on markdowns, store labor?
  • What is our promotional service level?  How much inventory does it take to support different types of promotions? 

Objectively assess our forecasting ability

Promotional forecasting is challenging, but some practices make sales even more difficult to predict:

  • Promotions that recur within a short time frame
  • Promotions with unpredictable audience, e.g. social media for example
  • Promotions that move majority of demand to short time frames

A review of promotional predictability could reveal more.

Reviewing promotions can help categorize the impact on the promoted item's sales, and the impact on other items and locations. Understanding the uncertainty associated with the promotions helps highlight promotional risk. 

These insights can inform stronger decisions upstream and downstream, encourage tighter collaboration.

Understand and agree upon stocking strategies

Imagine being able to have these discussions:

These items are frequently on promotion, have huge pantry loading effects. - 80% of sales are at a discount.  Do we want to continue these promotions? If so, what should our stocking strategy be?

Another group of products are expensive, highly price sensitive, and are rarely promoted.  We are confident in the total sales but to have high service level at the store, we will risk overstocks after the promotion.  We'll hold inventory back at the warehouse, replenish during the promotion and offer customers home delivery if we stock out.

Occasionally, let the tail wag the dog

Listening to your Inventory Managers and Supply Chain can help you see your promotions from a different perspective and may inspire you to tweak and align practices to improve your promotional performance.


About the author

Linda Whitaker has worked with hundreds of retailers applying data science and artificial intelligence to a wide variety of areas including merchandise planning, pricing, promotions, customer insights, demand forecasting, replenishment, allocation and supply chain. Prior to joining us at Cognira, Linda served as Chief Science Officer at Quantum Retail Technology (which she co-founded) and 8th Bridge (now part of Fluid, Inc.).