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Oct 4, 2017
Getting ‘Ship from Store’ right is tough. And it's not just about reducing your logistics and transportation costs. That's just the tip of the iceberg.
Your consumers demand fast delivery, while you struggle to balance multiple (often conflicting) fulfillment objectives: to meet customer expectations, keep delivery costs low, minimize canceled orders, reduce safety stock, and maximize inventory turns.
To deliver profitably, Celect optimizes order fulfillment objectives in real-time with predictive analytics to get the best total outcome. This means you can realize gross margin gains by improving inventory turns and speed to customer, while still reducing shipping costs. It’s a win-win for you and your customers.
Ship From Store
How to Optimize with Predictive Analytics
Dir of Retail Technology
Dir of Marketing
Chief Customer Officer
Bridge Solutions Group
© 2017 Celect, Inc. All Rights Reserved.
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• Predictive analytics SaaS platform to help
retailers optimize inventories through data-
• We leverage a groundbreaking advance in
Machine Learning and Optimization.
• An MIT Artificial Intelligence Lab Top 50
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• How optimization of typical order management
works and why it’s not enough
• Why optimizing your inventories is the key to
your success as a retailer
• How to make your Ship from Store program
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WHAT WE WILL COVER TODAY
Many retailers ship-from-store.
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Is it working to their
Well, it’s a double-edged sword …
Store location puts you much
closer to the customer, but:
‒ Does the store have the right
‒ Can the store successfully
pick and pack?
‒ Do you know the forward
looking demand for the store?
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Order Management Systems
are rigid and limited by rules.
Insights into why optimizing your fulfillment
program is critical to success.
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One Key Quantifiable Benefit
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IT’S NOT JUST
Shipping: Delivery Windows
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Source: IBM 2016 Consumer Expectations Study
In general, how
important is each of the
windows when deciding
whether or not to place
an online/mobile order?
0% 20% 40% 60% 80%
Importance of Delivery Times For Making a
NOTE: Males 13-39 consider 1-2 hour delivery to be more
important that females of the same age. Females 50+ consider it
more important than males 50+.
Shipping: Importance of Speed of Delivery
Choice of Retailer
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Source: IBM 2016 Consumer Expectations Study; Q27
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Shipping: Free Shipping – Choice of Retailer
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Source: IBM 2016 Consumer Expectations Study; Q23
of consumers will
buy from a retailer
who offers free
shipping vs one
who doesn’t, even
if they have never
ordered from them
13-39= 78% 40+= 75%
Shipping: Costs – Forfeited Sales
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Source: IBM 2016 Consumer Expectations Study; Q24
have chosen not
0% 20% 40% 60% 80% 100%
Shipping: Delayed Delivery
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When you experience
a delayed delivery of
purchase from a
particular retailer, how
likely are you to not
shop that retailer in the
Source: IBM 2016 Consumer Expectations Study; Q29
13-19 20s 30s 40s 50s 60+
Shipping Delays Prevent Future Purchases
Provides quantifiable benefits across your organization
Time to Ship
Unit Shipping Cost
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• Utilize Celect’s proven ability to predict customer
demand to determine which stores have more inventory
than demand requires.
• Balance multiple, competing objectives
simultaneously, attempting to get as close as possible
to the optimal value on each separate objective.
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Why is optimizing fulfillment so
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Real-Time Optimization is Impossible with
Traditional Order Management Systems
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adapt to and optimize
against pick declines.
No way of ‘sacrificing
now’ for a future gain.
costs, and delay.
Priority Rule Driven.
Unable to balance
shipping costs against
metrics that help
increase product turn.
It’s Usually One Extreme or Another with
Traditional Order Management Systems
These systems are unable to
balance competing objectives – to
maximize inventory turns and
Look for available
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What Our Real-Time Optimizer Does
True Demand across all channels
• Attempts to maintain a close-to-
optimal balance across multiple
• Underlying algorithms
recognized by multiple patents
and academic awards
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Optimized Ship from Store Scenario
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Faster time to customer
Improve Inventory Turns
Capture In-Store Lost Sales
Prioritize stores with higher
weeks of supply
Reduce pick declines
Reduced ship delay
Fewer split shipments
Lower shipping cost
Results in Opportunity
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In the first year.
Big Returns from Optimizing Fulfillment
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Celect Case Study: Fashion Footwear Retailer
• Objective: Compare current Order Management System rules
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When implementing Celect, comparisons against the status
quo are used to understand impact of prioritization, further
”train” the model, and quantify ROI.
Summary of Results
Optimizing across all the factors below with the main objective of maximizing
throughput, we see significant overall improvements in weeks of supply, net pick
decline rate and split shipments.
Total Throughput 20,471 28,433 28%
Average Weeks of Supply 7 15 114%
Total Split Shipments 1261 741 -41%
Average Daily Unit Shipping Cost $3.09 $2.93 -5%
Average Daily Net Pick Decline
35% 26% -25%
Average Daily Load Balancing 33% 44% 29%
1. Throughput (main objective)
2. Pick Decline Rate
3. Split Shipments
4. Weeks of Supply
5. Load Balancing
6. Shipping Costs
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1. Optimizing fulfillment is more than just transportation
2. Order Management Systems are a necessity but can’t
optimize against multiple objectives in real-time
3. Huge financial gains possible in a short timeframe
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© 2017 Celect, Inc. All Rights Reserved.28
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