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Welcome!
Sean GouldsonDir of Retail Technology
Celect
Todd HarrisDir of Marketing
Celect
Jim BengierChief Customer Officer
Bridge Solutions Group
© 2017 Celect, Inc. All Rights Reserved.
[email protected] [email protected] [email protected]
2
Background
• Predictive analytics SaaS platform to help
retailers optimize inventories through data-
driven decisions.
• We leverage a groundbreaking advance in
Machine Learning and Optimization.
• An MIT Artificial Intelligence Lab Top 50
Technology Innovation
© 2017 Celect, Inc. All Rights Reserved.3
• 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
profitable
© 2017 Celect, Inc. All Rights Reserved.4
WHAT WE WILL COVER TODAY
Many retailers ship-from-store.
© 2017 Celect, Inc. All Rights Reserved.5
Is it working to their
advantage?
Well, it’s a double-edged sword …
Store location puts you much
closer to the customer, but:
‒ Does the store have the right
inventory?
‒ Can the store successfully
pick and pack?
‒ Do you know the forward
looking demand for the store?
© 2017 Celect, Inc. All Rights Reserved.6
Insights into why optimizing your fulfillment
program is critical to success.
© 2017 Celect, Inc. All Rights Reserved.8
BRIDGE SOLUTIONS
GROUP
© 2017 Celect, Inc. All Rights Reserved.9
Typical Order
“Optimizer”One Key Quantifiable Benefit
Reduces Transportation Costs
© 2017 Celect, Inc. All Rights Reserved.10
IT’S NOT JUST
ABOUT COSTS.
DELIVERY SPEED
ORDER DELAY
SPLIT SHIPMENTS
Shipping: Delivery Windows
© 2017 Celect, Inc. All Rights Reserved.11
Source: IBM 2016 Consumer Expectations Study
In general, how
important is each of the
following delivery
windows when deciding
whether or not to place
an online/mobile order?
{Important/Very
Important}
0% 20% 40% 60% 80%
1-2 Hour
Same-day
Next-day
2-day
45%
52%
61%
72%
Importance of Delivery Times For Making a Purchase
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
© 2017 Celect, Inc. All Rights Reserved.12
Source: IBM 2016 Consumer Expectations Study; Q27
52%
60%
73%
80%
86%
81%
60%
71%
72%
79%
79%
78%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
60+
50s
40s
30s
20s
13-19
Age
Ran
ge
Female Male
Shipping: Free Shipping – Choice of Retailer
© 2017 Celect, Inc. All Rights Reserved.13
Source: IBM 2016 Consumer Expectations Study; Q23
77% of consumers will buy from a retailer
who offers free shipping vs one
who doesn’t, even if they have never ordered from them
before
$Shipping
FREEShipping
13-39= 78% 40+= 75%
Shipping: Costs – Forfeited Sales
© 2017 Celect, Inc. All Rights Reserved.14
Source: IBM 2016 Consumer Expectations Study; Q24
83% of consumers
have chosen not to purchase because of
shipping costs
88%
84%
80%
77%
71%
78%
87%
87%
86%
88%
79%
88%
0% 20% 40% 60% 80% 100%
60+
50s
40s
30s
20s
13-19
Age
Ran
ge
Female Male
Shipping: Delayed Delivery
© 2017 Celect, Inc. All Rights Reserved.15
When you experience
a delayed delivery of
an online/mobile
purchase from a
particular retailer, how
likely are you to not
shop that retailer in the
future? {Likely/Very
Likely}
Source: IBM 2016 Consumer Expectations Study; Q29
0%
10%
20%
30%
40%
50%
60%
70%
13-19 20s 30s 40s 50s 60+
58%
66%
56%
38%34%
23%
44%
51%48%
25% 25%22%
Shipping Delays Prevent Future Purchases
Male Female
Provides quantifiable benefits across your organization
Increase
Store Throughput
Decrease
Markdowns
Reduce Weeks
of Supply
Reduce
Time to ShipReduce
Unit Shipping Cost
Fulfillment Optimization
© 2017 Celect, Inc. All Rights Reserved.16
• 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.
© 2017 Celect, Inc. All Rights Reserved.17
FULFILLMENT
OPTIMIZATION
Real-Time Optimization is Impossible with
Traditional Order Management Systems
© 2017 Celect, Inc. All Rights Reserved.19
Issue #3:
Not Predictive.
Cannot organically
adapt to and optimize
against pick declines.
Issue #2:
Short-sighted.
No way of ‘sacrificing
now’ for a future gain.
Impacts splitting
shipments, shipment
costs, and delay.
Issue #1:
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
WE
EK
S O
F S
UP
PLY
(I
NV
EN
TO
RY
)
THROUGHPUT
These systems are unable to
balance competing objectives – to
maximize inventory turns and
utilization.
OMS RULE:
Maximize
Throughput
OMS RULE:
Look for available
inventory
© 2017 Celect, Inc. All Rights Reserved.20
What Our Real-Time Optimizer Does
5%
WE
EK
S O
F S
UP
PLY
(I
NV
EN
TO
RY
)
8%
Real-time Optimization
True Demand across all channels
THROUGHPUT
• Attempts to maintain a close-to-
optimal balance across multiple
objectives
• Underlying algorithms
recognized by multiple patents
and academic awards
© 2017 Celect, Inc. All Rights Reserved.21
Faster time to customer
Improve Inventory Turns
Capture In-Store Lost Sales
Reduce Cancellations
Prioritize stores with higher
weeks of supply
Reduce pick declines
Increase throughput
Reduced ship delay
Fewer split shipments
Lower shipping cost
Results in Opportunity
© 2017 Celect, Inc. All Rights Reserved.23
BENEFITS: METRICS:
In the first year.
$3.5M
Big Returns from Optimizing Fulfillment
© 2017 Celect, Inc. All Rights Reserved.24
Celect Case Study: Fashion Footwear Retailer
• Objective: Compare current Order Management System rules
against Celect
© 2017 Celect, Inc. All Rights Reserved.25
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.
Metric
Timeframe
Baseline Celect%
Change
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
Rate35% 26% -25%
Average Daily Load Balancing 33% 44% 29%
OPTIMIZATION FACTORS:1. Throughput (main objective)
2. Pick Decline Rate
3. Split Shipments
4. Weeks of Supply
5. Load Balancing
6. Shipping Costs
7. Delay
© 2017 Celect, Inc. All Rights Reserved.26
KEY TAKEAWAYS:
1. Optimizing fulfillment is more than just transportation
costs
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
© 2017 Celect, Inc. All Rights Reserved.27
THANK
YOU!
© 2017 Celect, Inc. All Rights Reserved.28
Questions?
More webcasts and information
celect.com/resources
@celect