Ship from Store: How to Optimize with Predictive Analytics

Ship From StoreHow to Optimize with Predictive Analytics

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


WHAT WE WILL COVER TODAY

Many retailers ship-from-store.


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?



Order Management Systems

are rigid and limited by rules.

Insights into why optimizing your fulfillment

program is critical to success.


BRIDGE SOLUTIONS

GROUP


Typical Order

“Optimizer”One Key Quantifiable Benefit

Reduces Transportation Costs


IT’S NOT JUST

ABOUT COSTS.

DELIVERY SPEED

ORDER DELAY

SPLIT SHIPMENTS

Shipping: Delivery Windows


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


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



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



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


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}


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


• 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.


FULFILLMENT

OPTIMIZATION

Why is optimizing fulfillment so

important?


Real-Time Optimization is Impossible with

Traditional Order Management Systems


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


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


Optimized Ship from Store Scenario


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


BENEFITS: METRICS:

In the first year.

$3.5M

Big Returns from Optimizing Fulfillment


Celect Case Study: Fashion Footwear Retailer

• Objective: Compare current Order Management System rules

against Celect


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


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


THANK

YOU!


Questions?

[email protected]

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Ship from Store: How to Optimize with Predictive Analytics

Retail

Transcript of Ship from Store: How to Optimize with Predictive Analytics