Omni-Channel Warehouse
Location with Competition
St. Louis Business Intelligence User Group
May 9, 2017
Mitch Millstein, James F. Campbell
College of Business Administration
University of Missouri-St. Louis
St. Louis, MO
1
Outline
• Problem Background
- Stores, Markets, Competition
- Convenience Economy
• Omni-Channel Inventory/Facility
Research
• Interesting Elements of the Model
• Results – Filling in a Knowledge Gap
2
Background
• Design an omni-channel distribution system for a
growing US sporting goods retailer (SG) in a
competitive environment.
 Locate warehouse(s).
 Assign e-commerce orders to a warehouse.
 Assign retail stores to a warehouse.
• SG Management strongly believed that e-commerce
market-share depends on fast delivery times.
• SG initially thought that adding a new US East-coast
omni-channel warehouse, along with its existing St.
Louis Missouri warehouse, could support expansion
and high service goals.
3
Company Background
• SG sells equipment primarily for one sport in 30 retail
stores in 11 states - and via e-commerce across the US
(currently 30% of sales).
 SG is one of 8 national or regional retail chains, all
privately held, that offer ecommerce sales.
 Single warehouse in St. Louis (HQ) supports all stores
and ships most e-commerce orders; holds 32,000 SKUs.
 Limited ability to use retail store inventory to fill ecommerce orders - via backroom of larger stores.
• SG plans large growth in e-commerce from better
service - and expansion to 86 stores across the US.
4
Top 15 Markets + 30 SG Stores
4
5
4
4
3
2
2
2
Top markets
SG retail stores
Current warehouse location
5
SG’s Future Stores (86 in 2020)
3
4+4
1+1
5+5
4
4
3+5
5
2+2 2+4 6
4
1+1
2+3
7
2
3
+3
Future stores
Current warehouse location
6
Current Stores + Chain Competitor Stores
4
5
4
4
2
2
3
2
2
2
2
2
2
2
Chain competitor stores
SG’s current stores
7
Omni-Channel Research
• Need to redesign distribution systems and supply
chains to support e-commerce and support high
service levels.
•
Swaminathan and Tayur, 2003; Agatz et al. 2008; Chao and Norton,
2016; Hübner et al. 2016; Ishfaq et al. 2016
• Ecommerce fulfillment methods
•
Alptekinoglu and Yang, 2005; Chiang and Monahan, 2005; Bendoly
et al. 2007; Mahar et al. 2009; Mahar and Wright, 2009; Bretthauer
et al. 2010; Gallino and Moreno, 2014; Torabi et al. 2015
• Optimization as a tool for strategic network design.
•
Shapiro and Wagner 2009, Bartolacci et al. 2012, Eiselt at al. 2015
• Little work specific to omni-channel location models.
•
Bretthauer et al. Computers & IE 2010; Liu et al. EJOR 2010.
8
Omni-Channel Research
• What network will best serve both retail stores and ecommerce (with delivery or in-store pickup)?
• Should ecommerce and retail stores be served by separate or
common warehouses and channels?
• Can retail store stock be used for ecommerce orders?
• Some research on dedicated vs. separate channels,
role of inventory pooling, benefits of postponement
with online orders, store pickup and return options,
etc.
9
1st: Estimate E-commerce Demand
• Annual revenue from sales of equipment for this sport
in the US is $600 million, with 20% being ecommerce.
• Developed regression model to allocate $120
million of annual e-commerce sales (891,000 orders)
to 359 US MSAs (cities) based on:
1. Address data for 600,000 members of the association
that governs the amateur sport in the US.
2. Income data for US zip codes.
3. Sales data from SG’s e-commerce and customer loyalty
programs.
• This provided the potential e-commerce demand
for each of the 359 MSAs across the US.
10
2nd: Estimate Demand Captured by SG
• No data is available on retail or e-commerce market
shares (private firms).
• With input from SG management, SG’s market share in
an MSA was adjusted as follows:
1. Increase market share when SG has stores in an
MSA due to brand presence. (reverse cannibalization)
2. Reduce market share for each competitor chain in
the MSA.
3. Reduce market share as delivery times increase.
11
Reverse Cannibalization?
• Analyzed historical data to evaluate the impact of SG
entering an MSA with retail stores, on e-commerce
orders from that MSA.
• Modelled opening stores in a new MSA as an “intervention”
on e-commerce orders using ARIMA
• “Intervention” is statistically significant for 3 of 6
MSAs; though results were quite variable and affected
by competitor actions…
12
Service Impact on Market Share
• Market share depends on travel time to MSA i from a
warehouse at j, Tij and a “market share factor” F (F
ranges from 1.5 to 4.0).
𝑀𝑖𝑗 = market share for MSA i captured by warehouse at j
𝑀𝑖𝑗 =
1−% 𝑙𝑜𝑠𝑠 𝑡𝑜 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑜𝑟 𝑐ℎ𝑎𝑖𝑛𝑠
𝑇𝑖𝑗 ×𝐹
+ % 𝑔𝑎𝑖𝑛 𝑓𝑟𝑜𝑚 𝑆𝐺 𝑝𝑟𝑒𝑠𝑒𝑛𝑐𝑒
F controls sensitivity to service; What should F be?
13
Fit of Market Share Model
• Compare modelled and “actual” SG market share for
service from existing warehouse.
F=2
F=3
MSAs with >70 orders/year
14
Inventory Costs
• Five sizes of warehouses based on # of e-commerce
orders shipped per year: 25,000 – 250,000.
• SKU mix and inventory investment calculated by
Inventory OptimizerTM software used by SG (forecasts
demand and sets safety stocks).
• SKUs and costs are
adjusted to use
inventories of retail
stores in the same
MSA as the warehouse,
if MSA traffic is not
too congested.
15
Objective Functions: Variables
1. Gross Profit = $50 x # of orders captured (GP).
2. Store labor cost savings when a warehouse is in the
same MSA as a store (due to better receiving and
handling operations) (LC).
3. E-commerce parcel delivery costs (ED).
4. Warehouse-to-store LTL pallet shipping costs (SS).
5. Annual warehouse operating costs based on size (WO).
6. Inventory holding costs (from assigned demand) (WH).
7. One-time warehouse opening costs based on size (OP).
16
Location Model
• Capacitated warehouse location model.
• Costs and profit depend on # of orders captured, which
depends on warehouse locations (Tij ), SG and
competitor store locations, and market share factor F.
• Variables:
𝑌𝑖𝑗 = 1 if market i is served by a facility at location j
𝑋𝑗𝑘 = 1 if a facility of size k is open at location j k={1,2,3,4,5}
𝑍𝑠𝑗 = 1 if store support in city s is served by a facility at location j
• Maximize 5-year NPV of net profit.
Max
5
𝑡=1
𝐺𝑃 + 𝐿𝐶 − 𝐸𝐷 − 𝑆𝑆 − 𝑊𝑂 − 𝑊𝐻 − 𝑂𝑃
Profit
Labor
Savings
Transportation
Warehouse / Inventory
17
Future Stores + 23 Potential Warehouses
3
8
2
8
10
4
3
7
2
4
4
5
66
4
5
2
Potential warehouse location
18
Results: Sensitivity to F
F
Customers
more
sensitive to
service
3 or 4
WH
Min + Det
+ Chi or
Phi
Customers
less
sensitive to
service
Min + Det
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
Minneapolis Chicago Detroit Philadelphia
125000
125000
125000
50000
50000
50000
X
50000
50000
50000
50000
50000
50000
50000
50000
75000
50000
50000
50000
X
50000
50000
50000
50000
50000
50000
X
X
125000
125000
125000
125000
125000
X
X
50000
50000
50000
50000
50000
X
X
X
X
50000
X
X
X
X
X
50000
125000
125000
50000
50000
50000
50000
X
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
125000
250000
125000
125000
125000
125000
125000
125000
125000
125000
125000
25000
X
X
X
X
X
25000
25000
25000
X
X
X
X
X
X
# Orders
431,967
374,881
367,829
350,000
343,971
332,460
300,000
299,956
294,915
287,215
245,469
225,000
221,659
215,844
210,430
200,000
199,893
200,000
198,614
175,000
175,000
175,000
174,545
171,500
168,612
Profit ( millions)
Ave. Days
$27.1
$25.1
$24.0
$22.3
$21.5
$20.0
$18.9
$18.5
$17.6
$16.6
$15.5
$15.4
$14.9
$14.1
$13.4
$13.0
$12.6
$12.6
$12.4
$12.0
$11.8
$11.8
$11.7
$11.3
$11.0
1.66
1.86
1.78
1.81
1.80
1.77
1.89
1.74
1.72
1.70
2.03
2.17
2.10
2.10
2.10
2.19
2.14
2.11
2.06
2.24
2.25
2.21
2.15
2.15
2.15
 Only 4 locations are used.
 Almost always Detroit and
Minneapolis.
 Not current location (St. Louis).
 Warehouse locations follow
service sensitivity.
19
Optimal Warehouse Locations
3
8
2
10
4
3
7
8
2
4
4
5
66
4
5
2
Warehouse location
20
Results: Use of Store Inventories
• Current warehouse alone – or adding an eastern
warehouse (in Philadelphia) without using store
inventories
• Optimal network without using store inventories is one
WH in Detroit
• Use of store inventories leads to more warehouses and
much greater profit!
Use of Store
Inventory
No
St. Louis (fixed)
NPV Profit
($1000)
-$2,226
Service
(average days)
2.58
No
St. Louis, Philadelphia (both fixed)
-$9,144
2.12
No
Detroit (optimal)
$1,907
2.31
Yes
Detroit, Minneapolis, Chicago, Philadelphia
$20,043
1.77
Warehouse locations
21
Service Results
80%
% of Customers or Revenue
70%
% of Customers
60%
% of Revenue
50%
40%
30%
20%
10%
0%
1
2
3
4
Delivery Days
22
Future Research
• Ship-From-Store versus Consolidating Warehouses in
certain MSA’s versus Dedicated E-Commerce
Warehouse
– Impact of store inventory and warehouse inventory to support
stores
• Joint optimization of facility location and inventory
levels
• Explore impact of other forms of market share model:
– Impact of delivery times
– E-commerce loss due to competitor stores
– Reverse cannibalization for own stores
• Use general data to derive more general insights
23
Postscript
• SG bought by competitor with 22 stores in the northeast –
who has a large warehouse near Philadelphia.
4
3
5
4
4
3
2
2
8
3
2
24
Omni-Channel Warehouse
Location with Competition
Thank you!
25
Constraints: Capacitated fixed charge
location problem
𝑗
𝑌𝑖𝑗 ≤ 1
∀𝑖
Every MSA is served by at most 1 warehouse
𝑗
𝑍𝑠𝑗 ≤ 1
∀𝑠
Every store is served by at most 1 warehouse
𝑋𝑗𝑘
∀𝑖, 𝑗
Link MSA assignment and WH opening
𝑋𝑗𝑘
∀, 𝑗
Link store assignment and WH opening
𝑖
𝑀𝑖𝑗 𝑌𝑖𝑗 ≤
𝛾𝑗𝑘 𝑋𝑗𝑘
𝑘
𝑋𝑗𝑘 ≤ 1
𝑌𝑖𝑗 ≤
𝑍𝑠𝑗 ≤
𝑘
𝑘
𝑘
∀𝑗
∀𝑗
Obey WH capacity
Only one WH size can be selected
𝑌𝑖𝑗 ∈ 0,1 , 𝑋𝑗𝑘 ∈ 0,1 , 𝑍𝑠𝑗 ∈ 0,1
26
Profit model
• Maximize 5-year NPV of Net profit.
• Net Profit = gross profit (GP)
GP = $50 x # of orders
captured
+ store labor cost savings (LC)
– e-commerce parcel delivery costs (ED)
– store support LTL delivery costs (SS)
– warehouse operating costs (WO)
– inventory holding costs (WH)
– warehouse opening costs (OP)
27