RiRiShun Logistics

Home Appliance Delivery Data for the 2021 MSOM Data Driven Research Challenge

2School of Management, University of Science and Technology of China @ 2020/10/24

Introduction to RiRiShun

Data Description

Potential Research Questions

1

2

3

Outline

3School of Management, University of Science and Technology of China @ 2020/10/24

RiRiShun: a logistics ecosystem

Logistics Subsidiary of Haier

Focus on logistics service for household appliances

Provide service for Ali, JD.com, and hundreds of

manufacturers, and online & offline retailers

4-dimational logics network

Warehousing: 100 centers, 2000 hubs, 5,000,000 m^2

storage area

Distribution: 100,000 trucks

Service: 6,000 service stations, 200,000 installation

servers

Information: open, smart, sharing, timely

7 national central distribution centers (CDCs)

26 regional distribution centers (RDCs)

100 local transfer centers (LTCs)

6000 last-mile hubs

Each province has one RDC or CDC

4School of Management, University of Science and Technology of China @ 2020/10/24

7 national central distribution centers (CDCs)

26 regional distribution centers (RDCs)

100 local transfer centers (LTCs)

6000 last-mile hubs

Each province has one RDC or CDC

A much denser network for the eastern and

southern providences

RiRiShun: a logistics ecosystem

5School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Field Description Data type Sample value

rrs_order_id RRS database identification bigint 4ee6fc7df5be07e6fffdd9ea0d90e54d

order_no Order unique identifier varchar 1e98af94feb4d32f875546fcdadbe0f4

order_date Order time datetime 2019-08-08 22:37:10

clit_code Client unique identifier varchar RRS0001

total_amt Product quantity in each order int 2

origin_center_code Origin center for the order varchar C12401

destination_center_code Destination center of the order varchar C12402

delivery_method Delivery method int 2

arrived_org_code Last-mile hub for the order varchar CPWD002812

distc_oper_dest Haul distance from the Origin Center to the Destination Center (km) int rrsomsapp61

distc_dest_org Haul distance from the Destination Center to the Last-mile hub (km) int 8/1/2019 00:00:09

system_time System record create time timestamp 8/2/2019 15:29:00

1. Orders: provides the information for over 14 million RRS orders from

October 2018 to September 2019

6School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Field Description Data type Sample value

rrs_order_detail_id RRS database identification bigint 4ee6fc7df5be07e6fffdd9ea0d90e54d

order_no Order unique identifier varchar 1e98af94feb4d32f875546fcdadbe0f4

order_item SKU sequential number in the order varchar 2019-08-08 22:37:10

mat_code SKU unique identifier varchar RRS0001

order_amt Product quantity of the SKU int 2

location Storage location varchar C12401

mat_length Length of the SKU (cm) decimal 92

mat_width Width of the SKU (cm) decimal 35

mat_height Height of the SKU (cm) decimal 62

mat_volume Volume of the SKU (cm^3) decimal 200210

mat_weight Weight of the SKU (kg) decimal 32.5

2. SKU details: SKU information for all the orders

7School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Field Description Data type Sample value

rrs_order_extend_id RRS database identification bigint 4ee6fc7df5be07e6fffdd9ea0d90e54d

order_no Order unique identifier varchar 1e98af94feb4d32f875546fcdadbe0f4

client_install_date Client required installation time datetime 2019-08-08 22:37:10

client_require_date Client required delivery time datetime 2019-08-08 22:37:10

oms_aging_name Effective time zone int 48

oms_aging_user_dateEstimated latest delivery time according to

its effective time zonetimestamp 2019-08-08 22:37:10

appointment_date Delivery time appointed with consumer timestamp 2019-08-08 22:37:10

3. Appointment details: detailed appointment information for all the orders

8School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Field Description Data type Sample value

rrs_pool_node_info_id RRS database identification bigint 4ee6fc7df5be07e6fffdd9ea0d90e54d

order_no Order unique identifier varchar 1e98af94feb4d32f875546fcdadbe0f4

operation_center_code Operation distribution center varchar RRSZX040

orig_code Origin code of each node varchar rrs_wd_2731

dest_code Destination code of each node varchar GB01336

node_code Code of the operation node varchar QS

node_operation_date Time when the node occurs timestamp 2019-08-08 22:37:10

4. Delivery details: detailed delivery information of each order, containing

all operation nodes in the entire logistics distribution process

9School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Field Description Data type Sample value

rrs_order_person_info_id RRS database identification bigint 4ee6fc7df5be07e6fffdd9ea0d90e54d

order_no Order unique identifier varchar 1e98af94feb4d32f875546fcdadbe0f4

person_name Person name varchar 8c42086fdfa4a41ebc8c5bba86adaac9

person_provence Province varchar dd823c023d1e7f8f6d33bdab5d70c3b8

person_city City varchar e285318eb0fe8aaf4ed5c25b041074e1

person_area County varchar 903528e60a0b4ee45ac2476dbd593ff7

person_town Town varchar 6f447cdfd36c18bd3398016d3f6ab4db

person_three_gbcode Third GB code varchar GB00412

person_four_gbcode Fourth GB code varchar GB26559

person_post_code Post code varchar 123456

5. Consumer details: detailed consumer information of each order

10School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Field Description Data type Sample value

clit_code Client unique identifier varchar RRS18

clit_type The type of the client int 1

6. Client details: client classification

Note: logistics operators: client_type =1, retailers: client_type =2, manufacturers: client_type =3.

7. Distance information

This table provides a travel distance matrix among all the centers in the network. Travel distance

between any two centers in the network is estimated by navigation data based on the geo-location

of each center, and we believe this information will give a more visualized picture of the network

structure of RRS.

11School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Number of Orders in

different months

From January to August, 2019

17,401,572 orders

Lowest in February (5.6%)

Highest in June (20%)

12School of Management, University of Science and Technology of China @ 2020/10/24

Data Description

Order channels 120 channels with more

than 10 Orders

The highest is 14,888,490,

takes a 85.3% of all the

orders

13School of Management, University of Science and Technology of China @ 2020/10/24

Potential Research Questions

Solution: Inventory Transshipment

Significant inventory transshipment among national distribution centers!

14School of Management, University of Science and Technology of China @ 2020/10/24

Potential Research Questions

Online customers can be very

sensitive to delivery speed, and

better service capabilities may not

only lead to decreased costs, but

also increased revenues through

attracting new customers (Basak et

al. 2019, Levi et al. 2019)

Unlimited choice available online

vs. Limited capacity at local

warehouses

Logistics Ecosystem

Cloud Warehousing

Cloud Logistics

Traditional Logistics

Decentralized Warehousing

Centralized Transshipment

CDC/RDC TC

Retailers

Experiencing Mall

Flagship store

Consumers

Cloud Logistics

Productionfactory

Transshipment in Cloud Warehousing

15School of Management, University of Science and Technology of China @ 2020/10/24

Potential Research Questions

Contract design

Transshipment strategy with cloud storage and cloud distribution

Cost and revenue allocation between involved centers for transshipments

Incentives and delivery successful rate, to reduce reassignments

Cross-dock warehousing optimization between different transportation modes (i.e., trunk

transportation, urban logistics, and last-mile delivery)

Distribution network design

Inventory strategy: pooled or dedicated

Cost or revenue allocation among involved centers for specific orders

Load scheduling for trucks facing products from upstream centers and local warehouses

16School of Management, University of Science and Technology of China @ 2020/10/24

Thank you !