Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
Abstract
:1. Introduction
2. Literature Review
2.1. Shippers/Consignees Behavior Segmentation
2.2. Association Rule
2.3. Clustering Technique
2.4. Decision Tree
2.5. Monte Carlo Simulation
3. Methodology
3.1. Step 1: Product Association
- (1)
- FP-Growth was used to determine items in the set that have been frequently delivered together in a certain fraction of transactions.
- (2)
- Evaluation of the association rule used Min sup and Min conf thresholds [47] measured by Equations (1) and (2). Thresholds are defined for expected values of minimum revenue.
3.2. Step 2: Shipper/Consignee Clustering
- (1)
- Determining R, F, M, NC, NP, W, and D variables for each shipper and consignee
- (2)
- Correlating variables to investigate relationships
- (3)
- Determining optimal numbers of clusters, with the K-optimal method [62]. The K-means technique was used to cluster a group of Y shippers and X==>Y consignees.
- (4)
- Analyzing different clusters of Y shippers and screening potential X==>Y consignee clusters.
3.3. Step 3: Prediction of Shippers/Consignees Matching
- (1)
- Classification of the delivery behavior of X==>Y consignees and Y shipper clusters through a decision tree algorithm. Predictions of X consignees were used as testing data (Figure 2).
- (2)
- Evaluation of the classification model through 10-fold cross-validation. Accuracy and confidence of rule thresholds were determined for the expected value of minimum revenue in Step 4.
3.4. Step 4: Revenue Simulation
4. Case Study
4.1. The Case and Data Collection
4.2. Implementation
4.2.1. Identifying Potential Products Using Association Rule
4.2.2. Identifying Potential Customers Using Clustering Technique
Variable Analysis
Statistical Correlation Analysis
K-means Clustering
4.2.3. Predicting Possible Matching of Shippers and Consignees Using Decision Tree
- Rule 1: if {Weight > 303.13 kg} then cluster_4
- Rule 7: if {Weight ≤ 303.13 kg} and {Monetary ≤ 24,502 THB} and {Number of customers ≤ 1.5} and {Frequency > 38} then cluster_2
- Rule 4: if {144.30 kg < Weight ≤ 303.13 kg} and {Monetary ≤ 24,502 THB} and {Number of customers > 1.5} then cluster_3
4.2.4. Simulating Expected Revenue Using Monte Carlo Simulation
5. Conclusions and Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Research Question (RQ) | DA Techniques | Description |
---|---|---|
RQ1 Which products/items do consignees most often receive together? Which items are likely to be recommended? | Association rule
| Discover relationships between product categories that are normally or frequently delivered together. |
RQ2 Which shippers and consignees are the most/least valuable? What are their distinct characteristics? | Clustering technique
| Group customers according to patterns of behavior and target potential customers to provide recommendations. |
RQ3 Which shippers and consignees have a greater likelihood of becoming new business partners? | Classification technique
| Learn to predict potential business matches between shippers and consignees. |
RQ4 Can the matching of consignees and shippers generate revenue? | Simulation
| Estimate revenue that would be generated to support investment decision-making and to simulate new business scenarios. |
Attributes | Data Type | Description and Data Examples |
---|---|---|
Shipper name | Text | Name of shipper e.g., Mr. Emily Brown |
Consignee name | Text | Name of consignee e.g., Mr. John Meily |
Date | Numeric | Transaction date e.g., 12/03/2018 |
Time | Numeric | Transaction time e.g., 10:31:44 AM |
Products group name | Nominal | Name of product group e.g., food and drink, clothes, garments, and accessories |
Products category name | Nominal | Name of product category e.g., perishable foods, clothes |
Start station | Nominal | Name of start station e.g., Chiang Mai |
Destination station | Nominal | Name of destination station e.g., Chiang Rai |
Quantity | Numeric | Quantity of cargo per transaction |
Weight | Numeric | Weight of cargo per transaction (kg) e.g., 15 kg. |
Freight charges | Numeric | Freight charges per transaction (THB) e.g., 695 THB. |
Membership application date | Numeric | Date of member registration (first transaction) e.g., 05/12/2017 |
Employee name | Text | Name of sale officer who recorded the transaction |
Payment type | Nominal | Payment type e.g., cash, debit, agent |
No. | Rule | Support | Confidence |
---|---|---|---|
1 | Garment products ==> Clothes | 0.090 | 80.81 |
2 | Garments and related products ==> Clothes | 0.019 | 60.79 |
3 | Bags and handbags ==> Clothes | 0.018 | 61.93 |
4 | Car accessories ==> Spare parts | 0.010 | 56.49 |
5 | Garment products ==> Bags and handbags | 0.008 | 87.76 |
6 | Meatballs ==> Perishable foods | 0.008 | 64.21 |
7 | Underwear ==> Clothes | 0.007 | 52.53 |
8 | Computers and spare parts ==> Electronics equipment | 0.007 | 50.00 |
9 | Garment products ==> Garments and related products ==> Clothes | 0.006 | 89.81 |
10 | Sewing equipment ==> Clothes | 0.006 | 75.61 |
11 | Steaks ==> Seasoning ==> Perishable foods | 0.006 | 67.18 |
Variables | Definition | Calculation |
---|---|---|
R (Recency) | Period of time between previous service and set date | Set date—last transaction date |
F (Frequency) | Total number of service users within a particular period of time | Count of sale transactions |
M (Monetary) | Total THB generated by customer’s service within a particular period of time | Sum of money for each customer |
NC (Number of customers) | Number of consignees/shippers | Count of total consignee/shipper customer numbers |
NP (Number of product items) | Number of product items | Count of consignee/shipper product items |
W (Weight) | Average weight per transaction (kg) | Average weight per transaction (kg) |
D (Day) | Interval between transactions (day) | Average number of days between transactions |
R | F | M | NC | NP | W | D | |
---|---|---|---|---|---|---|---|
R | 1 | ||||||
F | −0.098 | 1 | |||||
M | −0.097 | 0.895 | 1 | ||||
NC | −0.113 | 0.693 | 0.686 | 1 | |||
NP | −0.108 | 0.160 | 0.154 | 0.232 | 1 | ||
W | −0.067 | 0.056 | 0.127 | 0.054 | 0.009 | 1 | |
D | 0.317 | −0.145 | −0.142 | −0.180 | −0.203 | −0.125 | 1 |
R | F | M | NC | NP | W | D | Number in Cluster | Normalized SAW Score | Rank | |
---|---|---|---|---|---|---|---|---|---|---|
Cluster_0 | 160.84 | 2.32 | 313.65 | 1.44 | 2.09 | 11.59 | 272.95 | 14,534 | 0.03 | 5 |
Cluster_1 | 27.21 | 44.01 | 9920.17 | 9.34 | 3.44 | 39.52 | 11.75 | 228 | 0.12 | 4 |
Cluster_2 | 1.33 | 1960 | 408,641 | 302.00 | 15 | 36.28 | 1.38 | 3 | 0.49 | 1 |
Cluster_3 | 7.00 | 124.98 | 38,288.36 | 16.23 | 4.38 | 54.68 | 4.96 | 40 | 0.15 | 3 |
Cluster_4 | 1.69 | 334.15 | 100,562.4 | 64.69 | 9.77 | 48.06 | 2.29 | 13 | 0.22 | 2 |
Total | 158.20 | 3.98 | 734.59 | 1.72 | 2.12 | 12.17 | 267.92 | 14,818 | 1 |
No | Classification Rule | Prediction Shipper Cluster | Number of Consignees in Rule | Number of Shippers in a Cluster | Confidence of Rule-Based Classifier | Accuracy | Association Support |
---|---|---|---|---|---|---|---|
1 | W > 303.13 | Cluster_4 | 1 | 13 | 100% | 62.40% | 9% |
2 | W ≤ 303.13 M > 24,502 R > 10 | Cluster_2 | 0 | 3 | 50% | ||
3 | W ≤ 303.13 M > 24,502 R ≤ 10 | Cluster_3 | 0 | 40 | 50% | ||
4 | W ≤ 303.13 M ≤ 24,502 NC > 1.5 W > 144.30 | Cluster_3 | 6 | 40 | 60% | ||
5 | W ≤ 303.13 M ≤ 24,502 NC > 1.5 W ≤ 144.30 D > 5.1 | Cluster_0 | 381 | 14,534 | 72% | ||
6 | W ≤ 303.13 M ≤ 24,502 NC > 1.5 W ≤ 144.30 D ≤ 5.1 | Cluster_2 | 12 | 3 | 25% | ||
7 | W ≤ 303.13 M ≤ 24,502 NC ≤ 1.5 F > 38 | Cluster_2 | 1 | 3 | 100% | ||
8 | W ≤ 303.13 M ≤ 24,502 NC ≤ 1.5 F ≤ 38 | Cluster_0 | 1034 | 14,534 | 39% |
Rule | Consignees: Shippers | Fitness of Association Model | Fitness of Classification Model | Expected Revenue (THB) | ||||
---|---|---|---|---|---|---|---|---|
SA | CA | RC | AC | |||||
1 | 1:13 | Triang (4.05, 6.5015) (N = 2400) | 3884 | 0.09 | 0.80 | 1 | 0.624 | 1038 |
7 | 1:3 | 11,429 | 1 | 3063 | ||||
4 | 6:40 | Pareto (0.9681, 1076) (N = 6) | 0.60 | 12,266 |
Rule | Minimum, Mean, Maximum Expected Revenue (THB) | Continuous Distribution Functions |
---|---|---|
1 | (192, 1038, 2675) | Lognorm (1082, 939.8) |
7 | (558, 3063, 8161) | Lognorm (3186.2, 2789.7) |
4 | (334, 12,266, 22,200) | Pearson5 (1.095, 1561) |
Total | (2380, 16,368, 27,500) | Pearson5 (2.0665, 10556) |
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Jintana, J.; Sopadang, A.; Ramingwong, S. Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics. Appl. Sci. 2020, 10, 5585. https://doi.org/10.3390/app10165585
Jintana J, Sopadang A, Ramingwong S. Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics. Applied Sciences. 2020; 10(16):5585. https://doi.org/10.3390/app10165585
Chicago/Turabian StyleJintana, Jutamat, Apichat Sopadang, and Sakgasem Ramingwong. 2020. "Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics" Applied Sciences 10, no. 16: 5585. https://doi.org/10.3390/app10165585
APA StyleJintana, J., Sopadang, A., & Ramingwong, S. (2020). Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics. Applied Sciences, 10(16), 5585. https://doi.org/10.3390/app10165585