A Comprehensive 3-Phase Framework for Determining the Customer’s Product Usage in a Food Supply Chain
Abstract
:1. Introduction
1.1. Questions of the Research
1.2. Contributions and Novelties of the Research
1.3. Managerial Implications
2. Literature Review
2.1. Product Distribution in Supply Chains
2.2. Learning Methods
3. Research Methodology
3.1. K-Means Algorithm
3.2. K-Means Algorithm’s Steps
4. Results and Discussion
4.1. Identifying the Consumers, Availability and Society Factors in Chocolate Consumption (Phase 1)
4.1.1. Level of Chocolate Consumption in Malaysia
4.1.2. Statistical Society
4.2. Determining the Effective Factors in Chocolate Consumption (Phase 2)
4.2.1. Data Entry
4.2.2. Data Preparation
- ConsumersData.Gender=ConsumersData.Gender.apply([’Female’,’Male’].index)
- ConsumersData.Education=ConsumersData.Education.apply([’Below High School Diploma’,’High School Diploma’,’University Graduate’,’Post Graduate’].index)
- ConsumersData.Diabetics=ConsumersData.Diabetics.apply([’No’,’Yes’].index)
- ConsumersData.HealthBenefitAwareness=ConsumersData.HealthBenefitAwareness.apply([’Low’,’Medium’,’High’,’Very High’].index)
- ConsumersData.SocietyTaste=ConsumersData.SocietyTaste.apply([’No’,’Yes’].index)
- ConsumersData.ChocolateAvailability=ConsumersData.ChocolateAvailability.apply([’Low’,’Medium’,’High’].index)
- ConsumersData.BrandVariety=ConsumersData.BrandVariety.apply([’Low’,’Medium’,’High’,’Very High’].index)
- ConsumersData.City=ConsumersData.City.apply([’KualaLumpur’,’Sembilan’,’Pahang’].index)
4.2.3. Descriptive Analysis
4.2.4. Determining the Effective Factors Using Shapiro Ranking Method
4.3. Using a Hybrid PCA and Ward Agglomerative Clustering Method for Consumption Pattern (Phase 3)
4.3.1. Libraries
- from sklearn import linear_model
- from sklearn import cluster, datasets
- import numpy as np
- import pandas as pd
- from math import sqrt
- import scipy.cluster.hierarchy as shc
- from sklearn.preprocessing import StandardScaler
- from sklearn.decomposition import PCA
- from mlxtend.plotting import plot_decision_regions
- import matplotlib.pyplot as plt
- import matplotlib.gridspec as gridspec
- import itertools
- from sklearn.metrics import silhouette_score
4.3.2. Using PCA Method
array([[40.21847836, −1.34506094], |
[33.22742738, −1.39573043], |
[−22.78335868, 0.65705467], |
[22.22322185, −1.35425239], |
[24.22312134, −1.35532962], |
[−31.78233389, −1.31869809], |
[31.21332019, −1.44789902], |
[−19.78296358, 0.86418076], |
[12.2193131, 0.65062064], |
… |
[3.22312041, 2.85635524], |
[36.21900695, −1.39087729], |
[29.21857854, 0.77858665]]) |
4.3.3. Determining the Appropriate Number of Clusters Using a Dendrogram
4.3.4. Training the Agglomerative Algorithm
4.3.5. Silhouette Coefficient
4.3.6. Calinski-Harabasz Index
5. Conclusions
- Availability Factors: Chocolate Availability, Brand Variety
- Society Factors: Health benefit Awareness, Society Taste (Desire)
- Consumer’s Factors: Age, Gender, Education, Diabetic Status
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
How to Score Respond Column: Select amongst options 1 to 5. Depending on the questions, different values will be explained for each option. | ||||
| ||||
Group Factor | No. | Question | Your Response | Please use this section to add any comments which you feel would further clarify your response. |
RESPOND | ||||
(Use the above guideline) | ||||
Consumer’s Factors | 1 | Do you think different people will have different desires to use chocolates (in terms of volume)? | ||
2 | Do you think different people will have different desires to use chocolates (in terms of taste)? | |||
3 | Do you Believe gender will affect chocolate consumption? | |||
4 | Do you believe the different levels of education will affect chocolate consumption? | |||
5 | Do you think Diabetic people should use different types of chocolates? | |||
Society Factors | 6 | Do you think consuming chocolate is necessary for your health? If so, please explain | ||
7 | Do you think how much should a person at your age consume chocolate per month? | |||
8 | Which types of chocolates do you most like (milk chocolate, dark chocolate, flavored chocolates, etc.) | |||
Availability Factors | 9 | How easily can you purchase chocolates in your area? | ||
10 | Which chocolate brands can you easily find in the local market? | |||
11 | provided for executing the activities as estimated before? | |||
12 | Do you believe chocolate packages should be manufactured based on consumers’ needs? How? |
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Brand | Type | Weight (g) | Price (MYR) | Per 100 g (MYR) |
---|---|---|---|---|
1 | Ferrero Rocher Chocolate | 600 | 37 | 6.16 |
2 | Ferrero Rocher Chocolate | 38 | 8.8 | 23.15 |
3 | Cadbury Dairy Milk Oreo | 130 | 9.9 | 7.61 |
4 | Nutella B-ready | 132 | 10.8 | 8.18 |
5 | Ferrero Rocher Chocolate | 500 | 40.9 | 8.18 |
6 | Cadbury Dairy Milk Oreo | 165 | 8.61 | 5.21 |
7 | Cadbury Dairy Milk Oreo | 160 | 8.5 | 5.31 |
8 | Ferrero Rocher Chocolate 3 Pieces | 38 | 8.9 | 23.42 |
9 | Cadbury Dairy Milk Oreo | 90 | 9.6 | 10.66 |
10 | Nutella B-ready | 132 | 23.6 | 17.87 |
11 | Ferrero Rocher Raffaello | 50 | 19.45 | 38.9 |
Chocolate Price in the market per 100 g | 14.06 |
Row | Age | Gender | Education | Diabetics | Health Benefit Awareness | Society Taste | Chocolate Availability | Brand Variety | City |
---|---|---|---|---|---|---|---|---|---|
0 | 79 | Male | Below High School Diploma | No | Low | Yes | Medium | Medium | Pahang |
1 | 72 | Female | Below High School Diploma | Yes | Low | Yes | Medium | Medium | Sembilan |
2 | 16 | Female | High School Diploma | No | High | No | High | High | Kuala Lumpur |
3 | 61 | Female | Below High School Diploma | No | Low | No | Medium | Medium | Pahang |
4 | 63 | Female | Below High School Diploma | Yes | Low | No | Medium | Medium | Pahang |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
164 | 16 | Male | High School Diploma | No | High | No | High | High | Kuala Lumpur |
165 | 69 | Female | High School Diploma | Yes | High | No | Medium | Medium | Sembilan |
166 | 42 | Male | Post Graduate | No | Very High | Yes | Medium | Medium | Pahang |
167 | 75 | Female | Below High School Diploma | Yes | Low | Yes | Medium | Medium | Sembilan |
Row | Age | Gender | Education | Diabetics | Health Benefit Awareness | Society Taste | Chocolate Availability | Brand Variety | City |
---|---|---|---|---|---|---|---|---|---|
0 | 79 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 2 |
1 | 72 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 |
2 | 16 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 0 |
3 | 61 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |
4 | 63 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 2 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
164 | 16 | 1 | 1 | 0 | 2 | 0 | 2 | 2 | 0 |
165 | 69 | 0 | 1 | 1 | 2 | 0 | 1 | 1 | 1 |
166 | 42 | 1 | 3 | 0 | 3 | 1 | 1 | 1 | 2 |
167 | 75 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 |
168 | 68 | 1 | 1 | 0 | 2 | 1 | 1 | 1 | 1 |
Row | Age | Gender | Education | Diabetics | Health Benefit Awareness | Society Taste | Chocolate Availability | Brand Variety | City | |
---|---|---|---|---|---|---|---|---|---|---|
count | 169.00 | 169.00 | 169.00 | 169.00 | 169.00 | 169.00 | 169.00 | 169.00 | 169.00 | 169.00 |
mean | 85.00 | 38.781 | 0.432 | 0.745 | 0.213 | 1.224 | 0.289 | 1.272 | 1.272 | 1.082 |
std | 48.930 | 23.373 | 0.496 | 0.852 | 0.411 | 1.105 | 0.455 | 0.446 | 0.446 | 0.789 |
min | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 0.00 |
25% | 43.00 | 17.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 0.00 |
50% | 85.00 | 39.00 | 0.00 | 1.00 | 0.00 | 2.00 | 0.00 | 1.00 | 1.00 | 1.00 |
75% | 127.00 | 56.00 | 1.00 | 1.00 | 0.00 | 2.00 | 1.00 | 2.00 | 2.00 | 2.00 |
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Ali, M.F.B.M.; Ariffin, M.K.A.B.M.; Delgoshaei, A.; Mustapha, F.B.; Supeni, E.E.B. A Comprehensive 3-Phase Framework for Determining the Customer’s Product Usage in a Food Supply Chain. Mathematics 2023, 11, 1085. https://doi.org/10.3390/math11051085
Ali MFBM, Ariffin MKABM, Delgoshaei A, Mustapha FB, Supeni EEB. A Comprehensive 3-Phase Framework for Determining the Customer’s Product Usage in a Food Supply Chain. Mathematics. 2023; 11(5):1085. https://doi.org/10.3390/math11051085
Chicago/Turabian StyleAli, Mohd Fahmi Bin Mad, Mohd Khairol Anuar Bin Mohd Ariffin, Aidin Delgoshaei, Faizal Bin Mustapha, and Eris Elianddy Bin Supeni. 2023. "A Comprehensive 3-Phase Framework for Determining the Customer’s Product Usage in a Food Supply Chain" Mathematics 11, no. 5: 1085. https://doi.org/10.3390/math11051085
APA StyleAli, M. F. B. M., Ariffin, M. K. A. B. M., Delgoshaei, A., Mustapha, F. B., & Supeni, E. E. B. (2023). A Comprehensive 3-Phase Framework for Determining the Customer’s Product Usage in a Food Supply Chain. Mathematics, 11(5), 1085. https://doi.org/10.3390/math11051085