C HAPTER 7
5. Classification of Supply Chain Data by Using WEKA 1 Material and Methods
Fig. 15: SimpleCLI.
package which is at the much higher level, and the classifiers package is contained in weka package which is at the top- level. Like IBk> Lazy> Classifiers> Weka. Java packages are called with the java <classname> <args> command. In the example, k-Nearest Neighbour is run on iris.arff dataset with default parameters. The parameter “- t” refers to the dataset portion to be used for training.
As shown in the Figure 15, the Tab key is functional as a command complement. In the example, the Tab key lists matching packages/commands after “java.weka.a” and “java.weka.c”. If the command is composed until the classifier name, a description of both the general parameters used in each classifier and the classifier-specific parameters is displayed.
Detailed information on the schemes, algorithms and parameters of the packages can be purchased from the WEKA documentation pages (Bouckaert et al. 2018).
5. Classification of Supply Chain Data by Using WEKA
118 Logistics 4.0: Digital Transformation of Supply Chain Management HA(D)
Information Gain Ratio = (3)
H(D)
Here, HA(D) shows the entropy of the discriminating attribute, which has a value of s dividing the dataset into s subsets. The attribute with the minimum Information Gain Ratio, the ratio of the information acquisition of the tested attribute to the total information acquisition, is determined as the decision node.
5.1.2 Naive Bayes
The Naive Bayes Classification is a classification approach that is based on the work of the English Mathematician Thomas Bayes, who lived in 18th Century, on the probability theory. It aims to determine which possible class the new observation probably belongs to. Namely, C, representing the c possible class, X = {x1, x2,…, x n} to represents the n feature variables of the observation, the hypothesis of the probability of the X observation being of class C can be written as Equation 4.
P(X|C)P(C)
P(C|X) = (4)
P(X) Here,
P(C|X) is the posterior probability of Class given Observation.
P(X|C) is the likelihood which is the probability of Observation given Class.
P(C) is the prior probability of Class.
P(X) is the prior probability of Observation.
As it is seen in Equation 5, the Naive Bayes Classifier is the product of all the conditional probabilities, and as P(X) is equal to all classes, the X observation is considered to belong to the class that maximizes P(X|C) P(C).
n
P(X│C) =
∏
P(Xk |C) = P(X1│C)P(X2│C) … P(Xn │C) (5)i =1
Since the Naive Bayes Classifier is not an iterative calculation method, it can work quickly classifying big data sets with high accuracy rates.
5.1.3 k-Nearest Neighbor
The k-Nearest Neighbor (k-NN), which was proposed by Fix and Hodges in 1951, is a sample-based classification algorithm estimating the class of the new observation that is based on known observations in the training set (Fix and Hodges 1989).
It is also named as the Lazy Learning Method because there is no training stage in calculating the values of the variables of the method by using the training data, which is the case in some supervised learning algorithms. The k-NN algorithm examines the similarity between the new observation and other observations in the training set. The similarity is found by calculating the metric distance between the new observation, whose class is sought, and the attribute variables of previous observations. The following Figure 16 demonstrates in the two-dimensional space how the similarity is measured between the new sample whose class is sought and the neighbors whose classes are known.
Fig. 16: Neighborhood relationship between samples.
The distance between the attribute vector of the observation whose class is sought Xu and the Xi j, which is the attribute vectors of all observations in the training set is calculated as in the distance Equation 6.
distance = ||Xu – Xi j|| (6)
The most common metric that is used in similarity measurement is the Euclidean Distance, which is the application of Pythagorean Theorem, and is formulated in Equation 7. It is calculated by taking the square root of the sum of the squares the differences between the attribute variables of the new observation and the attribute variables of the neighboring previous observations.
Euclidean distance =
∑
n (xi – y )i 2 (7)√
i =1The class in which the majority of the most similar k observations belongs is considered as the class of the new observation in the similarity vector calculated with Equation 6.
5.1.4 Artificial neural network
It is a supervised classification algorithm, which imitates the biological nervous system behavior. The basic functioning element is the artificial nerve cell, which is called as the neuron, and the togetherness of a large number of neurons creates an artificial neural network. The artificial neural network consists of three basic parts as the input layer, the hidden layer, and the output layer. An example of the artificial neural network model is shown in Figure 17.
The number of inputs of the artificial neural network is represented with x, which is the attribute variable of the observations, and which is equal to the number. Outputs that represent the classes are shown with y. The hidden layer can be edited once or more, and there is no definitive method to determine the number of nodes in the hidden layers. The weight coefficients that are represented with W determine the relations between the input nodes, hidden layer nodes, and output nodes. Each neuron weighs its inputs, and transfers their sum to the activation function. The activation function may be linear or some special functions like Sigmoid. The purpose of the training stage is to calculate the final values, which will produce each observation class accurately by updating the weights that were determined randomly at the initial stage. To update the weights, linear approaches like extreme machine learning (Huang et al. 2006), or iterative approaches like back propagation algorithm are used (Hinton 2007).
Fig. 17: ANN architecture.
5.2 Organization of the Supply Chain Data
In this exemplary application, suppliers are considered according to 10 criteria. The following Table 1 shows the criteria along with related sources and detailed descriptions. The Quality and On-Time delivery criteria are in the form of number in percentages, and other criteria consist of discrete numbers between 1–9 according to Likert Scale. The data set contains 1000 instances generated randomly, and each of them contains 10 attributes. The class distribution in the data set is that 144 samples belong to the low-grade supplier represented by class 1, 764 samples belong to the middle-class supplier
120 Logistics 4.0: Digital Transformation of Supply Chain Management
Table 1: Supplier selection criteria with their related sources and definitions.
No. Criteria Name Papers Definitions
1 Price
(Dickson 1966; Weber et al. 1991; Sarkar and Mohapatra 2006; Jain et al. 2009;
Thanaraksakul and Phruksaphanrat 2009;
Deshmukh and Chttudhari 2011; Erginel and Gecer 2016; Taherdoost and Brard
Price can include unit price, transportation cost, production cost, taxes, and discount./Unit product price identified by potential suppliers.
2019)
(Dickson 1966; Weber et al. 1991;
Jain et al. 2009; Thanaraksakul and
2 Quality
Phruksaphanrat 2009; Buyukozkan and Cifci 2011; Deshmukh and Chttudhari 2011; Erginel and Gecer 2016; Taherdoost and Brard 2019; Sarkar and Mohapatra 2006; Chan and Kumar 2007; Wang et al. 2009)
Quality is defined as when the products meet customer demands and requirements and meet their specifications./
The ratio of the number of quality products to the total number of products.
3 On Time Delivery
(Dickson 1966; Weber et al. 1991; Sarkar and Mohapatra 2006; Jain et al. 2009;
Thanaraksakul and Phruksaphanrat 2009; Wang et al. 2009; Deshmukh and Chttudhari 2011; Erginel and Gecer 2016;
Taherdoost and Brard 2019)
Delivery can include lead-time, supply Ability, delivery time, location, and transportation./The ratio of the number of products delivered on time to the total number of products.
4 Environmental responsibility
(Chiou et al. 2008; Thanaraksakul and Phruksaphanrat 2009; Taherdoost and Brard 2019)
The supplier’s responsibility to use natural resources carefully, implement recycling-reusing-refurbishing- remanufacturing operations, minimize damage, reduce energy consumption and possess environmental certificates such as ISO 14000, environmental policies./The level of fulfillment of environmental responsibility by potential suppliers.
5 Social
responsibility (Thanaraksakul and Phruksaphanrat 2009;
Taherdoost and Brard 2019)
The supplier’s responsibility to present employee benefits and rights and stakeholders’ rights, disclosure of information, respect for policy and provide occupational health and safety and corporate social responsibility./The level of fulfillment of social responsibility by potential suppliers.
6 Industry
4.0-Maturity (Torğul and Paksoy 2019)
Industry 4.0 incorporates internet of things, cyber physical systems, sensors, RFID technologies, robotics technologies, artificial intelligence, big data, 3D printing, cyber security, augmented reality and cloud computing./The maturity level of Industry 4.0 concepts within the organization, how well these systems are transformed, designed and functioning.
7 Flexibility (Jain et al. 2009; Thanaraksakul and Phruksaphanrat 2009; Buyukozkan and Gocer 2017)
Supplier flexibility can be defined as the easy adaptation of the supplier to customer requirements./The flexibility level of potential suppliers.
8 Warranties and claim policies
(Dickson 1966; Jain et al. 2009;
Thanaraksakul and Phruksaphanrat 2009;
Deshmukh and Chttudhari 2011; Erginel and Gecer 2016)
After-sale tracking services, written warranties that promises to repair or replace the product if necessary within the specified period of time and claim policies for the scope or compensation of a loss or policy event.
9 Mutual trust and easy
communication (Taherdoost and Brard 2019)
Assurance on the quality of the service offered by the supplier and liabilities between the buyer and the supplier, Supplier’s communication system with information on the order’s progress data./The level of trust and communication with potential suppliers.
10 Reputation and position in industry
(Dickson 1966; Weber et al. 1991; Jain et al. 2009; Deshmukh and Chttudhari 2011;
Taherdoost and Brard 2019)
The factors such as market share, status, image, past performance and reputation of potential suppliers.
9 Mutual trust andeasy communication
(Taherdoost and Brard 2019)
Assurance on the quality of the service offered by the supplier and liabilities between the buyer and the supplier, Supplier's communication system with information on the order's progress data. / The level of trust and communication with potential suppliers.
10 Reputation and position in industry
(Dickson 1966,Weber et al. 1991,Jain et al.
2009, Deshmukh and Chttudhari 2011;, Taherdoost and Brard 2019)
The factors such as market share, status, image, past performance and reputation of potential suppliers.
Comment [S8]:Press: Please set and align Figure 18
represented by class 2, and 92 belong to the high-class supplier represented by class 3. The class information is in the latest column. The dataset constructed is shown in Figure 18 in the Arff format.
The dataset opened in WEKA Explorer is shown in Figure 19. The Preprocess Screen has several parts like the number of the samples that inform the user on the contents of the dataset, the number of attributes, statistical information on the values of the attributes, the attributes, and the bar-graph showing the class distributions. However, the main function of the Preprocess is its including a variety of filters to prepare the data for processing.
5.3 Classification Results
When the model is established, if the dataset is as whole as it is here, there are two approaches to create the training and test sets. The first one is the Percentage Split Method, which divides the dataset into two parts at a user-specified rate, the training and the test set. Here, the drawback is that the classification results may be very good or very bad because the training and test sets that are created with the division do not represent the overall characteristics well. The second is the Cross Validation. In this approach, the dataset is divided into specific subsets, widely to 10 subsets. Each step is considered as a subset test, and the rest is considered as a training set. The average results for each subset are considered as the final classification performance.
Figure 20 shows the classification perspective of the Supply Chain Data. The testing was done with the Cross-Validation Method. The Classifier Output Screen provides detailed statistical data for the classification process. The results of the statistics are given in Table 2 and confusion matrices of classifiers are given in Table 3.
@relation SupplyChain
@attribute Quality numeric
@attribute OnTimeDelivery numeric
@attribute Price {1,2,3,4,5,6,7,8,9}
@attribute EnvironmentRes {1,2,3,4,5,6,7,8,9}
@attribute SocialRes {1,2,3,4,5,6,7,8,9}
@attribute Industry40 {1,2,3,4,5,6,7,8,9}
@attribute Flexibility {1,2,3,4,5,6,7,8,9}
@attribute Warranties {1,2,3,4,5,6,7,8,9}
@attribute Trust {1,2,3,4,5,6,7,8,9}
@attribute Reputation {1,2,3,4,5,6,7,8,9}
@attribute Class {1,2,3}
@data
81,76,3,5,1,2,2,3,5,2,1 75,89,3,4,6,2,2,4,5,3,1 81,71,5,2,5,2,1,4,5,3,1 83,83,2,6,2,4,2,5,2,3,1 87,75,2,1,7,3,1,2,4,2,1 81,71,1,4,2,8,1,6,2,2,1
|||||||||||||||||||||||
Fig. 18: Supply Chain Data in the ARFF format.
122 Logistics 4.0: Digital Transformation of Supply Chain Management
Fig. 19: Preprocess perspective of Supply Chain Data.
Fig. 20: The classification perspective of the Supply Chain Data.
According to Table 2, the four methods used correctly classified suppliers by more than 80%. However, the ANN method showed the best performance with 92.6%. Looking at Table 3, the ANN method also predicted classes 1 and 2 better than other methods, and predicted Class 3 worse than only the NB method. As a result, we can say that the use of ANN would be correct in terms of achieving the best results in supplier evaluation.
The suppliers who are member of Class 1 are the suppliers with high-risk level and the conditions regarding the critical criteria should be improved first. The firm recommends reducing or eliminating the high risks identified in the process of guiding suppliers of this class. In case of a negative response in which recovery cannot be achieved, these suppliers are pruned. The suppliers who are member of Class 2 are the suppliers with medium risk level. Their basic characteristics (primary criteria) are in good condition and they are potentially recommended candidates, however the findings identified as risky for the company should be corrected. The evaluation process continues until these suppliers enter in Class 3. The suppliers who are members of Class 3 are the suppliers with low risk. They can be chosen to establish a long-term relationship and do not require any action for the firm.
Table 2: Statistics of classification results.
J48 NB k-NN ANN
Correctly Classified Instances 88.1 % 88.8% 84.3% 92.6%
Kappa statistic 0.684 0.726 0.5557 0.8061
Mean absolute error 0.109 0.1061 0.1503 0.0534 Root mean squared error 0.2501 0.2361 0.2788 0.1992 Relative absolute error 42.1171% 41.01% 58.1007% 20.6349%
Root relative squared error 69.6247% 65.7346% 77.6039% 55.4394%
TP-Rate 0.881 0.888 0.843 0.926
FP-Rate 0.227 0.137 0.345 0.137
Precision 0.879 0.895 0.837 0.925
Recall 0.881 0.888 0.843 0.926
F-Measure 0.880 0.890 0.835 0.925
MCC 0.673 0.717 0.548 0.799
ROC Area 0.886 0.955 0.877 0.977
PRC Area 0.883 0.956 0.862 0.977
Table 3: Confusion matrices of classifiers results.
J48 NB k-NN ANN
1 2 3 1 2 3 1 2 3 1 2 3
Class 1 103 41 0 124 20 0 92 52 0 127 17 0
Class 2 23 713 28 43 691 30 41 711 12 19 731 14
Class 3 0 27 65 0 19 73 0 52 40 0 24 68
6. Conclusion
With the growth of supply chains, complex and large amounts of data have become difficult to analyze and supplier selection has begun to be influenced by many attributes with too complex effects to be determined by conventional methods.
In this chapter, we introduced a new solution approach to supplier evaluation. 10 criteria from the current literature were selected primarily to evaluate suppliers, and each was assessed for their importance in selecting an appropriate supplier. Then, hypothetic data set was created for 1000 supplier profiles evaluated according to these criteria. Artificial neural networks, decision trees, bayesian classifiers and k-nearest neighbor were applied to classify these data in the WEKA machine learning tool. Three classes were determined for the risk profiles of suppliers and the attitudes of the enterprises to their suppliers according to each classes were proposed. The results show that the use of J48, NB, k-NN and ANN algorithms with WEKA machine learning tool can support supplier selection decision-making process and may lead to improvements in suppliers’ risk reduction decisions and efforts.
In the next step, different algorithms can be run for the data in the WEKA and the results can be compared. As a result, the proposed approach is flexible and so, can be used to find new partners or is easily applicable to other real case supplier selection problems however much the dataset size.