View of Evaluation Of Supply Chain Management In Heavy Equipment Industry Using Artificial Neural Network

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518 Jurnal Teknik Informatika dan Sistem Informasi ISSN 2407-4322 Vol. 10, No. 2, Juni 2023, Hal. 518-525 E-ISSN 2503-2933

Evaluation Of Supply Chain Management In Heavy Equipment Industry Using Artificial Neural Network

Sofyan Wahyudi

Department of Industrial Engineering, BINUS Graduate – Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480

e-mail: [email protected]

Abstract

In relation to the increase in coal prices worldwide in the post Covid-19 pandemic, the heavy equipment business in Indonesia has had a positive impact, one of which is the heavy equipment spare part business. It is very important for the company to maintain the supply of goods to satisfy customers, therefore supply chain management is essential to be evaluated. The aim of this study is to prove that Artificial Neural Network can be applied to assess the supply chain performance of a heavy equipment spare part company. This study will collect data from a holding company in the heavy equipment spare part business located in Jakarta, which will provide an assessment of supply chain performance to all of its subsidiaries located in Jakarta.

The research method will use Artificial Neural Network and its accuracy is proven with a confusion matrix. This research to prove that Artificial Neural Network for supply chain performance assessment of heavy equipment spare part companies that has never been done by previous research. The main results from this research are Artificial Neural Network can be applied to measure supply chain performance of heavy equipment spare part companies. State a brief limitation of the study is only limited to spare part companies under PT. XYZ. This study can provide a contribution to Supply Chain Management and Operation Management in the field of heavy equipment.

Keywords— Artificial Neural Network, Supply Chain, Spare Part, Heavy Equipment

1. INTRODUCTION

The heavy equipment business triggers an increase in world coal prices in the post Covid-19 pandemic [1]. The industry that has had a positive impact is the heavy equipment spare part business. Supply chain management is essential in order to maintain the supply of raw materials to satisfy customers and add value to companies in this business [2].

The researcher considers that it is necessary to analyze supply chain performance using methods that are in accordance with the industrial revolution 4.0. This research will use Artificial Neural network (ANN) because based on [3] concluded that ANN is the most accurate data mining model in predicting supply chain in food industry, so the author wants to prove whether this model can be used for the heavy equipment industry.

The main result of this research is to prove whether the Artificial Neural Network can be applied to measure the supply chain performance of heavy equipment spare parts companies.

The dataset in this paper will take data on the supply chain performance assessment of PT. XYZ on the valuation of its seven subsidiaries, the range of data for three years from January 2019 to December 2021. Measurement of accuration ANN model using confusion matrix tool.

Confusion matrixis is the predictive analytic tool that displays and compares the actual value or the actual value with the predicted value of the model [4].

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Sofyan Wahyudi, [Evaluation of Supply Chain Management In Heavy Equipment Industry Using Artificial Neural Network]

This study can provide a contribution to Supply Chain Management and Operation Management in the field of heavy equipment, either by a company or by a holding company that has several subsidiaries. The researcher hopes that this research will provide added value to the heavy equipment business in Indonesia.

2. METHODOLOGY

Figure 1. Reasearch Method

2.1 Data Envelopment Analysis (DEA)

This research begins by collecting sample data obtained from the company (PT. XYZ) and then data processing is carried out, by determining the supply chain performance assessment criteria through brainstorming with PT. XYZ. As the initial stage of data processing, this research applies the Data Envelopment Analysis (DEA) method, DEA is a mathematical programming technique that builds a linear program to identify non-parametric production boundaries [5]. DEA is a management tool to evaluate the relative efficiency level of a DMU (Decision Making Unit) which is non-parametric and multi-factor, both input and output [6]. In this research, DEA value status calculation will use DEAP 2.1 software.

2.2 Artificial Neural Network (ANN)

Furthermore, the results of the evaluation with the DEA method will be used as a basis for ANN, the next step is making the ANN model. The way ANN works is similar to how humans work, namely learning through examples, storing information, using information to draw conclusions, adapting to new circumstances, and communicating with users [7]. The layers that make up the ANN are divided into three layers, namely the input layer, the hidden layer, and the output layer [8].

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2.3 Confusion Matrix

The final step is to prove that whether the ANN model can be used to evaluate supply chain management performance in the heavy equipment spare part industry, the accuracy of its prediction will be tested with the confusion matrix tool. Confusion matrixis is the predictive analytic tools that usually use to displays and compares the actual value with the predicted value of the model [9]. There are 4 values, firtsly, TP (True Positive) is the amount of data whose actual class is a positive class with the prediction class being a positive class, secondly, FN (False Negative) is the amount of data whose actual class is a positive class with the prediction class being a positive class. negative class, additionally, FP (False Positive) is the number of data whose actual class is a negative class with the prediction class being a positive class, and TN (True Negative) is the number of data whose actual class is a negative class with the prediction class being a negative class [10].

To determine whether the accuracy results can be used, the accuracy values will be grouped in the accuracy value group. Based on [11] the model accuracy performance metric values for the classification system can be grouped into 5 groups, as shown in the table 1.

Table 1. Accuracy Value Grouping Score Classification

0.90-1.00 Very Good

0.80-0.89 Good

0.70-0.79 Fair

0.60-0.69 Poor

0.50-0.60 Incorrect

Source: Gorunescu (2011)

3. RESULT and DISCUSSION

3.1. Criteria for Supply Chain Evaluation

The selection of supply chain performance evaluation criteria by brainstorming with PT.

XYZ and based on the Supply Chain Operation Reference (SCOR) 10.0 criteria, namely plan, source, make, delivery, and return [12].

3.2. Decision Making Unit (DMU) Determination

The company to be analyzed is a subsidiary of PT. XYZ. each company is divided into Decision-Making Units (DMU), it is shown in table 2.

Table 2. Distribution of DMU Suppliers to PT. XYZ Decision Making Unit (DMU) Subsidiary

DMU 1 PT. XYZ 1

DMU 2 PT. XYZ 2

DMU 3 PT. XYZ 3

DMU 4 PT. XYZ 4

DMU 5 PT. XYZ 5

DMU 6 PT. XYZ 6

DMU 4 PT. XYZ 7

Source: PT. XYZ Brainstorming (2022)

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3.3. DEA Model

Variables that affect the performance of PT. XYZ is grouped into two categories, namely input and output variables. Determination of input and output variables is generated through a brainstorming process with PT. XYZ as the parent company.

The grouping of input and output variables is done by brainstorming with the company.

Where the input variable affects the performance of supply chain management, while the output variable is the expected result. In this study chose to use input oriented or output variable maximization. The results of the input oriented may recommend increasing the output while reducing the input at the same time. The DEA Decision Model can be seen in Figure 2.

Figure 2. Illustration of DEA Model Source: PT. XYZ Brainstorming (2022)

Based on the results of dataset processing using DEAP 2.1 software, the efficiency value will be known. The calculation of the status to be efficient and inefficient refers to the standards made by PT. XYZ with range values in table 3.

Table 3. Efficiency Score of DEA Result

Status Value

Efficient 0.920 - 1,000 Not efficient 0.000 - 0.919 Source: PT. XYZ Brainstorming (2022)

3.4. DEA Processing

There are 252 datasets, with details of the five DMUs, namely the seven subsidiaries of PT. XYZ has five criteria that are assessed over three years (from January 2019 to December 2021. Table 4 will show the results of the efficiency value using the DEA method, with the data shown only an example of the last three data (November and December 2021) from each company. Data processing using DEAP 2.1 software.

Table 4. Results of Efficiency Values with The DEA Method Supplier Period

INPUT OUTPUT Efficiency Score

Plan Source Make Delivery Return DEA

Result Status PT XYZ 1 Oct-21 94.00 88.00 87.00 85.00 92.00 0.957 EFFICIENT PT XYZ 1 Nov-21 86.00 91.00 83.00 90.00 90.00 0.910 NOT EFFICIENT PT XYZ 1 Dec-21 83.00 89.00 89.00 85.00 93.00 0.953 EFFICIENT PT XYZ 2 Oct-21 86.00 87.00 86.00 89.00 97.00 0.910 NOT EFFICIENT

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PT XYZ 2 Nov-21 88.00 87.00 82.00 87.00 93.00 0.931 EFFICIENT PT XYZ 2 Dec-21 93.00 82.00 88.00 84.00 91.00 0.967 EFFICIENT PT XYZ 3 Oct-21 83.00 94.00 95.00 88.00 92.00 0.933 EFFICIENT PT XYZ 3 Nov-21 86.00 83.00 83.00 87.00 97.00 0.931 EFFICIENT PT XYZ 3 Dec-21 93.00 96.00 91.00 85.00 92.00 0.967 EFFICIENT PT XYZ 4 Oct-21 92.00 92.00 95.00 93.00 98.00 0.879 NOT EFFICIENT PT XYZ 4 Nov-21 95.00 83.00 89.00 84.00 97.00 0.968 EFFICIENT PT XYZ 4 Dec-21 86.00 96.00 94.00 86.00 92.00 0.952 EFFICIENT PT XYZ 5 Oct-21 94.00 91.00 90.00 86.00 95.00 0.945 EFFICIENT PT XYZ 5 Nov-21 97.00 89.00 93.00 86.00 94.00 0.967 EFFICIENT PT XYZ 5 Dec-21 90.00 81.00 85.00 97.00 91.00 0.890 NOT EFFICIENT PT XYZ 6 Oct-21 93.00 86.00 92.00 83.00 87.00 0.982 EFFICIENT PT XYZ 6 Nov-21 85.00 82.00 94.00 82.00 91.00 0.988 EFFICIENT PT XYZ 6 Dec-21 84.00 89.00 87.00 90.00 84.00 0.964 EFFICIENT PT XYZ 7 Oct-21 82.00 81.00 85.00 96.00 84.00 0.964 EFFICIENT PT XYZ 7 Nov-21 97.00 96.00 97.00 82.00 92.00 1.000 EFFICIENT PT XYZ 7 Dec-21 95.00 82.00 81.00 88.00 91.00 0.930 EFFICIENT

Source: Processed Data by DEAP 2.1

3.5. Artificial Neural Network Model

Artificial Neural Network (ANN) is an information processing technique or approach that is inspired by the workings of the human biological nervous system [13]. In this study using a multilayer ANN design which tends to have good accuracy and more effective [14]. Neural Network consists of neurons that can adjust the value of the existing weights on each connectivity from input, neuron, output. In this ANN model data processing uses 100 neurons.

Figure 3. Illustration of Multilayer ANN Design Source: ORANGE Data Mining Simulation (2022)

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Figure 4. Illustration of ANN Evaluation Using ORANGE Data Minig Source: ORANGE Data Mining Simulation (2022)

3.6. Confusion Matrix

The level of model accuracy is calculated through the confusion matrix. The results of the confusion matrix from the ANN model using ORANGE Data Mining can be seen in the figure 5.

Figure 5. Confusion Matrix Results of ANN Model Source: ORANGE Data Mining Simulation (2022)

Based on the confusion matrix evaluation results in figure 4, the accuracy of the ANN model is 88%, referring to the accuracy in the table 1, the accuracy of ANN model is classified as good.

4. CONCLUSION

4.1. Conclusion

Based on this research, we can conclude that Artificial Neural Network can be used properly for predicting supply chain performance assessment of heavy equipment spare part companies due to ANN model is classified as good.

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4.2. Limitation

This research is limited to the supply chain performance assessment criteria for heavy equipment spare part companies.

5. SUGGESTION

Researchers suggest that further research can simulate other data mining algorithms and use data from spare part companies in other business fields.

ACKNOWLEDGMENT

The researcher would like to thank PT. XYZ which has provided support for the performance data of its subsidiaries in this study, and thank you to the previous research whose research results support this paper.

REFERENCES

[1] Camargos, P. H., & Villibor, J. (2022). The Socio-Economic Implications of The Coronavirus Pandemic: A Brazilian Electric Sector Analysis. In Interdisciplinary and Social Nature of Engineering Practices (pp. 265-281). Springer, Cham.

[2] Rezaei, J., Ortt, R., & Trott, P. (2015). How SMEs Can Benefit From Supply Chain Partnerships. International Journal of Production Research, 53(5), 1527-1543.

[3] Wahyudi, S., & Asrol, M. (2022). Designing A Supplier Evaluation Model in The Cheese Industry Using Hybrid Method. Academic Journal of Manufacturing Engineering, 20(2).

[4] Caelen, O. (2017). A Bayesian Interpretation of The Confusion Matrix. Annals of Mathematics and Artificial Intelligence, 81(3), 429-450.

[5] Fancello, G., Uccheddu, B., & Fadda, P. (2014). Data Envelopment Analysis (DEA) for Urban Road System Performance Assessment. Procedia-Social and Behavioral Sciences, 111, 780-789.

[6] Shafiee, M., Lotfi, F. H., & Saleh, H. (2014). Supply Chain Performance Evaluation with Data Envelopment Analysis and Balanced Scorecard Approach. Applied mathematical modelling, 38(21-22), 5092-5112.

[7] Dongare, A. D., Kharde, R. R., & Kachare, A. D. (2012). Introduction to Artificial Neural Network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194.

[8] Alaloul, W. S., & Qureshi, A. H. (2020). Data Processing Using Artificial Neural Networks. Dynamic Data Assimilation-Beating The Uncertainties.

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[9] Room, C. (2019). Confusion Matrix. Mach. Learn, 6, 27.

[10] Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An Improved Method to Construct Basic Probability Assignment Based On The Confusion Matrix For Classification Problem. Information Sciences, 340, 250-261.

[11] Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques (Vol. 12).

Springer Science & Business Media.

[12] Ntabe, E. N., LeBel, L., Munson, A. D., & Santa-Eulalia, L. A. (2015). A Systematic Literature Review of The Supply Chain Operations Reference (SCOR) Model Application With Special Attention to Environmental Issues. International Journal of Production Economics, 169, 310-332.

[13] Radha Krishnan, B., Vijayan, V., Parameshwaran Pillai, T., & Sathish, T. (2019).

Influence of Surface Roughness in Turning Process An Analysis Using Artificial Neural Network. Transactions of the Canadian Society for Mechanical Engineering, 43(4), 509- 514.

[14] Heidari, E., Sobati, M. A., & Movahedirad, S. (2016). Accurate Prediction of Nanofluid Viscosity Using A Multilayer Perceptron Artificial Neural Network (MLP-ANN).

Chemometrics and Intelligent Laboratory Systems, 155, 73-85.