Predictive Analysis in Business Analytics: Application of Decision Tree in Business Decision Making

Chee Sun Lee; Peck Yeng Sharon  Cheang

doi:10.47654/v26y2022i1p1-30

Authors

Chee Sun Lee School of Management, University Sains Malaysia Corresponding Author
Peck Yeng Sharon Cheang School of Management, ,11800 USM, Penang, Malaysia Corresponding Author

DOI:

https://doi.org/10.47654/v26y2022i1p1-30

Keywords:

Business Analytics (BA), Predictive Analysis (PA), Machine Learning (ML), Decision Tree (DT)

Abstract

Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on hand data with the final objective of improving performance of the corporation. Predictive analysis has been gaining much attention in the late 20th century and it has been around for decades, but as technology advances, so does this technique and the techniques include data mining, big data analytics, and prescriptive analytics. Last but not least, the decision tree methodology (DT) which is a supervised simple classification tool for predictive analysis which be fully scrutinized below for applying predictive business analytics and DT in business applications

Author Biographies

Chee Sun Lee, School of Management, University Sains Malaysia

Chee Sun Lee, is a PhD student in Business Analytics in the School of Management, Universiti Sains Malaysia. He received his Degree and Master Degree in Material Science and Engineering from Universiti Putra Malaysia. He is currently serving as a Supply Chain Engineer in Intel Corporation Malaysia which focuses on Global Supply Chain Integration. His research interests are supply chain performance, supply chain management, Business Analytics and predictive Analysis.
Peck Yeng Sharon Cheang, School of Management, ,11800 USM, Penang, Malaysia

Peck Yeng Sharon Cheang, (Ph.D.) is a lecturer at the School of Management, Universiti Sains Malaysia. She has vast experience in the area of technology transfer and commercialisation management. She obtained her Ph.D. degree in the field of Electrical & Electronics Engineering. Her Ph.D. research has been published in several international journals and has presented at various conferences. Her research interests are in the areas of business analytics, technology/knowledge transfer, entrepreneurship and academic-industry collaborations.

References

Abbas, S., Hodhod, R., & El-Sheikh, M. (2021). Retrieval of behavior trees using map-and-reduce technique. Egyptian Informatics Journal, 1.

Al-Akhras, M., El Hindi, K., Habib, M., & Shawar, B. A. (2021). Instance reduction for avoiding overfitting in decision trees. Journal of Intelligent Systems, 30(1), 438-459.

Al-Zuabi, I. M., Jafar, A., & Aljoumaa, K. (2019). Predicting customer’s gender and age depending on mobile phone data. Journal of Big Data, 6(1), 1-16.

Antosz, K., Pasko, L., & Gola, A. (2020). The use of artificial intelligence methods to assess the effectiveness of lean maintenance concept implementation in manufacturing enterprises. Applied Sciences, 10(21), 7922.

Arismendy, Luis, Carlos Cardenas, Diego Gomez, Aymer Maturana, Ricardo Mejía, & Christian G. Quintero M. (2021). "A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach." Sustainability 13, no. 8, 4311.

Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598.

Bawack, R. E., & Ahmad, M. O. (2021). Understanding business analytics continuance in agile information system development projects: an expectation-confirmation perspective. Information Technology & People, 1.

Bayrak, T. (2015). A review of business analytics: A business enabler or another passing fad. Procedia-Social and Behavioral Sciences, 195, 230-239.

Berry, M., & Linoff, G. (1999). Mastering data mining: The art and science of customer relationship management. John Wiley & Sons.

Biehler, R., & Fleischer, Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. Teaching Statistics, 43, S133-S142.

Bhatnagar, V., Poonia, R. C., Nagar, P., Kumar, S., Singh, V., Raja, L., & Dass, P. (2021). Descriptive analysis of COVID-19 patients in the context of India. Journal of Interdisciplinary Mathematics, 24(3), 489-504.

Bibri, S. E., & Krogstie, J. (2021). A novel model for data-driven smart sustainable cities of the future: A strategic roadmap to transformational change in the era of big data. Future Cities and Environment, 7(1).

Biecek, P., & Burzykowski, T. (2021). Explanatory model analysis: explore, explain, and examine predictive models. CRC Press, Taylor and Francis.

Brandt, T., Wagner, S., & Neumann, D. (2021). Prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning. European journal of Operational Research, 291(1), 379-393.

Brynjolfsson, E., Jin, W., & McElheran, K. (2021). The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance. Workplace Complements, and Business Performance, 1.

Cao, Z., Chen, T., & Cao, Y. (2021). Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree. Frontiers in Public Health, 9, 512.

Chee, W., Yi, J. S., & Im, E. O. (2021). Information Needs of Asian American Breast Cancer Survivors: A Decision Tree Analysis. Journal of Cancer Education, 1-10.

Chen, M., Liu, Q., Huang, S., & Dang, C. (2020). Environmental cost control system of manufacturing enterprises using artificial intelligence based on value chain of circular economy. Enterprise Information Systems, 1-20.

Dagnino, A. (2021). Industrial Analytics. In Data Analytics in the Era of the Industrial Internet of Things, 21-46. Springer, Cham.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.

de Magalhaes, D. J. A. V. (2021). Analysis of critical factors affecting the final decision-making for online grocery shopping. Research in Transportation Economics, 101088.

de Medeiros, M. M., Hoppen, N., & Maçada, A. C. G. (2020). Data science for business: Benefits, challenges and opportunities. The Bottom Line, 33(2).

Do, M., Byun, W., Shin, D. K., & Jin, H. (2019). Factors influencing matching of ride-hailing service using machine learning method. Sustainability, 11(20), 5615.

Emam, K. E., Mosquera, L., & Zheng, C. (2021_. Optimizing the synthesis of clinical trial data using sequential trees. Journal of the American Medical Informatics Association, 28(1), 3-13.

Espadinha-Cruz, P., Godina, R., & Rodrigues, E. M. (2021). A review of data mining applications in semiconductor manufacturing. Processes, 9(2), 305.

Foley, B., Degirmenci, K., & Yigitcanlar, T. (2020). Factors affecting electric vehicle uptake: Insights from a descriptive analysis in Australia. Urban Science, 4(4), 57.

Garcia, S., Cordeiro, A., de Alencar Naas, I., & Neto, P. L. D. O. C. (2019). The sustainability awareness of Brazilian consumers of cotton clothing. Journal of cleaner production, 215, 1490-1502.

Garcia Marquez, F. P., Segovia Ramirez, I., & Pliego Marugan, A. (2019). Decision making using logical decision tree and binary decision diagrams: A real case study of wind turbine manufacturing. Energies, 12(9), 1753.

Grover, S., McClelland, A., & Furnham, A. (2020). Preferences for scarce medical resource allocation: Differences between experts and the general public and implications for the COVID‐19 pandemic. British Journal of health Psychology, 25(4), 889-901.

Hartmann, J. (2021). Classification Using Decision Tree Ensembles. In The Machine Age of Customer Insight. 1.

Huo, D., & Chaudhry, H. R. (2021). Using machine learning for evaluating global expansion location decisions: An analysis of Chinese manufacturing sector. Technological forecasting and social change, 163, 120436.

Hussein, A. S., Khairy, R. S., Najeeb, S. M. M., & ALRikabi, H. T. (2021). Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression. International Journal of Interactive Mobile Technologies, 15(5).

Izagirre, U., Andonegui, I., Eciolaza, L., & Zurutuza, U. (2021). Towards manufacturing robotics accuracy degradation assessment: A vision-based data-driven implementation. Robotics and Computer-Integrated Manufacturing, 67, 102029.

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2021). Significance of Quality 4.0 towards comprehensive enhancement in manufacturing sector. Sensors International, 100109.

Jijo, B. T., & Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20-28.

Johnson, T. N., Abduljalil, K., Nicolas, J. M., Muglia, P., Chanteux, H., Nicolai, J., Gillent, E., Cornet, M., & Sciberras, D. (2021). Use of a physiologically based pharmacokinetic–pharmacodynamic model for initial dose prediction and escalation during a paediatric clinical trial. British Journal of Clinical Pharmacology, 87(3), 1378-1389.

Kalyankar, G. D., Poojara, S. R., & Dharwadkar, N. V. (2017). Predictive analysis of diabetic patient data using machine learning and Hadoop. In 2017 international conference on I-SMAC, 619-624.

Kaparthi, S., & Bumblauskas, D. (2020). Designing predictive maintenance systems using decision tree-based machine learning techniques. International Journal of Quality & Reliability Management, 37(4), 659-675.

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4(1), 60.

Kingsford, C., & Salzberg, S. L. (2008). What are decision trees?. Nature biotechnology, 26(9), 1011-1013.

Korstanje, J. (2021). The Decision Tree Model. In Advanced Forecasting with Python, 159-168.

Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429.

Kristoffersen, E., Mikalef, P., Blomsma, F., & Li, J. (2021). Towards a business analytics capability for the circular economy. Technological Forecasting and Social Change, 171, 120957.

Kumar, M., Shenbagaraman, V. M., Shaw, R. N., & Ghosh, A. (2021). Predictive data analysis for energy management of a smart factory leading to sustainability. In Innovations in electrical and electronic engineering, 765-773.

Kumar, V., & Garg, M. L. (2018). Predictive analytics: a review of trends and techniques. International Journal of Computer Applications, 182(1), 31-37.

Lalic, B., Marjanovic, U., Rakic, S., Pavlovic, M., Todorovic, T., & Medic, N. (2020). Big data analysis as a digital service: evidence form manufacturing firms. In Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing, 263-269.

Lana, I., Sanchez-Medina, J. J., Vlahogianni, E. I., & Del Ser, J. (2021). From data to actions in intelligent transportation systems: a prescription of functional requirements for model actionability. Sensors, 21(4), 1121.

Li, W., Ma, X., Chen, Y., Dai, B., Chen, R., Tang, C., Luo, Y. and Zhang, K. (2021). Random Fuzzy Granular Decision Tree. Mathematical Problems in Engineering, 5578682.

Liou, F., Spark, M. T., Flood, A., & Joshi, M. (2021). Applications of Supervised Machine Learning Algorithms in Additive Manufacturing: A Review. Preprints, 2021010588.

Lo, F. Y., Wong, W. K., & Geovani, J. (2021). Optimal combinations of factors influencing the sustainability of Taiwanese firms. International Journal of Emerging Markets, 1.

Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., & Reber, S. (2017). Descriptive Analysis in Education: A Guide for Researchers. NCEE 2017-4023. National Center for Education Evaluation and Regional Assistance.

Maass, W., & Storey, V. C. (2021). Pairing conceptual modelling with machine learning. Data & Knowledge Engineering, 101909.

Manogna, R. L., & Mishra, A. K. (2021). Measuring financial performance of Indian manufacturing firms: application of decision tree algorithms. Measuring Business Excellence, 1.

Mark, B. G., Rauch, E., & Matt, D. T. (2021). Worker assistance systems in manufacturing: A review of the state of the art and future directions. Journal of Manufacturing Systems, 59, 228-250.

Meire, M. (2021). Customer comeback: Empirical insights into the drivers and value of returning customers. Journal of Business Research, 127, 193-205.

Merayo, D., Rodriguez-Prieto, A., & Camacho, A. M. (2019). Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in Industry 4.0. Procedia Manufacturing, 41, 42-49.

Mirzaei, N. E., Hilletofth, P., & Pal, R. (2021). Challenges to competitive manufacturing in high-cost environments: checklist and insights from Swedish manufacturing firms. Operations Management Research, 275, 1-21.

Mișu, N. B, & Madaleno, M. (2020). Assessment of bankruptcy risk of large companies: European countries evolution analysis. Journal of Risk and Financial Management, 13(3), 58.

Mosavi, N. S., & Santos, M. F. (2020). How prescriptive analytics influences decision making in precision medicine. Procedia Computer Science, 177, 528-533.

Musy, S. N., Endrich, O., Leichtle, A. B., Griffiths, P., Nakas, C. T., & Simon, M. (2020). Longitudinal Study of the Variation in Patient Turnover and Patient-to-Nurse Ratio: Descriptive Analysis of a Swiss University Hospital. Journal of Medical Internet Research, 22(4), e15554.

Ning, J., Praniewicz, M., Wang, W., Dobbs, J. R., & Liang, S. Y. (2020). Analytical modeling of part distortion in metal additive manufacturing. The International Journal of Advanced Manufacturing Technology, 107(1), 49-57.

Nwankwo, W., & Ukhurebor, K. E. (2020). Data Centres: A Prescriptive Model for Green and Eco-Friendly Environment In The Cement Industry In Nigeria. International Journal of Scientific and Technology Research, 9(5), 239-244.

Ondeş, R. N. (2021). Research trends in dynamic geometry software: A content analysis from 2005 to 2021. World Journal on Educational Technology: Current Issues, 13(2), 236-260.

Panjwani, S., Cui, I., Spetsieris, K., Mleczko, M., Wang, W., Zou, J.X., Anwaruzzaman, M., Liu, S., Canales, R. and Hesse, O. (2021). Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes. Biotechnology Progress, e3135.

Pappalardo, G., Cafiso, S., Di Graziano, A., & Severino, A. (2021). Decision tree method to analyze the performance of lane support systems. Sustainability, 13(2), 846.

Punjabi, P., Vaswani, P., & Kubal, A. (2021). Modelling Stock Trading Platforms Leveraging Predictive Analysis Using Learning Algorithms, International Journal of Research and Analytical Reviews, 2348-1269.

Purnamasari, I., Handayanna, F., Arisawati, E., Dewi, L. S., & Sihombing, E. G. (2020). The Determination Analysis of Telecommunications Customers Potential Cross-Selling with Classification Naive Bayes and C4. 5. In Journal of Physics: Conference Series, 1641, 1, 012010.

Qian, Y., Li, Z., & Tan, R. (2021). Sustainability analysis of supply chain via particulate matter emissions prediction in China. International Journal of Logistics Research and Applications, 1-14.

Ruschel, E., Loures, E. D. F. R., & Santos, E. A. P. (2021). Performance analysis and time prediction in manufacturing systems. Computers & Industrial Engineering, 151, 106972.

Sabbeh, S. F. (2018). Machine-learning techniques for customer retention: A comparative study. International Journal of Advanced Computer Science and Applications, 9(2).

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. Springer Nature Computer Science, 2(3), 1-21.

Sawangarreerak, S., & Thanathamathee, P. (2021). Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 128.

Sawant, N. V., Panicker, V. V., & Anoop, K. P. (2021). Predictive Analytics in Food Grain Logistics: Supervised Machine Learning Approach. Optimization Methods in Engineering, 459-466.

Saxena, M., Bagga, T., & Gupta, S. (2021). Fearless path for human resource personnel through analytics: a study of recent tools and techniques of human resource analytics and its implication. International Journal of Information Technology, 1, 1-9.

Seera, M., Lim, C. P., Kumar, A., Dhamotharan, L., & Tan, K. H. (2021). An intelligent payment card fraud detection system. Annals of Operations Research, 1-23.

Sharma, S., & Gupta, Y. K. (2021). Predictive analysis and survey of COVID-19 using machine learning and big data. Journal of Interdisciplinary Mathematics, 24(1), 175-195.

Singh, M., & Chhabra, J. K. (2021). EGIA: A new node splitting method for decision tree generation: Special application in software fault prediction. Materials Today: Proceedings, 1.

Song, Y. Y., & Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130.

Surucu-Balci, E., Balci, G., & Yuen, K. F. (2020). Social media engagement of stakeholders: A decision tree approach in container shipping. Computers in Industry, 115, 103152.

Tolba, A., & Al-Makhadmeh, Z. (2021). Predictive data analysis approach for securing medical data in smart grid healthcare systems. Future Generation Computer Systems, 117, 87-96.

Wassouf, W. N., Alkhatib, R., Salloum, K., & Balloul, S. (2020). Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study. Journal of Big Data, 7(1), 1-24.

Xu, D. (2021). Analysis on the structure of port collection and distribution in China. In IOP Conference Series: Earth and Environmental Science, 791, 1, 012080.

Van Benthem, K., & Herdman, C. M. (2021). A virtual reality cognitive health screening tool for aviation: Managing accident risk for older pilots. International Journal of Industrial Ergonomics, 85, 103169.

Van Pelt, A., Glick, H. A., Yang, W., Rubin, D., Feldman, M., & Kimmel, S. E. (2021). Evaluation of COVID-19 testing strategies for repopulating college and university campuses: a decision tree analysis. Journal of Adolescent Health, 68(1), 28-34.

Vlahakis, G., Kopanaki, E., & Apostolou, D. (2020). Proactive decision making in supply chain procurement. Journal of Organizational Computing and Electronic Commerce, 30(1), 28-50.

Yeboah-Ofori, A., & Boachie, C. (2019). Malware Attack Predictive Analytics in a Cyber Supply Chain Context Using Machine Learning. In 2019 International Conference on Cyber Security and Internet of Things (ICSIoT), 66-73.

Zangaro, F., Minner, S., & Battini, D. (2020). A supervised machine learning approach for the optimisation of the assembly line feeding mode selection. International Journal of Production Research, 1-22.

Zeng, L., Guo, J., Wang, B., Lv, J., & Wang, Q. (2019). Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resources Policy, 64, 101501.