Weight-based and Digital Payment Process Flow Design to Decrease Quarry Sand Sales Fraudulence Risk

Meita Annisa Nurhutami


Databases have risen to the level of being one of the most important corporate assets. The availability of data in a company is required to make the right profit decision. In running a business aiming to achieve high profitability, companies must recognize profitable items by making decisions that require knowledge of the supply chain, knowledge of past, present, and future buying patterns, and costs derived from data obtained from many different systems. In this study, a company running a quarry sand mining business is experiencing disproportions of the resource availability of quarry sands, operating expense, and profit. It leads to suspicion of fraudulence risk during the operations and transaction of the product. This study aims to minimize the fraudulence risk of quarry sand sales by using data re-verification and analytical techniques. The study implemented a process analysis and root-cause method using process flowcharting and fishbone diagram tools. The root causes of this issue happen during the initial data input process caused by three main factors namely machinery or equipment, people, and systems. The newly designed system utilizes a digital weighing scaling system for trucks in recording the initial data and deposit payment system based on the accurate truck final weight.


Process flow design;

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Aad, G., Abbott, B., Abbott, D. C., Ambroz, L., Artoni, G., Backes, M., and Zgubič, M. (2020). ATLAS data quality operations and performance for 2015–2018 data-taking. Journal of instrumentation, 15(04).

Ahlawat, S., and Vincelette, J. (2012). Enhancing knowledge integration with REA modeling in an AIS project. Journal of Information Systems Education, 23(2), 119-128.

Andreescu, I. A., Anda Belciu, A., Alexandra Florea, A., and Diaconita, V. (2014). Measuring Data Quality in Analytical Projects. Database Systems Journal, 5(1), 15-25.

Brown, M., and Ghaffariyan, M. R. (2016). Timber truck payload management with different in-forest weighing strategies in Australia. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 37(1), 131-138

Chase, R. B., and Jacobs, F.R. (2011). Operations and Supply Chain Management. New York: McGraw-Hill Irwin.

Chen, J., Nanehkaran, Y. A., Chen, W., Liu, Y., and Zhang, D. (2023). Data-driven intelligent method for detection of electricity theft. International Journal of Electrical Power & Energy Systems, 148, 108948

Elgammal, A., Turetken, O., and Van Den Heuvel, W. J. (2012). Using patterns for the analysis and resolution of compliance violations. International Journal of Cooperative Information Systems, 21(01), 31-54.

Emeka-Nwokeji, N. A. (2012). Repositioning accounting information system through effective data quality management: A framework for reducing costs and improving performance. International Journal of Scientific & Technology Research, 1(10), 86-94.

Kaur, J., Kumar, R., Agrawal, A., and Khan, R. A. (2023). A neutrosophic AHP-based computational technique for security management in a fog computing network. The Journal of Supercomputing, 79(1), 295-320.

Niraj, R., Gupta, M., and Narasimhan, C. (2001). Customer profitability in a supply chain. Journal of marketing, 65(3), 1-16.

Olson, J. E. (2003). Data quality: the accuracy dimension. Elsevier.

Saritas, M. M., and Yasar, A. (2019). Performance analysis of ANN and Naive Bayes classification algorithm for data classification. International journal of intelligent systems and applications in engineering, 7(2), 88-91.

Taranto-Vera, G., Galindo-Villardón, P., Merchán-Sánchez-Jara, J., Salazar-Pozo, J., Moreno-Salazar, A., and Salazar-Villalva, V. (2021). Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature. The Journal of Supercomputing, 77, 11481-11513.

Tojiboyev, N., Appelbaum, D., Kogan, A., and Vasarhelyi, M. A. (2022). Basics of SQL for audit data retrieval and analysis. Journal of Emerging Technologies in Accounting, 19(1), 237-265.

Wilkin, C., Ferreira, A., Rotaru, K., and Gaerlan, L. R. (2020). Big data prioritization in SCM decision-making: Its role and performance implications. International Journal of Accounting Information Systems, 38, 100470.

Xu, Z., and Dang, Y. (2022). Data-driven causal knowledge graph construction for root cause analysis in quality problem-solving. International Journal of Production Research, 1-19.

DOI: https://doi.org/10.17509/jipdrs.v2i1.59648


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