A Meta Smart Inventory Management System for Stock Prediction of some Shopping Malls within Bauchi Metropolis

Ibrahim AbdulBasit Adamu, Aminu Ahmad, Kabiru Ibrahim Musa

Abstract


The effective use of inventory is an essential factor to the success of shopping malls, but the unpredictable and nonlinear nature of consumer demand can regularly be difficult to predict using conventional forecasting techniques. The proposed and assessed Hybrid Smart Inventory Management System Model is a statistical approach that combines the statistical strengths of the Autoregressive Integrated Moving Average (ARIMA) model with the nonlinear deep learning of Long Short-Term Memory (LSTM) networks. The study was based on shopping malls in the Bauchi metropolis with the use of a real dataset of 1300, we then ×2, ×5, and ×10 training records to compare the results of the baseline ARIMA model and the proposed hybrid ARIMA-LSTM architecture. The assessment was done based on three main measures namely Mean Absolute error (MAE), Root mean square error (RMSE) and Mean Absolute Percentage error (MAPE). The outcome of the experiment confirmed the fact that the hybrid model was significantly better than the baseline in all parameters. In particular, of 20868 datasets, the hybrid model showed an improvement in MAE of 19.15 percent, in RMSE of 17.49 percent, and in MAPE of 25.53 percent. Subsequent diagnostic evaluation of CUSUM (Cumulative Sum) charts showed that the ARIMA model had a systematic bias and upward error drift (with a maximum, cumulative error of 35) whereas the hybrid model had a stable error movement (near-zero range between -1 and 4). The use of error distribution histograms ensured that the hybrid model led to the creation of highly concentrated zero-peaked distribution, a characteristic of high precision and reliability. The results conclude that LSTM integration is an effective way of overcoming the linear constraints of the classical models and can be used as a strong scalable solution to the problem of smart inventory management in high volume retail settings. 


Full Text:

PDF

References


Addisu Jember Zeleke a,, Pierpaolo Palumbo a, Paolo Tubertini b, Rossella Miglio c, L. C. (2024). Comparison of nine machine learning regression models in predicting hospital length of stay for patients admitted to a general medicine department. Informatics in Medicine Unlocked, 47(March), 101499. https://doi.org/10.1016/j.imu.2024.101499

Adebunmi Okechukwu Adewusi, Abiola Moshood Komolafe, Emuesiri Ejairu, Iyadunni Adewola Aderotoye, Oluwatosin Oluwatimileyin Abiona, & Oyekunle Claudius Oyeniran. (2024). the Role of Predictive Analytics in Optimizing Supply Chain Resilience: a Review of Techniques and Case Studies. International Journal of Management & Entrepreneurship Research, 6(3), 815–837. https://doi.org/10.51594/ijmer.v6i3.938

Agarwal, A., & Jayant, A. (2019). Application of Machine Learning Techniques in Supply Chain Management. International Research Journal of Management Science & Technology, 10(6), 29–48.

Ahmad, R. B., Shobowale, K. O., Idris, M. M., Dandago, K. K., Yahaya, J. U., Zango, M. S., & Hassan, A. (2408). Latest Advances in Inventory Management Systems: a Review. FUW Trends in Science & Technology Journal, www.Ftstjournal.Com e-ISSN, 7(3), 95–105. www.ftstjournal.com

Aldhyani, T. H. H., & Alzahrani, A. (2022). Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms. Electronics (Switzerland), 11(19). https://doi.org/10.3390/electronics11193149

Andaur, J. M. R., Ruz, G. A., & Goycoolea, M. (2021). Predicting out-of-stock using machine learning: An application in a retail packaged foods manufacturing company. Electronics (Switzerland), 10(22). https://doi.org/10.3390/electronics10222787

Bhatti, M. A., & Bauirzhanovna, B. A. (2023). Impact of Intelligent Inventory System on Improvement of Reverse Logistics: a Case of Saudi Manufacturing Industry. Operational Research in Engineering Sciences: Theory and Applications, 6(1), 1–19. https://doi.org/10.31181/oresta/060101

Biswas, A. K., Ahmed, S. I., Bankefa, T., Ranganathan, P., & Salehfar, H. (2021a). Performance Analysis of Short and Mid-Term Wind Power Prediction using ARIMA and Hybrid Models. 2021 IEEE Power and Energy Conference at Illinois, PECI 2021, April. https://doi.org/10.1109/PECI51586.2021.9435209

Biswas, A. K., Ahmed, S. I., Bankefa, T., Ranganathan, P., & Salehfar, H. (2021b). Performance Analysis of Short and Mid-Term Wind Power Prediction using ARIMA and Hybrid Models. 2021 IEEE Power and Energy Conference at Illinois, PECI 2021, May. https://doi.org/10.1109/PECI51586.2021.9435209

Chien-Chih Wang 1, , Chun-Hua Chien 1, 2 and Amy J. C. Trappey. (2021). On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements.

Chin Wei, C., Ramiah, P., & Farhaini Razali, N. (2023). Inventory Management Systems (IMS). Journal of Applied Technology and Innovation, 7(3), 2600–7304.

Dave, A., Swamy, H., Nakra, V., & Agarwal, A. (2024). Machine Learning Techniques And Predictive Modeling For Retail Inventory Management Systems . 29(4), 698–706. https://doi.org/10.53555/kuey.v29i4.5645

Goltsos, T. E., Syntetos, A. A., Glock, C. H., & Ioannou, G. (2022). Inventory – forecasting : Mind the gap. European Journal of Operational Research, 299(2), 397–419. https://doi.org/10.1016/j.ejor.2021.07.040

Halima Fatima1, Bisma Tahir2, Khalid Hamid3 1,2, 3. (2025). Spectrum of Engineering Sciences HYBRID ARIMA AND LSTM DEEP LEARNING MODELS EMPOWERING Spectrum of Engineering Sciences. 3138, 117–133.

Ingale, K. Y., & Senan, R. (2023). Predictive analysis of GDP by using ARIMA approach. 12(5), 309–315.

Jondhale, N. S., & Khairnar, D. T. (2020). an Analytical Study of Use of an Artificial Intelligence in Inventory Management With Reference To Medium Scale Manufacturing Industries in Nashikindustrial Estate. Vidyabharati International Interdisciplinary Research Journal, 11(1), 212–218. www.viirj.org

Jondhale, P. S. D. (2019). Review of Inventory Management System. International Journal for Research in Applied Science and Engineering Technology, 7(6), 1894–1897. https://doi.org/10.22214/ijraset.2019.6317

Kontopoulou, V. I., Panagopoulos, A. D., & Kakkos, I. (2023). A Review of ARIMA vs . Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. 1–31.

Mashayekhy, Y., Babaei, A., Yuan, X. M., & Xue, A. (2022). Impact of Internet of Things (IoT) on Inventory Management: A Literature Survey. Logistics, 6(2). https://doi.org/10.3390/logistics6020033

Michael, L., Ullah, A., Oral, O., Roshan, M., & Purdy, A. (2021). Synthetic Data Generation and Its Applications in Model Training.

Obadire, A. M., Boitshoko, B. L., & Moyo, N. T. (2022). Analysis of the Impact of Inventory Management Practices on the Effectiveness of Retail Stores in South Africa. Global Journal of Management and Business Research, 1–7. https://doi.org/10.34257/gjmbrcvol22is5pg1

Olamide Raimat Amosu, Praveen Kumar, Yewande Mariam Ogunsuji, Segun Oni, & Oladapo Faworaja. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708–719. https://doi.org/10.30574/wjarr.2024.23.2.2394

Pirayesh Neghab, D., Khayyati, S., & Karaesmen, F. (2022). An integrated data-driven method using deep learning for a newsvendor problem with unobservable features. European Journal of Operational Research, 302(2), 482–496. https://doi.org/10.1016/j.ejor.2021.12.047

Prajapati, D., Chan, F. T. S., Chelladurai, H., Lakshay, L., & Pratap, S. (2022). An Internet of Things Embedded Sustainable Supply Chain Management of B2B E-Commerce. Sustainability (Switzerland), 14(9). https://doi.org/10.3390/su14095066

Praveen K B. (2020). Inventory Management using Machine Learning. International Journal of Engineering Research And, V9(06). https://doi.org/10.17577/ijertv9is060661

Purwono a, 1, Alfian Ma’arif b, 2,, Wahyu Rahmaniar c, 3, Haris Imam Karim Fathurrahman b, 4, Aufaclav Zatu Kusuma Frisky d, e, 5, Q. M. ul H. f. (2022). Understanding of Convolutional Neural Network (CNN): A Review. 2(4), 739–748.

Rajotte, J., Bergen, R., Buckeridge, D. L., Emam, K. El, & Ng, R. (2022). iScience ll Synthetic data as an enabler for machine learning applications in medicine. ISCIENCE, 25(11), 105331. https://doi.org/10.1016/j.isci.2022.105331

Ridwan, M., Sadik, K., & Afendi, F. M. (2024). Comparison of ARIMA and GRU Models for High-Frequency Time Series Forecasting Comparison of ARIMA and GRU Models for High-Frequency Time Series Forecasting. January. https://doi.org/10.15294/sji.v10i3.45965

Shamsuddoha, M. (2015). Integrated Supply Chain Model for Sustainable Manufacturing: A System Dynamics Approach. In Sustaining Competitive Advantage Via Business Intelligence, Knowledge Management, and System Dynamics (Vol. 22B, pp. 155–399). Emerald Group Publishing Limited. https://doi.org/10.1108/S1069-09642015000022B003

Sustrova, T. (2016). An artificial neural network model for a wholesale company’s order-cycle management. International Journal of Engineering Business Management, 8, 1–6. https://doi.org/10.5772/63727

Swani, L., & Tyagi, P. (2017). Predictive Modelling Analytics through Data Mining. 5–11.

Ummah, M. S. (2019). No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title. Sustainability (Switzerland), 11(1), 1–14. http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI

Yang, L., Ma, X., & Liu, Y. (2023). Application of multi-level inventory intelligent decision- making system in the automotive aftermarket (Issue Icem). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-308-5

Yeresime, S. (2023). Machine Learning Based Predictive Analytics for Agricultural Machine Learning Based Predictive Analytics For. May.

Zhao, Y., & Yang, G. (2023). Deep Learning-based Integrated Framework for stock price movement prediction. Applied Soft Computing, 133, 109921. https://doi.org/10.1016/j.asoc.2022.109921.


Refbacks

  • There are currently no refbacks.