LOCAL INVENTORY DEMAND FORECASTING OF E-COMMERCE WITH MAPREDUCE FRAMEWORK

Kazi Fardin  Islam; Mithun  Rahman; SK Alamgir  Hossain

doi:10.53808/KUS.2022.ICSTEM4IR.0082-se

Authors

Kazi Fardin Islam Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Mithun Rahman Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
SK Alamgir Hossain Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh

DOI:

https://doi.org/10.53808/KUS.2022.ICSTEM4IR.0082-se

Keywords:

Demand Forecasting, Inventory, Big Data, MapReduce, ARIMA

Abstract

With the rapid expansion of e-commerce businesses shortest delivery time is a challenging task. As well as price and quality, delivery time is becoming an important key-factor in the race of business growth. To deliver the product to the buyer in the shortest time, the product should be at the local inventory at the time of purchasing. Which is a difficult task for e-commerce companies to store the right products at right time in local inventories. Extracting useful patterns and forecasting demand from purchase history is a tough job because of the huge volume and high variety nature of e-commerce data. The MapReduce algorithm of Big Data ecosystem can be used to pre-process the enormous amount of data and to summarize it into a suitable format which later can easily be used to build any forecasting model. A comparison of different classical time series forecasting methods with the pre-processing of MapReduce algorithm is focused on this work.

Downloads

Download data is not yet available.

References

Thiebaut, R. (2019). AI Revolution: How data can identify and shape consumer behavior in ecommerce. In Entrepreneurship and Development in the 21st Century. Emerald Publishing Limited.

Ajibola, A. S., & Goosen, L. (2014). Missed opportunities in mobile E-commerce usability. Int. J. Sci. Eng. Res.(IJSER), 5(6), 954-956.

Harsoor, A. S., & Patil, A. (2015). Forecast of sales of Walmart store using big data applications. International Journal of Research in Engineering and Technology, 4(6), 51-59

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101.

Arne Holst. (2021). Amount of data created, consumed, and stored 2010-2025. https://www.statista.com/statistics/871513/worldwide-data-created/

Bernard Marr.(2018). How Much Data Do We Create Every Day, The Mind-Blowing Stats Everyone Should Read. https://www.forbes.com

/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowingstats-everyone-should-read/?sh=2c464e6160ba

RandyBean.(2021). NewVantage Partners Releases 2021 Big Data and AI Executive Survey. https://www.businesswire.com/news/home/20210104005022/en/NewVantage-Partners-Releases2021-Big-Data-and-AI-Executive-Survey

Alicia Tan. (2017). How Coca-Cola uses data to supercharge its superbrand status. https://www.adma.com.au/resources/how-coca-cola-uses-data-to-supercharge-its-superbrandstatus

John Kopanakis. (2018). 5 Real-World Examples of How Brands are Using Big Data Analytics. https://www.mentionlytics.com/blog/5-real world-examples-of-how-brands-are-using-bigdata-analytics/

Dr. Andreas Stephan Huber et al. (2014). Big data: Potentials from a risk management perspective.

imee Manning. (2019). How Big Data is Improving Inventory Management. https://www.bigcommerce.com/blog/data-inventory management/

Li, M., Ji, S., & Liu, G. (2018). Forecasting of Chinese E-commerce sales: an empirical comparison of ARIMA, nonlinear autoregressive neural network, and a combined ARIMA-NARNN model. Mathematical Problems in Engineering, 2018.

Verma, N., & Singh, J. (2017). An intelligent approach to big data analytics for sustainable retail environment using Apriori-MapReduce framework. Industrial Management & Data Systems.

Wang, X., Kang, Y., Hyndman, R. J., & Li, F. (2020). Distributed ARIMA models for ultra-long time series. arXiv preprint arXiv:2007.09577.

Jain, A., Karthikeyan, V., Sahana, B., Shambhavi, B. R., Sindhu, K., & Balaji, S. (2020, November). Demand Forecasting for E-Commerce Platforms. In 2020 IEEE International Conference for Innovation in Technology (INOCON) (pp. 1-4). IEEE.

Li, L., Ma, Z., Liu, L., & Fan, Y. (2013). Hadoop-based ARIMA algorithm and its application in weather forecast. International Journal of Database Theory and Application, 6(5), 119-132.

Bendre, M., & Manthalkar, R. (2019). Time series decomposition and predictive analytics using MapReduce framework. Expert Systems with Applications, 116, 108-120.

M. Zaharia et al., ‘‘Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing,’’ in Proc. 9th USENIX Conf. Netw.Syst. Design Implement. (NSDI), 2012, p. 2

C. Parker, ‘‘Unexpected challenges in large scale machine learning,’’ in Proc. 1st Int. Workshop Big Data, Streams Heterogeneous Source Mining Algorithms, Syst., Programm. Models Appl. (BigMine), 2012, pp. 1–6.