Prediction of Intermittent Demand Occurrence using Machine Learning




Intermittent Demand, Inventory Management, Demand Forecasting, Intermittent Demand Classification, Machine Learning, Industry 4.0


Demand forecasting plays a pivotal role in modern Supply Chain Management (SCM). It is an essential part of inventory planning and management and can be challenging at times. One of the major issues being faced in demand forecasting is insufficient forecast accuracy to predict the expected demand and fluctuation in actual vs. the predicted demand results in fore-casting errors. This problem is further exaggerated with slow-moving and intermittent demand items.

Every organization encounters large proportions of items that have small ir-regular demand with long periods of zero demand, which are known as intermittent demand Items. Demand for such items occur sporadically and with considerable fluctuation in the size of the demand. Forecasting of the intermittent demand entails the prediction of demand series that is characterized by the time interval between demand being significantly greater than the unit forecast period. Because of this there are multiple periods of no demand in the intermittent demand time series. The challenge with these products with low irregular demand is that these items need to be stocked and replenished at regular interval irrespective of the demand cycle, thus adding to the cost of holding the inventory. Since the demand is not continuous, Traditional Forecasting models are unable to provide reliable estimate of required inventory level and replenishment point. Forecast errors would resulting in obsolescent stock or unfulfilled demand.

The current paper presents a simple yet powerful approach for generating a demand forecasting and replenishment process for such low volume intermittent demand items to come up with a recommendation for dynamic re-order point, thus, improving the inventory performance of these items. Currently, the demand forecast is generally based on past usage patterns. The rise of Artificial Intelligence/Machine Learning (AI/ML) has provided a strong alternative to solve the problem of forecasting Intermittent Demand. The intention is to highlight that machine learning algorithm is more efficient and accurate than traditional forecasting method. As we move forward to industry 4.0, the digital supply chain is considered as the most essential com-ponent of the value chain wherein the inventory size is controlled, and the demand predicted.


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


B. Adur Kannan, G. Kodi, O. Padilla, D. Gray, and B. C. Smith, “Forecasting spare parts sporadic demand using traditional methods and machine learning-a comparative study,” SMU Data Sci. Rev., vol. 3, no. 2, p. 9, 2020.

G. O. Kaya, M. Sahin, and O. F. Demirel, “Intermittent demand forecasting: a guideline for method selection,” Sadhana - Acad. Proc. Eng. Sci., vol. 45, no. 1, 2020, doi: 10.1007/s12046-020-1285-8. DOI:

Flowspace, “No Title.”

S. Axsater, Inventory Control - second edition. 2006.

T. M. Williams, “Stock Control with Sporadic and Slow-Moving Demand,” J. Oper. Res. Soc., 1984, [Online]. Available: DOI:

S. T. Visser, “Forecasting the intermittent and slow-moving demand of spare parts Forecasting the intermittent and slow-moving demand of spare parts Master of Science,” 2017.

A. A. Syntetos, J. E. Boylan, and J. D. Croston, “On the categorization of demand patterns,” J. Oper. Res. Soc., vol. 56, no. 5, pp. 495–503, 2005, doi: 10.1057/palgrave.jors.2601841. DOI:

Ç. Pinçe, L. Turrini, and J. Meissner, “Intermittent demand forecasting for spare parts: A Critical review,” Omega (United Kingdom), vol. 105, 2021, doi: 10.1016/ DOI:

J. D. Croston, “Forecasting and Stock Control of Intermittent Demand,” 1972. DOI:

M. Z. Babai, A. Syntetos, and R. Teunter, “Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence,” in International Journal of Production Economics, 2014, vol. 157, no. 1, pp. 212–219, doi: 10.1016/j.ijpe.2014.08.019. DOI:

A. A. Syntetos and J. E. Boylan, “On the bias of intermittent demand estimates,” vol. 71, pp. 457–466, 2001. DOI:

R. H. Teunter and L. Duncan, “Forecasting intermittent demand : a comparative study,” pp. 321–329, 2009, doi: 10.1057/palgrave.jors.2602569. DOI:

K. Nikolopoulos, “We need to talk about intermittent demand forecasting,” Eur. J. Oper. Res., no. xxxx, pp. 1–11, 2020, doi: 10.1016/j.ejor.2019.12.046. DOI:

G. H. John and P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers,” 2013, [Online]. Available:

S. Yang, “An Introduction to Naïve Bayes Classifier,” Towards Data Science, 2019.ïve-bayes-classifier-fa59e3e24aaf.

V. Jakkula, “Tutorial on Support Vector Machine (SVM),” Sch. EECS, Washingt. State Univ., pp. 1–13, 2011, [Online]. Available:

E. García-Gonzalo, Z. Fernández-Muñiz, P. J. G. Nieto, A. B. Sánchez, and M. M. Fernández, “Hard-rock stability analysis for span design in entry-type excavations with learning classifiers,” Materials (Basel)., vol. 9, no. 7, pp. 1–19, 2016, doi: 10.3390/ma9070531. DOI:

L. Noriega, “Multilayer perceptron tutorial,” Sch. Comput. Staff. Univ., pp. 1–12, 2005, [Online]. Available:

I. K. Nti et al., “Predicting Monthly Electricity Demand Using Soft-Computing Technique,” Int. Res. J. Eng. Technol., vol. 06, no. 06, pp. 1967–1973, 2019, [Online]. Available:

F. Lolli, E. Balugani, A. Ishizaka, R. Gamberini, B. Rimini, and A. Regattieri, “Machine learning for multi-criteria inventory classification applied to intermittent demand,” Prod. Plan. Control, vol. 30, no. 1, pp. 76–89, 2019, doi: 10.1080/09537287.2018.1525506. DOI:

Y. Hong, J. Zhou, and M. A. Lanham, “Forecasting Intermittent Demand Patterns with Time Series and Machine Learning Methodologies,” 2018.




How to Cite

A. K. Singh, J. B. Simha, and R. Agarwal, “Prediction of Intermittent Demand Occurrence using Machine Learning”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.