Abstract
Demand Forecasting, undeniably, is the single most important component of any organizations Supply Chain. It determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match the demand. It goes without saying that if an organization does not get its forecasting accurate to a reasonable level, the whole supply chain gets affected. Understandably, Over/Under-forecasting has deteriorating impact on any organizations Supply Chain and thereby on P and L. Having ascertained the importance of Demand Forecasting, it is only fair to discuss about the forecasting techniques which are used to predict the future values of demand. The input that goes in and the modelling engine which it goes through are equally important in generating the correct forecasts and determining the Forecast Accuracy. Here, we present a very unique model that not only pre-processes the input data, but also ensembles the output of two parallel advanced forecasting engines which uses state-of-the-art Machine Learning algorithms and Time-Series algorithms to generate future forecasts. Our technique uses data-driven statistical techniques to clean the data of any potential errors or outliers and impute missing values if any. Once the forecast is generated, it is post processed with Seasonality and Trend corrections, if required. Since the final forecast is the result of statistically pre-validated ensemble of multiple models, the forecasts are stable and accuracy variation is very minimal across periods and forecast horizons. Hence it is better at estimating the future demand than the conventional techniques.
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Acknowledgements
The complete list of the team which has worked in this project: Rajiv Ranjan∗, Arashdeep Singh∗, Shaivya Datt∗, Aamir Ahmed Khan∗, Mitav Kulshrestha∗, Pawan Kulkarni∗, Anubhav Rustogi∗, Aditya Iskande∗, Mandar Shirsavakar∗, Rabiya Gill∗, Heine van der Lende†, Srinivas Deshpande†, Srijan A†.
∗Advanced Analytics Team, SS Supply Chain Solutions Pvt. Ltd., Bangalore.
†Enterprise Information Management, Philips Lighting, Bangalore.
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Adhikari, N.C.D. et al. (2019). An Intelligent Approach to Demand Forecasting. In: Smys, S., Bestak, R., Chen, JZ., Kotuliak, I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-8681-6_17
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DOI: https://doi.org/10.1007/978-981-10-8681-6_17
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