About the Project
The required machine learning model should compose of 2 processes:
· Feature engineering of the demand patterns by building a clustering process having as its goals to cluster demand patterns chunks into c clusters, where chunks with high membership to the same cluster represent similar temporal patterns, and at the same time to measure the outlierness degree of each chunk and consequently to measure the density of outliers. This to track the changes in the incoming demand patterns. The goal of this first process is to build a clustering model that is able to adapt to the changes in the demand patterns, by implementing a continuous learning that exploits the input chunks as they arrive. Intrinsic to this clustering model is a measure of outlierness that provides information about the goodness of ﬁt of each input chunk to the clustering model.
· The second process is applying a supervised learning algorithm that is used to mimic the expert opinion of the planner, and decide whether we should set an item for make to stock (MTS) or make to order (MTO). This is called MTS/MTO classiﬁcation.
· Integrate the domain knowledge and machine leaning to create a new classiﬁcation and level setting process, leveraging years of demand data and new statistical indicators for demand patterns.
· The validation phase should be reduced at each iteration as ML model can be re-trained to incorporate past validations, increasing eﬃciency and performances.
· Level setting problem (on top of the deployed classiﬁcation model) can be addressed by benchmark Machine Learning methods (e.g. Reinforcement Learning, Montecarlo simulations and traditional statistical methodologies). Regarding Reinforcement learning and Montecarlo you have to establish punishments for letting a particular item run out of stock and also punish the model for stock with too higher value for too long. For rewards, the focus was on ordering items within a safe window before the demand.
2. A. Abdullatif, F. Masulli, and S. Rovetta. Clustering of nonstationary data streams: A survey of fuzzy partitional methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1258, 2018.
3. A. Abdullatif, F. Masulli, S. Rovetta, and A. Cabri. Graded possibilistic clustering of non-stationary data streams. In A. Petrosino, V. Loia, and W. Pedrycz, editors, Fuzzy Logic and Soft Computing Applications, pages 139–150, Cham, 2017. Springer International Publishing.
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