Abstract Paper aims Several concerns regarding the lack of interpretability of machine learning models obstruct the implementation of machine learning projects as part of the demand forecasting process. This paper presents a methodology to support the introduction of machine learning into the forecasting process of a traditional direct sales company by providing explanations for the otherwise obscure results. We also suggest incorporating human knowledge inside the machine learning pipeline as an essential part of capturing the business logic and integrating machine learning into the existing processes. Originality Using explainable machine learning methods on real-life company data demonstrates that machine learning techniques are functional beyond the academy and can be introduced to everyday companies' production. Research method The project used real-world data from a company and followed a traditional machine learning pipeline to collect, preprocess, select and train a machine learning model, to conclude with the explanation of the model results through the implementation of SHAP Main findings The results provided insights regarding the contribution of the features to the forecast. We analyzed individual predictions to understand the behavior of different variables, proving helpful when interpreting complex machine learning models. Implications for theory and practice This study contributes to a discussion about adopting new technology and implementing machine learning models for demand forecasting. The methodology presented in this paper can be used to implement similar projects on interested companies. processes reallife real life companies production realworld world collect preprocess forecast variables

A discrete event simulation-optimization heuristic is presented for configuring a selective pallet rack system. To the rack system under study pallets arrive dynamically over time to be stored for a given period of time. The storage duration, arrival time and pallet height are assumed to be continuous random variables with known density functions. In such a system, if there is no available slot in the rack to store an arriving pallet, it is placed on the floor. The proposed heuristic aims at minimizing the number of racking banks needed so that the long term proportion of pallets that are placed in a rack slot reaches a minimum pre-specified value. The computational experiments conducted suggest that the proposed heuristic effectively solves the problem under study.

En este artículo se presenta un heurístico de simulación-optimización para la configuración de un sistema de almacenamiento en estantería selectiva simple. Al sistema de almacenamiento objeto del estudio llegan pallets dinámicamente en el tiempo para ser almacenados por un periodo de tiempo. Se asume que la duración del almacenamiento, el instante de llegada y la altura de los pallets son variables aleatorias continuas con funciones de densidad conocidas. En este sistema, si al momento de llegada de un pallet no hay disponible una posición en la estantería en la que éste se pueda almacenar, el pallet se almacena en el suelo. El heurístico propuesto busca minimizar en el número de módulos de estantería necesarios de tal manera que en el largo plazo la proporción de pallets ubicados en la estantería alcance un valor mínimo preestablecido. Los experimentos computacionales llevados a cabo sugieren que el heurístico propuesto es efectivo para resolver el problema en estudio.