a b s t r a c tForecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

The Industry 4.0 (i4.0) paradigm was conceived bearing smart machines enabling capabilities, mostly through real-time communication both between smart equipment on a shop floor and decision-aiding software at the business level. This interoperability is achieved mostly through a reference architecture specifically designed for i4.0, which is aimed at devising the information architecture with real-time capabilities. From such architectures, the Reference Architectural Model for Industrie 4.0 (RAMI 4.0) is considered the preferred approach for implementation purposes, especially within Small and Medium Enterprises (SMEs). Nevertheless, the implementation of RAMI 4.0 is surrounded with great challenges when considering the current industrial landscape, which requires retrofitting of existing equipment and the various communication needs. Through three different case studies conducted within footwear and cork industries, this research proposes a RAMI 4.0 SME implementation methodology that considers the initial stages of equipment preparation to enable smart communications and capabilities. The result is a methodological route aimed for SMEs' implementation of smart machines, based on RAMI 4.0, which considers both the technological aspects as well as the business requirements.

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In this work we present a metaheuristic method based on tabu search for solving the permutation flow shop scheduling problem with sequence dependent setup times, with the objective of minimizing total weighted tardiness. The problem is well known for its practical applications and for the difficulty in obtaining good solutions. The tabu search method proposed is based on the insertion neighborhood, and is characterized, at each iteration, by the selection and evaluation of a small subset of this neighborhood; this has consequences both on diversification and on speeding up the search. We also propose a speed-up based on book keeping information of the current solution, used for the evaluation of its neighbors.