Abstract
The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Simple Multi-Attribute Rating Technique (SMART). The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.

Abstract

Abstract
Forecasting 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.

Abstract

Abstract
Considering the new paradigm in the industry, where clients request small amounts of a growing range of products, the times of crisis and the fierce competition for businesses survival, it is crucial for Organizations to be able to reduce the waste of raw materials. In the manufacturing industry, the waste of materials could be minimized with the introduction of decision support systems in the process. In this paper we propose an information system that enables the optimization of purchases and consumption of materials, promoting waste reduction and boosting profitability. © 2015 AISTI.

Abstract
Considering the new paradigm in the industry, where clients request small amounts of a growing range of products, the times of crisis and the fierce competition for businesses survival, it is crucial for Organizations to be able to reduce the waste of raw materials. In the manufacturing industry, the waste of materials could be minimized with the introduction of decision support systems in the process. In this paper we propose an information system that enables the optimization of purchases and consumption of materials, promoting waste reduction and boosting profitability.