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The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.

Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.

A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.

The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.

Nowadays, most of the organizations have focused through the world on Six Sigma to reduce the costs, improve the productivity and enhance concerned individuals’ satisfaction, especially customers’ satisfaction. Annually, these organizations define and execute thousands of Six Sigma projects which involve a great deal of investments. But are all of these projects successful and do the organizations benefit from the above advantages? How can we reduce the risk of failure in Six Sigma projects? The first step to reduce the risk of failure in Six Sigma projects is selecting optimal ones which have the most profits and the least expected risks.

In this paper, the effective criteria are recognized and defined in selecting Six Sigma projects. Then, the analytic hierarchy process (AHP) is used to rank the results. Then, a real example is resolved by two important techniques in decision-making process, that is the AHP and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), as well as data envelopment analysis (DEA). The results from the above three methods are compared.

The results of this paper show that by using fewer criteria, the results from AHP and TOPSIS are very similar. Also, the results from these techniques vary from DEA’s ones in many aspects. So regarding the different results and the importance of criteria in selecting the Six Sigma projects, multi-criteria decision-making (MCDM) techniques are more reliable in comparison with DEA, because decision-maker’s point of view is more effective in MCDM techniques.

The paper, using a real case study, provides important new tools to enhance decision quality in Six Sigma project selection.

The purpose of this paper is to develop a slack-based measure (SBM) model in the presence of dual-role factor. Then it is applied in supplier selection problem.

The developed model in this paper is based on data envelopment analysis (DEA) technique.

The proposed evaluation platform is capable of identifying ill-performing suppliers which seek to future improvement. The findings provide valuable insights for practitioners, as well as academicians, policy makers and also integrate selection criteria under the supply chain.

This is the first time that a non-radial DEA model considers dual-role factors. The proposed model does not deal with dual-role factor as a non-discretionary factor. The proposed model considers dual-role factors on both the input and output sides in a similar manner. The proposed model can fully measure the inefficiency of suppliers. The proposed model can give a complete ranking of suppliers.