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Sustainable warehousing is essential for organizations to achieve overall supply chain sustainability. Warehousing facilities have the greatest potential for reducing socio-environmental impact. Yet, both research and practice have given relatively less attention to considering all aspects of sustainability in warehouses. In order to address this gap, this study proposes combining both input from professionals and from a literature survey of triple-bottom-line theory in order to develop a sustainable warehouse criteria framework, thus contributing to sustainable organizational warehouse evaluation.The method supporting the evaluation of this framework is based on the integration of a multicriteria AHPSort-traffic light visualization technique and novel post-optimal analysis. Furthermore, the authors deployed this framework and integrated methodology in an Indian manufacturing company to evaluate and classify seven of their warehouses for decision making. The traffic light visualization technique presents and conveys the results better than numbers. Finally, the post-optimal analysis provides recommendations for efficient improvements. The findings of this study present valuable insights and guidelines for industrial managers and practitioners, especially those from the Indian manufacturing industry, for sustainable warehouse decision-making, and for improving their overall corporate sustainability performance.

This paper seeks to evaluate the appropriateness of various univariate forecasting techniques for providing accurate and statistically significant forecasts for manufacturing industries using natural gas. The term "univariate time series" refers to a time series that consists of single observation recorded sequentially over an equal time interval. A forecasting technique to predict natural gas requirement is an important aspect of an organization that uses natural gas in form of input fuel as it will help to predict future consumption of organization.We report the results from the seven most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the Naive method. Naï ve method, Drift method, Simple Exponential Smoothing (SES), Holt method, ETS(Error, trend, seasonal) method, ARIMA, and Neural Network (NN) have been studied and compared.Forecasting accuracy measures used for performance checking are MSE, RMSE, and MAPE. Comparison of forecasting performance shows that ARIMA model gives a better performance.

Foundry based organizations consume significant amounts of energy for producing their final products. Recently, techno-commercial and environmental factors have started triggering change from fossil fuels to cleaner ones. In this paper, factors acting as driving forces for migration from one fuel to another in order to improve energy efficiency, including various performance parameters in support of environment preservation, have been identified. Focus is also given to challenges which encounter during fuel switching. A new framework has been applied that can be used for fuel switching in manufacturing organizations. A real case of switching from three types of fuels to a single fuel has been studied and the outcomes are evaluated. Analysis related to energy consumption before and after fuel switching with respect to corresponding production data have been performed.