Demand forecasting aims to optimize the production planning of industrial companies by ensuring that the production planning meets the future demand. Demand forecasting utilizes historical data as an input to predict future trends of the demand. In this paper, a new approach for developing an intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm is presented. Firefly algorithmbased gated recurrent units (FA-GRU) is used to tackle the production forecasting problem. The proposed model has been evaluated and compared with the standard gated recurrent unit (GRU) and standard long short-term memory model (LSTM) using historical data of 36 months of concrete block manufacturing at dler company in Iraq. The prediction accuracy of the three models is evaluated using the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the statistical coefficient of determination (R2 ) indicators. The outcomes of the study show that the proposed FA-GRU gives better forecasting results compared to the standard GRU and standard LSTM.

In unpredicted industrial environment, being able to adapt quickly and effectively to the changing is key in gaining a competitive advantage in the global market. Agile manufacturing evolves new ways of running factories to react quickly and effectively to changing markets, driven by customized requirement. Agility in manufacturing can be successfully achieved via integration of information system, people, technologies, and business processes. This article presents the conceptual model of agility in three dimensions named: driving factor, enabling technologies and evaluation of agility in manufacturing system. The conceptual model was developed based on a review of the literature. Then, the paper demonstrates the agility evaluation by developing a multi-grade assessment model. This model can be used by decision maker to evaluate their current degree of agility. Lastly, the paper examined the conceptual model of evaluation in the State Company for Vegetable Oils Industry in Iraq. The calculation show that the State Company for Vegetable Oils Industry is very agile.

Companies are required to integrate a set of critical dimensions in order to measure and evaluate their performance to compete in globally competitive markets. Overall Equipment Effectiveness (OEE) is a quantitative metric, in the context of the total productive maintenance (TPM), attempts to measure and improve the effectiveness of manufacturing operations in three dimensions namely: availability, performance rate and quality rate. This study applies OEE to evaluate and improve the performance of a concrete block manufacturing system at Dler Company in Iraq. The study was conducted in two years of operation during 2016 and 2017. Results from 2016 show that the level of effectiveness was lower than the world class. An improvement in the average value of the OEE for 2017 was recorded, where the OEE value is increased in 2017 to 75% in comparison with its value in 2016 at 67%. It was also found that the reasons for this improvement are due to the enhancement that are made in the availability and quality.

Process capability indices are a powerful tool used by quality control engineering to measure the degree to which the process is or is not meeting the requirements. This paper studies the application of process capability indices in the evaluation of a process with asymmetric tolerances. The analyzed collected data of the cleaning liquid “Zahi”, was used to investigate the ability of the filling process to meet the requested specifications. Matlab software was used to plot control charts, normal probability, and histogram of the data gathered from the production line and further performed statistical calculations. It was observed from the control charts that the filling process is under control. In addition, it was revealed by the process capability indices that the process of filling the cleaning liquid bottle is not fitted with the target value but it is adequate.

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