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This research has addressed a quantitative approach for improving energy management through applying statistical techniques aimed at identifying and controlling factors linked to energy consumption rates at manufacturing plants. The paper presents analysis and results of multiple linear regression models used to establish the significance of a number of energy related management factors in controlling energy usage. Regression models constructed for this purpose proved the existence of statistically valid relationships between electrical energy consumption and maintenance and production management factors, namely, failure rate and production rate, where R² values of the magnitude of 65 per cent were obtained. Furthermore, an economical treatment based on the derived regression models was formulated and demonstrated that effective management practices associated with proper maintenance, cost accounting and reporting systems can result in highly significant savings in energy usage.

Quality control charts are statistical process control tools aimed at monitoring a (manufacturing) process to detect any deviations from normal operation and to aid in process diagnosis and correction. The information presented on the chart is a key to the successful implementation of a quality process correction system. Pattern recognition methodology has been pursued to identify unnatural behaviour on quality control charts. This approach provides the ability to utilize patterning information of the chart and to track back the root causes of process deviation, thus facilitating process diagnosis and maintenance. Presents analysis and development of a statistical pattern recognition system for the explicit identification of unnatural patterns on control charts. Develops a set of statistical pattern recognizers based on the likelihood ratio approach and on correlation analysis. Designs and implements a training algorithm to maximize the probability of identifying unnatural patterns, and presents a classification procedure for real‐time operation. Demonstrates the system performance using a set of newly defined measures, and obtained results based on extensive experiments illustrate the power and usefulness of the statistical approach for automating unnatural pattern detection on control charts.