Improving manufacturing cycle efficiency through new multiple criteria data envelopment analysis models: an application in green and lean manufacturing processes

The Green & Lean Manufacturing organisational strategy emphasises waste and environmental impact reduction, efficiency increase, quality improvement, and cycle time reduction by eliminating non-value-added activities. Usually, companies adopt the Manufacturing Cycle Efficiency indicator to identify and make improvements to meet Lean Manufacturing requirements. This work proposes an innovative approach based on Multiple Criteria Data Envelopment Analysis to calculate a new indicator, hereby called Value-Added Activities in Manufacturing Processes, to measure the efficiency of productive processes through Green Manufacturing concepts. This new indicator was applied in a company with the purpose of validating and testing it. The Levene's test was used to statistically validate the proposed procedure. As an immediate advantage of this new indicator, it was possible for managers to insert more information, such as incorporating aspects associated with Green Manufacturing, in the calculation of the Manufacturing Cycle Efficiency, making it more consistent and representative of what is happening in practice. Among this additional piece of information on the real processes analysed, it is worth mentioning, for example, the volume of scrap generated, the amount of sales invoicing, the quantity of work in process, and the quantity of finished products in stock. Specialists in Green & Lean Manufacturing, managers of the studied company, validated the results of the new indicator because they observed greater adherence to the practice of these new calculated values, when compared to the traditionally adopted calculations. Also highlighted, are other advantages of the new indicator such as the establishment of an efficiency ranking between the productive processes analysed and the determination of feasible goals to turn processes identified as inefficient into efficient ones.