Ease of disassembly of products to support circular economy strategies.

Circular economy strategies encourage, among others, concrete actions to extend the product lifetime. Product's repair and reuse, and component harvesting for reuse, all require the facilitated access to product components. Consequently, a reduction of the disassembly time and the related costs will increase the economic feasibility of product lifetime extension and therefore increase the viability of a circular economy in industrialised regions. Furthermore, disassembly has the potential to significantly increase the recycling yield and purity for precious metals, critical metals and plastics. For this reason, the European Commission and several ecolabels have considered to include design for disassembly requirements in legislation or voluntary environmental instruments. However, up to date, there is no standardised method to evaluate the ease of disassembly in an unambiguous manner with a good trade-off between the efforts required to apply the method and the accuracy of the determined disassembly time. The article proposes a robust method "eDiM" (ease of Disassembly Metric), to calculate the disassembly time based on the Maynard operation sequence technique (MOST). A straightforward calculation sheet is employed in eDiM to calculate the disassembly time given the sequence of actions and basic product information. This makes the results fully verifiable in an unambiguous manner, which makes eDiM suited to be used in policy measures in contrast to the results of prior developed methods One of the innovative aspects of eDiM is the categorization of disassembly tasks in six categories, which provides better insights on which disassembly tasks are the most time consuming and how the product design could be improved. The proposed method is illustrated by means of a case study of an LCD monitor. The presented case study demonstrates how the proposed method can be used in a policy context and how the calculated disassembly times per category can provide insights to manufacturers to improve the disassemblability of their products. The results also demonstrate how the proposed method can produce realistic results with only limited detail of input data.