Pagone, EmanueleSalonitis, KonstantinosJolly, Mark R.2020-02-102020-02-102020-02-01Pagone E, Salonitis K, Jolly M. (2020) Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing systems. Journal of Cleaner Production, Volume 257, June 2020, Article number 1202720959-6526https://doi.org/10.1016/j.jclepro.2020.120272https://dspace.lib.cranfield.ac.uk/handle/1826/15116A common feature of Multi-Criteria Decision Analysis (MCDA) to evaluate sustainable manufacturing is the participation (to various extents) of Decision Makers (DMs) or experts (e.g. to define the importance, or “weight”, of each criterion). This is an undesirable requirement that can be time consuming and complex, but it can also lead to disagreement between multiple DMs. Another drawback of typical MCDA methods is the limited scope of weight sensitivity analyses that are usually performed for one criterion at the time or on an arbitrary basis, struggling to show the “big picture” of the decision making space that can be complex in many real-world cases. This work removes all the mentioned shortcomings implementing automatic weighting through an ordinal combinatorial ranking of criteria objectively set by four pre-defined weight distributions. Such solution provides the DM not only with a fast, rational and systematic method, but also with a broader and more accurate insight into the decision making space considered. Additionally, the entropy of information in the criteria can be used to adjust the weights and emphasise the differences between potentially close alternativeenAttribution 4.0 InternationalManufacturing systemDecision makingSustainable developmentCastingLifecycleArticle26127007