Abstract
A 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 alternatives.The proposed methodology is derived generalising a problem of material selection of automotive parts in metal casting manufacturing systems. In particular, three typical aluminium, magnesium and zinc alloys in a High-Pressure Die Casting (HPDC) process are compared using the deterministic Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combining 18 criteria organised in 4 main categories (cost, quality, time and environmental sustainability). A detailed and systematic approach to calculate the considered criteria is also provided and it includes Life Cycle Assessment (LCA) considerations. Results show that, although in most of the cases the aluminium alloy is the best option, there are a few areas in the decision making space where magnesium and zinc alloys score better without a simple correlation to categories. This shows how valuable the proposed mapping process is to understand the complex MCDA analyses. The methodology does not make specific assumptions about metal casting and can be applied to sustainable manufacturing in general.
Highlights
The ecological situation of the Earth under current trends has been considered not sustainable and cause of concern as exemplified prominently by the “imperative to act” urged by the winners of the Blue Planet Prize (Watson, 2014)
This work absorbs such considerations devising a framework for Decision Makers (DMs) to select the best material to produce automotive parts with a High Pressure Die Casting (HPDC) process combining crucial traditional criteria like cost, quality and productivity with sustainability metrics
The case study presented in this work combines several themes: sustainability and energy efficiency in metal casting, material selection for automotive components, elements of product Life Cycle Assessment (LCA) and Multi-Criteria Decision Analysis (MCDA)
Summary
The ecological situation of the Earth under current trends has been considered not sustainable and cause of concern as exemplified prominently by the “imperative to act” urged by the winners of the Blue Planet Prize (Watson, 2014). An alternative policy to the ETS that may overcome these issues is the taxation (as a component of the VAT, for example) based on the emissions and resource consumption embodied by the product In this way, more sustainable practices at a global, systemic level are promoted without affecting the competitiveness of environmentally virtuous players. More sustainable practices at a global, systemic level are promoted without affecting the competitiveness of environmentally virtuous players This work absorbs such considerations devising a framework for Decision Makers (DMs) to select the best material to produce automotive parts with a High Pressure Die Casting (HPDC) process combining crucial traditional criteria like cost, quality and productivity with sustainability metrics (including product life cycle concepts). The proposed framework does not make any specific assumptions to foundries, material selection or automotive products and it can be applied to automatically map the sustainability decision-making space of any manufacturing system
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