Ranking

Finally, RIPPLE ES can aid to point out ways to be used to better promote particular Quality Products and Services in the future, by ranking the assessed alternative strategies/ policies under different assumptions about the relative weight of their internal and regional effects. To do this, RIPPLE ES combines Multicriteria Analysis (Roy & Vandepooten (1995) and Sensitivity Analysis.
Multicriteria Analysis aims at ranking alternative strategies/ policies, by referring both to their internal effectiveness and to their regional implications. Knowing how it is very difficult to identify the correct weight for each internal objective and regional implication - notably because these ones depend on the [political] point of view of decision makers, Sensitivity Analysis provides a way to compute the variations in the preference indexes induced by the variations of the relative weights of each criteria.

Multcriteria Analysis consists of
(i)  making a pairwise comparison among all strategies/ positions by referring to their impact on each internal objective and to each of their regional implications,
(ii) of ranking them from the frequency of their preference (over-efficiency) and from the frequency of their reject (sub-efficiency). Like cost-benefit methods, they can only be used when alternatives are well defined in advance.

End-User of ES loads the Impact Matrix O describing the impact of each alternative strategy/ policy on each typical objective of producers/ institutions (ref. VARS A1/2) and each regional implication (ref. App. VARS H1/2). Then, ES computes the preference index of each strategy/ policy from this matrix, by giving the same weight to each internal objective and regional implication. Next, End-User of ES sets the relative weight of each (or sets of) criteria by referring to the points of view of a given decision maker (ref. system of values). So, ES computes and shows in a graphical format, the best and the worst actions. As well as the 'behaviour' of each strategy/ policy (i.e. the values of the preference index) for each distribution of weights.

The Sensitivity Analysis Module (SA) Module aims at pointing out the changes in the preference indexes introduced by different weights assignments to internal objectives and regional implications of alternative strategies/ policies. Knowing that it is very difficult to identify the correct weight for each internal objective and regional implication - notably because these depend on the [political] point of view of decision makers, Sensitivity Analysis provides a way to compute the variations in the preference indexes induced by the variations of the relative weights of each criteria (i.e. internal objective and regional implication).