Evaluation of Consumer and Producer Risk in Conformity Assessment Decisions

Abstract

Conformity assessment is defined as any activity that is undertaken to determine whether a product, system or process meets the relevant standards and performs certain requirements. The decision whether the object of interest is in accordance with a request is based on the measurement results. The quality of the measurement results is described by the measurement uncertainty that is relevant to conformity assessment. The consumer and producer risks are described in this paper, as well as their impact on the conformity assessment and the way in which they can be managed. This paper also describes the impact of measurement uncertainty on the acceptance and rejection of a product according to its specifications.

Is the shopping list a guarantee for rational consumer behaviour?

“…Rational consumer activities require making certain calculations (Boudon, 2003;D. P. Green & Fox, 2007;Runje et al, 2019;Nickerson, 2021). Additionally, all consumer decisions are dependent upon multiple factors, such as, for instance, economic factors, i.e.…”

Risk significance assessment on the basis of a fuzzy model

“…For effective risk management and obtaining reliable and accurate test results, the risk analysis tool (Ishikawa diagram), failure cause and effect analysis (FCEA), fault tree analysis (FTA), event tree, project risk analysis and risk matrix can be used [7,9,10], etc. To analyze risks, taking into account external and internal factors, special economic and mathematical methods are used [11,12]. Their use, as a rule, assumes that the failure events are statistically independent and have an accurate probability.…”

“…This obviously indicates the fuzzy nature of the expert information. The influence of measurement uncertainty on risks is noted in [11], where a binary rule is applied to make a decision within the framework of a probabilistic approach to risk assessment. In [13], the apparatus of the theory of fuzzy sets and Bayesian networks were used to describe uncertainties, combine expert opinions, and update a priori probabilities.…”