Many engineering decisions involve uncertainty and require tradeoffs between multiple attributes. Often it is possible to reduce or eliminate uncertainty through data collection, or analysis, or both. Although it generally leads to better outcomes, reducing uncertainty may increase cost. This metadecision of whether to reduce the uncertainty at a given cost is undertaken in the form of Value of Information (VOI) studies. Previous work has considered value of information in singleattribute cases, however, studies of the more challenging multiattribute case are less common. Multiattribute problems can have numerous uncertain variables across alternatives, not all of them easily measurable in money amounts, which makes the VOI analysis more complicated. This paper presents a simulation-based approach for calculating and interpreting the VOI in generic multiattribute decision problems and describes a simulation study that we conducted to explore how the decision problem parameters affect the VOI. The relationship between the VOI and the gain in expected utility (utility-gain) that will result from new information is also discussed. This relationship is not simple and yields some expected as well as counterintuitive results. For example, our results suggest that non-linearity of the utility functions may lead to different VoI calculation for the same utility-gain, depending on the relative position of the alternatives. Finally, this paper discusses the sensitivity of the utility from the decision as a function of decision problem parameters and shows that the magnitude of change in VoI can be predicted from these sensitivities. A vehicle alignment problem in an automotive assembly line is presented to demonstrate the approach. The results show that VoI calculation is a challenging problem that is very much contingent on the parameters of the problem. Our results provide insights into the benefits of different ways to evaluate the value of information in multiattribute problems.