The rapid expansion of the cyber-infrastructure has resulted in a wealth of data that previous generations of product designers could only dream of. It is now possible to obtain real-time data at all points in the product lifecycle. But without an organized framework for gathering, analyzing and making design decisions with it, this information goes to waste. This paper presents a method for tapping into this data without being overwhelmed by it. An ideal case study is product design for cost-effective compliance with product take-back laws. A previously developed constrained optimization model indicates that data from sources throughout the product lifecycle can be used make better design decisions regarding component reuse and remanufacture which simultaneously decrease cost and increase customer satisfaction. However, computational issues previously limited the analysis to a single set of static, industry average values for model inputs. Real-world implementation requires dynamic input from a variety of widely distributed data sources, ranging from material suppliers to the customer. However, existing computer programming methods that efficiently utilize widely distributed data are limited. This paper makes advances on two fronts. First, a decision model is presented which effectively utilizes distributed data sources regarding both cost and customer preferences. Second, the system is made fault tolerant by using the existing distributed shared memory infrastructure which involves the concepts of replicated data and quorums. Case study results indicate that information from distributed sources can be efficiently acquired using multiple replicas of data and a probabilistic read and write, leading to improved customer satisfaction.