Total acquisition cost of overseas outsourcing/sourcing: A framework and a case study

Purpose - The purpose of this paper is to develop a framework of total acquisition cost of overseas outsourcing/sourcing in manufacturing industry. This framework contains categorized cost items that may occur during the overseas outsourcing/sourcing process. The framework was tested by a case study to establish both its feasibility and usability. Design/methodology/approach - First, interviews were carried out with practitioners who have the experience of overseas outsourcing/sourcing in order to obtain inputs from industry. The framework was then built up based on combined inputs from literature and from practitioners. Finally, the framework was tested by a case study in a multinational high-tech manufacturer to establish both its feasibility and usability. Findings - A practical barrier for implementing this framework is shortage of information. The predictability of the cost items in the framework varies. How to deal with the trade off between accuracy and applicability is a problem needed to be solved in the future research. Originality/value - There are always limitations to the generalizations that can be made from just one case. However, despite these limitations, this case study is believed to have shown the general requirement of modeling the uncertainty and dealing with the dilemma between accuracy and applicability in practice. © Emerald Group Publishing Limited.

No evidence for an item limit in change detection.

Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models"). Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models"). Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color) in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.