Bin picking of various mixed items is an important research problem in robotics. As a bin-picking system is more than the sum of its elements, its performance needs to be analyzed in a macroscopic scale. Recent robotics competitions are an excellent platform for technology comparisons since some participants may use state-of-the-art technologies, while others may use conventional ones. Nevertheless, even though points are awarded or subtracted based on the performance in the frame of the competition rules, the final score does not directly reflect the suitability of the technology. Therefore, it is difficult to understand which technologies and their combination are optimal for various real-world problems.One key element for an effective bin-picking robot is the gripper, as it is the main tool to manipulate objects. If the target items are diverse, multiple grippers are normally used. A design of gripper combinations depends not only on the item variations but also on the state of the bins, which changes while robots pick items from them. This dissertation first proposes a strategy to change the gripper combination during a bin-picking task based on the sparseness of objects inside bins, and a bin-picking robot system using it. The evaluation results using successful picking rate as a metric are shown, and the effectiveness of this strategy is verified. However, this metric represents only one aspect of the robot system. Furthermore, this dissertation proposes a set of performance metrics selected in terms of actual field use as a solution to clarify the important technologies in bin picking. Moreover, we use the selected metrics to compare four original robot systems, which achieved the best performance in the Stow task of the Amazon Robotics Challenge 2017. Based on this comparison, we discuss which technologies are ideal for practical use in bin-picking robots in the fields of factory and warehouse automation.