The use of robotics at various points in the supply chain for many products and industries has been demonstrated in research and practice to be highly cost effective and efficiency increasing (Zhang et al. 2011). Further research is still necessary to determine what specific cost reduction drivers are affecting these technological advances, however, and to identify additional supply chin components that can benefit from automation. This research would help to address issues in the American manufacturing sector, and has the potential to increase employment and add to GDP by providing significant cost savings to businesses that can be used to fuel growth and increase productivity (White House 2009, CCC 2009). According to a 2010 report on warehouse productivity and safety published by the Aberdeen Group a transition to robotics can reduce ownership costs and accelerate ROI timescales, assuming appropriate technologies have been identified and adopted. It is in the area of such identification and adoption procedures that more research is strongly needed, and that this research will address.

Fully or primarily automated manufacturing technologies such as those that have been in place in the United States' automotive industry for over half a century are often cost prohibitive, with a recoupment of investment on the transition to such robotics too far in the future for most firms to seriously contemplate. Emerging robotics systems and other means of automation, which are necessarily iterative and become increasingly effective and diverse as foundational technologies themselves grow and become more functional and efficient, can provide benefits to other areas of the supply chain at lower costs yet with a similar impact on savings. An understanding of other technological tools and approaches that have increased efficiency and cost effectiveness through automation, such as Electronic Product Codes (EPC), RFID tags, and real-time inventory control systems, can provide a more holistic background view of supply chain automation and the potential for low-cost and easily adaptable technologies.

The various levels of automation and their impact on supply chain efficiency and cost also raises another issue that will be explicitly examined in the research, namely the need to develop a system for quantifying the efficacy of robotics/automation adoption at various points in the supply chain. Large degrees of difference between impacts make it difficult to compare different technologies and different uses in a meaningful way, and as of yet no clear system for making such a comparison has emerged in practical or theoretical research. Addressing this gap will not only have intrinsic research value, but it will also make the empirical research undertaken here more meaningful and valid. Quantification will enable the process of reviewing past and upcoming automation technologies and procedures much simpler, and will enable for more coherent and explicit surveying of companies in various stages of robotics and/or automation adoption, also allowing value chain analysis to include more quantitative and concrete components than are currently available.