Data in Action: Data-Driven Decision Making in U.S. Manufacturing

Manufacturing in America has become significantly more data-intensive. We investigate the adoption, performance effects and organizational complementarities of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for 2005 and 2010, we observe the extent to which manufacturing firms track and use data to guide decision making, as well as their investments in information technology (IT) and the use of other structured management practices. Examining a representative sample of over 18,000 plans, we find that adoption of DDD is earlier and more prevalent among larger, older plants belonging to multi-unit firms. Smaller single-establishment firms adopt later but have a higher correlation with performance than similar non-adopters. Using a fixed-effects estimator, we find the average value-added for later DDD adopters to be 3% greater than non-adopters, controlling for other inputs to production. This effect is distinct from that associated with IT and other structured management practices and is concentrated among single-unit firms. Performance improves after plants adopt DDD, but not before – consistent with a causal relationship. However, DDD-related performance differentials decrease over time for early and late adopters, consistent with firm learning and development of organizational complementarities. Formal complementarity tests suggest that DDD and high levels of IT capital reinforce each other, as do DDD and skilled workers. For some industries, the benefits of DDD adoption appear to be greater for plants that delegate some decision making to frontline workers. Disclaimer and Acknowledgements: Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. All errors are our own. We thank the MIT Initiative on the Digital Economy for generous funding for this research. 1 MIT Sloan School, 100 Main St, room E62-414, Cambridge, MA 02142. [email protected] 2 University of Toronto, 105 St. George Street, Toronto, ON M5S 3E6, [email protected]