Currently, NIST SP 1176 is among the most downloaded NIST reports and the Engineering Laboratory’s most downloaded NIST publication.
As discussed in NIST AMS 100-18, the cost of machinery maintenance is not well understood; however, Census data estimates that $50 billion was spent on maintenance and repair in 2016.
Each year, the NIST Applied Economics Office assembles a report that examines public data on US manufacturing. The most recent report identified that US manufacturing GDP recently return to its pre-recession high in 2007. Manufacturing declined significantly in 2008 and partially bounced back in the years immediately after. Meanwhile, the economy as a whole grew faster and more steadily.
The maintenance of machinery in the manufacturing industry consists of three primary approaches: reactive, preventive, and predictive. Reactive maintenance is when a manufacturer runs the machinery until it breaks down and requires maintenance. Preventive maintenance is when maintenance is scheduled based on time or cycles. This is similar to changing a car’s oil after 3 months or 3000 miles. Lastly, predictive maintenance is based on data and observations. This is similar to how newer cars track different variables and indicate when the oil needs to be changed.
Between 2005 and 2016, US multifactor productivity declined an average 0.3 % annually. Some debate has ensued about the cause of this decline or whether there even was a decline in productivity. One aspect of productivity that is neglected in the literature is the effect of flow time on production and productivity.
Modeling data can reduce redundant design and production activities which include an estimated $8.4 billion spent on engineers answering questions and creating additional drawing documentation and $3.8 billion for machinists to do the same. Costs associated with interoperability of varying data formats, a related issue, is estimated to be between $20.9 and $42.9 billion.
New Tool to Account for Uncertainty - Cost examinations often run into the challenge of data uncertainty, where variables in the calculation (e.g., price of gasoline) will fluctuate over the study period. For instance, consider a manufacturer that will invest
New NIST Tool for Estimating Manufacturing Industry Costs - It might seem that estimating manufacturing industry costs would be easy and have a great deal of data available for estimation. Unfortunately, there are many cost areas where data is not available, and some data only covers a portion of a cost category.