ransom olds invented the assembly line method of production.
ransom olds invented the assembly line method of production.
who invented the assembly line method of production?
ransom olds
used globally around the world, an assembly line is a manufacturing process in which interchangeable parts are added to a product in a sequential manner in order to create a finished product more quickly than with older methods. this type of manufacturing greatly reduces the amount of time taken to assemble a product, thus reducing production, material, and labor costs so that an affordable product cost can be passed onto consumers. according to a book entitled "michigan yesterday & today" authored by robert w. domm, the assembly line and its basic concept is credited to ransom olds, who used it to build the first mass-produced automobile, the oldsmobile curved dash. olds patented the assembly line concept, which he put to work in his olds motor vehicle company factory in 1901. this development is often overshadowed by henry ford, who perfected the assembly line by installing driven conveyor belts that could produce a model t in ninety-three minutes.
timeline of united states inventions (1890–1945)
although the mathematical support for the method is given in terms of asymptotic results, the algorithm has been empirically verified to work well in a variety of data sets with reasonable dimensionality. in addition to the localized jump method described above, there exists a second algorithm for choosing "k" using the same transformed distortion values known as the broken line method. the broken line method identifies the jump point in the graph of the transformed distortion by doing a simple least squares error line fit of two line segments, which in theory will fall along the "x"-axis for "k" < "g", and along the linearly increasing phase of the transformed distortion plot for "k" ≥ "g". the broken line method is more robust than the jump method in that its decision is global rather than local, but it also relies on the assumption of gaussian mixture components, whereas the jump method is fully non-parametric and has been shown to be viable for general mixture distributions.
determining the number of clusters in a data set