Manufacturing is a part of the income of any country, helping to grow the
economy by generating productivity, stimulating research and development, and
investing in the future. Therefore, this paper seeks to explain the productivity growth
performance of Ethiopian's manufacturing sector using a dataset of 14 types of
industries for the year of 2008; utilizing data envelopment analysis (DEA) techniques
either traditional or fuzzy DEA models. Data envelopment analysis (DEA) is a
methodology for measuring the relative efficiencies of a set of decision making units
(DMUs) that use multiple inputs to produce multiple outputs. Conventional DEA
models assume that input and output values should be certain (crisp data). However,
the observed values of the input and output data in real-world situations are sometimes vague or imprecise. In this paper, three approaches that transform the  original data (crisp data) into interval data, in the form of upper and lower frontier
data, are suggested. Then, by using these upper and lower frontier data; the interval
DEA efficiency scores can be achieved. These approaches are applied on the real-life
data and the results show that data envelopment analysis (DEA) techniques are
suitable to evaluate and compare the performances of industries that enable the
decision makers to analyze the situation better.