The proposed approach uses DEA model in steps 2 and 4 for evaluation of organizations’ leanness level and assessment of the leanness factors, respectively. Let us consider methodology where the evaluation of lean production efficiency is based on optimization algorithms. This includes so called multi-attribute, eco-efficiency models, and multi-criteria decision making models (Jablonsky, 2007). DEA models also belong to this Sirolimus class (Charnes, Cooper, & Rhodes, 1978). DEA is a linear programming model to maximize the efficiency of multiple decision-making units (DMUs) when the production process presents a structure of multiple inputs and outputs. In DEA model, one need not assume a priori the existence of a particular production function for aggregating and weighting inputs and outputs variables. Instead, the respective aggregation weights result from solving an optimization problem (Azadeh et al., 2011 and Rickards, 2003). Hence, Quaternary Period are solely dependent on the empirical observations involved. This fact gives the DEA model a decisive advantage over ordinary optimization procedures (Lee, Hsu, Chou, & Guo, 2011). Even if one does not value the inputs and outputs variables at market prices, or indeed in money, one still can execute the necessary aggregations (Rickards, 2003). The objective of this paper is to optimize lean performance of packing and printing organizations. By employing the DEA model, companies can monitor efficiency scores, which provide an indication of the levels of lean production performance. By doing this, organizations can set a strategic goal to achieve improved its sustainability performance in both the short-term and long-term. The proposed approach helps managers to assess and compare their ranking in terms of leanness degree with other similar organizations.