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Purpose To manage cash flow in supply chains, the purpose of this paper is to propose inverse data envelopment analysis (DEA) model. Design/methodology/approach This paper develops an inverse range directional measure (RDM) model to deal with positive and negative values. The proposed model is developed to estimate input and output variations such that not only efficiency score of decision making unit (DMU) remains unchanged, but also efficiency score of other DMUs do not change. Findings Given that auto making industry deals with huge variety and volumes of parts, cash flow management is so important. In this paper, inverse RDM models are developed to manage cash flow in supply chains. For the first time, the authors propose inverse DEA models to deal with negative data. By applying the inverse DEA models, managers distinguish efficient DMUs from inefficient ones and devise appropriate strategies to increase efficiency score. Given results of inverse integrated RDM model, other combinations of cash flow strategies are proposed. The suggested strategies can be taken into account as novel strategies in cash flow management. Interesting point is that such strategies do not lead to changes in efficiency scores. Originality/value In this paper, inverse input and output-oriented RDM model is developed in presence of negative data. These models are applied in resource allocation and investment analysis problems. Also, inverse integrated RDM model is developed.

This paper proposes a novel model of inverse data envelopment analysis (IDEA) based on the slack-based measure (SBM) approach. The developed inverse SBM model can maintain relative efficiency of decision making units (DMUs) with new input and output. This model can also measure the input and output volumes when a decision maker (DM) increases efficiency score. The inverse SBM model is a kind of multi-objective non-linear programming (MONLP) problem, which is not easy to solve. Therefore, we suggest a linear programming model for solving inverse SBM model. In this model efficiency score of DMU under evaluation remains unchanged. Furthermore, we suggest an optimal combination of inputs and outputs in the production possibility set (PPS). A case study is presented to demonstrate the efficacy of our proposed model.

The objective of this paper is to develop a hybrid decision making system using Data Envelopment Analysis (DEA) and linguistic fuzzy models for selecting the best supplier in the presence of multiple decision makers. In this hybrid system, first the weights of selected criteria are obtained from each of the decision makers as linguistic fuzzy numbers within the framework of group decision making. Then, to select the best supplier, absolute weight restriction (AWR) model is incorporated into the DEA model. A real case study demonstrates the application of the model.

This paper presents an implementation of system dynamics model to determine appropriate product mix by considering various factors such as labor, materials, overhead, etc. for an Iranian producer of cosmetic and sanitary products. The proposed model of this paper considers three hypotheses including the relationship between product mix and profitability, optimum production capacity and having minimum amount of storage to take advantage of low cost production. The implementation of system dynamics on VENSIM software package has confirmed all three hypotheses of the survey and suggested that in order to reach better mix product, it is necessary to reach optimum production planning, take advantage of all available production capacities and use inventory management techniques.