Optimization Approaches to Assess Manufacturing Agility

Optimization Approaches to Assess Manufacturing Agility

P. G. Saleeshya (Amrita Vishwa Vidyapeetham (University), India)
Copyright: © 2014 |Pages: 10
DOI: 10.4018/978-1-4666-5202-6.ch154
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A Combined Ahp- And Dea-Based Approach To Measure Agility Of Manufacturing Systems

The author proposes an approach to measure the agility of manufacturing systems by combining the Analytical Hierarchy Process (AHP) and the Data Envelopment Analysis (DEA) methodologies. DEA was used to compare and find out the priorities of various units of organizations, which is essential for the optimum deployment of resources among these units, and hence to achieve maximum agility, by making use of the values of EI and Importance Index for agility.

Data Envelopment Analysis (DEA) is a method for assessing the relative efficiency of decision-making units (DMUs) having, in the general case, multiple incommensurate inputs to produce multiple incommensurate outputs. The relative efficiency of a DMU is defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs, the weights having been determined so as to show the DMU at maximum relative efficiency (Bousso Glance et al., 1991).

Here we have different organizations identified as DMUs. These DMUs have common multiple inputs (xi) and outputs (yr). The values of these inputs and outputs in terms of index of importance and index of effectiveness respectively are also known. In order to achieve B maximum efficiency each DMU has to deploy its resources in the most effective way. For this the optimum values of the weightages given to different inputs (v1, v2 …) and outputs (u1, u2 …) have to be found out. The solution of this model will give the values for these weightages. In this model the objective is to maximize the output by keeping the input at a constant level and hence to increase the overall efficiency of the system.

DEA-AHP Model

The formulation used in the present study makes use of the following model reported by Charnes et al (1978).Max: ... (1) Subject to ... (2) ... (3) ur ≥ 0vi ≥ 0r = 1 … s i = 1 … mwhere:

  • s: Is the number of outputs.

  • m: Is the number of inputs.

  • ur: Weight given to the rth output.

  • vi: Weight given to the ith input.

  • yr: The rth output.

  • xi: The ith input.

Key Terms in this Chapter

Interpretive Structural Modeling: Is an interactive learning process in which a set of different and directly related elements are structured into a comprehensive systemic model.

Supply Chain: Flow of product from raw material to customer.

Agile Supply Chain: Is a highly responsive supply chain.

Agile Manufacturing: Is the ability of an enterprise to respond quickly and successfully to change.

Data Envelopment Analysis: Method for assessing the relative efficiency of decision-making units.

Analytic Hierarchy Process: Is a structured technique for dealing with complex decisions.

Reconfigurable Manufacturing: Systems which facilitate the demand for customized products.

Lean Manufacturing: Value enriched manufacturing.

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