Introduction to Analytic Hierarchy Process

The analytic hierarchy process (AHP) was developed by Thomas L. Saaty (1980) and is the well-known and useful method to obtain weights of each alternative in a multiple criteria decision-making problem. AHP requires the decision maker to provide judgments about the relative importance of each criterion, and then, specify a preference for each decision alternative using each criterion. The output of AHP is a prioritized ranking of the decision alternatives based on the overall preferences expressed by the decision maker. 

Xia and Wu (2007) state that AHP consists of three parts The hierarchy structure, the matrix of pairwise comparison ratios, and finally, the method for calculating weights. In AHP, a decision maker is asked to estimate pairwise comparison ratios with respect to strength of preference between subjects of comparison, thus the AHP is deeply related to human judgment. 

AHP has also been applied together with case study methods in different areas like Evaluation of critical success factors of ISO 14001 implementation in a case study in Malaysia (Sambasivan and Fei 2008), Case study to the selection of a multimedia authoring system in software selection (Lai et al. 1999), Case study of design and evaluation of automated cellular manufacturing systems with simulation modeling and AHP approach (Chan and Abhary 1996), just to give some examples available in the literature. 

It is not the goal of this book to provide a detailed description of how AHP works, therefore, anyone interested in a comprehensive review of how to use AHP method, please refer to Saaty (1980). For a literature review on the integrated analytic hierarchy process and its applications, please refer to the work developed by Ho (2008), where he reviews the five tools that are commOTily combined with the AHP process, like mathematical programming, quality function deployment (QFD), meta-heuristics, SWOT analysis and data envelopment analysis (DEA). 

In this book, it is proposed to apply the AHP method to identify the weights for each component of the Demand Driven Supply Chain, and also for each category within each component, in order to ensure a consistent comparison, and a reliable overall score performance in the demand driven supply chain maturity model. 

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