Transportation Research Part E: Logistics and Transportation Review
Strategic analysis of logistics and supply chain management systems using the analytical network process1
Introduction
Logistics plays an increasingly important strategic role for organizations that strive to keep pace with market changes and supply chain integration. Traditionally, supply management and logistics have been delegated to operational level personnel in purchasing and distribution departments. Logistics and supply management are currently evolving due to external factors such as strategic alliances, technological changes, cycle time compression, and the increasing competitive environment (Bowersox, 1990, Daugherty and Pittman, 1995, LaLonde and Mason, 1993). There have also been changing internal factors such as the implementation of decision support systems, information systems integration, spanning of logistics to impact traditional functional areas, and increasing performance expectations (LaLonde and Mason, 1993).
Recently, much work has been targeted to the area of logistics strategic development and its categorical modelling. A framework for logistics research includes strategy, structure, and performance (Chow, et al., 1995). Strategy can be defined as plans to meet relatively long-term organizational objectives that have broad corporate functional implications. These developments explain how a successful logistics strategy has moved from an internal focus emphasizing integration with other enterprise functions, such as production and marketing, with a linkage to the overall corporate strategy, to an external focus of integrating supply chains and cycle time compression. The complexity of logistics strategic decisions and choices has increased with the number of dimensions that need to be considered. Logistics now includes comprehensive supply chains which require coordination, if not optimization.
The model presented in this paper helps to structure this complexity by evaluating alternative logistics strategies by utilizing a systemic multiattribute analytical technique, known as the analytical network process (ANP) (Saaty 1996). An illustrative example provides additional insights for research and practical applications. First, a review of some analytical models for strategy development and decision making in logistics is presented.
Section snippets
Analytical models for logistics strategy analysis
In the strategic management literature, analytical models that incorporate the many dimensions of a logistics strategy are rare. A number of qualitative models based on strategy development do exist (e.g O’Neil and Iveson, 1992). Limitations of the analytical models that exist for logistics strategy evaluation include a focus on one dimension of the logistics strategy or are static in their approaches.
One popular model is the added value strategy modelling (AVSM) system. AVSM measures the
A logistics framework for supply chain coordination and integration
According to strategic alignment models, the success of organizational strategy is dependent upon the alignment of environmental uncertainties, strategy, and adopted technology, practices, or systems (Henderson and Venkatraman, 1993, Luffinan et al., 1993). For organizations to be competitive and thrive they should accurately measure and evaluate their logistics activities and processes. Understanding and measuring the dynamic nature of logistics will aid an organization’s transition to
Organizational/supply chain relationships
The organization needs to determine what type of supply chain strategy will help an organization achieve its greatest competitive advantage. Even at the supply chain strategy level a dynamic environment with various choices exist. Supply chain strategies include a continuum extending from commodity providers to virtual enterprise membership. Commodity, partnering, strategic alliances and virtual enterprises form a spectrum of relationships that may exist among enterprises, with the relations of
The principles of logistics and the principles attributes
The principles of logistics, identified by LaLonde and Mason (1993)provide a foundation for consistent evaluation of logistics activities and strategies. Yet, as with any model, limitations to these principles do exist. Even though these principles are comprehensive, they are not exhaustive (e.g. component standardization is another principle that is not included among LaLonde and Mason’s principles). In addition, these principles are not necessarily scientifically proven principles and may be
A network framework for assessing logistics strategies and systems
The discussion on logistics strategies, environments, and principles provides the elements for the development of a strategic assessment framework. This framework is summarized in Fig. 2. The framework is presented through a network of decision model relationships. The levels of the network framework include the organizational/supply chain relationships, the principles of logistics level, the attributes level, and the alternative selection level. These levels impact the overall goal of
Model extensions
The current model that is presented may still require additional extensions for modelling practical situations. For example we have not introduced the true dynamics of long and short term planning horizons and their influences on the principles and the model. Nor have we explicitly included risks, costs and benefits as specific attributes that need to be considered. To be able to handle these two circumstances we shall provide a general description of the necessary alterations to the basic
Discussion and conclusions
The framework that was used in this example could serve as one of the tools for making a strategic decision. The criteria and attributes that were used in the model focused on logistics strategy and requirements. The model is capable of taking into consideration both qualitative, quantitative, and multiple dimensions of information into the analysis, a powerful and necessary characteristic for any strategic evaluation.
Since any logistics system or strategy that is selected or recommended by the
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This work was partially supported by NSF Grants 9320949 and 9505967, and Texas Higher Education Coordinating Board ATP Grant Number 003656-036.