Multidimensional Analysis of Supply Chain Environmental Performance

Multidimensional Analysis of Supply Chain Environmental Performance

Antti Sirkka (Tieto Finland, Finland) and Marko Junkkari (University of Tampere, Finland)
Copyright: © 2013 |Pages: 19
DOI: 10.4018/978-1-4666-2625-6.ch051
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Abstract

Monitoring the environmental performance of a product is recognized to be increasingly important. The most common method of measuring the environmental performance is the international standards of Life Cycle Assessment (LCA). Typically, measuring is based on estimations and average values at product category level. In this chapter, the authors present a framework for measuring environmental impact at the item level. Using Traceability Graph, emissions and resources can be monitored from the data management perspective. The model can be mapped to any precision level of physical tracing. At the most precise level, even a single physical object and its components can be analyzed. This, of course, demands that the related objects and their components are identified and mapped to the database. From the opposite perspective, the authors’ model also supports rough level analysis of products and their histories. In terms of the Traceability Cube, multidimensional analysis can be applied for traceability data.
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Workflows

Generally, workflows are used to model the flow of materials, documents and other pieces of information from one process to another (van der Aalst & van Hee, 2002; Bonner, 1999). Modern software modeling methods such as UML contain activity diagrams for mapping real-world activities to the underlying software solution (or vice versa). There are also a number of commercial applications that have a component for drawing workflow diagrams. The common feature of these applications is that they support the illustration of different types of processes.

The workflows can be divided into two main categories, process- and data-centric. So far, the process-centric workflow modeling focusing on processes and the timing between them has been the dominant approach. However, recently the data-centric workflow modeling has gained popularity. In the data-centric workflow modeling the focus is on the transformation of data sets—initial, intermediate, and final (Akram, et al., 2006). The data sets are used as parameters to services that consume the input data set and create output data sets. The data-centric workflows are most commonly used in scientific problem solving.

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