1996 Electronics Industry Environmental Roadmap - Civil and ...

Information and Knowledge Systems
A paper included in the Proceedings of the 1995 IEEE International Symposium on Electronics
and the Environment by White and Corbet detailed some of the difficulty with environmental
data [16]:
Quantitative environmental release and transfer data are often media-specific, limited in
scope, subject to questionable verification, and may become outdated before becoming
publicly available.
Quantitative data frequently are inconsistent among data sources due to varying waste
definitions and reporting methods.
Few studies have addressed qualitative characteristics of firms that contribute to environmental
performance.
Almost no research links traditional quantitative data with qualitative information for
measuring environmental performance in an integrated manner.
To this list should be added the proprietary nature of much of the industry data that might prove
useful to effective environmental management or that might be suitable for environmental benchmarking,
as well as shortcomings in data collection within companies, especially financial information.
These shortcomings have several effects. Inefficiencies in decision-making, lack of industry-wide
benchmarks, unnecessary repetition of errors or analyses, absence of wellestablished
“best practices” standards, and difficulties in identifying alternative sources of
systems, technologies, and solutions are just a few of the challenges that confront electronics
manufacturers without an effective industry-wide information infrastructure in place.
3.3 Information Required for Environmental Decision-making
Table 3-1 defines several categories of information that, if made available to electronics manufacturers,
could significantly contribute to improved efficiency and productivity.
The objective addressed in this chapter is the ultimate creation of an environmental information
infrastructure that provides data to support decisions made at a variety of levels in the
organization, from the individual designer through senior corporate management. These
decisions will span a wide range of operational considerations: design trade-offs, optimization of
materials, processes, chemicals, and design strategies; equipment purchasing, seeking the lowest
cost provider of equipment that meets requirements; technology selection, evaluating from
among a variety of alternative technologies for applications such as packaging, interconnect, and
chip-attach; alternative manufacturing processes; life cycle impacts, seeking to minimize the
environmental consequences of individual decisions across the entire life cycle of a product;
investment strategies; and many more.
Transforming numbers and other data into knowledge for decisions can be viewed as a multistage
process of information access, retrieval, and analysis (see Figure 3-1). The process begins
with the assembly of raw data, most likely in the form of numbers but also in the form of
anecdotes, listings, or directories. These data can be drawn from a number of sources and
through a variety of approaches. Discrepancies occur, however, when individual corporations
identify and assemble the data on their own, creating duplication, the likelihood of data
inconsistency, and data gaps.
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