1995•Journal of Management in EngineeringRequires access

Measuring Effectiveness of Materials Management Process

James Keith Plemmons, Lansford C. Bell

Open publisher page 21 citations

Abstract

A need exists within the industrial construction industry to measure the effectiveness of the materials management process to provide a basis for analyzing the impact of process changes on process performance. Process changes, like the implementation of data-management technologies and reengineering, often take place concurrently with other changes. A concurrent situation complicates the task of identifying the actual impact on overall process performance. Although the initial costs are often available, the benefits are not readily determined in a manner that directly relates investment decisions and process changes to increased performance and profit margins. For the construction industry in general, and the materials management process in particular, process benchmarking holds considerable value. A recently completed research project has identified key materials management effectiveness measures within the industrial construction industry. A classification scheme, consisting of six effectiveness categories (accuracy, quality, quantity, cost, timeliness, and availability) was applied to categorize the measures. To facilitate the implementation of the effectiveness measures, an industry-wide benchmarking mechanism is proposed.

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What this paper is about

A need exists within the industrial construction industry to measure the effectiveness of the materials management process to provide a basis for analyzing the impact of process changes on process performance. Process changes, like the implementation of data-management technologies and reengineering, often take place concurrently with other changes. A concurrent situation complicates the task of identifying the actual impact on overall process performance. Although the initial costs are often available, the benefits are not readily determined in a manner that directly relates investment decisions and process changes to increased performance and profit margins. For the construction industry in general, and the materials management process in particular, process benchmarking holds considerable value. A recently completed research project has identified key materials management effectiveness measures within the industrial construction industry. A classification scheme, consisting of six effectiveness categories (accuracy, quality, quantity, cost, timeliness, and availability) was applied to categorize the measures. To facilitate the implementation of the effectiveness measures, an industry-wide benchmarking mechanism is proposed.

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Available abstract

A need exists within the industrial construction industry to measure the effectiveness of the materials management process to provide a basis for analyzing the impact of process changes on process performance. Process changes, like the implementation of data-management technologies and reengineering, often take place concurrently with other changes. A concurrent situation complicates the task of identifying the actual impact on overall process performance. Although the initial costs are often available, the benefits are not readily determined in a manner that directly relates investment decisions and process changes to increased performance and profit margins. For the construction industry in general, and the materials management process in particular, process benchmarking holds considerable value. A recently completed research project has identified key materials management effectiveness measures within the industrial construction industry. A classification scheme, consisting of six effectiveness categories (accuracy, quality, quantity, cost, timeliness, and availability) was applied to categorize the measures. To facilitate the implementation of the effectiveness measures, an industry-wide benchmarking mechanism is proposed.

Key concepts: Benchmarking, Process (computing), Process management, Business process reengineering, Risk analysis (engineering), Performance indicator, Computer science, Profit (economics)

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