Successful operations within manufacturing environments require both accurate and precise information flow from one operation to the next. Incorrect, too little, or too much information can slow the manufacturing process and/or result in poor quality output. The National Institute of Standards and Technology (NIST) is developing and testing new integrated information models for use in manufacturing and quality measurement equipment. Numerous information types are required and distributed during the steps of the manufacturing and quality measurement process. The predominant information analyzed are Key Performance Indicators (KPIs). KPIs are defined as quantifiable and strategic measurements that reflect an organization's critical success factors. KPIs must be recognized and understood in order to assess and improve manufacturing performance. KPIs exist to increase the understanding of lean manufacturing (minimizing waste) and to realize a company's vision of accomplishing their strategic objectives. Determining which KPIs are more important than others and the relative importance of the functional areas within a manufacturing facility (e.g. an inventory area v. an assembly line) are significant challenges that must be overcome. This paper begins to apply the Multi-Relationship Evaluation Design (MRED) methodology to this manufacturing problem to detail a process that devises test plan blueprints to assess the overall performance of a manufacturing operations facility along with its constituent functional areas and critical physical elements. MRED also provides evaluators with a means of capturing the relative importance of KPIs within a manufacturing environment. These blueprints are invaluable in that they can focus test plan development to verify and validate performance whether the manufacturing operations facility is still in development or fully-developed.
Citation: NIST Interagency/Internal Report (NISTIR) - 7911
NIST Pub Series: NIST Interagency/Internal Report (NISTIR)
Pub Type: NIST Pubs
Multi-Relationship Evaluation Design, Performance Evaluation, Manufacturing, Key Performance Indicators, Effectiveness