A framework for a structured problem-solving approach with experimental design as the focus for industrial processing environment

Abstract

This article focuses on developing a framework for industrial environments to provide a structured problem-solving approach based on experimentation as a basis to assist analysts and management for strategic decision making for process improvement. The process for developing this generic framework was through an analytical process improvement process case study for a company in an industrial environment. The research methodology includes an interpretivist approach followed by a positivist approach and ends with a constructivism worldview due to the Experimental design approach design depicted by this framework. The summarized goals were, expanding Design of Experiments (DOE) as a statistical approach to complement existing methods and methodologies used for Data Mining, validate the integrity of data through a refining process and applying DOE in combination with traditional Data Mining techniques. The importance for developing this framework was to experiment with historical data, based on real process data then to predict future process behaviour, using full and fractional DOE design scenarios which allows the analyst not to have a one-dimensional analytical approach but to evaluate which design fits the data best. No risk of costly process failures due to experimenting into the unknown by utilizing historic data by applying experimentation when evaluating different processing scenarios for possible product improvement and to provide an alternative statistical approach for Data Mining in an industrial environment for screening independent and dependent variables for a DOE model.