Implementing continuous improvement using genetic algorithms

Purpose – On the metaphoric level, much as been written about complex adaptive systems (CAS) for implementing total quality management (TQM) and organizational learning (OL) in turbulent or unpredictable environments. The aim of this paper is to add practical insights on how a specific CAS-technique called genetic algorithms (GA) can be used for designing quality management systems for keeping the organization in a constant state of continuous improvement in unpredictable environments. Methodology/Approach – The paper describes design, implementation and evaluation of a GA method for a TQM program in the context of the climate department of a Scandinavian meteorological institute. The author was at the time working as a quality engineer responsible for designing the genetic algorithm as a change intervention for improving inflow, quality control and computer-generated statistical use of meteorological observations. Findings – In the given organizational context, a genetic algorithm was easy to implement using elementary quality management tools such as statistical process control (SPC), Pareto Analysis and Business Model Assessment. Rather than going through conventional change management steps of unfreeze-change-freeze, the double loop structure of the chosen GA method made it possible to maintain “edge of chaos” stasis of continuous change. As expected from CAS theory, however, the algorithm caused stable improvement in unpredictable environment at the cost of producing redundancy, complexity and less than optimal efficiency. Originality/Value of paper – Although the idea of applying GA to TQM is not new, this paper appears to be the first attempt to go beyond metaphorical ideas and computer simulations in order to actually define, implement and evaluate a GA method for TQM implementation.