A Complexity Based Look-ahead Mechanism for Shop Floor Decision Making

Abstract

Manufacturing Complexity has been extensively reported in the literature in the ways it can be classified, modelled, assessed and measured. However, the use of complexity measures for shop floor decision making has not been studied exhaustively. The paper demonstrates the use of a complexity measure for shop floor decision making. Knowledge of the transition of shop floor complexity into undesirability can give a correct indicative for any decision maker to intervene and take the necessary corrective actions to bring the system back to its desirable state. The current paper presents an information-theoretic approach based model which considers production scheduling, maintenance and quality functions simultaneously to come up with a complexity quantifier for a shop floor. At any point in time, various environmental factors like machine failures, corrective maintenance time variability, due date alteration, process shifts etc. are simulated into the future and their effect on the job states both in terms of schedule and quality are observed. The probability of these state occurrences is then quantified as complexity of situation which is subsequently scaled to a penalty based “undesirability index”. The complexity is re-evaluated every time a fresh event occurs, thus enabling the decision maker to be more predictive about the future state of the system and in turn, the performance of each of the manufacturing functions. Thus, the model so developed, deploys a complexity based look-ahead mechanism for identifying undesirable state transitions. The novelty of the work lies in the way it has been developed keeping the perspective of a shop floor manager in view and also in the way it can be readily used for decision making.