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A SYSTEMATIC STRATEGY FOR OPTIMIZING MANUFACTURING OPERATIONS
Author(s) -
KERKHOFF JONELL,
EAGAR THOMAS W.,
UTTERBACK JAMES
Publication year - 1998
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/j.1937-5956.1998.tb00439.x
Subject(s) - computer science , process (computing) , asset (computer security) , new product development , manufacturing , industrial engineering , business , engineering , computer security , marketing , operating system
A manufacturing optimization strategy is developed and demonstrated, which combines an asset utilization model and a process optimization framework with multivariate statistical analysis in a systematic manner to focus and drive process improvement activities. Although this manufacturing strategy is broadly applicable, the approach is discussed with respect to a polymer sheet manufacturing operation. The asset utilization (AU) model demonstrates that efficient equipment utilization can be monitored quantitatively and improvement opportunities identified so that the greatest benefit to the operation can be obtained. The process optimization framework, comprised of three parallel activities and a designed experiment, establishes the process‐product relationship. The overall strategy of predictive model development provided from the parallel activities comprising the optimization framework is to synthesize a model based on existing data, both qualitative and quantitative, using canonical discriminant analysis, to identify main effect variables affecting the principal efficiency constraints identified using AU, operator knowledge and order‐of‐magni‐tude calculations are then employed to refine this model using designed experiments, where appropriate, to facilitate the development of a quantitative, proactive optimization strategy for eliminating the constraints. Most importantly, this overall strategy plays a significant role in demonstrating, and facilitating employee acceptance, that the manufacturing operation has evolved from an experienced‐based process to one based on quantifiable science.