Data Analytics Application for Non-Conformance Reports in a Cabinet Manufacturing Facility

Abstract

Industrialization of building construction through offsite construction and modularization is an effective approach for improving performance of construction projects. In a modular construction approach, building components are produced in a well-controlled factory environment. The components are then delivered to site, in sequence, for installation by site crews. This process reduces construction waste, improves product quality, and minimizes onsite safety incidents. As the market conditions are rapidly changing, the demand for more customized and unique products is increasing. Customers increasingly demand customized dwellings to reflect their cultural tastes and personal preferences. Cabinets in the house, kitchen or otherwise, are building components that constitute a large portion of the visible customization that customers are interested in. This paper focuses on the analysis of records in Non-Conformance Reports (NCRs) at a cabinet manufacturing facility in Alberta, Canada. An NCR record represents a defect in any product that needs a repair or rework; it captures several attributes of the defective part, such as the job number, wood species, stain, the date and time when the record is created, etc. The systematic approach presented in this study employs data analytics to the collection, cleaning, and analysis of the NCR dataset. The dataset is first analyzed as per existing operations. Various data pre-processing techniques, including attribute and instance selection and transformation, are then applied to clean the dataset. The results show that most of the “Rework” results from administrative or product handling errors, while the majority of “Repairs” result from product finishing errors. The impact of repairing the defective parts is discussed, and recommendations to reduce the number of NCRs and thereby enhance the performance of operations are presented.