This paper aims to provide an approach to JIDOKA (machine's autonomation) implementation via compute the proficiency value that based on processes' deviation monitoring to besiege any defects chance weaken competitiveness. The product produced after operating multi-surface spaces (OSS), which prone to deviation during its processes and may lead to defect. The proposed methodology interested in controlling processes' deviation via improving the spatiotemporal model with SARIMA via using 72 observations to study and predict the deviation's behavior to stop machine before defect appears (i.e., JIDOKA methodology). A case study of the manufacture of a lawn mower (carburetor's valve) is presented to explain the JIDOKA methodology results that demonstrate it accurately and effectively, whether in forecasting or defect suppression to face the evaporative emissions. The approach is guided by a six-sigma methodology to obtain improved proficiency. The model’s fitting and forecasting results are compared with the SES to prove the ability of applying JIDOKA. The proposed methodology implemented in one of the lawn mower Co. Egypt, which gain defect reduction by 0.7 % or rescue 7000 product every one million via SARIMA(1,1,1)x(0,1,1)₆ to improve the spatiotemporal methodology.