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The focus of this research is to establish control and planning management in the sewing production process of lingerie clothing to better prepare companies for demand growth. The lack of improvement tools in this sector, the lack of staff training and a lack of quality culture has led to companies, especially MYPES, not being able to meet the esta...
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Citations
... For example, generating cost overruns to repair machinery, reduction in their levels of service or productivity, longer production time due to downtime, non-compliance with their demand, among others, so it is important to propose new solutions or strategies that allow companies to stand out among their competitors and the labor market. [3] A success story raised by the authors Baitaneh and Al-Hawari in 2020, developed through the integration of Total Autonomous Preventive Maintenance and the 5S methodology a maintenance plan that works together with control and cleaning cards to reduce the number of failures found that affect the quality of the product and generate reprocesses for not complying with quality standards, in addition to generating that capacity is not used efficiently; resulting in a 13% increase in the availability of machinery and a 44% increase in the efficiency of the production line. [4] Another success story that justifies our research project is the case of the authors Hasanati and Permatasari in 2019, in which case the problem focuses on the number of hours stopped on a production line due to the constant breakdowns of raw materials due to the errors of quantity requested in the replenishment orders or on the dates that the request is made. ...
In the present scenario, the industry and manufacturers find hardship in running the business profitably by enhancing the production and efficiency of the system, Multiple factors influence the industry to meet the customer’s requirements and to sustain itself in this competitive environment. The purpose of this research is to develop an Intelligent VSM (IVSM) Model which integrates with Industry 4.0 and lean tools to monitor the real-time manufacturing setup with recent trends in information technology. Our research is focused on developing an IVSM system enabled by IoT for the industry which caters for the need to enhance the performance of any manufacturing setup. A study has been conducted in an electronics component industry through the current state IVSM, and the IVSM Model for the future state was designed and implemented. The IoT-enabled future state Intelligent VSM system facilitates the need of today’s dynamic manufacturing environment scenario, which continuously monitors through an integrated efficiency monitoring system (IEMS). The improved performance of the system is visualized in the dashboard at the specified regular intervals. A major highlight of this study is reduction in the lead time from 35.5 to 24 days, a reduction in the time required to verify the results of PDCA using IVSM from 30 to 1 h, and a 0.9% improvement in overall process cycle efficiency. Further to improve the effectiveness of the Intelligent VSM system, Kaizen activities have been implemented to eliminate the shortfalls and increase the efficiency in the future DVSM system. The proposed system integrates all the processes in the industry to predict the uncertainty. This research work can further extend to all other manufacturing and service support systems to integrate SMART sustainable systems and to predict critical conditions in advance.
In recent years, production environments have gone from predictable to dynamic, therefore factors such as delivery time and a high percentage of customer satisfaction have become fundamental criteria for this new environment. Production planning and control (PPC) systems must be aligned with the new objectives of the manufacturers, however, the most popular PPC don’t satisfy their needs. This is how this article proposes a PPC model based on the Demand-Driven MRP system supported by the stages of the PDCA cycle so that it can perform optimally in dynamic environments such as make-to-order manufacturing models. As a result of the simulation in the case study, 100% compliance with deliveries on time was obtained and a reduction in lead time by 50.33%. Therefore, it is concluded that the proposed model is applicable and beneficial in this context.
