Dimas Permadi Bagas Kara, Ridwan Hartono, Dewa Kusuma Wijaya

Perbaikan Manajemen Pengendalian Mutu Pada Proses Produksi Teh Hitam Berbasis Statistical Quality Control (SQC)

Introduction

Perbaikan manajemen pengendalian mutu pada proses produksi teh hitam berbasis statistical quality control (sqc). Perbaiki mutu teh hitam dengan Statistical Quality Control (SQC). Atasi cacat produksi (kelembaban, ukuran daun), tingkatkan kualitas, dan daya saing produk teh.

Abstract

PT. XYZ, a black tea factory located in Pemalang Regency, continues to grapple with product defect issues arising during its production process. This study aims to identify the root causes of quality degradation and propose strategic improvements to elevate black tea product standards. The research employs Statistical Quality Control (SQC) techniques, utilizing precise and measurable statistical analysis instruments. Data were assessed descriptively, focusing on quality conditions derived from available records. The quality control stages were executed using various SQC tools, including check sheets, Pareto diagrams, histograms, control charts, and cause-and-effect diagrams (fishbone diagrams). Based on the research findings, out of a total black tea output of 84,380 tons, 4,432 tons of products failed to meet standards, primarily due to non-compliant moisture levels and inconsistent tea leaf sizes. According to the Pareto diagram analysis, the most frequent defect is moisture content non-conformity. This finding confirms that weaknesses in moisture control are the primary factor in quality decline. Recommendations for improvement focus on enhancing moisture control procedures, standardizing harvesting operations, providing labor training, and optimizing the use of quality control tools to significantly reduce defects and strengthen the company's competitiveness in the future.

Review

This study by [Author Name(s) - if available, otherwise "The authors"] addresses a pertinent issue for the manufacturing sector, specifically the black tea industry, by investigating product defect problems at PT. XYZ. The paper's objective to identify root causes of quality degradation and propose strategic improvements using Statistical Quality Control (SQC) is highly relevant and demonstrates a practical approach to enhancing product standards. The chosen methodology, employing a suite of well-established SQC tools, is appropriate for diagnosing process inefficiencies and forms a solid foundation for the subsequent recommendations. The clear articulation of the problem and the direct application of quality management principles are commendable. The research effectively details the application of various SQC tools, including check sheets, Pareto diagrams, histograms, control charts, and cause-and-effect diagrams, to analyze quality conditions from available records. A key strength lies in its quantitative findings: a significant defect rate of 4,432 tons out of 84,380 tons, with non-compliant moisture levels and inconsistent tea leaf sizes identified as primary issues. The Pareto analysis specifically pinpointing moisture content non-conformity as the most frequent defect is a crucial finding, directly confirming a critical weakness in the production process. This systematic diagnosis provides actionable insights that are directly applicable to PT. XYZ's operational challenges. The recommendations proposed, such as enhancing moisture control procedures, standardizing harvesting operations, providing labor training, and optimizing the use of quality control tools, are logically derived from the research findings and offer practical pathways for improvement. While the abstract provides a good overview of the diagnostic process and proposed solutions, future studies might benefit from a more detailed discussion of the implementation strategy for these recommendations, including potential challenges and a cost-benefit analysis. Nonetheless, this paper presents a valuable case study in applying SQC to reduce defects and improve competitiveness, making a solid contribution to the field of quality management in the agri-processing industry.

Full Text

Comments

You need to be logged in to post a comment.