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All Journal Indonesian Journal of Innovation Studies Journal for Technology and Science
Sukmono , Tedjo
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Journal : Journal for Technology and Science

 1 
INCREASING THE EFFICIENCY OF WIRING HARNESS PRODUCTION USING THE RANGKED POSITIONAL WEIGHT AND LARGEST CANDIDATE RULE METHODS Arifin , Zainul; Sukmono , Tedjo
Journal for Technology and Science Vol. 1 No. 2 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i2.154

Abstract

In the wiring harness door production activities, the problem that occurs is the lack of achievement of the production target of 6.19% or 6.15 units in January 2023. This is caused by differences in production output at each work station or track which are not balanced resulting in an accumulation of work in progress. In making line balancing, working time measurements are carried out in each wiring harness production operation on the man power production line by using a stopwatch. The purpose of this research is to determine the optimal wiring harness production line and increase the efficiency of wiring harness production according to the company's target line efficiency. The methods used in solving this problem are the ranked positional weight method and the largest candidate rule method. From the results of the line balancing analysis using the ranked positional weight method and the largest candidate rule method, an efficiency value of 96.33% is obtained, the resulting output is 144.5 wiring haness units with 12 work stations. These results are better than before using both methods
ANALYSIS OF SPARE PART INVENTORY CONTROL USING ECONOMIC ORDER QUANTITY (EOQ) AND CONTINUOUS REVIEW METHODS Damayanti , Maharani Lutfiah; Sukmono , Tedjo
Journal for Technology and Science Vol. 1 No. 3 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i3.213

Abstract

General Background: In the concrete manufacturing industry, the reliance on machines for production activities necessitates a robust spare parts inventory management system to ensure operational continuity. Specific Background: However, fluctuating demand for spare parts often leads to overstock or stockout situations, significantly impacting inventory costs and cash flow. Knowledge Gap: Existing studies primarily focus on static inventory management approaches, neglecting the dynamic nature of spare parts demand in manufacturing environments. Aims: This research aims to optimize total inventory costs while determining efficient order and reorder point quantities by integrating the Economic Order Quantity (EOQ) method with continuous review techniques. Results: The findings reveal an optimal order quantity of 131 units and a reorder point of 18 units, resulting in a total inventory cost of IDR 2,414,609,989—an efficiency improvement of IDR 293,152,400, equivalent to an 11% cost saving compared to previous inventory management practices. Novelty: This study innovatively employs a probabilistic approach to account for demand variability, enhancing the accuracy of inventory control measures. Implications: The outcomes suggest that implementing the proposed inventory management strategy can mitigate the risks of overstocking and stockouts, ultimately fostering improved financial performance in the company. Furthermore, the research highlights the necessity for regular inventory reviews and suggests future studies to develop more dynamic inventory control models that incorporate price fluctuations for spare parts, thereby addressing potential risks associated with cost variability.
IMPLEMENTATION OF FUZZY INVENTORY METHOD AND ARTIFICIAL NEURAL NETWORK IN DETERMINING SAFETY INVENTORY OF BAG PRODUCTS Rahmansyah, Muhammad Miftah Arzaq; Sukmono , Tedjo
Journal for Technology and Science Vol. 1 No. 3 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i3.214

Abstract

General Background: Effective inventory management is crucial for small and medium enterprises (SMEs) to address fluctuating demand and avoid shortages, especially in sectors like handcrafted products. Specific Background: In the context of PTK MSMEs (Karangtanjung Bag Craftsmen) in Sidoarjo Regency, bag product sales often vary monthly, necessitating accurate demand forecasting and optimal inventory levels. Knowledge Gap: While previous studies have explored demand prediction and inventory management, few have integrated advanced methodologies like Artificial Neural Networks (ANN) and Fuzzy Inventory approaches to cater specifically to SMEs in the handicraft sector. Aims: This research aims to predict the sales demand for bag products and establish safety inventory levels using ANN and Fuzzy Inventory methods, ultimately to control demand and reduce inventory costs. Results: The study yielded a Root Mean Square Error (RMSE) of 45.031 from the ANN analysis, indicating a good forecasting performance, while the Fuzzy Inventory method calculated a safety stock of 43,647 pieces for 2023. Novelty: The integration of ANN for demand forecasting and Fuzzy Inventory for safety stock determination offers a novel approach for SMEs, enabling them to respond proactively to market fluctuations. Implications: The findings provide a framework for MSMEs to enhance their inventory management practices, thus improving operational efficiency and reducing holding costs, which can significantly impact their sustainability and competitiveness in the market.
COMPARING OF ARTIFICIAL NEURAL NETWORK AND MULTIPLICATIVE HOLT WINTERS EXPONENTIAL SMOOTHING METHODS IN FORECASTING DEMAND Safitri , Salsa Zulfa; Sukmono , Tedjo
Journal for Technology and Science Vol. 1 No. 3 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i3.215

Abstract

General Background: Fluctuations in raw material orders pose significant challenges for business operators, especially during peak seasons like holidays and the new year, often resulting in shortages or excess inventory. Specific Background: This study focuses on forecasting demand for wallet products from UMKM Pengerajin Dompet Khas Tanggulangin (PDKT) by comparing two forecasting methods: Artificial Neural Networks (ANN) and the Multiplicative Holt-Winters method, which is tailored for seasonal data. Knowledge Gap: While existing literature recognizes the effectiveness of various forecasting techniques, there is limited comparative analysis of ANN and Holt-Winters specifically in the context of UMKM wallet production, highlighting the need for empirical validation. Aims: This research aims to identify the most accurate forecasting method to optimize raw material usage and production planning. Results: The findings indicate that the ANN method yields a superior Root Mean Square Error (RMSE) of 14.249, compared to 93.436 for the Holt-Winters method, establishing its higher predictive accuracy. Novelty: The study contributes to the field by providing a comparative analysis of forecasting methods tailored to the specific context of UMKM, demonstrating the efficacy of ANN over traditional methods. Implications: These results suggest that adopting ANN for demand forecasting can significantly enhance inventory management and production efficiency for PDKT MSMEs, ultimately leading to better resource allocation and reduced operational costs.
Co-Authors Arifin , Zainul Atika, Dewi Nur Damayanti , Maharani Lutfiah Rahmansyah, Muhammad Miftah Arzaq Safitri , Salsa Zulfa