PROZIMA (Productivity, Optimization and Manufacturing System Engineering)

Design of Production System Improvement to Increase Productivity with Quality Control Circle Approach

2024-05-27T04:32:46+00:00

The increase in population has an impact on increasing community needs, especially in Indonesian sugar commodities. PG XYZ is one of the sugar production companies in Indonesia that targets a milling capacity of 5,750 tth. PG XYZ faced high sugar reject, so it needed to be reprocessed which had an impact hampering factory productivity. Data from the 2022 milling season shows that rejected sugar is at 5.15% with a total loss of IDR 1,619,300 for each ton. Therefore, it’s necessary to minimize the occurrence of rejected sugar. This research focuses on increasing quality productivity using a series of Quality Control Groups with PDCA stages on manufacturing parts. Data were collected through a FMEA questionnaire. The results showed the obstacles were pol blotong > 2.5%, outputting A < 55%, HK drops > 32%, and sugar reject due to black spots > 1%. The improvement plan is carried out by separating and creating a new clean water line for the supply of vacuum pump coolant, procurement, and installation of stirrer pans. The implementation was carried out for D1/D2 vacuum pan which showed a decrease in reject sugar due to black spots by 182.90 tons or 0.13% with a total of Rp 296,164,764.

Optimizing Distribution Center Network Design for a Cosmetic Manufacturer: A Case Study on Lightening Series Package Product

2024-07-02T04:55:54+00:00

Ensuring customer satisfaction while maintaining optimal cost efficiency is paramount in designing a successful supply chain structure. This study explores the vital role of service quality and delivery efficiency in achieving customer satisfaction and a superior overall experience through a cost-effective supply chain structure. Furthermore, the strategic positioning of Distribution Centers (DC) and the geographical distribution of consumers serve as additional determinants in the quest to devise the most effective DC scheme that meets consumer expectations. Four simulation scenarios, each with measurable parameters including response time, lead time, and operational costs, were evaluated to identify the most cost-efficient and responsive solution. These parameters encompassed the performance metrics of the supply chain structure model, namely response time and lead time, in conjunction with the total operational cost of each scenario. Our findings reveal that scenario 1 emerges as the most cost-effective with the lowest distribution costs and shortest customer lead time. This scenario consistently maintains the lowest distribution costs from the first to the seventh year. Despite a surge in forecasted demand in the 7th year, resulting in an increase in total distribution costs to Rp. 3,392,793,440, scenario 1 still prevails with overall vehicle costs decreasing and the smallest percentage cost at 17.50%, thus yielding the most economical outcome compared to alternative scenarios. Scenario 4 offers a similar outcome to scenario 1 but incurs higher daily vehicle operating costs. The cost-effectiveness demonstrated by scenario 4 further highlights the significance of strategic DC placement in achieving optimal supply chain outcomes.

Analysis of Bolt Former Machine Effectiveness Using Overall Equipment Effectiveness (OEE) and Six Big Lossess

2024-07-02T06:47:39+00:00

XYZ a manufacturer of nuts and bolts, frequently faced bolt former breakdowns that resulted in huge losses and disrupted the company's operational efficiency and reputation. To solve this problem, PT XYZ needs to implement an effective maintenance strategy and closely monitor machine conditions to reduce the risk of breakdowns and ensure smooth production. This can be analyzed using the OEE method that exists in the company to detect early symptoms of damage, and continuous monitoring of machine conditions. Based on the calculation (OEE) before maintenance scheduling is made on the bolt former machine is currently 69.34%, which is below the standard (JIPM) of 85%. Therefore, the machine still needs to increase productivity through the OEE and Six Big Losses methods and then proceed with analyzing the problem using a fishbone diagram. The results of the analysis found that the biggest factor of time loss was reduced speed losses of 23.15%. The value of setup and adjustment losses was 7.82%, idling and minor stoppages losses were 7.44%, equipment failure losses were 3.12%, defect losses were 0.99%, and scrap losses were 0.11%. After the implementation of OEE, it has an impact on the level of productivity with the calculation results increasing to 77.78%. The availability value reached 89.90%, the performance value was 87.75%, and the quality value was 99.57%.

Forecasting Analysis of Raw Material Needs for Manjakani and Pepet Key in the Manufacture of Madurese Herb Products at PT. XYZ

2024-07-17T07:38:05+00:00

Demand for Madurese herbal products at PT. XYZ is increasing, especially for women's herbal medicine products, namely fragrant rapet herbal medicine. Uncertain demand means that the supply of raw materials for fragrant rapet herbal medicine, namely manjakani and pepet key, must be precise. Therefore, to meet the demand for herbal medicine, it is necessary to predict the need for raw materials for fragrant rapet herbal medicine. This article aims to predict the need for raw materials in the form of pepet and manjakani keys using various forecasting techniques, including average MA, WMA, ES and ES with trends. Forecasting results with the lowest MAD, MSE, and MAPE values—which show very minimal forecasting errors—will be selected for further examination. The results of the research show that for raw material needs for manjakani, the best forecast result value is 24.43 kg with the smallest percentage error value, namely MAPE of 11.255%, while for key raw material needs for pepet, the forecast result value is 13.59 kg with the smallest error percentage, namely MAPE of 14.851%.

Food Safety Risk Analysis of Songkem Duck Using Failure Mode and Effect Analysis (FMEA) Method

2024-07-25T04:36:33+00:00

Food safety is an essential aspect of human health. Food processed without paying attention to food safety regulations can harm consumer health. This research aims to 1) identify activities with food safety risks, 2) determine the sequence of food safety risks that will be followed up, and 3) determine appropriate food safety risk mitigation. This research uses the Failure Mode and Effect Analysis (FMEA) method and fishbone diagrams. A case study was conducted on the Songkem pak Soleh Bangkalan duck supply chain. The research results show that activities that pose a food safety risk include receiving raw materials, storing raw material stocks, using raw materials and BTP, sanitizing production facilities, and storing finished products. The priority order of food safety risks in the Songkem duck supply chain is based on the FMEA method: storage of finished products, sanitation of production facilities, and storage of raw material stocks. Risk mitigation can be done to prevent food safety risks in storing raw material stocks and storing finished products, namely by controlling water activity (aw) and acidity factor (pH); and controlling the room's atmosphere. Meanwhile, to prevent risks due to sanitation hygiene, namely holding training to increase awareness of the importance of sanitation hygiene for restaurant employees monitoring and maintaining sanitation hygiene in the production environment.