scholarly journals ANALISIS KELAYAKAN ASPEK TEKNIS INDUSTRI PENGOLAHAN BIOFARMAKA BERBAHAN BAKU BAWANG TIWAI

2018 ◽  
Vol 7 (2) ◽  
pp. 99
Author(s):  
Muhammad Ilham Ramadhan ◽  
Muriani Emelda Isharyani ◽  
Muriani Emelda Isharyani ◽  
Farida Djumiati Sitania
Keyword(s):  
Production Process ◽  
Industrial Development ◽  
Power Plants ◽  
Moving Average ◽  
Facility Layout ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Scoring Systems ◽  
Ranking Procedure ◽  
Local Product

<p><em>From Latin </em><em>Eleutherina American L. Merr</em><em>, tiwai onion is one of species of flowering and bulbous plants in Borneo forest that can be developed as biopharmaceutical source for industrial scale. Tiwai onion beverage product is a product that has the benefits of tiwai onion plant in the tea bag form. Industrial development of tiwai onion processing into a tea bag product in UKM Solaindo still has many technical constraints. Therefore, it is necessary to conduct a research which aims to design technical aspect to ensure a smooth production process of tiwai onion tea. This research is expected to overcome the technical constraints faced by UKM Solaindo, so that tiwai onion product into a tea bag product in UKM Solaindo could get bigger and get increased to fulfill the market needs and also to become a competitive local product. Technical aspects examined include determining a factory location, production capacity, machinery and equipment, and factory facility layout. Determining a factory location using qualitative method (ranking procedure) determined that Tenggarong is the best location to establish an industrial factory of tiwai onion with the total score of 15.90. Production capacity is conducted by demand forecasting using </em><em>weighted moving average</em><em> method, and forecasting value obtained is </em><em>618 unit</em><em>s</em><em>/</em><em>month</em><em> </em><em>or </em><em>66,74 Kg/</em><em>month</em><em>.</em><em> Machinery and equipment used for production process from the factory that will be set up in every process are tray, automatic sealer machine, washing machine, oven, chopping machine, stamping equipment, and sealer machine. The most appropriate scoring systems for factory facility layout are ARC, ARD, SRD, and AAD that have 10 facilities such as administration room facilities with area of </em><em>80 m<sup>2</sup></em><em>, production facilities </em><em>37,5 m<sup>2</sup></em><em>, shipping facilities </em><em>7,5 m<sup>2</sup>, </em><em>material warehouse facilities</em><em> 15 m<sup>2</sup>, </em><em>finished product warehouse facilities </em><em>17,5 m<sup>2</sup>, </em><em>reception facilities </em><em>7,5 m<sup>2</sup>, </em><em>quality control facilities </em><em>12 m<sup>2</sup>, </em><em>power plants facilities</em><em> 6 m<sup>2</sup>, </em><em>waste shelter facilities</em><em> </em><em>4</em><em> m<sup>2</sup>, </em><em>and parking facilities </em><em>60 m<sup>2</sup>.</em><em> </em></p><p><em> </em></p><p><strong><em>Key-words: Factory layout, machine, production capacity, ranking Procedure, tiwai onions </em></strong></p>

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

2018 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
M. Tirtana Siregar ◽  
S. Pandiangan ◽  
Dian Anwar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Mean Absolute Percentage Error ◽  
Programming Method ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal&nbsp; not exceed the maximum or minimum control limit is &le; 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

Humanitarian and Technological Revolution: Experience and Challenges of Engineering Evolution

2020 ◽  
Vol 152 ◽  
pp. 54-63
Author(s):  
Aleksandr I. Ageev ◽  
◽  
Alexander V. Putilov ◽  
◽  
Keyword(s):  
Economic Development ◽  
Nuclear Power ◽  
Industrial Development ◽  
Power Plants ◽  
Life Support ◽  
Technological Development ◽  
Industrial Society ◽  
Human Potential ◽  
Technological Revolution ◽  
Post Industrial

Changing the priorities of economic development in transition to post-industrial society inevitably causes reviewing approaches to the role of innovation in modern economy. If in the era of industrial development of society innovations are considered mainly as a factor of technological development, in case of a post-industrial society innovations should be considered in a broader perspective. Innovative technologies in all their diversity are being introduced not only in the technological sphere, but also in education, in the service industry, housing and communal services, life support sphere, etc. The problem of shifting regions and separate territories to innovative development approaches is one of the key issues in forming an economy based on knowledge. “Nuclear” cities, where development of nuclear technologies is implemented both for defense and civilian purposes (nuclear power plants, nuclear fuel production, etc.), can be ideally used as territories of advanced social and economic development (TASED) primarily thanks to human potential of these cities. The article analyzes recent humanitarian and technological changes, called the “humanitarian technological revolution” (HTR), and their impact on the speed and effectiveness of innovative changes in this area.

scholarly journals EMPOWERMENT of WOMEN COMMUNITY THROUGH PROCESSING OF BANANA CHIPS

ICCD ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 623-626
Author(s):  
Diny Agustini Sandrasari
Keyword(s):  
Community Development ◽  
Production Process ◽  
Production Capacity ◽  
Development Activity ◽  
Women's Participation ◽  
The Family ◽  
Labeling System

Women’s participation to improve the family economic is through an entrepreneurship. One of the business have done by women’s in Cipayung area is to make a banana chips. Banana chips that they have produced have a savory and crispy taste but it has no longer. However, even though the business has been operating more than 5 years, that business not expanded. That is caused by low technology that they are used such as the slicing process, they still do this process manually so that it has unequal thickness, they used plastic to packaging the product with a simple seal so easy to broken and can make the product will be rancid quickly. The method used in this community development activity is participation of women’s community who have banana chips business in Cipayung sub-district, East Jakarta. This activity begins with a discussion with the community to identifying the problems, determine priority issues that must be endured resolved. The results of discussion is they agreed the main problem about the business is slicing process and packaging. To complete this problem which is an obstacle about the slicing process, it must be made with banana slicer machine, while for the packaging problem, improved packaging and labeling system must be done which aims to increase the production process of banana chips. The results showed that the introducing of technology will increased the production capacity and income of the community by 40%.

scholarly journals Penggunaan metode algoritma wagner within dalam upaya pengendalian persediaan scrap besi di PT XYZ

2020 ◽  
Vol 5 (1) ◽  
pp. 56-73
Author(s):  
Somadi ◽  
Syah Rajendra Hari Septa ◽  
Nila Dahlia Juita
Keyword(s):  
Control Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Lot Size ◽  
Total Size ◽  
Total Cost ◽  
Smoothing Methods ◽  
Before And After ◽  
Iron Scrap ◽  
Total Inventory

The research objective is to determine the total size of the lot of iron scrap orders, and the total cost of the company's inventory before and after carrying out the method of controlling iron scrap inventory using the Wagner Within Algorithm method. Demand forecasting uses the Single Moving Averge, Weight Moving Average, and Exponential Smoothing methods. Based on the results of the study, the total lot size of iron scrap material orders is smaller than the size of previous lot orders without using the inventory control method, which is 15,362 tons per year. Total inventory of Rp. 105,076,125,840 and the total cost is more optimal when compared with the total cost of inventory with the company system that is Rp. 109,734,165,840 so that the company can save costs by Rp. 4,658,040,000.

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

2021 ◽  
pp. 0734242X2110614
Author(s):  
AKM Mohsin ◽  
Lei Hongzhen ◽  
Mohammed Masum Iqbal ◽  
Zahir Rayhan Salim ◽  
Alamgir Hossain ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Production Capacity ◽  
Waste Recycling ◽  
Support Vector ◽  
Waste Recovery ◽  
Decomposition Procedure ◽  
Moving Average Model ◽  
The Government ◽  
Seasonal Data

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

A New Modeling and Application of Hierarchical Production Planning Approach

2013 ◽  
pp. 113-144
Author(s):  
Reza Tanha Aminlouei
Keyword(s):  
Power Systems ◽  
Production Planning ◽  
Power Plants ◽  
Production Capacity ◽  
Total Load ◽  
Hierarchical Production Planning ◽  
Time Horizons ◽  
Planning Approach ◽  
Utility Cost ◽  
Equal Space

In real power systems, power plants are not in the equal space from the load center, and their fuel cost is different. With common utilization conditions, production capacity is more than total load demand and losses. Therefore, there are different criteria for active and inactive power planning in each power plant. The best selection is to choose a framework in which the utility cost is minimized. On the other hand, planning in power systems has different time horizons; thus, for effective planning in power systems, it is very important to find a suitable mathematical relationship between them. In this chapter, the authors propose a modeling by selecting a Fuzzy Hierarchical Production Planning (FHPP) technique with zone covering in the mid-term and long-term time horizons electricity supply modeling in the Iran global compact network.

scholarly journals Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

2020 ◽  
Vol 60 (2) ◽  
pp. 336-353 ◽  
Author(s):  
Long Wen ◽  
Chang Liu ◽  
Haiyan Song ◽  
Han Liu
Keyword(s):  
Factor Model ◽  
Moving Average ◽  
Demand Forecasting ◽  
Mainland China ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Data Sampling ◽  
Tourism Demand ◽  
Composite Indices ◽  
Mixed Data Sampling

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

Modeling of Wind Power Production Forecast Errors for Wind Integration Studies

2010 ◽  
Author(s):  
Jason J. Kemper ◽  
Mark F. Bielecki ◽  
Thomas L. Acker
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Power Plants ◽  
Moving Average ◽  
Forecast Errors ◽  
Production Forecast ◽  
Wind Integration ◽  
Forecast Performance ◽  
Auto Regressive ◽  
Statistical Trends

In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.

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