Peramalan Penjualan Olein Curah di Perusahaan Pengolahan Kelapa Sawit Menggunakan Double Moving Average

Bulk olein is one of the products produced by Palm Oil Processing Company. Bulk cooking oil controls 75 percent of the production market share in Indonesia and about 77.5 percent of households in Indonesia use bulk cooking oil because the price is cheaper than packaged cooking oil. Demand for olein in the future is predicted to be continued to increase, so it is necessary to estimate future sales so that production activities become more effective and efficient. The method used in this study is the double moving average (DMA), which is one of the forecasting methods with data that has a trend. The calculation will be done by comparing the result of 3 moving, 4 moving and 5 moving. Forecasting error is calculated using mean absolute percentage error (MAPE). The calculation results show that the average MAPE from DMA with 5 moving has the smallest value. To verify these results, an analysis of the processed data was carried out, namely looking for data with the furthest distance from the linear line, namely t3 data and t7 data. The data is omitted in data processing and then the MAPE error value is recalculated. The results obtained are that DMA with 3 moving results have the smallest error, which is 11.863 percent. For this reason, the chosen forecasting calculation is a double moving average with 3 moving.

Sales Forecasting by Using Exponential Smoothing Method and Trend Method to Optimize Product Sales in PT. Zamrud Bumi Indonesia During the Covid-19 Pandemic

Forecasting is important for a company in achieving goals effectively and efficiently. Forecasting aims to determine the next steps to be taken based on historical data. PT. Zamrud Bumi Indonesia is one of the manufacturing companies in the management of agricultural liquid fertilizers with the trademark “Power Bumi”. The purpose of this study is to analyze the sales pattern of Power Bumi products during the covid-19 pandemic and compare the forecasting method that is able to produce the smallest error value in forecasting sales of Power Bumi products PT. Zamrud Bumi Indonesia. This study uses 2 methods, namely exponential smoothing and least square trend model. To calculate the error rate using MAD, MSE and MAPE. The results show that the exponential smoothing alpha 0.9 method has the smallest error value compared to other forecasting methods. In forecasting product sales, the MAD value is 130.329, MSE is 28251.23 and MAPE is 22.00% with a forecast of 627.628 boxes. Although the exponential smoothing a 0.9 method produces a forecast value that is relatively low than other methods. However, the comparison of products sold and forecasting results has a relatively small average difference (MSE). It can be interpreted that the exponential smoothing a 0.9 method is able to suppress the forecasting error value for the 2nd period. After getting the forecasting results, it can be concluded that the number of products sold for the 2nd covid-19 pandemic period will not differ much from the number of sales in the 1st covid-19 pandemic period. If the company applies this scientific forecasting method, then sales will be optimal so that excess or shortage of stock can be avoided and the predetermined sales target can be achieved. In addition, the costs incurred during the production process to sales will be more efficient.

Underappreciated Benefits of Reading Own and Others' Memories

These studies investigate underappreciated benefits of reading memories, including memories of other people, for happiness, psychological well-being, and loneliness. In the studies, college students (Study 1), residents of assisted-living facilities (Study 2), and MTurk workers online (Study 3) wrote down memories. They also predicted how they would feel after reading their own and others' memories at a later date. Then, later on, participants read memories that they or another participant had written. Individuals felt happier, less lonely, and higher in well-being after reading memories, regardless of whether those memories were their own or someone else's. Participants underpredicted the affect boost that they would gain from reading memories. This affective forecasting error was related to individuals' perceptions of the “mundaneness” of the memories, and the error was especially pronounced when individuals read others' memories rather than their own. Implications of reading memories for promoting well-being and reducing loneliness are discussed.

Kinerja Logika Fuzzy Sugeno dalam Menangani Prediksi Kain Tenun dengan Kombinasi Random Tree dalam Membangun Rule

This study describes the performance of Sugeno fuzzy logic in determining the amount of woven fabric production by using a combination of random tree decision trees in forming rules. The criteria used in determining the amount of production, namely, production costs, demand, and stock obtained from woven fabric entrepreneurs in Mlaki Wanarejan Utara Pemalang. The random tree decision tree is used, one of which is to automatically generate rules from the available data without consulting with experts, in addition to introducing random trees in the field of research because there are still few studies using this decision tree. The results of this study, it was found that the accuracy while the prediction results tested obtained an Average Forecasting Error Rate (AFER) of 42% with a value 58% truth after being compared with the actual production data.Keywords : Fuzzy Logic, Fuzzy Sugeno Method, Rule, Random tree, Prediction.

An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.

Identifying the root causes for inventory accumulation and prioritizing them using an MCDM-based TOPSIS approach

PurposeInventory accumulation is a major problem for any organization, as it not only occupies the valuable storage space, but it also blocks the company's capital, leaving the owners with less cash to run the company's business. Aggregation of inventory in any organization contributes to inventory carrying cost; it affects labor productivity, increases equipment expenses and creates a loss of opportunity associated with it. Therefore, it is essential for any organization to come up with a solution to deal with the stockpile of inventory.Design/methodology/approachThis research aims to examine the potential causes of inventory aggregation in an organization. First, the potential factors for the build-up of inventory are identified from survey data collection, such as questionnaire approach and discussion with industry experts, and then weights are assigned to attributes to study the effects for these factors. After the identification of probable causes, they are analyzed through a multi-criterion decision-making (MCDM) approach and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize the severity of these causes toward the accumulation of inventory and take corrective actions to prevent their disruptive effect on the business.FindingsThe top three causes identified from the TOPSIS analysis are sales and forecasting error, defects and quality related issues and communication gap between departments. Firstly, we focus on these major contributors and prioritize them using the TOPSIS analysis. Then, we proceed further toward other factors. The main reasons identified for the accumulation of inventory are (1) forecasting error, (2) bulk purchase, (3) data entry error, (4) communication gaps, (5) quality-related issues, (6) product category not traceable and (7) wrong material being procured.Research limitations/implicationsTo carry out the data analysis in this research paper, first survey data collection is done. Then, discussions with managers and executives in the particular domain are carried out, and weights are assigned to the attributes and the criteria to study the effects of the identified factors. After that root cause analysis (RCA) is performed to get to the genesis of the problem and to take necessary corrective action, for carrying out this study, a total of seven potential causes were identified and the contribution of these seven causes on five attributes or criteria, i.e. quantity (in tons), holding and carrying cost, effect on labor productivity, loss of opportunity cost and storage space were studied.Originality/valueThis research paper is the author’s original work, and all the analyses carried out are from the discussion with experts in the field and through the in-depth analysis carried out. This research aims to examine the potential causes of the accumulation of inventory in organizations and their contribution toward factors like inventory carrying cost, labor productivity, and opportunity loss and excessive storage space have been analyzed. This research provides great value to the readers in the respective domain.