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Companies often face various obstacles related to managing raw material inventory to meet demand, one of which is Peuyeum Ketan Istimewa. Working in the food processing industry, of course, raw material inventory management needs to be planned optimally to avoid various risks that can harm the company. The Quantity Discount model is used to take advantage of cost savings provided by suppliers when purchases are made in large quantities, while other efforts that can help manage raw materials in a company are by knowing the safety stock and reorder point of raw materials and also forecasting demand to predict future demand. This study will use the Quantity Discount model which optimizes inventory levels by considering storage costs, ordering costs, and quantity discounts. The calculations carried out are also to find the value of the company's Safety Stock and Reorder Point. The results of this study indicate that the use of the Quantity Discount method can reduce total costs by Rp26,319,267/year, while forecasting using the seasonality method increases the accuracy of demand predictions, thus enabling more efficient inventory management. The implementation of this model is expected to provide a significant contribution to operational efficiency and cost reduction at Peuyeum Ketan Istimewa

In the increasingly competitive Industry 4.0 era, companies need to forecast product demand to meet consumer needs and improve operational efficiency. CV Mamifood Sukses Abadi, an MSME that produces milk and cheese-based foods, has faced sales fluctuations in the last two years, thus requiring accurate forecasting to plan production strategies and resource management. This research aims to forecast demand using the Fuzzy Mamdani method and the POM-QM application. Fuzzy Mamdani was chosen for its ability to handle decision-making with multiple criteria and balanced weights, while POM-QM was used to validate predictions through quantitative methods. Product sales data for the years 2022 and 2023 were analyzed to produce accurate forecasts. The methods used include Moving Average for forecasting and evaluation of the results using MAPE. The analysis results show that the Moving Average method with N = 2 produces a MAD value of 402.523 and a MAPE of 22.155%, while the results of Fuzzy Mamdani show that product demand in the next period tends to decrease. This research is expected to provide insight for CV Mamifood Sukses Abadi in planning a more efficient production strategy.

This community service activity aims to increase the understanding and ability of business groups in Kassi Village, Rumbia District, Jeneponto Regency in making effective business projections. This program is expected to provide strategic provision for business actors in managing finances, planning business development, and facing market challenges. The technique of implementing this activity includes three stages starting from the preparation stage, the implementation stage and the activity evaluation stage. The results of the training showed that traditional business owners have difficulty in applying business forecasting techniques to their businesses because sales records are sometimes incomplete or non-existent. After conducting the simulation, participants were able to understand the basis of sales forecasting for future sales forecasts. In addition, business owners can know their business forecast with certainty. Knowledge of business forecasting is expected to improve business performance and competitiveness. The limitation of this training is that the implementation method does not use instructional techniques so that knowledge about business forecasting can be implemented in business activities.

CV Wangun Mandiri is a manufacturing company that produces tapioca flour. In order to achieve maximum profit targets and smooth production activities, the company is faced with problems relating to the amount of tapioca flour products due to the uncertainty of its demand that tends to fluctuate and the imbalance of machine capacity. Therefore, it is required to plan the amount of production using the forecasting and fuzzy inference system approach as an effective method to determine the optimal production level. This research relies on the availability of datasets to determine the appropriate forecasting method and fuzzy method. The Fuzzy Mamdani method concludes that CV Wangun Mandiri can produce 82.9 tons to maximize existing demand and the capacity of its machines. 

XYZ is a manufacturing company that produces Yamaha brand motorbike parts such as electrical switches, electrical sockets, lever assembly and horns. Many of the products sent were returned by customers because they did not meet quality standards, for example in March 2024 there were 1,420 pcs out of a total of 19,900 pcs that were returned. This means that companies have to increase production time and costs to replace defective products that are returned because they do not have safety stock. Forecasting is needed to control the production system so that it does not experience over stock and safety stock shortages. This research aims to provide recommendations for forecasting methods that companies can use to minimize the occurrence of production excesses and shortages in the company. Forecasting is done based on historical company sales data for 12 months. The method used is the exponential smoothing method which is then tested whether this method can be used in the future. This calculation uses the help of POM-QM (Production Operation Management – Quantitative Method) software. Calculations are carried out by testing the MAD, MSE, and MAPE values to obtain calculation error values. The results obtained by forecasting the main sw srtg lock assembly product using the exponential smoothing method were 15,708 for demand for the next period, MAD 3.38, MSE 22.84 and MAPE value 26%. Based on these results, the exponential smoothing method can be a recommendation for companies to forecast future demand. This is because the value of forecasting accuracy or MAPE is reasonable. The smallest percentage of MAPE values has a fairly minimal possibility of error in forecasting results.

Operational management is part of the work process, implementation, organization, or overall management in an organization or company. Our country has experienced difficulties since the implementation of PSBB. This also has an impact on the economy in Indonesia. Every company will compete to be at the forefront to meet consumer needs in the market in order to survive in the midst of competition. The type of research conducted in the discussion of this article is by using literature studies or literature reviews, The data sources used in this study are secondary data which are types of data that come from documents or other secondary sources Several supporting aspects for the company, Project Management, Process Strategy, Quality Management, Forecasting Management, and Strategic Location.

Every company is required to plan the need for goods as effectively as possible in order to maximize profits. Bintang Makmur Building Shop is a building shop that provides building materials, especially cement. Cement is one of the basic materials for buildings. The need for cement has recently continued to increase due to the large number of developments, both housing projects and road construction. In addition to the increasing demand for cement, cement prices also experienced price volatility which tended to fluctuate. This is done so that there is no stockpiling or even a shortage of cement. With prices that tend to go up and down if there is too much stock, it will cause losses if there is a price decrease. Vice versa if there is a shortage of cement stock, it can cause disappointment to customers. To deal with the above, it is necessary to build a prediction system that can predict cement needs in prosperous shops. The system that will be built uses an Artificial Neural Network (Artificial Neural Network) which is part of the science of artificial intelligence which has been widely used to solve various kinds of problems related to prediction or forecasting by utilizing the Backpropagation Method. The system is designed with the MATLAB programming application. From the results of the research that has been carried out, it was found that the total demand for Andalas cement for January of the following year is 0.2532 or 2532, thus the predicted total demand for Andalas cement is 2532 sacks.

MSMEs are productive economic businesses run by individuals or small business entities to grow and develop their businesses in order to build the economy, so that MSMEs become the most important pillars in the Indonesian economy. The large number of competitors in the business world requires entrepreneurs to find strategies that can increase the sales cycle and fulfill the number of requests. Solusi Cash & Kredit is a company engaged in cash and credit sales of smartphones, furniture, and electronics. According to the owner of Solusi Cash & Kredit, Vivo smartphones are one of the best-selling brands on the market. The phenomenon that occurred in the company during August 2023 to June 2024 was the occurrence of fluctuations or instability in product sales levels. The purpose of this study was to determine the results of the comparison of sales forecasting with the Exponential Smoothingi and Least Square methods. The results obtained were that the exponential smoothing alpha 0.3 method had a MAPE value of 18.27% with a forecast value for the next period of 34 units per month. Alpha 0.5 had a MAPE value of 14.74% with a forecast value for the next period of 36 units per month. Alpha 0.7 had a MAPE value of 12.64% with a forecast value for the next period of 36 units per month. Alpha 0.9 had a MAPE value of 11.7% with a forecast value for the next period of 37 units per month. The least square method had a MAPE value of 7.2% with a forecast value for the next period of 41 units per month.

This study aimed to know the prediction of rice sales for Employee Cooperatives Republic of Indonesia Bina Warga Benjeng in the following month. Rice sales are often difficult to predict market demand. When consumer demand increases, rice supplies sometimes suffer from shortages. If consumer demand decreases, stock builds too much and results in a decrease in rice quality. In order for the rice sales process to run smoothly, it is necessary to have a sales prediction so that there are no excesses or shortage in rice supplies. The method of discussion used to predict in this study using the Single Moving Average method which is a prediction method that uses new actual data requests to raise the predictive value of the next month’s demand. The best results were using the Single Moving Average methods using rice sales data variant 25 kg variant were successfully implemented with an RMSE value of 9.3% which means this prediction accuracy of 90.7% accurate.

The restaurant industry in Indonesia has experienced significant growth, driving the need for data-driven strategies to remain competitive. This study aims to apply and compare time series methods in forecasting sales at "Nasi Goreng Bacot" restaurant. The methods used are Simple Moving Average (SMA), Weighted Moving Average (WMA), and Single Exponential Smoothing (SES), with a focus on sales data from the year 2023.The research results indicate that SMA provides the most accurate predictions, with a Mean Absolute Error (MAE) value of 296.67, Mean Squared Error (MSE) of 129055.6, and Mean Absolute Percentage Error (MAPE) of 3.02%. WMA and SES, although useful in certain data conditions, show higher error rates in this case. This study confirms the effectiveness of SMA in the context of stable and less fluctuating restaurant sales data. With these results, restaurants can plan their inventory of raw materials and workforce more efficiently, reduce waste, and improve customer satisfaction.      

the external environment has an important influence on the sustainability of an organization. The purpose of this research is to analyze and describe the role of the external environment in educational institutions. The research method used in this research is a literature study approach, in other words, the research was conducted without a research location, but all data was taken through previous studies in the form of books and scientific articles relevant to the research theme. Data collection techniques in this study used documentation, with data analysis techniques in the form of data reduction, data presentation, and conclusion drawing. The results found that the external environment helps educational institutions to understand external factors that can affect their performance and success. SWOT is used as a tool to evaluate the strengths, weaknesses, opportunities, and threats of educational institutions. The external environment in organizations is divided into two: micro (small scope) involving competitors and partnerships, and macro (general scope) involving economic, political/legal, socio-cultural, technological, and global factors. The steps used in the analysis process involve scanning, monitoring, forecasting, and assessing.

Forecasting demand for screen-printed clothing products at the UMKM "D'mitz Screen Printing" in Sobrah Village, Wungu District, Madiun Regency helps with production control planning to maximize supply chain management for screen-printed clothing products. To predict future product demand, it is very important for UMKM to forecast market demand. Forecasting future demand is very important to avoid sales prediction errors that can cause waste, such as increased production costs due to sales predictions being too large, or stock outs due to sales predictions being too small, which results in customers having to wait longer to get the goods they want. Based on this problem, the UMKM "D'mitz Screen Printing" carried out a demand forecasting analysis for screen printed clothing with the aim of reducing waste and maximizing value. Forecasting demand for screen printed clothing for the next five months using time series analysis and moving average methods. Forecasting results for the period March 2022 to February 2023 show sequential forecasting values of 3266.67; 3300; 3250; 3283.33; 3233.33; 3316.67; 3333.33; 3372.22; 3305.56; and 3272.22. From the Mean Absolute Error (MAE) and Mean Square Error (MSE) calculations that have been carried out, the MAE value is 94.44 and the MSE value is 16018.593.

Sales forecasting is a technique that companies use to predict future sales volumes based on previous sales data. This research aims to help UMKM  XYZ determine the optimal production amount to maximize profits, by using forecasting methods in planning mocaf flour production. The methods used include the Time Series model with Moving Average, Exponential Smoothing, and Trend Analysis, which are calculated using POM QM Windows software. The analysis results show that the Trend Analysis method is the most accurate for forecasting, with the smallest error value, namely MAD of 76.997, MSE of 8161.672, and MAPE of 6.02%. The smaller the error value, the more accurate the forecasting results. Therefore, the Trend Analysis method is recommended for forecasting mocaf flour sales in XYZ UMKM in 2024, with the production of 15,100 kg to avoid excess stock and dead stock in meeting consumer demand.    

Amidst the growing competition in the industry, CV Berkah Jaya Klaten faces challenges in planning the production capacity of cleaning tools to meet market demand. This study aims to provide solutions to production capacity planning issues by applying the Rough Cut Capacity Planning (RCCP) method using the Capacity Planning Using Overall Factors (CPOF) technique and a system simulation approach. The planning process begins with demand forecasting using IBM SPSS Statistics 25 software, which produces the smallest Mean Absolute Percentage Error (MAPE) value using the Simple Seasonal method. These forecasting results are used to determine the Master Production Schedule (MPS). Processing RCCP data with the CPOF method requires MPS data, processing time for each workstation, and historical proportions calculated from standardized processing times. The system simulation of production capacity planning is conducted to model real conditions and evaluate various production scenarios. The simulation results reveal that the required production time capacity each month always exceeds the available time capacity, indicating the need for capacity adjustments to avoid bottlenecks and improve efficiency. With this approach, CV Berkah Jaya Klaten can plan production capacity more efficiently and effectively, ensuring product availability in accordance with customer demand.

The advancement of information technology, particularly in data analytics, has significantly impacted management accounting practices and strategic decision-making. This study aims to analyze the role of data analytics in management accounting and its impact on strategic decision-making in competitive markets. This study proposes a new framework that integrates data analytics into management accounting, focusing on its application in improving decision-making accuracy, competitive advantage, and operational efficiency. The methodology employed is a literature-based review, examining relevant journal articles, books, and industry reports. Secondary data is used to assess the impact of data analytics in management accounting and strategic decision-making. The findings reveal that the implementation of data analytics in management accounting improves budgeting and forecasting accuracy, provides a competitive advantage through faster market responses, and enhances operational efficiency by reducing costs and streamlining processes. The analysis indicates that while there are challenges such as technological barriers, organizational resistance, and ethical issues, the opportunities presented by data analytics in management accounting are significant. Proper implementation can support more accurate and efficient strategic decision-making, improving a company’s competitive positioning. Future research could focus on exploring the application of emerging technologies like artificial intelligence (AI) and machine learning in management accounting. Additionally, further investigation into the impact of data privacy regulations and ethical challenges is needed.

Human resources as a resource in a company have a strategic role compared to other resources. Humans play a very important role in the development of a company. If a company has higher quality human resources than its competitors, then its performance will definitely increase. Human resource and competency planning in a company plays an important role in improving human resources, especially in terms of employee performance, employee performance to help achieve company goals. In this case, employee performance is a result that can be achieved by an individual or group of people in a company organization according to quantitative or qualitative aspects, considering employee compensation. Apart from that, a mismatch between an employee's competency (the employee's educational background and skills) and their job can also make the employee uncomfortable with what they are doing. This of course greatly affects the level of work productivity in the company. Competencies can clarify work standards and expectations that the company wants to achieve and can make it easier for companies to align work behavior with organizational values. Apart from that, HR planning as an activity is a process of how to meet current and future workforce needs for an organization. In meeting current workforce needs, the HR planning process means efforts to fill/cover workforce shortages both in quantity and quality. HR planning emphasizes forecasting efforts regarding workforce availability based on needs in accordance with future business plans.

In the face of the digital era's rapid evolution, human resource planning has become increasingly crucial for organizations to maintain competitiveness and sustainability. This abstract explores strategic human resource planning (HRP) as a pivotal approach to navigating the complexities posed by digital transformation. The advent of digital technologies has revolutionized industries, requiring organizations to adapt swiftly to remain relevant and efficient. Strategic HRP involves aligning workforce capabilities and organizational goals with the demands and opportunities presented by digital advancements. Key considerations include forecasting future workforce needs, identifying critical skill gaps, and implementing robust talent acquisition and development strategies. Moreover, effective HRP entails leveraging data analytics and predictive modeling to anticipate HR requirements and optimize workforce planning initiatives.The challenges of the digital era necessitate proactive measures in talent management, such as upskilling current employees and recruiting digitally savvy talent. Organizations must foster a culture of continuous learning and adaptability to thrive in a digitally disrupted environment. Strategic HRP also encompasses developing agile HR policies and practices that can accommodate rapid technological changes and evolving employee expectations. Furthermore, integrating technologies like artificial intelligence and automation into HR processes can streamline operations and enhance decision-making capabilities.In conclusion, strategic human resource planning is indispensable for organizations seeking to harness the opportunities presented by the digital era while mitigating its inherent challenges. By prioritizing agility, foresight, and innovation in HR practices, organizations can build a resilient workforce capable of driving sustainable growth and competitive advantage in the digital age.    

Research on forecasting new student admissions at SD Negeri 10 Koto Tinggi Surian employs data analysis using simple linear regression to predict the number of new students based on previous years and formulate it into a mathematical model. In its calculation process, this approach can adopt both quantitative and qualitative methods. The prediction error rate for the next year is very low. Out of 41 predicted students, only one differs from the actual number. The study's data spans student admissions from 2019 to 2023. The research findings demonstrate the effectiveness of conventional linear regression techniques. The prediction for 2023 indicates an intake of 42 students. Therefore, it can be concluded that simple linear regression accurately predicts new student admissions at SD Negeri 10 Koto Tinggi Surian with high accuracy.    

Behavioral economics has highlighted how cognitive biases influence financial decisionmaking, often leading to suboptimal outcomes. This paper explores the impact of behavioral biases such as overconfidence, loss aversion, and herding behavior on accounting and economic forecasting. By reviewing empirical evidence from market behavior, the study assesses how these biases affect financial reporting, auditing practices, and economic predictions. The paper concludes with recommendations for accountants and economists to incorporate behavioral insights into their practices to improve decisionmaking and forecasting accuracy.

Furniture raw materials are still a major challenge in the industry, in line with the wishes of consumers to get good quality raw materials and soaring export demand, so there is a need for a control process to monitor the value of products using forecasting. The purpose of this study was to predict gross domestic product in the furniture industry in Indonesia in 2022. This study used secondary data on the quarterly trend of gross domestic product in the furniture industry in Indonesia 2010-2021 taken from the research industry data processed by BPS and Bank Indonesia, The method used is Double Exponential Smoothing-Holt. The results of the calculation using the double exponential smoothing-holt method obtained a value of α of 0.658 and β of 0.008 where the forecasting results for the 2022 period, namely the 1 quarter of 7.602 billion rupiah, quarter 2 of 7.676 billion rupiah, quarter 3 of 7.749 billion rupiah, and quarter 4 of 7.822 billion rupiah. Where the MAPE value is 0.737% which means forecasting has very good results.