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This research aims to evaluate the effectiveness of linear regression as a forecasting tool to estimate the Provincial Minimum Wage (UMP) in Indonesia. Utilizing UMP data from various provinces during the period 2002-2022, this study employs linear regression to analyze the factors influencing UMP determination. The predicted UMP for North Sumatra in 2023 demonstrates a high level of accuracy (R-squared = 0.9678), affirming the potential of linear regression as an effective tool to understand regional economic dynamics. The research provides a crucial foundation for policymakers in regional economic planning and suggests avenues for further investigation, including exploring alternative prediction methods and analyzing the impact of UMP regulation policies.

Mitsubishi Chemical Indonesia is a producer of Purify Terepthalic Acid (PTA) in Indonesia with an important role in meeting the primary needs of the Indonesian population by managing raw materials to produce purified terepthalic acid. Purified terepthalic acid is the main raw material for polyester fiber. The problem that occurs is the phenomenon of the bullwhip effect, namely the occurrence of fluctuations between orders and demand which results in a shortage of raw materials or low inventory which can cause production to stop. So the purpose of this study is to calculate the value of the bullwhip effect, determine the causes of the bullwhip effect, and determine solutions to reduce the bullwhip effect. This study begins by calculating the value of the bullwhip effect at the two echelons. The method used in this research is FMEA. The results of this study are the value of the bullwhip effect at echelon 1 (suppliers and PPIC) = 1.27 and echelon 2 (PPIC and distributors) = 0.93. This value indicates the occurrence of a bullwhip effect in echelon 1 while echelon 2 does not indicate the occurrence of a bullwhip effect. The dominant causes of the bullwhip effect are market competition, rising material prices, demand forecasting that is not close to accurate and the number of orders for raw materials in large quantities. The proposed improvement is to collaborate with suppliers and customers to get guaranteed orders and demand that are fixed and sustainable. This value indicates the occurrence of a bullwhip effect in echelon 1 while echelon 2 does not indicate the occurrence of a bullwhip effect. The dominant causes of the bullwhip effect are market competition, rising material prices, demand forecasting that is not close to accurate and the number of orders for raw materials in large quantities. The proposed improvement is to collaborate with suppliers and customers to get guaranteed orders and demand that are fixed and sustainable. This value indicates the occurrence of a bullwhip effect in echelon 1 while echelon 2 does not indicate the occurrence of a bullwhip effect. The dominant causes of the bullwhip effect are market competition, rising material prices, demand forecasting that is not close to accurate and the number of orders for raw materials in large quantities. The proposed improvement is to collaborate with suppliers and customers to get guaranteed orders and demand that are fixed and sustainable.      

Kruskal's algorithm in searching for minimum spanning trees can be applied to pipelines installed at the location of PDAM Tirtanadi Tuasan where problem identification starts with the water discharge reaching the consumer is small but the discharge flowing from the reservoir is sufficient, so this research is used as a solution to this problem and also as an optimization of the clean water distribution network in the Tirtanadi Regional Drinking Water Company (PDAM) of the Tuasan Branch with the intention of cutting the direction of the pipe flow to overcome this problem. The data obtained from PDAM Tirtanadi Tuasan Branch is in the form of a floor plan and formed into a weighted graph. After the data is obtained, then it is calculated manually that the length of the installed water pipe is 32,645 m with 86 vertices and 100 edges, then the pipe length is represented as a set of paths and the pipe connection ends are represented as nodes. The pipe length obtained using Kruskal's algorithm and inspection of iterations using the QM for windows software is 22,095 m, with 86 vertices and 85 edges. So, using the Kruskal Algorithm and the help of the QM for windows software, the difference in pipe length obtained is 10,610 m.

Every business must strive to remain stable in its performance in order to survive competition. In Kotagede Yogyakarta, UMKM Yangko Sari Roso produces food. Businesses that are developing in the current era of globalization are characterized by strong competition in all fields, such as business in industry, trade and services. Because the company only produces for one shop and entrusts products to several distributors, Yangko's MSME sales strategy is still not well chosen. In addition, making product packaging that is considered quite expensive and unattractive results in poor sales, which results in high inventories in warehouses and cost losses. The ultimate goal of this work is to find the best solution to the problem of marketing and production costs. The ARIMA method is used to forecast Yangko's short-term sales, production costs, and packaging proposals. The aim of this method is to provide the best results for finding marketing decisions. The results of this research, namely the analysis of marketing strategy observations, show that the company can overcome this problem and find ways to make production costs more efficient by increasing labor.

The daily needs of the people of Central Aceh cannot be separated from agricultural commodities such as tomatoes, shallots, garlic, and others. Some of these agricultural commodities have sharp price fluctuations, such as tomatoes. When the supply of tomatoes in the market is reduced, the price can be much higher than the normal price. Conversely, when the supply of tomatoes is excessive, the price will fall far below the normal price. This is influenced by various factors such as the harvest season, the amount of production, the amount of public consumption and others. Based on these problems, we need a method to be able to estimate the price of tomatoes so that it can be used to support decision making related to price issues. Forecasting is one of the solutions to be able to estimate the movement of tomato commodity prices. The method used for forecasting tomato prices is High Order Fuzzy Times Series Multifactors. In this method, subinterval formation is carried out using Fuzzy C–means. To calculate the error rate of forecasting results in this study using the Mean Square Error (MSE). Based on the results of the tests carried out, the large values ​​of the training and order data used in forecasting do not guarantee a low error rate.

Company CV. AM Nanda Putra is located in Sidoarjo and operates in the scaffolding industry. Currently the company is experiencing losses due to a lack of optimization in planning the amount and time of ordering raw materials, which results in shortages and excess material inventory. To overcome this problem, the company uses the MRP (Material Requirement Planning) method in optimizing raw material planning. In terms of the lot sizing approach, the company applies the LFL and EOQ methods. The forecasting methods used are Moving Average (MA), Weight Moving Average (WMA), and Exponential Smoothing (ES). The smallest MAD results were obtained using the Exponential Smoothing (ES) method for all scaffolding products. The forecasting results are obtained to determine the MPS (Master Production Schedule) for the next 10 months. After the determination of  MPS, the results of Material Requirement Planning (MRP) were obtained, namely the supply of raw materials for MF 170 AM scaffolding of 35402 units or 17700 sets, MF 170 K1 scaffolding of 28906 units or 14453 sets, MF 190 AM scaffolding of 16250 units or 8125 sets, and MF 190 K1 scaffolding of 7656 units or 3828 sets. From the results of calculating the cost of raw material requirements using the Lot for Lot (LFL) method and the Economic Order Quantity (EOQ) method, it can be seen that the total cost of planning the smallest raw material inventory with an amount of Rp. 12,975,818,022.

Electrical energy is one of the most important things in human life. Electrical energy is needed in the industrial sector. In meeting the needs of electrical energy, good planning is needed by predicting the needs of electrical energy. Holt's Double Exponential Smoothing method is a method that can be used to predict electrical energy needs. The results of forecasting the demand for electrical energy at PT PLN (Persero) for the North Sumatra Region for 2022 to 2030 use the Double Double Exponential Smoothing method from Holt (with a value of α = 0.99 and γ = 0.1 which has a MAPE value of 2.0372%. ) namely 13933.19 gwh, 14478.46 gwh, 15023.73 gwh, 15569.00 gwh, 16114.26 gwh, 16659.53 gwh, 17204.80 gwh, 17750.06 gwh, 18295.33 gwh.

Perum Bulog as a State-Owned Enterprise has the main task, which is to conduct a quality and adequate basic food logistics business for the survival of the people. The problem that occurred in Perum Bulog Sub Divre Medan is that the rice supply in Bulog does not consider the demand in the market. Forecasting is an important tool in effective and efficient planning. Therefore, prediction is indispensable for predicting future events. This method essentially uses past data initiated by performing an exponentially decreasing weighting of older observational values or newer values. Brown's double exponential smoothing is a linear model proposed by Brown. This double exponential smoothing method is used when the data indicate a trend. In this study, the terbaik best parameter for forecasting the Number of Rice Sales in Perum Bulog Sub Divre Medan was α = 0.2 with MAPE of 0.27%. And the results of the forecast for Rice Sales at Perum Bulog Sub Divre Medan in 2022 are decreasing every month.