Forecasting and Predictive Analysis

The Forecast and Predictive Analysis module is a powerful tool for predicting future trends using historical data. This module allows businesses to more accurately predict future opportunities and potential risks. Machine learning and statistical models support more strategic decision-making in critical areas such as sales forecasts, inventory levels, and market trends.

Data analytics-powered predictive models help decision makers make informed choices. This module allows businesses to minimize risks, seize opportunities early, and gain competitive advantage in the market. It also allows you to quickly adapt to changing conditions with scenario analysis, thus achieving more sustainable and robust growth.

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Estimation and Forecasting Module

This module performs analysis based on historical data to predict future trends and enable organizations to make more informed strategic decisions.

Data Set Preparation

Prediction Algorithms

Scenario Analysis

Reporting

Decision Support

Data Collection and Preparation

It ensures that the data sets required for the creation of forecasting models are collected and made ready for analysis. The data is meticulously prepared and organized to increase the accuracy of the forecasting models.

Features

Determining Data Sources
Data Cleaning and Filtering
Data Normalization
Preparing Time Series Data

Identifying Data Sources
Critical data such as sales history, customer behavior, and market analysis are collected from different sources. This data is pulled from various systems so that the forecasting model can produce accurate results. Data from different departments of the business, such as marketing and sales, is systematically combined.

Data Cleaning and Filtering
The accuracy of the prediction models is increased by cleaning the missing, incorrect or duplicate data. The cleaning process ensures the validity and reliability of the data. At the same time, only important and reliable data is included in the model with the filtering process, thus ensuring the model works more efficiently.

Data Normalization
Data from different sources are converted into standard formats for analysis. Data normalization ensures that the data is on a similar scale, allowing the model to produce comparable and accurate results. This process ensures homogeneity of the data and helps to obtain more efficient results in the analysis process.

Time Series Data Preparation
Historical data is organized according to the time axis, allowing trend analysis. Time series data is used to understand past trends and predict future movements. This type of data is especially important in demand forecasting and trend analysis.

Prediction Algorithms

It enables the selection and application of mathematical and statistical models used in forecasting analyses. Using different algorithms, the most accurate forecasts based on the data set are made and future trends are predicted.

Features

Time Series Analysis
Regression Models
Machine Learning Methods
Model Performance Analysis

Time Series Analysis
Algorithms such as ARIMA, SARIMA, and Holt-Winters are used to analyze future trends based on past data. Time series analysis is effective for predicting the future, especially in data where seasonal and trend effects are significant. These algorithms analyze the trend and cycles of data over a given time period so that future values can be predicted accurately.

Regression Models
Allows predictions to be made by analyzing relationships in data such as sales, production or cost. Regression models predict future values by examining the relationship between independent variables and dependent variables. Such models are particularly effective in data containing linear relationships and facilitate prediction processes.

Machine Learning Methods
Predictions are made from complex data using algorithms such as decision trees, support vector machines (SVM), and artificial neural networks. Machine learning methods make accurate predictions by extracting meaningful patterns from large and multivariate data sets. These algorithms are used to learn hidden relationships in past data and to obtain predictions from complex data sets.

Model Performance Analysis
The accuracy and reliability of the applied prediction models are tested to determine the most suitable model. Performance analysis is evaluated with metrics (e.g. MAPE, RMSE, R²) on how accurately each model makes predictions. The model with the highest accuracy is selected and used.

Scenario and Risk Analysis

It enables simulating various scenarios and evaluating risk factors according to each scenario. Scenario and risk analysis helps businesses make more conscious strategic decisions by predicting potential dangers and opportunities.

Features

Generating Possible Scenario
Risk Assessment
Best and Worst Case Analysis
Critical Decision Points

Generating Possible Scenario
Alternative scenarios are created for situations such as different market conditions, cost changes, or production problems. This process involves examining various possibilities based on variables and developing action plans for each possibility.

Risk Assessment
The potential risks of each scenario are analyzed and risk levels are determined. In this way, the company can take precautions for the scenarios that carry the most uncertainty and danger.

Best and Worst Case Analysis
The most positive and most negative scenarios are determined and a plan is made on how to evaluate opportunities and prevent threats. In this way, the business is prepared for both ends.

Critical Decision Points
The processes with the highest risk are identified and early warning mechanisms are activated. This provides speed and efficiency in decision-making processes.

Forecast Reporting and Visualization

It provides presentation of forecasting analyses in understandable graphics and reports. Forecast results are reported both visually and textually to guide managers and decision makers when making strategic decisions.

Features

Forecast Graphs and Table Reports
Trend and Deviation Analysis
Dynamic Filtering and Breakdown Analysis
Forecast Performance Evaluation

Forecast Graphs and Table Reports
Sales, production and cost trends are visualized with graphs. This makes data easy to understand and supports strategic decisions.

Trend and Deviation Analysis
The differences are analyzed by comparing the actual data with the forecast results. Large deviations are detected and necessary corrective actions are planned.

Dynamic Filtering and Breakdown Analysis
Data can be filtered based on customer, product group or region, enabling more detailed and targeted analysis.

Prediction Performance Evaluation
The success of the models is evaluated by measuring the prediction accuracy with metrics such as MAPE and RMSE. More reliable predictions are made by ensuring continuous improvement.

Predictive Decision Support

It enables the integration of forecasting results into decision-making mechanisms. It provides strategic guidance using forecasting data so that businesses can make proactive decisions based on future trends. This process enables data-based decisions to be made and more efficient business processes to be created.

Features

Predictive Inventory Management
Production Planning and Capacity Management
Market and Customer Trend Analysis
Proactive Decision Making

Predictive Inventory Management
Cost savings are achieved by optimizing stock levels based on historical demand and trend analysis. Forecasts ensure that stock levels are set correctly and that overstocking is prevented. This way, the business can avoid the costs associated with excess inventory and respond quickly to demand fluctuations.

Production Planning and Capacity Management
Production processes are organized according to forecasted order quantities and production capacity is used more efficiently. Forecasts prevent overloading of production lines by determining which products will be produced and when. This planning helps optimize the production process and use resources more efficiently.

Market and Customer Trend Analysis
Strategic planning is done according to customer demands and market changes, thus identifying opportunities and threats in the market. Market analysis helps the company determine its strategic direction by predicting which products or services will be in demand. This is important for providing better customer satisfaction and gaining competitive advantage.

Proactive Decision Making
Decision support suggestions are created to take preventive measures against expected trend deviations, which helps minimize risks. This feature allows early detection of potential problems, so businesses can proactively solve problems. Businesses can minimize potential risks by making strategic decisions based on forecast results.