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There is often confusion between forecasting methodologies and predictive modeling using supervised machine learning algorithms. While the latter relies on external information for its predictions, forecasting uses its own data.

This workshop aims to provide a comprehensive understanding of all forecasting methods and how to apply them for near-future predictions. It will cover basic models and then explore the evolution of various methods, enabling participants to use them effectively. Understanding all quality indicators will help participants select the best forecasting model for their businesses. 

• Compare forecasting with supervised machine learning.
• Learn how to select between forecasting models
• Evaluate the relationship between the future and the past.
• Measure the impact of the past on the near future
• Analyze all forecasting methods and their evolution.
• Develop all analytical models for estimation.
• Master the precision measures of models’ quality.
• Select the best forecasting model.
• Apply models with specialized software.

Day 1:

- Linear and Polynomial trends

- Exponential, Power, and Logarithm trends.

Day 2: 

- Averaging and Moving Averages.

Day 3:

- Simple, Double, and Triple models in exponential smoothing.

Day 4: 

- Time Series

- ARIMA and Box Jenkins method

- Comprehensive colored PPT documents.

- Supervised ML vs. Forecasting approach.

- Stationary, Additive, and Multiplicative models.

- Proprietary tools solutions.

- Quality measures of forecasting models.

- "White Noise” data.

- Selecting the Fit model.

Statistical Quality Control (SQC) is a fundamental approach to ensuring consistent product and process quality. This comprehensive five-day training provides participants with an in-depth understanding of SQC methodologies, from foundational concepts to advanced techniques. Covering Statistical Process Control (SPC), process capability analysis, and design of experiments, this workshop equips managers, engineers, and quality professionals with the tools to monitor, analyze, and improve process performance. Through practical case studies and hands-on exercises, participants will develop the skills needed to implement robust quality control systems in their organizations. 

• Understand the principles and importance of Statistical Quality Control.
• Apply Statistical Process Control (SPC) techniques for process monitoring.
• Conduct Phase I analysis for initial process stability assessment.
• Implement SPC for attributes and interpret control charts effectively.
• Perform process capability and process performance analysis.
• Utilize Design of Experiments (DOE) to optimize and improve processes.

Day 1:

- Essentials of statistics, and SQC and SPC Fundamentals.

Day 2: 

- Phase I and SPC for Attributes.

Day 3:

- Process Capability Analysis.

Day 4: 

- Process Performance Analysis and Case Studies.

Day 5: 

- Design of Experiments (DOE) and Practical Implementation.

- Introduction to Statistical Quality Control (SQC).

- Statistical Process Control (SPC) fundamentals and techniques.

- Phase I: Process Stability and Control Chart Implementation.

- SPC for Attributes: Methods and Applications.

- Process Capability Analysis: Cp, Cpk, Pp, and Ppk.

- Process Performance Analysis for continuous improvement.

- Design of Experiments (DOE): Principles and practical applications.

Under Fine Tuning

Under Fine Tuning

Under Fine Tuning

Under Fine Tuning