We may earn an affiliate commission when you visit our partners.
Jorge Morales
Supply shortages, pandemics, military wars, trade wars, and other disruptive events have a significant impact in both consumer behaviour and product availability. Companies are becoming aware that historical sales data sets might no longer be relevant; and that the customary forecasting methods are not the best for their new current situation.
Read more
Supply shortages, pandemics, military wars, trade wars, and other disruptive events have a significant impact in both consumer behaviour and product availability. Companies are becoming aware that historical sales data sets might no longer be relevant; and that the customary forecasting methods are not the best for their new current situation.
Read more
Supply shortages, pandemics, military wars, trade wars, and other disruptive events have a significant impact in both consumer behaviour and product availability. Companies are becoming aware that historical sales data sets might no longer be relevant; and that the customary forecasting methods are not the best for their new current situation.
This is the reason why, demand for skilled, critical and flexible Demand Planners with broad perspective is on the rise.
In this course you will be able to decide if previously used forecasting techniques are the right ones for today's "New Normal" business environment. You will be capable of forecasting customer demand of different offerings going through different stages in their product life cycles; using causal and judgemental techniques, market research, statistical methods, time series of past sales and most recent customer orders.
You will also be able to separate relevant from non-relevant data, and mitigate the impact of low forecast accuracy in demand planning, inventory management and profitability.
By the end of this course, that is part of the edX Professional Certificate program to become a Certified Forecaster and Demand Planner (CFDP), you will be able to choose the right forecasting method for each data pattern and understand how to improve forecast performance with Machine Learning and Lean Six Sigma principles.
CFDP certified professionals are globally preferred by recruiters for decision making positions because they are capable of forecasting both slow and rapidly changing seasonal, intermittent and new product demand.
To become an ISCEA Certified Forecaster and Demand Planner (CFDP), you must complete all three preparatory courses and successfully pass the CFDP Exam.
Register for this course and see more details by visiting:
OpenCourser.com/course/z23uqs/forecasting
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Use code at checkout.
Valid until December 4
Save on Select Programs
Stay ahead in a competitive job market and save with these programs.
Take
30%
off
EDXCYBER24
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Learning objectives
- To develop quantitative, judgmental, and causal forecasting models for seasonal, intermittent, and new product demand.
- How to choose the right forecasting method for each data pattern.
- How to improve forecast performance with machine learning and lean six sigma principles.
- To assess forecast performance based on forecast precision, forecast accuracy and forecastability
- How to mitigate the risk of inaccurate forecasts and deal with randomness, low forecastability, missing data, outliers, disruptive events, and overfitting.
- To develop quantitative, judgmental, and causal forecasting models for seasonal, intermittent, and new product demand.
- How to choose the right forecasting method for each data pattern.
- How to improve forecast performance with machine learning and lean six sigma principles.
- To assess forecast performance based on forecast precision, forecast accuracy and forecastability
- How to mitigate the risk of inaccurate forecasts and deal with randomness, low forecastability, missing data, outliers, disruptive events, and overfitting.
Syllabus
Section 2.1. Forecasting myths, realities and challenges
2.1.1. Forecasting myth 1 - About predicting the future
2.1.2. Forecasting myth 2 - About choosing a forecasting model
Read more
Syllabus
Section 2.1. Forecasting myths, realities and challenges
2.1.1. Forecasting myth 1 - About predicting the future
2.1.2. Forecasting myth 2 - About choosing a forecasting model
Read more
2.1.3. Forecasting myth 3 - About model fitting
2.1.4. Forecasting myth 4 - About model sophistication
2.1.5. Forecasting myth 5 - About About Artificial Intelligence
2.1.6. Forecasting myth 6 - About modeling data
Section 2.2. Forecasting based on historical data
2.2.1. The role of historical data in forecasting
2.2.2. Missing data, events and outliers
2.2.3. Naïve and Moving Average Methods
2.2.4. Exponential smoothing and linear regression
2.2.5. ARIMA models
Section 2.3. Seasonal demand forecasting
2.3.1. Decomposition method
2.3.2. Decomposition method example
2.3.3. Holt-Winters method
2.3.4. Holt-Winters method example
Section 2.4. Intermittent demand forecasting
2.4.1. Croston method
Section 2.5. Judgmental and causal forecasting models
2.5.1. Judgmental forecasting models
2.5.2. Causal forecasting models
Section 2.6. Forecasting with Machine Learning
2.6.1. Machine learning fundamentals
2.6.2. Decision trees in machine learning
2.6.3. Machine learning example
Section 2.7. New product forecasting
2.7.1. New product forecasting fundamentals
2.7.2. Assumptions-based modeling and scenario analysis
2.7.3. Quantitative analysis to forecast new product demand
Section 2.8. Forecast performance
2.8.1. Forecast error
2.8.2. Impact of aggregation on forecast accuracy
2.8.3. Forecast accuracy and forecastability
Section 2.9. Impact of randomness and disruptive events in forecasts
2.9.1. Understanding randomness and disruptive events
2.9.2. Mitigating risk
Section 2.10. Improving forecasting with Lean and Six Sigma Principles
2.10.1. Forecast Value-Added Analysis
2.10.2. Applying Six Sigma methodologies to forecasting
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps students choose the right forecasting technique to understand demand trends
Covers foundational methods such as Simple Exponential Smoothing (SES) and Holt's Exponential Smoothing (HES)
Provides insights into seasonal and intermittent demand forecasting, which are practical for various industries
Leverages machine learning and Lean Six Sigma principles to enhance forecasting performance
Focuses on improving forecast accuracy, which is crucial for effective decision-making
Requires no prior knowledge of forecasting, making it accessible to beginners
Save this course
Save Forecasting Techniques for Slow and Rapidly Changing Demand to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Forecasting Techniques for Slow and Rapidly Changing Demand with these
activities:
Review: Forecasting Principles and Applications
Show steps
Provides a solid conceptual basis for forecasting techniques.
Show steps
-
Read Chapters 1 and 2 to get an overview of forecasting principles and applications.
-
Complete the practice exercises at the end of each chapter.
Solve forecasting practice problems
Show steps
Reinforces understanding of forecasting concepts through repetition and application.
Show steps
-
Find practice problems online or in textbooks.
-
Solve the problems using the techniques learned in the course.
-
Review your answers and identify areas for improvement.
Follow tutorials on forecasting with Python or R
Show steps
Provides hands-on experience with forecasting tools and techniques, enhancing practical skills.
Show steps
-
Identify online tutorials or courses on forecasting with Python or R.
-
Follow the tutorials step-by-step, practicing the code and techniques.
-
Apply the learned skills to a small forecasting project.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a forecasting industry event or webinar
Show steps
Provides exposure to industry professionals and trends, broadening perspectives.
Browse courses on
Networking
Show steps
-
Research upcoming forecasting events or webinars.
-
Register and attend the event.
-
Engage with speakers, attendees, and industry experts.
Join a study group or discussion forum on forecasting
Show steps
Facilitates peer learning, knowledge sharing, and support, enhancing understanding.
Show steps
-
Find existing study groups or discussion forums related to forecasting.
-
Join the group and actively participate in discussions.
-
Share knowledge, insights, and experiences with other participants.
Develop a forecasting model for a real-world dataset
Show steps
Applies forecasting techniques to a practical problem, improving understanding and skill.
Show steps
-
Choose a dataset that interests you and is relevant to the course.
-
Explore the data and identify any patterns or trends.
-
Select and apply appropriate forecasting methods based on the data characteristics.
-
Evaluate the accuracy of your forecasts using appropriate metrics.
Create a presentation on forecasting techniques
Show steps
Strengthens understanding by explaining concepts to others and improving communication skills.
Show steps
-
Choose a specific forecasting technique to focus on.
-
Gather relevant information and examples.
-
Create visual aids to illustrate key concepts.
-
Practice presenting your material clearly and effectively.
Participate in a forecasting workshop
Show steps
Offers intensive training and hands-on practice, accelerating skill development.
Show steps
-
Find forecasting workshops offered by professional organizations or training providers.
-
Register and attend the workshop.
-
Actively participate in discussions, exercises, and case studies.
Review: Forecasting Principles and Applications
Show steps
Provides a solid conceptual basis for forecasting techniques.
Show steps
Show steps
Show steps
- Read Chapters 1 and 2 to get an overview of forecasting principles and applications.
- Complete the practice exercises at the end of each chapter.
Solve forecasting practice problems
Show steps
Reinforces understanding of forecasting concepts through repetition and application.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Solve the problems using the techniques learned in the course.
- Review your answers and identify areas for improvement.
Follow tutorials on forecasting with Python or R
Show steps
Provides hands-on experience with forecasting tools and techniques, enhancing practical skills.
Show steps
Show steps
Show steps
- Identify online tutorials or courses on forecasting with Python or R.
- Follow the tutorials step-by-step, practicing the code and techniques.
- Apply the learned skills to a small forecasting project.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a forecasting industry event or webinar
Show steps
Provides exposure to industry professionals and trends, broadening perspectives.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research upcoming forecasting events or webinars.
- Register and attend the event.
- Engage with speakers, attendees, and industry experts.
Join a study group or discussion forum on forecasting
Show steps
Facilitates peer learning, knowledge sharing, and support, enhancing understanding.
Show steps
Show steps
Show steps
- Find existing study groups or discussion forums related to forecasting.
- Join the group and actively participate in discussions.
- Share knowledge, insights, and experiences with other participants.
Develop a forecasting model for a real-world dataset
Show steps
Applies forecasting techniques to a practical problem, improving understanding and skill.
Show steps
Show steps
Show steps
- Choose a dataset that interests you and is relevant to the course.
- Explore the data and identify any patterns or trends.
- Select and apply appropriate forecasting methods based on the data characteristics.
- Evaluate the accuracy of your forecasts using appropriate metrics.
Create a presentation on forecasting techniques
Show steps
Strengthens understanding by explaining concepts to others and improving communication skills.
Show steps
Show steps
Show steps
- Choose a specific forecasting technique to focus on.
- Gather relevant information and examples.
- Create visual aids to illustrate key concepts.
- Practice presenting your material clearly and effectively.
Participate in a forecasting workshop
Show steps
Offers intensive training and hands-on practice, accelerating skill development.
Show steps
Show steps
Show steps
- Find forecasting workshops offered by professional organizations or training providers.
- Register and attend the workshop.
- Actively participate in discussions, exercises, and case studies.
Career center
Learners who complete Forecasting Techniques for Slow and Rapidly Changing Demand will develop knowledge and skills
that may be useful to these careers:
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts use data to identify and solve problems within the supply chain. They may also develop and implement new processes to improve efficiency. This course will help Supply Chain Analysts understand how to forecast demand, which is a key part of the supply chain. By learning how to forecast demand, Supply Chain Analysts can help their companies make better decisions about inventory levels, production schedules, and transportation.
Demand Planner
Demand Planner
Demand Planners forecast customer demand for various products across their lifecycles. They use causal, judgmental, and statistical techniques to create these forecasts. This course will help you develop the skills needed to become a successful Demand Planner. You'll learn how to choose the right forecasting method for different data patterns and how to improve forecast performance with Machine Learning and Lean Six Sigma principles.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketing teams to bring new products to market. This course will help Product Managers understand how to forecast demand for new products. By learning how to forecast demand, Product Managers can help their companies make better decisions about which products to develop and how to launch them.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data about customers, markets, and competitors. They use this data to help businesses make better decisions about product development, marketing, and sales. This course will help Market Research Analysts understand how to forecast demand, which is a key part of market research. By learning how to forecast demand, Market Research Analysts can help their companies make better decisions about which products to develop, which markets to target, and how to price their products.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. They develop sales strategies, set sales goals, and motivate their teams to achieve those goals. This course will help Sales Managers understand how to forecast demand for their products and services. By learning how to forecast demand, Sales Managers can help their teams develop more effective sales strategies and achieve their goals.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. They oversee production, inventory, and customer service. This course will help Operations Managers understand how to forecast demand for their products and services. By learning how to forecast demand, Operations Managers can help their companies make better decisions about production levels, inventory levels, and customer service.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data. They use this data to make recommendations about investments, trading strategies, and other financial decisions. This course will help Quantitative Analysts understand how to forecast demand for financial products and services. By learning how to forecast demand, Quantitative Analysts can help their clients make better financial decisions.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to businesses and individuals. They analyze financial data to make recommendations about investments, loans, and other financial decisions. This course will help Financial Analysts understand how to forecast demand for financial products and services. By learning how to forecast demand, Financial Analysts can help their clients make better financial decisions.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They work in a variety of industries, including finance, healthcare, and retail. This course will help Data Scientists understand how to forecast demand for products and services. By learning how to forecast demand, Data Scientists can help their companies make better decisions about product development, marketing, and sales.
Business Analyst
Business Analyst
Business Analysts help businesses improve their operations. They analyze data to identify problems and recommend solutions. This course will help Business Analysts understand how to forecast demand for products and services. By learning how to forecast demand, Business Analysts can help their companies make better decisions about product development, marketing, and sales.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with marketing teams to create and implement marketing strategies. This course will help Marketing Managers understand how to forecast demand for their products and services. By learning how to forecast demand, Marketing Managers can help their teams develop more effective marketing campaigns.
Salesforce Administrator
Salesforce Administrator
Salesforce Administrators manage Salesforce software for their companies. They work with sales teams to customize Salesforce to meet their needs. This course may be useful for Salesforce Administrators who want to learn more about demand forecasting. By learning how to forecast demand, Salesforce Administrators can help their companies make better decisions about sales strategies and goals.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers get the most value from their products and services. They work with customers to identify their needs and develop solutions. This course may be useful for Customer Success Managers who want to learn more about demand forecasting. By learning how to forecast demand, Customer Success Managers can help their customers make better decisions about their products and services.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with teams to achieve project goals. This course may be useful for Project Managers who want to learn more about demand forecasting. By learning how to forecast demand, Project Managers can help their teams develop more realistic project plans.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with teams to create and implement software solutions. This course may be useful for Software Engineers who want to learn more about demand forecasting. By learning how to forecast demand, Software Engineers can help their teams develop more scalable software applications.
For more career information including salaries, visit:
OpenCourser.com/course/z23uqs/forecasting
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Forecasting Techniques for Slow and Rapidly Changing Demand.
Save
Provides a comprehensive overview of forecasting practices in business.
A comprehensive guide to forecasting principles and practices.
A foundational reference for time series forecasting in econometrics.
Save
Provides a good background on time series forecasting and describes a variety of forecasting methods, their assumptions and limitations. Provides context for the course and is useful as a foundational reference.
Provides a very good introduction to machine learning concepts and techniques, which is especially useful for those who are new to the field.
Focuses primarily on techniques used for forecasting intermittent demand, which is common in many industries. Good reference for Croston's method, covered in the course.
A good introduction to judgmental forecasting methods and techniques, such as Delphi and expert panels.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/z23uqs/forecasting
Share
Similar courses
Here are nine courses similar to
Forecasting Techniques for Slow and Rapidly Changing Demand.
Certified Forecaster and Demand Planner - CFDP - Certification Exam
OpenCourser.com/course/gz4den/certified
Certified Forecaster and Demand Planner - CFDP -...
Most relevant
Improving Supply Chain Performance through Demand Planning
OpenCourser.com/course/pu7cq1/improving
Improving Supply Chain Performance through Demand Planning
Most relevant
Demand Management in a Demand Driven Supply Chain
OpenCourser.com/course/jknhrv/demand
Demand Management in a Demand Driven Supply Chain
Most relevant
Conduct Sales Projections with Seasonality in Google Sheets
OpenCourser.com/course/rxd27l/conduct
Conduct Sales Projections with Seasonality in Google...
Most relevant
Marketing Analytics: Forecasting Models with Excel
OpenCourser.com/course/vrz3yb/marketing
Marketing Analytics: Forecasting Models with Excel
Most relevant
Impute Data to Forecast Demand in Google Sheets
OpenCourser.com/course/hjds4z/impute
Impute Data to Forecast Demand in Google Sheets
Most relevant
Demand Analytics
OpenCourser.com/course/t6o1hy/demand
Demand Analytics
Most relevant
Demand Planning in RStudio: Create Demand Forecast
OpenCourser.com/course/6jibim/demand
Demand Planning in RStudio: Create Demand Forecast
Most relevant
Predict Sales and Forecast Trends in Google Sheets
OpenCourser.com/course/z2uir4/predict
Predict Sales and Forecast Trends in Google Sheets
Most relevant
Effort
2 to 8 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
International Supply Chain Education Alliance
Instructor
Jorge Morales
Language
English
Transcript
English
Good to know
Helps students choose the right forecasting technique to understand demand trends
Covers foundational methods such as Simple Exponential Smoothing (SES) and Holt's Exponential Smoothing (HES)
Provides insights into seasonal and intermittent demand forecasting, which are practical for various industries
Leverages machine learning and Lean Six Sigma principles to enhance forecasting performance
Focuses on improving forecast accuracy, which is crucial for effective decision-making
Requires no prior knowledge of forecasting, making it accessible to beginners
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser