What is a Forecast?

Forecasting methods can be qualitative or quantitative:

Qualitative methods

Qualitative forecasting relies on marketing experts to make short-term predictions. You can use qualitative methods when there is insufficient historical data. For example, these are two use cases:

• Market research techniques like polls and surveys identify consumer demand.
• Delphi modeling techniques poll experts in a particular field to collect their opinions and predict trends in that field.

Quantitative methods

Quantitative forecasting models use meaningful statistics and historical data to predict long-term future trends. We give examples of standard quantitative methods below:

• Econometric modeling analyzes financial data sets, like loan and investment data, to predict significant economic shifts and their impact on the company.
• The indicator approach compares data points to identify relationships between seemingly unrelated data. For example, you can use changes in GDP to forecast unemployment rates.
• In this scenario, GDP data is called the lead indicator, and the unemployment rate is the lagging indicator.
• Time-series forecasting analyzes data collected over different intervals of time to predict future trends. 

Time-series forecasting is a data science technique that uses machine learning and other computer technologies to study past observations and predict future values of time-series data. Let’s look at some examples of time-series forecasting:

• Astronomical data consists of repetitive movements of the planets over centuries. You can use this data to predict astronomical events like eclipses and comets accurately.
• Weather forecasting uses wind and temperature patterns to predict weather changes.
• Scientists can use birth rates and migration data to predict population growth.

Time-series analysis vs. time-series forecasting

Time-series analysis explores the underlying causes in any time-series data. This field of study seeks to understand the “why” behind a time-series dataset. Analysts must often make assumptions and decompose or break down the data to extract meaningful statistics and other characteristics.

While time-series analysis is all about understanding the dataset, forecasting is all about predicting it. These are the three steps of predictive modeling:

• Ask a question and collect a sample set of time-series data that answers this question for a past time period.
• Train the computer software or forecasting algorithm using the past values.
• Use the forecasting algorithm to make future observations.

Forecasting gives businesses relevant and reliable information about both the present and the future. We describe some example use cases of forecasting technology below:

Operations – How More Retail Limited uses automation to forecast product sales?

More Retail Ltd. (MRL) is one of India’s top four grocery retailers, with several billion dollars in revenue. They have an extensive store network and a complex supply chain of distributors. They were relying on the manual judgment of store managers to estimate and order stock, but this affected customer experience, especially in the fresh produce category. MRL used forecasting services by AWS to build an automated ordering system that reduced fresh food wastage by 30%.

Manufacturing – How Foxconn uses forecasting to manage manufacturing demand?

Hon Hai Technology Group (Foxconn) is the world’s largest electronics manufacturer and solutions provider. During the COVID-19 pandemic, Foxconn faced unprecedented volatility in customer demand, supplies, and capacity. The company collaborated with the Amazon Machine Learning Solutions Lab to predict accurate net order forecasts for their factory in Mexico. These forecasts led to an annual savings of over $500,000.

Customer support – How Affordable Tours uses sales forecasting to improve customer experience?

Affordable Tours.com is one of the largest vendors of escorted tours, cruises, river cruises, and active vacations in the United States. They were struggling to allocate resources when handling customer call volumes. Some days they had too many agents, and other days they had too few, which created inconsistent customer experiences and increased missed call rates. They used Amazon Forecast to anticipate customer call volumes better and improved their missed call rate by 20%.