Demand Forecasting

Demand Forecasting

In this lesson, you’re expected to:
– understand how demand forecasting is used in operations
– learn about commonly used forecasting methods
– explore the demand forecasting process

Demand forecasting is the basis for operations planning decisions

Forecasting is a process to predict future events that forms the basis for several operations planning decisions, such as those related to production, inventory, human resources, and capacity. Its use is applicable to every type of business, such as retail, manufacturing, services etc.

Some examples of the use of demand forecasting:
– Governments need to forecast the number of children in a region order to build or expand schools.
– A book retailer needs to forecast the number of books sold so that they can plan their inventory levels.
– A management consulting firm has to forecast the number of projects in order to allocate or hire staff.

Some decisions only need short-term forecasts, such as deciding how many newspapers should be ordered the next day or next week. On the other hand, capacity planning, such as building a new plant, should consider long-term forecasts.

Mature products, such as salt or milk, are usually more predictable and easier to forecast; however, some products, such as fashion goods, are unpredictable and difficult to forecast.

The essence of demand forecasting is analyzing its driving factors in the context of a specific product and market

For example, to forecast the number of children joining school within five years, one can think about the current birth rate, assuming 5 years old as the school-entering age, as shown in Figure A.

However, we might be missing important factors using such an approach. An improved model (Figure B) could include additional information such as child mortality rate and migratory movements, which can be influenced by economic prosperity and other factors.

Forecasts are always wrong and the uncertainty level varies according to the level of aggregation and time frame

1) The time frame of the forecast

Long-term forecasts are usually less accurate than short-term forecasts, i.e. it is easier to predict how many units are likely to be sold within the next hour than within the next decade.

2) The level of aggregation

Aggregated forecasts are usually more accurate than disaggregated forecasts. This is because, in aggregated forecasts, the errors are sometimes canceled out.
As you can seen in the table below, the absolute total aggregated forecast error (2%) is less than the forecast error of any isolated product.
[Optional] Demand Forecasting – Objectives, Classification and Characteristics of a Good Forecast
Demand Forecast Methods
Approaches to Forecasting

A combination of qualitative and quantitative forecast methods can be used by bringing together expert judgement and predictive models.

There are two main approaches to forecasting:
– Qualitative methods: based on opinions, past experience, and even best guesses.
– Quantitative methods: based on data.

It is important to note that no method will result in an accurate forecast.

Overview of Forecasting Techniques
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Examples of Qualitative Methods used in Demand Forecasting:

1) Panel Approach
2) Delphi Method
3) Scenario Planning

Overview of Qualitative Methods
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Examples of Quantitative Methods used in Demand Forecasting:

1) Time Series methods
2) Causal methods

1) Time Series methods

Time series examines the pattern of past behavior of demand, taking its level, trend, and seasonality into consideration in order to forecast future demand.

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2) Causal methods

Causal methods evaluate complex cause-effect relationships between key variables in order to devise the demand forecast.

The example below is of a regression analysis showing the relationship between demand and the previous week’s temperature.
Source: Slack et al. (2010)

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[Optional] Time Series Forecasting 
Watch this 18-minute video of Time Series Forecasting using Excel by Jalayer Academy:
The forecast process
Overview of a general process of demand forecasting
[Optional] Future of Supply Chain Management
Watch this 3-minute video featuring Dr. Larry Lapide of MIT talking about forecasts (scenarios) for the field of supply chain management:
Jim Rohn Sứ mệnh khởi nghiệp