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Lecture

Demand Management: Forecasting Methods

In course

ME-419: Production management

Production management deals with producing goods sustainably at the right time, quantity, and quality with the minimum cost. This course equips students with practical skills and tools for effectively managing demand, supply, and implementing advanced anal ...

Description

This lecture covers the demand management and forecasting methods in production management. It includes topics such as demand disruptions, forecasting steps, qualitative and quantitative methods, comparing causal models, time series analysis, and trend forecasting. The instructor discusses the importance of accurate demand forecasting for effective production planning and decision-making.

Instructor

Amin Kaboli

Dr. Amin Kaboli is an external lecturer for at the Mechanical Engineering Section at EPFL and teaches Production Management (ME-419), Continuous Improvement (ME-498) to graduate students and Digital Supply Chain, Innovation and Digitization, and Sustainable Change Leadership to business executives and managers at International Institute for the Management of Logistics and Supply Chain (IML). Amin supports many organizations ranging from Fortune 500 corporations to entrepreneurial firms and industries as diverse as Watch, Consumer Goods, Food and Beverage, MedTech, and Pharma. He obtained his Ph.D. in Manufacturing Systems & Robotics, specialized in Supply Chain Management from EPFL. You can watch his TEDx talk on “Leading Change Consciously” here. TEDx talk here.

Official source

Ontological neighbourhood

Statistics

Data analysis: Time series

Related concepts (32)

Forecasting

Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and resolution itself.

Time series

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. A time series is very frequently plotted via a run chart (which is a temporal line chart).

Demand forecasting

Demand forecasting refers to the process of predicting the quantity of goods and services that will be demanded by consumers at a future point in time. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods.

Economic forecasting

Economic forecasting is the process of making predictions about the economy. Forecasts can be carried out at a high level of aggregation—for example for GDP, inflation, unemployment or the fiscal deficit—or at a more disaggregated level, for specific sectors of the economy or even specific firms. Economic forecasting is a measure to find out the future prosperity of a pattern of investment and is the key activity in economic analysis.

Weather forecasting

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere, land, and ocean and using meteorology to project how the atmosphere will change at a given place.

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Explores model choice, prediction, and forecasting techniques in time series analysis.

Time Series

Explores Time Series, covering model specification, diagnostics, and forecasting methods.

Demand Forecasting Methods

Explores demand forecasting steps and the Holt-Winter model for time series analysis.

Demand Forecasting: Model Initiation

Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.

Time Series: Forecasting and Long Memory