Lecture

Demand Forecasting: Methods & Cognitive Biases

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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 demand forecasting methods such as qualitative and quantitative approaches, including causal models, time series analysis, and cognitive biases that can affect decision-making. It explores steps like goal determination, data analysis, and forecasting techniques. The instructor discusses the importance of considering various factors in forecasting and the potential pitfalls of cognitive biases in decision-making processes.

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

Related concepts (32)

Rubin causal model

The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis, though he discussed it only in the context of completely randomized experiments.

Causal inference

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

Singular spectrum analysis

In time series analysis, singular spectrum analysis (SSA) is a nonparametric spectral estimation method. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. Its roots lie in the classical Karhunen (1946)–Loève (1945, 1978) spectral decomposition of time series and random fields and in the Mañé (1981)–Takens (1981) embedding theorem. SSA can be an aid in the decomposition of time series into a sum of components, each having a meaningful interpretation.

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.

Cognitive bias mitigation

Cognitive bias mitigation is the prevention and reduction of the negative effects of cognitive biases – unconscious, automatic influences on human judgment and decision making that reliably produce reasoning errors. Coherent, comprehensive theories of cognitive bias mitigation are lacking. This article describes debiasing tools, methods, proposals and other initiatives, in academic and professional disciplines concerned with the efficacy of human reasoning, associated with the concept of cognitive bias mitigation; most address mitigation tacitly rather than explicitly.

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