Lecture

Demand Management: Forecasting Methods

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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 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 (31)

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.

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).

Technology forecasting

Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Researchers create technology forecasts based on past experience and current technological developments. Like other forecasts, technology forecasting can be helpful for both public and private organizations to make smart decisions. By analyzing future opportunities and threats, the forecaster can improve decisions in order to achieve maximum benefits.

Production planning

Production planning is the planning of production and manufacturing modules in a company or industry. It utilizes the resource allocation of activities of employees, materials and production capacity, in order to serve different customers. Different types of production methods, such as single item manufacturing, batch production, mass production, continuous production etc. have their own type of production planning. Production planning can be combined with production control into production planning and control, or it can be combined with enterprise resource planning.

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.

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