Demand Forecasting

Dmitry Ivanov, Alexander Tsipoulanidis and Jörn Schönberger
Additional contact information
Dmitry Ivanov: Berlin School of Economics and Law
Alexander Tsipoulanidis: Berlin School of Economics and Law
Jörn Schönberger: Technical University of Dresden

Chapter 11 in Global Supply Chain and Operations Management, 2019, pp 319-333 from Springer

Abstract: Abstract In this chapter, demand forecasting methods are considered. At the beginning, the role of demand forecasting in supply chain and operations management is discussed. Next, the role of expert methods in forecasting is analyzed and application of statistical methods for forecasting is demonstrated. Subsequently, it is shown how forecasts are calculated based on statistical methods, as well as how to understand and apply measures for forecast quality assessments. The chapter is enriched by an E-Supplement which provides additional Excel templates, tasks, and video streams.

Keywords: Demand Forecasting; Forecast Quality Assessment; Forecasting Methods; Double Exponential Smoothing; Autoregressive Integrated Moving Average (ARIMA) (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-319-94313-8_11

Ordering information: This item can be ordered from
http://www.springer.com/9783319943138

DOI: 10.1007/978-3-319-94313-8_11

Access Statistics for this chapter

More chapters in Springer Texts in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().