Machine Learning for New Product Forecasting

Mohsen Hamoudia () and Lawrence Vanston ()
Additional contact information
Mohsen Hamoudia: France Telecom Group (Orange) and PREDICONSULT
Lawrence Vanston: TFI

Chapter Chapter 4 in Forecasting with Artificial Intelligence, 2023, pp 77-104 from Palgrave Macmillan

Abstract: Abstract Forecasting the demand for new products is crucial given the level of investment required for a launch. It is also challenging and risky in an environment of vigorous economic competition, evolving customer expectations, and the emergence of new technologies and innovations. Given the high failure rate of new launches (70–80 percent for consumer-packaged goods), the accuracy of demand forecasts is a top priority for decision-makers. Underpredicting demand leads to a loss of potential salesSales; overpredictingOverpredicting it leads to costly excess inventory. Forecasting new product demand has traditionally been done using a variety of techniques: judgmental methods, market researchMarket research like surveys of buyers’ intentions, marketMarket testing, expert opinion methods like the Delphi method, diffusion models like the Bass model, and statistical modelingStatistical modeling through a variety of time seriesTime series and/or multivariate techniquesMultivariate techniques. More recently, machine learning has been added to the mix. The selection depends somewhat on whether the new product is: (a) new to the world, (b) new to the firm, (c) an addition to existing product lines, or (d) an improvement or revision to existing products. Machine learning is a good candidate when we have lots of data, including the salesSales history, on existing products that are similar to the new one. Although humans use this approach too, the idea is that machine learning should be able to do it faster and more accurately. Many papers and case studies are available on using machine learning to forecast existing products with historical data. However, when it comes to new products with little or no history, the literature is very limited. In this chapter, we will review the main techniques for predictingPredicting new product demand, focusing on machine learning. We also review four recent case studies that confirm that machine learning can improve accuracy of demand forecasts for new products.

Keywords: Forecasting; Machine Learning; New Products; Diffusion Models; Analogs (search for similar items in EconPapers)
Date: 2023
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:pal:paiecp:978-3-031-35879-1_4

Ordering information: This item can be ordered from
http://www.palgrave.com/9783031358791

DOI: 10.1007/978-3-031-35879-1_4

Access Statistics for this chapter

More chapters in Palgrave Advances in Economics of Innovation and Technology from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().