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Lifecycle forecast for consumer technology products with limited sales data

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  • Li, Xishu
  • Yin, Ying
  • Manrique, David Vergara
  • Bäck, Thomas

Abstract

Early lifecycle demand forecast is critical to consumer technology products with a fast innovation speed, as firms which compete on these products focus on timely responding to market changes through new product development and efficient product diffusion, rather than sustaining product sales. The challenge for obtaining an accurate long-range forecast is that sales volumes at the early lifecycle stages are small, which limits the forecast accuracy. We propose a two-step lifecycle forecast approach for consumer technology products with limited sales data. First, we segment products based on market and clustering. Second, we apply the Bass model to aggregated products in a group using the average periodic sales of all products in the group and then use the forecast for related new products. We validate our approach using a dataset collected from Philips Netherlands, which contains consumer healthcare products sold in US and China over an 8-year timespan. The results suggest that for forecasting the lifecycle of a new product, models based on aggregated products generally perform better than models based on an individual product. It highlights the value of data aggregation in product lifecycle forecasts. Clustering is also useful for improving the forecast accuracy: when aggregation is done using sufficient product sales data, the aggregated model based on products with which the new product has the most sales pattern similarities could provide a more accurate forecast than other aggregated models. Based on our results, we provide a practical guideline to firms for obtaining an accurate early product lifecycle forecast.

Suggested Citation

  • Li, Xishu & Yin, Ying & Manrique, David Vergara & Bäck, Thomas, 2021. "Lifecycle forecast for consumer technology products with limited sales data," International Journal of Production Economics, Elsevier, vol. 239(C).
  • Handle: RePEc:eee:proeco:v:239:y:2021:i:c:s0925527321001821
    DOI: 10.1016/j.ijpe.2021.108206
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    as
    1. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    2. Qin, Ruwen & Nembhard, David A., 2012. "Demand modeling of stochastic product diffusion over the life cycle," International Journal of Production Economics, Elsevier, vol. 137(2), pages 201-210.
    3. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.
    4. Pal, Shilpi & Mahapatra, G.S. & Samanta, G.P., 2014. "An EPQ model of ramp type demand with Weibull deterioration under inflation and finite horizon in crisp and fuzzy environment," International Journal of Production Economics, Elsevier, vol. 156(C), pages 159-166.
    5. Tseng, Fang-Mei & Lin, Ya-Ti & Yang, Shen-Chi, 2012. "Combining conjoint analysis, scenario analysis, the Delphi method, and the innovation diffusion model to analyze the development of innovative products in Taiwan's TV market," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1462-1473.
    6. Gaimon, Cheryl & Singhal, Vinod, 1992. "Flexibility and the choice of manufacturing facilities under short product life cycles," European Journal of Operational Research, Elsevier, vol. 60(2), pages 211-223, July.
    7. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    8. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    9. Matthew G. Nagler, 2011. "Negative Externalities, Competition And Consumer Choice," Journal of Industrial Economics, Wiley Blackwell, vol. 59(3), pages 396-421, September.
    10. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    11. Seol, Hyeonju & Park, Gwangman & Lee, Hakyeon & Yoon, Byungun, 2012. "Demand forecasting for new media services with consideration of competitive relationships using the competitive Bass model and the theory of the niche," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1217-1228.
    12. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    13. Sushil Punia & Konstantinos Nikolopoulos & Surya Prakash Singh & Jitendra K. Madaan & Konstantia Litsiou, 2020. "Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4964-4979, July.
    14. Dev, Navin K. & Shankar, Ravi & Swami, Sanjeev, 2020. "Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system," International Journal of Production Economics, Elsevier, vol. 223(C).
    15. Guo, Xuezhen, 2014. "A novel Bass-type model for product life cycle quantification using aggregate market data," International Journal of Production Economics, Elsevier, vol. 158(C), pages 208-216.
    16. Aditya Jain & Nils Rudi & Tong Wang, 2015. "Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need," Operations Research, INFORMS, vol. 63(1), pages 134-150, February.
    17. Daeseong An & Seonggoo Ji & Ihsan Ullah Jan, 2021. "Investigating the Determinants and Barriers of Purchase Intention of Innovative New Products," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    18. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    19. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    20. Antti Saaksvuori & Anselmi Immonen, 2008. "Product Lifecycle Management," Springer Books, Springer, number 978-3-540-78172-1, December.
    21. Minhi Hahn & Sehoon Park & Lakshman Krishnamurthi & Andris A. Zoltners, 1994. "Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model," Marketing Science, INFORMS, vol. 13(3), pages 224-247.
    22. Kejia Hu & Jason Acimovic & Francisco Erize & Douglas J. Thomas & Jan A. Van Mieghem, 2019. "Forecasting New Product Life Cycle Curves: Practical Approach and Empirical Analysis," Service Science, INFORMS, vol. 21(1), pages 66-85, January.
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