Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS

Hong, Jun; Koo, Hoon-Young; Kim, Tae-Gu

doi:10.1016/j.ejor.2015.07.034

Cited by 14 publications

(4 citation statements)

References 39 publications

(42 reference statements)

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“…The Lotka-Volterra model, abbreviated as the LV model, is a differential dynamic equation proposed by Lotka and Volterra [3,4]; it was initially applied to the dynamic competition among populations in the ecosystem. There are many models applied in describing the spread of species, for example, the Gompertz model [5], the logistic model [6,7], and the Bass model [8] were used to describe the spread of a species; these above models only describe the dynamic changes in a single species in a single population, ignoring information exchange between species. The LV model has been extended to many cases in recent years, such as market competition [9,10], global dynamics of a class of n-dimensional Lotka Volterra systems [11], the bank system [12], and energy and environment [13], for the model focussing on the interactions generated in an environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of the Dynamic Trade Relationship between China and the United States Using the Quantile Grey Lotka–Volterra Model

Wang,

Li,

Gao

2024

Fractal Fract

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The quantile regression technique is introduced into the Lotka–Volterra ecosystem analysis framework. The quantile grey Lotka–Volterra model is established to reveal the dynamic trade relationship between China and the United States. An optimisation model is constructed to solve optimum quantile parameters. The empirical results show that the quantile grey Lotka–Volterra model shows higher fitting accuracy and reveals the trade relationships at different quantiles based on quarterly data on China–US trade from 1999 to 2019. The long-term China–US trade relationship presents a prominent predator–prey relationship because exports from China to the US inhibited China’s imports from the United States. Moreover, we divide samples into five stages according to four key events, China’s accession to the WTO, the 2008 global financial crisis, the weak global economic recovery in 2015, and the 2018 China–US trade war, recognising various characteristics at different stages.

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Section: Literature Reviewmentioning

confidence: 99%

Identification of the Dynamic Trade Relationship between China and the United States Using the Quantile Grey Lotka–Volterra Model

Wang,

Li,

Gao

2024

Fractal Fract

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“…Furthermore, managers look for ease of use and reliability of the forecasting results; the Bass model is highly recommended due to its thriftiness, ease of interpretation and explanatory power (Hong et al. , 2016).…”

Section: Introductionmentioning

confidence: 99%

Scenarios for optimizing timing for new product exits: a trifecta of models' predictive performances

Sharma

Lichtenthal

2022

BIJ

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PurposeThe purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).Design/methodology/approachThe study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.FindingsWhile higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.Originality/valueThe study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

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“…For accurate demand forecasting using the Bass diffusion model, reliable parameter estimates are necessary. On the estimation of the three key parameters in the Bass Model, refer to Boswijk and Franses [10], and Hong et al [11]. Recently, Hong et al [11] presented the new estimation method, which combines non-linear least square (NLS) and ordinary least square (OLS).…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the estimation of the three key parameters in the Bass Model, refer to Boswijk and Franses [10], and Hong et al [11]. Recently, Hong et al [11] presented the new estimation method, which combines non-linear least square (NLS) and ordinary least square (OLS). However, estimating the key parameters for new products before the product launch is not that simple.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an Adaptive Forecasting System: A Case Study of a PC Manufacturer in South Korea

Jung

Lim

2016

Sustainability

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Abstract:We present a case study of the development of an adaptive forecasting system for a leading personal computer (PC) manufacturer in South Korea. It is widely accepted that demand forecasting for products with short product life cycles (PLCs) is difficult, and the PLC of a PC is generally very short. The firm has various types of products, and the volatile demand patterns differ by product. Moreover, we found that different departments have different requirements when it comes to the accuracy, point-of-time and range of the forecasts. We divide the demand forecasting process into three stages depending on the requirements and purposes. The systematic forecasting process is then introduced to improve the accuracy of demand forecasting and to meet the department-specific requirements. Moreover, a newly devised short-term forecasting method is presented, which utilizes the long-term forecasting results of the preceding stages. We evaluate our systematic forecasting methods based on actual sales data from the PC manufacturer, where our forecasting methods have been implemented.

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Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS

Cited by 14 publications

References 39 publications

Identification of the Dynamic Trade Relationship between China and the United States Using the Quantile Grey Lotka–Volterra Model

Identification of the Dynamic Trade Relationship between China and the United States Using the Quantile Grey Lotka–Volterra Model

Scenarios for optimizing timing for new product exits: a trifecta of models' predictive performances

Development of an Adaptive Forecasting System: A Case Study of a PC Manufacturer in South Korea

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