Lifecycle forecast for consumer technology products with limited sales data

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

doi:10.1016/j.ijpe.2021.108206

Cited by 11 publications

(5 citation statements)

References 46 publications

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“…According to Hamzah et al (2021), the ARIMA method is more accurate and can be used to predict chicken-based food products on weekly sales data. Other than that, Li et al (2021) develop research using a two-step product lifecycle forecast approach for consumer technology products with inadequate sales data. It is found that to predict the lifecycle of a new product, models based on aggregated products usually outperform the models based on an individual product.…”

Section: Comparison Of Actual and Intervention Forecasting Methodsmentioning

confidence: 99%

“…Also, the aggregate forecast accuracy is usually more than the disaggregate forecast accuracy since it has a smaller coefficient of variation (Athanasopoulos, Hyndman, Kourentzes, & Petropoulos, 2017). According to Li, Yin, Manrique, and Bäck (2021), when aggregation is achieved using adequate product sales data, the aggregated model could give a better forecast than other aggregated models. Wolters and Huchzermeier (2021) mentioned that no forecasting method can accurately predict the mixture of seasonal sales variations and promotion-stimulated sales heights over forecasting horizons of many weeks or months.…”

Section: (Neisyafitri and Ongkunaruk)mentioning

confidence: 99%

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The Use of Intervention Approach in Individual and Aggregate Forecasting Methods for Burger Patties: A Case in Indonesia

Neisyafitri

Ongkunaruk

2022

J AGRIB RURAL DEV RES

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The Indonesian beef consumption increases sharply during Ramadan and made a difference between supply and demand. The research aimed to study the demand pattern of burger patties and determine a suitable forecasting method compared between quantitative and intervention forecasting methods. The actual demand was intervened by experts based on reasons such as supply shortage, holidays, promotion, and government projects. The daily sales of burger patties were collected for a year. Then, the data were divided into training and testing data. Later, time-series forecasting was performed by software. Then, the best forecasting method for daily data was selected between Individual forecasting and Top-Down forecasting. Similarly, for weekly data, the best forecasting method was compared between aggregate forecasting and Bottom-Up forecasting. Then, repeat the process for the intervened sales data. The result revealed that the mean absolute percentage error was improved after intervention by about 3.64%-58.83%. The combination of quantitative and qualitative approaches improved forecast accuracy. In addition, the aggregate level or weekly sales forecast had higher forecast accuracy than the disaggregated level. The Bottom-Up forecast performs better than the aggregate forecast. Hence, we recommended the company plans based on weekly data and implement Every Low Price to reduce the demand fluctuation.

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Section: Comparison Of Actual and Intervention Forecasting Methodsmentioning

confidence: 99%

Section: (Neisyafitri and Ongkunaruk)mentioning

confidence: 99%

The Use of Intervention Approach in Individual and Aggregate Forecasting Methods for Burger Patties: A Case in Indonesia

Neisyafitri

Ongkunaruk

2022

J AGRIB RURAL DEV RES

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“…Malthouse and Derenthal [12] proposed aggregated scoring models by developing averaging predictions to target the right customers. Li et al [13] proposed a lifecycle forecast approach to predict product demand in each period. Kumar et al [14] conducted their study to investigate the contributions of the promotion marketing strategy to customers demand using fuzzy neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Marketing Resource Allocation with Two-Stage Decisions

Zhang

Liao

et al. 2022

JTAER

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In the precision marketing of a new product, it is a challenge to allocate limited resources to the target customer groups with different characteristics. We presented a framework using the distance-based algorithm, K-nearest neighbors, and support vector machine to capture customers’ preferences toward promotion channels. Additionally, online learning programming was combined with machine learning strategies to fit a dynamic environment, evaluating its performance through a parsimonious model of minimum regret. A resource optimization model was proposed using classification results as input. In particular, we collected data from an institution that provides financial credit products to capital-constrained small businesses. Our sample contained 525,919 customers who will be introduced to a new product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected borrowers when KNN was performed with an optimal resource allocation strategy. Our results also show that KNN is the most stable method to perform classification and that the distance-based algorithm has the most efficient adoption with online learning.

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“…However, there are still many fields, which cannot obtain a large amount of data due to factors, such as high experiment cost and long test cycle. Therefore, it is difficult to apply advanced deep learning algorithm to solve problems [1], such as voice print recognition [2] in multimedia field, disease diagnosis [3] [4] and water analysis [5] in biological and medical field, product sales prediction [6] in the economic field and life prediction of fuel cells [7] in the industrial and military fields. In the process of using machine learning to deal with the above problems, there are problems such as small data volume and data imbalance, etc.…”

Section: Introductionmentioning

confidence: 99%