Case Teaching of E-commerce Fresh Product Demand Forecasting in Logistics System Forecasting

Zhang, Mengya; Zhou, Zhen; Liu, Zhiping; Dai, Jinshan

doi:10.1007/978-3-031-04812-8_37

Cited by 1 publication

(1 citation statement)

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“…The internal strategies and plans adopted by business industries, sociological factors (education level, rural-urban habitation, family income, demographic conditions), psycho-logical factors (learning skills, attitudes, habits, perceptions), and competitive factors (quality improvement, advertisement, technical competence) create impact on the revenue and sales prediction. [11] Future customers' demands can be predicted based on historical information and data over certain time-period. Proper consumer demand prediction is an essential process for po-tential market growth plans, future sales, revenues, decision-making, and strategies.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analysis for Revenue and Sales Prediction using Support Vector Regression with Auto-regressive Integrated Moving Average

Sharma,

Patil

2023

SMSJ

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In e-commerce industry, customers’ demands get fluctuated throughout the year depending on the purchasing behavior and season. It may be a repetition period in the year, where sales may generally be down, moderate, and whilst some periods are extremely high. Studies reveal that machine learning techniques boosted much e-commerce industry, from supply chain management to business planning. In this paper, a hybrid big data analytical model which integrates Support Vector Regression (SVR) with Auto-Regressive Integrated Mov- ing Average (ARIMA) is proposed to predict product sales and revenues. The simulation results show that the proposed model presents lower relative error rate and higher accuracy that can be utilized for business planning and strategies.

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Section: Introductionmentioning

confidence: 99%