A demand forecasting model based on the improved Bass model for fast fashion clothing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoxi Zhou ◽  
Jianfei Meng ◽  
Guosheng Wang ◽  
Qin Xiaoxuan
Keyword(s):  
Historical Data ◽  
Consumer Preferences ◽  
Demand Forecasting ◽  
Impact Factors ◽  
Bass Model ◽  
Demand Forecast ◽  
Fast Fashion ◽  
Content Type ◽  
Sales Data ◽  
Demand Forecasting Model

PurposeThis paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.Design/methodology/approachFrom the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.FindingsThe objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.Originality/valueThe paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

scholarly journals Shipbuilding capacity optimization using shipbuilding demand forecasting model

2021 ◽  
Author(s):  
Yujiro Wada ◽  
Kunihiro Hamada ◽  
Noritaka Hirata
Keyword(s):  
Supply And Demand ◽  
Demand Forecasting ◽  
Sustainable Growth ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Shipbuilding Industry ◽  
Complex Issue ◽  
Proposed Model ◽  
Demand Fluctuations ◽  
Demand Forecasting Model

AbstractThe shipbuilding industry has been drastically affected by demand fluctuations. Currently, it faces intense global competition and a crisis because of an imbalance between supply and demand. This imbalance of supply and demand is caused by an excess of shipbuilding capacity. The Organisation for Economic Co-operation and Development has considered adjusting the shipbuilding capacity to reduce the imbalance based on the demand forecast. On the other hand, demand forecast of shipbuilding is a complex issue because the demand is influenced indirectly by adjustments in shipbuilding capacity. Therefore, it is important to examine the influence of construction capacity adjustments on the future demand of ships based on demand forecasting for the sustainable growth of the shipbuilding industry. In this study, shipbuilding capacity adjustment is considered using a proposed simulation system based on a demand-forecasting model. Additionally, the system dynamics model of a previous study is improved by developing a ship price-prediction model for evaluating the shipbuilding capacity-adjustment scenario. We conduct simulations using the proposed demand-forecasting model and system to confirm the effectiveness of the proposed model and system. Furthermore, several shipbuilding capacity-adjustment scenarios are discussed using the proposed system.

Parking Demand Forecast Method of Big City and its Application

2014 ◽  
Vol 587-589 ◽  
pp. 1753-1756
Author(s):  
Jing Fei Yu ◽  
Xiu Ling Gong ◽  
Xin Jie Zhang
Keyword(s):  
Motor Vehicle ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Urban Transport ◽  
Demand Forecast ◽  
Planning And Design ◽  
Big City ◽  
Demand Forecasting Model ◽  
Parking Demand ◽  
Parking Demand Forecast

Parking is difficult in today's social problems faced by big cities. To solve this problem, a new parking facility planning and design was required and the parking demand forecast is a very important step in this process. The paper first discusses the necessity of parking demand forecast and the development process of parking demand forecast model, then a few parking demand forecasting model were compared and analyzed, final the motor vehicle OD method was selected to forecast parking demand according to the characteristics of the parking demand forecast and urban transport planning simultaneously. The results show that the precision of prediction results is acceptable.

A hybrid method for forecasting new product sales based on fuzzy clustering and deep learning

Kybernetes ◽  
2020 ◽  
Vol 49 (12) ◽  
pp. 3099-3118
Author(s):  
Peng Yin ◽  
Guowei Dou ◽  
Xudong Lin ◽  
Liangliang Liu
Keyword(s):  
Deep Learning ◽  
Fuzzy Clustering ◽  
Demand Forecasting ◽  
New Product ◽  
Forecasting Model ◽  
Bass Model ◽  
Short Term ◽  
Content Type ◽  
Product Demand ◽  
Product Sales

Purpose The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors. Design/methodology/approach A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model. Findings The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored. Originality/value This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

2013 ◽  
Vol 433-435 ◽  
pp. 545-549
Author(s):  
Zhi Jie Song ◽  
Zan Fu ◽  
Han Wang ◽  
Gui Bin Hou
Keyword(s):  
Historical Data ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Influential Factors ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

scholarly journals Demand Forecast in Retail Assortment Optimization—Based on an Empirical Analysis of Beverage Sales

2020 ◽  
Vol 6 (1) ◽  
pp. p57
Author(s):  
Jun Chen ◽  
Xinyijing Zhang ◽  
Chenyang Zhao
Keyword(s):  
Empirical Analysis ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Revenue Maximization ◽  
Sales Volume ◽  
Demand Model ◽  
Total Revenue ◽  
Demand Forecast ◽  
Assortment Optimization ◽  
Demand Forecasting Model

This paper focus on establishing the demand forecasting model to optimize product assortments from a set of SKUs in the same category. The aim of the model is to achieve revenue maximization. Based on the attribute level, the demand model considers the consumers’ preference and the possibility of substitution between different attributes. Then it divides the product’s specific attributes and multiplies these attributes effects. Furthermore, one beverage case was applied to the demand model to do empirical analysis. Top beverage categories were selected and e-commerce sales data were collected to represent the pre-sale of whole categories. Moreover, a store named S with some beverage SKUs is assumed and applied to the model, which predicted sales volume of each existing SKU and the total revenue.

A study on behaviour of bullwhip effect in (R, S) inventory control system considering DWT-MGGP demand forecasting model

2019 ◽  
Vol 14 (2) ◽  
pp. 385-407 ◽  
Author(s):  
Sanjita Jaipuria ◽  
Siba Sankar Mahapatra
Keyword(s):  
Inventory Control ◽  
Demand Forecasting ◽  
Control Policy ◽  
Bullwhip Effect ◽  
Forecasting Model ◽  
Control Policies ◽  
Content Type ◽  
Inventory Control Policy ◽  
Smoothing Parameters ◽  
Demand Forecasting Model

Purpose The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013). Design/methodology/approach A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies. Findings A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ). Research limitations/implications This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy. Practical implications The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment. Originality/value This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

Expressing herself through brands: the Arab woman’s perspective

2015 ◽  
Vol 17 (1) ◽  
pp. 36-53 ◽  
Author(s):  
Baker Ahmad Alserhan ◽  
Daphne Halkias ◽  
Aisha Wood Boulanouar ◽  
Mumin Dayan ◽  
Omar Ahmad Alserhan
Keyword(s):  
Consumer Preferences ◽  
Arab Women ◽  
Global Marketplace ◽  
Content Type ◽  
Perceived Quality Of Care ◽  
Consumption Practices ◽  
Cultural Body ◽  
Word Of Mouth Communication ◽  
Beauty Care

Purpose – This paper aims to extend Wallström et al.’s (2010) six-nation study on brand use and notions of self-expression to Arab women in the UAE. Additionally, it extends the scope of investigation to include an extensive qualitative data corpus to inform and explain the consumption practices of this large, very wealthy and under-researched sector of the global marketplace. Design/methodology/approach – The paper uses mixed methodology emphasizing qualitative research as a means of building on the results of Wallström et al.’s (2010) quantitative study. Findings – Results reveal that Arab women are less committed to the idea that beauty care products are a locus of self-expression, and their purchase choices are based on perceived quality of care products, scene of use and their lack of value in the culture as vehicles of conspicuous consumption cues. Originality/value – The paper offers valuable insights to researchers and practitioners into the use of beauty care products as a means of self-expression, and emphasizes the value of word-of-mouth communication in enhancing reach in this category. The authors recommend the investigation of relationships between expressing self through brands and variables revealed in this study such as respondents’ relationships to religiosity and health concerns. An extension of this research is also recommended to produce a cross-cultural body of literature on women’s self-expression through brands and how the variable of self-expression can be an important driver of consumer preferences and choices in this population.

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