Product age based demand forecast model for fashion retail
- URL: http://arxiv.org/abs/2007.05278v1
- Date: Fri, 10 Jul 2020 09:44:59 GMT
- Title: Product age based demand forecast model for fashion retail
- Authors: Rajesh Kumar Vashishtha, Vibhati Burman, Rajan Kumar, Srividhya
Sethuraman, Abhinaya R Sekar, Sharadha Ramanan
- Abstract summary: Fashion retailers require accurate demand forecasts for the next season, almost a year in advance.
We present a novel product age based forecast model, where product age refers to the number of weeks since its launch.
We find a revenue uplift of 41% from our framework in comparison to the retailer's plan.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fashion retailers require accurate demand forecasts for the next season,
almost a year in advance, for demand management and supply chain planning
purposes. Accurate forecasts are important to ensure retailers' profitability
and to reduce environmental damage caused by disposal of unsold inventory. It
is challenging because most products are new in a season and have short life
cycles, huge sales variations and long lead-times. In this paper, we present a
novel product age based forecast model, where product age refers to the number
of weeks since its launch, and show that it outperforms existing models. We
demonstrate the robust performance of the approach through real world use case
of a multinational fashion retailer having over 300 stores, 35k items and
around 40 categories. The main contributions of this work include unique and
significant feature engineering for product attribute values, accurate demand
forecast 6-12 months in advance and extending our approach to recommend product
launch time for the next season. We use our fashion assortment optimization
model to produce list and quantity of items to be listed in a store for the
next season that maximizes total revenue and satisfies business constraints. We
found a revenue uplift of 41% from our framework in comparison to the
retailer's plan. We also compare our forecast results with the current methods
and show that it outperforms existing models. Our framework leads to better
ordering, inventory planning, assortment planning and overall increase in
profit for the retailer's supply chain.
Related papers
- F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm.
By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases.
Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
arXiv Detail & Related papers (2024-06-23T21:28:50Z) - Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking [59.87055275344965]
Job-SDF is a dataset designed to train and benchmark job-skill demand forecasting models.
Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023.
Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels.
arXiv Detail & Related papers (2024-06-17T07:22:51Z) - Making forecasting self-learning and adaptive -- Pilot forecasting rack [0.0]
This paper presents our findings based on a proactive pilot exercise to explore ways to help retailers to improve forecast accuracy for such product categories.
We evaluated opportunities for algorithmic interventions to improve forecast accuracy based on a sample product category, Knitwear.
Our outcomes show an increase in the accuracy of demand forecast for Knitwear product category by 20%, taking the overall accuracy to 80%.
arXiv Detail & Related papers (2023-06-12T03:26:11Z) - Improved Sales Forecasting using Trend and Seasonality Decomposition
with LightGBM [9.788039182463768]
We propose a new measure to indicate the unique impacts of the trend and seasonality components on a time series.
Our experiments show that the proposed strategy can achieve improved accuracy.
arXiv Detail & Related papers (2023-05-26T18:49:42Z) - Multimodal Neural Network For Demand Forecasting [0.8602553195689513]
We propose a multi-modal sales forecasting network that combines real-life events from news articles with traditional data such as historical sales and holiday information.
We show statistically significant improvements in the SMAPE error metric with an average improvement of 7.37% against the existing state-of-the-art sales forecasting techniques.
arXiv Detail & Related papers (2022-10-20T18:06:36Z) - Approaching sales forecasting using recurrent neural networks and
transformers [57.43518732385863]
We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques.
Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort.
The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
arXiv Detail & Related papers (2022-04-16T12:03:52Z) - Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies.
Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times.
This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
arXiv Detail & Related papers (2022-02-26T02:07:25Z) - Forecasting sales with Bayesian networks: a case study of a supermarket
product in the presence of promotions [0.0]
We develop a BN model to forecast promotional sales where a combination of factors such as price, type of promotions, and product location impacts sales.
This paper confirms that BNs can be effectively used to forecast sales, especially during promotions.
arXiv Detail & Related papers (2021-12-16T08:52:22Z) - PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction.
We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines.
As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
arXiv Detail & Related papers (2021-05-04T15:23:59Z) - Predicting seasonal influenza using supermarket retail records [59.18952050885709]
We consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets.
We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence.
arXiv Detail & Related papers (2020-12-08T16:30:43Z) - Improving Sales Forecasting Accuracy: A Tensor Factorization Approach
with Demand Awareness [1.8282018606246824]
We propose a novel approach called the Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS)
ATLAS achieves accurate and individualized prediction for sales by building a single tensor-factorization model across multiple stores and products.
The advantages of ATLAS are demonstrated on eight product category datasets collected by the Information Resource, Inc.
arXiv Detail & Related papers (2020-11-06T16:04:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.