Paper #1 - Product age based demand forecast model for fashion retail
Key Notes
- 300 stores, 35k items and around 40 categories.
- Accurate demand forecast 6-12 months in advance
- Age-based prediction model
- Time series models are based on forecasts obtained from previous year’s sales of similar items
- Clustering, classification and prediction
- Determine the appropriate cluster for a new fashion item
- Attributes such as Sleeve length, Color, Pattern, Fastening type and Neck shape
- color and sleeve length are some of the crucial attributes for demand forecasting
- The average selling price at which people buy Dresses is 25$ and is 36$ for Kids wear
Paper #2 - Demand Forecasting in the Presence of Systematic Events: Cases in Capturing Sales Promotions
Key Notes
- Demand uplift by analyzing historical sales data and different combinations of promotions
- Promotion frequency and magnitude of demand uplift
- Promotions, holidays and special events
- contextual information include: changes in promotional plans, competitor activities, market intelligence, sudden climate changes and dynamic influencers
- Type of promotion (e.g., single-buy, buy one get one free, multi-buy)
- Advertisement type (e.g., in-store, online, catalogue)
- Baseline + Uplift and Predicted value
- Major and minor promotions are advertised in retailers’ weekly catalogues and are typically associated with discounts of approximately 50% and 30% off regular price, respectively
- Single buy, Multiple buy transactions per week
Paper #3 - Elasticity Based Demand Forecasting and Price Optimization for Online Retail
Key Notes
- Price elasticity demand value
- Relative change of demand and retail price in percentage, Compute it and add it
- Data Pre-processing module integrates data aggregation, missing data processing, data transformation, data
- normalization and outlier detection, Additional binary features: is_holiday and is_weekend, is_festiveweekednd, is_festiveweekday
- Optimal pricing formulation
Forecasting: theory and practice
Notes from Supply chain section
- Forecasting has always been at the forefront of decision making and planning
- A supply chain is ‘a network of stakeholders (e.g., retailers, manufacturers, suppliers) who collaborate to satisfy customer demand’
- Sales and Operations Planning (S&OP)
- The ‘bullwhip effect’ occurs whenever there is amplification of demand variability through the supply chain (Lee et al., 2004), leading to excess inventories
- Zero sales due to stock-outs or low demand occur very often at the SKU × store level, both at weekly and daily granularity
- Product level (PL) information consists of the time series of sales and returns, alongside
- information on the time each product spends with a customer
- Average custom spend per month
- Average sales per month per brand per category
- Unsold inventory count
More Reads
- An Experimental Review on Deep Learning Architectures for Time Series Forecasting*
- Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
- Retail forecasting: research and practice
- CHALLENGES AND APPROACHES TO TIME-SERIES FORECASTING IN DATA CENTER TELEMETRY: A SURVEY
- FEW-SHOT LEARNING FOR TIME-SERIES FORECASTING
- Forecasting: theory and practice
- The challenges and realities of retailing in a COVID-19 world: Identifying trending and Vital During Crisis keywords during Covid-19 using Machine Learning
- Learnings from Kaggle’s Forecasting Competitions
- Towards forecast techniques for business analysts of large commercial data sets using matrix factorization methods
- Methods for Intermittent Demand Forecasting
Keep Exploring!!!
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