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Warehouse Optimization based on Machine Learning

Fayrix Machine Learning solution for efficient warehouse management, minimising risk of product unavailability and costs or warehouse ownwership
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Warehouse management based on individual approach to corporate data using Data Science can significantly increase inventory turnover rate, decrease surplus inventory and "emergency" orders, improve customer service and eventually overall customer satisfaction. Forecasting based on conventional warehouse management strategies does not bring accurate results. Analysts often have to adjust their results manually depending on their knowledge and experience. Modern technologies and accumulated knowledge in predictive analytics enable to process a wide range of corporate data better and more effectively and make forecasts customized for business needs of a particular company and for a specific industry.
RISKS OF POOR
Warehouse Management

SOURCE DATA
Key to proper warehouse management is an advanced Machine Learning solution, based on rich internal and external business data
Product type
Similar and interchangeable articles
Affiliate warehouses
Historical data on sales and write-offs
Historical data on reasons for write-offs
Products cost
Inventory shipment time
Inventory shortage cases
Production plan
Parameters to forecast demand in warehouse articles are selected automatically based on unique corporate data. Preferably, historical data for 3 preceeding years should be analysed and used as a training data set for the Machine Learning model in order to identify seasonality and make an accurate forecast.
Warehouse optimization
project milestones
1
Data analysis
Sales analysis, write-offs analysis, delivery routes analysis, seasonal factor and trend identification, shortage cases analysis, illiquid stock identification, identification of data correlation patterns, abnormal cases detection
2
Predictive model development
Forecasting demand, shipment time, insurance supply level and other probability-related factors which have significant impact on warehousing costs
3
Recommendation model development
Development of optimization model which minimizes warehousing costs by recommending the necessary value of orders and appropriate suppliers
4
Implementation
UI development and/or integration with related systems for sending modeling results

Problems
SOLVED

As a result of integrating a Machine Learning based model, you get automated recommendations for solving the most vital problems in warehouse management, including:
What articles should be ordered and how many?
What's the optimal time to order
What's the optimal amount of insurance inventory?
What's the overall cost of warehouse management?
Benefits of Machine Learning in predictive
MODEL DEVELOPMENT

Conventional approach and
MACHINE LEARNING

Machine Learning in inventory ordering decreases warehousing costs by 10% compared to conventional methods.
Machine Learning used for warehouse management helps to deeply and accurately calculate optimal parameters for each article eliminating human error factors. This results in increased accuracy of forecasting, decreased the risk of goods unavailability, increased volume of met demand and client satisfaction. In addition Machine Learning algorithms allow to achieve the optimal balance between minimal warehouse costs (release of assets), minimised losses due to some articles unavailability and minimised ordering costs due to bulk purchase discounts.
Conventional warehouse management
  • The formula to estimate an order point and time is created by a human
  • The formula is based on corporate data of companies, started their business back in the 20th century
  • The formula structure is rigid and does not take into account the client's corporate specifics and does not change when the relevant market experiences changes
  • The single formula for all warehouse articles (article groups)
ML-powered warehouse management
  • The formula to estimate an order point and time is powered by machine learning algorithms
  • The formula is based on the client's generalised data for the last 2-5 years
  • The formula structure is being defined during the model learning process on the client's data and is subject to change if the marker changes
  • The formula can be different for different warehouse articles (article groups)
BENEFITS
of warehouse optimization
Substantially lower labor costs
Lower risk of goods unavailability
Higher and faster warehouse turnover
Better customer service
Higher profitability
Less dead stock
Lower warehousing costs
Improved customer satisfaction

How to minimize risks and cut costs of warehouse management?

Use predictive analytics powered by Machine Learning. Contact us now to start optimizing your warehouse management!