Warehouse Optimization based on Machine Learning

Fayrix Machine Learning solution for efficient warehouse management, minimising risk of product unavailability and costs or warehouse ownership

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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

Warehouse optimization
project milestones

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

Predictive model development

Forecasting demand, shipment time, insurance supply level and other probability-related factors which have significant impact on warehousing costs

Recommendation model development

Development of optimization model which minimizes warehousing costs by recommending the necessary value of orders and appropriate suppliers

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), minimized losses due to some articles unavailability and minimized 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)