AI News, How Machine Learning Can Improve Supply Chain Efficiency
- On Thursday, June 7, 2018
- By Read More
How Machine Learning Can Improve Supply Chain Efficiency
In today's volatile and complex business world, it is very difficult to make a reliable demand forecasting model for supply chains.
So supply chain management is the planning, control and execution of daily supply chain activities, with the aim to improve the business quality and customer satisfaction, while negating wastage of goods, in all the nodes of a business.
They cannot properly understand the changing market patterns and market fluctuations, and this hampers its ability to properly calculate market trends and provide results accordingly.
However, with newer data generation technologies coming into play, the data has become very complex and nearly impossible to manage with existing technology.
This means that as more data enters the machine learning system’s reservoir, it will become more intelligent and the data will become more manageable and easier to interpret.
These problems and the improvement of these aspects through machine learning are discussed below: Planning Team’s Problems Often, planning teams use old forecasting techniques, which involve manually evaluating all the data.
However, with machine learning, the system can take many variables according to their priorities based on the data, and make a highly accurate model.
(To learn more about using data to plan ahead, see How Contextual Integration Can Empower Predictive Analytics.) Safety Stock Levels With traditional planning methods, a company has to keep its safety stock levels high nearly all the time.
Sales and Operations Planning If the forecast from your sales and operations planning (S&OP) team is unsatisfactory and inaccurate, or isn’t flexible enough to adapt according to the market behavior, then maybe it is time to upgrade the system.
Machine learning finds a perfect use here, as it can improve the quality of forecasting by learning the current market trends through different kinds of data.
So, in order to provide full customer satisfaction, while coping with the expansion process, Lennox integrated machine learning with its forecasting architecture.
With the help of machine learning, Lennox could accurately predict the needs of its customers, which further helped the company to understand common customer demands better.
It's not necessary to completely change an entire system now, but in the very near future, every supply chain will certainly use machine learning to improve forecasting capability by the creation of dynamic models that will be updated regularly by the machine learning system.
- On Monday, July 15, 2019
Basic Excel Business Analytics #56: Forecasting with Linear Regression: Trend & Seasonal Pattern
Download file from “Highline BI 348 Class” section: Learn: 1) (00:11) Forecasting using Regression when we ..
Best Practices for Demand Forecasting and Inventory Planning – A Practical Demonstration
In this webinar, we will discuss how to improve forecasting and planning in your company. We will show and perform loading data into Streamline (our flagship ...
AI for Supply Chain
Only a few days left to signup for my Decentralized Applications course! Every product in your home is there as a result of being ..
How Finance Teams Chart a Course to Improve Planning and Forecasting with Advanced Analytics
This webinar—sponsored by IBM and Financial Executives International—examines key focus areas for finance highlighted in the 2016 Hackett Key Issues ...
Deepmind and the Future of the Finance Industry | Matthew Dixon | TEDxIIT
Can Google's Deepmind predict the future of financial markets? Google has released tensor-flow, an open source software, that provides special types of neural ...
S&OP: Are You Outgrowing Your Forecasting and Inventory Planning Software? ToolsGroup
S&OP: Are You Outgrowing Your Forecasting and Inventory Planning Software? We have seen hundreds of companies start with a basic forecasting solution, ...
Empowering Your Retail Business with Advanced Analytics through Demand Forecasting
As the digital transformation of the retail market accelerates, advanced analytics and big data technologies can offer retailers gather critical customer intelligence ...
Random Projection Estimation of Discrete-Choice Models with Large Choice Sets
Matthew Shum of Cal Tech discusses the use of machine learning ideas in the estimation of discrete choice models, the workhorse model of demand in ...
Retail Store Forecasting and Planning Demo
Quickly forecast, model and manage store level revenues. With complete historical data sets, business planners can model complex drivers like seasonality ...
How to Build a Forecasting Model in Excel - Tutorial | Corporate Finance Institute
How to Build a Forecasting Model in Excel - Tutorial | Corporate Finance Institute Enroll in the Full course to earn your certificate and advance your career: ...