A basic presentation of how we can use Machine learning to sort out different problems faced by supply chain management and How we can also use it to model Inventory management.

1. Machine Learning - A giant leap for
supply chain forecasting
By:
Shaswat Mandhanya
Undergraduate Student
Indian Institute of Technology,
Hyderabad

2. Machine Learning - Forecasting
▪ The main objective of this project is to use machine learning
algorithms to precisely predict or forecast the supply chain
management
▪ The idea behind this project is that nearly all the companies
today has none of less same type of manufacturing
equipment so manufacturing cost is same for all, but to be
ahead in race with your competitor, you need to optimise all
areas of your production
▪ This is where supply chain management comes in. We can
simply forecast the risks, demands on the basis of previous

3. Machine learning - Forecasting {Contd.}
Datas and we can use different algorithms to optimise our
production.
▪ Only few of the top guns today use machine learning
algorithms to forecast, but we need to ensure that each and
every industrialist use it because it is very precise and also
uses more factors into consideration.

4. Machine learning - Forecasting
There are many problems faced today in supply chain like the
bullwhip effect etc. For this project I am discussing a particular
problem faced by a fictional company which is a common problem
for many companies today:
Lets say X is an online fashion sales company offering extremely
limited-time discounts (“flash sales”) on designer apparel and
accessories.
One of X’s main challenges is pricing and predicting
demand for these first exposure styles, which was reflected in
either quick sale-outs or too much leftover inventory.

5. Machine learning - Forecasting
The above problem dealt with inventory management.
▪ In technical terms, It is a classic case of Newsvendor model
where you have only one chance to buy and you have to
predict the demand and get the maximum profit.
▪ The problem faced by X was that during the flash sale ,
either they were stock out or lots of inventorys were left
over.
▪ Here we can use Machine learning to forecast demand and
provide X maximum profit

6. Machine learning - Forecasting
▪ The basic component of machine learning is Neural
Netwroks . We can break forecasting down to ML terms:

7. Machine learning - Forecasting
Approach to the problem:
Our approach is two-fold and begins with developing a
demand prediction model for first exposure items; we then
use this demand prediction data as input into a price
optimization model to maximize revenue.
The two biggest challenges faced when building our demand
prediction model are estimating lost sales due to stockouts,
and predicting demand for items that have no historical sales
data.

8. Machine learning - Forecasting
▪ We use machine learning techniques to address these
challenges and predict future demand. Regression trees - an
intuitive, yet nonparametric regression model - are shown
to be effective predictors of demand in terms of both
predictability and interpretability.
▪ We then formulate a price optimization model to maximize
revenue from first exposure styles, using demand
predictions from the regression trees as inputs.

9. Machine learning - Forecasting

10. Machine learning - Forecasting
▪ It will surely create impact on lots of manufactures who
uses sales and each and every manufacturer in the world to
optimise their inventory and they can get reasonable
estimate for their production.
▪ The current system of inventory management and
forecasting is based loosely on experience but I hope that
things are now changing and all the companies now use big
data and machine learning to forecast their demand and
inventory levels.