1. Machine Learning for Inventory Management
July 5, 2019

2. BHGE Introduction

3. Product Companies Delivering market-leading solutions across the energy value chain
OILFIELD
SERVICES
OILFIELD EQUIPMENT
DIGITAL SOLUTIONS
TURBOMACHINERY & PROCESS SOLUTIONS
Our Oilfield Services (OFS) business lowers the cost per barrel of oil equivalent for the life of a well by improving well efficiency, optimizing production and increasing ultimate recovery.
In Oilfield Equipment (OFE), we provide customers with a portfolio of ultra-reliable technologies, including subsea trees, manifolds, blowout preventers (BOPs), flexible risers and advanced control systems.
Our Digital Solutions business (DS) combines sophisticated hardware technologies with enterprise-class software products and analytics to connect industrial assets, providing customers with the data, safety and security needed to reliably and efficiently improve operations.
In Turbomachinery & Process Solutions (TPS), we provide industry-leading availability and reliability in mechanical-drive, compression, and power- generation applications across a diverse range of industry segments.
July 5, 2019

4. Turbomachinery & Process Solutions (TPS)
Structured to serve our customers
10,000+ employees
Operating across 120 countries in 7 Regions: Europe, Russia & CIS, Africa, Middle East & India, Asia Pacific & China, North America and Latin America
30 manufacturing facilities
10 service facilities
1,000 Field Service Engineers
Solutions tailored to industry segment needs
Portfolio
Aeroderivative and heavy-duty gas turbines
Small- to medium-sized steam turbines
Centrifugal and axial compressors
Reciprocating compressors
Process, control and safety valves
Integrated power, compression and LNG modular systems
Service solutions
Onshore & offshore production
Pipeline & gas processing
Liquefied natural gas
Refinery & petrochemical
Industrial
Confidential. Not to be copied, distributed, or reproduced without prior approval.

5. Segment financial performance ($ in millions)
Orders – Revenue = 341 M$
This is the official Shareholders book presented for 2018, we will focus on the top right quadrant, you see orders, revenue, their difference is the amount of orders received by customers that we did not convert into cash in 2018, we will do that in 2019, don’t worry, it would have been better for our shareholders if we did it in The amount of this difference is 341 M$, we would like to reduce it, in order to maximise the conversion rate, it depends on the items Lead Time.
Conversion Rate = Revenues/Orders = 84%
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

6. The Supply Chain Model (simplified)
Customer Order
Goods Shipment
LOGISTICS
ORDER FROM CUSTOMER
ORDER TO SUPPLIER
PRODUCTION
ORDER FULFILLMENT
FORECASTING
Let’s dig a little bit more into the Lead Time problem, a conversion rate of 84% means that all the orders taken from mid November to end of the year are not fulfilled on time because we are phisically waiting for them to arrive, but what if we already had them in house? We can achieve this result with a good forecast, actually a good forecast can reduce the Lead Time by more that 70% in average, so that means that the we can recover a fair % of conversion rate. (16*0.7=11% conversion rate that means ~200 M$ sales in the year) t
TOTAL LEAD TIME
NEW LEAD TIME
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
July 5, 2019 6
Confidential. Not to be copied, distributed, or reproduced without prior approval.

7. What is the most important thing for the Forecasting Manager?
The Turns indicator
𝑻𝒖𝒓𝒏𝒔= 𝑹𝒆𝒗𝒆𝒏𝒖𝒆𝒔 𝑰𝒏𝒗𝒆𝒏𝒕𝒐𝒓𝒚
So recapping, what is the n.1 thing for the Forecasting Manager? The conversion rate right? Wrong! It’s the bonus...everybody knows that...
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
July 5, 2019 7
Confidential. Not to be copied, distributed, or reproduced without prior approval.

8. How to increase Turns? 1st Option: Increase Revenues 2nd Option:
Reduce Inventory
How do we reduce the inventory while keeping the same Revenues?
We need to choose the ‘’right’’ parts to stock!!
Right parts = MTS
Wrong parts = MTO
So let’s get a little bit real here...how do we get the bonus? There is a metric...the Turns, its the ratio between Revenues and inventory, it’s the number of times our inventory rotates in sales, so if a have big sales and thin inventory Turns will be high and we like that...so Option 1 increase Revenues, how do we do that? Increase Prices? Mmmmhhhh...don’t like it. Increase market share? Out of Forecasting span of control no? Expand the product Range? Engineering job... So? Let’s go to the Option 2, Reduce Inventory, Yes we can! We just need to stock the right parts...the ones that we will sell, and not the ones that will get old in the warehouse we call them MTS and MTO...
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019

9. Machine Learning Approach
Binary Classification
(MTO-MTS)
Of course this is a Binary Classification problem and I introduce You Laura De Stefanis to talk to you about that. Thanks, Laura?
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

10. Machine Learning Application

11. Machine Learning for Materials Forecast
The team scope is to maximize Service Level for strategic and frequently asked items, buying them in advance,
while minimizing inventory.
Parts classification means deciding which are the items to hold in stock (MTS) and their reorder levels.
Pre-Machine Learning Process
Machine Learning Application
Based on historical sales order shipments
(6 years) and items features (cost, price..)
25% Make To Stock items
Improvement areas:
Huge amount of data managed manually
Multi-validation process
Level calculation based on static statistical model
ML is used to improve the two main steps of the process:
Parts Classification
Supervised neural network algorithm
Stock Levels Optimization
Reinforcement Learning algorithm
Heuristic process can be efficiently conveyed into a machine learning algorithm
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

12. Machine Learning Steps
Data preprocessing
Features extraction
Classification & Validation
ML automates classification and highlights inconsistencies
Supervised algorithm (KERAS dense layers with Drop Out)
Full deep learning same Accuracy
200 K items, 6 years, 1M sales transactions
Huge amount of data require time consuming analysis
Domain knowledge translated into features to
condense items demand pattern
Statistics & Unsupervised learning
Integrated in Python
99% Accuracy
Machine learning highlights clusters, learns human validation and allows single flow process
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

13. Reinforcement learning approach for levels setting
MIN and MAX levels decide when we have to buy material
Optimal policy (MIN and MAX values) is found through epsilon-greedy, every visit MC algorithm
Reward Function is made up of:
Carrying cost
Purchasing cost
Sales of items ready in stock
Sales of items not in stock
Discount factor due to delayed sale
Demand quantities are sampled from historical pattern
Optimal levels allow 95% + service level
Customer demand
Inventory
Optimal level
Reinforcement learning explores possible levels and exploits previous attempts.
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

14. Main Interface (Django & Keras)
Django app makes results available also for non-expert users
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

15. Conclusions Rapid adaptability Faster decision making Accurate results
Model continous improvement
Validation will require less time any time
Faster decision making
Workload reduction 50%
Accurate results
Single flow process
Errors risk mitigation
Performance evaluation
Key indicators (stock plan, sales..)
automatic calculation
Inventory optimization
Regular items identification, levels optimization
Inventory reduction 15%
Domain expertise combined with Machine Learning drives Inventory optimization & Workload reduction
Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif
July 5, 2019
Confidential. Not to be copied, distributed, or reproduced without prior approval.

16. Thank you

17. Model input features Attribute Description Price
Price of the item in Millions
Cost
Cost of the item in Millions
Lead_time _monthly
The latency between the initiation and execution of a process
CI_w
Confidence interval of 6 years weekly demand patterns
CI_ly_w
Confidence interval of last year of weekly demand patterns
Hits_all_w
Binary representation of the 6 years weekly demand patterns
Hits_ly_w
Binary representation of the last year of the weekly demand patterns
CI_m
Confidence interval of 6 years monthly demand patterns
CI_ly_m
Confidence interval of last year of monthly demand patterns
HITS_all_m
Binary representation of the 6 years monthly demand patterns
HITS_ly_m
Binary representation of the last year of the monthly demand patterns
q/mean
Quantity coefficient of variation confidence interval

18. DNN Model parameters for MTO/MTS classifier
Choice
Hidden neuron activation function
RELU
Output activation function
Sigmoid
Optimizer
Adam
Regularize
dropout(0.3)
Learning rate
0.001
Number of layers 3
Hidden units
fixed to [128, 128, 128]
Batch size
1000
Number of Epochs
200
Accuracy 98%
July 5, 2019

19. DNN Model parameters for Capital/Flow classifier
Cap/Flow
Model parameter
Choice
Hidden neuron activation function
RELU
Output activation function
Sigmoid
Optimizer
Adam
Regularize
dropout(0.3)
Learning rate
0.001
Number of layers 3
Hidden units
fixed to [64, 32, 32]
Batch size
10000
Number of Epochs
200 with Early Stopping
Vocabulary size
50000
Embedding dimension 30
Pre-trained weights
False
Accuracy 98.9%
July 5, 2019

20. Models comparison Models for MTO/MTS Choice Shallow NN 0.94
Random forest
0.98
DNN
Models for Cap/Flow
Word vectorizer
Test accuracy
Multilayer NN
TF-IDF(0.9, 0.1)
0.93
Random forest
0.96
DNN
Word2Vec(300d)
0.98
DNN model was selected as it run more efficiently on the GPU
DNN with Word2Vec achieve an accuracy of 0.98% on the test set
July 5, 2019