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Machine Learning for Inventory Management

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Presentation on theme: "Machine Learning for Inventory Management"— Presentation transcript:

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


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