Interactive Application for Product Demand Forecasting · Interactive Application for Product...

#TDPARTNERS16 GEORGIA WORLD CONGRESS CENTER

Interactive Application for Product Demand Forecasting

James BirdData Scientist, NXP Semiconductors

Richard ArebaloSupply Chain Forecast Team, NXP Semiconductors

Outline

2

• NXP overview

• About me

• EBI team overview

• Effective Data Science

• Motivation for Project

• Project Overview

• Teradata in NXP overview

• Solution Architecture – R / Shiny

• Demand Forecasting Application

• Benefits / Conclusion

Outline

3

• NXP overview

• About me









NXP Overview

✓ 5th Largest semiconductor

company2

✓ 45,000 employees

✓ 11,000 engineers

✓ 9,000 patent families

✓ 50+ year history

✓ $9.8B annual revenue3

Sources for market data: HIS, ABI Research, Strategy Analytics, The Linley Group

1MCU market excluding Automotive2Excludes memory3Pro forma revenue resulting from Dec 2015 acquisition of Freescale Semiconductor and Nov 2015 divestiture of Bipolar Power business

✓ #1 Automotive

✓ #1 Broad-Based MCUs1

✓ #1 Secure Identification

✓ #1 Communications Processors

✓ #1 RF Power Transistors

✓ #1 Small Signal Discretes

NXP SOLUTIONS

Secure,

Connected

Vehicle

• ADAS: Radar, V2X, Vision, Fusion, network processor

• Car entertainment

• In-vehicle networking

• Secure car access

• Secure car

End-to-end

Security &

Privacy

• Mobile transactions

• E-Government

• Smart bank cards

• User authentication

• Embedded security

• Cloud & Infrastructure Security

Smart,

Connected

Solutions

• Smart home & buildings

• Smart cities, smart grid

• M2M, Industry 4.0

• Intelligent logistics

• 4.5G/5G Networks

• Mobile audio

• High-speed Interfaces

• Smartphone RF

• Personal health & fitness

• Healthcare

IndustrialConsumer

TODAY: 90% OF AUTO INNOVATION FROM ELECTRONICS

Leader in Auto Analog/ RF Leader in Auto Processing Leader in Auto Sensors

#1 INFOTAINMENTTUNERSSOFTWARE-DEFINED DIGITAL RADIOMULTIMEDIA PROCESSORSSOUND SYSTEM DSPs & AMPLIFIERSNFC BT PAIRINGWIRELESS POWER CHARGINGPOWER MANAGEMENT

#1 SECURE CAR ACCESSIMMOBILIZER/ SECURITYREMOTE KEYLESS ENTRYPASSIVE KEYLESS ENTRY/ GOBI-DIRECTIONAL KEYSNFCULTRA WIDE BAND

POWERTRAIN & CHASSISMICROCONTOLLERSPRESSURE/ MOTION SENSORSBATTERY MANAGEMENTDRIVERS

STANDARD

PRODUCTSLOGICPOWER DISCRETES #1 VEHICLE NETWORKING

CAN/LIN/ FLEXRAYETHERNETCENTRAL GATEWAY CONTROLLERSECURITY

#1 SAFETYMICROCONTROLLERS AIRBAGANALOG AIRBAGMICROCONTROLLERS BRAKINGANALOG BRAKINGSENSORS BRAKINGTIRE PRESSURE MONITORING

#1 BODYMICROCONTROLLERSPOSITION/ ANGLE SENSORSSYSTEM BASIS CHIPS

RADAR FRONTEND & MICROCONTROLLERSV2X COMMUNICATION BASED ON ROADLINKVISION & LIDAR PROCESSING SENSOR FUSION

ADASSecurity1. SECURE INTERFACES (SE)2. SECURE GATEWAY3. SECURE NETWORKING4. SECURE PROCESSING (MCU/MPU)(+1) SECURE CAR ACCESS

Outline

7

• NXP overview

• About me


• Data Science in Action / Definition







About Me

8

• Began career as process engineer in semiconductor wafer manufacturing

• Always had intense interest in data analysis

• Six Sigma Black Belt under Motorola

• Worked in Quality organization with focus on data analysis for customer returns

• Began coding with R language about 10 years ago

• Led me to the Enterprise Business Intelligence team where I lead the Advanced Analytics group

• BS Material Science / MS in Operations Research & Industrial Engineering (ORIE)

data

Source: Dr. Eric Siegel, www.predictThis.org

About Richard

9

• From Alice Texas – BA in Finance from the University of Texas

• Worked for Motorola/Freescale/NXP for 33 years in Finance,

Accounting, Marketing, Sales, IT, Supply Chain

• Currently works on Demand Forecasting team in Supply

Chain

• Trained employees all over the world on new software adoption

• Worked on Diamond Chip winning project for Freescale

supporting disaster responses in Japan following Tsunami

• Fluent in Spanish and French

Outline

10

• NXP overview

• About me









EBI Team

11

EBI Team – Data Warehouse

12

NXP’s EDW Architecture principles are largely adapted from Teradata’s Solution Architecture principles, which have been proven successful for Teradata consultants globally and were defined through many years of expertise.

We have adapted these, integrating our own principles developed through our years of experience and corporate culture.

Outline

13

• NXP overview

• About me








• Conclusion

What is a Data Scientist

14

NSF Report (Johnstone & Roberts, 2014)

• Computational aspects of carrying out a complete data analysis, including acquisition, management, and analysis of data

Deborah Nolan, Univ of California, Berkeley

• A Blend/Integration of computational and statistical thinking when working with data

Effective data science requires

• Tight collaboration with business

• Understand the business question you want to answer

Awesome nerds!!Hilary Mason, Fast Forward Labs

Outline

15

• NXP overview

• About me









Background / Motivation

16

Sales $$$

Product AProduct BProduct CProduct D …

Distributor ADistributor BDistributor C

• Highly complex monthly forecasting process• Thousands of products• Products belong to product family hierarchies• Monthly Forecasts for each product by Customer/Region

Customer xxxCustomer yyyCustomer zzzCustomer xyz

Old Process

17

• manual and highly labor intensive• manually pull data from

warehouse• manual ETL process• Excel based• 5 person team to support effort

Messy record keeping

Charts built in excel

Changes manually noted and tracked

Outline

18

• NXP overview

• About me









Solution Architecture

R is a programming language and

software environment for statistical

computing and graphics supported by

the R Foundation for Statistical

Computing. The R language is widely used

among statisticians and data miners for

developing statistical software and data

analysis.

- Wikipedia

• Shiny is a web application framework for R created by Rstudio using WebSocketstechnology

• Shiny is a reactive programming environment (event driven)

• Combines the computational power of "R" with the interactivity of modern web pages

• Fast bidirectional communication between the web browser and R

• Default UI theme based on bootstrap

• Complex interactivity / Highly flexible

− Shiny User Showcase (examples)

TM

TM

http://www.rstudio.com/

http://www.rstudio.com/products/shiny/shiny-user-showcase/

Outline

20

• NXP overview

• About me









Current Project Scope

Data Warehouse

ETL

BillingsBacklogCurrent modelInventoryResalesMeta data

Pull journals & configsfrom db

Build graphs and models

Apply Seasonality & user choices

Disaggregate Forecasts

Save User Selections

Outline

22

• NXP overview

• About me









Cycle on Cycle Table

23.

The Forecasting App opens to the Cycle on Cycle page.

This provides a top level and comparison of prior year to current year.

User selects business group here

MAPE Accuracy

24.

Forecast

accuracies

are

calculated

and tracked.

Journal of Change History

25.

Change history is maintained in a searchable, sortable journal.

Seasonal Weights

26.

Seasonal decomposition can be set at different levels of the product hierarchy.

This allows the forecast team flexibility in setting seasonal weights.

User Configuration

27.

Some products are analyzed at the product line level of the hierarchy.

The user can easily move a product family up or down the hierarchy for forecasting.

Demand Forecast Dashboard

28.

This is the Demand Forecast dashboard.

The graph displays billings, backlog, inventory, resales, and several modeling options. Summary statistics for the selected product family are shown on the right.

The panel on the left allows the user to experiment with different forecast models. There is a second level menu for Model Choice which is not currently displayed. Seasonality weighting can be turned on/off for the selected model.

Model selection menu.2nd menu level with detailed options appears after making model choice.

Exponential State Space Models

29

Exponential State Space ModelsIn 2000, Robert Hyndman et al developed a state space framework for exponential

smoothing methods which incorporates stochastic models, likelihood calculation, prediction

intervals and procedures for model selection.

A state space model for an N-dimensional time series yt consists of a measurement

equation relating the observed data to an m-dimensional state vector at, and a Markovian

transition equation that describes the evolution of the state vector over time.

Holt’s Linear Method utilizes an additive treatment of the trend component. Alternatively, it

is possible to treat the trend component in a multiplicative manner. Seasonal components

can also be included in the models and can be either additive or multiplicative. The

Exponential State Space models cover all feasible combinations of models where the error

term, the trend term, and the seasonal term can be additive or multiplicative. The models

are described with a 3-letter naming convention indicating additive, multiplicative, or none

for each term in the model. For example, the “MAM” model indicates a multiplicative error

term, additive trend term, and multiplicative seasonal term. This is equivalent to the

multiplicative Holt-Winters’ seasonal model with multiplicative errors. Dampening terms can

also be included in the models.

The Demand Forecast R Model uses Hyndman’s exponential smoothing state space model

framework. The algorithm optimizes model parameters by minimizing the prediction error

for all feasible model choices (models based on the 3-letter exponential state space code).

The best model of all the choices is then determined by comparing values of each model’s

Akaike Information Criterion (AIC).

The algorithm automatically identifies the best model family with the optimal parameter settings

Detailed Model Info

30.

If interested, users

can delve into the

details of the

forecast model.

Forecast Disaggregation - part 1

31.

The forecasts must be disaggregated down the lowest level – saleable part by customer.

Several algorithms are available to the user. The disaggregation is then calculated automatically.

Forecast Disaggregation – part 2

32.

After the user makes their selections, the final disaggregated results are shown.

Forecast Results Summary

33.

Model forecast results are compiled into a table which is sent downstream to the next application in the forecast process.

Outline

34

• NXP overview

• About me









Benefits

• Dashboard generation of analytics provides a tremendous improvement cycle time

• Generates the MGF model on first possible day instead of taking ~ 1week to build

• Model reviews with MBGs can now happen 1+ weeks earlier in the month

• Business specific seasonal indexes improve accuracy of business forecasts

• Ability to exclude specific Product Lines improves forecast accuracy at aggregated levels

• Easier review of model changes improves impact of model reviews

• Journal tracking improves change management

• Easy configuration changes enables flexible options for forecasting team

• Greatly increases the efficiency of the forecasting process

• Automated model generation and dashboard analytics changes the emphasis from model and data production to

• Focus on improved forecast mix

• Better handling of product transitions (new products / end of life products)

• More accurate forecasts

Conclusion

• Data Science & Machine Learning algorithms are readily

available through platforms such as R

• Effective application of algorithms can provide significant

business benefits

• Integrating data science tools & algorithms into an easy to use

web application can also deliver tremendous efficiency gains

allowing teams to focus energy in improving their business rather

than building reports

• Tight collaboration with the business client and understanding

the business problem are critical to the success of any such

project

Thank You

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Interactive Application for Product Demand Forecasting · Interactive Application for Product...

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