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Interactive Application for Product Demand Forecasting · Interactive Application for Product...
Transcript of 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
• EBI team overview
• Effective Data Science
• Motivation for Project
• Project Overview
• Teradata in NXP overview
• Solution Architecture – R / Shiny
• Demand Forecasting Application
• Benefits / Conclusion
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
• EBI team overview
• Data Science in Action / Definition
• Motivation for Project
• Project Overview
• Teradata in NXP overview
• Solution Architecture – R / Shiny
• Demand Forecasting Application
• Benefits / Conclusion
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
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• 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 overview
• Effective Data Science
• Motivation for Project
• Project Overview
• Teradata in NXP overview
• Solution Architecture – R / Shiny
• Demand Forecasting Application
• Benefits / Conclusion
EBI Team
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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
• EBI team overview
• Effective Data Science
• Motivation for Project
• Project Overview
• Teradata in NXP overview
• Solution Architecture – R / Shiny
• Demand Forecasting Application
• Conclusion
What is a Data Scientist
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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
• EBI team overview
• Teradata in NXP overview
• Effective Data Science
• Motivation for Project
• Solution Architecture – R / Shiny
• Project Overview
• Demand Forecasting Application
• Benefits / Conclusion
Background / Motivation
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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
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• 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
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• NXP overview
• About me
• EBI team overview
• Teradata in NXP overview
• Effective Data Science
• Motivation for Project
• Solution Architecture – R / Shiny
• Project Overview
• Demand Forecasting Application
• Benefits / Conclusion
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
Outline
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• NXP overview
• About me
• EBI team overview
• Teradata in NXP overview
• Data Science in Action / Definition
• Motivation for Project
• Solution Architecture – R / Shiny
• Project Overview
• Demand Forecasting Application
• Benefits / Conclusion
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
• EBI team overview
• Teradata in NXP overview
• Data Science in Action / Definition
• Motivation for Project
• Solution Architecture – R / Shiny
• Project Overview
• Demand Forecasting Application
• Benefits / Conclusion
Cycle on Cycle Table
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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
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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
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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
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Model forecast results are compiled into a table which is sent downstream to the next application in the forecast process.
Outline
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• NXP overview
• About me
• EBI team overview
• Teradata in NXP overview
• Data Science in Action / Definition
• Motivation for Project
• Solution Architecture – R / Shiny
• Project Overview
• Demand Forecasting Application
• Benefits / Conclusion
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
Questions/Comments
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