Enhancing Demand Forecast through Advanced Analytics

Gil Graciani, José Mejías

RapidMiner’s Wisdom October, 2018

Agenda

• Selecting a Data Science Platform

• Enhancing Demand Forecast • Problem Statement

• Key Innovations

• Results

Analytic IDE Objectives

Tool Objectives Business Benefits

Single, multi-tenant platform that supports advanced analytics for both Data Scientists and Citizen Data Scientists

Increased data driven decision making through adoption & greater collaboration & algorithm sharing

Platform that scales (preferably horizontally) by compute capability as well as data size

Faster speed to insights through flexibility & agility to quickly adapt to growth

Platform that interfaces with EA Analytics platform applications providing in-database, distributed computing via Spark and Hadoop and streaming analytics.

Ability to drive business transformation through embedded analytics

Platform that supports Data Preparation needs for advanced analytics Increased agility and speed to insights through automation

Data Prep and Algorithms (E2E) that can be automated (scheduled) Ability to leverage Dev Ops via Automation

Platform output that can be embedded into BI analytic reporting tools and applications

Expanded use of algorithmic intelligence via ability to distribute insights across broader BI ecosystem

Platform supports self-service Greater agility, faster speed to insight and lower support costs

Analytic IDE Tool Evaluation Process and Outcome

Research Conducted

• Gartner ratings

• Magic quadrant ratings

• Individual capability scores

• Input from Analytic SMEs

Vendor Demos

• Dataiku

• RapidMiner

• Data Science Inc.

• Knime

• Alteryx

In-house proof-of-concept

• RapidMiner

RapidMiner chosen based on:

• Scalability / Open Source

• Big data ecosystem connectivity (Hadoop)

• Integration with Python and R

• One of the most intuitive UIs

• Templated Business Use Case (e.g.

“Predictive maintenance”, etc.)

• “Wisdom of crowds” social analytic

recommendation feature

Evaluation Process

EA Platform Tools Landscape

5

Data Lake Domain Views Consumption

Process

Tools

Framework

IDE

Skills

Management

Basic-advanced data managementBasic-advanced analytics skills

Advanced DM/BI/Visualization/WebDev

Scheduler:

Ingestion Enrichment Transform Analysis Delivery

Consumption DM

Analytics DM

Domain DM

Atomic Data

Raw Data

Stream

Batch

Proof

Enhance

Merge

Aggregate

Normalize

Descriptive

Diagnostic

ML

BI

Insights

Actions

Governance Cooperative: DEV: IT Enable - Business Produce PRO: IT Enable – Business/IT Produce

Workflow:

IDE = Integrated Development Environment

Catalog:

Data Governance:

Admin: Security: Automation:

OLAP:

Discovery Layer

6

SERP

SFDC

Business Managed

Data

Edge

Node

Discovery Layer

Consumption LayerRefined LayerRaw Layer

Data Sources Enterprise Analytics PlatformData

ConsumersData Ingestion

IF

BI Reports

Data Scienceas needed

Archive

Big Data Engineer Toolkit

BMT

Personas will determine the tools in the toolkit. Technologies listed are subject to change based on needs of the user community.

Data Science Toolkit

BMT

Environment

• 4 TB Storage limitation for Discovery Layer• Environment dedicated to Data Science & Big Data

Engineers/Analysts• Access Through Visualization Tools subscribe through

UAM

External DataRM

Server

Data Driven Organization

7

Problem Statement

We are building a next gen model...• Unique, Artificial Intelligence(AI) based

Predictive Capability

• with a long-term (15yrs), econometric, total market perspective,

• leveraging new inputs & next gen analytics• Machine & Deep Learning, Customer Analytics,

Channel Inventory data & Lifecycle Analytics

• Optimized for accuracy improvement

• Intelligent Design: Will deliver increased value in future years with continuous improvement

Solution: Better & Broader Data Set

+ Next Gen Advanced Analytics

= Improved Planning

Goal/Value: Improve planning accuracy by at least 40%

Problem Statement: Advanced analytics, econometric, market & long term

considerations are excluded from our current planning, which is primarily

bottoms-up

Stats Models: ARIMA

Machine Learning: Random Forest, KNN, GBM, Neural Nets

Deep Learning: Recurrent Neural Nets

Ensemble Approach for Final Model Selection

Market Data: IDC

Econ Data: OECD, Duke CFO Survey

Internal: Orders, Customer, SC

ALFA Solution Overview

Our Journey

10

Issue: Excellent concept, but team lacked ingestion, automation

& modeling expertise

Final Solution: RapidMiner as an end to end analytics solution

Initial Solution: Cover gaps with consultants

Results: • Aspiring Data Scientist Mentality• New capabilities developed and

leveraged for planning tools• Saved consulting & SaaS fees

Why RapidMiner?

• Ingestion, Enrichment, Transformation and Analysis in one automation tool

• One vs Many tools to learn and link for automation

• Leveraging for Demand Planning tool dev.

• It’s “where the puck is going”• RapidMiner’s first company deployment

• IT supported • Investing our time in higher value add tasks vs

supporting ad-hoc / unique tools

• Leaders in Gartner Magic Quadrant

11

RapidMiner Benefits• Integration with R

• Intuitive UI / Easy to Learn

• Excellent Expert Support

• Big data ecosystem connectivity (Hadoop)

• “Wisdom of crowds” social analytic

recommendation feature

• Scalability / Open Source

Taking the capabilities of the team to the next level

The Model

12

Total Line Fam A Fam B

AMS Pilot: Mar-Aug

Current MAPE ALFA Pilot MAPE

Results Overview

13

49% Better

65% Better

10% Better

68% Better

58% Better

75% Better

Total Line Fam A Fam B

EMEA Pilot: Mar-Aug

Current MAPE ALFA Pilot MAPE

Accelerated time to Insight

~6 months

RapidMiner Key Enabler

Ingestion, Enrichment, Transformation, Analysis

Discovery, Modelling, Integration

Outstanding Support

Model Achieving stable, sustainable performance

Beating the 40% improvement goal

Future improvements potential

Key Messages

>60%Sys Volume Coverage

up to 75%Improvement

All LinesDelivering Improvement over Current Process

Thank you!