Hub16: Why Bespoke Supply Chain Analytics?

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Why BespokeSupply Chain Analytics?

Tom DavisMay 10, 2016

5© 2016 End-to-End Analytics, LLC

Why Bespoke Supply Chain Analytics? Introduction The Supply Chain Analytics Domain A Quick Look: Analytics in Practice Bringing Analytics to the Organization The Anaplan Connection

5/10/2016

5/10/20166© 2016 End-to-End Analytics, LLC

Founded in 2005Based in Palo Alto, CA

40+ professional staff12 Ph.D.’s from top universitiesResources in CA, MI, MA, Peru, Brazil

Over 75 clientsMore than 500 projects to date30+ published articlesMore than 15 patents

End-to-End Analytics: Who Are We?

Company

Team

Work

Specialist consulting firm focused on supply chain analyticsPreviously employed by top companies

Articles published in leading publications

Project work across leaders in many industries

HP Philips

McKinsey & Co. SAS Institute

Procter & Gamble Nestlé

Networking Equipment Food & Beverage CPG Medical & Healthcare

Automotive Semiconductors Cosmetics Internet Search


Why Bespoke Supply Chain Analytics? Introduction The Supply Chain Analytics Domain

Planning Framework Data & Analysis Framework Roles

A Quick Look: Analytics in Practice Bringing Analytics to the Organization The Anaplan Connection

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What Does “Analytics” Mean?

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What should we do?What will happen?What happened?

Descriptive

Predictive

Prescriptive


The Classic S&OP Framework

Demand-Supply Matching

Financial Forecasting

Inventory Targeting

Capacity Planning

Demand Planning

Supply Planning

Production Scheduling

Material Planning

Core of S&OP

Holistic View of S&OP

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Promotion Planning

Planning Solution Architecture


Data Layer

Analytics Layer

Interaction Layer

Visualization Layer

ERP

EDW

• Disseminating Information• Dashboards / Consensus Meetings• Exception Management

• Data management• Data preparation• Different from a data warehouse

• Making & Capturing Decisions• Data Manipulation (“Pivoting”)

• Calculation engine• Analytics tuning / solution development


Demand Analysis vs. Demand Planning

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Step Analysis PlanningValidate and Prepare Data

• Access to EDW• Ability to insert and manage new signals

• Automatically loaded into a DP tool• Metrics and charts to validate data

Aggregate and Interpret

• Ad-hoc analysis and reporting for new signals

• Metrics and charts that support aggregation and drill-down

Explore • Sandbox to conduct ad-hoc analysis and reporting for new signals

• Comparison of multiple vetted signals in a single view

• Exception dashboards

Adjust • Adjust the models • Overwrite the default forecast

Define Modeling Strategy

• Sandbox to conduct segmentation strategies • Exception dashboards to determine when to override

Create & Run Forecast Models

• Sandbox to build models on the fly • Exception dashboards to determine when to override

Validate Forecasts • In and out of sample tests • Metrics and charts that support aggregation and drill-down

• Comparison against other signals and the business plan

Publish Results • Methodology for implementation in DP tool • Actual forecast



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Forecast “Reality Check”

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Past Sales forecasts were too high

Marketing forecast is in line with the statistical range.

Current Sales forecast looks optimistic again.


Forecast Data Entry

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Forecast Analysis & Entry



Forecast Entry

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Forecast Analysis & Entry

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Supply Planning Analytics

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Why Bespoke Supply Chain Analytics? Introduction The Supply Chain Analytics Domain A Quick Look: Analytics in Practice Bringing Analytics to the Organization

Visualize the Data Avoid Black Boxes Build Your Own

The Anaplan Connection

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Our Beliefs about Decision SupportVisual, intuitive interfaces are key

Interaction with the math is as important as the math itself Don’t just tell people the answer – show them why it’s the answer

Decision Support is best developed in an iterative manner Decision Support usually needs to be customized to your business Most software development is linear:

requirements – build – test – deploy But building good analytics needs multiple iterations:

mock-up – rapid prototype – try out and learn – revise prototype – repeat

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Avoid “Black Box” Syndrome

Many analytics projects fail because of the “black box” effectGood visualizations are key to helping stakeholders understand the calculations – and therefore buy in to the results

Drill Down

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Change policy and instantly see the impact on inventory and backlog

Building Often Better Than Buying


Packaged Software

Perception Reality PerceptionReality

Custom Tools Building Your Own Can Improve the Result and Saves Time Solves your problem No unnecessary data gathering No “shoe-horning” problem into

a model that doesn’t fit Almost as fast to build as

configuring a packaged solution Stakeholders understand the

calculations and buy in to the results Result: Better outputs and more

efficient analysis outweigh potentially small increase in build / configure time

Off-the-Shelf Package Myths“One solution can solve every problem.”“With a little training, anybody can use it.”“It saves time.”

Configure Configure

Analyze

AnalyzeAnalyze

Analyze

Build BuildTota

l Effo

rt


Typical Supply Chain Packages/Modules

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+ Candidate for atailored solution

x Best served“off-the-shelf”

+++

+

+

x

xxxx

xx

+ +++

x

x



5/10/2016

Hub16: Why Bespoke Supply Chain Analytics?

Business

Transcript of Hub16: Why Bespoke Supply Chain Analytics?