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Why Bespoke
Supply Chain Analytics?
Tom Davis
May 10, 2016
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© 2016 End-to-End Analytics, LLC 5
5/10/20166© 2016 End-to-End Analytics, LLC
Founded in 2005
Based in Palo Alto, CA
40+ professional staff
12 Ph.D.’s from top universities
Resources in CA, MI, MA, Peru, Brazil
Over 75 clients
More than 500 projects to date
30+ published articles
More than 15 patents
End-to-End Analytics: Who Are We?
Company
Team
Work
Specialist consulting firm focused on supply chain analytics
Previously 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
5/10/2016© 2016 End-to-End Analytics, LLC 7
What Does “Analytics” Mean?
5/10/20168© 2016 End-to-End Analytics, LLC
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
5/10/2016© 2016 End-to-End Analytics, LLC
Promotion
Planning
9
Planning Solution Architecture
5/10/201610© 2016 End-to-End Analytics, LLC
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
5/10/201611
Step Analysis Planning
Validate 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
© 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© 2016 End-to-End Analytics, LLC 12
Forecast “Reality Check”
5/10/2016© 2016 End-to-End Analytics, LLC
Past Sales forecasts
were too high
Marketing forecast
is in line with the
statistical range.
Current Sales forecast
looks optimistic again.
13
Forecast Data Entry
5/10/2016© 2016 End-to-End Analytics, LLC 14
Forecast Analysis & Entry
5/10/201615© 2016 End-to-End Analytics, LLC
Forecast Entry
5/10/2016© 2016 End-to-End Analytics, LLC 16
Forecast Analysis & Entry
5/10/2016© 2016 End-to-End Analytics, LLC 17
Supply Planning Analytics
5/10/201618© 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
 Visualize the Data
 Avoid Black Boxes
 Build Your Own
 The Anaplan Connection
5/10/2016© 2016 End-to-End Analytics, LLC 19
Our Beliefs about Decision Support
Visual, 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
5/10/2016© 2016 End-to-End Analytics, LLC 6
Avoid “Black Box” Syndrome
Many analytics projects fail because of the “black box” effect
Good visualizations are key to helping stakeholders understand the
calculations – and therefore buy in to the results
Drill Down
5/10/2016© 2016 End-to-End Analytics, LLC 21
Change policy and instantly see the
impact on inventory and backlog
Building Often Better Than Buying
5/10/201622© 2016 End-to-End Analytics, LLC
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
Analyze
Analyze
Analyze
Build Build
Typical Supply Chain Packages/Modules
5/10/201623© 2016 End-to-End Analytics, LLC
+ Candidate for a
tailored solution
x
Best served
“off-the-shelf”
+
++
+
+
x
xxxx
x
x
+ +
+
+
x
x
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© 2016 End-to-End Analytics, LLC 24
5/10/201625© 2016 End-to-End Analytics, LLC
Smarter Planning for
Numbers-Driven Companies
Q A

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Hub16: Why Bespoke Supply Chain Analytics?

  • 1.
  • 3. Smarter planning apps for numbers-driven companies
  • 4. #AnaplanHub16 Why Bespoke Supply Chain Analytics? Tom Davis May 10, 2016
  • 5. 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© 2016 End-to-End Analytics, LLC 5
  • 6. 5/10/20166© 2016 End-to-End Analytics, LLC Founded in 2005 Based in Palo Alto, CA 40+ professional staff 12 Ph.D.’s from top universities Resources in CA, MI, MA, Peru, Brazil Over 75 clients More than 500 projects to date 30+ published articles More than 15 patents End-to-End Analytics: Who Are We? Company Team Work Specialist consulting firm focused on supply chain analytics Previously 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
  • 7. 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 5/10/2016© 2016 End-to-End Analytics, LLC 7
  • 8. What Does “Analytics” Mean? 5/10/20168© 2016 End-to-End Analytics, LLC What should we do?What will happen?What happened? Descriptive Predictive Prescriptive
  • 9. 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 5/10/2016© 2016 End-to-End Analytics, LLC Promotion Planning 9
  • 10. Planning Solution Architecture 5/10/201610© 2016 End-to-End Analytics, LLC 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
  • 11. Demand Analysis vs. Demand Planning 5/10/201611 Step Analysis Planning Validate 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 © 2016 End-to-End Analytics, LLC
  • 12. 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© 2016 End-to-End Analytics, LLC 12
  • 13. Forecast “Reality Check” 5/10/2016© 2016 End-to-End Analytics, LLC Past Sales forecasts were too high Marketing forecast is in line with the statistical range. Current Sales forecast looks optimistic again. 13
  • 14. Forecast Data Entry 5/10/2016© 2016 End-to-End Analytics, LLC 14
  • 15. Forecast Analysis & Entry 5/10/201615© 2016 End-to-End Analytics, LLC
  • 16. Forecast Entry 5/10/2016© 2016 End-to-End Analytics, LLC 16
  • 17. Forecast Analysis & Entry 5/10/2016© 2016 End-to-End Analytics, LLC 17
  • 18. Supply Planning Analytics 5/10/201618© 2016 End-to-End Analytics, LLC
  • 19. 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 5/10/2016© 2016 End-to-End Analytics, LLC 19
  • 20. Our Beliefs about Decision Support Visual, 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 5/10/2016© 2016 End-to-End Analytics, LLC 6
  • 21. Avoid “Black Box” Syndrome Many analytics projects fail because of the “black box” effect Good visualizations are key to helping stakeholders understand the calculations – and therefore buy in to the results Drill Down 5/10/2016© 2016 End-to-End Analytics, LLC 21 Change policy and instantly see the impact on inventory and backlog
  • 22. Building Often Better Than Buying 5/10/201622© 2016 End-to-End Analytics, LLC 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 Analyze Analyze Analyze Build Build
  • 23. Typical Supply Chain Packages/Modules 5/10/201623© 2016 End-to-End Analytics, LLC + Candidate for a tailored solution x Best served “off-the-shelf” + ++ + + x xxxx x x + + + + x x
  • 24. 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© 2016 End-to-End Analytics, LLC 24
  • 25. 5/10/201625© 2016 End-to-End Analytics, LLC Smarter Planning for Numbers-Driven Companies
  • 26. Q A

Notas do Editor

  1. This is a reference flow chart – the idea is a collaboration so that demand and supply can, indeed, be matched. Most businesses have settled on calling it some close variation of S&OP – Sales and Operations Planning. What can we do to shape demand? Slow down sales to push it out? Offer a discount to pull it in? How about the supply side – cancel orders? Expedite? We focus a lot on planning, which is what you do with a forecast. Forecasts change all the time; your plan doesn’t have to. One of the keys to planning is to have something of a playbook to follow. What are we going to do if orders don’t materialize? What if that big government order comes in? There are some really neat analytical techniques that can be deployed in each of these boxes. However, one of the hardest challenges – and one with great payback – is simply consolidating the data on past demand and anticipated future demand. Simply being able to see what it looks like gives rise to good discussions. This is a case where underlying analytics enable a human discussion and decision process. This process has been followed in the tech world for years, and we’ve worked with several clients who are essentially tech players in the healthcare space. All have benefited form this ability to blend views from the demand side (e.g., regional demand, new launches) and the supply side (capacity, constrained supply, etc.). The techniques translate easily to the pharmaceutical manufacturers and distributors.
  2. This picture emphasizes the importance of data visibility without particularly fancy analytics. This is what the S&OP discussion is all about. Different parties have different perspectives; why do they differ? What do you know that I don’t know? In this example, why is it that the sales force is eternally optimistic, while marketing thinks demand is going down? I should add – this is real data! Folding in a statistical “reality check” helps to ground people, and taking pains to identify the uncertainty is especially important for a quality conversation. Sometimes you just ID bad data … typos, crazy forecasts … and correct them. Other times there are major disconnects between organizations that need to be resolved. Again, note the use of spreadsheet tools to make data accessible and visible. There’s some analytical rigor behind the scenes, but much of the value is in just getting the story the numbers tell in front of people. Other tools will help draw attention to the elements seemingly requiring the most attention – a sorted action list, really. Note the ability to click to roll up and roll down. This allows you to look at your data from different perspectives – perhaps showing how demand patterns are evolving at each of several regional hospitals, then turning it around and looking at it from a supplier-centric view.