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Decisions, Decisions: Optimizing With Alteryx

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Embracing prescriptive analytics to optimize decision making can be thrilling with Alteryx. Formal optimization methods can help you make more effective decisions. Attend this session to learn all about these methods through different case studies that look at applications in direct marketing, manufacturing operations, and financial portfolio management.

Dr. Dan Putler - Chief Data Scientist, Alteryx

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Decisions, Decisions: Optimizing With Alteryx

  1. 1. # A L T E R Y X 1 9 PRESENTED BY DECISIONS, DECISIONS : OPTIMIZING WITH ALTERYX Dan Putler, Chief Data Scientist, Alteryx
  2. 2. # A L T E R Y X 1 9 2 With Alteryx, I can find a way to provide the needed information DAN PUTLER When I use Alteryx, I feel like I’m accomplishing something A L T E R Y X U S E R S I N C E 2 0 1 2
  3. 3. # A L T E R Y X 1 9 3 TODAY’S AGENDA 1. Where optimization fits in the analytics toolbox 2. The two broad classes of optimization methods 3. The optimization methods supported by Alteryx’s Optimization tool and how they conceptually work 4. Applying the tool to applications in manufacturing, retailing, and finance
  4. 4. # A L T E R Y X 1 9 BI VS PREDICTIVE VS OPTIMIZATION 4 • All three methods have the same underlying goal – Requires an understanding of business issues in order to provide the right information to inform business decisions • What the three approaches provide is different – BI and reporting provide information that is currently available – Predictive analytics provides information that is not currently available, but which can be reasonably predicted – Optimization provides prescriptive information on what specific action(s) should be taken
  5. 5. # A L T E R Y X 1 9 THE TWO MAJOR CLASSES OF OPTIMIZATION METHODS 5 • Exact – Unconstrained: Commonly used under the hood for many predictive models – Constrained: The focus of the Optimization tool, and what is generally what is meant by optimization • Approximate – Currently implemented for spatial applications through Location Optimization macros in Alteryx – Custom code (mostly Python or R based) has also been implemented • Why two classes of methods? – Exact methods require mathematically “well behaved” functional relationships, which works for many use cases, but far from all
  6. 6. # A L T E R Y X 1 9 EXACT OPTIMIZATION METHODS IN ALTERYX 6 • Linear Programming (LP) • Quadratic Programming (QP) • Mixed Integer-Linear Programming (MILP) • These three methods will be explained and illustrated via three use cases
  7. 7. # A L T E R Y X 1 9 THE THREE USE CASES 7 • A bottler and distributor of wine wants to create a Rhone style blend that will be perceived to be of high quality, but at a low cost – Linear programming • A financial services firm is hoping to develop a portfolio of transportation stocks with a fixed expected return, but with as low a level of risk as possible – Quadratic programming • A supermarket retailer needs to determine how to allocate store shelf-space to maximize revenues – Mixed integer-linear programming
  8. 8. # A L T E R Y X 1 9 DEVELOPING A LEAST COST RHONE WINE BLEND 8 • A bottler and distributor wants to market 300 cases of a Rhone style red wine blend • The firm can make a bulk wine purchase of two different wines, one from Grenache grapes, and the other from Syrah grapes to make the blend • The information the firm needs is the amount of each wine they should purchase that will allow them to achieve the twin objectives of creating a blended wine that will perceived to be of high quality, but at the lowest possible cost
  10. 10. # A L T E R Y X 1 9 THE CHARACTERISTICS OF THE TWO WINES 10 • Grenache – Price: $6.34 per liter – Alcohol: 13.1% by volume – Volatile acidity: 0.31 grams/liter • Syrah – Price: $4.73 per liter – Alcohol: 11.2% by volume – Volatile acidity: 0.38 grams/liter • One other thing – A case of wine is 9 liters, resulting in the need to produce 2700 liters of the blended wine to produce 300 cases
  11. 11. # A L T E R Y X 1 9 GETTING THE BOUNDS INTO THE RIGHT UNITS 11 • Based on the perceived quality model, we would like to have the final blend to have an alcohol content that is 11.5% or more by volume, a volatile acidity of 0.36 grams/liter or less, and produce 2700 liters of blended wine – In this form these requirements are not directly comparable – Since we will be minimizing cost, only 2700 liters of wine will be produced, which is a fact we can use • Given that 2700 liters will be produced, the non-volume bounds become: – Alcohol: ≥ 310.5 liters – Volatile acidity: ≤ 972 grams
  12. 12. # A L T E R Y X 1 9 THE LINEAR PROGRAMMING PROBLEM 12 Minimize: 6.35(Grenache) + 4.73(Syrah) Subject to: Grenache + Syrah ≥ 2700 0.131(Grenache) + 0.112(Syrah) ≥ 310.5 0.31(Grenache) + 0.38(Syrah) ≤ 972 0 ≤ Grenache ≤ 2700 0 ≤ Syrah ≤ 2700
  13. 13. # A L T E R Y X 1 9 HOW LP WORKS 13
  14. 14. # A L T E R Y X 1 9 HOW LP WORKS 14
  15. 15. # A L T E R Y X 1 9 HOW LP WORKS 15
  16. 16. # A L T E R Y X 1 9 HOW LP WORKS 16
  17. 17. # A L T E R Y X 1 9 HOW LP WORKS 17
  18. 18. # A L T E R Y X 1 9 HOW LP WORKS 18
  19. 19. # A L T E R Y X 1 9 DEVELOPING A PORTFOLIO OF TRANSPORTATION STOCKS 19 • A financial services firm wants to create a portfolio of stocks selected from those in the S&P, Dow-Jones Transportation Index (or DJT) which has an expected return of 1.25% a month, with the minimum possible level of risk, as measured by expected variance • The information the firm needs to acquire is the percentage of the portfolio’s value to place in each of the 20 stocks that make up the DJT
  20. 20. # A L T E R Y X 1 9 THE QUADRATIC PROGRAMMING PROBLEM 20 Minimize: Subject to: 1 2 𝑥 𝑇 𝐶𝑥 ෍ 𝑖=1 20 𝑥𝑖 = 1 0 ≤ 𝑥𝑖 ≤ 1 ෍ 𝑖=1 20 𝑚𝑖 𝑥𝑖 ≥ 1.25
  21. 21. # A L T E R Y X 1 9 ALLOCATING SHELF-SPACE IN A SUPERMARKET 21 • A supermarket retailer needs to select the shelf-space arrangement for up to 355 different categories (174 of which are considered “required” and 161 of which are considered “optional”) within a store that has 10,000 feet of shelf space that maximizes store sales based on 5020 category shelf- space “planograms” provided by their suppliers • The information the retailer needs is which planogram to select (if any) for each of the 355 different categories among the 5020 planograms available in a way that maximizes sales while maintaining the 10,000 feet of available shelf- space
  22. 22. # A L T E R Y X 1 9 THE MIXED INTEGER-LINEAR PROGRAMMING PROBLEM 22 Maximize: Subject to: ෍ 𝑖=1 𝑅 𝑗 𝑥𝑖 = 1 𝑥𝑖 ∈ {0, 1} ෍ 𝑖=1 𝑁 𝑠𝑖 𝑥𝑖 ෍ 𝑖=1 𝐶 𝑘 𝑥𝑖 ≤ 1 ෍ 𝑖=1 𝑁 𝑓𝑖 𝑥𝑖 ≤ 10000
  23. 23. # A L T E R Y X 1 9 23 This presentation demonstrates the types and breadth of business use cases that can be addressed using mathematical programing models using Alteryx, along with showing how these models are formulated, how they can be implemented in Alteryx, and providing an illustration of what is going on “under the hood” with these models. Finally, it indicates where optimization fits into the overall analytics toolkit, and how the information it provides differs from other types of analytics. OPTIMIZING DECISIONS WITH ALTERYX FIVE KEY POINTS As with all analytics, the goal of optimization is to provide information of decision making There are a number of strong requirements needed to create “exact” optimization models Optimization models are applicable to a wide range of use cases and industries Both an objective and one or more constraints need to be developed There are three ways of formulating an optimization model in Alteryx 1 2 4 5 3
  24. 24. # A L T E R Y X 1 9 THANK YOU 24 (650) 375-2919 DAN PUTLER