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Introduction to Prescriptive Analytics: Solving Real World Optimization Problems using IBM ILOG CPLEX Optimization
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Housekeeping
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• Link to Webinar Recording and Presentation Slides will be shared after the presentation.
• Comments and Questions in the GoToWebinar Control Panel.
• Additional questions should be directed to Nabeel Nazeer - [email protected]
www.newcomp.com
A dedicated analytics practice since 1997 with over 400 successful project implementations and satisfied clients.
About Newcomp Analytics
Newcomp Analytics is a leading Analytics partner that provides software, services, support, renewals, training and education for IBM Solutions
• IBM Platinum Business Partner• 2017 North American IBM Strategic Partner of the Year
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Focus on 5 Analytics Pillars
1. Business Intelligence2. Planning & Forecasting3. Advanced Analytics4. Information Management 5. Open Source
Trusted Analytics Advisors
www.newcomp.com
Alkis Vazacopoulos Optimization Expert
Today’s Presenter
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Introduction to Prescriptive Analytics: Solving Real World Optimization Problems using IBM ILOG CPLEX Optimization
Alkis VazacopoulosOptimization Expert
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Why Optimization?“Plans are nothing; planning is everything” – Dwight D. Eisenhower
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What is the difference between industry leaders?
vs.
vs.
vs.
vs.
vs.
vs.
Source: The Optimization Edge, Steve Sashihara (New York, NY: McGraw Hill, 2011) p. 3
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Optimization solutions – documented ROI
2 Chilean Forestry firms Timber Harvesting $20M/yr + 30% fewer trucks
UPS Air Network Design $40M/yr + 10% fewer planes
South African Defense Force/Equip Planning $1.1B/yr
Motorola Procurement Management $100M-150M/yr
Samsung Electronics Semiconductor Manufacturing 50% reduction in cycle times
SNCF (French RR) Scheduling & Pricing $16M/yr rev + 2% lower op ex
Continental Airlines Crew Re-scheduling $40M/yr
AT&T Network Recovery 35% reduction spare capacity
Grantham Mayo van Otterloo Portfolio Optimization $4M/yr
Source: Edelman Finalists, http://www.informs.org or http://www.scienceofbetter.org
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Hard Benefits of Optimization
- Calculable ROIs, paybacks within months, sometimes even weeks- Capital expense avoidance or deferral- Operating expense reductions- Total revenue, revenue mix, and margin improvements
- Improved customer satisfaction- Provide better and more customized customer service- Reduce costs; improve customer service
- Improved operations- Increase productivity- Better planning and scheduling processes - Minimize Costs, Maximize Profits
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What is Decision Optimization?
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The advanced analytics picture
What will happen in the
future?
What should I do about it?
What is happening in my business
today?
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
IBM’s Advanced Analytics Portfolio
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Decision Optimization is the “secret sauce” of
Prescriptive Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
IBM’s Advanced Analytics Portfolio
The advanced analytics picture
What will happen in the
future?
What should I do about it?
What is happening in my business
today?
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Business Analytics and Optimization
Optimization
From the book “Competing on Analytics” by Thomas H. Davenport , Jeanne G. Harris
Standard Report
Ad hoc reports
Query/ Drill Down
Alerts
Statistical Analysis
Forecasting/ Extrapolation
Predictive Modeling
Co
mp
etit
ive
Ad
van
tag
e
Complexity
Stochastic Optimization
Descriptive
Predictive
Prescriptive
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Decision Support Applications
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Capture price, product, location and date for each transaction.
Historical & Master Data ETL
Determine important variables, predict trends, seasonality etc.
Predictions and Insights
Allow multiple users to experiment with multiple scenarios.
Collaboration & What-if
Set policies, promotions etc. Allow reviewers and auditors to have a say.
Rules & ProcessManagement
Automatically generate decisions, allow user interaction with decisions.
Decision Making
Key steps for a mature decision support application leveraging advanced analytics
Descriptive
Predictive
Prescriptive
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Prescriptive Analytics by Industry
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Challenge
Solution
Benefits/ROI
Indeval (Mexican Central Securities Depository)
• Process security transactions in real-time rather than daily.
• Provide a better service to the Mexican Stock Exchange.
• Decision Optimization for assignment and scheduling
Profile
A private securities depository organization in Mexico.
• Real time reconciliation and completion of trading operations for more than USD$250 Bin average, every day
• Reduced liquidity requirements for trading partners by 52 percent
• Increased the volume of operations by 26 percent
• Reduced the costs of each trading transaction for electronic trading facilities, the Stock Exchange and trading brokers
Testimonial
“By building a unique technology solution for our securities services, we now better serve the Mexican Financial Community and trading partners. We are very proud that this solution has played a key role in helping elevate the economy of Mexico.”Jaime VillaseñorChief Risk Officer, INDEVAL
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Challenge
Solution
Benefits/ROI
Auditing Tax Returns at New York State
• Questionable tax refunds can total about $400-500M.
• 98,000 exception returns are processed.• 800-1200 people analyze exceptions.
• BPM for process automation
• SPSS predictive analytics for scoring
• Decision Optimization for assignment and scheduling
Profile
• New York State• 20M total population• ~$200B total state tax revenue
(third highest)
• Increased collections of outstanding debt by $83M
• Average age of cases when assigned to field agents decreased by 9.3%
• Dollars per staff day increased by 15%
• 35,000 fewer taxpayers had serious enforcement actions taken against them.
• Collections process is more productive, efficient and fairer to all taxpayers.
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Flash Memory Supplier Management
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Challenge
Solution
Benefits/ROI
• Excel spreadsheets were replaced by dedicated optimization solution for supplier planning• Solution allowed for freeze period, limited flexibility and full flexibility periods. Customer Profile
• Planning team can now collaborate on a global plan• Consistency in decision making • Audit capability on old plans
§Place orders for flash memory from manufacturers§Determine how memory will be used. Which product line, which market and which assembly shop will utilize provided raw materials. §Initial 4 week freeze period. 4-8 weeks of limited flexibility. 8-52 week of full flexibility.§Constant changes in the market, technology and suppliers
• Global consumer electronics retailer•Multiple markets, multiple product lines, multiple suppliers•Multiple global suppliers
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Detailed Scheduling, Monitoring and Re-Scheduling
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Challenge
Solution
Benefits/ROI
• IBM ILOG optimization solution to schedule, monitor and re-schedule production. • Complementing SAP • Delivering understandable and executable schedule in minutes with the capability to plan activities, outstanding work, and manage unexpected events that can build a detailed schedule in a few minutes. • Ability to monitor progress and re-schedule on demand
Customer Profile
Airbus is a leading aircraft manufacturer whose customer focus, commercial know-how, technological leadership and manufacturing efficiency have propelled it to the forefront of the industry.
• One unique tool for all Final assembly lines for all AIRBUS aircraft families• Starting deployment for aircraft component• Planning effort reduced from 7 days to 3-4 hours. • Re-scheduling in minutes
• Increasing complexity in the Final Assembly Line • Volatile market demand• Tough competition, pressure on costs & on time deliver • Need to Increase productivity, utilization and efficiency. • Ability to manage unforeseen event during production• Capitalize very experienced planners expertise
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Market Leading MP & CP Optimization Engine
Model complex business problems.
Solve with IBM CPLEX Optimizer.
Prescribe precise and logical decisions.
SaaS Delivery via Decision Optimization on Cloud
Prescriptive analytics as a service.
No install, no setup.Embed in other applications via
Rest API.http://ibm.co/docloudtrial
IBM Decision Optimization portfolio
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Decision Optimization in end-to-end IBM AnalyticsExample: Advanced S&OP Lifecycle
Import historical sales transaction
data and master data
Determine demand trends, seasonality
and key correlations
Let planners review and edit forecast and
master data
Allow planners to create multiple what-
if scenarios
Automate capacitated plan
generation
Decision Optimization
SPSS
TM1
TM1TM1
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Optimization for LoB – typical use cases
Finance HRITMarketing OperationsSales
Prioritizingaccounts
Receivable
Portfolio optimization
Employee retention
Compensation & training planning
Helpdeskcase
Analysis
Staff assignment
ROI analysis
Campaignplanning
Warrantyanalysis
Sales & operations planning
Customer retention
Territory optimization
Descriptive & predictive analytics
Prescriptive analytics
(optimization)
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Bridget’s story
What are the predicted sales per region?
How should I create territories
to maximize quota
achievement fairly?
What is the revenue
breakdown by region?
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Bridget HartSales
Manager Descriptive analytics
Descriptive analytics
Descriptive analytics
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What are the predicted sales per region?
How should I create territories
to maximize quota
achievement fairly?
What is the revenue
breakdown by region?
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Descriptive analytics
Descriptive analytics
Descriptive analytics
Bridget HartSales
Manager
Bridget’s story
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Combining Machine Learning with Prescriptive Analytics – Used Cases
26IBM Analytics University 2018
Send a proposal X to customer ASend a proposal Y to customer B…
Customer A is about to churn (score = 95%)
Customer B might need product C (score = 20%)
…
Decision support : Don’t stop at the insight level…
ML model
HistoricalData
Data Score
Insight
DO model
Forecast
Data Plan
Decision
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TransportationMachine learning
Traffic Weather Past trips
Forecasteddemand
Travel times
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TransportationMachine learning
Decision optimization
Traffic
Policies
Weather
Capacities
Past trips
Actual demand
Forecasteddemand
Travel times
Routing
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Insurance and fraudMachine learning
Weather patterns
Customer class Claim/casetype
(house, car, etc.)
Claim casesForecasted claims
andfraudulent cases
Decision optimization
AgentsSchedule
Actual claims/cases Skills
Capacity Routing
Past claims
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FinanceMachine learning
Scenario 1
Scenario 2
Scenario 3
Market
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FinanceMachine learning Decision optimization
Scenario 1Budget Risk
Rulesdiversification
Commissionfees
Scenarios
Scenario 2
Scenario 3
New portfolioMarket
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ManufacturingMachine learning
Customers
Output:expected orders,
durations
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ManufacturingMachine learning Decision optimization
Customers Lines and recipes
R1 R2 R3
Line 2
Output:expected orders,
durations
R2R1
Line 1
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ManufacturingMachine learning Decision optimization
Customers Lines and recipes
Productionschedule
R1 R2 R3
Line 2
Output:expected orders,
durations
R2R1
Line 1
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Use Case - Retail
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Pricing using promotions, markdowns, and clearance strategies
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Retail optimization use case- Vertical: Retail
- Products: Apparel & Accessories
- Objective: Maximize Revenue, Maximize margin, Reduce Inventory
- Decisions: Dynamic Pricing
- What do I have: Initial Plan
- Status: Review the week- Decisions: Pricing- Dynamic Pricing- Markdowns- Price Points- Clearance- Promotions
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PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 53.73%
Our sales plan for last week was:
REVENUE TARGET
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PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 53.73%
Actual – Last Week
Sales $ Units Sold Margin
$7,083,935 559,390 51%
How did we do? Plan vs. Actual
REVENUE TARGET ACTUAL Revenue
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PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 54%
Actual – Last Week
Sales $ Units Sold Margin
$7,083,935 559,390 51%
How did we do? Plan vs. Actual
We missed both in sales revenue, units sold and
margin
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PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 53.73%
Actual – Last Week
Sales $ Units Sold Margin
$7,083,935 559,390 51%
How did we do? Plan vs. Actual
Which season/s was the problem?
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PLAN – Last week – SPRING SEASON
Sales $ Units Sold Margin
$5,515,500 310,000 61.73%
Actual – Last Week
Sales $ Units Sold Margin
$4,571,196 269,470 61.48%
Where we miss?
We missed on Revenue and on units
SPRING 2016 is the problem!
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What can we do?
- Using TM1/IBM Planning Analytics, we can analyze the data and identify Variance in the Plan vs. Actual
- How can we affect the demand? - Promotions- Markdowns- Clearance
- How do we decide which products , groups, when to act?
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Technology
- We use Predictive analytics- To predict the sales for next week/s- To identify slow and fast moving products- To identify products that react well in markdowns and promotions
- We use Prescriptive analytics – optimization - To decide optimal prices that maximize our revenue- To decide when to offer promotions to maximize our revenue
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What are the data we need for each SKU?
SKU ID Price Cost Days on the Floor
Total QuantityOrdered
Revenue Cost of Sold
Current Margin
Total Sold Units
Total Length of Selling period
Liquidation price
SKU999 $24.04
$6.85 77days
1794 $9969 $4247 57.4% 620 24 weeks
$6.85
Total Sold * Price IS NOT EQUAL to REVENUES SO FAR
Average Price$16.07
Avg. Sales through3.14%
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What is the output of the optimization?
SKU ID Price Cost Days on the Floor
Total QuantityOrdered
Revenue Cost of Sold
Current Margin
Total Sold Units
Total Length of Selling period
Liquidation price
SKU999 $24.04 $6.85 77days
1794 $9969 $4247 57.4% 620 24 weeks
$6.85
PROMOTE 30% NEXT WEEK
Average Price
$16.07 Avg. Salesthrough3.14%
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50% off
20% off 30% off40% off 50% off
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Reve
nues
Weeks
Markdown & Promotion strategy for a “slow-moving”product
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10% off30% off
20% off 50% …
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Reve
nues
Weeks
Markdown & Promotion strategy for a “fast-moving”product
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$79,000
$133,000
$187,000
$100,608
$174,846
$204,279
$60,000
$90,000
$120,000
$150,000
$180,000
$210,000
$240,000
Reve
nue
Effect of Markdowns & Promotions on Revenue
Revenue without Markdown & Promotion
Very slow
Slowmoving
Fast-moving
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20.9%
53.0%
66.6%
37.9%
64.3%69.4%
10.0%
25.0%
40.0%
55.0%
70.0%
Original Sales Rate
Effect of Markdowns & Promotions on Margin
Margin without Markdown & PromotionMargin with Markdown & Promotion
VERY SLOW
SLOW
FAST
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Learn more
Product
Slow moving
Fast Moving
Bad Sales Lift
Good Sales Lift
Bad Sales Lift
Good Sales Lift
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50% off 60% off
40% off 50% off60% off
40% off
50% off
30% off40% off
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Reve
nues
Weeks
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$79,000
$133,000
$100,608
$174,846
$81,064
$142,379
$60,000
$90,000
$120,000
$150,000
$180,000
$210,000
Revenue
Revenue without Markdown & Promotion Revenue with good sales lift
SLOW FAST MOVING
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20.9%
53.0%
37.9%
64.3%
22.4%
56.1%
10.0%
25.0%
40.0%
55.0%
70.0%
Margin without Markdown & Promotion Margin with good sales liftMargin with bad sales lift
FAST MOVING PRODUCTSLOW MOVING PRODUCT
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Call to Action
- Connect with Newcomp Analytics to discuss your current use cases- Where can you see Decision Optimization adding value in your organization?
- Proof of Concept with your data – showcase the art of the possible
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Thank You!
www.newcomp.com
