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Charlotte (US) - London (UK) – Rome (IT)
www.act-operationsresearch.com
FROM DATA TO DECISIONS.
THE CHALLENGES OF THE DIGITALIZATION AND
THE FRANZ EDELMAN AWARD EXPERIENCE
Operations Research Meets Machine Leering –
BOLOGNA March 12th 2019
Raffaele Maccioni
CEO – R&D Executive
ABOUT US - THE PASSION FOR MODELING
ACT OR is a math-technology company founded in 1996, with a strong engineering background.
We have a passion for Advanced Analytics, Operations Research, Statistical models, the theory of Systems and Control.
APPLIED SCIENCES &
TECHNIQUES
• Predictive & Stochastics models
• Math-Optimization & AI
• Simulation (continuous & discrete domain)
• Theory of Control
ACT OR’S VALUE PROPOSITION
At ACT OR we strongly believe that our math-technology with the right mix of competences in:
• Business processes• Advanced analytics skills• Computer science skills
Gives us the competitive advantage to offer to our Customers Business Solutions that generate immediate VALUE
MODELING & ALGORITHMS ENGINEERING
PROCESS KNOWLEDGE
COMPUTER SCIENCE
ESTABLISHED IN THE 1996 AS MATH-COMPANY
MARKET POSITIONin between research & the
“operations world”
BACKGROUNDEngineering and Mathematics
SOLUTIONSDecision Support Systems
& Services
ENABLING SUPERIOR BUSINESS PROCESSES GOVERNANCE
USA
• Charlotte
Italy• Rome•Varese• Salerno
UK• London
Spin-off Sapienza, University Of Rome
International bridge between theory and application
SOME OF THE REFERENCES
TOPICS
OPERATIONS PLANNING& SCHEDULING
CAPACITY, REVENUE & PRICE OPTIMIZATION
RISKs-MANAGEMENTTrading & Procurement
OPTIMAL DESIGN & Predictive Maintenance
DEMAND & SALES FORECAST
WAREHOUSE SIMULATION & OPTIMIZATION
FLEET SCHEDULING& ROUTING - TMS
Life Science & Healthcare
WORKFORCEMANAGEMENT
INVENTORY & REPLENISHMENT
Technology Tips
Bloomy Decision is a “decision-science-platform” , where multiple modules interacts: math-optimization, dynamic simulators, predictive & Artificial Intelligence models.
SOLUTION ARCHITECTURE
Bloomy Decision include the web user interface.Can be used in service (SaaS) or on premise
Transactional Systems
(ERP, etc)
Math-Optimization
Engines
Simulation
Environment
Real-Time Control System &
Field-Devices
Predictive &
Learning Machines
HIGH PERFORMANCE DECISION-SCIENCE PLATFORMWEB
FROM DATA TO DECISION.
THE CHALLENGES.
MAKING DECISIONS
A business decision today will impact the future….
.. is not so easy to make the “best” decisions.
MAKING DECISIONS
Sometimes, it is not so clear what "best" really means.
MAKING DECISIONS
Often the outcomes depend on the sum of multiple-decisions and "multiple-bests“, from different people within the organization.
MAKING DECISIONS
Attested: an appropriate, data-driven and analytical decision-making approach
enables better performances.
… BUT IS NOT SO EASY !!
#AI
#MachineLearing
#Optionzation
#DigitalTwins
#PrescriptiveAnalytics
Charlotte (US) - London (UK) – Rome (IT) www.act-operationsresearch.com
THE FRANZ EDELMAN AWARD EXPERIENCE
EDELMAN OPERATIONS RESEARCH 2018 AWARD
ACT OR and the partner Europcar (HQParis - FR) have been selected asfinalists by the Institute for OperationsResearch and Management Science(INFORMS) for Franz Edelman Award,the world’s most prestigiousinternational award for achievementin the practice of Operations Research.
INFORMS is the leading international association for professionals in Operations Research and Analytics.
Let’s imagine a hotel in which you could add one floor with 100 additional rooms in the peak period instead of being fully booked.“
“
“WITHOUT DEVIATION
FROM THE NORM,
PROGRESS IS
NOT POSSIBLE”
Frank Zappa
CHANGE MANAGEMENT
Operations Department
Fleet DepartmentFleet Capacity
9X
COMMON SYSTEM
OPERATIONAL CONSTRAINTS
SPECIFIC TO EACH COUNTRY
Successful Roll out:
Time
Skills
Expertise
BEST PRACTICESANNUAL REVENUE
MANAGEMENT MEETING
THE 4Rs to have the RIGHT car
at the RIGHT place,
for the RIGHT
customer,
sold at the RIGHT price
WIN – WIN Customer
Satisfaction
Profitability
ANALYTICAL MODELS STRUCTURE
OPT Allocation Plan
(Mid-long term)
OPT Capacity & Revenue OPT
Short-Mid term
Predictive & Learning Machines
Business Process Simulator
ANALYTICAL MODELS STRUCTURE
FORECASTDISCRETE EVENT
SIMULATOR
FLEET
OPTIMIZATION
PRICE
OPTIMIZATION
Upload input data
Data consistency check
Warning system
Updated Rental Agreements: 1.6 Mil
Forecasted Times Series: 18 k
Calculated Statistical Distributions: 760 k
Automatic Scenario: starting point for the daily activities
Run Scenarios:
Impact Evaluations
Fleet Optimizations
Prices Optimizations
Every day 300 users log in the system
Every month 600 users scenarios
Stop Sales
Infleet
Defleet
Transfers
Pricing strategies
Follow competitors
FORECAST
EXPECTED RENTAL AGREEMENTS
CHECK OUT DATE
DURATION
ONE WAY
EXTENSION
UPGRADE
EXPECTED DEMAND
More complex than forecasting a number
FORECAST INSIGHTS
Business customer on Friday
Leisure customer on Friday
FORECAST
DEMAND FORECAST
• Forecast for the upcoming 24 weeks
• Bank holidays and special events management
DISCRETE EVENT
SIMULATION
STATISTICAL MODELS MACHINE LEARNING TECHNIQUES
DEMAND FORECAST
FORECASTDISCRETE EVENT
SIMULATOR
DEMAND DRIVEN SCENARIO
FLEET DRIVEN SCENARIO
• Fleet Planning and Long-Term Actions
• Identification of possible actions for matching demand
• Fleet allocation and stop sales
• Fleet Allocation and Short-Term Actions
• Infleetment and transfers
• Different vehicle or demand loss
SIMULATION
OPTIMIZATION
FLEET
OPTIMIZATION
THE PROBLEM
• Three separate but correlated actions:
infleet, transfer and defleet
• Linear Programming Optimization Problem
• Fleet composition and utilization simultaneously considered
Factor Size Range
Zones from 10 to 54
Car Groups from 6 to 20
Fleet from 800 to 48.000
Rental Requests from 5.600 to 40.000
Days from 7 to 90
Large scale problem
Solved within 2 hours
Objective Function: Profit Maximization
Variables: Number of vehicles infleeted, defleeted and transferred
OPTIMIZATION
PRICE
OPTIMIZATION
• Main Input: expected demand, competitors actions
• Probabilistic Global Optimization Meta-Heuristic
• Black-box objective function
• Optimal Pricing Strategy
• Near Real Time Optimization
THE PROBLEM
THE ALGORITHM
THE STRUCTURE
Objective Function: Profit Maximization
Variables: Prices
NOT JUST TECHNOLOGY
PROCESS
ANALYTICS AND OPERATIONS
RESEARCH
CULTURE
TECHNOLOGY
HIGH-LEVEL SCENARIO VIEW
Predictive ALERT VIEW based on demand forecast
Week +1
Week +3
Week +2
Week +4
Portfolio Orders
Utilization Scale
Forecasts Available cars
FORECASTS VS. BOB
Real- time price optimization Vs Competitors
PRICE OPTIMIZATION VIEW
7 TAKEAWAYS FOR EXECUTIVES
1) Focus on the problem: are not the techniques the starting point
2) What data enable today? Which are the decision variables?
3) Will the “to-be” require a change in the processes and a change management?
4) Implement the solution or a POC? 5) It is not only a question of software and features.
Models behind such software have a dramatic importance.
6) Analytical experts should support the above steps, since the early stage;
7) Perseverance
CONTACTS
ACTOperationsResearch
www.act-operationsresearch.com
ACT Operations Research