FROM DATA TO DECISIONS. THE CHALLENGES OF THE ... · FROM DATA TO DECISIONS. THE CHALLENGES OF THE...

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

act@act-operationsresearch.com

ACTOperationsResearch

www.act-operationsresearch.com

ACT Operations Research