The document discusses technical lessons learned from selling optimization solutions to business users. It provides tips for understanding the needs of different stakeholders in a business, including ensuring the optimization project solves the right problem and delivers return on investment. It also offers ways to improve optimization performance, such as moving to the latest solver release, changing the mathematical model, using multithreading, and tuning the solver.

1. Technical Lessons Learned While
Selling Optimization To Business Users
Jean-François Puget
Industrial Solutions Analytics and Optimization
IBM, France
SponsorSession IBM1
Monday July 1 – 14:30-16:00

2. © 2013 IBM Corporation
Industry Solutions
Optimization
Lessons Learned When Selling Optimization To
Business Users
Jean-François Puget, IBM Distinguished Engineer, IBM Software Group
July 1, 2013
@JFPuget

3. © 2013 IBM Corporation3
Industry Solutions
Optimization
Disclaimer
 I work for IBM
– The views expressed here are mine, not IBM’s
 I worked for ILOG
– The views expressed here are biased towards ILOG and IBM past
engagements in this area
– They are also biased towards IBM products in this area
• IBM ILOG CPLEX Optimization Studio (CPLEX), IBM ILOG ODME
 But I think there is some general truth here
 Some ideas expressed here have been discussed on my blog :
http://ibm.co/ITcbUn
(easier to find me on twitter: @JFPuget)

4. © 2013 IBM Corporation4
Industry Solutions
Optimization
Agenda
Business users needs
– What do they care about?
– How to sell them an optimization project?
Solve the right problem
– Really solve the right problem
Position against other techniques
– Eg Simulation
It is not fast enough
– How to get a 200x speedup?

5. © 2013 IBM Corporation5
Industry Solutions
Optimization
Solving a Business Problem with Optimization
Business Problem
Mathematical Problem
Solver
Supply Chain Optimisation Programme
RASA Benefit Realisation Weekly Summary
-35
-25
-15
-5
5
15
25
35
2008Jan*
Feb*
Mar*
Apr*
w19
w20
w21
w22
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w28
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ContributionRelativetoApr08QS61Baseline(£k)
Realised Benefit Missed Opportunity Actual ≠ QS61 or Optimal
Business Results
min cT
x
s.t. Ax ≤ b
x integer
x1 = 3, x2 = 0, ...
Solution to
Mathematical Problem
OR Specialist
Business
Expert
Evaluation
 What are the key decisions?
 What are the constraints?
 What are the goals?

6. © 2013 IBM Corporation6
Industry Solutions
Optimization
Business users
 They don’t care about the
technology
 They care about their problem
– Eg schedule next day plant
operations, next month roster
for bus drivers, etc
 They want
– Return on investment
– Help to solve their problem
– To be in charge
Business Problem
Supply Chain Optimisation Programme
RASA Benefit Realisation Weekly Summary
-35
-25
-15
-5
5
15
25
35
2008Jan*
Feb*
Mar*
Apr*
w19
w20
w21
w22
w23
w24
w25
w26
w27
w28
w29
w30
w31
w32
w33
w34
w35
w36
w37
w38
w39
w40
w41
w42
w43
w44
w45
w46
w47
w48
w49
w50
w51
w52
ContributionRelativetoApr08QS61Baseline(£k)
Realised Benefit Missed Opportunity Actual ≠ QS61 or Optimal
Business
Expert
Evaluation
Business Results

7. © 2013 IBM Corporation7
Industry Solutions
Optimization
Business users
 They don’t care about the
technology
 They care about their problem
– Eg schedule next day plant
operations, next month roster
for bus drivers, etc
 They want
– Return on investment
– Help to solve their problem
– To be in charge
 Iterative process
– Monitor their business
– Construct a plan
– Analyze trade offs
– Validate
– Publish new plan

8. © 2013 IBM Corporation8
Industry Solutions
Optimization
Return on investment for optimization is amazing
After INFORMS 2011 Edelman Award Brochure – Jeffrey M. Alden

9. © 2013 IBM Corporation9
Industry Solutions
Optimization
Sales Made Simple: Sell ROI
 Find relevant problems already solved
–Lots of literature
–Edelman finalists
 Show the results
–Show you can do it too
 Get the sale
–(get hired as a consultant)
 That’s it?
No!

10. © 2013 IBM Corporation10
Industry Solutions
Optimization
Undertand who you speak to
 There are several stakeholders
– The buyer, interested in ROI
• Eg a COO, a line of business manager, the person who has the budget
– The first line manager
• The manager of the user, responsible for the operations to be optimized
– The OR expert, who will deliver the software solution
• Eg a consultant
– The user, who will use the software for delivering a business function
• Eg a plan operations planer,
 Each of them has different needs
 It is very important to understand these
– What follows is barely touching the issues

11. © 2013 IBM Corporation11
Industry Solutions
Optimization
The Budget Owner
 Speaking about return on investment is great
– Very compelling
 That’s not all, she will be looking for hidden cost
– Is there a risk on project completion?
– Will her company/department be ready to implement the results computed by
the optimization software?
– Will people buy in?
– Will the solution be scalable?
– Will it evolve correctly over time
– Can the company take over, ie hire the right skills to further develop it?

12. © 2013 IBM Corporation12
Industry Solutions
Optimization
The Manager
 He is in charge
– Wants to stay in charge
– Keep him in the decision loop
 Does’t want to look stupid
– Why didn’t he though of it before?
At a railway company, an employee found a nice way to use
optimization to reduce the need for herbicids (7% less)
–Was fired

13. © 2013 IBM Corporation13
Industry Solutions
Optimization
The OR Expert
 The more promise on ROI
– The easier the sale
– The harder the job
 Do not overpromise
– Solve the right problem
– ROI comes second
 Use you experience to manage expectations

14. © 2013 IBM Corporation14
Industry Solutions
Optimization
The user
 She has to actually solve the problem
– Must be convinced that the proposed project will lead to a solution of her
business problem
– Keep her in the loop while the application is being developed
 Start with a small part of the problem
– Validate solutions for the partial problem with the user
– Move to a larger part of the problem only when current piece is validated
 The more we promise the COO or the manager on ROI
– The more complex the task for the user and for the consultant
• They are expected to deliver the ROI!
• The OR expert can rely on his experience
• The user has to rely on the consultant
– Frightening!
 Solving the business problem comes first, improving ROI is second

15. © 2013 IBM Corporation15
Industry Solutions
Optimization
Solve the right problem
 Better have an approximate solution to today’s problem than an optimal solution to
yesterday’s problem
 Make sure to get problem statement right
– Objective (often multiple conflicting objectives)
– Constraints (often too many)
• Test with a known solution
 Data Quality is key
– Garbage in, garbage out
 Make sure we always output a solution
– Relax the problem, move constraints to objective
 Make sure we convey solution clearly
– Nice graphics always win!
– Use what is familiar for the customer

16. © 2013 IBM Corporation16
Industry Solutions
Optimization
Optimization
16
Business Problem
Mathematical Model
Min cT
x
s.t. Ax ≤ b
x integer
OR Specialist
Raw Data
Historical
Simulated
Text Video, Images Audio
 Data instances
 Predicted data
Optimization Data OptimizationOptimization
SolverSolver
Business
Expert

17. © 2013 IBM Corporation17
Industry Solutions
Optimization
Solving in reasonable time
 Which time?
– Time to compute a solution
• Often time boxed, best solution found in limited time
– Time to develop the software application
• Boxed too by project funding
 Trade off
– Fast solver with poor development tool
• Not much time to tune mode/data, poor performance in the end
– Slow solver with great development tool
• Lots of time to tune model/data, poor performance in the end
– Great Solver with great development tools
• Lots of time to tune model/data, great performance in the end

18. © 2013 IBM Corporation18
Industry Solutions
Optimization
How To Spot Opportunities
Find the low hanging fruit
– Data must be available and of good quality
– Business need must be pressing (competition)
Implement the solution
– Can be *very* hard if it implies process changes
– Can be tough if it implies to move or fire people
– Easier when optimization is used to do more
• More revenue, better service, new services, etc

19. © 2013 IBM Corporation19
Industry Solutions
Optimization
How To Please The Customer?
Is it a less expensive solution?

20. © 2013 IBM Corporation20
Industry Solutions
Optimization
Is it a less expensive solution?
No!
How To Please The Customer?

21. © 2013 IBM Corporation21
Industry Solutions
Optimization
How To Please The Customer?
Is it a less expensive solution?
No!
It is a local optimum

22. © 2013 IBM Corporation22
Industry Solutions
Optimization
Predictive Analytics
Why did it happen? What will happen?
Descriptive Analytics
What has happened?
Prescriptive Analytics
What should we do?
The Analytics Maturity Model : How Much Do We Automate?
Data Insight Action
Analytics is a mean to an end:
Its value comes from the decisions it enables
Optimization is a tool of choice for computing decisions
DecideAnalyze
Business Value
22

23. © 2013 IBM Corporation23
Industry Solutions
Optimization
Optimization vs Other Decision Technology
Predictive Analytics
– Statistics, machine learning
– Learn from past, then predict
Business Rules
– Predefined decision policy
Simulation
– Behavioral model
Optimization complements these
– None is a replacement for another one

24. © 2013 IBM Corporation24
Industry Solutions
Optimization
– 3 machines
• Each machine can process various wafer types
– For example, Machine 2 can process two types while Machine 3
accepts 3 types
– Wafer flow
• One operation on Machine 1
• One operation on either Machine 2 or Machine 3
– All operations last the same amount of time, one time unit
Machine 2
Machine 3
Simulation and optimization
A Very simple Semi Conductor Plant
Machine 1

25. © 2013 IBM Corporation25
Industry Solutions
Optimization
 When there is a choice between machines, the MES (Manufacturing
Execution System) dispatch wafers using rules
– Wafers wait before a machine can process them
– This is called WIP (Work In Progress)
 Various rule sets are possible
– Improving plant operations requires changes in rule set
– Simulation is used to evaluate the plant performance for a given rule set
– Alternate rule sets can be evaluated using simulation of the plant
– The best rule set is kept
 Let’s simulate this rule set:
– First rule: selects one WIP and disptch it to one of the available machine
– Second rule: In case of tie assign to the less loaded machine
 We start with this WIP:
Solution Using Simulation
Machine 2
Machine 3
Machine 1

26. © 2013 IBM Corporation26
Industry Solutions
Optimization Solution Using Dispatching Rules
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Rules are applied, resulting in this WIP dispatch
•Then we advance time by one time unit,
•One operation is processed by each machine
•Processed wafers move to the new stage in flow
•Then they are dispatched by rules
•Result is a new plant state :
We repeat this and get a sequence of plant states, see next slide

27. © 2013 IBM Corporation27
Industry Solutions
Optimization
5 time units are required for processing WIP
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1

28. © 2013 IBM Corporation28
Industry Solutions
Optimization
Optimization Solution
 Optimization outputs a schedule
– Assigns operations to machines
– Computes starting time for each operation
– While meeting all constraints
– And optimizing the objective
 The result can be displayed in a Gantt chart
– It shows the state of the plant over time
– No need for a simulation tool to know what will happen when the schedule is executed
 An optimal schedule for our example is shown below
– It only requires 4 time units
We can easily compute the state for the plant at anytime from the schedule
The sequence corresponding to the above Gantt chart is shown next slide

29. © 2013 IBM Corporation29
Industry Solutions
Optimization
4 time units are required for processing WIP
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1
Machine 2
Machine 3
Machine 1

30. © 2013 IBM Corporation30
Industry Solutions
Optimization
Optimization solution is quite different
 Simulation requires a behavioral model
– Compute plant state at time T+1 knowing state at time T, and knowing events that occur betwen T
and T+1
– Here, events are operation completion on each machine
 Optimization requires a descriptive model
– Operation sequence for each wafer
– Processing time for each operation
– WIP capacity for each machine
– Set of operations each machine can process
…
 Optimization requires an objective, for instance
– Minimize processing time
– Maximize throughput
– Maximize machine utilization rate

31. © 2013 IBM Corporation31
Industry Solutions
Optimization
It is too slow
Is it really an issue?
Move to latest CPLEX release!
Change your model!
Use multithreading
Tune CPLEX

32. © 2013 IBM Corporation32
Industry Solutions
Optimization
It is too slow
We had to solve a locomotive assignment problem in less than a minute
We managed to get it done in a second
We thought the customer would be happy…
They immediately tried the weekly problem
It was taking more than a minute
They asked us to solve under a minute
I predicted that if we managed that then they’ll try the monthly problem…
Guess what happened …
Customers are complaining because optimization results are great.
They can’t wait for them!

33. © 2013 IBM Corporation33
Industry Solutions
Optimization
Move to latest CPLEX release (free for academia)

34. © 2013 IBM Corporation34
Industry Solutions
Optimization
Change Your Model
Eg D-Wave vs CPLEX
Input: a QUBO
minimize sumij Qij xi xj
where all the variables xi are binary.
D-Wave 3600x faster than CPLEX [McGeoch&Wang 2013]

35. © 2013 IBM Corporation35
Industry Solutions
Optimization
Change Your Model
Linearize xi xj
Introduce new variables zij
minimize sumij Qij zij
Subject to
zij ≤ xi
zij ≤ xj
zij ≥ xi + xj - 1
Zij ≥ 0
Only state two of the constraints depending on the
sign of Qij
D-Wave 3600x 110x faster than CPLEX

36. © 2013 IBM Corporation36
Industry Solutions
Optimization
Use Multi Threading
Run with 8 threads
D-Wave 3600x 20x faster than CPLEX
Run with 32 threads
D-Wave 3600x 20x faster than CPLEX
Worst case time divided by 2

37. © 2013 IBM Corporation37
Industry Solutions
Optimization
Tune CPLEX
Look at log file
Zero-half cuts applied: 206
Lift and project cuts applied: 1
Gomory fractional cuts applied: 12
Then use these cuts more aggressively
set mip cuts gom 2
set mip cuts zero 2
D-Wave 3600x 14x faster than CPLEX

38. © 2013 IBM Corporation38
Industry Solutions
Optimization
Business users
 They don’t care about the
technology
 They care about their problem
– Eg schedule next day plant
operations, next month roster
for bus drivers, etc
 They want
– Return on investment
– Help to solve their problem
– To be in charge
 Iterative process
– Monitor their business
– Construct a plan
– Analyze trade offs
– Validate
– Publish new plan Have a look at ODM Enterprise