Lessons learned while selling optimization technology to business people. Issue is to map the value of this technology to business value without having to explain how it works. replay available here http://www.youtube.com/watch?v=l1LieKG_Q8Y&t=2m49s

1. Industry Solutions
Optimization
Lessons Learned When Selling Optimization To
Business Users
Jean-François Puget, IBM Distinguished Engineer, IBM
January 15, 2013
https://www.ibm.com/developerworks/mydeveloperworks/blogs/jfp/?lang=en
© 2013 IBM Corporation

2. 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, IBM ILOG ODME
 But I think there is some general truth here
 Some ideas expressed here have been discussed on my blog :
https://www.ibm.com/developerworks/mydeveloperworks/blogs/jfp/?lang=en
2 © 2013 IBM Corporation

3. Industry Solutions
Optimization
Solving a Business Problem with Optimization
min c Tx
s.t. Ax ≤ b
x integer
Mathematical Model
OR Specialist
Business Problem
 What are the key decisions?
Evaluation
 What are the constraints?
Business  What are the goals?
Expert
Supply Chain Opt imisat ion Progr amme
RASA Benefit Realisat ion Weekly Summary
35
25
Solver
Cont ribut ion Relat ive t o Apr08 QS61 Baseline (£k)
15
5
x1 = 3, x2 = 0, ...
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Mar*
Feb*
Apr*
2008 Jan*
-5
Solution to
-15
-25
-35
Mathematical Model
Realised Benefit Missed Opport unit y Act ual ≠ QS61 or Opt imal
Business Results
3 © 2013 IBM Corporation

4. Industry Solutions
Optimization
Business users
 They don’t care about the
technology
 They care about their problem
Business Problem – Eg schedule next day plant
operations, next month roster
for bus drivers, etc
Evaluation
Business  They want
Expert – Return on investment
Supply Chain Opt imisat ion Progr amme
RASA Benefit Realisat ion Weekly Sum mary
35
– Help to solve their problem
25
– To be in charge
Cont ribut ion Relat ive t o Apr08 QS61 Baseline (£k)
15
5
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Mar*
Feb*
Apr*
2008 Jan*
-5
-15
-25
-35
Realised Benefit Missed Opport unit y Act ual ≠ QS61 or Opt imal
Business Results
4 © 2013 IBM Corporation

5. Industry Solutions
Optimization
Return on investment for optimization is great
After INFORMS 2011 Edelman Award Brochure – Jeffrey M. Alden
5 © 2013 IBM Corporation

6. Industry Solutions
Optimization
Don’t sell on ROI!!
 There are three stakeholders
– The buyer, interested in ROI
• Eg a COO
– The user, who will use the software for delivering a business function
• Eg a plan operations planer,
– The OR expert, who will deliver the software solution
• Eg a consultant
 The more we promise on ROI
– The happier the buyer
– 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
6 © 2013 IBM Corporation

7. Industry Solutions
Optimization
What is a good enough solution?
Is it a less expensive solution?
7 © 2013 IBM Corporation

8. Industry Solutions
Optimization
What is a good enough solution?
Is it a less expensive solution?
No!
8 © 2013 IBM Corporation

9. Industry Solutions
Optimization
What is a good enough solution?
Is it a less expensive solution?
No!
It is a local optimum
9 © 2013 IBM Corporation

10. Industry Solutions
Optimization
Solving the right problem
 Better have an approximate solution to today’s problem than an optimal solution to
yesterday’s problem
 Make sure we 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
10 © 2013 IBM Corporation
 Make sure we convey solution clearly

11. Industry Solutions Optimization
Optimization
Business Min cTx
Expert
s.t. Ax ≤ b
x integer
Mathematical Model
OR Specialist
Business Problem
Raw Data Optimization Data Optimization
Solver
Historical
 Data instances
 Predicted data
Simulated
Text Video, Images Audio
11 © 2013 IBM Corporation
11

12. 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
12 © 2013 IBM Corporation

13. Industry Solutions
Optimization
Other issues
 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 optimizaiotn is used to do more
• More revenue, better service, new services, etc
13 © 2013 IBM Corporation

14. 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
14 © 2013 IBM Corporation

15. Industry Solutions
Optimization
Predictive Analytics and Business Rules
Input: offer generator, output: offers for selected customers
 Collect offers for a given customer
 Validate offer using business rules
Act!
 Score offers
C  Propose best offer to customer
Context data Potential Business Response Expected
(channel, contact reason, planned NPV Expense
actions, IVR selections, etc.) actionss rules probability value
A
C B 90 54% 49 30
Customer data
(current portfolio, segmentation,
baseline behavior, preferences, etc.) C 200 32% 64 60
D 150 42% 61 40
Budget <= 100
15
15 © 2013 IBM Corporation

16. Industry Solutions
Optimization
Predictive Analytics and Business Rules and Optimization
Input: offer generator, output: offers for selected customers
 Collect offers for all customers
 Validate offers via business rules
Act!
Score eligible offers
C  Select best set of offers
Context data Potential Business Response Expected
(channel, contact reason, planned NPV Expense
actions, IVR selections, etc.) actionss rules probability value
A
C B 90 54% 49 30
Customer data
(current portfolio, segmentation,
baseline behavior, preferences, etc.) C 200 32% 64 60
D 150 42% 61 40
Budget <= 100
16
16 © 2013 IBM Corporation

17. Industry Solutions
Optimization Simulation and optimization
A Very simple Semi Conductor Plant
Machine 2
Machine 1
Machine 3
– 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
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18. Industry Solutions
Optimization
Solution Using Simulation
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 require 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: 1
Machine 2
Machine
– 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
Machine 3
We start with this WIP:
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19. Industry Solutions
Optimization Solution Using Dispatching Rules
Rules are applied, resulting in this WIP dispatch
Machine 2
Machine 1
Machine 3
•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 :
Machine 2
Machine 1
Machine 3
We repeat this and get a sequence of plant states, see next slide
19 © 2013 IBM Corporation

20. Industry Solutions
Optimization
5 time units are required for processing WIP
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
20 © 2013 IBM Corporation

21. 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
21 © 2013 IBM Corporation

22. Industry Solutions
Optimization
5 time units are required for processing WIP
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
Machine 2
Machine 1
Machine 3
22 © 2013 IBM Corporation

23. 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
23 © 2013 IBM Corporation

24. 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
24 © 2013 IBM Corporation