© 2013 IBM Corporation
Industry SolutionsOptimization
Lessons Learned When Selling Optimization To Business Users
Jean-François Puget, IBM Distinguished Engineer, IBMJanuary 15, 2013
https://www.ibm.com/developerworks/mydeveloperworks/blogs/jfp/?lang=en
© 2013 IBM Corporation2
Industry SolutionsOptimization
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
© 2013 IBM Corporation3
Industry SolutionsOptimization
Solving a Business Problem with Optimization
Business Problem
Mathematical Model
Solver
Supply Chain Opt imisat ion Progr amme RASA Benefi t Real isat ion Weekly Summary
-35
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-5
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2008
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b*Ma
r*Ap
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Cont
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Rela
tive t
o Ap
r08
QS61
Bas
eline
(£k)
Realised Benefit Missed Opport unit y Act ual ≠ QS61 or Opt imal
Business Results
min cTxs.t. Ax ≤ b
x integer
x1 = 3, x2 = 0, ...
Solution toMathematical Model
OR Specialist
BusinessExpertE
valu
atio
n What are the key decisions? What are the constraints? What are the goals?
© 2013 IBM Corporation4
Industry SolutionsOptimization
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 Opt imisat ion Progr amme RASA Benefi t Real isat ion Weekly Summary
-35
-25
-15
-5
5
15
25
35
2008
Jan* Fe
b*Ma
r*Ap
r*w
19w
20
w 21
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w 48
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w 51
w 52
Cont
ribut
ion
Rela
tive t
o Ap
r08
QS61
Bas
eline
(£k)
Realised Benefit Missed Opport unit y Act ual ≠ QS61 or Opt imal
BusinessExpertE
valu
atio
n
Business Results
© 2013 IBM Corporation5
Industry SolutionsOptimization
Return on investment for optimization is great
After INFORMS 2011 Edelman Award Brochure – Jeffrey M. Alden
© 2013 IBM Corporation6
Industry SolutionsOptimization
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
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Industry SolutionsOptimization
What is a good enough solution?
Is it a less expensive solution?
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Industry SolutionsOptimization
Is it a less expensive solution?
No!
What is a good enough solution?
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Industry SolutionsOptimization
What is a good enough solution?Is it a less expensive solution?No!It is a local optimum
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Industry SolutionsOptimization
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
Make sure we convey solution clearly
–Nice graphics always win!–Use what is familiar for the customer
© 2013 IBM Corporation11
Industry SolutionsOptimization
Optimization
11
Business ProblemMathematical Model
Min cTxs.t. Ax ≤ b x integer
OR Specialist
Raw Data
Historical
Simulated
Text Video, Images Audio
Data instances
Predicted data
Optimization Data OptimizationOptimizationSolverSolver
BusinessExpert
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Industry SolutionsOptimization
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
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Industry SolutionsOptimization
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
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Industry SolutionsOptimization
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
© 2013 IBM Corporation15
Industry SolutionsOptimization
15
32%
54%
Responseprobability
54%
32%
90
200
NPV
A
B
C
Potentialactionss
Businessrules
64
49
Expectedvalue
Context data(channel, contact reason, planned
actions, IVR selections, etc.)
C
Customer data(current portfolio, segmentation,
baseline behavior, preferences, etc.)
C
Validate offer using business rules
Score offers
Propose best offer to customer
Act!
Collect offers for a given customer
Predictive Analytics and Business RulesInput: offer generator, output: offers for selected customers
42%D 150 61
Budget <= 100
60
30
Expense
40
© 2013 IBM Corporation16
Industry SolutionsOptimization
16
32%
54%
Responseprobability
54%
32%
90
200
NPV
A
B
C
Potentialactionss
Businessrules
64
49
Expectedvalue
Context data(channel, contact reason, planned
actions, IVR selections, etc.)
C
Customer data(current portfolio, segmentation,
baseline behavior, preferences, etc.)
C
Validate offers via business rules
Select best set of offers
Act!
Collect offers for all customers
Predictive Analytics and Business Rules and OptimizationInput: offer generator, output: offers for selected customers
42%D 150 61
Budget <= 100
60
30
Expense
40
Score eligible offers
© 2013 IBM Corporation17
Industry SolutionsOptimization
– 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
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Industry SolutionsOptimization
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:– 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
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Industry SolutionsOptimization 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
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Industry SolutionsOptimization
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
© 2013 IBM Corporation21
Industry SolutionsOptimization
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 unitsWe can easily compute the state for the plant at anytime from the scheduleThe sequence corresponding to the above Gantt chart is shown next slide
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Industry SolutionsOptimization
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
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Industry SolutionsOptimization
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
© 2013 IBM Corporation24
Industry SolutionsOptimization
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