Lessons learned

© 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


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

https://www.ibm.com/developerworks/mydeveloperworks/blogs/jfp/?lang=en



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

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Business Results

min cTxs.t. Ax ≤ b

x integer

x1 = 3, x2 = 0, ...

Solution toMathematical Model

OR Specialist

BusinessExpertE

valu

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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

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Return on investment for optimization is great

After INFORMS 2011 Edelman Award Brochure – Jeffrey M. Alden



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



What is a good enough solution?

Is it a less expensive solution?



Is it a less expensive solution?

No!

What is a good enough solution?



What is a good enough solution?Is it a less expensive solution?No!It is a local optimum



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



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



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



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



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



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Businessrules

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Context data(channel, contact reason, planned

actions, IVR selections, etc.)

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baseline behavior, preferences, etc.)

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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

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Businessrules

64

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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

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Score eligible offers



– 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



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


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



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



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



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



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



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

Documents

Lessons learned