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Lessonslearnedeuro

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My talk at EURO INFORMS Conference

My talk at EURO INFORMS Conference

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  • Optimization does not start with data. Very often we start working on the optimization model without any data. The lack of data isn’t an issue per. It becomes an issue during the tuning phase, but not during the elaboration phase of a modeL Here are two examples with the Empty Container Repositioning (ECR) asset The optimization model is quite stable, what differs is how data is collected by the customer. Forecast of where empty containers will be needed in particular vary a lot from customers to customers. Last customer we visited do not have any relevant data. It is once they understood the potential ROI enable by the optimization model that they started to think about how they could collect the required data. That data does not exist prior the ECR discussion. The previous customer, once interested, asked us to validate the ROI duing a POC. The POC lasted 8 weeks, and all the work in the POC was about getting the right data in the ECR asset. Once this was done we proved a 7.5% redution in transportation cost.
  • JFP

Lessonslearnedeuro Lessonslearnedeuro Presentation Transcript

  • 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
  • © 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
  • © 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)
  • © 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?
  • © 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 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 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?
  • © 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
  • © 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
  • © 2013 IBM Corporation8 Industry Solutions Optimization Return on investment for optimization is amazing After INFORMS 2011 Edelman Award Brochure – Jeffrey M. Alden
  • © 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!
  • © 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
  • © 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?
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 2013 IBM Corporation19 Industry Solutions Optimization How To Please The Customer? Is it a less expensive solution?
  • © 2013 IBM Corporation20 Industry Solutions Optimization Is it a less expensive solution? No! How To Please The Customer?
  • © 2013 IBM Corporation21 Industry Solutions Optimization How To Please The Customer? Is it a less expensive solution? No! It is a local optimum
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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
  • © 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!
  • © 2013 IBM Corporation33 Industry Solutions Optimization Move to latest CPLEX release (free for academia)
  • © 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]
  • © 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
  • © 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
  • © 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
  • © 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