Lessons learned

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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

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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.
  • Lessons learned

    1. 1. Industry SolutionsOptimization 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. 2. Industry SolutionsOptimizationDisclaimer 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=en2 © 2013 IBM Corporation
    3. 3. Industry SolutionsOptimization 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, ... w 19 w 20 w 21 w 22 w 23 w 24 w 25 w 26 w 27 w 28 w 29 w 30 w 31 w 32 w 33 w 34 w 35 w 36 w 37 w 38 w 39 w 40 w 41 w 42 w 43 w 44 w 45 w 46 w 47 w 48 w 49 w 50 w 51 w 52 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 Results3 © 2013 IBM Corporation
    4. 4. Industry SolutionsOptimization 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 w 19 w 20 w 21 w 22 w 23 w 24 w 25 w 26 w 27 w 28 w 29 w 30 w 31 w 32 w 33 w 34 w 35 w 36 w 37 w 38 w 39 w 40 w 41 w 42 w 43 w 44 w 45 w 46 w 47 w 48 w 49 w 50 w 51 w 52 Mar* Feb* Apr* 2008 Jan* -5 -15 -25 -35 Realised Benefit Missed Opport unit y Act ual ≠ QS61 or Opt imal Business Results4 © 2013 IBM Corporation
    5. 5. Industry SolutionsOptimizationReturn on investment for optimization is great After INFORMS 2011 Edelman Award Brochure – Jeffrey M. Alden5 © 2013 IBM Corporation
    6. 6. Industry SolutionsOptimizationDon’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 second6 © 2013 IBM Corporation
    7. 7. Industry SolutionsOptimizationWhat is a good enough solution?Is it a less expensive solution?7 © 2013 IBM Corporation
    8. 8. Industry SolutionsOptimizationWhat is a good enough solution?Is it a less expensive solution?No!8 © 2013 IBM Corporation
    9. 9. Industry SolutionsOptimizationWhat is a good enough solution?Is it a less expensive solution?No!It is a local optimum9 © 2013 IBM Corporation
    10. 10. Industry SolutionsOptimizationSolving 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 objective10 © 2013 IBM Corporation Make sure we convey solution clearly
    11. 11. Industry Solutions OptimizationOptimization 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 Audio11 © 2013 IBM Corporation 11
    12. 12. Industry SolutionsOptimizationSolving 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 end12 © 2013 IBM Corporation
    13. 13. Industry SolutionsOptimizationOther 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, etc13 © 2013 IBM Corporation
    14. 14. Industry SolutionsOptimizationOptimization 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 one14 © 2013 IBM Corporation
    15. 15. Industry SolutionsOptimizationPredictive Analytics and Business RulesInput: 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 <= 1001515 © 2013 IBM Corporation
    16. 16. Industry SolutionsOptimizationPredictive Analytics and Business Rules and OptimizationInput: 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 <= 1001616 © 2013 IBM Corporation
    17. 17. Industry SolutionsOptimization 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 unit17 © 2013 IBM Corporation
    18. 18. Industry SolutionsOptimization 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:18 © 2013 IBM Corporation
    19. 19. Industry SolutionsOptimization 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 slide19 © 2013 IBM Corporation
    20. 20. Industry SolutionsOptimization 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 320 © 2013 IBM Corporation
    21. 21. Industry SolutionsOptimizationOptimization 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 slide21 © 2013 IBM Corporation
    22. 22. Industry SolutionsOptimization 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 322 © 2013 IBM Corporation
    23. 23. Industry SolutionsOptimizationOptimization 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 instance23 © 2013 IBM Corporation
    24. 24. Industry SolutionsOptimizationBusiness 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 plan24 © 2013 IBM Corporation

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