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e-Procurement Using Goal Programming

e-Procurement Using Goal Programming. S Kameshwaran kameshn@csa.iisc.ernet.in March 4, 2003. e-Procurement. Internet-based business process for obtaining material and services. Current Solutions: Reverse Auctions www.edeal.com Optimization Techniques www.emptoris.com, www.rapt.com

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e-Procurement Using Goal Programming

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  1. e-Procurement Using Goal Programming S Kameshwaran kameshn@csa.iisc.ernet.in March 4, 2003

  2. e-Procurement • Internet-based business process for obtaining material and services. • Current Solutions: • Reverse Auctions • www.edeal.com • Optimization Techniques • www.emptoris.com, www.rapt.com • Multiple Criteria Decision Analysis • www.frictionless.com, www.perfect.com eEnterprises Lab, CSA, IISc

  3. Reverse Auctions • Supplier with lowest bid price is selected • Negotiation is only one dimension: Price • Competing on price alone makes the supplier feel de-branded and commoditized • Competitive bidding on price drives the manager down to the point of suppliers’ survival • With multiple attributes to bid, the suppliers can differentiate themselves eEnterprises Lab, CSA, IISc

  4. Optimization Techniques • Business rules are considered in selecting the winning bid(s) • # of winning suppliers: not more than 3 • % of business for a supplier: no more than 40% • Shipment constraints: not less than 20% • Cannot handle multiple attributes eEnterprises Lab, CSA, IISc

  5. Multiple Criteria Decision Analysis • Multiple attributes/criteria are considered in selecting the bids • Example: • Attributes: Cost, lead time, part failure rate • Weights: 0.5, 0.25, 0.25 • Scoring Function: • Cost: {(<$1000, 100), ([$1000-$1200], 80), ([$1200-1500], 60), …} • Lead Time: {([<5 days], 100), ([5-8], 70), …} • Part Failure Rate: {[<20%], 100], ([20-30%], 80), …} eEnterprises Lab, CSA, IISc

  6. Multiple Criteria Decision Analysis (contd) • Scoring function: converts the attributes values to a common virtual currency • Bid: • Bidding language (Attribute, Value): {(Cost, $1100), (Lead Time, 6 days), (Part failure rate, 10%)} • Bid Score: Weighted additive scoring • 0.5 x 80 + 0.25 x 70 + 0.25 x 100 = 82.5 • Bids are ranked by their total score and selected eEnterprises Lab, CSA, IISc

  7. Multiple Criteria Decision Analysis (contd) • Weights and scoring functions are subjective assessments (suppose to reflect the buyer’s true preferences and utility) • Effects of the weights on the output are clear • The weighted additive scheme does not reflect true utility of the buyer if the attributes are interdependent eEnterprises Lab, CSA, IISc

  8. Our Proposal • Configurable bids as bidding language • Goal programming for selecting winning bid(s) • Combines optimization techniques + multiple criteria decision analysis • Allows the suppliers to express their bids in more negotiable format eEnterprises Lab, CSA, IISc

  9. Configurable Bids • Example: Procuring SLR cameras • Attributes: Price, lens focus, warranty • Bid: Functions of Price • Warranty: • 2 yrs: $50 • 5 yrs: $150 eEnterprises Lab, CSA, IISc

  10. Configurable Bids • Each attribute can have more than one value: (lens focus: 3 values, warranty: 2 values) • For each value, Price is given as a function of quantity • Function: Piecewise linear (lens focus) or linear (warranty) • Piecewise linear: Price depends on quantity • Total Cost: Sum of all costs • Buyer configures the bid by choosing the best values for each attribute and the best quantity for each value • 100 units with lens focus28-200mm and 2 yrwarranty + 250 units with lens focus75-300mm and 5 yrwarranty eEnterprises Lab, CSA, IISc

  11. Multiple criteria decision analysis For less # of alternatives Utility and preferences are precisely known Useful for ranking few bids Multiattribute theory, AHA etc. Multiple criteria optimization For very large or infinite # of alternatives Utility and preferences are not explicitly known Useful for configurable bids: finding the best configuration of bids Goal programming, Compromise programming, etc Multiple Criteria Decision Making eEnterprises Lab, CSA, IISc

  12. Goal Programming (GP) • The objectives are formulated as Goals • Minimize cost as Cost <= $20,000 • Buyer specifies target or aspiration level for each objective • Cost: $20,000 • Lead Time: 5 days • Part failure rate: 20% • GP simultaneously satisfies all goals as closely as possible eEnterprises Lab, CSA, IISc

  13. GP Techniques • Weighted GP • Minimizes the weighted sum of deviations from target levels • Lexicographic GP • Minimizes the deviations of goals in preemptive manner • Interactive Sequential GP • Interactive with human intervention • The buyer can change the target levels of goals by learning from the intermediate solutions eEnterprises Lab, CSA, IISc

  14. Thank You • Our technique can handle: • Multiple criteria in bid evaluation • Business rules as side constraints For more details refer: “e-Procurement Using Goal Programming”: S Kameshwaran, Y Narahari, 2003. eEnterprises Lab, CSA, IISc

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