mysap Supply Chain Management

Transcription

1 ptimizing the Supply Network in mysap Supply Chain Management SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 1 SAP / 1 Dr. Dirk Meier-Barthold GBU SCM

2 Agenda 1 ntegrated Supply Network Planning and ptimization with AP 2 Modeling the Supply Network 3 ptimizing the Supply Network 4 Selected planning scenario SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 2 SAP / 2

3 Supply Network Planning Decision support for supply network planner: Decisions to be made: Global sourcing decisions Global load-balancing decisions Global lot-sizing decisions Supply Chain Planning Horizon Level of Detail SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 3 SAP / 3

4 ntegrated Supply Network Planning and ptimization Network Design Supply Network Planning Demand Planning Procurement Planning Production Planning Distribution Planning Purchasing Workbench Detailed Scheduling Vehicle Scheduling Available to Promise Supply Network Planning ptimizer Heuristics Sourcing Balancing Lot-Sizing LP MLP Propagation CTM DP/MP esult: Network wide supply decisions products, locations, periods and quantities SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 4 SAP / 4

5 Agenda 1 ntegrated Supply Network Planning and ptimization with AP 2 Modeling the Supply Network 3 ptimizing the Supply Network 4 Selected planning scenario SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 5 SAP / 5

6 Modeling the Supply Network External Procurement T G Production S P Transportation G T G SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 6 SAP / 6

7 Decision Variables External Procurement T G Production S P Production Quantity Transportation G T G External Procurement Additional Capacity Transportation Quantity SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 7 SAP / 7

8 Decision Constraints External Procurement T G Transportation G T G Production S P Product Constraints - Consumption (fix, variable) - Minimal lot size - Fixed lot size - Shelf life esource Constraints (Production, Transport, Handling, Storage) - Capacity (normal, additional, calendar) - Consumption (set up, variable) Customer Constraints - Back order -Lost sales - Safety stock SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 8 SAP / 8

9 Cost of Decisions External Procurement T G Production S P Cost of Procurement (piecewise linear cost function) - Production quantity - Transportation quantity - External Procurement Transportation G T G Cost of Product Constraints - Cost of violating Shelf Life Cost of esource Constraints (Production, Transport, Handling, Storage) - Cost of additional capacity - Cost of nventory consumption Cost of Customer Constraints (Demand classes) - Cost of back order - Cost of lost sales - Cost of using safety stock SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 9 SAP / 9

10 Problem Complexity 3 classes of problem complexity: -> linear program (LP) -> all decision variables are proportional -> mixed integer linear program (MLP) a) yes/ no decisions -> set up -> minimal lot size -> piecewise linear cost function b) integer decisions -> fixed lot size -> full truck loads SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 10 SAP / 10

11 eduction of Problem Complexity (1) Are mixed integer really necessary? -Set up -> not reasonable, if a lot of products are on resource per bucket - Minimal lot size -> not reasonable, if minimal lot size is small to average lot size - Piecewise linear cost function -> only reasonable, if few pieces are modeled - Discrete lot size/ rounding -> not reasonable, if lot size is very high (e.g. 97.5) -> not necessary, if production over buckets is allowed SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 11 SAP / 11

12 eduction of Problem Complexity (2) How can we create a reasonable SNP model? - Use aggregated time-buckets - Focus on Supply Chain relationships - Use key products and bottleneck resources, only - Design easy PPM s SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 12 SAP / 12

13 Agenda 1 ntegrated Supply Network Planning and ptimization with AP 2 Modeling the Supply Network 3 ptimizing the Supply Network 4 Selected planning scenario SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 13 SAP / 13

14 ptimizing the Supply Network bjectives for the SNP-ptimizer: - good performance of planning result - good performance of planning runtime Planning untime Planning untime Planning esult Planning esult SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 14 SAP / 14

15 ncremental ptimization Can be necessary due to: - Problem size - User experiences -> mportant: Focusing on strongest constraints - with Selection -> horizontal aggregation -> vertical aggregation -> DP/ MP-like planning - within Selection -> product decomposition -> time decomposition -> priority decomposition -> activate constraints -> restrict runtime SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 15 SAP / 15

16 Horizontal Aggregation Aggregation of demands by classes Aggregation of shortage costs Advantage: Customer information are taken into account SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 16 SAP / 16

17 Vertical Aggregation Production Aggregation by product-location hierarchy -> supply, demand, stock, costs 2. ptimization on aggregated level 3. Disaggregation by deployment algorithm push fair share A -> product-location hierarchy, ppm hierarchy -> Vertical aggregation for special production structure, only! SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 17 SAP / 17

18 DP/ MP-like Planning Step by Step Planning over the Supply Chain To get feasible solution: Set secondary and distribution demand as soft constraint (demand class) SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 18 SAP / 18

19 ptimization with Decomposition Decomposition via Product Product 1 : Product n Decomposition via Priority Decomposition via Time Demand class 1 : Demand class n Time SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 19 SAP / 19

20 Activate Constraints External Procurement T G Transportation G T G Production S P Variable Constraints (end date, bucket oriented) - set up -> production - minimal lot-size -> production - piecewise linear cost function -> production, transport, external procurement - fixed lot-size/ rounding -> production, transport esource Constraints - production capacity - transportation capacity - handling capacity - inventory capacity SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 20 SAP / 20

21 Agenda 1 ntegrated Supply Network Planning and ptimization with AP 2 Modeling the Supply Network 3 ptimizing the Supply Network 4 Selected planning scenario SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 21 SAP / 21

22 Combination of Vertical and Horizontal Aggregation 2a. 3. 2b. Production Selection of bottleneck part of supply chain 2. ptimization with 2a. Horizontal Aggregation 2b. Vertical Aggregation 3. ptimization of non bottleneck part SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 22 SAP / 22

23 SNP ptimizer - Customer Problems (1) Discrete industry Model: 13 Buckets, Locations-Products, 4887 Arc-Materials, 7581 PPMs Solution: optimal after 10 minutes Consumer industry Model: 30 Buckets, Locations-Products, Arc-Materials, PPMs LP: Variables, Constraints Solution: optimal after 30 minutes Chemical industry Model: 3 Buckets, 2131 Locations-Products, 1461 Arc-Materials, 356 PPMs MP: Variables (1.050 discrete, binare), Constraints Solution: < 1% optimality-gap after 1 minute SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 23 SAP / 23

24 SNP ptimizer - Customer Problems (2) Consumer industry Model: 22 Buckets, 916 Location-Products, 333 Arc-Materials, 741 PPMs MP: Variables ( discrete), Constraints Solution: < 5% optimality-gap after 5 minutes < 3% optimality-gap after 80 minutes Financial sector Model: 23 Buckets, 1 Product, 22 Locations, 30 Lanes MP: 3000 Variables (300 binare), 1600 Constraints Solution: < 1% optimality-gap after 1 minutes SAP AG 2001,ptimizing the SN, Dr. Dirk Meier-Barthold page 24 SAP / 24