Quantitative Supply Chain Optimization

Transcription

1 1 Quantitative Supply Chain Optimization Dorus van der Linden - Competence Manager Process Modeling DSM Advanced Chemical Engineering Solutions (DSM ACES) DSM Mission Our purpose is to create brighter lives for people today and generations to come We connect our unique competences in Life Sciences and Materials Sciences to create solutions that nourish, protect and improve performance Slide 1

2 2 DSM Modeling Competence (Simulation & Optimization) Creating value with integrated advice & knowledge based on quantified analysis, using mathematical process descriptions. Production Process Modeling Process Design Quantification Batch Schedule & Recipe Modeling Model Supported Operations Supply Chain Modeling Advanced Forecast & Market Predictions Production & Inventory Optimization Network & Inventory Optimization Slide 2 DSM Building on History Bioterials / Biologics Life Science Products Performance Materials Petrochemicals Fertilizers Coal Slide 3

3 3 Constant Change DSM Setup and Surroundings Development of profit / EBITDA ( m) * Normalized public reported price of 161 products in the DSM value chain * 2013 EBITDA from continuing activities only Slide 4 Supply Chain Management - Great Impact on Businesses Supply Chain Management has large effect on business results Purchasing and supply management Demand planning & sales forecasting Manufacturing and operations strategies Supply chain logistics Slide 5

4 4 Supply Chain Management - Great Impact on Businesses Supply Chain Management is optimization Minimize supply chain costs Meet required service level Maximize capacity Minimize inventory Minimize logistics cost Slide 6 Supply Chain Management - Competing Objectives Meet required service level Minimize supply chain costs Maximize capacity Minimize logistics cost Minimize inventory Slide 7

5 5 Difficulty in Balancing Supply Chain Slide 8 Re-Design Efficient, Flexible & Responsive Supply Chains Existing methods DSM Stochastic demand Single echelon Steady state Low level of information availability Not applicable to DSM s market situation DSM Modeling approach Discrete event simulation based on demand data Multi echelon approach Automated scenario analysis Making use of available data Slide 9

6 6 Supply Chain Optimization Toolset Discrete event simulation based on demand data Production & Inventory Optimization Multi echelon systems approach Automated scenario analysis Network Optimization Big Data Advanced Forecast & Market Predictions Monte Carlo & Sensitivity Optimizer Slide 10 Supply Chain Optimization Toolset Monte Carlo & Sensitivity Optimizer Production & Inventory Optimization Network Optimization Advanced Forecast & Market Predictions Slide 11

7 7 Forecast & Market Prediction Correlations and Early Warning System (1-3 month) Inventory and Demand Modeling (6-18 month) Time shifted cross-correlations Database solutions for Big Data Market Capacity Modeling (1-8 years) Upstream demand propagation Systems approach Monte Carlo Simulation Business Intelligence Slide 12 Production & Inventory Optimization Challenge Multi product Multi echelon Multi line With competition for resources Approach Discrete event simulation (DES) Balancing KPI s and $/ optimization Scale able Discrete Event Model Automated scenario analysis Setup Sales Orders Forecast Slide 13

8 Conclusions Method successful in DSM supply chain challenges Discrete Event Modeling advantages of scalable models Flexible (different supply chains) Scalable (via data, number of products and orders) Fast & lower cost Strong Business results 10-20% increase in perfect order rating 10-20% reduction in supply chain costs 20-50% reduction of inventory More efficient, flexible & responsive supply chains Further improvement and development needed Incorporate responsive dynamic market behavior Expand to more complex network setups Slide 14 Acknowledgements Gerben Bas - ISPT & Delft University of Technology Telli van der lei - DSM Ruud Barendse - DSM Willem Godlieb DSM 8

9 9 Integral supply chain simulation tool enables management to make strategic choices based on quantified scenarios and optimization\