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Multi-echelon inventory management at Sligro Food Group N.V. Tim van Pelt Who I am • 23 years old • Live in Eindhoven, the Netherlands • Master study Operations Management & Logistics, Eindhoven University of Technology • Graduation project at Sligro Food Group • Supervised by dr. ir. R. Broekmeulen and prof. dr. T. de Kok • Master study Strategic Management, Tilburg University Key facts Sligro Food Group (2013) • Established in 1935 • Food retail activities: 130 EMTÉ supermarkets • Foodservice activities: 9 delivery centers (BSs) and 46 cash-and-carry wholesalers • 5.829 full-time equivalent employees • Net sales of almost 2.5 billion euros • Operating profit (EBIT) of 89 million euros Problem introduction • Literature: importance of improving inventory management in retail and wholesale cannot be overemphasized • Focus on supply chain efficiency – offering a certain desired service level at least costs • Integrated inventory management: key role information sharing “What is the cost saving potential of the application of an installation stock policy with central demand pooling?” Problem scope Multi-item two-echelon distribution system ZB (46) CDC Focus only on inventory holding costs Supply chain breakdown Supplier Supplier BS (8) CDC CDC EMTE RDC 1 CDC (67) RDC 2 CDC (63) EMTE Current situation (benchmark) Regular installation stock policies CDC BS 1 BS 2 BS 8 Scenarios 1 Benchmark scenario • Application regular installation stock policy • Benchmark for determination cost saving potentials 2 Demand pooling scenario • Application installation stock policy with central demand pooling • Expectation: lower demand uncertainty at CDC 3 Optimal scenario • Near-optimal situation: application of multi-echelon inventory control policy • Expectation: Most inventory positioned downstream and lower internal fill rates Demand pooling scenario (2) Installation stock policies with central demand pooling CDC BS 1 BS 2 BS 8 Optimal scenario (3) Multi-echelon inventory control policy CDC BS 1 BS 2 BS 8 Modelling • Customer fill rates • Demand pattern: mean and variation • Review periods • Lead times: mean and variation • MOQs and Qs • DoBr tool: exact evaluation scenario 1 & 2 • ChainScope: accurate approximation scenario 3 Inventory value index numbers BS supply chain part RDC supply chain part 120% 120% -27% 100% 80% 100% -22% 60% 60% 40% 40% 20% 20% 0% 0% Benchmark scenario Demand pooling scenario -58% 80% Optimal scenario CDC Demand variability: -49% -11% Benchmark scenario Demand pooling scenario Optimal scenario CDC Demand variability: -62% Inventory weeks BS supply chain part 7 6 Pipeline 0,4 BS 4 12 0,4 10 Pipeline 0,4 0,7 RDC CDC - 6,3 8 -1,3 6 3 2 CDC -1,6 5 2,0 RDC supply chain part 2,0 3,4 1 0,4 4 2,1 2 2,0 0,5 0 Benchmark scenario 9,2 2,9 18% : 82% Fill rate: ±45% - 1,1 0,4 1,6 0,9 0 Demand pooling Optimal scenario scenario 0,4 0,7 Benchmark scenario Demand pooling Optimal scenario scenario 36% : 64% Fill rate: ±65 % Conclusion & recommendation • Substantial potential inventory holding cost reductions – relatively and absolutely • Should be practically validated • Impact on inventory planning activities and other logistical operations should be determined • Therefore, it is recommended to Sligro to set up a pilot project with an adapted or acquired inventory management system