for Fresh Produce Authors: Saran Limvorasak and Zhiheng Xu Advisor: - PowerPoint PPT Presentation

Multi-Echelon Inventory Optimization for Fresh Produce Authors: Saran Limvorasak and Zhiheng Xu Advisor: Dr. Francisco Jauffred Sponsor: A Mass Discount Retailer MIT SCM ResearchFest May 22-23, 2013

Fresh Produce for Retail Business • Product Freshness and Availability are key attributes for a company competing in grocery segment in retail business. May 22-23, 2013 MIT SCM ResearchFest 2

Introduction Thesis Questions: Should a mass discount retailer add upstream produce facilities into its current network? • What are key benefits from an additional node? • What are the impacts to supply chain networks? Scope and Expected Outcomes: • Analyze Top 21 fresh produce categories • Develop a Predictive Model to compare a network with and without an additional facility May 22-23, 2013 MIT SCM ResearchFest 3

Supply Chain Network Under Two Scenarios for Comparison Scenario 1: Existing Supply Chain Network Grocery Supplier Distribution Retail Store Center Hold Inventory Replenishment Average Average Frequency 3 times/week 7 times/week Scenario 2: Supply Chain Network with Fulfillment Center Grocery Fulfillment Supplier Distribution Retail Store Center Center Hold Inventory Average Average Replenishment 3 times/week 7 times/week Frequency May 22-23, 2013 MIT SCM ResearchFest 4

Risk Pooling Key benefit from moving inventory upstream is from Risk Pooling Supplier Supplier Central Warehouse Regional Regional Regional Regional Warehouse Warehouse Warehouse Warehouse Network 1: No Central Warehouse Network 2: With Central Warehouse Network 2 with Central warehouse has less Safety Stock and Average Inventory because • The concept of Risk Pooling is a powerful tool to address variability in the supply chain • Benefit from having central warehouse is greater in a system in which demand has higher volatility May 22-23, 2013 MIT SCM ResearchFest 5

Methodology STEP I: A Predictive Model Total supply chain cycle time “A total time which a product spends in the supply chain from supplier until it is sold” T GDC T Store Tr GDC Tr Store Grocery Supplier Retail Store Distribution Center T FFC Tr FFC Tr GDC Scenario 1 Fulfillment Scenario 2 Center Inventory Dwell Time (T) : Average time which product is stored at facility Transit Time (Tr) : Average transportation time between facilities Safety Time (T Sf ) : Safety Time in the supply chain captures the effect of demand volatility at Retail Store May 22-23, 2013 MIT SCM ResearchFest 6

Methodology STEP I: A Predictive Model May 22-23, 2013 MIT SCM ResearchFest 7

Methodology STEP II: Simulations of the inventory levels in supply chain: DOS Store DOS GDC Grocery Supplier Retail Store Distribution Center DOS FFC Fulfillment Scenario 1 Center Scenario 2 • Focus on the inventory level at each inventory facility • Relax assumptions on Inventory Policy by using current periodic inventory policy (R, s, S) • Average Inventory Level and Days of Supply (DOS) are supply chain performance metrics May 22-23, 2013 MIT SCM ResearchFest 8

Methodology STEP II: Simulations of the inventory levels in supply chain: May 22-23, 2013 MIT SCM ResearchFest 9

Total Supply Chain Cycle Time • Demand Characteristics Incremental / (Saving) From 21 Product Average Standard Enhanced Safety Time Transit Time Total Supply Product Category Categories, Total Supply Demand Deviation Coefficient (days) (days) Chain Cycle (lbs) of Variation Time (days) Chain Cycle of 6 1 Berries 15 30 1.97 (0.97) 0.3 (0.47) Product Categories in 2 Watermelons (0.24) 57 62 1.10 (0.54) 0.3 3 Cherries 64 60 0.93 (0.38) 0.3 (0.08) Supply Chain Network 4 Mixed Melons 57 49 0.86 (0.34) 0.3 (0.04) with Fulfillment Center 5 Stone Fruit 165 133 0.80 (0.32) 0.3 (0.02) 6 Strawberries 176 11 0.64 (0.31) 0.3 (0.01) is Less than existing 7 Citrus 0.08 195 88 0.45 (0.22) 0.3 network 8 Nuts-Snacks- 35 15 0.44 (0.21) 0.3 0.09 9 Grapes 317 117 0.37 (0.18) 0.3 0.12 • All Product Categories 10 Avocadoes 340 125 0.37 (0.18) 0.3 0.12 11 Potatoes 107 35 0.32 (0.16) 0.3 0.14 have reduction in safety 12 Cut Fruit 0.14 109 35 0.32 (0.16) 0.3 stock 13 Apples 331 105 0.32 (0.13) 0.3 0.17 14 Mushroom 44 11 0.26 (0.13) 0.3 0.17 15 Mixed 151 47 0.31 (0.12) 0.3 0.18 16 Carrots 104 26 0.25 (0.10) 0.3 0.20 17 Onions 297 74 0.25 (0.10) 0.3 0.20 18 Lettuce 203 49 0.24 (0.10) 0.3 0.20 19 Tomato 434 86 0.20 (0.07) 0.3 0.23 20 Pkg Salads 292 57 0.19 (0.07) 0.3 0.23 21 Bananas 1,427 238 0.17 (0.06) 0.3 0.24 May 22-23, 2013 MIT SCM ResearchFest 10

Enhanced Coefficient of Variation • Enhanced Coefficient of Variation (ECV) is created to measure the relative demand volatility May 22-23, 2013 MIT SCM ResearchFest 11

Enhanced Coefficient of Variation Break-Event Point • Sensitivity Analysis on Vendor replenishment frequency is tested to determine the break-event point for Enhanced Coefficient of Variation Vendor replenishment frequency ECV Break-Event Point 1 time a week 0.45 2 times a week 0.63 3 times a week 0.76 4 times a week 0.88 5 times a week 0.99 6 times a week 1.08 7 times a week 1.18 May 22-23, 2013 MIT SCM ResearchFest 12

Average Inventory in Supply Chain • All 21 product categories, Total Inventory in Supply Chain (lbs) Scenario 1 Scenario 2 Incremental / except Tomato, have less total Product Category (saving) inventory in supply chain of 1 Bananas 1,305,661 1,237,040 (68,622) scenario 2 2 Avocadoes 301,327 280,422 (20,905) 3 Citrus 179,321 164,461 (14,860) • Net saving in total inventory in 4 Apples 286,792 272,313 (14,479) 5 Stone Fruit 166,995 152,759 (14,236) supply chain results from 6 Grapes 277,742 264,727 (13,015) 7 Strawberries 167,591 154,646 (12,945) o Inventory at retail stores will 8 Mixed 133,407 126,201 (7,206) increase due to longer lead 9 Cherries 71,453 65,699 (5,754) 10 Cut Fruit 97,094 92,149 (4,945) time 11 Watermelons 65,781 60,959 (4,822) o Inventory at FFC will 12 Potatoes 94,608 90,232 (4,375) 13 Mixed Melons 61,672 57,376 (4,297) decrease in a larger amount 14 Carrots 90,730 87,941 (2,789) due to risk pooling effect 15 Berries 24,770 22,265 (2,504) 16 Pkg Salads 247,322 245,231 (2,092) 17 Nuts-Snacks- 34,900 32,954 (1,947) 18 Onions 254,099 252,711 (1,388) 19 Mushroom 41,782 40,873 (908) 20 Lettuce 173,904 173,469 (436) 21 Tomato 366,371 370,612 4,242 May 22-23, 2013 MIT SCM ResearchFest 13

Conclusion 1. A Fulfillment Center provides benefits to Some Product Categories The decision to add upstream produce facilities significantly depends on Product Categories and Locations which indicates Demand Volatility and Supplier Replenishment Schedule Products for Channel 1: Channel Decision Low Demand Volatility Product Average Standard ECV Category demand deviation Grocery Channel 1 Retail BANANAS 1,423 238 0.17 Supplier Distribution Store MUSHROOM 44 11 0.26 Center BERRIES 15 30 1.97 WATERMELONS 57 62 1.10 Fulfillment Center Products for Channel 2: Channel 2 High Demand Volatility May 22-23, 2013 MIT SCM ResearchFest 14

Conclusion 2. A Fulfillment Center adds Agility to the system • Safety Time of supply chain and Total safety stock in the supply are reduced from Risk Pooling. • However , a Fulfillment Center adds another “touch” to the system and may increase total time for all product categories May 22-23, 2013 MIT SCM ResearchFest 15

Q&A May 22-23, 2013 MIT SCM ResearchFest 16